Health of an

Aging America

Issues on Data

for Policy Analysis

The papers in this report were background

to a study conducted by the Panel on

Statistics for an Aging Population, of the

Committee on National Statistics, focusing

on data needed over the next decade for

health policy analysis for an aging America.

Documents and Committee Reports

Series 4, No. 25

n

DI+HS Publicahon No. (PHS) 89--1488

U.S. Department of Health and Human

Services

Public Health Service

Centers for Disease Control

National Center for Health Stahstics

Hyattsville, Md.

December 1988
Copyright Information

The National Center for Health Statistics has obtained permission from the

copyright holders to reproduce various sections of this report. Further

reproduction of this material is prohibited without specific permission of the

copyright holders. All other material contained in the report IS in the pubhc

domain and may be used and reprinted without special permission; citation as

to source, however, IS appreciated.

%ggeated Citation

National Center for Health Statistics. t 988. Health of an aging America: issues

on data for policy analysis. Vital and Hea/fh Statistics. Series 4, No. 25. DHHS

Pub. No. (PHS] 89–1 488. Public Health Service. Washington: U.S. Government

Printing Office.

Library of Corigreas Cataloging-in-publication Data

Health of an aging America.

(Series 4, Documents and committee reports ; no. 25) (DHHS publication ;

no. (PHS) 89-1488)

Includes b!bhographies.

Supt. of Dots no.: HE20.6209:425

1. Aged—Health and hygiene-lhted States—Stabstlcal methods.

2. Health surveys-Untted States—Statistical methods. 3. Aged—Health and

hygiens+hted States—Statistical services. 1.Manton, Kenneth G. Il. Nattcmal

Center for Health Statistics (U.S.) Ill. Series: Vital and health statistics. Series

4, Documents and committee reports ; no. 25. IV. DHHS publication ; no. (PHS)

89-1488. [DNLM: 1. Data Collection. 2. Geriatrics-United States-statistics.

3. Heaith Poiicy-United States W2 A tW 48vd no. 25]

HA37.U1 893 no. 25 362.1 ‘0723 S 86-600392

[RA408.A3] ~362.1‘989700973]

ISBN 0-6406-0362-2
National Center for Health Statistics

Manning Feinleib. M.D.. Dr.P. H.. Director

Robert A. Israel, Deputy Director

Jacob J. Feldman, Ph. D., Associate Director,fiw Amlysi.s

atd Epidemiology

Gail F. Fisher, Ph. D.. A.wociate Director for Planning ami

E.wamurd Pro,qratns

Peter L. Hurley. A.w)ciate Director ,ji)r Vital awl Health

Statistics Systetns

Stephen E. Nieberding. A.wociate Director jhr Mamigement

George A. Schnack, A.v.wciate Director for Dda Processing

(ml Services

Monroe G. Sirken, Ph. D., Associate Director f[)r Re.rewdj

aml Methdolo,qy

Sandra S. Smith, Information Oficer

Division of Health Care Statistics

W. Edward Bacon, Ph. D., Director

Joan F. Van Nostrand, Deputy Director
Health of an Aging

America: Issues on Data

for Poiicy Analysis

Foreword

We tire witnessing the “graying” of America. Today one

in every eight Americans is 65 years of age or older. By

the yew zoos, one in every five will be older Americans.

This dcm~lgraphic shiti is unprecedented in our history and

in that of other industrialized nations as well. Life expectancy

has incvtwed dramatically. In 1900. life expectancy was 47

yews. Today. it is 75 years—an addition on the average

of 28 ycttrs. This increase forecasts changes that will reshape

American society. especially in the health area. We can expect

nmjtw differences in the health sttatus of older persons. in

their usc t~fhealth care. and in its costs.

The graying of America is challenging our ability to make

health policy to meet the changing needs of older persons.

Whit dutti will be needed in the next decade to develop,

cwaluutc, and modify health policy for an aging America’?

With this question in mind. the National Center for Health

Statistics joined with other Federal agencies that produce data

ub(wt the health of older persons to commission a study.

These agencies are the Health Care Financing Adrninistmtion,

the National Institute on Aging, the National Institute of Mental

Heulth, the Office of the Assistant Secretary for Planning

and Evaluation, and the Social Security Administration—all

of the Department of Health and Human Services—and the

Veterans Administration. The study, commissioned in 1984,

WM conducted by the Panel on Statistics for an Aging Population of the Committee on N&ionaI Statistics, which is part

of the National Research Council. The charge to the Panel

wtis to focus on data needed over the next decade for health

policy iumlysis for an aging America. Within this context,

the Ptinel was charged with making recommendations in three

areas:

● Ditta requirements and appropriate remedies to major

shortcomings

● Actions for refining relevant statistical methodology

. Chtinges in the decentralized Federal statistical system

to facilitate integrating data across components

To address this charge, the Panel divided into three work

groups. Each commissioned several papers as background.

These papers constitute the content of this report. The papers

are categorized here according to the working group that commissioned them:

● Health issues and data requirements

● Methodology issues

● Issues in improving data resources

The work groups used the papers to provide indepth information for their delibemtions and development of recommendations. In many instances, the concepts. views. and recommendations from the papers are retlected in the Panel’s tlnal

report-Tl~e Agittg Population in the T}t’ewyjit-.rt Cetmu-y:

Stmi.wic.rfix-He(llth Policy (National Research Council. 1988).

These papers were written in the summer of 1985. They

were the focus of the Symposium on Statistics for an Aging

Population convened in September 1985 by the National Research Council. Over 100 experts in health policy, data analysis, and statistical policy attended the symposium. Its purpose

was to provide a forum for presentation of the papers and

their critique by both invited discussants and symposium participants. The papers were subsequently revised to reflect the

discussions at the symposium. Final copy was submitted to

the work groups at the close of 19S5. although minor revisions

were made to some pdpers within the last year. These papers

reflect the views of the authors and not those of their employers

nor of the Department of Health and Human Services.

These papers are presented here as a companion volume

to the Panel’s final report. They provide a fuller treatment

of some of the issues addressed by the Panel in its development

of recommendations. It is important to add the insights of

these papers to our growing body of knowledge for strengthening data to meet the challenges of making and modifying

health policy for an aging America.

-

Manning Feinleib, M. D., Dr.P.H.

Ill
Chapter 1. Health issues and data requirements

Measurements of health and disease, a transitional perspective by Kenneth G. Manton, Ph.D. . . . . . . . . . . . . . . 3

Maintenance of health, prevention of disease, a psychosocial perspective by Li.w F. Berktnun, Ph.D. . . . . . . . . . . 39

Economic data and the analysis of health-related issues of the elderly by John A. A4enefee, Ph.D. . . . . . . . . . . . . 56

Chapter IL Methodology issues

Methodologic issues in linkage of multiple data bases by Fritz Scheuren, Ph.D. . . . . . . . . . . . . . . . . . . . . . 75

Statistics for an aging population, dealing with uncertainty by Michae/ A. Stoto, Ph.D. . . . . . . . . . . . . . . . . . 96

Health indicators for an aging population by Laurence G. Branch, Ph. D., Julius B. Richtnond, M. D., Dm’id E. Rogers,

M. D., Rotutld W. Wilson, M. A., wdMar-v M. E. Adcitns, Ph.D. . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

Chapter Ill. Issues in improving data resources

Research for tomorrow’s elderly by George C. Myers, Ph.D. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

Data needs for policy analysis of long-term care by Joan F. Van No.waml, M.P.A. . . . . . . . . . . . . . . . . . . . 131

Data requirements for long-term care insumnce by Mat+ R. Meiners, Ph.D. . . . . . . . . . . . . . . . . . . . . . . . 142

Coordination of Federal statistics related to the elderly by K<irherine K. W(illtnan . . . . . . . . . . . . . . . . . . . 151

Lessons from longitudinal studies by Clifford H. Patrick, Ph.D. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163

State datti issues for an aging population I)y Chrlene A. Hmrington, Ph.D. . . . . . . . . . . . . . . . . . . . . . . . 174

. . .
Chapter U

Health issues and data requirements
Measuremmts of health

and disease, a transitional

perspectives’ b

by Kenneth G. Manton, Ph.D., Duke University Center for

Demographic Studies and Duke University Medical Center

L introduction

The identification of the rate and nature of health transitions at advanced ages is a complex analytic issue that has

not been satisfactorily resolved. For example, many studies

of the loss of physiological function with age have recently

been undergoing revision because they were based upon representative samples of elderly persons which included chronically

ill persons. Since the age-specific prevalence of chronic morbidity tends to increase with age (which could be due simply

to the greater time of exposure to these diseases), the actual

age rate of physiological decline for the individual was confounded in the population with the physiological deficits due

to disease; that is, the physical impairments generated by

age-related diseases were confounded with functional decreases associated with “general” aging processes. Studies

of “healthy” elderly populations suggest very different rates

of loss with age, with some functions showing little decline

even to very advanced ages (e.g., cardiovascular output can

be maintained in healthy persons at least up to the eighth

decade of life; Lakatta, 1985).

Frequently, epidemiological studies have not included

populations at very old ages, so we currently lack the same

amount of evidence about risk factor change and disease-risk

factor associations that we possess for persons in late middle

age. The available evidence suggests that risk factors may

have different effects at different ages. Demographic studies

have often assumed that mortality at advanced ages follows

a Gompertz-t ype hazard function, which many studies indicate

predicts too rapid an increase in the hazard rate (Perks, 1932;

Spiegelman, 1968; Wilkin, 1982; Manton, Stallard, and Vaupel, 1986), or have assumed a risk-homogeneous population—

despite extensive epidemiological evidence documenting major

differentials in chronic disease risk factor exposures and the

effects of systematic mortality selection.

Another major problem in analyzing health changes at

advanced ages is the fact that chronic diseases evolve over

a lengthy period of time, have complex natural histories,

interact with other chronic diseases, and are related to physiological aging changes in a multidimensional fashion. This

suggests that the study of health changes at advanced ages

must be a study of interrelated processes; that is, the concep-

“Conknts we [ho opininns of the author and nut thusc of the Department

of HMth and Humtin .!+xviccs nr the organization hc wurlis for.

II,c,l~g~ Na[ionnl Academy nf.%kmw.

tualization of disease as an event or fixed state will be inadequate. In order to conceptualize health changes at advanced

ages, the World Health Organization (1984) has proposed

a model that describes such health changes in terms of three

age-directed and interacting processes—morbidity, disability,

and mortality. Researchers at the World Health Organization

were able to describe the linkage (though not the detailed

interaction) of these processes according to figure 1, which

is based upon standard life table concepts.

The outermost curve in the figure represents the survival

probability to age x from a standard life table model. The

life tables may be formulated on either a cohort or period

basis, where the period life table is a synthetic rendition

of what current prevalence and mortality rates would imply

free of the effects of differences in birth cohort size and

prior survival. The innermost line represents the probability

of survival to age x free of disease. The middle line represents

survival to age x with disease but free of disability. The

positioning of the morbidity and disability curves represents

the assumption that chronic diseases, as they progress, generate

disability. Though disability is generated by chronic disease,

this does not require that chronic disease map one-to-one

on disability (Manton, 1985). Both advanced cancer and a

severe stroke can produce a bedridden incontinent patient

with total dependence in activities of daily living. Thus, the

mapping of disease processes to disability outcomes is a probabilistic many-to-one mapping.

The prime advantage of the curves in figure 1 is that

they describe health changes in duration (i.e., person-year)

terms and show how those duration effects are correlated

with age. The concepts represented in figure 1 have been

used by Wilkins and Adams (1983) to describe various aspects

of health changes in Canada. For example, in table 1 we

see a decomposition of life expectancy based upon institutionalization or degree of activity restriction. We see that

a significant portion of life (5.4 years for males, 8.7 years

for females) is expected to be spent with restriction in a

major activity. Because the portion of life spent with restriction

of major activity is based upon life table measures, it is

possible also to portray this decomposition on an age-specific

basis.

In table 2 we present (a) life expectan~y remaining at

age x; (b) quality-adjusted life expectancy based upon a weighting of person-years of survival by certain qualitative health

characteristics (i.e., each year of survival in an institution

is counted only as 0.4 year in quality-adjusted terms; a person

with no disability contributes 1.0 quality-adjusted year for

3
W’(;lJI{P; 1

The observed mortality and hyprXtwtical morbidity and disability

Amwicu ill 1980

survival curves fw females In 1116United Stales of

100

[

----- ----~ ------

-- .<...., DISABILi?Y - -“:~.: z.:.: --- ~ -

90 :.. ““.... ..

/

+. ,, MORTALITY

-.. ““..,

80 %, ““”..., \

\. .....

\

. ...O J

~ 7(3 .

‘°FiB’D’Cx\ ““””...

.? 60 —

\

“,

\’

.-

2

\

\

= 50 ~

.

W

g 40 —

.-

r

g 30 —

: 20 –

10 –

L—--l__ll—.—..—l————l—

o 10 20 30 40 50 60 i

Age

-\ \

\

\

,... \

... \

..

:

\

... \

\ ““..., \.

\

\.

—. 1

8(

1“

90 100 110

;;

SOURCE: World Health Organization,

each actual year lived); and (c) the number of years expected

to be lived after age x free of disability. We can see that,

with advancing age, the proportion of remaining life expectancy that will be disability free declines (e.g., for males

from 85 percent at birth to 57 percent at age 65).

Such. measures can also be used to represent temporal

trends, This is exemplifiedin table 3, for the period 1951–78.

This table addresses the question that has frequently emerged

in the debate over whether the improvement in health status

on an age-specific basis has paralleled recent life expectancy

gains. We see, fmm 1951 to 1978, that Canadian life expectancy increased 6.0 years. We also see that this gain was

a function both of increases in disability-free years (1,3 years)

and in disabled years (4.7 years). Thus some improvement

is evident, though not as much as might be expected. The

gain in quality-adjusted life years is larger (3.7 years) than

for disability-free years (1.3), indicating that much of the

disability was at low levels. These figures serve to emphasize

the need to develop health policy to increase active life expectancy as one increases total life expectancy (i.e., to minimize

the area between the two outside curves in figure 1). Such

policies would seem consistent with the goals of geriatric

medicine to maintain function at advanced age to the degree

possible (Minaker and Rowe, 1985).

Constructionof these tablesrequiresthe synthesisof data

from multiple sources (i.e., population survey data of various

1984.

types). Though this has been done for several other countries

such as Japan (Nihon University, 1982), a similar comprehensive exercise has not been conducted in the United States

(Lute, Liu, and Manton, 1984).

Though the above concepts are useful to describe health

changes at advanced ages in the population, they do not describe the physiological mechanisms underlying the interacting

processes at the individual level. It is the individual-level

mechanisms that are important clinically for the appropriate

management of the geriatric patients with multiple, interacting

chronic diseases, and for the investigation of the basic physiology of aging mechanisms+specially in terms of a complex

multidimensional process and the discrimination of “aging”

from “morbid” physiological changes. This manuscript is designed to explore health transitions in terms of these individuallevel processes.

IL Health transitions

at advanced ages:

Population data

A major issue in describing health transitions at later

ages is to distinguish between the evidence provided on these

4
transitions at the aggregate level and the actual transitions

of the individual. Typically. in survival studies, it is assumed

that the survival curve generated in the population is representative of the failure process at the individual level. A consequence

of this argument is that the population survival curve is also

representative of the age-related loss of physiological homeostilsis tind thus represents the basic dynamics of physiological

oging processes, e.g., Gompertzian aging dynamics (Fries,

1980). In fact, the population hazard function is representative

of the tige increase in mortality risks only if the population

is completely risk homogeneous. Clearly a large body of

epidemiological evidence tells us that it is not. Consequently,

the population hazwd rate represents only the average risk

among survivors to a given age. Since the survivors to a

given W are Wematically determined by mortality, the

population hazard rate is not representative of the physiological

process in any given individual. Of course, one can statistically

“reduce” the risk heterogeneity of any population by stmtifying

(or by using other means of statistical control) for risk factor

level. In general, many factors (e.g., genetic endowment)

will not be measured, so that this reduces the problem only

in degree.

Given the confounding effect of risk heterogeneity, it

is reasonable to ask if there are analytic tools which can

resolve our problem, One mode of resolving the problem

is to construct a model of population risk heterogeneity, on

either theoretical or empirical grounds, and unconfound the

effects of selectivity analytically. In the development of such

techniques, we can utilize the fact that the effects of

heterogeneity can be shown mathematically to bias the relation

of the individual to population heterogeneity in a given direction (Manton and Stallard, 1984). Specifically, regardless of

the actual shape of the distribution of risk differentials in

the population. we find that the relation of the population

(P-) to individual (P) hazard functions involves the following

type of function:

which expresses the average force of mortality at age a as

a function of (a) the distribution of frailty (i.e., the fixed

level of susceptibility to death) at a prior age x and (b) the

cumulative mortality hazard for individuals at the frailty value

z = 1. This relation holds for any distribution with a moment

generating function (MGF), i.e., a distribution with finite

moments. The implications of this relation can be illustrated

by examining the relation of individual to population mortality

rates, Iq,r(z) ’s, in figure 2, i.e., where Iqr(z)s I – exp[ – P(.Y

+ Yqz)].

In this figure we have plotted the theoretical mortality

rate for four types of individuals (i.e., at frailty level z =

1.0 for frailty distributed as a gamma distribution with shape

parameter, k, of 0.5, 1.0, and 2.0 and for frailty level 0.33

in a population with gamma parameter 1.0) and for the observed cohort.

We see that the relative rate of increase with age of

the individual mortality rate is greater than in the population.

FIGURE 2

Observed and individual age-specific mortality rates after adjustment for population

heterogeneity, white females in the U. S., 1875 birth cohort.

{/

Individual Mortality

k=0.5 k=l.O

Z=l.o Z=i.o

I / “k=l.O ‘

rIndividualMortal ity

? Individual hlortolitv

z ““

.3-

.2–

.1-

0. {

50 55 60 6’5 7’0 7!5 8’0 85 90

/

)

i Z’O.33

/ IndividualMortaiity

I k=2.O

/ Z:l.o

/

/

/

/

.’ ~Abserv$dh&!rtO I i!y

AGE

5
Equation (I) tells us this is the case for any distribution

with an MGF, i.e., for most distributions we are likely to

encounter in nature.

To illustrate this relation quantitatively, we present the

parameter describing the age rate of increase in risk for the

Weibull hazard function (i.e., the hazard rate increases as

the m – I power of age, cm P-1) for a series of elderly

female Medicare cohorts aged 65–84 in 1968 (i.e., the birth

cohort of 1883–1902) and followed to 1978.

The first column in table 4 presents the values of m – 1

= (3 in the Weibull function if one assumes the population

to be homogeneous. The second column presents the values

of ~ estimated from the data using a model which adjusts

for risk heterogeneity (by assuming that the individual hazard

curves are proportional and that the proportionality factors

for individuals follow the gamma distribution). In the third

and fourth columns, we present the exponents for a Gompertz

function (i.e., 0 in ee’) with and without a similar gamma

adjustment for individual differences in risk.

The Weibull function was found to explain the cohort

risks better than the Gompertz (Manton, Stallard, and Vaupel,

1986). The Gompertz parameters are included because of

their general use and because the exponent (3can be simply

interpreted as the annual percentage increase in the mortality

rate.

We can see that, for both the Gompertz and Weibull

hazard functions, the shape parameters (i.e., (3 and t)) are

larger for the functions adjusted for heterogeneity than for

the functions which assume the population to be homogeneous.

The larger parameter values mean a more sharply curved

function. Since the Gompertz and Weibull parameters estimated for the population have often been used to signify

the rate of aging changes (e.g., Sacher, 1977; Fries, 1980),

this suggests that the estimates of the “aging rate” from the

population are not truly descriptive of the aging rates for

individuals. Most particularly, since the ~ (and 6) coefficients

are larger for the adjusted hazard function, this suggests that

these risks rise relatively more rapidly than the hazard function

assuming heterogeneity. Consequently, the period during

which a person is at high risk of death is shorter than would

be suggested by the population estimates. This simply reflects

the fact that the hazard function parameters in the population

are confounded with interindividual differences in risk and

so do not directly describe individual aging changes. The

more rapid increase in risks for individuals seems consistent

with evidence on the “terminal decline” often noted for persons

in longitudinally followed populations and with concepts of

mortality resulting from a loss of physiological homeostasis

at advanced ages.

A second important insight resulting from the estimates

provided in table 4 is that there is a decline in the curvature

parameters for individuals over cohorts, i.e., the age rate

of increase of mortality declines for the younger cohorts.

This is not evident in the Gompertz hazard function estimated

assuming risk homogeneity, where there is actually a slight

tendency to increase (i.e., from 5.86 percent for the 1883

cohort to 6.26 percent for the 1902 cohort), suggesting an

acceleration of age-related risks. In the case of the Gompertz

function adjusted for heterogeneity, we see a sharp decline

in the (3 parameter from 10.37 ( 1883 birth cohort) to 7.34

(1902 birth cohort). Thus, there are apparent changes in the

aging process for the individual when we statistically adjust

for heterogeneity. This may be a result of clinical innovation

over the 20-year span of cohorts that allows the younger

cohorts to maintain homeostasis in the face of greater physiological challenge. For example, improved management of

chronic diseases such as diabetes or hypertension may have

helped younger cohorts to maintain physiological homeostasis

in a more impaired condition and thus altered the form of

the hazard function. Altemativel y, improved health at later

ages may have led to greater homeostatic reserve due to a

lessened prevalence of chronic diseases. Regardless of its

possible sources, none of this effect is evident in the Gompertz

hazard function assuming homogeneity because of confounding of the trajectory of the hazard function with interindividual

differences. (For example, risk heterogeneity may have increased for younger cohorts because of the greater survival

of diabetics due to the reduction of stroke mortality. )

The basic conclusion is that, in contrast to analyses of

the population mortality data with the Gompertz function assuming homogeneity, where it was often concluded that there

was little change in the rate of aging changes (e.g., Gavrilov,

1983), the hazard function adjusted for risk heterogeneity

suggests that there has been considerable change in the rate

of aging processes. Furthermore, even for the unadjusted Gompertz hazard functions, our estimates of 6 (the basic aging

parameter) differ (being much smaller) from values cited in

the literature (typically on the order of 0.08) because we

employed cohort data rather than cross-sectional data. The

estimates of about 8 percent per year in the increase in population mortality risks are confounded with intercohort differences

in risk.

The effects of heterogeneity on our ability to make inferences about basic individual aging changes from population

data also have important implications for cross-population

comparisons. One well studied comparison is that of the agespecific differentials in mortality rates for U.S. black and

white persons. After being much elevated in middle age,

black mortality rates at advanced ages first approach and

then drop below white mortality rates. This has been referred

to as the “black-white” mortality crossover (Manton, 1982a).

Indeed, even if there is not an actual crossover, there is

a convergence of the hazard rate trajectories beginning in

midlife. One possible explanation for this crossover (or convergence) is the differential rate of mortality selection between

black and white persons, i.e., the fact that higher black mortality mtes at earlier ages tend to remove more rapidly black

persons with chronic morbid conditions so that the smaller

proportion of black persons surviving to advanced ages are

relatively healthier than white persons surviving to those ages.

Manton and Stallard (198 1) show how a model of mortality

selection could explain the crossover. Clearly, if the differences

in mortality rates at later ages were due to mortality selection,

then one would not need to posit different physiological aging

processes to explain the different trajectory of age-specific

mortality rates between black and white persons.

6
It is interesting that this black-white crossover can also

be demonstrated in a number of longitudinal studies. For

example, we see that black persons who survive to later ages

in the Duke Longitudinal Study have greater residual life

expectancies than white persons who survive to advanced

ages (Manton, Siegler, and Woodbury, 1986). A black-white

mortality crossover has also been demonstrated in the Evans

County study population (Wing, Tyroler, and Manton, 1985).

In addition, preliminary analyses (Wing et al., 1987) have

also shown a clear black-white mortality crossover in the

Charleston Heart Study—a crossover which is present even

after socioeconomic factors are controlled.

One point of confusion that often emerges in discussions

of heterogeneity and selection is the effect of “debilitation,”

i.e., the fact that early adverse mortality conditions may reflect

environmental exposure which “damages” the physiology of

those persons who do survive, For example, systematic differences in cause-specific mortality patterns, some of which are

clearly environmentally generated, exist for middle-aged black

and white persons (Manton, 1982a). What is not always recognized is that this damage, or “debilitation,” behaves just like

risk heterogeneityy a~ter the age at which it occurs. Thus,

if black persons have higher alcohol consumption rates than

white persons, and consequently greater risks of deaths from

cirrhosis, we would observe a systematic selection (and earlier

age at death) of black persons after the ages at which the

physiological damage from alcohol consumption occurred.

We will illustrate thi;effect below for lung can;er.

Lung

To this point we have examined the effects of population

heterogeneity on the trajectory of population-level hazard functions only for total mortality. There is a second, critically

important source of heterogeneity that we have not yet controlled for. This is the heterogeneity due to the multiple chronic

disease processes that are operational in an elderly population.

The tendency has been for mortality and aging at advanced

ages to be viewed as due to a unidimensional process. One

theoretical justification for this view is that a single factor

can become overriding and dominant in the homeostatic control

mechanisms that determine age-specific mortality risks.

Sacher ( 1977) showed, in a model of human aging and mortality, that such a dominant factor could produce the Gompertzian

type of aging model that has often been applied in the analysis

of unadjusted population mortality rates. The emergence of

a single dominant mortality function seems not to be supported

by the evidence (Schneider and Brody, 1983). instead, it

appears that multiple aging processes, each associated with

different chronic disease processes, continue to operate into

very advanced ages. Clearly the progression of these processes

is time related. Furthermore, there may be interaction among

the different chronic disease processes. Nonetheless, the effects

of discrete disease processes can be identified and tracked—

even at advanced ages (e.g., Howell, 1963a, 1963b, 1981).

To illustrate this, consider the cases of lung and breast cancer.

In the case of lung cancer, we can see in figures 3 and

4 that there are strong cohort differentials in risk, probably

due to changes in the smoking history of birth cohorts. FurtherFIGURE 3

WHITE MALES

Cancer Mortality

0“ ?

●

/(

rl

;

AGE

7
FIGURE 4

WHITE FEMALES

Lung Cancer Mortality

155

A~ A4~ ~

~~~ ~ 4> L/

more, it has been observed that lung cancer mortality tends

to decline at advanced ages. Both ofthese phenomena may

be observed in the cohort plots for white males and females

presented in figures 3and 4.

ln the figures we follow the experience of nine male

and nine female birth cohorts (born 1880-1920) for28 years

(1950-77). Thejagged Iinesindicate theobserved cohort agespecific mortality rates, while the smooth lines indicate the

values fit ~o those points by our analysis—i.e., by fitting

a gamma-mixed Weibull hazard function (Manton and Stallard,

1982). The most recent male cohorts (i.e., those marked by

YSO* w v403 and w) show OnV small increases. In contrast>

females show rapid increases for the more recent birth cohorts.

This is consistent with the fact that reductions in female smoking lag 20 years behind reductions for males (Harris, 1983).

ln addition to the large cohort differences in figures 3

and 4, we also observed the tendency for mortality to increase

less rapidly at advanced ages. The slowing again may be

a consequence of the effect of selection on the age trajectory

of risks described in equation (1), i.e., that the higher risk

persons are more rapidly removed by mortality from the

populations.

The decline in risk at more advanced ages for lung cancer

is also observed for other chronic diseases with an early mean

age at death; e.g., diabetes, cirrhosis, and chronic respiratory

disease (Manton, 1982b). interestingly, as the force of mortality for disease decreases, its prevalence is Iik:ly to increase.

For example, as stroke and other conditions associated with

diabetes decrease in mortality risk, the population prevalence

of diabetes may increase. The decrease in stroke mortality

has, in part, been attributed by some to be a consequence

of the reduction in risk of other associated conditions, e.g.,

heart disease (Hachinski, 1984).

The tendency for some chronic diseases to decline in

risk at advanced ages is probably a function of their typical

age at death. Thus, for diabetes (and cirrhosis) the mean

age at death is low relative to other chronic diseases. Thus

persons with the risk attributes for these diseases (e.g., obesity

for diabetes, drinking for cirrhosis) will die out of the population at earlier ages, causing the cause-specific rates to decline

(due to selection) at later ages. lf, as modeled for lung cancer,

the individual risks tend to increase with age, future shifts

in the mean age at death for different diseases will have

important implications for manifest disease prevalence patterns

atlater ages.

One version of this problem can be illustrated for breast

cancer. lt has been argued that there may be two distinct

forms of the disease process generating the complex hazard

function form known as Clemmensen’s hook (Manton and

Stallard, 1980). (See figure 5.) We see that the “hook” is

manifest as a flattening of the observed hazard function (top

line) after age 50. It has been hypothesized that early increases

in breast cancer mortality hazards are due to a “premenopausal”

disease component and that this component has a genetic

a
FIGURX 5

Observed and predicted single year of age probabilities of death due to breast cancer

for white females in the United States in 1969.

AGE

NOTE: A= Observed probability of death caused by breast cancer, 0= predicted

probability of death caused by breast cancer, += hypothesized probability of

death caused by premenopausal disease, X= hypothesized probability of death

caused by postmenopausal disease.

base. The late portion of the hazard function, it is argued,

is due to a “postmenopausal” disesase component that is not

linked so strongly to family pedigree.

In Manton and Stallard ( 1980) it was found possible to

explain this complex hazard function using a two-disease

model. Thepersons succumbingtothe earlydiseasecomponent

were found to have a high degree of risk heterogeneity—a

degreeofheterogeneity high enough that’’susceptible” persons

are exhausted at a very high rate from the population after

age 50. The degree of heterogeneity estimated by the model

for this component of the disease process is consistent with

the high relative risks determined for women with certain

high-risk pedigrees (Anderson, 1975), The late disease component, in contrast, behaved much more like other solid tumors,

where the age increase in the risk of the disease increased

as the sixth or seventh power of age. The individual contribution of the two diseases to the total breast cancer mortality

riskisillustrated infigure5bythe twobottomlines.

We illustrate in table 5 the effects of debilitation by

examining the very early stages of,lung cancer mortality for

birth cohorts 1880- 1940, followed from 1950-77. Thecoefficients in this table represent the risk multipliers for the function

describing theage increase inthe lung cancercohortmortality

rates. Ifwe read down thedlagonals, we see that, for cohorts

9
at least age 35 in 1950. the relative risk is constant, i.e..

we have assumed that the major risk factor exposures have

occurred by age 35. If we read across the first row in the

table, we see that the risk at ages 35–39 of the 191540

cohorts pealied for the 1930 cohort (in 1965–69, shortly after

the 1964 Surgeon General’s report on smoking hazards). This

suggests that male birth cohorts born after 1930 will have

declining risks so that. after the birth cohort of 1930 reaches

the peak lung cancer risk age (around age 70), there could

be a decline in male rates. Actually, this cohort experienced

the highest risk in the interval 1965–69. After that period

the cohort risk declined, suggesting that the risk of lung cancer

changed within cohorts. probably due to changing exposure

to cigarettes. Such changes in disease risk can reflect the

impact of debilitation, i.e.. the period-specific accumulation

of risk factor exposure.

The issues of population heterogeneity, mortality selection, the multidimensionality of the aging process, and the

different age pattern of mortality for specific diseases raise

the issue of “competing risks” (Chiang, 1968). Competing

risks represent the censoring of the manifestation of a particular

disease by mortality from another cause. Competing risks

can be dealt with in one of two ways. The first, the usual

demographic approach, assumes that diseases operate independently. Thus. ifa person were nottodie ofa given condition

at the observed time, then his theoretical second age at death

would be determined by the age-specific mortality risks observed for all other causes of death in the population. The

second approach views causes of death, or, more precisely,

h (

the morbid processes generating death. as interacting. To illustrate, consider the simple illness-death process in figure 6

(Manton. 1985).

For simplicity, we have divided the set of all discaws

into a disease of interest and a group of all other diseases.

In the figure we describe the effects of the two sets of discuses

on the population by five health states. The first is the well

state. Since we are dealing with chronic disease, all persons

are assumed to be born into the well state. free of disease.

The person can make one of two types of transitions from

this state. The first, v](a), is the age-specific mortality risks

for all other causes of death. The second. k,(a). is the agespecific incidence function for the disease of interest. If wc

are describing a model of individual health changes in figure 6. this incidence would increase without the effects of

selection. At the population level, however, we could observe

a decline in risks if the most highly susceptible persons were

removed fromthe population (Brody, 1985).

The second health state describes the preclinical phase

of the disease. The individual can also make two types of

transitions from this state. The first is to the death from

other causes state. with a transition rate (1 + 13z) o vi(a).

The parameter ~z reflects the degree of interaction between

the condition of interest and the risk of death from all other

causes. The interaction may function in one of two ways.

One way the interaction may function is for the morwlity

risk of one disease process to be enhanced by another disease

process. This is illustrated by the interaction of diabetes melIitus and stroke. Diabetes causes circulatory degeneration and

I “WELL” ●

------ ------ ------ ------ ------

VI (a)

I

I Clinically I

Unobserved ~

V2 (a)=(l~ N 1 (a) Clinically

------ ------ .

FIGURE 6: Illness-Death Process

~4(t**)

10
enhances the risk of stroke (possibly from hypertension) and

other chronic circulatory diseases. Generally, the person will

die ofa stroke before the primary effects of diabetes cause

death, Indeed, the example given earlier of the increase in

the prevalence of diabetes due to the decrease in the risk

of stroke death illustrates a case where ~z was reduced (by

reducing the mortality risks of stroke through hypertension

control), so that the prevalence in the preclinical (and clinical)

state fordiabetes increased (Whisnant, 1984).

Another way a dependency (represented by ~z # O)

may be generated is through changes in the population distribution of a risk factor common to two diseases. In this case,

persons who develop a particular disease will have a greater

probability of having certain risk factor exposures than those

who do not. The risk factor exposures increase the risk of

both causes of death. Thus ~z > 0.0 because people who

enter the preclinical disease state area subpopulation systematically selected into that morbid state because of particular

risk factor exposures.

The third state is the clinically observed portion of the

disease process. The transition to this state is a function of

the rate of progression of the disease process. Clearly, at

the population level, the rate of disease progression may vary

over individuals, so that we will again find that persons who

clinically manifest their disease will be selected to have the

most rapidly progressing disease processes. For example, many

elderly males manifest prostatic tumors at autopsy, but far

fewer manifest them clinically, Those who manifest the disease

clinically (for a given age at onset) probably have a greater

susceptibility to onset and rapid growth. This may engender

an entirely different disease dependency, say P3, so that the

risk of death from other cause is ( 1 + ~Jvl (a). Likewise,

since there may be a selected group of persons who clinically

manifest the disease, there may be implications for the probability of disease cure or remission.

To illustrate the implications of dependency, consider

table 6, in which ~2 = (33 = (3, by assumption. In this

table we present (1) the number of persons who died of

lung cancer in 1977, (2) the number of persons projected

to die of another disease with a tumor growing with (~ =

l) and without (~ = O) dependency in 1977, and (3) the

number of persons in the population projected to have the

disease but who died of another cause with (~ = 1) and

without (f) = O) dependency in the year 2000. Since the

number of deaths in 1977 is an observed quantity, it cannot

be changed by assumptions about p. However, the number

of persons with the disease in the population is strongly affected

by assumptions about (3.For example, we see that the number

of persons who can be projected to die of another disease

but with a tumor increases dramatically under the assumption

of dependency, Thus, if the risk of death from other causes

decreased (equivalent to an increase inthemean age at death

from those causes), we could expect much larger increases

in the amount of disease manifested (and the number of deaths

generated by the target disease) if the true value of @ is

1.0 (dependent competing risks) than if it is 0.0 (independent

competing risks).

One empirical manifestation of such disease interactions

is the high prevalence of multiple chronic diseases at advanced

ages. This is a particular reason why the “multiple” cause

of death mortality data are so informative about mortality

at advanced ages (e.g., Manton, 1985). For example, in table 7 we present the age-specific ratio of total to underlying

cause mentions of lung and prostatic cancer. We see that

the relative prevalence of prostatic cancer as other than the

under]ying cause of death is much greater than the relative

prevalence for lung cancer and that the relative prevalence

increases with age. This is due to the slower rate of progression

of prostatic cancer, with the greater likelihood of death from

other causes. There is a consistent positive relation, for different tumor sites, between the ratio of total to underlying occurrence and the clinical survival time determined from the Survey

of Epidemiology and End Results study (Manton, Myers and

Cohen, 1987).

Ill. Health transitions

at advanced ages:

Categorical patterns

In the prior section we discussed the relation of individuallevel and population-level morbidity and mortality hazards

and how these relationships had to be analytically adjusted

in order to use population data to make inferences about

the aging trajectory for individuals. In this section we will

describe individual physiological changes in a population

where the physiological and health state of individuals was

measured longitudinally. In describing those longitudinal aging

changes, we will not make assumptions about either (a) the

form of the age trajectory of physiological status or mortality

risks or (b) the distribution of health or physiological parameters in the population. In order to avoid making assumptions

about either factor, we will need as an analytic tool a general

pattern recognition model that can identify patterns both in

the grouping of cases and in the trajectory of change in a

complex multivariate profile of physiological and health

measures.

A pattern recognition model which fulfills these restrictions is called the Grade of Membership (GOM) model. This

model was originally developed in order to identify characteristic symptom profiles for persons with particular disease clusters

(Woodbury and Manton, 1982). The basic logic of the model

is that there exist K profiles that simultaneously describe (a)

the grouping of individuals according to their attributes and

(b) the multivariate profile describing each subgroup. The

K profiles are obtained by first recording the information

on each person (i = 1, 2, 3,.. J) into k binary responses

(!2= 1, 2,..., Lj) for J different variables where an individual

can have only one (i.e., have the value 1.0) of the possible

responses for a given variable. We will designate these binary

variables as XUi,

To explain each binary variable, two types of coefficients

are estimated. The first type, k~jg,describes the multivariate

pattern for the K profiles in terms of the J variables and

11
Lj responses. These coefficients represent the probability distribution of the Lj responses for the jth variable (hence ~ A~jL

= 1.0). The hh profile may be interpreted. in much the

same fashion as factor analysis, by examining the set of ~~jl

for the kth profile. The second type of coefficient. .gik.represents how strongly the ith person’s observed responses (i.e..

his xii!) relate to the pattern of responses for the ,Rh profile.

It is a measure of the closeness of XVLto the h~jv When

a gi~ = 1.0, then .Yijl and kLjLwill be identical. The gik

are constrained to have the properties ~ gi~ = 1.0 and gi~

= 0.0. Thus the .~ijl for a person will be explained as a

weighted combination of some set of h~jv The basic equation

can be written .f,J1= ~gi~ ~ljfi

The .ti,l are equal to l+-(xijl = 1.0). or the probability

predicted by the model that the ith person has the property

.~ijl= 1. The values of the coefficients gik and ALjLare determined by maximum likelihood procedures with Kuhn-Tucker

constraints to impose boundary restrictions on the numerical

range of the two sets of coefficients.

The mathematical structure of the model gives it special

properties for studying longitudinal change. First. the ,qi~and

kLjLare simultaneously estimated in the model. Since the

~i~represent the distribution of cases on each of the K profiles,

this means that no assumptions are required in the model

about the distribution of cases. Second, since all information

is coded in binary form and we attempt to explain the probability of each binary response in a pattern, no assumptions are

made about the functional form of specific types of response.

Third, the model deals appropriately with missing data. As

a consequence, we do not lose cases from an analysis of

a longitudinal data set because of gaps in measurement. Finally, the amount of information in the model increases as

a function of both the number of variables and the increase

in the number of cases. As a result, small data sets with

lengthy followup periods can yield considerable statistical

power.

This general model can be specifically structured to identify patterns of temporal and age change in a longitudinally

followed population. This can be done in one of two ways.

The first way involves representing the temporal path of individual bhanges. In this situation, the number I refers to the

number of individuals or individual episodes in the study.

In the simplest case (i.e., where the episode refers to the

full event history of the person observed in the study), the

number.7 will refer to the number of variables that are measured

at each wave of measurement in the study times the number

of waves of measurements made. If five physiological measurements were taken at each of five biennial exams, there would

be 5 x 5, or 25, variables. Aging would be reflected in

the change of the response probabilities for each type of

variable over the five times of measurement. Thus, if the

distribution of blood pressure for a given profile shifted toward

higher levels across waves, we would infer a tendency toward

blood pressure increasing with age. Since, however, there

are no constraints on how the distribution of response levels

for a variable changes from one wave of measurement to

the next, the blood-pressure measurements could first increase

and then decrease with age. Indeed, this nonlinear pattern

is what is typically observed for diastolic blood pressure among

survivors, with the highest levels observed in the late sixties.

In contrast, pulse pressure tends to remain unchanged through

late midlife, then to gradually increase.

In this way of describin~ change, the GOM model isolates

K patterns of change which represent the shift with age of

the distribution of responses for a specific type of measurement. In any longitudinally followed population, one is likely

to have considerable individual heterogeneity in the trajectory

of physiological (and cognitive) aging changes. These individual differences would be captured in the gi~;that is, while

the model identifie> K typicalpatterns of change, the gif

relate each of those basic patterns of change to the observed

pattern of change for each individual. This type of analysis

may be viewed as a generalization of event history analysis,

where unobserved factors are allowed to affect the operation

of the discrete state jumps, or shifts.

This heterogeneous nonparametric event history analysis

can be roughly illustrated by an evaluation of the use of

type D living arrangements (i.e., nursing homes) by Supplemental Security Income (SS1) recipients. In table 8 we present

the probability that each of 5,482 episodes of cases funded

under the SS1 program ended by one of four types of changes

(i.e., left the active payment state, changed living arrangements, died, or came to the end of the study) occurring after

1, 3, 12, 18, or 24 months. Also contained in the table

isthe distribution of the episodes overage and program type.

At the left side of the table, the variables and their response

levels are described. Next, the column labeled “Frequency”

contains the observed distribution of responses in the sets

of individual episodes. The last four columns describe the

four profiles (K = 4) by presenting the probability that an

episode with a given profile will exit the SS1 population

in one of several ways. We can see that the first profile

describes episodes with a high probability of changing living

arrangements, which is consistent with its strong likelihood

(44.2 percent) of representing episodes for children who return

to their parents. The fourth profile describes episodes with

a strong likelihood of ending in death, which is consistent

with persons in those episodes being very elderly (85 percent

65 years and oveq 63 percent 75 years and over). The second

group represents episodes for young adults ( 19–50 years) who

have low probabilities of death. The third group represents

episodes for elderly persons (somewhat younger than group

4) but with roughly one-half the risk of ending in death.

To relate these patterns to individual episodes, we need

the distribution of the gif. This is presented in table 9. The

high values of gi~ indicate episodes which are very closely

represented by the kth pattern. We see that the fourth group

of episodes is the most prevalent (i, e., 473 episodes with

a gik = 1.0 and a total ~ gi4 = 1,827). Group 3 is least

i

prevalent.

As suggested above, this analysis can be viewed as extending event history analysis (Tuma, 1982) in two ways: (a)

the transition probabilities vary over individuals because of

unobserved covariates affecting the level of the transition rate,

and (b) the hazard function describing the time dependency

of the transition to one of the outcome states is nonparametric.

The reason that we can analyze the history of multiple-state

changes in this way (i.e., instead of as in mortality analysis,

12
where there is only one type of absorbing transition to the

death state) is that the likelihood function can be shown to

independently factor into event episodes (Manton, Stallard,

Woadbury, and Yashin, 1986). Given this factorization, the

unobserved heterogeneity is reflected in the distribution of

the gi~ where they are similar to the posterior probability

of a person’s observed transition history being a given type.

The fact that we have four types is a generalization of the

models of mortality for heterogeneous populations discussed

earlier because, instead of the one-dimensional heterogeneity

in those models, we have four different unobserved dimensions. It should also be noted that, in addition to the information

on transitions, a set of fixed covariates is included. Thus

the four-event history protlles represent information on fixed

individual characteristics as well as their transition histories.

A second way of describing transitions is by describing

the states that a person passes through. In this case, the

number 1 no longer represents individuals or episodes. Instead,

it is expanded to represent the number of measurements made;

thut is, if a person were observed at three of five measurement

times, we would treat the three observations as independent

cases. Thus [, the number of observations in the analysis,

would represent the number of persons times the average

number of waves of measurements at which each person was

assessed. For example, in a recent analysis of the 267 persons

in the Duke Longitudinal Study of Aging ( 1955–76), persons

were assessed at an average of 4.3 waves. Hence I = 1,167,

or 267 X 4.3. Clearly, this reorganization of the data is

possible because, in the model, given the selection of the

correct number of profiles. the observed quantities, .r,il, are

.-

independent. Thus the measurements of the same variable

made at different times are independent. This means that

J, the number of variables used to represent the set of measurements made on each individual, will have to be redefined.

Instead of. as in the prior analysis, having five variables

measured at five waves, in the current analysis we have only

five variables (but five times as many “cases”).

In this type of analysis, the hk,,l represent the states that

pers~ms possess at a specific point in time and do not represent

the transitions between those states. Change is described in

terms of the change in the prevalence of each of the K states

over time, Hence the information on transitions is now contained in the ,q,k instead of the AAj~ Heterogeneity is still

reilcctcd in this analysis, since the gik represent how close

a person is at a given ti~e to one of the K states. Since

etich person will have as many sets of ,yi~as waves of measurements in which he participated, we can examine the pattern

of change in an individual ,g,Lto see how he transitions between

multivuriate states (defined by the A1jJ.

TO illustrate this type of analysis, we present table 10.

In this table we present the AL,,!derived from an analysis

{)f the 21 years of followup on the first Duke Longitudinal

Study of Aging ( 1955–76). These coefficients describe five

basic stfites manifested in the study population over the study.

As we can see, the five profiles describe a consistent pattern

of physiological decline.

Whtit is interesting is to examine the probtihility distribution for the additional measurements in ttible I I. We see

that the first variable describes the distribution of ages at

which each profile was observed. It is interesting that the

mean ages across the profiles are 75.0, 75.6, 79.3, 83.2,

and 74.8. Thus there is a correlation of physiological decline

with the mean age of persons in each state. What this suggests

is that there is a morbid, relatively young subgroup that entered

the study and died out quickly. The exhaustion of this population from the study is evidenced by the next two variables,

which show that no one in profile 5 survived past the seventh

wave of the study. Also of interest is the distribution of

these profiles by sex, race, and education. We find that the

probability of being black is high in both profiles 4 (long

survivors) and 5 (short survivors). This is consistent with

the model of the black–white crossover. It is interesting that

the fourth group is less educated than the fifth group-so

it is not possible to explain these profiles by socioeconomic

differences. Also, though there are differences in the proportion

female in the two profiles, they are not as great as differences

between other profiles where the differences in survival are

less. Thus the GOM analysis has picked up a pattern of

state changes that seems to identify a terminal drop subpopulation with poor, early physiological characteristics.

IV. Mechanisms of health

transitions at later ages

In the previous section we described analyses which could

identify basic health transition patterns and subgroups in longitudinal data. In this section we describe models that go

beyond simply identifying patterns to attempting to describe

the basic mechanisms of physiological aging changes. The

model we will employ to do this is the Random Walk model

of human aging and mortality (Woodbury and Manton, 1977).

This model describes aging changes in terms of two correlated

stochastic processes.

The first type of process describes changes in individual

risk factor values over an interval among persons who survive

the interval. For a set of J risk factors, these equations may

be simply written,

.$, +[ = y, + (~ — /4,).:, + 6’,. (~)

where ~-represents the vector of measurements made on person

i (index i suppressed) at times r and / + 1. q, is the vector

of regression constants, ({ – ~,) is the matrix of regression

coefficients (J x Y), and ~,is the vector ofj error terms.

These J equations describe the change in the risk Fdctors

as a function of three types of change—’”drift. ””“’regression.”

and ‘-diffusion .“ These three types of dynamics are illustrated

in the univariate case in figures 7 to [().

“Drift’” (change between figures 7 and 8) represents systematic changes in the mean of the risk factor values over

time. It is retlected in the consttmt, //,. and in the coefficients

for the other risk fwors in the multivariale case (i.e., in

the matrix / – #). ‘-Regression”’ (tlgure 9) represents the

13
Figure 7

I

AREA UNDER CURVE IS

POPULATION SIZE IS ~

Q.

P(-J-2q) f+)-co !-l. l-+)+%l+)+%

Figure 8

FREQUENCY FUNCTION OF RISK VARIABLE AT TIME OABOVE

1BELOW
Figure9

REGRESSION

TO THEMEAN

Figure10
stochastic tendency for an individutil to return to the ‘Utvcragc”

value of the risk tkctor-with the temporal change in the

average risk fMor value being determined by “drift. ” “DitYusion’” (figure 10) represents the lack of predictability in risk

factor values over time—represented by the error terms in

the equations. These three types of dynamics describe both

systematic and stochastic aging among individuals in a population to the degree that the physiological age status of the

person is described by the risk factors in the system. Age,

as a risk factor, is a proxy measure for the physiological

factors not explicitly measured.

One potentially important modification of the model in

order to explain aging mechanisms is the effect of “tracking”-

i.e.. the tendency for persons to persist at certain physiological

risk factor levels (e.g.. high diastolic blood pressure levels).

Tracking could be represented, for example, by allowing individual constants (i.e.. g,i) in the regression equations. in which

case all stochasticity would involve diffusion about individual

level means (with the individual means changing over time

due to “drift”). Tracking is one method to adjust aging dynamics for the effects of influential but unobserved physiological

processes (e.g.. Yashin. Manton. and Stallard. 1987).

Each of the types of dynamic forces represents possible

ways that one could intervene in the mortality process by

controlling risk factor change. For example. one could reduce

the upward drift of a risk factor with age. Alternatively.

one could reduce diffusion. which would reduce mortality

because higher variances imply persons at more extreme risk

factor levels.

To illustrate this type of process, we present table 12.

The coefficients in this table represent the change in nine

risk factors over 2-year intervals for roughly 2,330 males

followed for 20 years in Framingham, Massachusetts. We

see that there is potentially a large amount of diffusion for

certain variables, like diastolic blood pressure (i.e., because

the diagonal term is small). Others exhibit very strong persistence, e.g., weight-height index.

Associated with the dynamics of physiological changes

among survivors is a discrete state process that describes

the risk of death (or some other discrete health status change)

as a function of the current risk factor levels. This risk is

modeled as a quadratic function of the risk factors. The hazard

functioticould be written

.. .

#, (x,) = po, + /J .:, + 1/2// tl, x,. (3)

The extended matrix, including the constant (p.{),)and linear

(% q;) coefficients, say Q,, as well as the quadratic and

interaction terms (l/z~f), is presented in table 13.

Note that the matrix does not contain terms for agc.

This is because we represent the effects of age by multiplying

the whole matrix by a Gompertz function, or

where the exponent in the Gornpertz term was estimated to

be 0.0887 (compared with the value for (3 in table 4), and

a = AGE.– 50.

This means that each type of term in Q is multiplied

by a function of age. Specifically,

.

p-o = #.oxx7)a .

f-%$ (4a)

/7,, = e(()”()887)”“ ~(),

~,, = e(()()X87)(’. f/(J

(4b)

(4C) -.”

This representation of age is critical to the model being

biologically realistic. For example, an alternative strategy for

analyzing the effects of age is to include it simply as a risk

t%ctor. This. however. does not truly represent age changes

in the model. Rather, it simply means that age is statistically

controlled. Consequently. not all risk factors have their effects

modified by age. The result is that the age effect may be

isolated from the other risk factor coefficients.

The problem with the model where age is simply introduced as a risk factor is not statistical. It can produce

nearly as good a statistical fit to the data as the Gompertz-type

model (equation 3). and it can produce forecasts that are

very similar. The problem is that. if the quadratic function

is to be taken seriously as a model of physiological processes,

its minimum point must represent the physiological homeostatic point. When age is included as a covariate in the hazard

function. it forces the minimum point for the other risk factors

to physiologically unrealistic ranges. The Gompertz (and

Weibull) quadratic hazard models produce physiologically

plausible values. This is illustrated in table 14. where we

present the minimum mortality points for pulse pressure and

diastolic blood pressure over age for the two models.

We see that the changes when age is incl~ded as a risk

factor are not realistic (e.g.. a pulse pressure of - I5 at

age 45), whereas they are reasonable for the Gompertz model.

Therefore. though a risk model may perform well statistically.

and forecast well. it may not be an acceptable model of

the actual underlying biological risk mechanisms.

The dynamic and hardrd equations—i.e., (3) and (4)—

may be used to describe the mortality and physiological change

of a population by integrating their effects in a forecasting

model. The change in the probability of surviving to the

next measurement time may be generated by

{

p(ll,) + p(yq

exp 2 -d~l}=~f+l. “)

We can see that the probability of survival (Sp) is a function

of the variability of persons on risk factors (~,) as well as

the average risk factor values (y,). This variability increases

mortality risks regardless of whether it is due to fixed factors

or diffusion. Clearly mortality is also driven by drift. i.e.,

systematic changes in the risk Factor means.

The next two equations show how the metin and variance

of the risk factor distribution among survivors is changed

by systematic mortality:

16
Finally, to alter the risk factor values to their future values,

wc must apply the dynamics as,

y,+l =14, +( J-/ll)Iy (8)

y,+, = ;, + ({–+t,)y~(/-/t,)~. (9)

We can utilize the coefficients in tables 12 and 13 in

these tlve equations to produce a synthetic cohort life table

starting at age 30 with risk factor covariates that describe

both survival and physiological changes. This is illustrated

in table 15. Clearly, this table differs from a standard life

table in that, in addition to S, and life expectancy, we present

the means and standard deviations of the nine risk factors

among survivors to age a, (i.e., v, and (diag ~J “2). The

S, values in this table are very close to the S, values in

the cohort life table for U .S, males provided by the Social

Security Administration ( 1983). The risk factor means, since

they are derived from equation (8) applied to projections for

30-year-oId males. can be compared to the observed agespecitlc means in the Framingham population. The correspondence of the risk factor means (projected and observed) is

quite close over time (up to age 80-the last age observed

in the 20-year followup). Thus, the life table provides an

accurate picture of the survival and physiological status of

the cohort followed over time.

These tables, and the equations that generate them, can

be used in several ways to understand the mechanisms underlying physical changes. First, since the tables are adjusted for

mortality as it is ‘systematically related to the individual values

on the physiological variables, they unconfound individual

changes from the temporal trajectory of the population mortality rates. Second, if we decompose total mortality into its

disease-specific elements (e.g., cancer and heart disease), we

can study the dependency of different disease processes. Specifically, if we estimate separate quadratic hazard functions

for diseases, we can examine the coefficients for the same

risk factors in the different equations. If the coefficients are

positive, there is, conditional upon risk factor values, “positive” dependency between the diseases (implying that the elimination of one condition would be compensated for by the

other conditions so that the effect on survival would be less

than expected under the assumption of independence). If the

signs are different, then there is “negative” dependency. Thus

one could determine what the effect would be on the force

of mortality of all other conditions if a condition were eliminated and persons at certain risk factor levels survived to

later ages. These simulations would also Udke into account

the change in the impact of a risk factor on cause-specific

mortality over age. In a series of such simulations, we found

the actual level of dependency to be relatively small. This

is illustrated in table 16.

We see that the effects of dependency are largest for

cardiovascular disease. However, even in that case it is only

0.65 year-or 6.1 percent of the change under the assumption

of independence. Clearly, if there are major unobserved risk

factors, this could increase the estimate of the dependency

effect, We are investigating the effect of such unobserved

factors by including tracking effects in the dynamic equation.

The small effect for disease dependency occurred because

diffusion tended to cloud systematic shifts in the risk factor

distributions caused by systematic mortality.

The current model could be used to investigate the effects

of proposed risk factor interventions in detail. It has been

observed that, in some community-based trials, the effects

of intervention were not as great as implied by the association

of risk factors with risk in longitudinal studies would suggest.

The model presented earlier may help us to understand why.

The failure to observe an effect of the anticipated size might

result because several types of dynamic factors were not represented in the original risk factor analyses. Consequently,

the appropriate age and time dynamics of risk factor interventions in a population were not obtained from those analyses.

Specifically, those analyses generally did not represent

(a) age and risk factor interactions, (b) the trajectory of change

in risk factor values, and (c) the duration of specific chronic

disease states. The potential effects of these factors in terms

of the economic cost of illness are illustrated in table 17.

We see that reducing diastolic blood pressure and pulse

pressure to what are usually viewed as clinically desirable

levels produces an actual decline in life expectancy at age

30 and increased costs (though by age 47 this has reversed).

In contrast, smoking control produces a large positive effect

at age 30. The reason for the early increased costs for blood

pressure control is evident. The trajectory of the blood pressure

mean under intervention is not like the actual population trajectory, which first increases and then declines. Furthermore,

the standard cost function strongly weights premature mortality

at early ages (i.e., the life expectancy decrease is quite small).

After age 60 there is a very large positive impact of blood

pressure control on both costs and life expectancy. The reason

for the early discrepancy is that the age X risk factor interaction

effects were quite strong for blood pressure, and the intervention in no way reflected those effects. Thus intervention should

reflect the age-specific optimal risk factor levels. This can

be validated in table 17 by examining the columns where

all risk factors are fixed at “optimal” levels. This produces

a large improvement in survival because the

risk factor implications are controlled.

V. Implications of health

transitions

effects of the

status

In the preceding sections we (a) described the relation

of individual health transitions to the average rate of change

of population health characteristics, (b) showed how patterns

of physiological change and subgroups could be identified,

and (c) illustrated how the physiological mechanisms of aging

changes could be described. In this section we will briefly

identify some of the implications of those health transition

models.

One set of implications has to do with changes in the

Social Security entitlement age. These changes were introduced because it was assumed that life expectancy increases

at an advanced age (i.e., 65) reflected improvements in the

health status of the elderly. In a minority report to the Commission on Social Security Reform (Feldman, 1983), it was

17
suggested that this might not be so because improved survival

might be concentrated among persons with chronic diseases

and impairments, so the prevalence of such conditions in

the population might increase. We examined such relations

in the first section and suggested that, in the case of diabetes

mellitus, reductions in stroke mortality may have increased

the population prevalence of diabetes at later ages. The implications of multiple-cause mortality data and dependent competing

risks were also discussed.

A second area where there are serious implications in

inappropriately modeling health transitions is in estimating

the need and demand for health services-especially long-term

care services for the extreme elderly population. Certain projections (e.g., Brody, 1985) show the serious consequences of

population aging on the prevalence of hip fracture and

Alzheimer’s disease. What the analysis of certain longitudinal

data sets suggested is that survivors to extreme old age are

generally a highly selected group. Therefore, under current

mortality conditions, their average health status is kept relatively high because the survival time, once a person enters a

morbid state, is short. If life expectancy increases, this may

allow certain morbid subgroups to survive longer in a disease

state. This would imply even higher levels of demand for

health services.

The third set of implications has to do with the clinical

case management of chronic diseases and risk status over

age. We have begun to recognize that the management of

elderly patients is very different from that of younger patients.

What the results on the physiological aging mechanisms

suggest is that case management should change over age.

Indeed, the results suggest that optimal physiological values

for risk factors change in complex ways with age and must

be evaluated in the context of other physiological variables.

The fact that such factors were not fully taken into account

in past risk factor intervention attempts may be one reason

why certain interventions failed to produce the anticipated

population effects in some community population trials.

VI. Data needs

The statistical methodologies and analyses reviewed in

this paper also serve the purpose of identifying critical data

needs. We can identify some of these.

One need is for long-term followup of well characterized

cohorts. In this followup, there is a need for repeated measurements of detailed physiological Characteristics. Such data

would better help us understand the age dynamics of physiokJgical risk factors at advanced ages. For example, we might

be able to determine why certain risk factors cease to be

so strongly associated with disease risks at advanced ages.

Such followup data are necessary to help us disentangle the

effects of systematic mortality selection from physiological

aging dynamics. A cost-effective way to produce these data

is to have further followup on existing longitudinal populations.

A second need is to determine morbidity and mortality

risks among nationally representative samples of already morbid and disabled persons. This would help us resolve some

of the questions about the societal impact of life expectancy

gains at later age. Illustrations of such surveys are the 1982

and 1984 National Long Term Care Surveys. A nationally

representative sample of 6,300 persons with chronic limitations

in activities of daily living and instrumental activities of daily

living were followed for 2 years to examine (a) their risk

of death, (b) their risk of institutionalization, and (c) the

trajectory of change in dependency level. Such data are very

important in forecasting future health service needs.

A third type of data that would be useful could be obtained

by systematically extending data collection for clinical studies

to measure comorbidities. Not only is the issue of comorbidity

important for describing “normal” aging changes, but it is

important for determining what are the appropriate clinical

responses to disease at advanced age.

A fourth data requirement is for data that systematically

relate chronic disease diagnosis to level and type of disability.

Such data would allow us to generate the many-to-one mapping

functions of diseases to disabilities. This would enable us

to utilize the extensive epidemiological data on disease risk

factor associations in forecasting future demand for long-term

care services.

A fifth data requirement is for more spatially disaggregate

data. Spatial disaggregation would allow us to take better

advantage of naturally occurring population differences in risk,

This would be a low-cost way of increasing the power of

currently existing data.

Finally, it is clear that the interactions of chronic diseases

and aging changes are such complex phenomena that no single

data source will suffice to fully characterize them. This

suggests the need for biologically motivated models of the

basic physiological processes that can be used to integrate

parameter estimates developed from different data sources.

Such a strategy for integrating and evaluating multiple data

sources must be pursued both as a way of systematizing our

knowledge about health dynamics and as a stmtegy for identifying critical data needs.

18
References

Anderson, D. E,: Familial Susceptibility, in J. F. Fraumeni, cd.,

Persons at High Risk of Cancer. New York. Academic Press, 1975.

Brody, J. A.: Decreasing Mortality and Increasing Morbidity in

the 20th and 2 Ist Centuries, in Philosophy and Medicine Series.

D. Rcidcl Pub. Co. Norwell, Mass., 1985.

Chiang, C. L.: [introduction to Stochastic Processes in Biostatistics.

New York. Wiley, 1968.

Fcldmim, J. J.: Work ability of the aged under conditions of improving

mortality. Mi/bank Metn. Fund Q. 61(3):430444, 1983.

Fries, J. F.: Aging, natural death, and the compression of morbidity.

N, Etrg/. J. Med. 303:130-135, 1980.

Gavrilov, L. A.: Human lifespan stopped increasing, Why? Gerontolo~y 29:176-180, 1983.

Huchinski, V.: Decrcascd incidence and mortality of stroke. S/roke

1S:376-378, 1984.

Hamis, J. E.: Cigarette smoking among successive birth cohorts

of mcn and women in the United States during 1900- I980. J.N.C.I.

7 I:473-479, 1983.

Howell, T. H.: Causes of death in nonagenarians. Geronrol. C/in.

5:139–143, 1963a.

Howell, T. H.: Multiple pathology in nonagenarians. Geriayr. 899-

902, 1963b.

Howell, T. H.: Heart weights among octogenarians. Y. Am. Geriatr.

Sot. 24:572-575, 1981.

Laktitta, E. G,: Health, Disease and Cardiovascular Aging, in AmeriCU’SAgin,q:Heahh in an Older Society. Washington, D.C. Committee

on tin Aging Society, Institute of Medicine and National Research

Council, National Academy Press. 1985, pp. 73-104.

Lucc, B. R,, K. Liu, and K. G. Manton: Estimating the Long-Term

Care Population and Its Use of Services, in Long-Term Care and

Sociu/ Secwri(y. ISSA Studies and Research No. 21. International

Social Sccrrrity Association. Geneva, Switzerland, 1984.

Mwrton, K. G.: Temporal and agc variation of the U.S. black/white

citusc specific mortality differentials: A study of the recent changes

in the rcltitive health status of the U.S. black population. Gerontologist

22(2):170-179, 1982a.

Manton, K. G.: Changing concepts of morbidity and mortality in

the elderly population. JfilbankMem. Fund Q. 60: 183–244, 1982b.

Manton, K. G.: Paper presented at Quality of Life and Well-Being

Workshop, National Center for Health Statistics. Bethesda, Md.,

July IS-16, 1985.

Mtinton, K. G., G. C, Myers, and H. Cohen: National Patterns

of Reporting of Cancer on Death Certificates. Manuscript, 1987.

Manton, K. G., I. C. Siegler, and M. A. Woodbury: Patterns of

intellectual development in later life. J. Geronrof. 41:463-499, 1986.

Manton, K. G., and E. Stallard: A two-disease model of female

breast cancer Mortality in 1969 among white females in the United

States. J. Natl. Cancer Inst. 64( 1):9-1 6, 1980.

Manton, K. G., and E. Stallard: Methods for evaluating the

heterogeneity of aging processes in human populations using vital

statistics data: Explaining the black/white mortality crossover by

a model of mortality selection. Hum. Bio/. 53:47-67, 1981.

Manton, K. G., and E, Stallard: Bioactuarial models of national

mortality time series data, Strategies for making full information

estimates of national morbidity distributions. Heal/h Care Financing

Review. Vol. 3. Office of Research and Demonstrations, Health

Care Financing Administration. Washington. U.S. Government Printing Office, 1982.

Manton, K. G., and E. Stallard: Heterogenei~y and Its Effect on

Mortality Measurement, in J. Vallin, J. H. Pollard, and L. Heligman,

eds. Methodologies for the Collection and Analysis of Mortality

Da/a. Liege, Belgium. IUSSP, Ordina Editions, 1984.

Manton, K. G., E. Stallard, and J. W. Vaupel: Alternative models

for the heterogeneity of mortality risks among the aged. Journal

of the American Statistical Association81 :635–644, 1986.

Manton, K. G., E. Stallard, M. A. Woodbury, and A. 1, Yashin:

Application of the grade of membership technique to event history

analysis: Extension to multivariate unobserved heterogeneity.

Mathematical Modelling 7:1375-1391, 1986.

Minaker, K. L., and J. W. Rowe: Health and disease among the

oldest old, A clinical perspective. Mi/bank Mere. Fund Q. 63(2):

324-349, 1985.

Nihon University: Population Aging in Japan, Problems and Policy

/ssues in the 21s/ Century. International Symposium on an Aging

Society, Strategies for 2 Ist Century Japan. Nov. 24-27, 1982.

Perks, W.: On some experiments in the graduation of mortality

statistics. J. Insl. Actuaries 63: 12–57, 1932.

Sacher, G. A.: Life Table Modification and Life Prolongation, in

J. Birren and C. Finch, eds., Handbook of the Biology of Aging.

New York. Van Nostrand Reinhold, 1977.

Schneider, E. L., and J. A. Brody: Sounding board—Aging, natural

death and the compression of morbidity, Another view. N. EngI.

J. Med. 309:854-856, 1983.

Social Security Administration, Office of the Actuary: Life Tables

for the U.S., 1900-2050. SSA Pub. No. 11–11536. Actuarial Study

No. 89, Dec. 1983.

Spiegel man, M.: The Curve of Human Mortality, in Proceedings

of Seminars of Duke University Council on Aging and Human Development, Mar. 1968.

Tuma, N. B.: Nonparametric and Partially Parametric Approaches

to Event-History Analysis, in S. Leinhardt, ed. Sociological

Methodology, 1982. San Francisco. Jossey-Bass, 1982.

19
Whisnant, J. P.: The decline of stroke. Stroke 15:160-168, 1984.

Wilkin, J. C.: Recent Trends in the Mortality of the Aged, in Trunsoctions, 1981, Vol. XXXIII. Society of Actuaries, 1982.

Wilkins, R., and O. Adams: Healthfulness of Life, A Unijied View

of Morlulity, Itzstitlltiotzuli2cltion, and Non-Instiu[tionalizution in

Canada, 1978. Institute for Research on Public Policy. Montreal,

Canada. 1983.

Wing, S., K. G. Manton, E. Stallard, et al.: The Black–White

Mortality Crossover in Two Community-Based Studies. Manuscript,

1987.

Woodbury, M. A., and K. G. Manton: A random-walk model of

human mortality and aging. Theor. Popul. Biol. 11(1):3748, 1977.

Woodbury, M. A., and K. G. Manton: A new procedure for analysis

of medical classification. Merlr.Inform. Med. 21:210-220, 1982.

World Health Organization: The uses of epidemiology in the study

of the elderly. Report of a WHO Scientific Group on the Epidemiology

of Aging. Technical Report Series 706. Geneva. WHO, 1984.

Yashin, A. 1., K. G. Manton, and E. Stallard: The propagation

of uncertainty in human mortality processes operating in stochastic

environments. Theor. Popu/. Bio/. (In review) 1987.

Wing. S.. H. Tymlcr. and K. G. Mantmr: The participant effect:

Mortality in a community-based study cornparcd to vital statistics.

./. ChwnicDis. 38(2): 135-144.1985.

20
Health expectancy at birth, by se% Canad+ 1978 (in years)

State of health lMales Females Total

Long-term institutionalization 0.8 1.5 1.1

Cannot do major activity 3.0 1.3 2.1

Restric~ed in major activity 5.4 8.7 7.1

Minor activity restric~ion 1.3 2.2 1.8

Only short-term disability 1.1 1.8 1.4

Not restricted in activities 59.2 62.8 61.0

Total Life expectancy 70.8 78.3 74.6

SOURCE: Wilkins and Adams, 1983.

TABLE 2

Health expectancy by age and sex, Canada, 1978 (in years and

percentage of remaining life expectancy)

SexlAge Life Expectancya Quality-Adjustedb Disability-Freec

Remaining Life Expectancy Life Expectancy

N % N % N %

Males

o

15

25

45

65

75

Females

o

15

25

45

65

75

Total

o

15

25

45

65

75

70.8

57.2

48.1

29.6

14.4

8.9

78.3

64.5

54.8

35.7

18.7

11.7

74.6

60.9

51.5

32.7

16.7

10.6

(loo)

(loo)

(loo)

(loo)

(loo)

(loo)

(loo)

(loo)

(loo)

(loo)

(loo)

(loo)

(loo)

(loo)

(lCo)

(loo)

(loo)

(loo)

65.8

52.4

43.5

25.6

11.5

6.9

71.7

58.1

48.7

30.5

14.7

8.6

68.7

55.2

46.1

28.1

1~.~

7.9

(93) 59.2

(92) 46.2

(90) 37,6

(86) 20.6

(80) 8.2

(78) 4.6

(92) 62.8

(90) 49.4

(89) 40.4

(85) 23.6

(79)

(74) ;:;

(92) 61.9

(91) 47.8

(90) 39.0

(86) 22.1

(80)

(75) ?:;

(85)

(81)

(78)

(70)

(57)

(52)

(80)

(77)

(74)

(66)

(53)

(44)

(82)

(78)

[::]

(54)

(47)

lNotes: a. All states of health together

b. Weighting factors used were as follows: INST = 0.4,

CDMA3 = 0.5, RMAJ = 0.6, MINOR = 0.7, OSTD = 0.5,

NODLS = 1.0.

c. Free of any health-related activity restriction (all

categories of severity, institutionalized or not).

SOURCE: Wilkins and Adams, 1983.
TABLE 3

!-kalth q&uw=Y at bi~ by sex, Canad% 1951 and 1978 (years)

Health Expectancy Males Females Total

Life expectancy

1951

1978

Change: 1951-1978

DisabiIitv exDec:ancvb

1951

1978

Change: 1951-1978

Disabiliwfree !ife exoecarwc

1951

1978

Change: 1951-1978

d Q~~itv+c~us.:ed !ife exw5czmCY

1951

1978

Change: 1951-1978

66.3 70.8 68.6

70.8 78.3 74.6

+4.5 +7.5 +6.0

6.6 6.1 6.3

9.7 12.2 11.0

+3.2 +6.1 +4.7

59.8 64.7 62.2

61.1 66.1 63.6

+1.3 +1.4 +1.3

63.1 67.8 65.4

66.9 72.2 69.1

+2.9 +4.4 +3.7

Notes: a. All states of health combined.

b. Long-term disability or activity limitation, excluding

institutionalization and purely short-term disability.

>

c. Life expectancy 16s disability expectancy.

d. Disability expectancy counted as 50 per cent of disabilityfree life ●xpectancy.

SOURCE: Wilkins and Adams, 1983.
TABLE 4.: Parameter Describing Age Rates of Increase in Risk for Weibull

Hazard Function for Female Medicare Recipients Aged 65-84 in 1968

Pd tin

Weibull Function I Gompertz Function

Cohort/ (m-i-l= 6 parameter) (e”parameter)

Age Range Homogeneous Heterogeneous Homogeneous Heterogeneous

1902

65-75

1$101

66-76

lgoo

67-77

1899

68-78

1898

69-79

1897

(70-80)

1896

71-81

1895

72-82

1894

73-83

1893

7&!34

1892

75-85

1891

76-86

18cjo

77-87

1889

78-88

1888

79-89

1887

80-90

1886

81-91

1885

82-92

1884

83-93

1883

84-94

5.44

(0.04)

5.46

(o. 04)

5.46

(0.04)

5,59

(0.04)

5.69

(0.04)

5.69

(0.05)

5.75

(0.05)

5.90

(0.05)

5.51~

(0.05]

6.15

(0.05)

6.11

(0.05)

6.29

(0.05)

6.33

(0.06)

6.38

(0.06)

6.36

(0.06)

6.53

(0.07)

6.33

(0.07)

6.42

(0.07)

6.38

(0.08)

6.23

(0.08)

5.77

(0.06)

5.82

(0.07)

5.86

(0.07)

6.02

(0.07)

6.16

(0.08)

6.20

(0.08)

6.31

(0.09)

6.51

(o. 10)

6.58

(0.10)

6.89

(0.11)

6.91

(0.12)

7.17

(o. 13)

7.29

(0.14)

7.43

(0.16)

7.51

(0.17)

7.78

(0.19)

7.69

(0.20)

7.90

(0.22)

7.98

(0.24)

7.97

(0.25)

6.26

(0.06)

6.20

(0.06)

6.11

(0.06)

6.21

(0.06)

6.yj

(0.06)

6.18

(0.06)

6.18

(0.06)

6.30

(0.06)

6.22

(0.06)

6.$6

(0.06)

6.32

(0.06)

6.48

(0.07)

6.44

(0.07)

6.44

(0.07)

6.34

(0.07)

6.46

(0.08)

6.17

(0.08)

6.20

(0.08)

6.09

(0.09)

5.86

(0.09)

7.34

(0.10)

7.36

(0.10)

7.38

(0.11)

7.57

(0.11)

7.73

(0.12)

7.75

(0.13)

7.88

(o. 14)

8.13

(o. 14)

8.~o

(0.15)

8.60

(0.17)

8.65

(0.18)

8.97

(0.20)

9.14

(o.~~)

9.35

(0.22)

9.&8

(0.24)

9.85

{0.26)

9.81

(0.28)

10.12

(0.30)

10.29

(0.32)

10.37

(0.34)

(Standard errors in parentheses)

23
TABLE.:5 MIX’S Of ~ ~th m = 6,

as a Step 3’unctiOnfor

Cancer, United States

and t estimated to be 20.4 Years, Where & is

Cohorts Born in 1920 or hter. White Males,

modeled

Lung

.-

Year

of

Age At

Start Of

PERIOD

1950- 1955- 1960- 1965- 1970- 1975-

Birth Internal 195.4 1959 1964 1969 1974 1977

Xti a~ 31md* 9

19151

19151 35 2.38

1910= 40 2.17

19051 45 1.75

19001 50 1.50

18952 55 1.35

18902 60 1.05

18853 65 0.73

18803 70 0.56

75

80

85

90

95

1920-

3.Q1

2.38

2.17

1.75

1.50

1.35

1.05

0.73

0.56

1925L

3.77

2.8a

2

2<

.

38

L*75

1.50

1.35

1.05

0.73

0.56

%atio of s/; cm.st,ai-nedequal fo= 1.900-1940birth c.h.r.s

2

Ratio of s/; constrained equal for 1890 and 1895 birth cohorts

3

Ratio of s/; constrairiedequal for 1880 and 1885 birth cohorts

19311L

4.56

3.20

~Jg

2.38

2.?.7

1.75

1.50

1.35

1.05

0.73

0.56

19351

4.40

3..61

3*Z4

2.58

2.38

2.17

1.75

1.50

1.35

1.05

0.73

0.56

19401

4.20

3.26

3.13

2.83

2.47

‘~38

2.17

1.75

1.50

1.35

1.05

0.73

0.56

Nom : In allmodels parameter estimates for cohorts age 35 and over in 19S0

were constrained to be constant over entire interval 1950-1977. The

sequence of parameter estimates for each cohort is obtained following

diagonally down the table from the entry in the first column or row.

24

..
TABLE 6: Lung Cancer Mortality Observed in 1977, and Lung Cancer Morbidity Observed

and Predicted Under Two Dependency Assumptions

Number Number Projected to

Observed Number Who Died of Have Died of Another

Dead From Another Cause With Cause but with a

Lung Cancer Tumor Growing Tumor Growing

1977 1977 2000

AGE B=l 6=0 ~=1 $=0

o-9

10-19

20-~9

30-39

40-49

50-59

60-69

70-79

80-89

90+

2

7

42

448

3,223

12,056

21,592

17,649

5,381

373

0

5

76

442

3,027

12,462

45,667

84,464

76,784

9,040

0

2

37

208

1,371

5,259

17,601

25,993

14,091

1,796

0

3

64

644

4,708

13,580

31,125

79,324

112,339

26,253

0

2

31

302

2,135

5,749

10,935

18,096

21,495

7,652

TOTAL 60,773 231,968 66,359 268,040 66,397

TABLE,7: Age and Sex Specific Ratios of Total to Underlying Cause of Death

Occurrence of Cancer of Bronchus and Lung (ICD 162.1) and Prostate (ICD

185) in 1970 for White Males for Five-Year Age Groups

Lung and Bronchus Prostate

# Underlying # Underlying

Age Cause # Total Cause # Total

Range Occurrences Occurrences Ratio Occurrences Occurrences Ratio

35-39 351 358 1.02 2 2 1.00

40-44 1,114 1,161 1.04 14 16 1.14

45-49 2,314 2,391 1.03 45 49 1.09

50-54 4,063 4,247 1.05 150 167 1.11

55-59 6,540 6,934 1.06 445 508 1.14

60-64 8,295 8,882 1.07 1,065 1,287 1.21

65-69 8,605 9,382 1.09 1,801 2,345 1.30

70-74 7,352 8,232 1.12 2,677 3,730 1.39

75-79 5,130 5,794 1.13 3,414 5,128 1.50

80-84 2,415 2,780 1.15 2,957 4,867 1.65

85-89 775 910 1.18 1,609 2,834 1.76

90+ 197 250 1.27 496 973 1.96

TOTALS 47,149 51,321 1.09 14,675 21,906 1.49

25
TABLE 8: Probability of Being Ended by One of Four Types of Change for 5,482 Episodes of

Cases Funded by Supplemental Security Income Program, and Distribution Over Age

and Program Type

PROFILE

Variable Frequency 1 2 3 4

REASON FOR TERMINATION

Up to One Month Length of Stay

Non-Payment 0.0487

Change in Living Arrangement 0.0657

Died 0.0327

End of Study 0.0117

One to Three Months Length of Stay

Non-Payment 0.0456

Change in Living Arrangement 0.0618

Died 0.0224

End of Study 0.0161

Three to Twelve Months Length of Stay

Xon-Payment 0.0540

Change in Liting Arrangement 0.0925

Died 0.0547

End of Study 0.0560

Twelve to Eighteen )fonthsLength of Stay

Non-Payment 0.0139

Change in Liting Arrangement 0.0239

Died 0.0208

End of Study 0.0412

Eighteen to Twenty-Four Months Length Of Stay

Non-Payment 0.0093

At

Change in Living Arrangement 0.0109

Died 0.0144

End of Study 0.1474

Twenty-Five !lonthsLength of Stay

End of Study 0.1563

PROGR.4M TYPE

Adult

Blind Adult

Disabled Adult

Disabled Child

0.0930

0.0923

0.2258

0.1468

0.1419

0.2379

0.0622

0.3594

0.0104

0.5328

0.0974

0.0650

0.0861

0.0

0.0

0.0

0.0352

0.0

0.0270

0.0795

0.0

0.0

0.0880

0.0267

0.(!

0.0

0.0747

0.0185

0.0191

0.0

0.2462

0.2341

0.5346

0.0

0.0

o.~757

0.1896

0.0

0.0

0.1033

0.0139

0.4408

0.4420

0.0379

0.0827

0.0

0.0342

0.0

0.1128

0.0

0.0143

0.0878

0.2011

0.0

0.1012

0.0296

0.0513

0.0

0.0536

0.0098

0.0

0.0

0.0760

0.1078

0.0

0.2901

0.7099

0.0

0.0

0.0

0.0

0.0

0.0049

0.9584

0.0367

0.0574

0.0319

0.0474

0.0146

0.2109

0.0390

0.0

0.0338

0.0234

0.0

0.0612

0.0284

0.0064

0.0230

0.0464

0.0190

0.0086

0.0165

0.0224

0.1428

0.1669

0.0

0.0

0.0

0.2685

0.2115

0.3882

0.1318

0.6192

0.0074

0.3734

0.0

0.0419

0.0632

0.0654

0.0

0.0

0.0542

0.0654

0.0

0.0345

0.1240

0.1210

0.0227

0.0

0.0180

0.0314

0.0275

0.0043

0.0108

0.0279

0.1466

0.1412

0.0

0.0

0.0

0.1533

0.2162

0.5133

0.1172

0.6763

0.0146

0.3092

0.0

26
TABLE 9: Distribution of gik, Supplemental Security Income Program

PROFILE

Range 1 2 3 4

<(). 1% 2,818 2,493 3,098 2,179

0.1-25% 752

25.1-50%

753 441 373

884 1,238

50.1-75%

945 1,190

704 625 582 921

75.1-99.9% 132 89 186 346

100% 192 234 230 473

sum of gik’s 1,199 1,293 1,160 1,827

Prevalence of

episode type 1199/5482 1293/5482 1160/5482 1827/5482

27
TABLE 10: The Probability of Five Analytically Defined States (K=5) Having Specific

Physiological Characteristics

PROFILE

Variable Frequency 1 2 3 4 5

Pulse Pressure

<50

50-60

61-70

71-80

81-90

90+

Diastolic Blood Pressure

<96

86-95

96-115

116+

Infarction (IKG)

Injury (EKG)

Ischemia (EKG)

ArterioscleroticEtiology

Hvgertensive Etiologv

Rhemuatic Etiology

Cholesterol

>206

206-255

256-305

306+

Hematocrit

>40

40-41

42-45

46+

0.135

0.173

0.181

0.187

0.123

0.201

0.134

0.610

0.208

0.049

0.142

0.022

0.243

o.4g6

0.294

0.068

0.246

o.~lo

0.270

0.074

0.253

0.199

0.386

0.161

ArterioscleroticRetinopathy

None 0.162

Grade 1 0.527

Grade 2 0.286

Grade 3 0.025

Grade 4 0.001

Hypertensive Retinopathy

None 0.625

Grade 1 0.263

Grade 2 0.097

Grade 3 0.015

0.324

0.427

0.222

0.0

0.027

0.0

0.414

0.586

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.602

0.319

0.080

0.0

0.372

0.439

0.189

0.0

0.997

0.0

0.0

0.003

0.682

0.318

0.0

0.0

0.0

0.0

0.206

0.794

0.0

0.0

0.0

1.000

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.692

0.308

0.0

0.0

0.0

0.0

0.546

0.454

0.999

0.0

0.0

0.0

0.001

1.000

0.0

0.0

0.O

0.0

0.0

0.100

0.0

0.220

0.680

0.0

0.0

1.000

0.0

0.0

0.0

0.0

1.000

1.000

1.000

0.0

0.396

0.440

0.165

0.557

0.250

0.194

0.0

0.0

0.999

0.0

0.0

0.001

0..556

0.444

0.0

0.0

0.0

0.142

0.398

0.0

0.459

0.0

0.0

1.000

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.804

0.196

0.0

0.0

0.659

0.0

0.341

0.0

0.0

0.0

0.942

0.057

0.001

0.537

0.463

0.0

0.0

0.303

0.184

0.0

0.514

0.0

0.0

0.0

0.0

0.550

0.450

1.000

1.000

1.000

1.000

1.000

0.0

0.0

0.311

0.610

0.079

0.0

0.402

0.425

0.173

0.0

0.0

0.893

0,107

0.0

0.0

0.0

0.870

0.130

28
TABLE 10 (continued)

Overall Cardiovascular Functional Status

No Disease 0.546 1.000

Deiinite Disease. Not

Limiting

Moderate to

Disease

Severe

Tobacco Use

SJl!2K

Absent

Trace

1+

2+ More

Albumin

Absent

Trace

1+

2+ Yore

Subjective Health

Very Poor

Poor

Fair for My Age

Fair

Good

Good for Yy Age

Excellent

Excellent for ?@ Age

Verbal Scaled Scores

>31

31-50

51-70

71-90

91+

Performance Scaled Scores

>26

26-40

41-55

56-70

Height (Inches)

<fjo

60-62

63-65

66-68

69-IObesity

2

0.280

0.174

0.253

0.965

0.006

0.010

0.019

0.877

0.105

0.012

0.006

0.029

0.073

0.037

0.202

0.280

0.181

0.108

0.090

0.114

0.296

0.295

0.253

0.042

0.412

0.410

0.165

0.014

0.094

0.253

0.273

0.221

0.160

0.053

0.672

0.237

0.038

0.0

0.0

0.0

1.000

0.0

0.0

0.0

1.000

0.0

0.0

0.0

0.0

0.0

0.0

0.011

0.314

0.363

0.312

0.0

0.0

0.0

0.0

0.798

0.202

0.0

0.421

0.521

0.058

0.0

0.0

0.610

0.390

0.0

0.0

0.770

0.230

0.0

1.000

0.0

0.0

1.000

0.963

0.012

0.025

0.0

0.924

0.0

0.076

0.0

0.0

0.0

0.0

0.215

0.366

0.0

0.0

0.420

0.0

0.0

1.000

0.0

0.0

0.0

1.000

0.0

0.0

0.0

0.0

0.0

0.243

0.757

0.0

0.557

0.443

0.0

0.0

0.693

0.307

0.0

0.918

0.0

0.0

0.082

1.000

0.0

0.0

0.0

0.0

0.0

0.070

0.0

0.560

0.370

0.0

0.0

0.0

0.0

0.0

1.000

0.0

0.0

0.643

0.358

0.0

0.452

0.0

0.548

0.0

0.0

0.0

0.804

0.083

0.113

0.0

0.0

1.0

1.000

0.965

0.0

0.035

0.0

1.000

0.0

0.0

0.0

0.0

0.371

0.124

0.506

0.0

0.0

0.0

0.0

0.251

0.749

0.0

0.0

0.0

1.000

0.0

0.0

0.0

0.077

0.923

0.0

0.0

0.0

0.0

1.000

0.0

0.0

0.0

0.512

0.488

0.0

0.961

0.039

0.0

0.0

0.230

0.770

0.0

0.0

0.311

0.0

0.0

0.569

0.0

0.0

0.120

0.0

1.000

0.0

0.0 “

0.0

0.0

1.000

0.0

0.0

0.0

0.0

0.0

0.340

0.660

0.0

0.375

0.0

0.519

0.105

29
TABLE U: Conditional Probability of the Five States Defined on Physiological Variable

Having Certain Survival and Demographic Characteristics

PROFILE

Variable Frequency 1 2 3 4 5

*

59-64

65-69

70-74

75-79

80-84

85-89

90+

Years to Death

<1

1

2-4

5

“6-10

11-15

16-20

21+

Sex

Vi%le

Female

Race

White

Black

Occupation

Professional

Management

Clerical

Laborer

l?everEmployed

Education (Years)

o-4

5-9

10-15

16+

Wave

-i2

3

4

5

6

7

8

9

10

11

0.053

0.119

0.208

0.250

0.209

0.115

0.047

0.028

0.079

0.234

0.078

0.305

0.162

0.088

0.027

0.412

0.588

0.650

0.350

0.218

0.124

0.231

0.128

0.300

0.265

0.190

0.281

0.264

0.200

0.145

0.159

0.103

0.081

0.080

0.059

0.050

0.047

0.041

0.034

0.073

0.169

0.280

0.229

0.188

0.052

0.010

0.012

0.0

0.114

0.089

0.254

0.285

0.183

0.064

0.417

0.583

0.765

0.235

0.298

0.153

0.220

0.010

0.318

0.059

0.136

0.292

0.513

0.292

0.224

0.156

0.077

0.050

0.044

0.046

0.059

0.012

0.035

0.006

0.116

0.099

0.233

0.300

0.118

0.120

0.014

0.047

0.044

0.178

0.039

0.394

0.194

0.104

0.0

0.788

0.212

0.860

0.140

0.400

0.241

0.216

0.035

0.108

0.184

0.197

0.333

0.286

0.194

0.180

0.144

0.174

0.072

0.021

0.086

0.046

0.030

0.010

0.042

0.0

0.104

0.155

0.256

0.284

0.160

0.041

0.0

0.118

0.277

0.141

0.299

0.103

0.043

0.020

0.127

0.872

0.662

0.318

0.237

0.096

0.097

0.063

0.506

0.106

0.117

0.531

0.247

0.079

0.128

0.110

0.035

0.146

0.162

0.059

0.043

0.073

0.082

0.083

0.0

0.0

0.091

0.225

0.314

0.206

0.164

0.056

0.200

0.390

0.010

0.323

0.021

0.0

0.0

0.315

0.685

0.437

0.563

0.043

0.002

0.341

0.326

0.288

0.580

0.263

0.090

0.067

0.0

0.0

0.213

0.128

0.100

0.123

0.111

0.091

0.132

0.062

0.039

0.083

0.199

0.237

0.26!3

o ● 104

0.072

0.036

0.055

0.068

0.291

0.073

0.310

0.133

0.051

0.019

0.536

0.464

0.411

0.589

0.022

0.187

0.288

0.238

0.216

0.471

0.291

0.199

0.039

0.447

0.093

0.207

0.171

0.036

0.045

0.001

0.0

0.0

0.0

0.0

30
TASLE 12: Dynamic Equation Coefficientsfor Risk Factor Changes, Framlngh~ Hales Over Two-Year Inte=als

(I - A)

AGEt PPt DBPt QI~ CHOLt

u

BLDSUG HEMO

t t

VITCt CIG

t

AGEt+2 2.00 1.0 0.0 0.0 0.0 0.0 0.0 0.0 :0:0 0,0

PP -5.89 2.84x1O-1

t+2 .6.04x10-1 1.49XI0-1 -8.01xIO-3 -2.17X1O-3 2.68XI0-* 2.23x10-3 -3.66x10-3 2.42x

-2

‘Bpt+2 19.65 -5.9OX1O 7.39X10-2 6.10X10-1 2.4OX1O-2 7.05xlo=4 5.OOX1O-3 3.O3X1O-2 4.6x10-3 -7.76x

QIt+2 23.64 -8.63x10-2 -3.99X10-2 -3.58x10-2 9.51XI0-1 -7.ooxlo-3 -1.26x10-2 4.71xlo-3 8.63x10-3 -8.43x

CHOLt+2 78.21 -2.07x10-1 -6.24x10-3 -3.82x10-2 -7.28x10-3 7.88x10-1 -4.76x10-3 -8.91x1O-2

8.33x1O

-3 4.50X

BLDSUGt+2 2.29 1.42x10-1 4.22x10-2 3.44xlo-3 1.23x1O-2 -4.60x10-3 7.36x10-1 7.34xlo-2 -7.89x10-3 -3.1OX

HEMOt+2 56.03 6.32x10-2 2.03x10-3 -1.83x1O-2 2.03xI(I-2 6.24x10-3 -1.38x1O-2 5.69x10-1 2.80x10-3 5.05X

VITCt+2 21.06 -1 .90X10-1 4.63x10-3 -4.61x10-3 1.35X1O-2 1.35X1O-3 -7.80x10-3 -4.46x10-2 9.22X10-1 -3.37x

CIG 4.63 -5.83x10-2 -1.5OX1O-3 -6.27x10-3 -5.60x10-3

-4

t+2 -2.55x1O 1.71X1O-3 7.2OX1O-3 4.45xlo-4 8.93>

cd
a

NY TABLE 13: Extended Matrix, Quadratic and Interaction Terms, Frwingham Study

Diastolic

Pulse 1!1ood Quetlet B]00d Hemo- Vltal Cigarette

Constant Pressure Pressure fndex Cholesterol Sugar globin Capacity Consumption ——

Constant 2.98x1O-3

(2.40)

Pulse

Pressure 4.31X10-5 9*34X10-7

(3.16) (1.29)

Diastolic

Blood

Pressure -8.15x1O

-6 -1.o~xlo-6

6.8OX1O

-6

(-o.20) (-1.48) (3.75)

Quetlet

-1.62x10-5 -1.56x10-7 –6 Index -1.51X1O

(-1.40) (-0.55) (-2.99)

Cholesterol -1.44x1O-5 -2.68x10-7 -4.84x10-7

(-1.45) (-1.39) (-1.47)

Blood -6 -2.34x10-7 -6 Sugar -3.19X1O 1.O1X1O

(-0.22) (-0.79) (1.65)

Hemo-

-9.95X10-6 1.37X10-7 -6 globin -2.71x1O

(-0.33) (0.18) (-2.14)

Vital

Capacity -8.42x1O-6 -5.95X1O-8

-3.31X1O

-7

(-2.23) (-0.72) (-1.66)

Cigarette

Consumption 4.88x10-5

(2.45)

5.19X10-7 -3.38x10-7

(1.00) (-0.29)

5.71X1O-7

(2.89)

3.13X10-7

(3.32)

-1.76x1O-7

(-0.99)

7.80x10-7

(2.35)

1.18X1O

-7

(1.99)

-3.77X1O

-8

(-(-).13)

2.I5x1O-7

(2.25)

-2.76xI0-8

(-0.24)

3.47X1O

-7

(1.24)

5.94X10-8

(1.61)

-5.43X10-8

(-0.26)

1.63x10-7

(0.91)

-7

-3.66x1O

(-o.99)

-4.64x10-8

(-1.34)

-2.66x1O

-8

(-(-).10)

1.21X1O-6

(1.30)

I.64x10-7 4.30X10-8

(1..66) (1.78)

5.54XI0

-8

-1.41X10-7 1.O7X1O

-6

(0.12) (-1.71) (2.20)

t values in parentheses
TABLE 14: Minimum Mortality Points

GOIXPERTZMODEL AGE X AGE4 AS RISK FACTORS

Diastolic Diastolic

Pulse Blood Pulse Blood

AGE Pressure Pressure Pressure Pressure

31

33

35

37

39

41

43

45

47

49

51

53

55

57

59

61

63

65

67

69

71

73

75

77

79

81

83

85

87

89

91

93

95

97

99

30.38

35.05

37.94

39.88

41.25

42.26

43.02

43.60

44.04

44.36

44.58

44.71

44.76

44.74

44.65

44.51

44.32

44.09

43.82

43.52

43.21

42.89

42.58

42.28

42.01

41.37

40.46

39.58

38.74

37.96

37.27

36.66

36.16

35.76

35.48

72.25

73.91

75.36

76.58

77.56

78.32

78.89

79.28

79.52

79.62

79.60

79.47

79.24

78.92

78.53

78.07

77.55

76.97

76.35

75.70

75.01

74● 31

73.60

72.88

72.18

71.54

70.96

70.44

69.99

69.60

69.28

69.05

68.89

68.82

68.82

26.95

23.70

19.56

14.50

8.50

1.55

-6.37

-15.26

-25.14

-36.02

-47.89

-60.77

-74.66

-89.57

-105.48

-122.42

-140.37

-159.35

-179.35

-200.36

-222.40

-245.$6

-269.53

-294.63

-320.74

-347.93

-377.99

-409.10

-441.23

-474.41

-508.62

-543.86

-580.13

-617.42

-655.74

81.32

84.36

86.41

87.70

88.36

88.49

88.15

87.40

86.28

84.82

83.05

80.99

78.64

76.04 I

73.18

70.08 t

66.74 ~

63.18

59.39 I

55.39

51.17

46.75

42.12

37.28

32.25

27.02

21.55

15.91

10.08

4.06

-2.14

-8.54

-15.13

-21.90

-28.86

33
cd

* ‘1’A8LE15: Life Table Parameters for the Total Fk)rtal.ity aIId Kfsk Factor (Xlange Experience of FramingharnMales

—..-..- —.——-——— —

MI;ANS/S’l’ANl)AIU) DJWIATIONS

Stxlos 2dt 2% lht :

t Cos’1’ _AGK 1’1’ 1)111’ Xu.-___ww B1.DSUG HEMO VITC CIG

100000.

99785.

99533.

99234.

98877.

98450.

97941.

97335.

96614.

95761.

94753.

93564.

92166.

90526.

88606.

86360.

0.002

0.003

0.003

0.004

0.004

0.005

0.006

0.007

0.009

0.010

0.012

0.014

0.016

0.019

0.022

0.026

0.00

0.00

0.00

0.00

0.00

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.02

0.02

0.03

0.03

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.01

0.01

0.01

0.01

0.01

0.02

44.26

42.35

40.46

38.58

36.71

34.87

33.04

31.24

29.47

27.72

26.01

24.32

22.68

21.07

19.50

17.99

1.8359.75

17674.08

16916.32

16066.75

15197.04

14217.09

13265.68

12196.79

10998.90

9904.98

8685.49

7664.26

6531.12

5276.49

4373.15

3377.35

30

32

34

36

38

40

42

44

46

48

50

52

54

56

58

60

45.83

13.70

41.85

13.70

40.25

13.7(-)

39.95

13.7(-)

40.37

13.70

41.21

13.7(-I

42.31

13.70

43.55

13.70

44.9(-1

13.70

46.31

13.70

47.76

13.70

49.24

13.70

50.73

13.70

52.24

13.70

53.75

13.69

55.26

13.69

79,57

12.53

81.53

12.53

82.44

12.53

82.86

12.53

83.07

12.53

83.17

12.53

83.24

12.53

83.29

12.53

83.34

12.52

83.37

12.52

83.40

12.52

83.42

12.52

83.42

12.52

83.41

12.52

83.38

12.52

83.34

12.51

261.88

34.44

264.75

34.44

267.36

34.43

269.66

34.42

271.64

34.42

273.29

34.41

274.63

34.40

275.65

34.39

276.38

34.37

276.82

34.36

276.99

34.34

276.90

34.32

276.56

34.29

275.99

34.26

275.21

34.22

274.23

34.18

215.22

4].41

224.65

41.40

231.43

41.40

236.20

41.40

239.43

41.40

241.46

41.39

242.55

41.39

242.91

41.39

242.71

41.38

242.06

41.38

241.06

41.37

239.80

41.36

238.33

41.36

236.71

41.34

234.96

41.33

233.13

41.32

79.35

29.63

78.28

29.63

77.73

29.63

77.63

29.62

77.89

29.62

78.46

29.62

79.26

29.62

80.24

29.62

81.37

29.62

82.59

29.62

83.89

29.62

85.25

29.62

86.65

29,62

88.08

29.61

89.53

29.61

90.99

29.61

142.11

10.25

144.02

10.25

145.36

10.24

146.34

10.24

147.11

10.24

147.73

fO.24

148.24

10.24

148.67

10.24

149.03

10.24

149.34

10.24

149.60

10.24

149.83

10.24

150.02

10.24

150.18

10.24

150.32

10.24

150.44

10.24

139.29

31.11

1/,().(34

31.11

140.29

31.11

140.09

31.11

139.52

31.11

138.61

31.11

137.40

31.11

135.90

31.11

134.15

31.11

132.16

31.10

129.96

31.10

127.57

31.10

124.99

31.10

122.25

31.09

119.37

31.09

116.35

31.08

13.24

11.53

13.83

11.52

14.23

11.52

14.45

11.52

14.53

11.52

14.47

11.52

14.29

11.52

14.00

11.52

13.63

11.52

13.17

11.52

12.64

11.52

12.04

11.51

11.39

11.51

10.69

11.51

9.94

11.51

9.15

11.50
TABLE l!i(cent’d)

83744.

80705.

77193.

73155.

68552.

63357.

57575.

51253.

44497*

37476.

30468.

23583.

17194.

11661.

7250.

4064.

2015.

865.

314.

93.

0.030

0.035

0.040

0.046

0.052

0.058

0.063

0.068

0.070

0.070

0.069

0.064

0.055

0.044

0.032

0.020

0.012

0.006

0.002

0.001

0.04

0.04

0.05

0.06

0.08

0.09

0.11

0.13

0.16

0.19

0.23

0.27

0.32

0.38

0.44

0.50

0.57

0.64

0.70

1.00

0.02

0.02

0.03

0.03

0.04

0.05

0.06

0.07

0.09

0.10

0.13

0.16

0.19

0.24

0.29

0.35

0.42

0.51

0.61

0.61

16.52

15.10

13.74

12.45

11.22

10.05

8.97

7.95

7.(31

6.14

5.33

4.61

3.97

3.41

2.92

2.50

2.15

1.86

1.65

1.65

2822.50

2214.56

1551.40

1247.62

920.78

742.96

556.23

363.58

263.81.

165.98

116.36

70.35

30.05

19.79

12.07

6.80

3.61

1.91

1.14

0.85

62

64

66

68

70

72

74

76

78

80

82

84

86

88

90

92

g!+

96

98

1(-)0

56.77

13.69

58.28

13.69

59.78

13.69

61.27

13.69

62.75

13.68

64.21

13.68

65.66

13.68

67.09

13.67

68.50

13.67

69.87

13.66

71.25

13.65

72.61.

13.64

73.94

13.63

75.23

13.62

76.48

13.61

77.68

13.60

78.83

13.59

79.93

13.57

80.96

13.56

81.94

13.54

83.28

12.51

83.21

12.51

83.11

12.50

83.01

12.50

82.88

12.49

82.74

12.48

82.58

12.47

82.41

12.46

82.22

12.45

82.02

12.43

81..78

12.41

81.52

12.40

81.26

12.37

80.98

12.35

80.70

12.32

80.42

12.29

80.13

12.25

79.85

12.21

79.57

12.16

79.31

12.12

273.08

34.13

271.75

34.08

270.29

34.01

268.71

33.93

267.04

33.84

265.30

33.73

263.52

33.60

261.74

33.46

260.00

33.30

258.33

33.11

256.57

32.90

254.94

32.66

253.46

32.40

252.17

32.11

251.08

31.81

25(3.22

3[.48

249.60

31.14

249.21

30.79

249.(34

3(-).43

249.07

30.07

231.24

41.30

229.31

41.28

227.36

41.26

225.41

41.23

223.48

41.20

221.57

41..16

219.71

41..12

217.91

41.07

216.20

41.01

214.58

4(-).94

213.17

40.86

211.91

40.77

210.79

40.67

209.86

40.56

209.12

40.43

208.59

40.29

208.28

40.13

208.20

39.96

208.34

39.77

208.70

.39.57

92.46

29.60

93.92

29.60

95.39

29.59

96.84

29.59

98.29

29.58

99.72

29.57

101.14

29.56

102.54

29.55

103.92

29.54

105.29

29.52

106.57

29.49

107.81

29.47

109.01

29.44

110.18

29.41

111.32

29.38

112.43

29.35

113.52

29.31

114.59

29.26

115.64

29.21

116.67

29.16

150.54

10.24

150.62

10.23

150.69

10.23

150.75

10.23

150.80

10.23

150.85

10.22

150.89

10.22

150.94

10.21

150.99

10.21

151.04

10.20

151.19

10.14

151.38

10.12

151.61

10.10

151.87

10.09

152.15

10.08

152.47

10.07

152.82

10.06

153.19

10.05

153,58

10.04

154.00

10.03

113.21

31.08

109.96

31.07

106.62

31.07

103.21

31.06

99.73

31.05

96.20

31.03

92.63

31.02

89.06

31.00

85.48

30.98

81.92

30.96

78.28

30.91

74.67

30.87

71.09

30.82

67.58

30.77

64.15

30.72

60.83

30.67

57.66

30.61

54.65

30.55

51.88

30.49

49.34

30.43

8.32

11.50

7.45

11.49

6.55

11.49

5.62

11.48

4.66

11.47

3.66

11.46

2.62

11.45

1.55

11.44

0.43

11.42

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.OO

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00
TABLE 16: Cauae Elimination Life Expectancy at Age 30 Under Independent

and Dependent Competing Risk Models

Independent Competing Risks Dependent Competing Risks

Hazard Multiplied Hazard Multiplied

Cause by Gompertz by Gompertz

Cancer 46.94 46.85

cvD/cHD 55.27 54.62

Residual 46.33 46.26

Change in Life Expectancy at Age 30

Cancer 2.41 2.32

cvD/cHD 10.74 10.09

Residual 1.80 1.73

Cancer

CVD/CHD

Residual

---

---

---

Effect of Dependency

-0.09

-0.65

-0.07

36
TABLE 17: Effects of Cmtrol on Risk Varlmles w Costs of Ilortallly

.

Lost Future Lifetime Incme (.1977 Dollars) Due to Hortallty,at and Beyond Given Age

.’

RISK VMIABLES CllANGFJl ONE AT A TIME

Baseline AN Risk

Control PP+DBP Q.I. C11OL . BLDSUG IILWO VITC CIG Variables

Value “Ideal” 45.87 81.38 258.82 215.24 78.26 143.76 140.74 15.02 Changed

Age costs . 45.00 65.00 250.001 lJI1 150.00 7!j .00 145.00 145.00 0.0 Shuitaneously

30

32

34

36

38

40

42

44

46

48

50

52

54

56

58

60

62

64

66

68

70

72

74

~ 76

22270.10

20554.99

18998.95

17506.96

16160.82’

14801.37

13605.98

12374.70

11090.54

9978.58

8779.14

7788.30

6686.17

5448.78

4539*55

3514.94

2932.60

2285.15

1573.69

1248.14

901.37

716.18

526.69

337.30

34091.38 23526.07

31403.23” ‘--”- ‘-

28543.90

25325.41

22277.46

19208.56

;6609.87

14’102.79

11709.70

9850.88

8079.53

6797.55

5548.22

4308.72

3491.31

2647.94

2199.49

1723.12

1213.36

982-50

735.66

601.64

461.43

316.30

zliltJ.lz

20093.50

18562.76

17201.10

15832.68

14621.77

13350..08

12009.12

10817.67

9508:22

8411.78

7183.44

5803.07

4793.26

3665.06

3031.19

2335.85

1582.79

1243.35

‘ 087.18

899:72

510.61

328.04

29548.39

26768.68

24263.14

21994.50

20086.94

18269.93

16728.22

15163.05

13528.93

12102.18

19550.38

9264.10

7638.72

6236.10

5113.32

3853.90

3156.24

2401.85

1596.60

1238.90

869.10

677.40

486.98

302.18

21500.23

19814.33

18263.06

16783.31

~5111i6.3.9

]4103.06

12924.22

11717.11

10467.39

9294.52

8247,75

7308.71

6212.23

5115.62

4269,36

3$17.93

2777.47 ‘

2175.88

1513,02

1208.43

882.17

706.69

525.62

342,83

22111.04

20409.36

18852.27

17365.28

16034.6,7”

147(3(3*79 ,

13532.36

12330.08

11073.49

9980.95

8746,44

7812.88

6713.83

5975,36

4562.94

3532.97

291;7.04

2295.38

1579.38

1251.90

903..31

717.30

20738.20

19063,44

17534.40

16064.26

14738.14

13404.39

12236.99

11048.11

9825.49

8$84.97

;683.97

6792.01

5817.64

4740.31

3957.95

3083.25

2588.11

2037.79

1151.69

851.09

688.42

527.14 519.17

337.28 346.56

17363.38 15847.04

15989.34 14515.45

14738.07 13270.91

13564.75 12102.98

12540.97 11099.32

11544.28 10139.59

10697.58 9334.22

9850.13 8532.19

8981.42 7711.09

8230.68 7003.73

7408.82 6236.77

6709.57 5595.68

5900.30 - 4871.64

4950.12 4045.15

4219.41 3427.59

3359.33 2719.91

2850.52 2310.83

2264.07 1847.87

1598.66 1320.27

1285.45 1086.85

943.40 822.26

756.67 676.94

561.91 523.32

363.95 363.87
83 TAIJLE17: (.Cent inued)

78

80

82

84

86

88

90

92

94

96

98

100

232,59

152.91

108.65

69.17

35.30

26.63

19.83

14.75

11.15

8.76

7.26

6.38

240.66

165.50

126.25

69.19

55*O4

45,59

37.50

30.87

25.67

21.80

19.07

17.25

30 43*70 43,52

60 18.03 20.67

231.86

145.50

10302~

65.93

34.07

25.93

1.9.53

“’J4*74

11.32

9.01

7.85

6.68

212.41

129.82

90.15

55.78

27.12

19.99

14.54

16.57

7.84

8.08

4.98

4.38

250.35

161.81

117.07

76.66

41.32

32.08

24.64

18.94

14.78

11.92

10.06

8.43

242.42

152.79

108.38

68.91

35.09

26.44

19.67

14.61

11.04

8.66

7.17

6.31

Residual Life Expectancy in Years

43.11 41..24 hl,,[,g 43.70

17.70 S6.79 18.76 17.99

258.12

171.98

127.98

87.31

50.86

41.06

32.92

26.44

21.52

17.97

15.55

13.98

45.50

19.74

263.50

167.20

119.10

76.07

39.03

29,52

22.04

16.43

12.45

9.79

8.12

7.15

45.25

18.26

280.54

197,52

153.99

112.66

74.46

63.79

54.52

46,73

40.42

35.52

31.88

29.30

48.31

21,$7
Maintenance of health,

prevention of disease, a

psychosocial perspectiveaYb

by Lisa F. Berkman, Ph.D.,

Department of Epidemiology, Yale School of Medicine

Social variations in the distribution of health and disease

have been observed for centuries; however, the reasons for

these differences in physical health status are not well understood. For many years, it was assumed that these differences

could be explained by variations in ethnicity or genetic stock,

exposures to noxious stimuli in the physical environment,

or access to medical care resources. While these factors undoubtedly account for some of the differences in disease rates

among members of different social groups, evidence is now

beginning to accumulate which indicates that certain conditions

and circumstances within the social environment or sociocultural milieu per se have disease consequences.

What is striking about the associations between these

social conditions and physical health is the nonspecific nature

of the associations—that is, social conditions are associated

with a very broad and diverse set of diseases and conditions.

These findings force us to question traditional psychosomatic

models of disease causation in which only certain diseases,

for instance, peptic ulcer, hypertension, or colitis, are influenced by psychological and social conditions. Several investigators, notably Cassel (1976), Hinkle (1973), and Syme

(1974), have pointed out that it may be more fruitful and

accurate to envision psychosocial process as enhancing susceptibility to disease. As Hinkle stated in 1973: “It is evident

that any disease process and in fact any process within the

living organism, might be influenced by the reaction of the

individual to the social environment or to other people. ” This

line of reasoning fits the findings to date more closely than

a traditional psychosomatic perspective, since social conditions

have been found to be associated with infectious diseases,

e.g., tuberculosis, pneumonia, and mononucleosis, as well

as noninfectious diseases ranging from cardiovascular diseases

to cancer.

How do social conditions

influence health status?

Social conditions may influence human health in many

different ways and through many different pathways, and

none can be a priori rejected as irrelevant. Psychosocial factors

“Contentsarc the opinions nf the uu[hor and not [how of the Department

of Hdth and Human .Wrvirxs or of the organization she works for.

h,c)IYX5Natim-d Acxicmy of.sciwrccs.

can be represented (see Kasl, 1979) as those that affect:

(a) initial immunity or susceptibility status, (b) exposure among

susceptible, (c) development of inapparent versus clinical

illness or disease onset (incidence of new events), (d) course

of illness or disease (exacerbations, repeat events), and

(e) outcome of disease process and/or degree of recovery,

including case fatality.

Of particular relevance to the field of aging research

is the possibility that these factors could influence aging processes in some way that, in turn, advances or retards the

onset of disease. One of the most persistent notions in gerontology is the concept of biological aging as an intrinsic, sui

generis process that can be separated from the accumulation

of environmental influences and insults that the aging organism

also experiences. For example, Busse (1969) distinguishes

between “biological aging,” those genetic processes that are

time related and independent of stress, trauma, or disease,

and “secondary aging,” which consists of disability resulting

from trauma and chronic illness. However, despite a great

accumulation of normative data on age and biological fi.mctioning (e.g., Finch and Hayflick, 1977), there are few biological

aging processes that appear not to be modified or modifiable

by stress, trauma, and disease. Biological theories of aging

(Shock, 1977) can, at the moment, point us toward those

age-dependent changes in biological functioning that are particularly uniform or particularly variable across diverse population groups. But it may be more fruitful to conceptualize

“biological aging” as being influenced by both intrinsic (genetic, constitutional) and extrinsic (environmental, psychosocial) processes. This possibility opens up numerous pathways

for research that focus on the wide variability reported for

almost all physiological processes associated with aging.

The mechanisms by which conditions in the social environment might influence health status in human populations constitute a relatively unexplored area. In a search for such

mechanisms, it is important to keep in mind that social conditions seem to be associated with a wide range of outcomes

rather than any single disease entity. This observation would

suggest that there are several pathways leading from social

circumstances to illness and/or that such circumstances lead

to a compromised resistance to disease in general. As Cassel

(1976), a proponent of the latter hypothesis, suggests, it may

also be that there are several clusters of diseases associated

with different psychosocial situations. An elucidation of these

issues is critical, since by understanding the process by which

social factors lead to poor health, we can learn where to

39
intervene most effectively and prevent unnecessary illness

and death.

If we take the approach that there are likely to be multiple

biological pathways, several potential processes can be outlined: (1) behavioral processes whereby people living in certain

social and cultural circumstances maintain health practices

which are either beneficial (e.g., physical activity) or harmful

(e.g., cigarette smoking) to their health or obtain a variable

quality of medical care; (2) psychological processes whereby

people respond to circumstances by becoming depressed or

changing their coping and appraisal processes; (3) direct physiological changes in both known biological risk factors (e.g.,

blood pressure, serum cholesterol) and unidentified processes

which are directly altered by exposure to certain environmental

circumstances. In the exploration of this chain leading from

environmental conditions to health outcomes, we must also

recognize that factors which in this chain are considered

mediators may also directly influence socioenvironmental factors, reversing the causal chain. Thus it is possible, for instance, that psychological states such as depression could

influence social conditions. Studies in which these variables

are being investigated will be most useful when the temporal

chain of events can be identified. It is also likely that characteristics of the person and the environment, combined, will ultimately be the best predictors of disease risk.

Behavioral factors, including health practices, preventive

utilization of health services, and compliance with treatment

regimens, are obvious and likely candidates for linking

socioenvironmental conditions to disease consequences; however, the evidence available thus far indicates that health

practices are capable of explaining only a part of the environmentidisease association. Studies in which this hypothesis

was directly addressed (Marmot et al., 1978; Berkman and

Syme, 1979; Marmot and Syme, 1976; Prior, 1974) suggest

that other pathways must also be involved, although in most

cases across-sectional association between the social condition

and health behavior was noted.

Psychological factors such as depression or coping processes also seem likely candidates as mediators between environmental conditions and illness responses; and there is

some literature to support this view. Of particular interest

are the work by Seligman (1974) and earlier work by Rotter

(1966)and others interested in feelings of internal and external

control and learned helplessness, as well as a review of such

coping mechanisms and social circumstances by Satariano

and Syme (1979). These investigatorshave hypothesizedthat

when individuals are habitually confronted with situations in

which their responses either are or appear to be ineffective,

they ultimately come to conclude that events in general are

uncontrollable and that they are powerless to effect a particular

outcome. Seligman (1974) indicates that such feelings are

related to depression and perhaps to other health problems.

Psychological responses may also predispose an individual

to suicide or risk-taking behavior which could result in accidents. Engel and Schmale (1972) and Antonovsky (1979)

also refer to the importance of “giving up” versus maintaining

a sense of control or “coherence” to health maintenance. There

is, as yet, little strong evidence in support of these positions

in terms of prospectively predicting physical health status

or of linking socioenvironmental processes to health outcomes

in human populations. However, it is interesting to note the

similarity of these positions with Cassel’s (1976) characterization of stressful social situations as ones in which “the actor

is not receiving adequate evidence (feedback) that his actions

are leading to anticipated consequences.”

Furthermore, this pathway also closely parallels the findings by Rodin (1977) and Langer and Rodin (1976) on controlrelevant interventions among the elderly in nursing homes

and the findings on the effects of depression on neuroendocrine

function (Sachar, 1976; Amsterdam et al., 1983) and lymphocyte function (Schleifer et al., 1984).

While the previous hypothetical pathway suggests that

the critical role social factors play in disease causation is

not due to objective circumstances themselves but to the way

in which such circumstances are more subjectively perceived

and mediated, it is also possible that there may be a pathway

leading directly from social circumstances to physiologic

changes in the body which increase either general or specific

disease susceptibility. Findings from the Human Population

Laboratory study indicating that no psychological factors developed from items on the 1965 survey mediated between

social isolation and mortality risk support this view, although

none of the factors were created to tap the psychological

dimensions just described (Berkman, 1977).

In this discussion it is important to keep in perspective

that socioenvironmental conditions may serve as predisposing

factors (Cassel, 1976) which affect a person’s general resistance to disease and that other factors (e.g., genetic susceptibility, exposure to noxious stimuli) may determine specific disease vulnerabilities. Alternatively, socioenvironmental conditions may modify the impact of other risk factors on disease,

or the progression of disease, or the impact of the disease

on mortality risk.

Stressful conditions may lead to alterations both in known

biologic risk factors (e.g., blood pressure, serum cholesterol)

and in as yet unidentified processes. If they function, in part,

by altering known risk factors, it will be a mistake in analyses

to treat such risk factors as simple confounders of the relationship instead of more properly considering them as potential

mediators or pathways leading from a social experience to

a health outcome, While this might seem to some a technical

point, in fact, it has profound implications for the design

of effective interventions in this entire area. To give a hypothetical example, if social isolation influences cardiovascular disease by raising blood pressureand cholesterollevels, “controlling” for these biologic risk factors in an analysis may wipe

out an association between social isolation and cardiovascular

disease. The implication for intervention is clear (but misleading), that we should focus efforts on lowering blood pressure

and cholesterol without any attention to the social condition

that caused the original elevations. Similar examples can be

found linking physical activity to coronary heart disease (CHD)

via weight change or linking high-density lipoprotein/lowdensity lipoprotein ratios or diet to CHD via blood cholesterol

levels or obesity. In all three cases, environmental factors

must influence health status via some biologic pathway. Controlling for that pathway obscures the relationship between

the environmental factors and detracts attention from focusing

40
interventions at that level of disease causation. We will return

to this issue of appropriate interventions at the conclusion

of the paper.

The impact of risk factors on

the health status of the elderly

Biological and psychosocial risk factors for a particular

disease, or for adverse health status changes in general, may

change in their potency as one goes from younger and middleaged study samples to older subjects. The general issue here

is the extent to which older age changes vulnerability to disease

for a given level of risk factors. Of course, we can invoke

many plausible explanations for why risk factors may change

potency with age. If risk factors increase in potency with

age, it may be that they simply represent an accumulation

of exposure over time and may not be related to age per

se, that the risk factor is actually occurring in a more severe

and threatening form, or that the older person is physiologically

or psychologically more vulnerable to the same exposure or

stress and is unable to mobilize resoutces as effectively as

younger or middle-aged people. Decreases in risk may simply

represent a selective skvival of the fittest. It may also be

that older people are wiser, that past learning facilitates adaptation to the next experience, and that we have a very incomplete

understanding of the positive factors in the lives of older

people. Events may change in significance because of lifestyle

changes in aspirations, expectations, and perceptions.

Changes in risk factors

with age

Some of the most intriguing changes in risk occur among

sociodemographic variables, Kitagawa and Hauser (1973)

analyzed mortality data on U.S. residents in relation to age

and education. Among white men, standardized mortality

ratios (SMR’S) for all causes revealed a negative gradient

in relation to years of education only among those aged 25-64;

white men 65 and older showed no gradient whatever. Among

white women, a somewhat stronger negative gradient was

observed in the 25-64 age group than in the group 65 and

older, but the latter still revealed a definite negative association

between SMR and education. Analyses by cause revealed

that the disappearance of the gradient among men 65 and

older held for all major causes of death except two: for influenza and pneumonia and for accidents, the higher levels

of education conferred an advantage even in those men 65

and over. These data, overall, suggest -a simple interaction

between SMR, education level, and age, and a more complex

interaction invcdving sex.

Analyses by socioeconomic level of city tracts and age

(Antonovskyj 1967) reveal the expected negative gradient in

SMR’S and its weakening in the older age groups. However,

unlike the data on education, these data show that the gradient

never quite disappears even in the oldest group (75 and older)

and is about the same for both sexes. Computations of SMR’S

for men 20-64 by age and occupation (Antonovsky, 1967)

reveal that nonfarm laborers, the one group with a notable

excess mortality, have an SMR of more than 170 in the

2544 age group, but it drops to a little more than 120 in

the 55-64 age group.

Race remains an ambiguous social indicator in health

studies and presumably reflects many sociocuhural and genetic

and constitutional variables in addition to average social class

differences. Consequently, the race-dependent differences in

various health status indicators that are also modified by age

are difficult to interpret. For example, age-specific death rates

are higher for persons of races other than white only until

age 74; the situation then reverses (National Center for Health

Statistics, 1979). A recent paper from Evans County further

indicates that the crossover effects are genuine and are not

the result of misclassification of age or small numbers (Wing

et al., 1985). However, these do not appear to be additional

years free of disability; days of restricted activity and days

of bed disability continue to be higher among persons other

than white even in the age bracket 75 and older (National

Center for Health Statistics, 1972). Other studies also suggest

changing race differentials for specific diseases with change

in age. For example, the strong increase in rheumatoid arthritis

with age holds only for white persons; black persons have

higher rates until age 64, while white persons have higher

rates in the age group 65 and older (Cobb, 1971). Prevalence

rates of diabetes, determined in a mobile multiphasic screening

program, are much higher among black than white persons,

but at ages 65 and over, the rates are virtually identical (Adler,

Bless, and Mosley, 1966).

Analyses of 1959-61 mortality by marital status (National

Center for Health Statistics, 1972) clearly document the wellknown excess mortality associated with the three marital status

categories of single, widowed, and divorced (compared with

married). Moreover, in 11 of the 12 sex-by-race-by-status

groups, the elevated SMR goes down in the older age groups.

Only among divorced women of races other than white does

the SMR show no change with age, remaining in the vicinity

of 135. Additional analyses of similar mortality data from

1940 and 1949–5 1 reveal one interesting secular trend: among

divorced women of both races, the SMR went up with older

age groups for the 1940 and 1949–5 1 years. In other words,

in 1940 divorced older women had much higher SMR’S than

divorced younger women; by 1959-61, this trend was reversed

for white women and wiped out for black women. Additional

analyses of the 1959-61 mortality data by cause-e. g., arteriosclerotic heart disease, International Classification of Diseases (ICD) 420; vascular lesion’saffecting the central nervous

system, ICD 330-334; and malignant neoplasm of digestive

organs and peritoneum, ICD 150-1 59—repeatedly confirm

the decline in SMR for single, ‘widowed, and divorced people

in older age groups.

Data from the Human Population Laborato~ (HPL) survey

of almost 7,000 adults in Alameda County have been examined

for the relationship between health habits and mortality. Preliminary analyses (Belloc, 1973) suggested that only the bene41
fits of physical activity persist into old age. More recent

analyses with a refined health practices index (smoking, alcohol consumption, physical activity, sleep, and relative

weight), however, reveal that even among subjects 70 and

older, a low score on positive health practices is associated

with significantly greater mortality (Kasl and Berkman, 1981).

It is also worth noting that there was not much of an association

between age and health practices for either men or women.

These associations were not found in data from Massachusetts,

where few behaviors persisted as risk factors in the older

age groups (Branch and Jette, 1984).

The changing risk factor picture with advancing age becomes most complex—and most fascinating—when some factors become less potent, while others emerge as more potent.

Haynes et al. (1978), in their report on the relationship of

selected psychosocial factors to coronary heart disease in the

Framingham study, showed that the Type A personality dimension was significantly associated with CHD among both men

and women, but only in the younger age group (45-64);

among older (65 and older) men and women, this association

did not hold. On the other hand, marital dissatisfaction and/or

marital disagreement were associated with CHD among men

and women, but only in the older age group. Interestingly,

an index called “aging worries” showed an association with

CHD among younger men and women only. Equally fascinating results are to be found in the study of acculturation and

CHD among Japanese men living in California (Marmot and

Syme, 1976). The basic finding was one of low CHD prevalence among the more traditional Japanese-Americans, a result

that could not be accounted for by differences in the major

coronary risk factors. How did this picture change for different

age groups’?On one index, “culture of upbringing,” the benefits

of being traditional weakened considerably in the oldest of

three age groups (55 and older). On another index, “cultural

assimilation” (degree of retention of Japanese cultural forms),

it was the oldest group that showed the biggest differences

in CHD prevalence due to degree of assimilation.

In summary, then, the selected findings discussed in this

section were primarily intended to alert the readers to a particularly interesting issue: the changing risk factor picture with

age, and what that may say about the vulnerability of older

people. Certainly it is possible that, in part, we are simply

observing the selective survival of the fittest. Similarly, calculations intended to reveal the strength of the impact of a

particular variable on health status, such as standardized mortality ratio or relative risk, are ratio calculations that encounter

a ceiling effect when the outcome variable is quite common

in the total group under observation (e.g., mortality among

the very old). Calculations based on linear differences (e.g.,

difference in percentage between two groups) might lead to

somewhat different statements regarding the changing risk

factor picture. However, neither of these explanations is particularly compelling when one finds subgroup differences,

such as the difference between men and women in the impact

of education on mortality among younger and older persons,

or when new risk factors emerge as others weaken among

the progressively older age groups in the study sample.

In the long run, we need a substantive rather than

methodological interpretation of the findings on the changing

risk factor picture. This will call for abroad life-cycle perspective and for an understanding of the adaptation processes

of different age groups. For example, the impact of social

status variables may differ because lifestyle adaptations and

aspirations change with age and because, in retirement, the

effects of some components of social status may be considerably blunted. Ultimately, in order to understand the changes

in biosocial vulnerability of people as they age, we will need

to know more about the intrinsic, sui generis process of biological aging (if it exists), the cumulative impact of environmental

conditions and experiences, and the changes in the meaning

and dynamics of the psychosocial risk factors under scrutiny.

Clearly differences in risk factors may vary not only

among age groups but among different subgroups of the population or among different cultures. The most striking differences in risk are frequently observed between men and women.

Evidence to date indicates that widowhood is associated with

greater mortality risk for men than women, that is, men do

not seem to fare as well following the loss of their wives

as women do following the loss of their husbands. Gender

also seems to interact with many other traditional risk factors

such as smoking, serum cholesterol, and alcohol consumption

(Wingard, 1982) so that men with the same level of risk

factor have a greater morbidity and mortality than women

with the same amount of exposure to risk. The reasons for

this phenomenon are poorly understood. Similar interactions

may well exist across socioeconomic groups and across different cultures, though most research has not focused on these

differences. We will come back to this topic in relationship

to the impact of networks and support on health status in

the conclusion of this paper.

Social networks and social

SUppO~: A selected social condition

and its influence on health status

The importance of maintaining social connections for the

physical and mental well-being of an individual has been

recognized for some time (Durkheim, 1951; Freud, 1926;

Bowlby, 1958). Recently, investigators have hypothesized that

social and community ties may alter host resistance and consequently play a role in the etiology of a wide range of

diseases (Cassel, 1976; Kaplan, Cassel, and Gore, 1977; Antonovsky, 1972). Social isolation has also been proposed as

a critical aspect of the aging process itself (Lowenthal, 1964;

Cumming and Henry, 1961). It is commonly expressed that

the elderly are an isolated group, with widowhood, retirement,

and grown children leaving home bringing about an irreversible

loss of social attachments and community ties. Thus, if the

relationship between social isolation and poor health is a causally strong one, we would expect this to have profound effects

among the elderly, for whom such losses are common. In

fact, while there is some evidence to suggest that social networks play a role in disease etiology, there is less convincing

42
evidence regarding increasing social isolation as an age-linked

phenomenon, and even less evidence supporting the notion

that therelationship between sociai networks and health status

may be unique to, or different for, the elderly.

In the following pages, the research relating social and

community ties to health status among people of all ages

will be reviewed. I will then return to the issues pertaining

to the social networks and social support of the elderly. I

will start with a review of the literature on marriage and

widowhood and move to a review of other kinds of social

connections.

Marriage: A special kind of tie

Marriage is one of the most fundamental and intimate

ties among people. Those who are not married, whether single,

separated, widowed, or divorced, experience higher mortality

rates than married people (Ortmeyer, 1974; Durkheim, 1951;

Carter and Glick, 1970). This mortality risk does not appear

to be as great for women as for men and seems to decline

with age.

Mortality differences are not explained by any one cause

of death, Using national data (ortmeyer, 1974), divorced

white men have been found to have higher mortality rates

than their married counterparts for virtually every major cause

of death except leukemia. This is also true of single white

men, with the added exception of malignant neoplasms of

genital organs, Compared to married men, those who are

single have a mortality rate from cancer of the digestive organs

and peritoneum that is four times higher. Widowed men have

seven times the homicide rate, four times the suicide rate,

and over six times the mortality rate for tuberculosis and

cirrhosis of the liver. Divorced white women, compared to

those who are married, experience higher death rates from

almost all causes, and single white women have higher rates

than married white women for most causes. The cause-specific

ratios are not as great for women, however, as they are for

men (Ortmeyer, 1974).

In the Israeli Ischemic Heart Disease Study, a prospective

cohort study of almost 10,000 Israeli adult male civil service

and municipal employees, psychosocial problems (particularly

family ones) and wife’s love and support were found to be

important predictors of the development of angina pectoris

over a 5-year followup period (Medalie and Goldbourt, 1976).

Incidence cases were subjects at risk for angina pectoris (excluding known angina and myocardial infarction cases in 1963)

who in 1968 were diagnosed as having definite angina (Medalie

etal,, 1973).

In this study family problems were measured by responses

to four questions: (1) Did you have any problems (conflicts)

with your family (wife, children) in the past? (2) Do you

have any problems with your family at present? (3) How

does it affect you when your wife or children do not listen

to you or even oppose you? (4) Does your wife show you

her love? In an analysis of 5-year angina incidence rates,

family problems emerged as strong predictors after controlling

for other biomedical and demographic covariates. In another

analysis, wife’s love and support were shown to buffer the

effects of anxiety. When anxiety was low, wife’s love and

support were not associated with angina. When anxiety was

high, men who did not have the love and support of their

wives were 1.8 times as likely to develop angina during the

followup period as those with support (p < .05). In this

study, these same variables did not predict myocardial infarction in multivariate analyses (Goldbourt, Medalie, and

Neufeld, 1975).

The existence of a supportive marriage has been shown

to mediate between stressful life events and other poor health

outcomes. In one study of the mental and physical health

consequences of job loss due to a factory shutdown, Gore

(1981) reports that those men who had “the emotional support

of their wives while unemployed for several weeks had few

illness symptoms, low cholesterol levels, and did not blame

themselves for loss of job.” In general, men who were both

unemployed for a longer time and unsupported tended to

have the worst health outcomes (Cobb and Kasl, 1977). In

another study of psychiatric disturbance among women,

Brown, Bhrolchain, and Harris (1975) found that having a

husband or boyfriend who was a confidant served as a powerful

mediator between a severe event or major difficulty and onset

of psychiatric disorder. In this sample of women aged 18-65,

38 percent of those who had a stressful event and no husband

or boyfriend as confidant had onset of disturbance. (For those

without such a confidant or without a severe event, the percentage of psychiatric disturbance was under 4 percent. ) When

the confidant named was a sister, mother, or friend seen

weekly, the relationship was not observed to mediate between

life events and psychiatric disturbance.

Widowhood seems to have a profound effect on physical

and mental health. Also, as noted earlier, this effect appears

to be stronger for men than women and stronger in younger

rather than older ages. Maddison and Viola (1968), Marris

(1958), and early studies by Parkes (1964) indicate that

widows, especiali y in the first year following bereavement,

report many more complaints about their health, both mental

and physical, and believe they have sustained a lasting deterioration to their health. The relationship between widowhood

and increased mortality risk from a wide variety of diseases

has been reported both in studies using vital statistics (Kraus

and Lilienfeld, 1959; Young, Benjamin, and Wallis, 1963;

Cox and Ford, 1964; McNeil, 1973) and in cohort studies

(Rees and Lutkins, 1967; Clayton, 1974; Gerber et al., 1975;

Ward, 1976).

In one of the first major studies, Parkes, Benjamin, and

Fitzgerald (1969) report that of 4,486 widowers 55 and older,

213 died during the first 6 months following bereavement.

This is 40 percent above the expected rate for married men

the same age. After 6 months, the rates gradually fell back

to those of married men and remained at that level. Through

an analysis of husband’s and wife’s concordance rates for

cause of death, the authors concluded that neither the sharing

of similar pathogenic environment nor the tendency toward

the selection of the unfit to marry the unfit (homogamy)

was likely to explain more than a part of the increased 6-month

43
mortality rate. While the Parkes study is a landmark study

of widowhood, as Minkler (1985) notes, it suffered from

its own methodological weakness, such as lack of attention

to loss by migration, slightly older subjects than controls,

and retention of remarried (and therefore possibly healthier)

widows in the cohort.

To date, the most comprehensive and well-controlled analysis of widowhood is by Helsing, Szklo, andComstock(1981 ),

who found that being a widow or widower catried an increased

mortality risk among men but not among women. Their 10-year

retrospective cohort study of men and women in Washington

County, Maryland, showed that the excessive risk in men

persisted when age, education, age at first marriage, cigarette

smoking, church attendance, and a proxy economic status

measure were controlled. In contrast to other investigators,

Helsing and coworkers reported that the increased mortality

risk among widowers was not confined to the first 6 months

of the bereavement but persisted throughout i 10-year followup

period. If further studies confirm this finding, viewing widowhood as a chronically stressful situation, rather than attributing

the increased mentality to the acute effects of the spouse’s

death, would seem reasonable (Helsing and Szklo, 1981; Susser, 1981). In addition, the increased risks were not observed

in only one or two causes of death but were spread across

many causes. Notable increases were found for infectious

disease, accidents, and suicide for males and cirrhosis of

the liver for females (Helsing, Comstock, and Szklo, 1982).

These data suggest that there is something protective about

a supportive spouse or partner that is capable of shielding

an individual against the otherwise deleterious effects of some

objective life circumstances. The morbidity and mortality findings also indicate that the loss of a spouse, a major enduring

tie to another person, may be at least a precipitating factor

in the increased death rates found among widowers. The relationship appears to be at least to some extent independent

of the traditional CHD physiological risk factors, homogamy,

and equal exposure to pathogenic environment.

The elderly, particularly elderly women, are increasingly

likely to lose a spouse with advancing years. Jacobs and

Ostfeld (1977), in a comprehensive review on the mortality

of bereavement, estimate that 2–3 percent of married Americans over the age of 50 will become widowed each. year.

At ages 65–74, 10 percent of men and 43 percent of women

are widowed. For those 75 years and older, the respective

percentages are 29.5 and 70.2 (Siegel, 1975). Clearly, this

is an extremely frequent event among the elderly. .However,

there is also some evidence to suggest that the nature of

bereavement is in some ways modified for the elderly. Several

investigators (Stem and Williams, 1951; Gramlich, 1968;

Gerber et al., 1975; and Heyman and Gianturco, 1973) indicate

that grief among the elderly is not the same as grief at younger

‘ages.Jacobs and Douglas (1979) Iiave summarized this work,

reporting that among the elderly a loss is faced with more

cognitive acceptance and is associated with less numbness,

denial, and guilt. However, hallucinations and illusions and

psychosomatic symptoms may occur with greater frequency.

Jacobs and Douglas (1979) suggest that these differential characteristics of grief among the elderly may be the result of

the process of anticipation of death, which is probably associated with older age. This ability to anticipate and accept

widowhood may also play some role in the decreased relative

risks associated with bereavement at older ages reported by

researchers (Kraus and Lilienfeld, 1959; McNeil, 1973). This

finding also supports Neugarten’s (1970) viewpoint that what

makes events traumatic is their being “off time” and unanticipated. “Major stresses,” she emphasizes, “are caused by events

that upset the sequence and rhythm of the life cycle ...when

the empty nest, grandparenthood, widowhood, or retirement

occur off time” (pp. 86-87). It may also be that the experience

of becoming a widow or widower at a time when one’s peers

are also becoming single provides a structural support that

buffers against some of the stressful circumstances encountered

by younger widows and widowers. Economic and cultural

conditions are likely to modify the impact of widowhood

in the bereaved spouse, although little research has been done

to explore these potential relationships.

Other social ties

In addition to spouses, other social ties are relied on

by most people to fulfill a variety of emotional and pragmatic

needs. Some of these ties are characterized by enduring and

emotionally important relationships, while other contacts are

characterized primarily by their task orientation. Some people

in these links may fulfill a variety of needs; others may have

one specific need for which they are relied on. Taken as

a whole, these relationships and informal associations form

a web or a social network in which most people spend a

significant part of their lives. Although network configurations

have been described in detail by many social scientists, relatively little is known about the impact that various network

configurations have on health status. We will briefly review

some of the evidence relating social networks to health status

and then focus on situations of special interest to the elderly.

For more extensive reviews on the topic of health and social

networks, the reader is referred to several recent reviews

(Kaplan, Cassel, and Gore, 1977; Cobb, 1976; Broadhead

et al., 1983; Minkler, 1985; Berkman, 1984; Cohen and Syme,

1985).

Mortality

There have been three studies indicating that particular

network or support characteristics are associated with mortality

risk. One of these is an analysis of 9-year mortality data

from 6,928 adult residents of Alameda County, California

(Berkman and Breslow, 1983; Berkman and Syme, 1979),

In the survey, basic information on several essential aspects

of an individual’s personal network was collected. A Social

Network Index was developed based on four types of social

connection: (1) marriage, (2) contacts with extended family

and close friends, (3) church group membership, and (4)

other group affiliations. Contacts with friends and relatives

44
were measured by the number of close friends and relatives

a respondent reported and the frequency with which he or

she saw them. The age- and sex-specific mortality rates from

all causes in relation to the Social Network Index reveal

it consistent pattern of increased mortality rates associated

with decrease in social connection. The age-adjusted relative

risks were 2.3 for men and 2.8 for women (p<.001). When

the Social Network Index was examined in relation to the

separate causes of death, people with few connections were

found to be at increased risk of dying from many separate

causes of death: ischemic heart disease, cancer, cerebrovascuIar and circulatory disease, and a final category of all other

causes of death.

In subsequent multivariate analyses using a multiple logistic risk model, the Social Network Index was found to be

associated with mortality risk independent of initial physical

health status, socioeconomic status, cigarette smoking, alcohol

consumption, level of physical activity, obesity, race, life

satisfaction, and use of preventive health services. In the

multivariate analyses, when all variables were considered

simultaneously, the approximate relative mortality risk was

reduced but still remained over 2.

A prospective community study of adults in Tecumseh,

Michigan, by House, Robbins, and Metzner (1982) has both

extended and, in part, replicated the findings of the Alameda

County study. In this analysis, 2,754 men and women who

were interviewed and medically examined during the third

round of the Tecumseh Community Health Study in 1967-69

and who were 35-69 years old at that time were included

in a 1O-yearmortality followup.

Measures of social relationships and activities fell into

four major categories: (1) intimate social relationships (marital

status, visits with friends and relatives, going on pleasure

drives and picnics), (2) formal organizational involvements

outside of work (going to church or meetings or voluntary

associations), (3) active and relative]y social leisure (going

to classes, movies, affairs, museums, etc.), and (4) passive

and relatively solitary leisure (watching television, listening

to radio, reading). Confounding variables considered in the

analyses were age, cigarette smoking, alcohol consumption,

education, employment status, occupation, weight and height,

and a variety of morbidity measures assessed from physical

examination. One of the major strengths of the study was

the ability of the investigators to control for such factors

as coronary heart disease-defined as probable history of

myocardial infarction (MI), or angina, or electrocardiographic

evidence of MI; chronic bronchitis or persistent cough of

phlegm; probable hypertension; levels of serum cholesterol

and blood glucose; and forced expiatory volume.

Among seven individual components of social integration

and activities, four were statistically significant for men in

multiple logistic analyses after adjusting for age and other

risk factors: marital status, attendance at voluntary associations, spectator events, and classes and lectures. For women,

only church attendance was significant. Frequencies of visits

with friends and relatives and of going out on pleasure drives

and picnics were not significant for either men or women.

In two cumulative indexes of social integration using the

same seven factors, multiple logistic analyses revealed that

among men the indexes were related to mortality when confounding variables were considered. Among women, the relationships of both indexes to mortality were significant independent of age, but became nonsignificant when all risk factors

were covariates.

A third study of mortality risk was conducted in Durham

County, North Carolina, by Blazer (1982) on 331 men and

women 65 years of age and older. Eleven items tapping social

support were included in the original survey. These items

from the Older Americans Resources and Sources Community

Survey (OARS) Questionnaire were divided into three dimensions: ( 1) roles and attachments available (marital status,

number of living children and siblings), (2) frequency of

interaction (telephone calls and visits with friends and relatives

during the past week), and (3) perception of social support

(lonely even with people, someone cares what happens to

you, difficulty speaking to new people, enough contact with

confidant, seems like no one understands, someone would

help if you were ill or disabled). Control variables considered

in analyses were age, sex, race, economic resources, physical

health, activities of daily living, stressful life events, symptoms

of major depressive episode, cognitive dysfunction, and

cigarette smoking.

In several types of analyses, the three parameters of impaired social support were significant risk factors for 30-month

mortality independent of the 10 covariates. Using three binary

regression analyses, the estimates of relative mortality risk

were found to be 3.40 for impaired perceived social support,

2.04 for impaired roles and attachments, and 1.88 for impaired

frequency of social interaction. Impaired perceived support

and roles and attachments predicted mortality when the other

two support parameters were controlled for. This was not

the case for the third measure, tapping frequency of interaction.

Thus, in this survey of older men and women, the parameter

with the highest value was perceived social support, a measure

(as Blazer correctly pointed out) that is a subjective appraisal

of the adequacy of support rather than a more objective characteristic of a social network.

Following this study, Reed et al. (1983) examined the

impact of social networks on CHD in the Hawaii cohort of

Japanese-Americans. In 1971, 7,639 men who had completed

the initial examination from 1965 to 1968 and who were

thought to be alive and residing in Hawaii were given a

psychosocial questionnaire: 61 percent of these people returned

the questionnaire (n= 4,653). Nonresponse, although high,

does not seem to reflect any systematic nonresponse bias

(Reed et al., 1982). Prevalent cases were defined as those

CHD cases existing at the time of the survey; incident cases

were new ones occurring during the followup period. At the

time of the survey, 264 prevalent CHD cases were identified.

In the subsequent time through 1978, 218 men developed

new coronary heart disease.

In this study, nine questions were used in two network

scales. The first included all nine items: (1) geographic proximity of parents, (2) geographic proximity of parents-in-law

(3) marital status, (4) number of living children, (5) number

of people in household, (6) frequency of social activities with

45
coworkers, (7) frequency of discussing serious personal problems with coworkers, (8) frequency of attendance at religious

services, and (9) number of social organizations attended regularly. The second scale included the first five of these items,

which involve more intimate contacts. Among individual

items, men who were never married and/or were living alone

had a particularly high risk of both prevalent and incident

heart disease, although these risks were not statistically

significant.

In logistic analyses of the prevalence data, both scores

predicted total CHD (an outcome measure including cases

of myocardial infarction and angina pectoris) independent of

such standard biologic risk factors as high blood pressure,

serum cholesterol and serum glucose levels, as well as cigarette

smoking, alcohol consumption, physical inactivity, and body

mass. The set of five items tapping family contacts and household composition predicted angina pectoris; however, neither

score predicted MI alone.

Different findings emerged from the analyses of incidence

or prospective data. In logistic analyses with all potentially

confounding variables, there was no significant association

of either score with total CHD, fatal myocardial infarction,

or angina. The second network score of more intimate contacts

was of borderline significance @= .08) in predicting nonfatal

myocardial infarction. These findings must lead to the question

of what might be responsible for the differences in prevalence

and incidence data.

The most obvious possibility is that the experience of

disease is antecedent to and influences a person’s ability or

desire to maintain social ties, thereby causing an association

to appear in the prevalence data but not in the prospective

incidence data. Some variables in this analysis are more likely

to be influenced by health status than others (e.g., attendance

at social activities and religious services as well as membership

in and regular attendance at organizations). In fact, the scales

including these items were more predictive of CHD prevalence

than of incidence. Other variables are much less likely to

be influenced by concurrent health status (e.g., never being

married, geographic location of parents and in-laws, number

of children, and household composition). If these variables

predicted prevalence, then other explanations must be

examined. In fact, these items were found exclusive of the

others in the second scale. This second scale of marital status,

contacts with relatives, and household composition predicted

CHD prevalence and angina prevalence and was of borderline

significance in predicting nonfatal MI incidence.

Is there another explanation for these associations? Because prevalence data obviously do not include fatal cases,

there is a suggestion in this cohort that the network variables

may have been more predictive of nonfatal CHD than of

fatalities, The authors noted that the MI case fatality rate

was higher in the group with most connections than in the

group with fewer contacts (43 percent compared to 34 percent).

In the univariate analyses of incident cases, the network scales

showed no association or clear pattern with fatal MI. However,

the scale of intimate contacts was significantly associated

with nonfatal MI; the relative risk was 1.5 (p= .04). Thus,

although the association of the network variables with prevalence data and nonfatal MI cannot fully be explained, they

also cannot be dismissed lightly as being artifactual. In the

long run, developing more substantive hypotheses to explain

these findings will be useful.

In the Framingham cohort of women, clerical workers

(n= 142) who had a nonsupportive supervisor were found

to be at increased risk of developing CHD over an 8-year

followup period (Haynes, Feinleib, and Kannel, 1980). Incidence of CHD was defined as diagnosis of MI, coronary

insufficiency syndrome, angina pectoris, or CHD death. Nonsupport from supervisor predicted CHD incidence independent

of other standard risk factors (high systolic blood pressure,

high serum cholesterol levels, and number of cigarettes

smoked) and other psychosocial scales (anger, job changes,

and family responsibilities). This factor was not a significant

CHD predictor for other working women or for men (Haynes,

Feinleib, and Kannel, 1980). Framingham working women

with the highest CHD risk were those in clerical jobs. Thus,

women in this position may experience stress from several

sources, including lack of autonomy and control over the

work environment, underutilization of skills, and lack of recognition of accomplishment.

A recent study by Rubberman et al. (1984) of survivors

of acute myocardial infarction who were participants in the

B-Blocker Attack Trial (BHAT) reveal striking effects on

3-year mortality associated with social isolation and stress.

The BHAT was a multicenter, randomized, double-blind, and

placebo-controlled research project designed to test whether

the regular administration of propranolol hydrochloride to men

and women who had at least one myocardial infarction would

result in a significant reduction in total mortality during a

period of 2 to 4 years. Six weeks after MI, men were given

an interview on a range of psychosocial topics covering life

events, stress, difficulty coping, social isolation, depression,

and Type A behavior. Twenty-five of the 31 BHAT clinical

centers agreed to participate in this study. Among those 25

centers, the response rate to the interview was 90 percent,

yielding 2,320 men. Social isolation in this study was defined

by three responses to questions: (1) respondent did not talk

with medical personnel (while in the hospital or soon after

discharge) about any possible need for life changes; (2) respondent (at the time of MI) was not a member of any club,

social or fraternal organization, church or synagogue; (3) respondent “hardly ever” visited friends or relatives in their

homes.

Mortality analyses were based on standard life-table

methods. High levels of social isolation were associated with

roughly double the risk found among patients with low levels

of isolation in uncontrolled analyses. The risk was comparable

to that of persons who experienced other forms of stress

and was slightly less than the risk associated with few years

of education. When a combination of life stress and social

isolation was entered into a multivariate regression mode!

along with other biomedical risk factors, the stress/isolation

measure was independently associated with both all causes

of death and sudden death and among men in high-amhythmia

and low-arrhythmia groups. Social isolation alone was independently associated with mortality. Relative risks resulting

46
from the Cox multivariate regression models, however, are

provided only for the combined measure. They are 4.56 for

all causes of death (comparing people who are high on both

stress and isolation measures to those who are low on both

factors) and 5.6 for sudden death from arteriosclerotic heart

disease.

These results are similar to those reported by Chandra

et al. (1983), who found marital status predictive of survival

after myocardial infarction when adjusted for biomedical

covariates, including clinical complications.

Conclusions and

recommendations

research

for future

What is known about the relationship between social ties

and physical health has been reviewed in the previous section.

The focus of this section is on what we need to know—for

it is clear that the studies produced so far raise many more

issues than they resolve. For instance, they do little to inform

us about which network characteristics are important or how

such a social phenomenon “enters” the body to influence

disease processes. In this section, I will discuss four issues

raised by these studies.

1.Specificityof socialnetwork and

supportmeasures

Perhaps the greatest problem with the studies reviewed

is the inadequacy of the social network and support measures.

With few exceptions, the measures used in these studies have

been developed post hoc from a few items included in questionnaires for other reasons. Thus, many studies purporting to

measure social networks and support do not in fact do so,

or do so in a very limited way. This is understandable in

early studies when preliminary relationships are being uncovered; however, we now need to move on and take full advantage

of the more sophisticated measures currently available which

allow the measurement of spec~fic network and support

characteristics,

For instance, many of the studies completed to date have

intermingled measures of support with measures of networks.

As was stated earlier in this report, social networks might

be seen as the web of social relationships that surround an

individual and the characteristics of those social ties—that

is, their size, composition, geographic dispersion, density,

homogeneity, level of reciprocity, intimacy, frequency of contact, etc. (Fisher et al., 1977; Mitchell, 1969; Laumann, 1973).

Social support might then be viewed as the aid (emotional,

instrumental, financial) that is transmitted among network

members (Mitchell and Trickett, 1980; House, 1981; Kaplan,

~assel, and Gore, 1977). As Wellman (198 1) has so cogently

argued, networks may or may not be supportive; that is an

empirical question. We need to know.specifically what network

and support characteristics are related to morbidity and mortality, with the understanding that certain characteristics may

even increase risk of one type of outcome and decrease risk

of another. For example, some evidence indicates that individuals with large networks may increase their utilization of health

services and independently decrease their risk of disease. This

may come about because large networks may be stress reducing

but at the same time be comprised of “cosmopolitan” members

who have pro-medical-care orientations. We need to know

if dispersed networks are as effective as networks of equal

size where members are geographical y proximate. Most of

all, we need to move beyond measures of household composition, or presence of kin, or group affiliation as adequate

measures of social networks and support.

It is also evident that social networks and social support

are shaped by various cultural, social, environmental, and

psychological forces and that they, in turn, influence others.

Psychologists and psychiatrists point to the possibility that

individuals with mental disorders are unable to maintain extensive social networks or mobilize support because of their

mental state. It is this mental condition, they argue, which

is etiologically related to the disease outcome. Other investigators point out that the social structure, to a great extent,

determines the composition of networks and types of interaction among network members. Still others point to the

psychological sequelae of social experiences—sense of coherence, internal/external control, fatalism, even Type A behavior—and suggest that these are the cause of disease since

they are more proximate to it.

All of these positions are plausible, and all are at least

in part correct. The choice of which of these important factors

to study depends upon our orientation—professional, political,

and personal—and upon where we foresee interventions to

be most effective. No matter what our orientation is, however,

our research efforts will be greatly strengthened by our ability

to distinguish among these factors in terms of both measurement and temporal sequencing. Studies in which these variables

are lumped together will do little to enhance our understanding

of which psychosocial experiences are most noxious and how

those factors interact with others to produce disease.

2. Social networks: Buffers or stressors?

Whether we consider social networks and support as buffers or stressors is dependent, in part, on statistical tests and

model building, but also upon our conceptualization and measurement of social networks and support. Whether we consider

death of a spouse or divorce as a break in networks or an

acute life event is dependent upon our’conceptualization, not

a statistical “manipulation. It has now been pointed out by

several investigators (Mueller, 1980; Thoits, 1982: Gore, 198 l;

and Berkman, 1982) that stress and support are neither operationally nor conceptually independent variables. Many life

changes are actually losses or breaks in social ties; others

are not really life events at all but reflect ongoing poor or

deteriorating social relationships. Many other events “may

seriously disrupt, distort, reduce, or otherwise change existing

network relationships” (Mueller, 1980). Moving, changing

jobs, and retiring almost inevitably cause changes in networks.

47
Mueller has hypothesized that much of the impact of life

events may result from the profound disturbances they introduce into one’s social network. This possibility was discussed at the very beginning of this report. The extent to

which life events and networks are overlapping and redunddnt

severely hampers our ability to assess their relative impacts

on health status.

How questions are framed can also influence the kinds

of effects networks and support will have. For instance, if

we have a study of occupational stress and the network measure

involves discussing work-related problems with network members, such an item can have only buffer effects because one

does not talk about job problems to anyone in the absence

of such problems. On the other hand, having a “supportive”

boss may be independent of other job stresses. Therefore,

the Framingham data that show the significance of a supportive

boss for clerical workers but not other workers probably reveal

the buffering effect of this variable on the job stress of clerical

employees.

The findings reviewed in this paper indicate the existence

of both main effects and buffer effects. It may well turn

out that certain network or support characteristics have buffer

effects (perhaps emotional support buffers certain occupational

stresses) and others have main effects (network size may

influence mortality). In either case, we need to examine these

relationships empirically, not assuming the validity of one

model versus another a priori.

3. Cultural expectations and wellintegrated communities

It is a cliche to say that responses to survey questions

are influenced by cultural attitudes and expectations, yet this

obvious fact has been ignored in most research to date. The

reason that it is so important to pay attention to this possible

influence is that there is a hint in the findings reviewed that

in some populations known to be very socially cohesive and

well integrated (i.e., women in Tecumseh, Japanese-Americans in Hawaii), differences in risks between isolated and

nonisolated people are not great. One possible explanation

for these findings is that those communities have such overall

high levels of social contact and support that very few people

are isolated in severe enough ways to reveal significant increases in risk. For instance, only about 2 percent of Japanese

men in Hawaii live alone (Reed et al., 1983). Another possibility is that social contacts are so much a part of a routine

in the lives of these people that they go unnoticed and unreported. As a result, the measures of social activities do not

differentiate well in these groups (House, Robbins, and Metzner, 1982). For instance, there is some evidence that, in

studies of older people, the daily visits to parents who live

in in-law apartments or nearby commonly go unreported by

their children as “visits.” This seems to occur because they

are so much a part of normal daily life that they are not

seen as formal occasions worthy of reporting (Stueve and

Lein, 1979). Similar experiences have been reported for

women in Tecumseh (House, Robbins, and Metzner, 1982)

and for the Japanese. In a cross-cultural study comparing

the experiences of older Japanese and Americans, 42 percent

of Japanese in Japan, compared to 78 percent of Americans,

reported feeling rooted, that they belonged and were part

of things (Hashimoto, 1983). This is in contrast to the fact

that 68 percent of Japanese had lived in their town for 50

years or more, compared to only 24 percent of older Americans. Even comparing only those who lived in their town

since before World War II, 46 percent of Japanese and 86

percent of Americans reported feeling that they belonged and

were rooted (Hashimoto, 1983). Reporting those kinds of

feelings may be very foreign to the Japanese in spite of our

belief that this sense of fit is a common theme of Japanese

culture.

Also, questions which tap discussion of personal or financial problems may be culturally unacceptable to different

groups. For instance it appears to be very difficult for Japanese

men to discuss personal problems with coworkers, since in

the Japanese-American study in Hawaii only about 1Y2 percent

reported that they do so (Reed et al., 1983). Studies designed

to tap networks and support must take this into consideration

in the future by probing more fully and developing more

culturally sensitive questions.

4. Pathways leading to disease

Social networks seem to be related to disease states in

a remarkably nonspecific way. It would be premature at this

time to say that social ties are related to one category of

disease and not to another. In fact, they may turn out to

influence biologic mechanisms which increase an individual’s

vulnerability to a host of conditions. With regard to the spectrum of disease, the most consistent findings that have emerged

to date are those relating network variables to mortality. In

all mortality studies reviewed, including those on bereavement,

various aspects of networks predicted mortality risks. Results

are more mixed in morbidity studies. Two studies on case

fatality (following MI) again show strong associations between

social ties and survival.

The next question is: how might social networks and

the functions they serve influence physical health status; that

is, what are the biologic pathways linking these two

phenomena? While there has not been much work in this

area and space limitations preclude a lengthy discussion of

this issue, several potential pathways leading from social networks to morbidity and mortality can be described.

The first is that, through the provision of advice, services,

and access to new social contacts, individuals with particular

network ties obtain better medical care than others. Through

the transmission of certain pro-medical-care values, knowledge

of how to obtain those services, subsequent access to and

utilization of them, people simply get superior health care

which, in turn, influences their physical health.

A second possible pathway is by the direct provision

of aid, services, and tangible or economic assistance to individuals. According to this hypothesis, some networks take

better care of their members than others—independently of

professional medical services-and it is this factor that influences a member’s health status.
A third mechanism related to social integration has to

do with social control and peer pressure; that is, individuals

in a network frequently feel constrained to behave like other

network members. Thus, people who have ties with people

who smoke cigarettes, drink alcohol, are physical]y active,

or maintain certain dietary practices may follow the patterns

set forth by their group simply to maintain their group identity

(not because of the health value of the behavior). Therefore,

groups or networks have the potential to be either health

promoting or not; and this may influence the health status

of individuals.

A final mechanism is a more direct physiologic pathway

by which people who are lacking ties that provide for intimacy,

a sense of belonging, opportunities for nurturance, and reassurance of worth are stressed. Some individuals may respond

to this situation with a changing psychological state (becoming

depressed or fatalistic, for example), and the psychological

state may lead to alterations in physiologic functioning. In

this instance, the psychological state would be viewed as

a mediating factor, The stressful condition may also directly

alter physiologic patterns without notable changes in

psychological states. Social networks may well turn out to

influence generalized susceptibility to illness, as has been

hypothesized by some (Antonovsky, 1972; Berkman and

Syme, 1979; Cassel, 1976), or they may affect the course

of many diseases through multiple mechanisms. In any of

these cases, the biologic responses to this stressful condition

may lead to alterations in both known biologic risk factors

(e.g., blood pressure, serum cholesterol) and in as yet unidentified processes. These findings were reviewed at the beginning

of this paper.

Relevant issues in the study

of the effects of social

networks on the health

status of the elderly

1. Social networks and social support may well play

a role in the etiology of disease; however, there is little evidence

to support the notion that the elderly are particularly fragile

and vulnerable to the effects of social isolation. This relationship must be clearly documented and the reasons for it better

understood. Also, as we learn more, there may be special

subgroups of the elderly population identified (i.e., those who

are ill or with limited functional abilities) for whom informal

caretaking is the critical factor determining institutionalization

or significant declines in physical and mental health.

2. There is a difference between social networks and

social support, At this time, we have very little information

on those networks that are supportive. Are daughters better

than sons at providing help? Do neighbors help more than

we suspect’? While all self-reports are colored by social response patterns, feelings of supportedness are certainly

molded, in part, by psychological factors. For accurate answers

to questions about which networks are used for different support functions and which are most influenced by social desirability patterns, we must await information from new studies.

3. Social networks have most often been conceptualized

and measured in static terms, as if they were a stable property

of an individual. Clearly, this is not truly the case. Social

networks are dynamic, perhaps especial] y so for the older

person, who is confronted with significant exits and entrances.

To date, there is very little information on social network

patterns over time or longitudinally. Whether people maintain

certain network patterns (although individuals may change)

throughout the life course is unknown. The association between

earlier patterns and patterns of social relationships in old age

and their respective risks to health are also unclear. Some

evidence indicates that the elderly who are lifelong isolates

have better mental health than recent isolates (Lowenthal,

1964). However, these data are suggestive and need to be

further corroborated. It is also possible that lifelong isolation

represents a cumulative risk which becomes manifest in terms

of disease consequences late in life, after a long latent period.

Thus, the critical aspects of network and support would be

the structure during early life and middle-age. Network structure measured in old age may or may not be a good “proxy”

for the extent of earlier relationships.

4. It is most likely that networks both influence and

are influenced by physical and mental health. It is important

to realize that social networks and support are also correlated

with a multitude of other psychosocial and behavioral factors

that are also associated with disease risk. The degree to which

they are colinear presents not only a statistical challenge to

the investigator but a conceptual one as well. To the degree

that studies are longitudinal and it is possible to sort out

which factor precedes the others, we will be able to solve

some of these problems of collinearity.

Although space does not permit a lengthy discussion of

causal inference here, in order to assess the effect of social

networks on subsequent physical health prospectively, one

must adequately control for baseline health status. Generally

in epidemiologic studies one identifies a cohort initially free

of the specific disease in question and follows up the cohort,

identifying incident cases. This method poses two problems

with regard to studying the health impact of social networks

in the elderly. First, social networks appear to be associated

with a very wide range of health outcomes. Thus, it is questionable whom one should exclude from prospective analyses,

except anyone with any disorder or condition. Secondly, in

the elderly this poses an additional dilemma, since so few

people are totally free of disease. Therefore, in order to study

this issue most fruitfully, the researcher must pay very careful

attention to the development of new conditions, the rate of

progression or recovery from old conditions, declines in functional ability and general well-being, instances of hospitalization and institutionalization, and, of course, mortality. By

rigorous monitoring of this broad range of outcomes and assessments at baseline, the investigator should be able to identify

any impact social networks might have.

Another approach would be to focus on one particular

49
outcome, say coronary heart disease, exclude all prevalent

c&es, and include the assessment of social networks along

with other known risk factors in subsequent analyses of incidence. By doing this for a whole range of diseases, researchers

might again profitably piece together from many studies the

health effects of social disconnection.

5. Finally, while social and community ties are probably

important to the maintenance of health, as yet we do not

have three straightforward questions which clinicians can use

to predict disease risk. It is not at all clear that living alone,

being single, not having family necessarily imply social isolation. In,fact, the data produced thus far argue for more complex

measures. It may well turn out that some kinds of support

have a negative impact on health and functioning, and others

have a positive impact. Furthermore, most people, even older

people, on the whole seem amazingly resilient and flexible.

They seem to make new relationships and make suitable

tradeoffs and substitutions. Older people seem to obtain support

from many different sources of contact, kin and nonkin alike.

If this is true, then our task must be to uncover the circumstances under which older men and women derive adequate

support.

Data bases and future research

approaches

There are currently a great many studies that have included

measures of social networks and support on questionnaires

and are of potential usefulness for our purposes. They can

be divided into three categories (table 1). The first is comprised

of studies using short instruments that were used to predict

mortality (Alameda, Tecumseh, Durham). While these instruments are crude and were not originally intended to measure

Table 1. Types and examplesof survey instruments used to measure

aociel networks and social support

Brief instruments with predictive ability (predict physical health outcomes):

1. Human Population Laboratory Study of Alameda County (8)

2. Tecumseh Community Health Study (53)

3. Elderly in Durham County (13)

4. Rand Corporation, Social Contacts and Resouces (31)

Lengthier instruments of social networks and support used in physical and

mental health studies:

1. Schedule for social interaction (50)

2. Norbeck social support questionnaire (80)

3. Social support scales (30)

4. Social supparf questionnaire (92)

5. Social support questionnaire (94)

6. Social networks in adult life (57)

7. Johns Hopkins study of widowhood and networks (Goldberg)

8. Establishment of populations for epidemiologic studies of the elderly

(Boston, New Haven, Iowa, North Carolina)

9. Systolic Hypertension in the Elderly

Instruments to measure social networks

1. Northern California communities study (35)

2. East York social networks project (106)

3. Attitudes and relationships in the Detroit area (64)

networks and support, all three have been shown to predict

mortality. Another instrument developed by Donald and Ware

(1982), which is similar to them in its focus on tapping the

quantity of social ties, has been shown to be reasonably reliable

in a test-retest assessment. Correlations of items over a 1-year

period were 0.80 for church attendance, 0.64 to 0.66 for

group membership activity, and 0.4 for contacts with friends

and relatives. Thus, these measures are reasonable for use

in situations where networks and support are not the major

focus of studies but are to be included with a host of other

risk factors. The problem with these instruments is that they

tell us very little about what characteristics of ties are important

or how they are interrelated. If they do not predict, we do

not-know if it was due to the “crudeness” of the measurement

or because of a more substantive issue.

The next set of questionnaires represents attempts by investigators to measure, in a sophisticated way, characteristics

of social networks and social support in studies concerning

health outcomes. Nearly all these studies are in the field,

and they have produced few results so far. They are promising,

however, since they were based on earlier theoretical works

on networks and support. In some of the studies, individual

scales have been created; in others, such scales are currently

being developed. These survey instruments take much longer

to administer (minimally 15 to 20 minutes). Two approaches

are typically taken to elicit social contacts. One is to ask

respondents to name a number of contacts, ranging from very

intimate ones to more extended and weaker ties, and then

to focus questions on each of the people specifically named

in the respondent’s network. This approach provides rich and

detailed information on a subset of a respondent’s network.

Although time consuming, it is the only way to develop measures of network structure, as indicated by sociologists who

do social network analysis. The second approach is to ask

questions about friends, relatives, and neighbors without asking the respondent to name specific individuals. This method

allows the investigator to construct variables similar to those

used in social network analysis but not identical to them.

For example, measures of density traditionally are developed

by counting the number of people mentioned by the respondent

who know each other. Using the second method, density

is measured by responses to one item, “How many of your

friends know each other?’ The advantage of this method is

that it is less time consuming, but it does not provide the

rich information obtained by the first approach.

Choosing instruments of these latter two types allows

the investigator with a major interest in networks and support

to assess these characteristics in a substantial way. These

instmments have disadvantages; we do not know yet whether

they will predict health outcomes of major interest, and they

are lengthy. In the next several years, the items included

on these surveys may well be reduced and refined as their

psychometric properties and predictive ability become known.

At this time, however, these instruments will allow us, most

definitively, to assess which network and support characteristics are of most value.

The third set contains instruments that were developed

by investigators in the field of social network analysis. They

50
represent the state of the art in this field. They uniformly

ask the respondent to name specific individuals and then ask

questions concerning these contacts. These questionnaires are

the most lengthy ones and were developed to describe social

network configurations in different populations. Since they

were not initially developed to assess health outcomes, we

must question whether the characteristics of interest to these

investigators are the ones that we suspect of having health

ccmsequences. If they are, we may well want to adapt these

instruments for use in health surveys. If they are not, we

may not need to use such time-consuming instruments.

Concerning the use of all these groups of instruments,

investigators must ask themselves what networks and support

characteristics they suspect are important in causing disease

and disability, which instruments best capture these dimensions, and how much time they can devote to obtaining the

informtition. At this time, there are no clear frontrunners;

however, the most potentially valuable instruments are probably included in the second group. We will know more about

these instruments as findings from studies emerge.

The need now is to piggyback social network and support

measures into ongoing studies in which health outcomes are

measured rigorously (the Systolic Hypertension in the Elderly

Project is a fine example) or to supplement social surveys

in which the network and support dimensions are measured

extensively with careful assessment of physical health. Both

longitudinal and cross-sectional studies will be valuable. From

cross-sectional studies, we can learn the association between

social network factors and support and physiologic markers

of aging, risk factors for disease, biological responses to stress,

etc. Of course, associations between these factors will be

of most interest in situations where the physiologic factor

is unlikely to influence the maintenance of network contact

or support. Longitudinal studies will be more helpful in sorting

out cause and effect relationships, but they cannot be considered a panacea either, since they can do no more to overcome

poor measurement of variables than cross-sectional studies

can. Both well focused cross-sectional and longitudinal studies

are needed.

Social intervention in the

prevention of disease

Public health efforts have traditionally focused on primary

prevention. With the increased prevalence of chronic diseases

such as cancer and coronary heart disease, for which there

are currently many unidentifiable causes, primary preventive

efforts obviously must be limited. Clearly, the identification

of causal factors in these diseases is of the utmost importance.

If, as growing evidence suggests, there is a relationship between diseases and the social environment, primary preventive

efforts aimed at modifying the disease-promoting aspects of

the social environment should be considered. Though such

interventions are relatively new with regard to improving health

status, there is a long history in the United States of using

social intervention to improve mental health as well as social

and economic well-being (Zurchen and Bonjean, 1970; Caplan,

1974). In the following discussion, intervention in the area

of social networks will be taken as an example of potential

interventions.

At least three routes may be taken in modifying an individual’s social network, depending upon the factors influencing

the network: (l) sociostructural intetientions: promoting opportunities for social contact; (2) social interventions: improving opportunities and skills for social contacts; (3) psychosocial

interventions: providing psychological treatment and opportunities for social contact.

A. Sociostructural intewention

Interventions directed at the sociostructural level assume

that network configurations are at least in part determined

by structural aspects of the social environment. Networks

and the extent of social connections maintained by an individual are seen as being primarily determined not by personal

variables, but by the very real lack of opportunities providing

for such contact by social situations. According to this view,

such social circumstances as poverty, urbanization, migration,

housing situations, and deviancy may be the major barriers

to making social connections. If these factors are found to

be determinants of networks, several interventions might be

useful in promoting adequate contacts:

1.

2.

Poverty: Poor people appear to have economic barriers

which prevent them from maintaining many kinds of social

connections. Lack of adequate transportation and communication channels across distances, lack of economic

ability to provide for families, as well as to join organizations, may encourage isolation and social disconnectedness. Interventions in this area may be mostly economic,

providing income supplements, job opportunities, and access to good transportation and communication resources.

Community disintegration: Frequently, because of

changes in industrial sites, urban planning or renewal

efforts, or population shifts, older communities begin to

disintegrate, disrupting both family and community organizations. Ethnic working-class communities, communities of older people, and often even neighborhoods

filled with transients have developed ways of functioning

and surviving which are complex. When these communities are disbanded or relocated, new social connections are difficult to form. An example of such social

disruption resulting from an urban renewal program in

which predominantly Italian working-class residents of

Boston’s West End were relocated is poignantly

documented by Gans and Fried (Gans, 1963;Fried, 1966).

Appropriate community intervention may, in some cases,

be a form of nonintervention. Leaving intact communities

alone and helping other communities to remain intact

may be, Iiterall y, the healthiest kind of action. Of course,

urban planning has the potential both to alleviate unhealthy

housing conditions and to promote adequate social support

systems. Planning which takes into consideration the importance of both the physical and social environment might

be a powerful form of intervention.

51
3. Deviancy: Though this word often carries a negative connotation, many people are deviants in the sense that they

are placed in a position different from many of their

peers, a situation which often makes it difficult or awkward

for them to maintain many social ties. People who are

widowed early in life, single parents, migrants who come

to a country and live isolated from other people with

similar backgrounds, and anyone who is surrounded by

people in a different social position frequently find themselves segregated from people around them. Blau (1973),

in her work on structural constraints on friendships in

old age, has described this phenomenon in some depth.

Two sorts of interventions are possible to increase these

people’s social connections: (1) Integration into the larger

community through interventions aimed at increasing conA form of intervention, appropriately called Social Network Intervention, has evolved which was created to deal

with situations of just this nature. In this intervention, a team

of interveners assembles all members of a kinship system,

all friends and neighbors, and anyone else who is of sigrtificance to the individual. The process which then ensues is

complex and has been described in greater detail than is possible here by Speck andAttneave(1971 ).

This form of network intervention provides both opportunities and skills for utilizing the potential links in an individual’s network structure. In this way, it is possible to structure networks in homes for the aging, residence clubs for

younger people, and work groups in which many members

are highly mobile, as well as in traditional family structures.

tact among these people and their work associates, neighbors, extended family. Examples of ways to bolster the C. Psychosocial intervention

older person’s integration into an already existing community might take the form of income supplements to children

of the elderly to keep them home, or adult day care

and respite care. Both provide resources to caretakers

to enable them to maintain their own lives and take care

of the elderly. (2) Development of what has been called

“an intentional community,” based on some common interest. Co-ops, child-care centers, retirement communities,

dmg and alcohol abuse centers (e.g., Alcoholics Anonymous), Reach for Recovery, Parents Without Partners,

organizations for people who are physically disabled (such

as the Center for Independent Living), and communes

are just some examples of groups organized for some

common purpose by people who were otherwise having

a difficult time maintaining enough social contacts to

fulfill emotional and/or pragmatic needs.

B. Social intervention

While the only barrier against adequate social contact

for some people is a simple matter of opportunity, others

lack the social skills necessary to maintain and create opportunities for social contact. In fact, our culture, by encouraging

independence and self-sufficiency, may in many ways be discouraging the development of such skills necessary to the

development of strong and supportive social networks. For

example, some men are socialized so that it is difficult for

them to maintain intimate relationships. Other people are temporarily caught by a crisis, move, or job change with neither

social contacts nor a vision of how to create opportunities

for such contacts. Older lower class women and women from

certain ethnic groups have been described as not ever being

socialized to make friends outside their family. All these

people are similar in that they probably have a variety of

potential sources of social support of which they are either

unaware or feel unable to utilize.

Although many people are isolated because of few opportunities for contact or because they lack certain social skills,

some people are isolated either by choice or because serious

psychological difficulties prevent them from maintaining relationships with other people. The issue of appropriate intervention for the individual who actively refuses social contact

is a difficult one. Certainly, if a person does so by choice

and is satisfied with his or her circumstances, no intervention

ought to be attempted. After all, many people who are isolated

enjoy good health, are very satisfied with their lives (Berkman

and Breslow, 1983), and would be far more unhappy being

forced into social situations.

For the group of isolates who would prefer having more

social contacts but appear to be psychologically unable to

do so, several different forms of intervention might be

examined. Social Network Intervention might be appropriate

under certain circumstances; other forms of family, crisis,

and group therapy might also be attempted. Encouraging those

individuals to engage in nonthreatening social encounters or

providing protected living conditions such as halfway houses

could be suggested.

These ideas are only examples of potential forms of intervention and are not presented as definitive answers to the

problems of isolation and social disconnection. Rather, they

are offered with the aim that people in the fields of public

health, and medicine, will perceive that intervention in the

social environment is not an impossible or utopian task. The

issues raised here are complex and will not be resolved by

a single formula or in a short time. However, if future research

supports the importance of the social environment to the

maintenance of health, hopefully this work will contribute

to the development of sound and constructive social and health

policies,

52
References

Adler, J. J., C. M. Bless, and K. T. Mosley: The Oklahoma State

Department of Health mobile multiphasic screening program for

chronic disease. Part II. Am. J. Public Health 56:2066-2082, 1966.

Amsterdam, J. D., A. Winokur, and I, Lucki, et al.: A neuroendocrine

test battery in bipolar patients and healthy subjects. Arch. Gen.

Psychiatry 40:515-521, 1983.

Antonovsky, A.: Social class, life expectancy, and overall mortality.

A4ilbank Mere. Fund Q. 45(2):3 1-73,1967.

Antonovsky, A.: Breakdown, A needed fourth step in the conceptual

armamcntarium of modem medicine. Sot. Sci. Med. 6:537–544,

1972.

Antonovsky, A.: Health, Stress and Coping. San Francisco. JosseyBass Publishing Co., 1979.

Bclloc, N. B.: Relationship of health practices and mortality. Prev,

Med. 2:67-81, 1973.

Berkman, L. F.: Social Networks, Host Resistance, and Mortality,

A Follow-Up Study of Alameda County Residents. Unpublished

doctoral dissertation; Berkeley, University of California, 1977.

Berkman, L. F.: Social networks and analysis and coronary heart

disease. Adv. Cardio[. 29:37-49, 1982.

Berkman, L. F.: Assessing the physical health effects of social

networks and social support. Annu. Rev. Public Health. 5:413-432,

1984.

Berkman, L., and L. Breslow: Health and Ways of Living, Findings

From fhe Alameda County Study. New York. Oxford University

hSS, 1983.

Berkman, L. F., and S. L. Syme: Social networks, host resistance,

and mortality, A nine-year follow-up study of Alameda County residents. Am. J. Epidemiol. 109:186-204, 1979.

Blau, Z.: Old Age in a Changing SocieQ. New York. Franklin

Watts, 1973,

Blazer, D.: Social support and mortality in an elderly community

population, Am. J. Epidemiol. 115:684-694, 1982,

Bowlby, J.: The nature of the child’s tie to his mother. Itrt. J.

Psychoanal. 39:350-373, Sept.-Get. 1958.

Branch, L. G., and A. M. Jette: Personal health practices and mortality among the elderly. Am. J. Public Health7411 26-1129, 1984.

Broadhead, W. E., B. H. Kaplan, S. A. James, et al.: The

epidemiologic evidence for a relationship between social support

and health. Am. J. Epidemiol. 117:527–537, 1983.

Brown, G. W., M. N. Bhrolchain, and T. Harris: Social class

and psychiatric disturbance among women in an urban population.

Sociologv 9:225–254, 1975.

.-

Busse, E, W,: Theories of Aging, in E. W. Busse and E, Pfeiffer,

eds., Behavior and Adaptation in Late Life. Boston. Little, Brown,

1969.

CapIan, G.: Support Systems and Communi~ Mental Heahh. New

York. Behavioral Publications, 1974.

Carter, H., and P. C. Glick: Marriage and Divorce, A Social and

Economic Study. American Public Health Association, Vital and

Health Statistics. Cambridge, Mass. Harvard University Press, 1970.

Cassel, J.: The contribution of the social environment to host resistance. Am. J. Epidemiol. 104(2):107–123, 1976.

Chandra, V., M. Szklo, R. Goldberg, and J. Tonascia: The impact

of marital status on survival after acute myocardial infarction, A

population-based study. Am. J. Epidemiol. 117:320-325, 1983.

Clayton, P. J.: Mortality and morbidity in the first year of widowhood.

Arch. Gen. Psychiatry 30:747–750, 1974.

Cobb, S.: The Frequency of Rheumatic Diseases. Cambridge, Mass.

Harvard University Press, 1971.

Cobb, S.: Social support as a moderator of life stress. Psychol.

Med. 38:300-314, 1976. .

Cobb, S., and S. V. Kasl: Termination, The Consequences of Job

Loss. (NIOSH) Pub. No. 77–224. Cincinnati, Ohio. National Institute

of Occupational Safety and Health, 1977.

Cohen, S., and S. L. Syme, eds.: Social Support and Health. New

York. Academic Press, 1985.

Cox, P. R., and J. R. Ford: The mortality of widows shortly after

widowhood. Lancet 1:163, 1964.

Cumming, E., and W. Henry: Growing Old. New York. Basic

Books, 1961.

Dean, A., N. Lin, and W. M. Ensel: The epidemiological significance

of social support systems in depression. Research in Community

Mental Health 2:77–109, 1983.

Donald, C. A., and J. E. Ware: Classification of Social Contacts

and Resources. Santa Monica, Calif. Rand Corporation, 1982.

Durktreim, E.: Suicide. Glencoe, Ill. The Free Press, 1951.

Engel, G. L., and A. H. Schmale: Conservation-Withdrawal, A

Primary Regulatory Process for Organismic Homeostasis, in Ciba

Foundation, Symposium 8, Physiology, Emotion and Psychosomatic

Illness. Amsterdam, Elsevier, 1972.

Finch, C. E., and L. Hayflick, eds.: Hartdbook of the Biology of

Aging. New York, 1977.

Fischer, C.: To Dwell Among Friends., Chicago. University of

Chicago Press; 1982.

Fisher, C., R. Jackson, C. A. Stucve, et al.: Networks and Places,

Social Relations in the Urban Setting. New York. The Free Press,

1977.

Freud, S.: Inhibitions, Symptoms, and Anxiety, in Standard Edition

of the Complete Psychological Works of Sigmund Freud. London.

Hogarth Press Ltd., 1926.

53
Fried, M.: Grieving for a Lost Home, Psychological Costs of Relocation, in J. Q. Wilson, ed., Urban Renewal, The Record and the

Controversy. Cambridge, Mass. M.I.T. Press, 1966.

Gans, H.: The Urban Villagers. Glencoe, Ill. The Free Press, 1963.

Gerber, 1., R. Rusualem, N. Hannon, et al.: Anticipating grief

and widowhood. Br. J. Psychiatry 122:47–51, 1975.

Goldbourt, U., J. Medalie, and H. Neufeld Clinical myocardial

infarction over a five year period, III, A multivariate analysis of

incidence. J. Chronic Dis. 28:217-237, 1975.

Gore, S.: Stress-Buffering Functions of Social Supports, An Appraisal

and Clarification of Research Models, in B. S. Dohrenwend and

B. P. Dohrenwend, eds., Stressful Lije Events and Their Contexts.

New York. Prodist, 1981.

Gramlich, E. P.: Recognition and management of grief in elderly

patients. Geriatrics 23:87-92,1968.

Hashimoto, A.: Personal communication, 1983.

Haynes, S. G., M. Feinleib, and W. Kannel: The relationship of

psychosocial factors to coronary heart disease in the Framingham

Study, III, Eight year incidence of coronary heart disease. Am.

J. Epidemiol. 111:37-58, 1980.

Haynes, S. G., M. Feinleib, S. Levine, et al.: The relationship

of psychosocial factors to coronary heart disease in the Framingham

Study, II, Prevalence of coronary heart disease. Am. J. Epidemiol.

107:384-402, 1978.

Helsing, K., G. Comstock, and M. Szklo: Causes of death in a

widowed population. Am. J. Epidemiol. 116524-532, 1982.

Helsing, K., and M. Szklo: Mortality after bereavement. Am. J.

Epidemiol. 114:41-52, 1981.

Helsing, K., M. Szklo, and G. Comstock: Factors associated with

mortality after widowhood. Am. J. Public Health71 :802-809, 1981.

Henderson, S., D. G. Byrne, and P. Duncan-Jones: Neurosis and

Ihe Social Environment. Sydney, Australia. Academic Press, 1982.

Heyman, D. K., and D. T. Gianturco: Long term adaptation by

the elderly to bereavement. J. Gerontol. 28:259–262, 1973.

Hinkle, L. E.: The concepts of “stress” in the biological and social

sciences. Sci. Med. Man. 1:31-48, 1973.

House, J.: Work, Stress, and Social Support. Reading, Mass.

Addison-Wesley, 1981.

House, J., C. Robbins, and H. Metznen The association of social

relationships and activities with mortality, Prospective evidence from

the Tecumseh Community Health Study. Am. J. Epidemio/. 116:123-

140, 1982.

Jacobs, S., and L. Douglas: Grief, A mediating process between

loss and illness. Compr. Psychiatry 20(2):165-174, 1979.

Jacobs, S., and A. Ostfeld: An epidemiological review of the mortality

of bereavement. Psychosom. Med. 39(5):34&357, 1977.

Kahn, R. L., and T. C. Antonucci: Social Networks in Adult Life

(Questionnaire) From Social Supports of the Elderly, Family/Friends/

Professionals. NIA Grant (AG-01 6-32).

Kaplan, B. H., J. C. Cassel, and S. Gore: Social support and

health. Med. Care Supplement 15(5):47–58, 1977.

Kasl, S. V.: Changes in Mental Health Status Associated With

Job Loss and Retirement, in J. E. Bamett, ed., Stress and Mental

Disorders. New York. Raven Press, 1979.

Kasl, S. V., and L. F. Berkman: Some Psychosocial Influences

on the Health Status of the Elderly, The Perspective of Social

Epidemiology, in J. L. McGaugh and S. B. Kiesler, eds., Aging,

Biology and Behavior. New York. Academic Press, 1981.

Kitagawa, E. M., and P. M. Hause~ Dtfierential Mortality in the

United States. Cambridge, Mass. Harvard University Press, 1973.

Kraus, A. S., and A. M. LHienfeld: Some epidemiologic aspects

of the high mortality rates in the young widowed group. J. Chronic

Dis. 10:207–217, 1959.

Langer, E. J., and J. Rodin: The effects’ of choice and enhanced

personal responsibility for the aged, A field experiment in an institutional setting. J. Pers. Sot. Psychol.3419 1–198, 1976.

Laumann, E.: Bonds of Pluralism, The Form and Substance of

Urban Social Networks. New York. John Wiley&Sons, 1973.

Lowenthal, M. F.: Social isolation and mentaI illness in old age.

Am. Sociol. Rev. 29(1):54-70, 1964.

Maddison, D., and A. Viola: The health of widows in the year

following bereavement. J. Psychosom. Res. 12:297-306, 1968.

Marmot, M., and S. L. Syme: Acculturation and coronary heart

disease in Japanese-Americans. Am. J. Epidemiol. 104(3):225-247,

1976.

Marmot, M., G. Rose, M. Shipley, et al.: Employment grade and

coronary heart disease in British civil servants. J. Epidemiol. Community Health 32:244-249, 1978.

Marris, P.: Widows and Their Families. London. Routledge & Kegan

Paul, 1958.

McNeil, D. N.: Mortality Among the Widowed in Connecticut.

Master’s of Public Health essay. New Haven, Corm. Yale University,

1973.

Medalie, J., and V. Goldbourt: Angina pectoris among 10,000 men,

II, Psychosocial and other risk factors as evidenced by a multivariate

analysis of a five year incidence study. Am. J. Med. 60:910-921,

1976.

Medalie, J., M. Snyder, J. J. Green, et al.: Angina pectoris among

10,000 men, 5 year incidence and univariate analysis. Am. J. Med.

55:583-594,1973.

Minkler, H.: Social Support and Health of the Elderly, in S. Cohen

and S. L. Syme, eds., Social Support and Health. Orlando. Academic

Press, 1985.

Mitchell, J. C.: The Concept and Use of Social Networks, in

J. C. Mitchell, ed., Social Networks in Urban Situations. Manchester,

England. Manchester University Press, 1969.

Mitchell, R., and E. TricketL Task force report, Social networks

as mediators of social support, An analysis of the effects and determinants of social networks. Communi~ Merit. Health J., 1980.

Mueller, D.: Social networks, A promising direction for research

of the relationship of the social environment to psychiatric disorder.

Sot. Sci. Med. 14:147-161,1980.

National Center for Health Statistics: Disability days, United States,

1968. Vital and Health Statistics. Series 10, No. 67. DHEW Pub.

No. (HSM) 72–1034. Health Services and Mental Health Administration. Washington. U.S. Government Printing Office, Jan. 1972.

National Center for Health Statistics: Vital Statistics of the United

States, 1975. Vol. H, Mortality, Part A. Public Health Service.

Washington. U.S. Government Printing Office, 1979.

Neugarten, B.: Adaptation and the life cycle. J. Geriafr. Psychiatry

471–100, 1970.

Norbeck, J. S.: The development of an instrument to measure social

support. Nurs. Res. 30(5):264-269, 1981.

Ortmeyer, C.: Variations in Mortality, Morbidity, and Health Care

by Marital Status, in C. F. Erhardt and J. E. Berlin, eds., Morrality

and Morbidizy in the United States. Cambridge, Mass. Harvard University Press, 1974.

54
Parkes, C. M.: The effects of bereavement on physical and mental

health, A study of the medical records of widows. Br. Med. J.

2;274-279, 1964.

Parkes, C. M., B. Benjamin, and R. G. Fitzgerald: Broken heart,

A statistical study of increased mortality among widowers. Br.

Med. J. 1:740-743,1969.

Prior, I. A.: Cardiovascular epidemiology in New Zealand and the

Pacific. N. Z. Med. J. 80:245-252, 1974.

Recd. D,, D. McGee, J. Cohen, et al.: Acculturation and coronary

heart disease among Japanese men in Hawaii. Am. J. Epidemiol.

115:895-905, 1982.

Reed, D., D. McGee, K. Yano, and M. Feinleib: Social networks

and coronary heart disease among Japanese men in Hawaii. Am.

J. Epidemiol. 117:384-396, 1983.

Rccs, W. P., and S. G. Lutkins: Mortality of bereavement. Br.

Med. J. 4:13-16,1967.

Rodin, J.: Long-term effects of a control-relevant intervention with

the institutionalized aged. J. Pers. Sot. Psychol. 35(12):897–902,

1977.

Rotter, J.: Generalized Expectancies for Internal Versus External

Control of Reinforcement. Psychological Monograph. 80(1) Whole

No. 609, 1966.

Rubberrnan, W., E. Weinblatt, J. Goldberg, and B. Chaudhary:

Psychosocial influences on mortality after myocardial infarction.

N.Etrg/.J. Med. 31 1(9):552-559, 1984.

Sachar, E. J.: Neuroendocrine dysfunction in depressive illness.

Annu. Rev. Med. 127:389-396, 1976.

Sarason, I. G., H. M. Levine, R. B. Basham, and B. R. Sarason:

Assessing social support, The social support questionnaire. J. Pers.

Sot. Psychol.44(1 ):127–139, 1983.

Satariano, W., and S. L. Syme: Life Change and Disease, Coping

With Change. Paper prepared for presentation to the Conference

on Biology and Behavior of the Elderly, National Academy of Sciences. Woods Hole, Mass., June 22–24, 1979.

Schaefer, C., J. C. Coyno, and R. S. Laxarus: The health-related

functions of social support. J. Beltav. Med. 4(4):381406, 1981.

Schlcifer, S. J., S. E. Keller, A. T. Meyerson, et al.: Lymphocyte

function in major depressive disorder. Arch. Gen. Psychiatry 41:484-

486, 1984.

Seligman, M.: Helplessness. San Francisco. Freeman and Co., 1974.

Shock, N. W.: Biological Theories of Aging, in J. E. Birren and

K. W. Schaie, eds., Handbook of the Psychology of Aging. New

York. Van Nostrand Reinhold, 1977.

Siegel, J. S.: Some Demographic Aspects of Aging in the United

States, in A. M. Ostfeld and D. C. Gibson, eds., Epidemiology

of Aging. Pub. No. 77, 1975.

Speck, R. V., and C. L. Attneave: Social Network Intervention,

in J. Haley, ed., Changing Families. New York. Grime & Stratton,

1971.

Stem, K., and G. M. Williams: Grief reactions in later life. Am.

J. Psychiatry 108:289-294, 1951.

Stueve, A., and L. Lein: Problems in Network Analysis, The Case

of the Missing Person. Paper presented at the 32d Annual Meeting

of the Gerontological Society, 1979.

Susser, M.: Widowhood, A statistical life event or a stressful life

event. Am. J. Public Health71 :793-795, 1981.

Syme, S. L.: Behavioral factors associated with the etiology of

physical disease, A social epidemiological approach. Am. J. Public

Health 64 1043–1045, 1974.

Thoits, P.: Conceptual, methodological, and theoretical problems

in studying social supports as a buffer against life stress. J. Health

Sot. Behav. 23:145–159, 1982.

Ward, A. W.: Mortality of bereavement. Br. Med. J. 1:700-702,

1976.

Wellman, B.: Applying Network Analyses to the Study of Support,

in B, H. Gotdieb, ed., Social Networks and Social Support. Beverly

Hills, Calif. Sage Publications, 1981.

Wellman, B.: From Social Support to Social Networks, in I. G.

Sarason, ed., Social Support. The Hague. Martinus Nijhoff, in press.

Wing, S., K. G. Manton, E. Stallard, et al.: The black/white mortality

crossover, Investigation in a community-based study. J. Gerontol.

40(1):78-84, 1985.

Wingard, D. L.: The sex differential in mortality rates, demographic

and behavioral factors. J. Epidemiol. 117(2):205-21 6, 1982.

Young, M., B. Benjamin, and C. Wallis: The mortality of widows.

Lancet 2:454457, 1963.

Zurchen, L. A., and C. M. Bonjean: Planned Social Intervention.

Scranton, Chandler publishing Company, 1970.

55
Economic data and the

analysis of health-related

issues of the elderlyayb

by John A. Menefee, Ph.D.,

Economic Consultant, The Wyatt Company

Introduction

Economic factors play a vital role for the elderly’in maintaining their health and independence. The quality of life

of the aged individual is in many ways a result of the allocation

of available economic resources to sustain a desired standard

of living. The choices the aging individual makes are in response to aggregate external economic factors such as

economic growth and changes in prices, as well as individual

decisions concerning consumption and savings preferences.

The adequacy and availability of individual and aggregate

economic data to describe such elderly economic behavior

are the focus of this paper.

The paper is composed of three sections. The first section

focuses on issues related to the distribution of income and

wealth among the elderly. The second section discusses consumption and expenditure patterns of the elderly. The third

section looks at economic factors related to access to health

care. In each section the discussion will center around the

availability and adequacy of existing economic data to address

a series of topical issues. Introductory remarks in each section

will address how various economic factors and data typically

have been defined and used, and the subsequent results of

such applications. An evaluation of general data requirements

based on the scope of each issue will be included along

with a discussion of needed data items and known data sources.

The three-part examination of data requirements will be

prefaced by a discussion of the general types of economic

information currently available, differences in its format,

scope, and use. The discussion of data sources focuses primarily on the applicability of economic-oriented data sets. Data

sets that generally can be described as primarily health,oriented

are, to a lesser extent, incorporated in this paper. The potential

for integrating new private survey information also is

highlighted.

Economic factors and health

Figure 1 presents a simple schematic drawing of the basic

components and linkages related to individual health and wellbeing. In the discussion of various topics in this paper, the

importance of economic data elements in terms of the various

‘Contents are the opinions of the author and not those of the Department

of Health and Human Services or the organization he worksfor.

b~ 1985National Academyof Sciences.

components and linkages in this system will be analyzed.

It is important to remember that the type and pattern of health

services utilization are the result of individual or collective

actions. Discretionary resources are inputs in the heaIth care

system composed of income, assets, and wealth. These system

inputs are made available as a result of individuals’ productive

efforts or by societal transfer. They are transformed into health

services, a consumable product or service, and utilized to

maintain or improve health status. The effectiveness of the

allocation and subsequent consumption process is the individual’s resulting quality of life and state of health. To effectively achieve a preferred state of health, the individual must

have the resources available to utilize health services in an

efficient and effective manner. Thus, the system is an economically motivated and directed process, and can be evaluated

within such a context.

A series of economic relationships need to be examined

to describe the elderly population’s utilization of health care

services and the impact on their quality of life. The pivotal

relationships involve how availability, access, and effectiveness of health care goods and services interact as the elderly

individual attempts to maintain a preferred state of health.

The type of tradeoffs elderly individuals make—work versus

retirement, present versus future consumption, accumulation

or depletion of assets, and use of private or public facilities

and programs+ither directl,y or indirectly are related to

economic factors. Although this paper focuses on the role

of economic factors, the primary determinants of the elderly’s

health status and utilization of health services are noneconomic

factors. The elderly’s health status, prior and current, has

been found to be the most important explicans of health expenditures and utilization. The importance of such noneconomic factors will be incorporated in the discussion in

conjunction with economic factors. A complete discussion

of the interaction and importance of]noneconomic factors separately is,”however, beyond the scope of this paper.

General data requirements

Economic information can be expressed in two principal

formats. Aggregate economic data, macrodata, describe the

allocation and distribution of society’s total resources in response to collective actions. The value of aggregate economic

data is in their use to describe the overall actions or status

of a large group in conjunction with changes in the health

care environment and the economy. Such measures help define

56
❑

Elderly

population ~ Availability ~ Re.so.rce. + Uthzat,o. +

H-E”ectveness+E

Figure 1. Components and Iinkagea in health care system

the extent to which the resources of the economy are being

allocated and consumed by the elderly proportion of society.

Information about individual economic behavior, microdata,

describes individuals’ responses to external and personal factors, such as changes in prices or changes in individual health

status. The value of disaggregated economic microdata is

in their use to describe individual elderly economic behavior

and preferences. It is important to be able to isolate and

identify individual actions and decisions in order to ascertain

how important health, health care, and quality of life issues

are relative to the other uses of available individual and public

resources.

Data sources

The adequacy of data sources to examine the importance

of economic factors pertaining to elderly health issues has

improved greatly in the past few years. Several large national

surveys have been completed and are now becoming available

for public use. (A nonexhaustive listing of major data sets

pertaining to health and economic factors is included in appendix C.) The availability of current survey information will

provide more accurate and timely information related to

economic status and expenditure patterns. However, many

of these new survey panels are based on a general population

sample with only a limited number of elderly respondents.

The heterogeneity of the elderly population in terms of

economic resources, health status, and health care utilization

necessitates a large broad-based sample of elderly individuals.

The limited representativeness of many of the new large public

data sets will restrict their use in analyzing the elderly population unless they are coordinated with other public and private

data sources.

Private data sets

Large public data sets have been the primary source researchers have employed in doing analytical studies related

to the analysis of the elderly. However, changing demographic,

political, and legislative factors will cause the adequacy and

applicability of many of these data sets to diminish. For example, recent legislation related to private retirement plans has

had considerable impact on the type and availability of private

income sources that are now being offered as retirement support

for the elderly. In addition, these regulatory and legislative

changes also will have an important impact on the way private

income sources and mechanisms will be coordinated with

public retirement sources such as Social Security, Medicare,

and Medicaid in the future. In lieu of such changes, it seems

that research and policy studies should attempt to make more

use of available private data as a supplemental source to

update existing data bases.

The majority of private data are gathered on a specific

industry, regional, or worker-demographic basis. In most instances, health and economic data are related to changes in

worker pension, health, and retirement benefits on an industrywide, and in some cases on a nationwide, basis. Although

privately gathered data are seldom nationally representative,

they can be quite informative and beneficial in addressing

specific questions. For example, recent changes in the rules

governing retiree medical benefits have resulted in a significant

shift in the type and provisions of medical retiree coverage

provided by the private sector. (See Washington Business

Group on Health, 1985.) Although public data gathered as

recently as 2 years ago would indicate that the majority of

retirees have medical coverage, they would fail to reveal

that over two-thirds of the plans providing benefits have shifted

from a noncontributory to a contributory basis within the

last 2 years. This shift in terms of copayments will act only

to further raise the elderly’s out-of-pocket medical expenditures

and reduce their discretionary income, important changes that

need to be documented and analyzed.

Initially, the use of private data may be slowed because

of issues of confidentiality. However, private sources do release and sell data as well as participate in cooperative survey

projects with various public agencies. The limited use of

private sources in many instances may have been a consequence of researchers not being aware of the existence of

the data. Therefore, the coordination of a better informational

network in this area appears to be an important step to ensure

access to the most current and appropriate data. Throughout

this paper, the adequacy and availability of heretofore lesser

known private data to supplement or complement existing

public data sources will be examined. The emphasis on private

data sources, relative to better known public sources, should

not be construed, however, as an argument for their exclusive

use. The purpose is to reveal how such data sources can

provide additional updated economic information to facilitate

the analysis and understanding of elderly economic/health issues.

Appendix A provides a list of organizations, insurance

companies, hospitals, and software firms now involved in

57
the collection of health data. Appendix B gives a list of

the types of data typically found in private data sets pertaining

to health.

Section I

Income and wealth

The distribution of income and wealth among the elderly

reflects the receipt of income from a wide range of sources

(Schieber and George, 1982). These sources can be grouped

into five general categories:

1.

2.

3.

4.

5.

Earnings from full- and part-time employment.

Financial assets and other wealth, such as home equity.

Employer pensions, including private and public sector

benefit plans, individual retirement accounts (IRA’s) and

Keogh’s.

Social Security, including retirement benefits under the

Railroad Retirement Program.

Cash and in-kind benefits from public assistance programs

such as Supplemental Security Income, Workers’ Compensation, Medicare, Medicaid, food stamps, housing,

energy assistance programs, family and external private

support networks.

Table 1 presents the major income sources for aged individuals based on the latest study of Social Security beneficiaries

(Reno and Maxfield, 1985). Panel A of the table gives the

percent of elderly receiving income from various sources,

based on marital status. Panel B of the table provides the

mean and median levels of these various income sources.

The distribution of major income sources presented in table 1 provides the latest information as to the composition

of the elderly population’s financial resources.

The distribution of income sources varies among specific

subgroups of the elderly because of prior work history, savings

patterns, asset accumulation, and changes in the economic

environment. The level of some types of income, such as

earnings, can be altered by the individual modifying the

number of hours worked or by simply choosing to retire.

On the other hand, some income sources are fixed and vary

only in response to the action of outside forces. For example,

the level of an employer-provided pension is based on the

individual’s past work history. It is subject to change only

at the discretion of the employer. Therefore, it is important

in evaluating the distribution of income to carefully assess

the elderly’s ability and inability to control various income

sources.

The assessment of the distribution of income and wealth

of the elderly requires specific economic data elements that

describe the major income categories (Grad, 1984). Employment earnings from full- or part-time work are important

in determining the extent of labor force involvement, eligibility

for Social Security, and private pensions. Financial assets

in the form of accumulated savings, investments, and home

equity are necessary to develop a measure of the net worth

of the individual or family unit. Social Security benefits,

employer pensions including private and public sector plans,

individual retirement accounts, and Keogh plans help to define

retirement income adequacy. Finally, cash benefits in the

form of Workers’ Compensation, veterans’ benefits, public

assistance cash payments, and in-kind benefits such as Medicare and food stamps help to determine the total financial

resource base available to the elderly.

Data pertaining to elderly income also must be compared

carefully in terms of equivalent economic units. Significant

income differentials can result as a consequence of family

size and composition, living arrangements, and age. The presence of one or more wage earners, pension recipients, or

nonaged individuals can affect current and future income levels

and assets. Individuals’ living arrangements affect income

in relation to the proportion of income needed to maintain

a preferred standard of living in their own home, in an institution, or living with family or friends. The age of the individual

affects income in terms of the rate of income and asset depletion, eligibility for pubIic and private pensions, and the cost

of health care. All of these factors alter the composition and

receipt of income and need to be considered in the analysis

of the elderly’s economic status.

Data sources

Data sources on cash and noncash components of the

elderly’s income are numerous. Two recent nationally representative surveys containing such information are the New

Beneficiary Study (NBS), conducted by the Social Security

Administration in 1981, and the ongoing Survey of Income

and Program Participation (SIPP), now being directed by the

Bureau of the Census. NBS provides an indepth look at a

new cohort of Social Security beneficiaries 62-72 years of

age. Extensive information is provided in this cross-sectional

sample on individual employment history, income, assets,

and individual health. The SIPP survey is designed to provide

information on income based on money and nonmoney income

sources.

SIPP should become the primary source of economic

information as its sampling frame expands, replacing information gathered in Current Population Surveys (CPS). The SIPP

sampling procedure will interview selected households every

4 months, gathering an extensive array of economic information over a 2%-year period. This sampling procedure will

give the file a longitudinal dimension that previous CPS surveys lacked. In addhion, SIPP’S sampling procedure will help

to identify changes in both household composition and income

over the survey period by continuing to interview individuals

asthey leave or enter the surveyed households.

Data elements

In the use of large data sets such as the NBS, SIPP,

CPS, the Retirement History Survey (RHS), and the Program

Study of Income Dynamics, income and wealth typically are

analyzed in terms of some measure of economic well-being

or retirement income adequacy. Economic well-being usually

58
Table 1. Reoeipt of income shsres from msjor sources by msrital status

Panel A

Married men and their wives Unmarried beneficiaries

Income source Couples Husbands Wives Total Men Women

Total number (inthouaantJs) .,.... . . . . . . . . . . . . . 580.0 580.0 580.0 295.9 112.5 183.4

Percent receiving income from

Social Security . . . . . . . . . . . . . . . . . . . . . . . . . 98.3”/. 97.60/o 51 .O%

Pensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

97.1 “/0 97.6% 96.7%

55.6

Private . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

53.1 9.7 42.4 41.3

38.3

43.1

Public . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

36.1 5.9 26.8 26.0

21.0

27.3

19.0 3.9 16.5 16.6

Assets’ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83.6

16.5

(2) (2) 69.1

Earnings . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

63.0

43.7

72.8

26.9 26.7

0thersources3 . . . . . . . . . . . . . . . . . . . . . . . . .

27.1 21.9

16.5

30.2

4

(4) 15.2

Welfare . . . . . . . . . . . . . . . . . . . . . . . . . . . .

16.3 14.5

2.0 !; (4) 7.1 6.6 7.2

‘Includes receipt of income from at least one of the fol!awing: money market accounts, certificates of depesit, All Saver’a mrtfficates, checking or savings accounts, credit union accounta, a!ccks,

bends, annuilies, IRA or Keogh plans, income from rental prc$wly or roomers, loan repayments, and estate, tmst, or royalty income.

2Datawere collectti forthemmbln4 aasetholdngs andinmmeofcoupf ea.

‘Includes recelpl of at least one of the following: veterans’ pensions or compensation, Workers’ Compensation, unemployment insurance, black lungbenefits,contributions from others wifhirr or

outside of the household, and welfare (Supplemental Security Inmme, Aid to Families With Dependent Chikiren, and food stamps).

‘Not tabulaled separately.

Panel B‘

Monthlyincomeamount forrecipients

Irtcomesource Percent receiving Mean Median

Married men and their wives

Social Security, total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Onlyhusband receives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Bothreceive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Onlywlfe receives . . . . . . ...’. . . . . . . . . . . . . . . . . . . . . . . .

Esrrdngs, total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Onlyhusband receives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Bothrecelve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Onlywif ereceive s........ . . . . . . . . . . . . . . . . . . . . . . . . .

Pensions, total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Onlyhusband receives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Both receive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Onlyyife receives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Assetincome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

98.3%

47.3

50.3

0.7

43.7

17.0

9.9

16.8

55.8

46.1

7.0

2.7

83.8

$ 671

500

836

324

972

803

1,613

786

656

640

899

332

539

.S 846

504

838

304

617

418

1,054

688

490

471

757

214

180

Unmarriedmenand women

Socisl Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97.1 % .$ 432 $ 421

Earnings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.1 566

Pension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

392

42.4 426 291

Assetincome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69.1 299 99

1Percentages in panels may vary due to rounding.

SOURCE: BasWmtabulations from: Distribution ofinwme wurcesof recant rstirees Htingsfrom ths New BaneFciay Suwey. %. S&ur. Bu//.48(l):l&13, Jan. l985.

is defined by two different, yet interrelated measures: objective

and subjective assessments of income adequacy. Objective

economic status is measured in terms oftotal family income,

including gross income from employment, rent, interest, dividends, SocialSecurity ,otherprivateandgovernment pensions,

welfare benefits, andintrafamily transfers. A complete measure of objective economic status also includes financial assets,

defined as stocks and bonds, loans and mortgages, checking

and savings accounts, life insurance, and annuities. While

home equity is not considered an easily accessible liquid asset

to most elderly people, it is usually included in a measure

of economic status to obtain an” overall gross measure of

networth.

Subjective measures of economic well-being attemptto

reveal the stress-inducing effectsofchanges in financird status.

Ideally,subjectivemeasurescaptureelderlypersons’perceived

income adequacy, i.e., their ability to get along on their

resources. They offer a different indicator of the elderly individual’s perception of income adequacy in terms of his place

in the income distribution and his ability to maintain his

standardofliving.

Acornplete measure of economic status would take into

account both objective and subjective differences in income,

wealth, and consumption. Incorporated in this measure would

be adjustments for taxes, differential costs associated with

working, the value derived from owner-occupied housing,

and the value of in-kind benefits. The measure would then

be standardized for the size and type of the household. However, there are not adequate data available to measure all

the variables mentioned, or even agreement on the methods

needed to measure them. The New Beneficiary Survey and

the Survey of Income and Program Participation are two new

59
data sources that provide extensive information on the necessary income compounds needed to evaluate the distribution

of income and wealth. If the NBS can be matched with administrative records to provide information on earnings profiles

and work histories, it can be a valuable cross-sectioned data

source for development of a complete measure of economic

status. As soon as SIPP panels are completed and released,

it also will be an important data set for analytical research

related to economic status. At present, use of the Retirement

History Survey matched with Social Security records is a

viable source of economic data for analysis of economic status

in terms of both objective and subjective measures.

Income measurement

The most common approach currently used in evaluating

the elderly income distribution is to define the individual’s

objective economic status in terms of a retirement income

adequacy standard. The value of using some type of income

adequacy standard is that it provides a frame of reference

for evaluation among different subpopulation groups of the

general elderly population. Three different types of standards

are used: relative, absolute, and wealth measures of income.

Relative standards usually base income needs on the adequate

replacement of some proportion of preretirement income. Absolute income standards are based on judgments regarding

the income level adequate to maintain a minimum subsistence

standard of living. Wealth measures base adequacy on the

amount of all available income sources necessary to sustain

individuals during retirement or old age.

Whatever measure of income adequacy is used, complete

and detailed information is needed on all forms of cash income,

asset values, and in-kind benefits. In relation to income survey

information, two general criticisms can be made about reliability and validity of elderly data. Similar to nonaged individuals,

the elderly understate their economic status. Elderly individuals

tend to have limited recall about the type and value of their

income sources. They also have limited knowledge about

the present value of their asset holdings, especially for stocks,

bonds, and home equity. Therefore, the adequacy of such

data must be closely scrutinized to determine whether it is

representative of the elderly’s asset base.

The poverty line

The poverty line, an absolute measure of income adequacy, has become the most widely used income distribution

standard. Its extensive use reflects the fact that it is the U.S.

Government poverty standard. It is adjusted for family size

and characteristics, which other standards are not. Unlike

the Bureau of Labor Statistics family budgets, an official

poverty income level has been established for elderly families.

Finally, unlike the relative income replacement rate concept,

the poverty line measure can be used in evaluating the income

levels of workers as well as nonworkers.

The poverty line has been criticized as a valid measure

of income adequacy because of the definition of income used

in its construction. Income is defined as all family members’

cash income, including employment earnings, earnings from

financial assets, and cash transfer payments. There are no

adjustments for income tax payments or any in-kind benefits,

The omission of taxes and the value of in-kind benefits has

a substantial impact upon the viability of this measure as

an indication of the elderly’s financial status.

Taxes

The preferential tax treatment afforded the elderly provides

for substantially higher tax-free income levels and discretionary

income. Under current law, single elderly have a tax-free

income of $10,640, including Social Security benefits, while

elderly couples have a tax-free income of $18,990. In contrast,

nonelderly singIe individuals have a tax-free income of $3,560,

on]y 33 percent the size of the elderly exempt tax level income.

Furthermore, nonelderly couples claiming four dependents

have a tax-free income level of only $10,598, 56 percent

of the elderly couple’s exempt tax level income.

The impact of the special tax provisions also is substantial

for the low-income elderly. The poverty line for nonelderly

singles is $5,800, while the level of tax-free income is $3,560,

making $2,240 subject to possible taxation. The poverty

threshold for elderly singles is $5,400, but their income is

not subject to tax through $10,640. A similar treatment is

found for couples. The poverty line for nonelderly couples

with four dependents is $15,000, with income through $10,598

exempt from taxes. The poverty line for elderly couples is

$6,800, but with an allowance of $18,990 of tax-exempt

income. Therefore, without consideration of elderly preferential tax provisions, estimates of the distribution of the elderly’s

real economic status and their relative poverty status will

be biased upward.

A valuable data source to refine and analyze the real

effects of taxes on the elderly’s disposable income and relative

income status is the March 1983 Current Population Survey

data. The data file is the March 1983 CPS microdata, expanded

to include estimated taxes paid (Federal and State individual

income taxes, property taxes on owner-occupied housing units,

Social Security taxes, retirement taxes) and after-tax money

income. In addition, supplemental data are provided on work

experience, types and amounts of “before tax” money income

received during the previous calendar year, and information

on nine noncash income sources.

In-kind benefiis

The omission of the value of in-kind benefits in the evaluation of elderly income and poverty status is quite significant.

If a person receives an in-kind benefit, his well-being is improved. However, the magnitude of the improvement is difficult to evaluate. Unless the individual can sell or trade his

in-kind benefit, no substitute for such a benefit is available

to estimate its worth. It may or may not have an equivalent

cash value to that individual. Therefore, a dollar of cash

may be worth more to the recipient than a dollar’s worth

of an in-kind benefit. The extent to which one measures

the differential is based on the value one assigns to the benefit.
One valuation approach gives in-kind benefits “full market

value,” i.e., a $1 food stamp coupon is worth $1. A second

evaluation approach, called the “poverty budget share,” values

in-kind benefits equivalent to the amount usually spent on

such items by people at the poverty level. A third evaluation

method, called the “cash equivalent approach,” values benefits

by the amount of cash the recipient would accept if he could

trade his benefit for cash. Estimating the value of in-kind

benefits with any one of the three methods significantly alters

poverty rates among the elderly (Clark et al., 1984).

Table 2, Panel A, presents poverty rates for elderly singles

and couples with income defined as combinations of different

cash, in-kind benefits, and wealth values. The inclusion of

selected income sources can reduce the poverty rate from

15 percent to 60 percent. Using only the cash income definition, 9.4 percent of elderly married couples and 26.3 percent

of single persons are defined as living in poverty. The addition

of asset income, benefits, and taxes into the income definition

results in the poverty rate dropping to 5.4 percent for married

couples and 14.4 percent for single elderly persons, Finally,

the inclusion of Medicare and Medicaid values causes the

poverty rate to fall to only 3.7 percent for married couples

and 11.5 percent for single elderly individuals.

Alternative income standards

The use of alternative standards of income adequacy also

can result in widely varying poverty rates, Panel B of table 2 presents the percentage estimates of elderly families

with annual incomes below four different income standards

in 1979 (Schieber and George, 1982). The four standards

are:

1. The official poverty line.

2. The revised Orshansky Index.

3. The Bureau of Labor Statistics (BLS) lower, intermediate,

and higher standard of living budgets for elderly couples.

4. Minimum wage index.

The poverty level is set at three times the cost of providing

the economy food plan to a family unit, updated to reflect

price changes of food budget items. The Orshansky Index

is a revision of the poverty line family unit concept which

uses a more recently developed food plan with a ratio of

total expenditures to food expenditures of 3.4:1. The BLS

Elderly Couples Budgets are based on the cost of maintaining

alternative living standards by the purchase of a typical market

basket of goods consumed by the elderly. The minimum wage

standard is based on the annual earnings for full-time workers

paid atthe minimum wage.

Based on income, defined as cash income only and the

after-tax value of cash income plus in-kind benefit values

and the potential annuitized value of assets, the four adequacy

income standards indicate widely different proportions of elderly individuals and families below the poverty income level.

In reference to the more generous definition of income that

includes in-kind benefits and asset values, the revised Orshansky Index indicates nearly twice as many elderly in poverty

than the official poverty line standard indicates. Use of the

minimum wage index indicates three times as many elderly

families, 24.6 percent, had annual incomes below the poverty

TdAe 2. Poverty rates among ekferty mam”edooupfes snd single persons under alternative income definitions and standards

Panel A

home definition Married couples Single pareons All elderly families

Total cash income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.49f0 26.3% 18.5%

Medicare and Medicaid values not included in income definition

Cash income, food stamps, and housing assistance . . . . . . . . . . . . . . . 8.3% 22.9% 16.2%

Cash Income, in-kind benefits, and annuitized wealth values . . . . . . . . . . . 5.3 14.3 10.2

Cash Income, in-kind benafits, and annuitized wealth values,

Iesstaxes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 14.4 10.2

Medicsre and Medicaid vslues included in income definition

Cash income, food stamps, and housing assistance . . . . . . , . . . . . . 5.6% 18.3% 12.47.

Cash income, in-kind benefits, and annuitized wealth values . . . , . . . . . . 3.7 11.5 7.9

Cash Income, in-kind benefits, and annuitized wealth values,

lesstax8a . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 11.5 7.9

Panel B

Married couples Single Persons All elder~ families

Cash Cash Cash

income A@sted income A@usted income A@sted

Alternative standards only income’ only income’ only income 1

Official poverty level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4% 3.7% 26.3% 11.5% 18.5% 7.9%

Revised Orshansky Poverty indexes . . . . . . . . . . . . . . . . . . . . . . . . 12.9 9.4 26.6 19.3 20.4 14.8

BLSElderly Couples’ Low Budget . . . . . . . . . . . . . . . . . . . . . . . . . 12.6 9.4 NIA NIA N/A N/A

Minimum wage index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.0 9.7 50.3 37.5 33.0 24.6

‘Incoms Includes the after-tax value of cash income, the value of in-kind benefits, and the annuitized value of wealth.

SOURCE: ICF, Inc., analyela for Employed Banefita Resaarch Institute of March 19s0 Current Population Survey data.

61
income line. Using cash income only as the income measure

results in a dramatic increase in the percentage of elderly

in poverty. Over one-quarter of the single elderly are defined

to be in poverty using the official poverty level and the Orshansky Index. The use of the minimum wage index results

in one-half of the elderly being defined with incomes below

the poverty line.

Home equity

Another important component of the elderly individual’s

wealth holdings is homeowner equity. It was estimated in

1981 that approximately 70 percent of the elderly own their

homes. This provides a substantial, yet in many cases, an

illiquid asset. The problem of illiquidity of home ownership

stems from the fact that the elderly homeowner would have

to either sell or take out some type of loan to gain use of

his net home equity, As an alternative, reverse annuity mortgages have been proposed as a way to alleviate the problem.

The idea behind the reverse annuity mortgage is that an elderly

individual sells his home to a bank or lending institution.

In return, the elderly homeowner will be paid a monthly

stipend based on the value of his home and be able to remain

in his home for a specified period of time or until his death.

Upon death or the end of the specified period, the payment

of the stipend would terminate and the home and the remaining

equity in the hbme would go to the financial institution holdlng

the house deed. The advantage of this type of arrangement

is that it allows the elderly to tap home equity as a source

of money income. A major drawback with such amangements

is the difficulty in estimating the payout time period. Secondly,

a substantial number of elderly homes are not attractive to

financial institutions because of their location andor condition.

A good illustration is among the low-income elderly homeowners. The vast majority of these elderly are in rural areas

or the inner city. Although over one-half of this population

own their home, the attractiveness of these older type dwellings

in terms of an investment for a financial institution is quite

limited.

In the future, reverse annuity mortgages may become

more viable as an increasing proportion of elderly are homeowners in outlying suburban areas. As these individuals age,

reverse mortgage arrangements may become attractive to financial institutions and act as a new vehicle for increased liquidity

for this select group of the elderly population. However, these

elderly typically are middle and higher income elderly, who,

as a group, are not in the same financial need as low-income

elderly, the group that would benefit proportionately much

more by such arrangements.

The analysis of housing equity as a potential income

source is restricted. by the availability of reliable data. Various

data sets do provide housing information for the elderly population. Matching county data based on the City/County Data

Book with various Social Security and census files would

be one way to look at the relationship between elderly homeowners and the income characteristics of the neighborhood.

However, the difilculty with the use of such data is the reliability of the home’s assessed value, its condition, and its potential

market value. Fhally, a series of estimation problems related

to financial markets makes anything but broad aggregate findings difficult to verify in such an analysis.

Wealth profiles

Over the life cycle, individuals accumulate wealth (i.e.,

income-producing financial assets) through several mechanisms, such as savings, annuities, pensions, certificates of deposit, money market funds, securities, stocks and bonds, corporate equities, and life insurance. In examining the distribution of wealth, it is important to correlate changes in wealth

holdings relative to various life events, such as retirement,

death of spouse, and changes in health status. Interest in

wealth accumulation relative to health has increased because

of the need for additional information about the effect of

changes in health on the depletion of wealth holdings, especially in terms of the exhaustion of assets to cover chronic

and long-term care health expenditures.

Most studies of wealth accumulation have used crosssectional survey data. Such survey information, however, introduces a selectivity bias into the analysis by looking only

at surviving members of a particular cohort. As a cohort

ages, the survivors represent an increasingly healthy proportion

of the original sample. Healthier individuals tend to accumulate

more wealth to provide for longer retirement periods. As

a result, a spurious positive correlation between wealth and

age has been observed.

The use of longitudinal data from retirement to death

to determine age-wealth profiles and the correlation with health

status is one approach to try to correctly analyze the problem.

One data source to develop wealth-age profiles is the longitudinal Retirement History Survey (RHS). The RHS collected

information on the net worth of respondents. It includes data

on the value of owner-occupied housing, equity in business

or from the net value of other property holdings, cash and

financial assets (including stocks, bonds, bank accounts,

checking accounts, and loans) minus total household debt.

The RHS also provides extensive information on pensions

and annuities, The ability to match the RHS with Social

Security administrative records also enables calculation of

earnings profiles. Although the use of RHS is hampered by

incomplete data for some of the survey panels, it still may

be the best available source at present to use in this type

of analysis. With the release of the] complete SIPP file and

the new Survey of Consumer Finances, two additional data

sources will become available to help develop wealth profiles

and address concerns related to selectivity bias.

Projected income and weatth distributions

of the elderly

In looking at the distribution of income and wealth for

future elderly cohorts, it is evident that many of the forces

that help determine the economic status of today’s elderly

will undergo substantial change. Improvements in health and

life expectancy will make possible extended worklives,

resulting in higher lifetime earnings profiles and greater

62
accumulations of work-related benefits, income, and assets

(Schieber and George, 1982). Extension of the working life

also may be encouraged by employers as the number of younger

workers declines and employers try to retain quality older

workers.

The possibility of longer worklives may well be offset,

however, by some countervailing forces. The most significant

will be the continuing early retirement trend, especially among

males. For the past 20 years, men have been leaving the

work force at the earliest possible age based on receipt of

retirement benefits. The trend toward early withdrawal from

the labor force also may be exacerbated by the increased

prevalence of dual career families. As the number of women

in the labor force continues to grow, dual earner families

should be able to take greater advantage of tax-deferred savings

arrangements, such as Individual Retirement Accounts and

401(k) plans. These savings vehicles will provide new additional sources of retirement income, providing the means to

retire early and maintain a desired standard of living.

Eligibility for private pensions also is expected to increase

from slightly less than one-half to nearly three-quarters of

the retiree population by the turn of the century. Increased

private pension coverage as well as the expansion of provisions

that expand joint survivor coverage and retiree medical benefits

will act to ensure broader and more comprehensive retirement

income security, Fhlally, the increasing prevalence of coordination of benefits among dual career families, i.e., having

coverages between spouses coordinated to eliminate overlap,

will help create more efficient and comprehensive benefit

coverage. All of these factors should contribute to an improving

economic status among future elderly.

The projected growth in various sources of income and

accumulated wealth may be diminished somewhat by the interaction between public and private income and support programs. At present, Social Security provides the major source

of income for nearly one-half of the retired population (Clark

et al., 1984). The combination of Social Security benefits

with the value of Medicare benefits accounts for more than

one-half of the total retirement income for all but the wealthiest

retirees. As various factors come into play and these programs

are changed in terms of eligibility, coverage, and benefit

levels, the direct impact upon the retirees’ economic status

could be significant. For example, legislative changes now

being considered will eliminate and restrict many types of

deferred compensation arrangements. With such changes, employers will provide more cash income but less generous

types of benefit and savings retirement vehicles, resulting

in lower future levels of the elderly population’s savings and

wealth, Moreover, the potential burden that health costs could

place on all private employers may well result in the continuing

reduction, if not total exclusion, of health coverage from

the retiree benefit package. If this occurs, increasing pressure

will be put on the public Medicare system and the elderly

population itself to pay increased private insurance and out-ofpocket expenditures.

The analysis of future patterns of income and wealth

requires detailed statistical information on labor force patterns

of men and women. Longitudinal socioeconomic survey data

that can be statistically or exactly matched with earnings and

work history data would provide the most appropriate information. Complete data of a longitudinal nature concerning work

histories also will help evaluate the role and impact that health

has on work force patterns and future retirement income and

wealth. Finally, work and earnings data that describe not

only the type of private income support provided but also

the characteristics of those income support vehicles would

help clarify the extent to which future income needs will

be met by private support mechanisms.

The adequacy and availability of data elements to analyze

future elderly income issues continues to improve. Earnings

and work history data have been made available by the Social

Security Administration to match with the RHS. Employment,

income, asset, and pension coverage information is available

in the Department of Labor’s Level of Benefits Survey and

the Pension Beneficiary Master File. Another data source,

the Survey of Consumer Finances, also provides extensive

asset, wealth, work, and pension data. Finally, the recent

supplement done in conjunction with the March 1983 CPS

Survey is a new data source for information on pension eligibility and coverage.

Privately generated survey data related to income sources,

pension eligibility, coverage, and plan characteristics are

another source yet to be fully exploited. Information on individual earnings and work histories, pension benefits, and integration with public pensions is available. Although most private data sets are not designed to be nationally representative,

characteristic profiles can be developed from these private

data sources that can be used to describe key components

of income and wealth for select populations of future retirees.

Perhaps the usefulness of private data best can be illustrated

in terms of postretirement medical benefits. Currently, over

four-fifths of major employers provide some type of retiree

medical coverage. However, in the past 2 years nearly threefifths of these employers have altered their retiree medical

plans, increasing retiree copayments and offering less generous

coverage (Wyatt Company, 1984). It is such changes that

need to be more closely examined in order to better predict

the elderly’s future health care utilization and quality of life.

Private data constitute one potentially valuable source of such

information. The types of private data available in relation

to health and medical utilization are presented in table 3.

63
Table 3. Economic data in private aurveya

Pension benefits Economic data Medical data

. Annual benefit accrued at retirement . Average lifetime earnings . Pre/post-retirement medical coverage

. Coordination and size of pension and Social . Work histories — Coverage limits

Security benefits . Earnings profiles — Continuations

. Benefits as percentage of final pay . Retirement patterns Coordination with Medicare

. Postretirement banefit increases Early . Dental coverage

. Postretirement death benefits — Normal . Flexible health plans

. Joint eurvivor benefits Late Choice of low or high options

.

Income replacement levels . Utilization patterns

Section II

Consumption and savings in later life

The basic decision the elderly individual makes in terms

of income and expenditures is how much to allocate for the

purchase of goods and services or to save. Consumption depends on the amount of income available, existing prices,

and personal preferences relative to the goods or services

needed. Savings depend on the availability of income in conjunction with the expected rate of return on the money invested

and its perceived use.

Elderly expenditure patterns differ in comparison to the

general nonaged population. Not only do elderly households

spend less, they spend different proportions of their income

for certain items relative to younger individuals. To examine

how consumption and savings decisions change over time,

it is necessary to look at the impact of various socioeconomic

factors on expenditures. The availability of different income

sources, such as public and private pensions, private insurance,

and cash and in-kind benefit programs modifies the way the

elderly consumer allocates his income. Reduced household

size and labor market earnings also change the elderly household’s consumption and savings patterns (Clark and Schrimper,

1984).

A major contributing factor to changes in the type and

size of expenditures made by the elderly is their health status.

Health status has been found to be a principal factor responsible

for a reallocation of expenditures away from such items as

apparel, transportation, and recreation and toward health care,

insurance, and personal care. The examination of health and

health-related expenditures is, however, complicated due to

several specific data problems. Survey information on elderly

expendhure patterns generally does not include specific price

and quantity data. Without some disaggregation of expendltures, relative price movements are difficult to discern. For

example, if the price of prescription drugs goes up while

the price of physicians’ fees declines, the response of the

elderly consumer well may be masked if an aggregate medical

services expenditure category is used to analyze elderly health

expendhures. Furthermore, comparing changes in expenditures

requires differentiating the effects of inflation from changes

in consumer preferences due to age.

The most reliable and current data source for analyzing

general elderly expenditures is the Consumer Expenditure Survey, conducted in 1980 and 1981. The survey provides comprehensive expenditure data which can be used to establish

benchmarks for determining different expenditure patterns and

the effect of differential changes in relative prices among

subgroups of elderly households. The RHS is another data

set that can be used to look at differential expenditure patterns.

By using this longitudinal sample, one can evaluate changes

in expenditure patterns in response to a host of both marketrelated and institutional factors, such as impact of eligibility

for Medicare on consumption of both personal and subsidized

health care. The drawback to the RHS information is that

it is based on respondents’ recall over the past year, raising

concern about its completeness and reliability.

Two other large public data sets that provide extensive

and detailed information about types of health care, services

consumed, and the amount of dollars expended for such care

are the National Medical Care Utilization and Expenditure

Survey (NMCUES) and the National Medical Care Expenditure

Survey (NMCES). The NMCES data set is a useful source

of survey information on insurance coverage, use of health

service expenditures, and sources of payment based on a

national sample of over 40,000 individuals. Extensive

economic and demographic data also are included in the data

set. A sample group of approximately 4,560 persons ages

65 and over is included in the NMCES sample group, providing

an adequate elderly survey population to address a number

of health expenditure questions among specific subpopulations

of the elderly. NMCUES is a second national health care

survey that focuses directly on health-related characteristics

of Medicare and Medicaid beneficiaries. It combines field

and survey information collected directly from respondents

and existing administrative data systems. This dual aspect

of NMCUES is important because it gives detailed statistics

on beneficiary characteristics and out-of-pocket expenditures

that are not available from other data or administrative sources.

The NMCUES data are based on three separate survey

components: a randomly selected national household sample,

selected State Medicaid households, and Medicaid and Medicare administrative records linked to the national and State

sample respondents. Through a series of five interviews, information was obtained from each household on the subject

of sociodemographic characteristics, utilization, expenditure,

and mobility for the calendar year 1980. NMCUES also

gathered information related to health care access, health

status, out-of-pocket expenditures, diagnosis and surgical

64
procedures, income and employment, and health insurance

coverage. The usefulness of the NMCUES sample is that

it allows for analysis of different groups in terms of their

health insurance coverage, poverty level, and other

sociodemographic characteristics. The NMCUES survey is

limited, however, in that it does not have a large elderly

sample or include institutionalized individuals.

Health expenditure patterns

A number of factors can be correlated with the changes

in health care expenditures among the elderly. One factor

responsible for the increasing and disproportionate shwe of

health care spending is a payment structure that tends to

distort the typical income-consumption pattern associated with

the purchase and consumption of most goods and services.

Although employment status and money income influence

the elderly’s ability to finance consumption of health care,

it is the availability of third-party reimbursement that alters

the typical income-consumption link. The presence of a thirdparty payer has resulted in more health care being utilized

with limited concern for price than would otherwise be the

case. The almost universal eligibility for Medicare benefits,

the availability of Medicaid, and increasing coverage through

Medigap private insurance have effectively reduced concern

for cost constraints and have resulted in increasing aggregate

health expenditure and utilization patterns.

Numerous studies have been done using the Health Interview Survey, Medicare Administrative Data, the NMCUES

and NMCES data that analyze various aspects of expenditures

and utilization patterns related to third-party payment and

individual out-of-pocket expenditures (Cafferata and Meiners,

1984; McMillan and Gornick, 1984; Dobson, Scharff, and

Korder, “1983;Kovar, 1983). The findings from these studies

could be verified and expanded by analyzing aggregate as

well as individual elderly health care expenditures from another

perspective, by making use of health care data from the health

care provider community. In general, three types of data are

available from the health care community: claims data related

to benefits or entitlements; clinical data related to individual

patient assessments or medical histories; and program management data related to monthly admissions and discharges.. These

data are used extensively now by employers to identify areas

of overutilization and excessive costs for current and retired

employees, It also could be used to verify and test hypotheses

about elderly and nonelderly utilization patterns and expenditures related to third-party payments and out-of-pocket

expenses.

Privately sponsored surveys and data projects also have

evolved in response to employers’ needs for improved data

collection and data base systems to deal effectively with health

care cost containment. For example, an independent health

research organization, The Health Research Institute, began

developing a national statistics data base project in 1982.

The Institute solicits claims data from employers, edits the

data, and then provides feedback to employers and carriers.

At present this data base has medical information on over

10 million lives. Other types of organizations that also have

become involved in data collection and analysis are employee

benefit consulting firms, data consulting firms, insurance companies, third-party administrators, multihospital systems,

health care software firms, and professional review organizations, including Foundations for Medical Care. A representative list of various types of privately oriented firms involved

in the process of gathering health cost and utilization data

is included in appendix A.

The fact that private organizations are gathering data on

a large-scale basis has made many new additional data sources

of potential use in health care analysis now available. Such

data bases are of interest because of their focus on the problems

of health care costs and utilization from an employer or health

care provider perspective. They also are important as a source

of information on the impact of the emerging alternatives

to the traditional health care delivery systems such as health

maintenance organization arrangements and the impact of vanous cost-containment procedures. With the utilization of such

data, a wider range of questions can be addressed and analyzed

related to the aged’s health care use and expenditures.

Health care utilization, expenditures,

and functional status

The elderly’s expenditures for health care are correlated

with the increasing incidence of chronic disease, acute conditions, and functional impairment. The demand for health care

services and related expenditures are, however, subject to

wide variation among. the aged population. To clarify such

variation, several important aspects of utilization related to

health status and health care expenditures must be addressed.

One important aspect of utilization is the correlation between differences in functional capacity and personal health

care expenditures. The tradeoffs the elderly make during a

period in their life when they are in a declining financial

position and are faced with buying increasingly expensive

health care to maintain, let alone improve, their health status

is a key component to understanding utilization. Health expenditures should be correlated with functional capacity to

determine whether the health care being purchased and utilized

by different economic subgroups of the elderly is beneficial.

Not only should the level of expenditures be analyzed, but

the type of health care also needs to be evaluated. Several

researchers have addressed these issues, primarily focusing

on health-specific factors using the NMCES and NMCUES

data sets (Kovar, 1983; Berk and Wilensky, 1984; Berk,

Cafferata, and Hagan, 1984; Kasper and Barrish, 1982;Farley,

1985).

From an economic perspective; the observed behavior

of the elderly in terms of health care utilization, expenditures,

and functional status also has been examined utilizing different

explanations of individual behavior (Grossman, 1972;

Menefee, 1980; Menefee, 1985). One approach looks at the

relationship between age-related health changes and the perceived value of additional expenditures on health care. This

approach defines health care as a priority consumption expenditure. Expenditures on health care services are viewed as an

investment that is likely either to result in improved functional

65
status or at least to ameliorate functional decline. Therefore,

elderly individuals invest in health care because they perceive

that they can slow the loss of their functional capacity. A

second, somewhat seemingly unrealistic, approach suggests

that the elderly consider personal health expenditures in an

objective context. An expectation of a decline in functional

capacity with age is assumed. Possibly faced with limited

financial resources andlor access to care the elderly consider

increased investments or expenditures in health care unlikely

to reverse or change health or functional status significantly.

Therefore, no attempt is made to offset the decline in functioning by continually increasing individual expendhures on health

care services.

The use of these alternative views about the relationship

between functional status and health expenditures results in

very different findings. The first hypothesis suggests that older

adults will increase investment and expenditures on health

care based on the perception that additional expenditures will

reduce or limit changes in their functional capacity. Adopting

such an approach would restrict an individual only in terms

of his available economic resources. In contrast, the second

hypothesis suggests that age-related reductions in health care

expenditures represent a logical response to the reduced value

of increased health care expenditures to offset declining functional capacity with age. In this framework the increasing

costs of medical care and/or limited individual resources are

of secondary importance relative to the individual’s belief

that increasing health care expenditures are inefficient.

Analyzing functional capacity and health expenditures in

either context requires a data source containing a wide range

of multiple indicators of health status as well as detailed

health expenditures data. Notwithstanding the problem of defining functional capacity, many data sets do contain multiple

indicators of physical mobility, work-related impairments,

mental outlook, sense of happiness, and capacity for self-care.

Such information can be adapted to measure various dimensions of functional capacity. Past research has found that

the most reliable use of such information involves the development of some type of functional capacity index to be used

in a multidimensional assessment strategy of functional ability

(Fillenbaum and Maddox, 1977; Fillenbaum, 1979). With

such indexes, individuals can be classified in terms of physical,

behavioral, and self-care functional capacity. Furthermore,

the adaptability of these functional indexes for use with various

data sets, specifically the RHS, has provided a viable data

source to perform longitudinal analysis of functional capacity

and health.

Finally, a somewhat different, yet potentially valuable

way to look at aggregate patterns of elderly health costs is

to forecast the incidence and prevalence of chronic diseases

customarily associated with the elderly population. From an

economic perspective, the value of estimating the distribution

of chronic diseases is to refine the cost estimates for publicly

funded and private third-party insurance contracts and, to

a lesser extent, estimates of future capital outlays for health

care facilities.

The development of such aggregate estimations of health

costs has been proposed by Manton and Liu (1984) through

the application of bioactuarial models. The attractiveness of

using such a modeling framework is that it offers an estimation

approach distinct from traditional economic projection

methodologies. It provides additional information that can

be drawn from existing census, epidemiological, and biomedical data about how private as well as public resources and

funding should be allocated to improve the health care network.

Further, linking the costs of health care with various states

of chronic illness will facilitate forecasts of future costs associated with morbidity and disability among the elderly. With

the projected deficit in the Medicare trust fund and continued

reductions in private pension provisions for retiree medical

costs, addhional information on the trends in elderly health

costs will be useful benchmarks in assessing future total health

care costs.

Section Ill

Access to medical care

The enactment of Medicare helped reduce the financial

barriers that increasingly had limited the elderly’s access to

adequate medical care. In addition, the commitment to provide

adequate health care facilities, spurred by an expansive building program of hospitals during the 1950’s and 1960’s period,

increased the size of the health care network. The increased

provision of health care facilities and the expanded public

health benefit structure made quality health care more accessible to the majority of older adults.

A thorough analysis of the distribution of health care

facilities in a spatial context, however, reveals limited availability and access to medical care for specific aged. Using

county-specific data from the Area Resource File on the location of health care facilities, a spatial access health network

can be developed (Menefee and McCoy, 1982). Matching

this data with elderly demographic patterns identifies the concentrations of the elderly population relative to health facilities.

Such a spatial examination reveals that the elderly, especially

the poor elderly, are more heavily concentrated in rural areas

and the inner city. Similar, yet broader, findings related to

access to medical care have been found in the analysis of

the NMCES data set (Berk, Bernstein, and Taylor, 1983;

Berk and Bernstein, 1982; Berk and Bernstein, 1985). Proximity to health care facilities was found to be a significant

indicator of access and use, primarily for minority aged. Also,

the importance of access was found to be correlated with

the elderly’s ability to pay for care and the type and extent

of health insurance coverage (Berk and Wilensky, 1984; Cafferata and Meiners, 1984). In lieu of these findings, the analysis

of access to care needs to be expanded, concentrating on

specific elderly subgroups based on income, race, and place

of residence. The use of the Area Resource File with the

NMCES data would provide an appropriate data source.

66
Alternative delivery systems and access

The emergence of health maintenance organizations

(HMO’s) and comprehensive medical plans is providing a

new type of option in terms of elderly medical care. An

HMO receives a predetermined fixed payment which makes

the HMO at risk for the total cost of health services. An

HMO attempts to control the expense or cost side of the

financial picture, in contrast to the “fee for service” system,

which tends to concentrate on increasing the revenue size.

The attractiveness of the HMO arrangement to the elderly

is that HMO’s provide more services for the health care dollar.

This is especially important for the older person on a fixed

income. HMO’s eliminate the incentive to hospitalize in order

to ensure reimbursement. They provide comprehensive continuous health care services and take the responsibility to

organize and arrange for the delivery of services. Therefore,

elderly persons may receive the greatest benefit in a managed

health care system, such as an HMO, that will provide experienced personal management of their total health care. There

are also drawbacks for the elderly in terms of HMO-type

arrangements. Fkst, HMO’s require a “lock-in” provision,

which means that all services must be provided at the HMO’s

service area unless they have been authorized. Secondly, in

order to maintain lower costs, HMO’s may tend to undertreat

and offer fewer services to their covered population. The

interest of HMO’s in elderly patients is only now becoming

an area of research and evaluation by HMO’s themselves.

Information related to HMO’s and other alternative health

delivery systems can be found, to a limited extent, in privately

sponsored surveys. However, the growing use of HMO’s and

similar health care arrangements necessitates the need for

extensive and detailed utilization data related to the elderly’s

use of such services. A more concentrated effort should be

made to ensure that the development of data sources containing

such information is given high priority.

●

●

●

●

●

Summary

Economic factors play an important part in determining

how the elderly maintain their health and independence. From

a data perspective, this means that individual and marketrelated economic information is needed to integrate and express

economic relationships pertaining to the elderly’s quality of

life. The adequacy and availability of economic data to describe

elderly economic behavior pertaining to health can be summarized with the following general points.

In examining the elderly’s health and quality of life,

the value of any type of economic information is subject

to how various health and economic constructs are defined

and formatted. Care must be given, when analyzing the

adequacy of available data, to determining how restrictive

the data are in terms of the way that various types of

economic factors or economic relationships can be

analyzed and evaluated.

A number of new public data sets currently provide updated economic information that is helpful in analyzing

questions related to economic issues. The usefulness of

these data sets for analyzing the elderly population is,

however, limited. Even though the new data sets provide

new, better, and more detailed economic information,

the elderly sample groups often are not large enough

to allow for broad representative studies of specific elderly

subpopulations.

The current distribution of income and wealth reveals

a growing percentage of the elderly with greater financial

resources and a higher standard of living. Current trends

indicate that the financial resources of future cohorts of

the elderly should continue to improve. Expanded

economic data concerning new income sources and saving

mechanisms will become increasingly important to proper]y evaluate the elderly’s economic status.

Health utilization and quality of life among the elderly

have increasingly become economic and political issues.

The increasing impact of political decisions on the type,

sources, and levels of various income sources has caused

the economic status of the elderly to change rapidly.

Thus, the usefulness and reliability of some data sources

may be limited as a basis for policy.

The changing nature of the health market will necessitate

a closer look at the provision of health cafe not only

from the user but also from the provider side. Such dual

analyses will require more extensive information and data

from the provider side of the market and may well be

an opportunity to make more use of a growing number

of private survey data sources as well as databases.

67
Selected references

Andrisani, P. J.: Effects of health problems on the work experience

of middle-aged men. Industrial Gerontology 4(2):97-1 12, Spring

1977.

Berheim, B. D.: Dissaving After Retirement, Testing the Pure Life

Cycle Hypothesis. Cambridge, Mass. National Bureau of Economic

Research, July 1984.

Berk, M. L., and A. B. Bernstein: Regular source of care and

the minority aged. American Geriatrics Society 30:25 1–54, 1982.

Berk, M. L., and A. B. Bernstein: Use of home health services.

Home Health Care Quarterly, Spring 1985.

Berk, M. L., A. B. Bernstein, and A, K. Taylo~ The use and

availability of medical care in health manpower shortage areas. inquiry 20:369–380, Winter 1983.

Berk, M. L., G. L. Cafferata, and M. M. Hagan: Persons with

limitations of activity. Data Preview. No. 19. National Center for

Health Services Research. Hyattsville, Md., Oct. 1984.

Berk, M. L., and G. R. Wilensky: Health Care of the Poor Elderly,

Supplementing Medicare. National Center for Health Services Research. Hyattsville, Md., Mar. 1, 1984.

Binstock, R. H., and E. Shanas: Aging and the Social Sciences.

New York. Van Nostrand Reinhold Company, 1976.

Bixby, L. E.: Retirement patterns in the United States, Research

and policy interaction. Sot. Secur. Bull. 3-19, Aug. 1976.

Bosse, R., and D. Ekerdt: Effects of retirement on physical health.

Am. J. Public Health 73:779-783, July 1983.

Cafferata, G. L.: Knowledge of their health insurance coverage

by the elderly. Med. Care 22(9), Sept. 1984.

Cafferata, G. L., and M. Meiners: Public and Private Insurance

and the Medicare Population’s Out-of-Pocket Expenditures, Unpublished paper. National Center for Health Services Research, Oct.

1984.

Changes in self-reported health with retirement. Inf. J. Aging Hum.

Dev. 5:213–223, 1982.

Clark, R., and D. Barker: Reversing the Trend Toward Early Retirement. Washington, D.C. American Enterprise Institute, 1981.

Clark, R. L., et al.,: Inji’ation and the Economic Well Being of

the Elderly. Baltimore, Md. The Johns Hopkins Press, 1984.

Clark, R., and R. Schrimpec Health Expenditures and the Elderly.

Unpublished paper. Raleigh, N.C. North Carolina State University,

Mar. 1984.

Dobson, A., J. Scharff, and L. Cordec Six months of Medicaid

data. Health Care Financing Review. Vol. 4, No. 3. Office of

Research and Demonstrations, Health Care Financing Administration.

Washington. U.S. Government Printing Office, Mar. 1983.

Ellet, T. V.: Medigap: State Responses to Problems with Health

Insurance for the Elderly. Prepared for the Health Care Fhsancing

Administration. Washington, D.C. George Washington University,

Oct. 1979.

Farley, P.: Private insurance and public programs, Coverage of health

services. Data Preview. No. 20. National Center for Health Services

Research. Hyattsville, Md., Mar. 1985.

Farley, P. J., and G. R. WiIensky: Household Wealth and Health

Insurance as Protection against Medical Risks, in R. David and

T. Smeeding, eds., Horizontal Equity, Uncertainty and Economic

Well-being. Chicago. NBER (Forthcoming).

Feldman, J. J.: Work Ability of the Aged Under Conditions of

Improving Mortality. Statement before the National Commission

on Social Security Reform. Washington, D.C. National Center for

Health Statistics, 1982.

Fillenbaum, G. G.: Social context and self-assessments of health

among the elderly. J. Healrh Sot. Behav. 20(1 ):45–51, 1979.

FiIlenbaum, G. G., and G. L. Maddox: Assessing the Functional

Stare of LRHS Participants. Durham, N.C. Center for Aging and

Human Development, 1977.

Gamick, D. W., and T. Short: Utilization of hospital inpatient scrviccs

by elderly Americans. Research Note. No. 6. Public Health Scrvicc,

Hospital Studies Program. Rockville, Md., June 1985.

Grad, S.: Incomes of the aged and nonaged, 1950-82. Sac. Secur.

Bull. 47(2), June 1984.

Grossman, M.: The Demand for Health: A Theoretical and Empirical

Invesrigarion. New York. National Bureau of Economic Research,

Inc., 1972.

Kasper, J. A., and G. Barrish: Usual sources of medical care and

their characteristics. NHCES Da/a Preview Series. No. 12. DHHS

Pub. No. (PHS) 82-3324, 1982.

Kingson, E.: The Early Retirement Myth: Wh~ Men Retire Before

Age 62. Report prepared for the SeIect Committee on Aging, U.S.

House of Representatives, Washington. U.S. Government Printing

Office, 1981.

Kovar, M. G.: Expenditures for the medical care of elderly people

living in the community throughout 1980. Data Report. No. 4.

National Center for HeaIth Statistics. Hyattsville, Md., Nov. 1983.

Lave, J., A. Dobson, and C. Walton: The potential use of Health

Care Financing Administration data sets for health care services

research. Health Care Financing Review. Vol. 5, No. 1. Office

of Research and Demonstrations, Health Care Financing Administration. Washington. U.S. Government Printing Office, Fall 1983.

Maddox, G. L., and E. B. Douglas: Self-assessment of health,

A longitudinal study of elderly subjects. J. Health Stx. Behav.

14:89-93, 1973.

Manton, K. G., and K. Liu: Projecting chronic disease prevalence.

Med. Care 22(6), June 1984.

Martin, J., and A. Doran: Evidence concerning the relationship

between health and retirement. Socio/. Rev. 14:340-342, Nov. 1966.

McMillan, A., and M. Gomick: The dually entitled elderly Medicare

and Medicaid population living in the community. Health Care

66
Financing Review. Vol. 6, No. 2. Office of Research and Demonstrations, Health Care Financing Administration. Washington. U.S. Government Printing Office, Winter 1984.

Medical Benefits: Charlottesville, Va. Kelley Communications, May

1985.

Menefee, J.: The Demand for Health, in R. L. Clark, ed., Retiremerz/

Policy in an Aging Society. Durham, N.C. Duke University Press,

1980.

Menefee, J.: The Role of Health in the Work and Retirement Decision

oj Older Workers. Prepared for the Administration on Aging.

Washington, D.C., 1985.

Mcncfce, J., and J. McCoy: The Spatial Distribution of Health

Care Facilities and the Elderly Population. Unpublished paper prepared for Social Security Office of Research and Statistics.

Washington, D.C., 1982,

Newquist, D., and P. Robinson: Health and Extended Workll~e.

Paper prepared for Administration on Aging, National Policy Center

on Employment and Retirement. Los Angeles. Andtus Gerontology

Center, 1983,

Ostficld, A.: Frequency and Nature of Health Problems of Retired

Persons, in F. M. Carp, ed., The Retirement Process. Public Health

Service Publication No. 1778. Washington. U.S. Government Printing Office, 1968.

Palmore, E,, ed.: Normal Aging: Report from the Duke Longitudinal

Study, 1955-1969. Durham, N.C. Duke University Press, 1970.

Palmore, E., L, George, and G. Fillenbaum: Predictors of retirement.

J, Gerontol., 1982.

Pamcs, H. S., and J. A. Meyers: Withdrawal From the Labor Force

by Middle Aged Men, 1966-1967, in G. M. Shatto, ed., Employment

of Middle Aged Men, Springfield, Ill. Charles C. Thomas, 1972.

Parries, H. S., and G. Nestel: Early Retirement, in The Pre-Retirement

Years (Vol. 4), A Longitudinal Study of the Labor Market Experience

of Men, by H. S. Pames, A. V. Adams, P. J. Andrisani, et al.

Washington. U.S. Government Printing Office, 1975.

Parsons, D. O.: The male Iabour force participation decision, Health,

reported health, and economic inventives. Economics 8 1–91, Feb.

1982.

Reno, V.: Why Men Stop Working At or Before Age 65, in Reaching

Retirement Age. Baltimore. U.S. Social Security Administration,

1976.

Rcno, V. P., and L. D. Maxfield: Distribution of income sources

of recent retirees, Findings from the New Beneficiary Survey. .SOC.

Secur. Bull. 48(1), Jan. 1985.

Schieber, S. J., and P. M. George: Coverage and Benefit Entitlement.

Washington, D.C. Employee Benefit Research Institute, 1981.

Schieber, S. J., and P. M. George: Income Levels and Adequacy.

Washington, D.C. Employee Benefit Research Institute, 1982.

Schwab, K.: Early Labor Force Withdrawal of Men: Participants

and Non-Participants Aged 58–63, in Almost 65: Baseline Data

From /he Retirement History Study. Baltimore. U.S. Social Security

Administration, 1976.

Sheppard, H. L.: Factors Associated With Early Withdrawal From

the Labor Force, in S. L. Wolfbein, ed., Men in the Pre-Retirement

Years. Temple University, 1977.

Streib, G. F., and C. J. Schneiden Retirement in American Society,

Impact and Process. Ithaca, N.Y. Cornell University Press, 1971.

U.S. Congressional Budget Office: Work an~ Retirement: Options

for Continued Employment of Older Workers. Washington. U.S.

Government Printing Office, 1982.

Waldo, D. R., and H. C. Lazenby: Demographic characteristics

and health care use and expenditures by the aged in the United

States, 1977–1984. Health Care Financing Review. Vol. 6,

No. 1. Office of Research and Demonstrations, Health Care Financing

Administration. Washington. U.S. Government Printing Office, Fall

1984.

Washington Business Group on Health: Post Retirement Medical

Benefits: Survey Report. Washington, D.C. Institute on Aging, Work

and Health, 1985.

Wyatt Company: Group Benejit Survey. Washington, D.C., 1984.

69
Health care software firms Appendix A

Organizations involved in data collection

and analysis include:

Alexander & Alexander

— George B. Buck Consultants

A. S. Hansen

Data Resources, Inc. (a McGraw-Hill Company)

— Ernst & Whinney

Frank B. Hall

Hewitt

Johnson &Higgins

Meidinger/Mercer

Tillinghast, Nelson&Warren, Inc.

- TPF&C

Data Consulting Firms

— Commons Management Group

Cost Care, Inc.

– Health Data Institute

Health Risk Management

Health Management Services

Intracorp

– Ludy and Associates

Medical Data Systems

— The Wyatt Company

Insurance companies

– The Metropolitan

– Corporate Health Strategies

– - Connecticut General

—

—

—

—

Health Systems International

Travelers Corporation

Center for Corporate Health Promotion, Inc.

Massachusetts Mutual

Puter Associates

Health Care Alternatives, Inc.

Blues

The Travelers

Equitable

Health Data Institute

Third Party Administrators (TPA’s)

Executive and Employee Benefit Plans Inc.

James Benefits

Alexander &Alexander

U.S. Administrators

Multihospital systems

Bain and Company

The Commission on Professional and Hospital Activities

McDonnell Douglas Corp.

Appendix B

Private data base information

A, Individual company data

1. Sources of data

– Claims information

– Hospital medical abstracts

– Utilization review program data

– Medical department records

– Medical benefit plans

2. Types of data

– Hospital procedures

– Medical services

– Medical diagnoses

– Source of medical service

– Cost of service

– Health status measures

– Length of stay

– Other health care services

– Type or extent of insurance coverage

– Medical coverage

– Copayments

B. Comparative data for community

1. Sources of data

– Hospital associations

– Private utilization data systems

– Medical foundations and professional standards

review organizations

– Health systems agencies

– Other State and Federal agencies

2. Types of data

– Aggregate data comparable to individual company data

– Community health resources

– Community measur:s of use of hospitals and

other health resources

– Cost and price data for the community’s health

care services

– Epidemiological data

— Humana

— National Medical Enterprises

Hospital Corporation of America

70
Appendix C

Public data sources

Annual Housing Survey

Housing Information, Decennial Census

Residential Finance

Employment & Unemployment Analysis

Eamings<urrent Population Survey

Local Area Unemployment

Occupational Employment &Unemployment

State and Area Data—Demographics

Consumer Expenditure Survey

Consumer Price Indexes

Family Budgets

Producer Price Indexes

Retail Prices—Fuels & Utilities

Cost-of-Living

Area Wage Surveys

Level of Benefits Survey

Pension Beneficiary Master File

Survey of Low Income Aged and Disabled

New Beneficiary Survey

Summary Earnings Records

Survey of Disabled and Nondisabled Adults

Master Beneficiary Records

Retirement History Survey

The Medicare Statistical System

The Health Insurance Master File

The Provider of Service File

The Utilization File

Medicaid Minimum Data Set (MMDS)

The Medicaid Financial Data Set

The Current Medicare Survey

National Medical Care Utilization and Expenditure Survey

(NMCUES)

The Health Interview Survey

Survey of Consumer Finances

National Longitudinal Survey

Program Survey of Income Dynamics

Hospital Discharge Survey

National Nursing Home Survey

Survey on Income Program Participation (SIPP)

Current Population Surveys (CPS)

CPS—Pension Supplement

CPS—Tax Supplement (1983)

Income Survey Development Program (ISDP)

Home Health Care Supplement to Health Interview Survey

Caregivers Supplement to Long Term Care Survey (1982)

Aging Supplement (1984)

71
Chapter M
Methodologic issues in

linkage of multiple data

basesayb

by Fritz Scheuren, Ph.D., Internal Revenue Service

Data linkage offers several obvious benefits in studying .

the dynamics of aging. Retrospective and prospective approaches are possible, Many ad hoc epidemiological studies

could serve as examples here (e.g., Beebe, 1985). Perhaps

of even more importance are broad-based statistical samples

composed of linked administrative records, used either alone

or in conjunction with survey data (e.g., Kilss and Scheuren,

1980;Scheuren, 1983).

In general, linked administrative records, when structured

longitudinally (e.g., Buckler and Smith, 1980), can be very

effective in tracing changes with age in income and family

relationships—including the onset of some forms of morbidity

(e.g., Klein and Kasprzyk, 1983) and, with the advent of

the National Death Index, mortality as well (e.g., Patterson

and Bilgrad, 1985).

Survey data can be used, among other things, to explore

the underlying causal mechanisms for these administratively

recorded outcomes. The design challenge, of course, is how

to build a data collection process which exploits the comparative advantages of both administrative and survey information.

,The present paper examines settings where linkages of

U.S. Federal Government records for individuals are feasible

and of interest in the study of the dynamics of aging. Both

administrative and survey records will be considered. Our

focus will be on the barriers to and benefits from data linkages,

with examples drawn from studies conducted using records

from the Social Security Administration (SSA), the Health

Care Financing Administration (HCFA), the National Center

for Health Statistics (NCHS), the Bureau of the Census, and,

of courw, the Internal Revenue Service (IRS).

Organizationally, the paper has been divided into three

main sections. Structural questions (e.g., legal and procedural)

in the development of a data linkage system are taken up

first (Section l). Technical issues in the matching process

itself are discussed next (Section 2). The paper concludes

(in Section 3) with some recommendations on areas for future

study. An extensive set of references is also provided, along

with some additional bibliographical citations. (See appendix A.)

“Conttxs[s we the opinions of the author and not those of the Department

ot’ Width tind Human Services or the organization he works for.

‘w IC)KfjNotiontil Academy of Sciences.

1. Structural design

considerations

During the last several decades numerous data systems

have been built by linkage techniques in an attempt, among

other objectives, to study various aspects of the aged population. Some of these, like the Continuous Work History Sample,

remain enormously valuable (e.g., Kestenbaum, 1985) but

are no longer fully exploited because of access problems and

severe resource constraints (e.g., Cartwright, 1978). Others,

notably the Retirement History Survey (Irelan and Flnegar,

1978), have not been continued. Many studies had an ad

hoc character to begin with. While successful, they have

not been repeated (e.g., the 1973 Exact Match Study, Kilss

and Scheuren, 1978; the Survey of Low Income Aged and

Disabled, Barron, 1978). Still other studies originally envisioned as stand-alone survey systems have not exploited

available data linkage opportunities to extend their useful life

beyond the point at which interviewing has stopped (e.g.,

the National Longitudinal Survey, Pames et al., 1979). What

can we learn from these experiences and others that are similar?

. First, agency support for the activity has to be very strong

and continuing. The Social Security Administration,

which supported most of the projects listed previously,

has moved away from such general research efforts and

shifted toward examining improvements in program operations (Storey, 1985). A sustained Iongrun commitment

to basic research simply may not be possible in what

is inherently a policy-oriented environment (President’s

Reorganization Project for the Federal Statistical System,

1981).

● Second, strong user support is essential. The products

must have high perceived public value, be delivered in

a timely manner and with sufficient regularity to sustain

continued interest. Startup problems with the Retirement

History Survey caused it some major difficulties from

which it may never have been able to fully recover (Maddox, Fillenbaum, and George, 1978). The Continuous “

Work History Sample has, especially in recent years,

been unable to sustain user interest outside of Social

Security because of access issues raised by the 1976 Tax

Reform Act. Also, the emphasis on employee-employer

relationships, long a main feature of the Continuous Work

History Sample, may not have been seen to be as important

as the resource commitment required to maintain it.

75
Third, startup costs may be high for data linkage systems,

especially if based in part on survey data. Linkage systems

tend to be easily maintained at low cost unless continued

surveying is done; however, certain data problems, due

to insufficient attention to obtaining good matching information,’ can cause continuing expense and difficulty at

the analysis stage. Obviously also, as turned out to be

the case with the Continuous Work History Sample, data

quality limitations in the administrative records may necessitate considerable additional expense.

Fourth, data linkage systems employ methods that may

not be seen as entirely ethical (e.g., Gastwirth, 1986)

or that have confidentiality constraints that make the systems hard to maintain, as with the Retirement History

Survey, or hard to use, as with the Continuous Work

History Sample (e.g., Alexander, 1983). These controversial elements in data linkage techniques, it may be speculated, could be one of the reasons linkages to the National

Longitudinal Survey (NLS) have never been attempted

(despite the collection of social security numbers in the

NLS).

It is only with the last of these points that we touch

on risks that data linkage systems encounter, which are not

also encountered to some degree in more conventional datacapture approaches. The force of these concerns will be

discussed.

Confidentiality and disclosure concerns

Data linkage operations bring us face-to-face with a “dense

thicket” of laws, regulations, and various ad hoc practices

justified on heuristic grounds. There are statutory considerations which apply either to the particular statistical agencies

involved or to the Federal Government as a whole. These

include the Privacy Act; the Freedom of Information Act;

special legislative protections afforded to statistical data, for

example, at the Census Bureau and the National Center for

Health Statistics; and, of course, legislative protections afforded to administrative data, notably the 1976 Tax Reform

Act. The paper by Wilson and Smith ( 1983) gives a good

summary of the legal protections afforded mx data. For a

more general treatment of legal issues and one which advocates

change. see Clark and Coffey ( 1983). Also see Alexander

and Jabine ( 1978).

The regulations and practices of each Federal statistical

agency differ too. not only because of the different legislative

statutes under which they operate, but also because of the

varying approaches that they have taken in the accomplishment

of their missions. Indeed, interagency data sharing arrangements almost defy description; they vary, among other reasons,

depending on )t’hich agencies are sharing wfzose data and

for }ikzr purpose. One excellent, albeit incomplete, taxonomy

of current practice is found in the work of Crane and Kleweno

(1985).

Despite the complexity of this topic, several general trends

emerge that are worth noting:

. First. the American people are at best ambivalent about

letting their governme~t conduct linkages across data sys76

terns, specifically between different agencies and for purposes not obviously central to the missions of both agencies.. For example, in a recent survey, questions were

asked about the sharing of tax records with the Census

Bureau, something which is a longstanding practice specifically permitted by law. Three-fourths of those surveyed

did not support this use of administrative records even

though an attempt was made to put the matter in a very

favorable light, arguing for it on efficiency grounds (Gonzalez and Scheuren, 1985). See also appendix B for exact

question wording.

. Second, bureaucratic practices which do not respect this

general unease about linkage may need to be reexamined

(e.g., Gastwirth, 1986). It is the duty, after all, of govern-’

ment statisticians to uphold both the letter and the spirit

of the law. The whole tenor of the post-Watergate, Privacy

Act and Tax Reform Act era has been to limit administrative initiatives (both big and little “a”) and to permit

the expansion of access only after the enactment of positive

law. The failed initiative regarding Statistical Enclaves

illustrates this point quite nicely. The Enclave proposal

(Clark and Coffey, 1983) sought what many regarded

as a degree of reasonable discretion on data linkage and

data access; however, the authority requested was too

broad for the current political climate. The arguments

put forward in the proposed legislation’s defense—for

example, that it would increase efficiency and bring order

to a patchwork of disparate practices—simply did not

carry the day. In summary, we do not seem to be even

close to a general solution on access to data for statistical

purposes.

. Third, absent new legislation, many statistical agencies

have begun to reexamine their traditional access arrangements and tighten still further their practices (e.g., Cox

et al., 1985). For example, the use of special Census

agents to facilitate linkages or to improve their subsequent

analysis has been drastically curtailed, resulting in a clear

shortrun loss in the utility to outsiders of linkage methods

at the Census Bureau. On the other hand, new linkage

practices have emerged from such reviews which may

be superior to what otherwise might have been done.

The linkage between the Current Population Survey (CPS)

and the National Death Index is an excellent example

(Rogot et al., 1983). Neither the Census Bureau nor the

National Center for Health Statistics felt it could give

up access of its data to the other agency; however, a

compromise was worked out where joint access was maintained during the Iinkagt? operation, and this has proved

satisfactory. In fact, similar arrangements have been made

successfully between the Center and the Internal Revenue

Service as part of a study of occupational mortality (Smith

and Scheuren, 1985b).

● Fourth, the extent to which public use files can be made

available from linked data sets has been greatly curtailed

because of new concerns about what is called the “reidentification” problem (Jabine and Scheuren, 1985). Simply

put, this means that if enough linked data are provided

in an otherwise unidentifiable (public-use) form. then each

contributing agency could reidentify at least some of the
mkd units, almost no matter what efforts at disguise

are attempted (Smith and Scheuren, 1985b). The only

major exception occurs when the data made public from

the contributing agencies are extremely limited (Oh and

Scheuren, 1984; Paass, 1985); but usually the incentives

for cooperation on the part of the contributing agencies

are limited as well. In practice, of course, there is almost

no incentive for the contributing agencies to reidentify;

thus, legally binding contractual obligations might be entered into that could stipulate that there was no such

interest. Contractual guarantees, however, may not satisfy

all parties to the linkage because of the public perception

issues mentioned earlier. It is conceivable, moreover,

that no degree of legal or contractual reassurance would

be adequate at the present time to permit the release

of certain public use linked data sets—for example, those

involving Census surveys linked to Internal Revenue

Service information. Historically it was only the impossibility of reidentification which made the release of

matched CPS-IRS-SSA public use files possible (Kilss

and Scheuren, 1978).

It goes almost without saying that confidentiality and

disclosure concerns pose the greatest barriers to the development of data linkage systems for studying aging. We will,

however, defer to Section 3 a discussion of what might be

done to deal with such issues and go on to explore the technical

side of matching,

2. Matching design

considerations

This section is intended to provide a brief discussion

of matching design questions that must be looked at in developing data linkage systems. We begin with some historical background and then focus specifically on “person” matches, where

the social security number is a possible linking variable.

Linkage systems based in part on survey information are emphasized. Analysis problems also are covered, particularly

ways of estimating and adjusting for errors arising from erroneous links or nonlinks.

Historical observations

The main theoretical underpinnings for computer-oriented

matching methods were firmly established by the late 1960’s

with the papers of Tepping (1968) and especially Fellegi and

Sunter ( 1969). Sound practice dates back even earlier, at

least to the 1950’s and the work of Newcombe and his collaborators (e.g., Newcombe et al., 1959).

The Fellegi-Sunter approach is basically a direct extension

of the classical theory of hypothesis testing to the problem

of record linkage. A mathematical model is developed for

recognizing records in two files which represent identical units

(said to be matched), As part of the process there is a comparison between all possible pairs of records (one from each

file) and a decision is made as to whether or not the members

of the comparison pair represent the same unit or whether

there is insufficient evidence to justify either of these decisions.

These three decisions can be referred to as a “]ink,’’’’nonlink,”

or “potential link. ”

In point of fact, Fellegi and Sunter contributed the underlying theory to the methods already being used by Newcombe

and showed how to develop and optimally employ probability

weights to the results of the comparisons made. They also

dealt with the implications of restricting the comparison pairs

to be looked at, that is, of “blocking” the files, something

that generally has to be done when linking files that are

at all large.

Despite the early seminal work of Newcombe, Fellegi,

and others, ad hoc heuristic methods abound. There are many

reasons for this state of affairs:

●

●

●

●

●

First, until recently (and maybe even now) there has

been only a handful of people whose main professional

interest is data linkage. This means, among other things,

that most of the applied work done in this field has

been carried out by individuals who maybe solving matching problems for the first time. Because the basic principles

of matching are deceptive] y simple, ad hoc solutions have

been encouraged that could be far from optimal.

Second, statisticians typically get involved very late in

the matching step, often after the files to be matched

have already been created. Even when this is not the

case, little emphasis may be placed on the data structures

needed for linkage because of other higher priorities.

Design opportunities have, therefore, been generally limited to what steps to take given jiles which were produced

largely for other purposes.

Third, until the late 1970’s, good, portable, generalpurpose matching software had not been widely available

(e.g., Howe and Lindsay, 1981), despite some important

early attempts (e.g., Jaro, 1972). Even in the presence

of general-purpose software, the uniqueness of each

matching environment may lead practitioners to write complex customized programs, thereby absorbing resources

that might have been better spent elsewhere.

Fourth, especially for matches to administrative records,

barriers to the introduction of improved methods have

existed because cruder methods were thought to be more

than adequate for administrative purposes.

Fifth, the analysis of linked data sets, with due consideration to matching errors, is still in its infancy (Smith

and Scheuren, 1985a). Qualitative statements about such

limitations typically have been all that practitioners have

attempted.

More will be said below concerning these issues in the

context of computerized person matching.

Person matching

Typically in a computerized matching process there are

a number of distinct decision points:

. First, design decisions have to be made about the linking

variables that are to be used, including the extent to

77
●

●

●

which resources are expended to make their reporting

both accurate and complete. (This step may be the most

important, but it is likely also to be the one over which

statisticians have the least control, especially when matching to administrative records. )

Second, decisions have to be made about what preprocessing will be conducted prior to linkage. Some of the

things done might include correcting common spelling

errors, calculating SOUNDEX or NYSIIS Codes, etc.

(Winkler, 1985). Decisions about how to sort and block

the files also fall here (Kelley. 1985).

Third. decisions about the match rule itself come next.

If a probdbi]istic approach is taken, as advocated by Fellegi

and Sunter ( 1969), then we have to estimate a set of

weights that represent the extent to which agreement on

any particular variable provides evidence that the records

correspond to the same person (and conversely, the extent

to which disagreements are evidence to the contrary).

Fourth, invariably there are cases where status is indeterminate regardless of the approach taken and a decision

has to be made about excluding them from the analysis,

going back for more information, etc.

To give some realism and specificity to our discussion,

let us consider potential linkage settings in which we could

bring together two files based on common identifying information: name, social security number, sex, date of birth, and

address. As appropriate. we will contrast the linkage as taking

place either entirely in an administrative context or between

survey and administrative data.

Linki}zg vw-idde.~-The social security number (SSN) is

the most important linking variable that we in the United

States have for person matching purposes. SSN’S were first

issued so that the earnings of persons in employment covered

by the social security program could be reported for eventual

use in determining benefits. SSN’S were also used as identifiers

in State-operated unemployment insumnce programs, but no

other major uses developed until 1961, when the Internal

Revenue Service decided to use the SSN as the taxpayer

identification number for individuals. Other uses by Federal

and State Governments followed rapidly. and now the social

security number is a nearly universal identifier. The Privacy

Act of 1974 placed restrictions on the use of SSN’S but exempted those formally established prior to 1975. So far these

restrictions have had only a minor impact on the widespread

use of the social security number by governments and private

organizations (Jabine. 1985).

The social security number is nearly a unique identifier

all by itself and extremely well reported, even in survey

settings and on records such as death certificates (e.g., CobIeigh and Alvey. 1975: Alvey and Aziz. 1979). In survey

contexts. error rates may run to 2 or 3 percent; but this

depends greatly on the extent to which respondents are required

to make use of records in order to provide the requested

information. Typically. driver’s licenses. pay stubs. and the

like are excellent sources (in addition to the use of the social

security card itselfj.

Both administrative and survey reporting of ’social security

numbers are subject to possible mistakes in processing, but

these can be guarded against by using part of the individual’s

surname as a confirmatory variable. For example, IRS and

55A use this method as one way of spotting keying errors.

A difficulty with current administrative approaches is that

name changes (especially for females) may lead to considerable

extra effort in confirming (usually through correspondence)

that the social security number was indeed correct to begin

with. (It is a requirement of the social security system that

notification is to be made when name changes occur, but

many people fail to do this until the omission is called to

their attention. )

One disadvantage of the social security number is the

absence of an internal check digit allowing one to spot emors

by a simple examination of the number itself. At the time

the social security system started in the midthirties, the widespread use of the SSN as an identifier was not envisioned.

Indeed, there is not a one-to-one correspondence between

individuals and the social security numbers they use. In some

instances, more than one person use the same social security

number. Historically, the most important cases of this type

arose because SSN’S were used by advertisers in promotional

schemes. Perhaps the best known such instance is the number

078-05-1120 (Scheuren and Hernot, 1975). It first appeared

on a sample social security number card contained in wallets

sold nationwide in 1938. Many people who purchased the

wallets assumed the number to be their own. The number

was subsequently reported thousands of times by different

individuals; 1943 was the high year, with 6,000 or more

wage earners reporting the number as their own.

While there have been over 20 different “pocketbook”

numbers, like 078-05-1120, they are probably no longer the

main cause of multiple use of the same number. Confusion

can arise (and go largely undetected) when one member of

a family uses the number of another. Also, there are incentives

for certain individuals, like illegal aliens, to simply “adopt”

the social security number of another person as their own.

The extent to which these problems exist is unknown, but

they are believed, at least by some authorities, to be less

prevalent than the opposite problem—issuances of multiple

numbers to the same person (Secretary of Health, Education,

and Welfare’s Advisory Committee, 1973).

Until 1972, applicants for SSN’S were not asked if they

had already been issued numbers, nor was proof of identity

sought. This led to perhaps as many as 6 million or more

individuals having two or more social security numbers

(Scheuren and Herriot, 1975). A substantial fraction of the

multiple issuances have been cross-referenced so that multiple

reports for the same individual can be brought together if

desired. Based on work done as part of the 1973 Exact Match

Study, it appears that, despite the frequency of the problem,

multiple issuances can largely be ignored unless one is looking

at longitudinal information stretching back to the early days

of the social security program. (In other words, people tend

consistently to use only one of the numbers they have been

issued.)

While the social security number is nearly ideal as a

linking variable, it is not always available. For example,

in the Current Population Survey for adults the number is

78
missing 20-30 percent of the time (Scheuren, 1983). Evidence

exists, however. from work done in connection with the Survey

of Income and Program Participation, suggesting that with

a modest effort the SSN missed rate can be lowered significantly, to less than 10 perc%nt in Census surveys (Kasprzyk,

1983). Recent experience with death certificates shows a

missed rate of about 6 percent for adults (Patterson and Bilgrad,

1985).

What. then, do we do when the SSN is missing or proves

unusable’? We are obviously forced either to seek more information or to try to make a match using the other linking

variables. Now, as a rule, none of these other linking variables

is unique alone, and all of them, of course, are subject in

varying degrees to reporting problems of their own. Some

examples of the problems typically encountered are—

●

●

●

●

●

●

Surnanle-As already mentioned, name changes due to

marriage or divorce are, perhaps, the main difficulty.

For some ethnic groups, there can be many last names

and the order of their use may vary.

Given nanre-The chief problem here is the widespread

usc of nicknames. Some are readily identifiable (“Fritz”

for “Frederick”). but others are not (like “Stony” for

“Paul”).

Middle initia-People may have many middle names (including their maiden name), and the middle name they

employ may vary from occasion to occasion. Often, too,

this variable may be missing (Patterson and Bilgrad,

19M).

St’.r—Thisis generallywell reportedand, exceptfor processing errors, can be relied upon. The main difficulty

with this variable is that it is not always available in

administrative records. (IRS does, not have this variable

except through the recoding of first names, which simply

cmmot be done with complete accuracy. )

Date ofbirrh-Day and month are generally well reported

even by proxy respondents. Year can be used with a

fdetwce togood effect as a matching variable. Again,

as with “sex,” this item is not available on all the administmtive files we are considering.

AdJre.t-This’ is an excellent variable for confirming

otherwise questionable links. Disagreements are hard to

interpret, however, because of address changes; address

variations (e.g., 2 Ist and Pennsylvania Avenue for 2122

Pennsylvania Avenue): and, of course, differences between mailing addresses (usually all that are available

in administrative files) and physical addresses (generally

all that are obtained in a household survey). Recent reseurch on this variable has been done by Childers and

Hogan ( 1984).

Still other linkage variables could have been discussed,

for exttmple. race and telephone number. Race is a variable

that is similar to sex except not nearly as well reported,

unless it is recoded as black and nonblack (e. g., U.S. Bureau

of the Census. 1973). Telephone numbers have problems similar to those of addresses and. while potentially of enormous

value eventually. are not now widely available in administrative files.

fr(~pro(.c’.v.si)l,q step.s-in general, any method of standardization of identifier labels. such as names and addresses. will

improve the chances of linking two records that should be

linked during the actual matching process. However. it will

also. to an unknown degree. result in some distortion and

loss of information in the identifying data and may even

,increase the likelihood of designating some pairs of records

as a positive link when, in fact. the pair is not a match.

Typically, for person matches to SSA or IRS information,

two preprocessing steps have been undertaken: ( 1) to validate

reported social security numbers; and (2) if missing or unusable, to search for SSN’S using surname and other secondary

“linking variables. Both of these steps have had to be conducted

largely within the existing administrative arrangements. The

cost of mounting a wholly separate effort has been judged

to be prohibitive. (The data sets involved are simply enormous.

Social Security has roughly 300 million SSN’S now issued.

In recent years IRS has been processing about 100 million

individual income tax returns annually, conmining well over

150 million taxpayer social security account numbers.)

The “validation step” itself consists of two parts: first,

a simple match on SSN alone is attempted; and, if an SSN

is found, then secondary information from Social Security

or Internal Revenue records is made available on the output

computer file. Further processing then takes place so that

the confirmatory matching information (names, etc. ) can be

examined and coded as to the extent of agreement. It is

possible that this part of the current administrative procedure

can be readily modified to accord with modern matching

ideas. What is needed is to institute probability-based weights

for the agreements (disagreements) found. At present administrators and statisticians alike simply employ a series of ad

hoc rules to separate what will be considered a link from

cases that have questionable SSN’S (e.g., Scheuren and Oh,

1975; Jabine, 1985).

The “search step” is an elaborate and fairly sophisticated

computerized procedure (which differs in detail at SSA and

IRS). The files used are in sort; and, for the most part,

the only possible links that can be looked at are cases that

agree on surname. Since other blocking variables are used

as well, the current administrative methods tend to be very

sensitive to small reporting errors. This is believed to be

true despite the fact that the computer linkage procedures

go to great lengths to protect against more common reporting

errors (such as those mentioned earlier). At Social Security

they do this by systematically varying the linking information

on the record for which an SSN is being searched. An extensive

set of manual procedures also exists for cases where computer

methods prove unsuccessful.

Unlike the “validation step,” it may not be possible to

bring the “search step” into full accord with modern practice.

First of all, we would need to reexamine the decisions about

what blocking variables to use (Kelley, 1985). Ideally we

want variables that are without error themselves, or nearly

so, in both sources (Fellegi, 1985) and that divide the files

into blocks or “packets” of reasonably small size, within which

we can look at all possible linkage combinations (e.g., Smith,

1982). Research is now underway in both agencies to find

79
ways of improving the blocking variables, but it is unlikely

that the current deterministic methods wil I ever be replaced

by probability-based ones, and for good reason. Linkage techniques for administrative purposes must be employed with

high frequency in a great variety of situations and hence

be extremely efficient in the use of computer time, since

the basic files involved are so large.

A compromise that naturally arises within the world of

large computer files is to employ some form of multiple,

albeit still deterministic, scheme. This is the approach taken

with the National Death Index (NDI). The NDI currently

employs over a dozen different combinations of matching

variables. Some give a primary role to the social security

number, some to the surname; still others place primary emphasis on the given name or on date of birth (Patterson and

Bilgrad, 1985). Adopting the NDI approach at SSA or IRS,

if feasible, might be one way to make a real advance.

March rufes—Usually the computerized matching phase

in a data linkage system consists of three steps: ( 1) comparisons

between the linkage variables on the files being matched;

(2) generation of codes which indicate the extent to which

agreements exist or disagreements are present; and (3) decisions regarding the status of each comparison pair. This structure is the same, whether probability y-based methods are being

implemented (e.g., Howe and Lindsay, 1981 ) or heuristic

approaches are taken (e.g., Scheuren and Oh, 1975).

● Comparison step-In a sense, we have already discussed

this step earlier. It depends heavily on what linkage variables are present; the reformatting, etc., done of those

variables to facilitate comparisons; and the degree to which

blocking is required because of resource or other considerations. What is desired here conceptually is to compare

every record on each file with every record on the other.

Blocking, of course, limits (sometimes severely) the extent

to which such comparisons can be carried out. Any recoding of the linkage variables (say SOUNDEX for surname)

may possibly, as we have noted, reduce the utility of

this step. Generally, if resources permit, all the linking

variables should be used in the computer comparisons.

When this is not possible, they can still be employed

later in manually settling cases where the outcome might

otherwise be indeterminate. However, it almost goes without saying that manual intervention needs to be carefully

limited and closely controlled. Manual matching is extremely costly and, while individual manual decisions

can sometimes be better than computer matching, usually

humans lack consistency of judgment and can be distracted

by extraneous information, such that they act more decisively than the facts would warrant.

● Coding step-As a result of the comparison step, a series

of codes can be generated indicating the degree of agreement which has been achieved. These agreement outcomes

may be defined quite specifically, e.g., “Agrees on Surname and the value is GILFORD. ” They might be defined

more generally: agree, disagree, or unknown (the last

arising because of missing information, perhaps).

It becomes very difficult to talk about the coding step

without looking ahead to the decision step and the specific

approach that will be taken there. Nonetheless, some

general observations can be made. Obviously, when we

have, in fact, brought together records for the same person,

we would like the agreement coding structure not to

obscure this point. For example, to protect against trivial

spelling errors, we might use the same agreement code

even though there are transposition or single-character

differences in the name. (The preprocessing of the files

should have taken care of some of this but it may, again,

be a consideration in the agreement coding itself. )

In most applications of the Fellegi-Sunter approach, the

assumption is rhade that agreement (or disagreement) on

one linking variable is independent from that on any

other, conditional only on whether or not the records

brought together are, in fact, for the same person. To

aid in making this assumption plausible, special care needs

to be taken in structuring agreement codes for such variables as sex and first name, which are inherently related

(Fellegi, 1985).

● Decision step-An assessment can now be made as to

the extent to which an agreement on any particular linking

variable, or set of variables, constitutes evidence that

the records brought together represent the same person.

Conversely, an assessment can be made as to the extent

to which disagreements are due to processing or reporting

errors or are evidence that the records do not represent

information for the same person. Typically, the records

are divided into those (I) where a positive link is deemed

to have been “definitely” established, (2) where a “possible” link may exist but the evidence is inconclusive,

and (3) where it can “definitely” be said that no link

exists.

In probability-based methods a statistical weight function

is calculated to order the comparison pairs. The weights

are developed by examining the probability ratioProb (result of comparison, given match)

Prob (result of comparison, given nonmatch)

The numerator represents the probability that comparison

of two records for the same person would produce the

observed result. The denominator represents the probability that comparison of records for two different persons,

selected at random, would produce the observed result.

In general, the larger the ratio, the greater our confidence

that the two records match, i,e., are for the same person,

Let us consider a particular example in which we are

matching on both sex and race, where sex is always

represented as either male or female and where race has

been recoded black or nonblack. Further suppose that

the proportion of males and females is each 50 percent

and that black persons constitute 10 percent of the population and nonblack persons 90 percent. Also suppose that

80
the chonces of a reporting w-ror on race w-e l/100 and

for sex 1/1.000. Finally. we will assume that sex and

rticc are independently distributed in the population w-d

that report in: emrs are independent as well.

With these stipulations and assumptions, we have the

following table of possible probability or “odds” ratios.

say for black persons. Usually. given the independence

assumption, theprobability ratio is broken upinto a series

of ratios. one for each agreement or disagreement, and

Iogs are taken (tothebase 2). One is now working with

simple sums, such that the larger (more positive) the

total, the more Iikelythat thepair isamatch; conversely,

the more negative the sum, the greater the likelihood

that the two records are not for the samS person.

Base 2

Probability log of

Outcome ratio ratio

Race and sex agree:

Race is black . . . . . . . . . . 167.7369 7.3901

Race is nonblack . . . . . . 2.4569 1.2980

Race agrees, sex does not:

Race ls black . . . . . . . . . .

Race is nonblack . .

0.3358 – 1.5743

0.0049 – 7.6730

Sex agreea, race does not . , 0.2051 - 2.2656

Neither agrees . . . . . . . . . 0.0004 -11.2877

NOTE See computational note a! end of paper,

In our particular example,, it is only when both sex and

race agree that the sum of the logs is positive. If the

race is black, the log is between + 7 and +8, moderately

strong evidence in favor of a match. If the race is nonblack,

however, the log is only slightly more than + 1. As

one would expect, the strongest evidence in favor of

a nonmatch occurs when both race and sex disagree;

for this outcome, the log of the probability is about – 13.

(Parenthetically, it might be noted that this example illustrates nicely the fact that outcomes that are frequent in

the population do not add very much to one’s ability

to decide if the pair should be treated as a link; but

if there are disagreements on such variables and reporting

is reasonably accurate, then the variable may have a great

deal of power in identifying comparison pairs that represent nonlinks. )

Now it can be shown in general, as by Fellegi and Sunter

( 1969) or by Kirkendall (1985), that we can divide the weight

distribution into three parts, as seen in figure 1. The points

%“ and “b” optimally divide the distribution of weights so

thtit we can simultaneously minimize the error of accepting

as a positive link cases that we should not have matched,

plus minimize the error of rejecting as nonlinks cases that

we should have kept, Assumptions such as independence must

be made, as a rule, and formidable computational problems

exist. Nonetheless, the approach is entirely workable, especially since the development of the Generalized Iterative

Record Linkage System (GIRLS), which provides a state-ofthe-art solution to the major computational problems (Howe

find Lindsay, 1981). Other notable approaches in advanced

linkit.gesoftware include the work of Jaro and his collaborators

(Jaro, 1985).

I

Links A

Indeterminate

A

Nonlinks

I

a b

I

+ 4 Linkage Weights > —

SOURCE Adapted from Felleg!. 1985

NOTE: Comparison paira above the fine are matched (M). those below unmatched {u)

I

Fgure 1. Hypothetical distribution of linkage weighta

Indeterminate outcomes-VirtuaIly all computerized

record linkage schemes may leave at least some cases where

the status is indeterminate. Three kinds of indeterminacy might

be distinguished:

● Nonlinks-Cases that were “definitely” determined by the

method to have no suitable match, given the approach

taken, but which ,might have been matched if another

technique had been used (e.g., if we had employed a

different set of blocking variables). The difficulty here

is that, while all the potential links that get looked at

may have proved inadequate, not all poss~ble links are

examined, and we cannot necessarily telI the difference

between a case that should have been a link and one

that should not. The only way this issue can be dealt

with is in the implausible situation when the probability

of a match between blocks is zero. (An indirect “solution”

to this problem can be developed using contingency table

ideas, as will be discussed later. )

● Multiple links—These can occur in the Feliegi-Sunter formulation; that is, there may be more than one comparison

pair for a unit whose match weight or score exceeded

the threshold for acceptance. In some cases, these manyto-one links might be appropriate, but usually a further

step has to be taken to select “the best” one. This problem

also can occur with some frequency in administrative

contexts and with the National Death Index. Manual resolution is usually the approach taken, especially if further

information is going to be sought or is available to help

make the selection. Jaro ( 1985) offers a computerized

transportation algorithm to solve” multiple linkage problems. His approach is most effective when all the linking

information has already been computerized and when there

are contention problems in the linkages, that is, n records

on one file are matching m records on another. Smith

and Scheuren (1985a) suggest ways of carrying through

the statistical analysis using all the links.

. Potential links—This type may be the largest form of

81
indeterminacy. These arc the cuscs that fall in the middle

mea in figure 1. The usual advice. resources permitting,

is tt~collect more information to resolve the match status.

!f statistictil ~stilna[es arc to be made and the resources

nmhxl to seek further information are not available, the

potential links may be treated M norrlinks and a surveytype mmrcsponse adjustment mi.ry be made (Scheuren.

1980). It is possible. 01s0. to consider keeping some

of the potential links tind then conducting the analysis.

with an adjustment being made for mismatching (Scheuren

and Oh. 1.975).

Often. the difhdty with indeterminate cases can be traced

back to a design tlaw in the data linkage system. For example,

not enough linking information may have been obtained on

one or both files to assure uniqueness. Maybe the degree

of redundancy in the identifiers was insufficient to compensate

completely for the reporting errors. In an administrative context, the linkage process may be so constrained for operational

reasons that. even if there are sufficient linkage items, they

cannot be brought fully to bear.

Analysis issues

Statements about the nature of the matching errors are

typically provided in data linkage studies; generally, however,

there is no real attempt to quaw@ the implications of matching

errors for the specific inferences being drawn. Data linkage

systems. like other survey-based or sample-based techniques,

need to be “measurdb]e” and to be structured to be as robust

as possible in the Face of departures from underlying assumptions. What can be done to achieve this is a separate and

sizable subject (Smith and Scheuren, 1985a). For our present

purposes. it may be enough to sketch some of the issues

and indicate general lines of attack.

. .Linkuge d~~(’u/ne/~rt/(i~~tJ—Documentation should routinely

be provided which tabulates the results of the match effort

along dimensions that turned out to be important in the

analysis. A distribution of the weights would be one

example, perhaps shown for major subgroups. If a public

use file is being created. then the match weight might

be placed in the file along with summary agreement codes

so that secondary analysts can “second-guess” some of

the decisions made. Providing potential links, at least

near the cutoff point. is another example of good practice.

Most of the foregoing information, by the way. was part

of the documentation and computer files made available

from the 1973 Exact Match Study (Aziz et al., 1978).

. Adjustitlg for tu)tditdx-It is generally worthwhile to consider reweighing the linked record pairs actually obvained

to adjust for Failures to completely link all the proper

records to each other (Scheuren, 1980). Conventional

nonresponse procedures can be followed (Oh and

Scheuren. 1983). Imputation strategies are also possible,

but may be less desirable because they tend to disturb

the estimated relationships across the two files being

brought together (Oh and Scheuren, 1980; Rodgers, 1984).

An important problem in this adjustment process. however

conducted. is in being able to estimate whether a link

should have occurred. Sometimes, by the nature of the

problem, we know that all the records should have been

linked. In other cases (Rogot et al., 1983), one of the

key things we are interested in is, in fact, the linkage

rate. Elsewhere (Scheuren, 1983; Smith and Scheuren,

1985a), we have advocated a capture-recapture approach

to this estimation problem. Such an approach, in the

presence of blocking, will actually allow us to improve

the links obtained, as well as make it possible to measure

the extent to which our best efforts still lead to erroneous

nonlinks. Capture-recapture ideas are well described in

the Iitemture (e.g., Bishop et al., 1975: Marks et al.,

1974). Here we will only indicate the application.

If we employ more than one set of blocks and, for each

blocking procedure, keep track of whether we would have

found (and linked) the case in every other blocking

scheme, then for any subpopulation of linked records

we can construct the usual 2“ table, where we look at

the link/nonlink status for each blocking (with /Z being

the number of separate blocking schemes). To estimate

the number of records not caught by any scheme, three

or more sets of blocks are recommended; otherwise, the

assumptions made may be unrealistically strong. (The

National Death Index, or NDI, already employs many

more than this, as we have noted earl ier. ) For best results

the blocks need to be as independent functionally and

statistically as is possible, given the linkage information.

(Improvements in the current NDI would be recommended

here. but these seem to be coming in any case. ) Application

of these ideas in an IRS or SSA context seems w(mthy

of study (Scheuren, 1983). although the expense of developing such an approach. say at SSA. may never bc

incurred unless there were a compelling administrative

need.

. Adjustingfor mismatches-In most linkage systems, practitioners have operated in what they considered to be

a conservative manner with regard to the links they would

accept. Sometimes this may have meant heavy additional

expense in obtaining more information or the risk of

seriously biasing results by leaving out a large number

of the potential links. In any event, further research is

needed on how to apply more complex analytic techniques

that take explicit account of the mismatch rate, possibly

by use of errors-in-variable approaches where the mismatch rate is estimated, e.g., as in Scheuren and Oh

(1975), so that a correction factor can be derived. We

must also attempt to find ways of estimating the mismatch

rate that make weaker assumptions than those made in

most Fellegi-Sunter applications. (Some further ideas on

this are found in Smith and Scheuren. 1985a. )

In summary, the main issues in the analysis of linked

data sets are that, at a minimum, we need to examine the

sensitivity of the results to the assumptions made in the linkage

process. Where possible, we need to quantify uncertainties

in the results; specifically, indeterminacies in the linkages

should translate into wider confidence intervals in the estimates. To achieve these goals we need to bring in techniques

from other areas of statistics and apply them creatively to

82
linked data sets. Examples here include information theory,

wror-in-vtiriable approaches, and contingency table (capturerecapturc) ideas.

3. Some conclusions and areas

for future study

In this paper we have dealt with the topic of data linkage

in a broad conceptual framework, using examples from recent

practice. It is appropriate now to draw out the implications

of the point of view expressed for studies of aging and to

use that summary as a basis for recommending further research.

Overall perspective

We have argued elsewhere that the potential for the s~atistiCU1use of data linkage systems is truly enormous (e. g., Kilss

and Scheurcn, 1980; Jabine and Scheuren, 1985). The suggestion htis even been made that data linkages among administrative records (with some supplementation) might eventually

replace conventional censuses in the United States (Alvey

and Scheuren, 1982). Such ideas are not new, certainly not

in Europe, where many developed nations have been rapidly

moving in this direction (e. g., Redfern, 1983). Indeed some

countries, like Denmark (Jensen, 1983), may have “already

arrived .“

In the United States there has been some reluctance and

resisttmce to accepting the inevitability of such a future. Grave

concerns have been expressed (Butz, 1985) about moving

too thst or in the wrong way, After all, while Denmark has

succeeded in its efforts, other countries (notably West Germany) have encountered major problems which did grave

damage to their statistical programs.

In view of what has happened elsewhere and, especially,

given the current state of public opinion, we would caution

that any planned use of data linkage systems be grounded

firmly in existing practice and not be based on new legislation

designed to expand on what it is currently possible to do.

On the other hand, it is important to conceptually integrate

whtit is now possible with what might be possible 10 or

20 years from now, Some further observations are—

●

●

●

First, if a data linkage approach is going to be taken,

it should be a necessary means, not just a sufficient one,

for achieving some required specific purpose. It is simply

not enough to argue the need for data linkage on efficiency

grounds.

Second, the linkage should be seen as important by all

the cooperating agencies and part of their mission, It

is simply not enough that the law can be interpreted

to permit such linkages. Positive law, and indeed social

custom, must exist which encourages the research, at

least in broad outline (Cox and Boruch, 1985).

Third, strong continuing user support is essential if a

long-term basic research effort is to be successful. Program

agencies cannot be relied on for really long-run undertakings without this support. Opportunity costs are simply

●

●

too high. If the linkage system is to be placed in a statistical

agency, user involvement is, again, essential (from the

outset, if possible). Without strong user involvement,

statistical agencies will tend to emphasize continuity of

measurement over relevance (while program agencies tend

to the reverse).

Fourth, cost considerations suggest that most data linkage

systems should be based on, or augment, an existing

survey or administrative system. Further, maintenance

costs should be low so that in the long run most of

the resources can be focused on exploiting the analytic

potential of the system.

Fifth, access to the results of the linkage system must

be basically open not only to the primary user(s), but

to seconda~ users as well. Ways to solve the “reidentification” problem must be built into the undertaking from

the beginning and firmly rooted in the best statistical

practice.

Still other considerations come to mind, such as adequate

physical security during the linkage operation and minimizing

the risks by removing identifiers from working files as soon

as possible (Kilss and Scheuren, 1978; Steinberg and Pritzker,

1967; Cox and Boruch, 1985; and Flaherty, 1978).

Many ad hoc efforts have succeeded without strictly adhering to one or more of these points. Nonetheless, if one is

working toward a future which encompasses still more data

linkages, it is essential that the strategy taken be absolutely

sound and above reasonable reproach.

Potential data systems deserving further study

Within the framework just given, there seems to be a

clear need to intensively examine the potential of particular

data :inkage systems to answer certain questions. We will

illustrate this point by looking at one of the most pressing

areas in the United States where better data are needed—this

is on our rapidly growing aged population. Even if we confine

ourselves to this single area, many subsidiary issues must

be addressed. For example, where are the greatest gaps: in

data on health, general demographic information, financial

data, or the extent to which Federal programs provide support?

In what follows, there has been no attempt to answer this

question. To do so, we would go well beyond the scope

of the present paper. Instead, there is a discussion of four

data linkage environments that, depending on the answer to

the question, may warrant further study. Special emphasis

has been placed on the limitations of working in each of

these settings and of the role that a strong outside user might

play in overcoming those limitations.

Sociul Securip and Health Care Financing Administra-

[ions—The Social Security and Health Care Financing Administrations are unlikely to take the lead in building and maintaining general purpose statistical data linkage systems, in part

because of a reduced emphasis on basic and applied research.

Nevertheless, the program-oriented statistical activities of these

agencies will continue to give them an important role in data

linkage efforts which are consistent with agency missions.

The potential at SSA and HCFA for providing improved

83
sources of statistics on the aging population depends on the

extent to which they are able to: (1) maintain major in-house

data linkage efforts. like the Continuous Work History Sample

(e.g.. Buckler and Smith. 1980) and the Medicare Statistical

System (Health Care Financing Administration, 1983):

(~) continue to sponsor or cosponsor periodic or ad hoc surveys:

and (3) cooperate in linkage studies sponsored elsewhere (for

example, in the Survey of Income and Program Participation

or in the Health Interview Survey) if they are in support

of the agencies’ missions.

However, these efforts would need to be coupled with

strong outside user support. At SSA and HCFA. there may

be a particularly pressing need for outside users to aid in

the resumption of some form of public release of subsets,

at least, of the administrative samples now being employed

almost solely for in-house purposes.

lnw-nal Resenue Sert’ice—It seems pointless to speculate

upon the degree to which interagency data linkages involving

Internal Revenue Service data can or should take place. Formidable statutory barriers narrowly limit access to tax records

and, even when the legal requirements can be met. many

other agencies, notably the Census Bureau, feel they would

be unable to engage in a cooperative study because of concerns

about public perception. American social customs, particularly

concerns about “Big Brother,” stand as nearly insurmountable

obstacles in the shod run.

It is possible, though, to use IRS records essentially all

by themselves as a basis for studying the aged population.

This may seem surprising because the statistical program of

the Internal Revenue Service is not looked at typically as

a source of such information. Certainly the Statistics of Income

publication series has focused very little on the aged, and

then mainly through the use of the age exemption to identify,

taxpayers 65 years or older (e.g., Holik and Kozielec, 1984).

Broader based research has been possible through occasional

linkages between the IRS’s Individual Income Tax Model

File and Social Security information. In a few cases, these

linkages have resulted in public use files (DelBene, 1979).

What has not been done is to look at the aging population

longitudinally, although this is fairly straightforward, at least

back to 1972. Furthermore, with the recent addition of complete SSA year-of-birth information to IRS files, it will. be

possible to routinely study age cohorts by means other than

the age exemption. It is also worthy of mention that linkages

between IRS files and the recently instituted National Death

Index have just been successfully instituted (Bentz, 1985)1

Tax returns probably represent the single best source of

financial information and could, therefore, prove of value

in studying the aging process. There are, however, three main

limitations to their use:

. First, “the income data, while of exceedingly high quality

(relative to surveys), are incomplete since certain nontaxable incomes. have been omitted (e.g., tax-exempt bond

interest and welfare payments). Until recently, data on

social security benefits were unavailable, but they are

now potentially taxable (beginning with 1984).

. Second, the population coverage of income tax returns

is incomplete. In fact, only about one-half of the population ages 65 years or older show up as taxpayers on

income tax returns. Again, recent changes have a bearing

here, since information documents, notably Forms 1099

from Social Security, are filed with the Internal Revenue

Sewice for all social security beneficiaries. This change

permits an expanded population concept that could be

essentially complete for the aged population.

. Third, the tax return is exceedingly awkward as a unit

of analysis for some purposes since it does not always

conform to conventional family and household concepts

(Irwin and Herriot, 1982). It is possible, though. using

information documents like Forms W-2 (for wages),

Forms W-2P (for private pensions), and Forms 1099 (for

social security payments, dividend. interest, etc. ), to

develop approximate financial profiles of virtually all individuals aged 65 or older. (Major gaps would exist, of

course, for Supplemental Security Income recipients and

recipients of veterans’ disability benefits. ) There does

not appear to be much hope for inferring changes in

lifestyles directly from the current IRS information, although the proposed addition of dependent social security

numbers could lead to real progress (Alvey and Scheuren,

1982).

Depending on its extent, the cost of maintaining an IRS

data linkage system to study aging could be quite modest.

Public-use files are possible; but, as with the Social Security

and Health Care Financing Administrations, strong outside

support would be needed.

Ndiond Center fi)r Health Statistics-Recent changes

(Sirken and Greenberg, 1983) at the Natiomd Center for Heidth

Swtistics suggest that the Center may be assuming a leading

role in sponsoring data linkage systems. Ntiturully and appropriately, the focus of these systems will be quite niu’row,

looking almost solely at health concerns. The National Health

Interview Survey (NHIS ). involving about 40.000 households

annually, appears to be the Center’s main survey vehicle

for the approach it is planning to take. Continued periodic

matching to Medicare records seems planned (Cox and Folsom,

1984) a’nd,of course. the National Death Index can be expected

to be fully exploited (Patterson and Bilgmd. 1985). Still other

linkage efforts are underway (e.g.. Johnston et al.. 1984)

which. taken together. suggest that the Center is pursuing

a coherent. fully integrated approach. both among its surveys

and toward needed vital record systems.

When the social security number’ questi(m wus added

to NHIS a few years ago. it was largely for mtitching to

the National Death !ndex. Great care, initially wtis given to

securing int’ormed consent from respondents Iwjiwt] obtaining

the information. This approach proved tedious and expensive.

Now the social security number question is simply asked

without much explanation; and only if requested are reasons

given for why the information needs to be obttiinecl. (See

appendix C.) Response rates are quite high. tibout 90 percent.

and it appears that NHIS may constitute a major vehicle

for a successful data linkage approach to Wdying aging.

Concerns exist about the reidentification problem. but exactly

how the Center will deal with this factor is unclear.

Bureau of the Census—Historically, the Census Bureau

84
hus pltiyed a major role in Federal data linkage systems involving surveys, sometimes as the sole sponsor (e.g., Chiiders

and Hogan, 1984), but often as a partner in conducting a

ptwticukir study, e.g., as with Social Security (Bixby, 1970).

Much of this work has focused on the Current Population

Survey (Kilss and Scheuren, 1978). Of more promise in future

studies of aging has been the development of the Survey

of Income and Program Participation (SIPP), which has as

one of its design elements the notion that data linkages would

be attempted, at least to Social Security information (Kasprzyk,

1983). SIPP, which may settle down to a sample size of

about 30,000 households annually, is certainly of sufficient

size and scope to look at many general demographic, financial,

and program-related questions concerning aging, The SSN

reporting rate is on the order of 90 percent; hence, the needed

resources to “perfect” the linkage (and the analysis problems

resulting from faulty or incomplete linkage) should be entirely

mw-wgcable,Oversampling is possible for particular subgroups

(e.g., t’hose aged 65 or older); however. unfortunately, SIPP.

like the NH IS. is confined to the noninstitutionalized population, ml for studies of the very old. it may not be suitable

ahme.

TwtJ difficulties exist with SIPP that further research may

rmdvc. First is the extent to which informed consent is being

uhtaineci when the social security number is being secured.

(SIPP’S approach is similar to that in NHIS: see appendix

D.) Related to this concern, of course. is the extent to which

such ctmsent is felt to be needed, The second issue, and

one that seems exceedingly troublesome to the Census Bureau.

is the “reidentification” problem. (Brietly stated, the reidentificutitm prl~hlem is particularly acute where linkage is concerned,

bcctitrw the cooperating agencies might have enough data

on the linked file to reidentify virtually all of the individuals

linked. )

The Census Bureau appears to be searching for a solution

thtit involves either simply not releasing public use files of

linked data or releasing public use files where only very

limited linked data have been provided and some kind of

masking technique has been employed to prevent reidentification, Given these restrictions, it must be said, there seem

to be real difficulties in concluding that there are sufficient

benefits to outside users of a SIPP-based data linkage system.

Some further comments on this dilemma and ways a general

research program could address it are given next.

General issues deserving further study

Further research is needed on a wide range of data linkage

issues, both structural and technical. Four, in particular, stand

out from the rest and deserve special attention: ethical and

legal concerns, public perception questions, finding solutions

to the reidentification problem, and finally, analysis issues

in the presence of matching errors.

Ethical concerns such as those raised by Gastwirth ( 1986)

secm to need a more specific answer than they have been

given so far (as by Dalenius, 1983). What might be done

is to obtain some data directly bearing on how respondents

actually think about data linkage. We could approach this

in a way similar to the earlier study by the Committee on

National Statistics concerning confidentiality guarantees (Committee on National Statistics. 1979). Within the context of

current survey efforts in NHIS and SIPP. it might be extremely

valuable to know how often respondents ask for clarification

before providing social security numbers and to code the

cases accordingly so we can look at differential refusal rates,

for example. Again, exactly what is said typically (by respondents and interviewers) when respondents do ask? Legal and

procedural issues abound here, too. For example, how long,

even assuming informed consent, can the consent be treated

as binding? Social Security practices with outside researchers

(when they obtain consent to gain access to individual records)

is to treat the consent as binding potentially only once; thus,

requests for information on the same subjects may require

a renewal of the consent. Signed consent agreements are also

required of outside researchers. Such a requirement has never

been imposed, say, in Census Bureau surveys, but should

it be? If it were, what would be the costs of such a practice

in interview time, reduced response, and cooperation

generally?

Public perception concerns deserve to be examined in

depth. To what extent are we already violating the public’s

sense of the social customs within which statisticians are

supposed to work? The public opinion polling results reported

in Gonzalez and Scheuren (1985) need to be followed up.

It does not seem defensible simply to speculate about whether

this or that approach to data linkage would be acceptable

to the public. While we can never use opinion polling to

answer all the many specific issues that exist here, much

can be done. Of particular interest may be the extent to which

the public knows or assumes that such linkages take place

now and for what purposes; the perceived legitimacy of actual

and perceived purposes; whether statutory or contractual prohibitions against efforts at reidentification would be seen to

be adequate; and so on.

We do not believe that an entirely satisfactory technical

solution to the reidentification problem is possible; but a great

deal more can be done to allow for at least limited release

of linked information. The work of Paass (1985) and Smith

and Scheuren (1985a) is suggestive here. The line of attack

that appears most promising is what might be termed a threestep process. First, “slice” the data up into small enough

bits so that each of the “bits” can be adequately masked.

(The data, for example, might be divided up into disjoint

subsets and for each subset of observations, say, only 2 to

4 different items of administrative data would be provided.)

Second, if the slices are chosen appropriately, then one can

“splice” back together the complete data set using statistical

matching; but in a setting where the conventional—and usually

false conditional—independence assumption (e.g., Rodgers,

1984) does not have to be made. Finally, the masking step

can add “noise” to the data set in such a way that either

certain analytic results are invariant under the noise transformation or correction factors can be calculated and readily applied.

There are some serious losses in this approach. For example, the effective sample size of the linked data items may

have shrunk considerably. In any case, more research on

this problem is definitely warranted (maybe even if contractual

and legal solutions turn out to be eventually possible). Either

85
way, public access to the linked data sets must be seen as

a key objective when such studies are undertaken and, to

the extent possible, release practices should be as open as

with any other data set (Committee on National Statistics,

1985).

Finally, a number of analysis issues have been mentioned

which deserve further research, especially in measuring matching errors and adjusting the matched results accordhgly. In

particular, we need to find a way to escape the historical

dilemma that the dissemination and growth of sound theory

and practice have been retarded by the perceived uniqueness

of many linkage problems (and the customized solutions this

perception has led to). The profound nature of the common

sense principles upon which good practice is based are not

widely enough appreciated. Insufficient attention has been

paid to the analysis issues in data linkage systems, perhaps

because so much creative energy and financial resources typically go into the linkage steps (Smith and Scheuren, 1985a).

It maybe too optimistic to suppose that things are now changing, but there is some evidence to this effect in the success

of the 1985 Washington Statistical Society Workshop on Exact

Matching Methodologies (Kilss and Alvey, 1985). In any

case, it is time to stop treating matching as a necessary but

dirty business, isolated from other parts of statistical theory

and practice.

Acknowledgments and

afterwords

The ideas in this paper owe much to my associations

with other professionals in the field of matching. Particular

thanks are due to Dan Kasprzyk for his useful remarks, and,

especially, Tom Jabine, whose insightful comments were much

appreciated, even though I was unable to incorporate them

all in the present version. Tom also acted as a discussant

when this paper was originally given and, among other things,

corrected a computational error in the calculation of the probability ratios shown in the example. All the remaining errors

are, of course, my responsibility.

Computational note

The probability ratios shown in the table were calculated

as follows:

Race andsexugree (ruce is black)

[(.99 )~+(.ol )~][(.999)~+(.ool)2]/[(.l)~( .99)~+(.9)~(.ol)~+

2(. 1)(.9)(.99) (.01 )][.5] = 167.7369

Race und se.ragree (race is nonblack)

[(.99)~+(.ol )7[(.999)~+ (.ool)7/[(.9)?.99 )~+

(. I)Z(.O1)Z+2(.9)(. 1)(.99)( .01)[.51 = 2.4589

Race agrees, sex does not (race is black)

[(.99)z+(.01)2][2(.999) (.OOl)]/[(.l )2(.99)z+(.9)z(.01 )2+

2(.1) (.9)( .99)( .01)][.5] = 0.3358

Race agrees, sex does not (race is nonblack)

[(.99)2+(. 01)21[2(.999)(.001)]/[(.9)2(.99 )z+(.l)2(.01 )2+

2(.9)(. 1)(.99)( .01)][.5] = 0.0049

Sex agrees, race does not

[2(.99)(.01 )][(.999)z+(.001 )z]/[2(. 1)7.99)(.01 )+

2(.9)Z(.99)(.01 )+2(.9 )(.1 )(.992 +.012 )][.5] = 0.2051

Neither agrees

[2(.99)(.01 )][2(.999)(.001 )]/[2(.I)Z(.99)(.01 )+

~(.9)7.99)(.01) +~(.9)(.1)(.99~+ .017][.5] = ().()()04

86

/’
References

Alcwmdcr. 1,.: There Ought to bc a L~w..., in Ptvceedings, Section

(JH SUtqqYRe,wcw/t MLI//IOA.American Stat istical Association, I %$3.

Alcximdcr. L., and T. Jabinc: Access to Social Security micmdata

film for rwxu-ch and statistical purposes, An overview. Sot. .%cur.

Bull. , I 97x.

Alvcy. W.. and F. Azir,: Mortality reporting in SSA Iinkcd data,

Prclimimwy results. SW. SecMr. Bu//., I 979.

Alvcy. W., wrd F. Schcurcn: Background for an Administrative

Record Census, in Ptvceedin,qs,Soci(ii .$tatistic.s.Wtion. American

Statistical Associtition, 1WC!.

A/i/. F.. ct al.: .$mdies From lmer~Igen(;v Data Linkages. Report

No. X. U.S. !+>citslSecurity Administration, 1978.

B;umm, E.: The Survey of Low-1’ncom Aged and Disabled, Survey

design ;md data systcm, Po/icy Ana/ysi.y Wi/h .$ocial Securify Reseorch

Fi/rs. U.S. Social Security Administration. 197X.

RAw. G.: Why m-c cpiclcmi;logists intcrcstcd in matching alg~~rithm~’~R~’c~~rd~.ink~l,w)T~’(’/lllif//l(s.1989f.f. U.S. InternalRcvcnuc

Scrvicc. I 9X5.

Bent/, M.: The lntcrgcncr~tional Wealth Study, Pmspccts for Data

Amdysis and Mcthodologicul Research. Prcscntcd at the CwraditmU.S. Symposium on Ttix Modclling. Sept. 19X5.

Bishop. Y.. ct al.: Discrete Multitwria!e Analysis: Theory aml Prac-

/i(v. Cwnbridgc. MIT Press. 1975.

13itby. 1..: [rwomc of people aged 6s or older. Overview from

the 196X Survey of the Agctl. Sot. .Wcnr. fJn/1., 1970.

flud-.lcr, W.. and C. Smith: The Continuous Work History .%nplc

(CWHS ). Description tsnd contents. Economic (md l)emo,qtwphic

Sltl/i.\/i[.\. U.S. Social Security Administration, 1980.

But/. W.: The futtrw of administmtivc records in the Census Burcmt’s

demographic uctivitics. .lomwd of~n.~iness md Economic S/a/is/its,

10X5

Cartwright. D.: Major limitations of CWHS files wrd pmspccts for

improvmncnt. Poli(y An[(lwi.s WiIil Sociol Security Rtl.uwrch Files.

U.S. Social Security Administration. 197X.

Chil&rs, D., and H. Hogtin: Mtitching IRS Records to Census Records, Some Problems ond RcsuIts, in Proceedings, Sw!i(m on Survey

R~’st’(Ir(”/Itl~c/h(x/.Y.American statistical Association, I~XQ.

Clark. C.. and J. CotYcy: How Many Pcrrplc Can Keep a Sccrct?

Statistical Data Exchange Within a Dcccntrtrlizcd Systcm, in Procecdin,~.s, ,Sw/ion on .’illrl’t~y/?esr({rc/] Mwhods. Amcrictm Sttttistictil

Association, 19X3.

Coldcigh, C.. and W. Alvcy: Validtiting the social security number.

Stm/iu,\ From /nter[{,qenc,v DW(I Link(~ges. Rcpm-t No. 4. U .S. Social

Swwrity Administration. 1975.

Committw on National Statistics: Priiw;v and C[>t!/j{/t~)lti(:/ity<IS

Fw’totx in .Snrlyy Response, Nistiomd Academy of Scicnccs, 1979.

Committct on National Statistics: Sh~wintq Re.wwch Lwa. National

Ac;dcmy d’ Scim-wcs, 19X5.

Cok. B.. and R. Folsom: Evaluation of Altcrmttc Designs tok a

Future NM CUES. in Proceedin,qs, Section on Snr\s(,J Resewch

MIY/IodY. Anwricwr Statistical Association. 19X4.

Crrx. L., and R. Boruch: Emcr.ging Policy lSSUCSin Record Linkage

m-tdPrivacy. Prcscntcd at the 45th Session of the Intcmational Statistical Institute, 1985.

Crrx, L., ct al.: Confidentiality Issues at the Census Bureau, in

Proceedin,y.s of the First Antwd Census Bureau Reseurch Conference.

U.S. Bureau of the Census, i 985.

Crmc. J., and D. Klcwcno: Project LINK-LINK, An interactive

ddtabasc of administrative record Iinkagc studies. Record Linkage

T~~c/?tliq{((~.s-/985. U.S. Internal Rcvcnuc Service, 1985.

Dalcnius, T.: Informed Consent or R.S. V. P., in tncornplete Du/a

in Sompk .!jnrreys. Vrrl. 1. Academic Press, 1983.

Dcl Bcnc, L.: 1972 Augmented Individual Income Tax Model Exact

Match File. S\ttdies From Interagency DUIU Linku,qes. Report

No. 9. U.S. Social Security Administration, 1979.

Fcllcgi, 1.: Tutorial on the Fcllcgi-Suntcr model for record Iinkagc.

Record Linkqy Tt~(/itliq[fe.s-/985. U. S. Internal Revenue Service,

1985.

Fcllcgi, 1., and A. Suntc~ A theory of record linkage. Jmowd

oj’ihe Anrericwl Stati.stictd Associ~[tion 64: I I83-1210, 1969.

Flahcrty, D.: The Bcllagio Confcrcncc on Privacy, Confidentiality

and the Usc of Government M icrodata, in New Direc~ions in Program

Ev(dm:tion, Vol. 4, 1978.

Gastwirth. J.: Discussion comments to paper by George Duncan

and Diane Ldmbcrt, A Model for Statistical Disclosure Control Based

on Prcdictivc Distributions and Uncertainty Functions. .fowtud of

the American Sta!isticd Associtui on,1986.

Gonzalez. M., and F. Schcurcn: Future Work by the Conference

of European s.~atisticians on Population and Housing Censuses. Presented before the Thirty-Third Plenary Session of the U.N. Confercncc of European Statisticians, 1985.

Health Care Financing Administration: Medicare Statisiicul Fi/es

Mom{(d, 1983.

Holik. D., and J. Koziclcc: Taxpayers agc 65 or older, 1977-81.

Statistics of/ncon?e Bul/e[in. U.S. Internal Revenue Service, 1984.

Howe, G., and J. Lindsay: A Gcncralizcd Iterative Record Linkage

Computer Systcm for Usc in Medical Follow-up Studies, in Compl{ter

and Biwnedicd Research, Vol. 14, 1981.

Irclan. L.. and W. Fincgzm Surveys relating to rctircmcnt and survivors ip. Polic;v Analv.sis With Social Secwil~ Reseurch Files. U.S.

Social Security Administration, 1978.

Irwin, R.. und R. Hcrriot: An Initial Look at Preparing Local Estimates of Household Size from Income Tax Returns, in Proceedings,

See/ion on .s((rlit~yResewch MeIh(xLs. American Statistical Association. 1982.

Jabinc. T.: Properties of the social security number relevant to its

usc in record linkages. Record Linl.w,qe T<,(.}ltziqlle.r—1985. U.S.

Internal Rcvcnuc Scrvicc. 1985.

Jabinc. T.. and F. Schcurcn: Goals for statistical uscsof administrative

records. The next tcn years. Jontwl (j” Business and Economic

SIalistics. 19X5.
Jaro, M.: UNIMATCH—A Computer Systcm for Generalized Record

Linkage Under Conditions of Uncertainty, in AF/PS-Conference Proceedings. 1972.

Jaro, M.: Current record Iinkagc research, Record Linkage Techniques-1985. U.S.lntcrnal Rcvenuc Scrvicc, 1985.

Jensen, P.: Towards a register-based statistical systcm—some Danish

cxpcricncc. Statistical Journal ofthe United Nations 1:341-365,

1983.

Johnston, D., ct al.: 1980 AHAHospital and National Natality/Fetal

Mortality Survey Linkage Methodology, in Proceedings, See/ion

on Survey Re.~earckMe/h~~d.s.American Statistical Association, 1984.

Kasprxyk, D.: Social Security Number Reporting, the Usc of Administrative Records and the Multiple Frame Design in the Income

Survey Dcvclopmcnt Program. in Technico/, Concep/uu/ ut]d Adnzinistrative Le.wons of the Income Survey De}’elopmenl Progrton. Ncw

York. Social Scicncc Research Council, 1983.

Kcllcy, R.: Advances in rccm-d Iinkagc methodology, A method

for determining the best blocking strategy. Record Linka#e Techniques-/985. U.S. Internal Rcvcnuc Scrvicc, 1985.

Kcstcnbaum, B.:Thc mcasurcmcnt ofcarly rctircmcnt. Journa/f#

the Americun Stutistictd Association 80:38-45, 1985.

Kilss, B., and W. Alvcy, cds.:Record Linkuge Technique.~-1985.

U.S. Internal Rcvcnuc Scrvicc, 1985.

Kilss, B., and F. Schcurcn:Thc 1973 CPS-IRS-SSA Exact Match

Study. Sot. Secur. Bull., 1978.

Kiiss, B., and F. Schcurcn: Goals and Plans for a Linked Administrativc Statistical Sample, in Proceedings, Sec/iotr on Survey Research

Me/hods. American Statistical Assocaticm, 1980.

Kirkcndall, N.: Weights in computer matching: Applications and

an information theoretic point of view. Record Linkage Teclrniques—

/985. U.S. Internal Rcvcnuc Scrvicc, 1985.

Klein, B., and D. Kasprzyk: Designing an Integrated Disability

Data System from Social Security Administrative Records, in Proceedings. See/ion on Surve.v Research Merhods. American Statistical

Association, 1983.

Maddox, G., G. Fillcnbaum, and L. Gcor.gc: Extending the uscs

ofthc LRHS’datasct. Po/icy Antdyvi sWithSociu lSecurityReseurch

Fi/es. U.S. Social Security Administration, 1978.

Marks, E., ct al.: Population Gro}tvh Estimutiotr, A Hundbook of

Vittd Statistics Measurement. Ncw York. The Population Council,

1974.

Ncwcombc, H., ct al.: Automatic Iinkagc of vital records. Science

130:954-959, 1959.

Oh, H. L., and F. Schcurcn: Differential Bias Impacts of Alternative

Census Bureau Hot Deck Proccdurcs for Imputing Missing CPS

Jncomc Data, in Proceedings, Section on Survey Reseurch Methods.

American Statistical Association, 1980.

Oh, H. L., and F. Schcurcn: Weighting Adjustments for Unit Nonrcsponsc, in Incomplete Datu in Sumple Surveys, Vol. 2. Panel on

Incomplctc Data, National Academy of Sciences, 1983.

Oh, H. L., and F. Schcurcn: Statistical Disclosure Avoidance. Presented before a May 1984 meeting of the Washington Statistical

Srrcicty.

Paass, G.: Disclosure Risk and Disclosure Avoidance for Microdata.

Prcscntcd at the May 1985 meetings of the International Association

for Social Scrvicc Information and Technology.

Pamcs, H., ct al.: From the Middle to Luter Yew-s: Longituditud

Studies of the Preretiretnent und Postretirernent Experiences of Men.

Ohio Sratc University, 1979.

Patterson, J., and R. Bilgrad: The National Death lndcx cxpcricnce.

1981–1985. Record Linkge Techniques-/ 985. U.S. Intcrrad Rcvcnuc Scrvicc, 1985.

President’s Rcor.ganization Project for the Federal Statistical Systcm:

Improving the Federal Statistical Systcm, Issues and options, Sktti.\rictd Reporter. 1981.

Rcdfcrn, P.: A Study of the Future of the Census of Populcili(m:

Alrernutitv Approaches. Commissioned by the Sttitistical Otllcc of

the Eumpcan Communities, 1983.

Rrrdgcrs, W.: An evaluation of statistical matching. Joutvud (f Bu.\iness turd Economic Statistics 2:91– 102, 1984.

Rogot, E., ct al.: The Usc of Probabilistic Methods in Mtitching

Census Samples to the National Death Index, in Proceetfin,qs, !ic(”tiou

on .!hr~’eyResearch Methods. American Statistical Association, 1983.

Schcurcn, F.: Methods of cstimaticm for the 1973 Exact Mtstch

Study. Studies From Interagency Dutu Linkuges. Rcprsrt No. 10.

U.S. Social Security Administration, 1980.

Schcurcn, F.: Design and Estimation for Large Fcdmal Surveys,-

Using Administrative Records, in Proceedings, Section on Surrey

Reseurch Methods. American Statistical Association, 1983.

Schcurcn, F., and R. Hcrriot: The role of the social security number

in matching administrative and survey records. Studies From /ntcragency Data Linkuges. Report No. 4. U.S. Social Security Administration, 1975.

Schcurcn, F., and H. L. Oh: Fiddling Around with Nonmatchcs

and Mismatches, in Proceedings, Social .Wutistics Sectirm. Amcrican

Statistical Association, 1975.

Secretary of Health, Education, and Welfare’s Advisory Committee:

Records, Computers and the Rights of Cilizens. U.S. Dcp~rtmcnt

of Health, Education and Welfare, 1973.

Sirken, M., and M. Greenberg: Redesign and Integration of a Pcrpulistion-Based Health Survey Program. Presented at 44th Session of

the International Statistical Institute, 1983.

Smith, M.: Development of a National Record Linkage Program

in Canada, in Proceedings, Section on Sur\’ey Reseurch Methoth.

American Statistical Association, 1982.

Smith, W., and F. Scheuren: Multiple Linkage and Measures of

Inexactness, Methodology Issues. Presented at the Workshop on

Exact Matching Methodologies, Arlington, Virginia, May 9-10,

1985a.

Smith, W., and F. Scheuren: Some New Methods in Stiitistical

Disclosure Avoidance. Presented at the 1985 Annual Meetings of

the American Statistical Association in a session sponsored by the

Section on Survey Research Methods, 1985b.

Steinberg, J., and L. Pritzkec Some experiences with and reflections

on data 1inkagc in the United States. Bu//etin of the /nternu/ionu/

StatisticulInstitute 42:786-805, 1967.

Storey, J.: Recent Changes in the A vuilubility of Federal DUIU on

?heAged. Report prepared for the Gerontological Society of America,

1985.

Tepping, B.: A model for optimum Iinkagc of records. Journu/

of(he Americtm Statistical Association 63:1321-1332, 1968.

U.S. Bureau of the Census: The Medicure Record Check: An EYUIUUtion of the Coveruge of Persons 65 Years of Age und O\wr in

the /970 Census. PHC(E)-7, 1973.

Wilson, O., and W. Smith: Access to Tax Records for Statistical

Purposes, in Proceedings, Section on .%-t’ey Reseurch Methods.

American Statistical Association, 1983.

Winklcr, W.: Preprocessing of lists and string comparison. Record

Linkage Techniques—/985. U.S. Internal Revenue !Xv-vice,19X5.

88
Appendix A

Supplemental bibliographic sources

In this paper we have cited some of the literature on

exact and statistical matching when the discussion warranted.

Further bibliographic material can be found in the following

publications:

Record LinkageTechniques-1985 (1985), U.S. Internal

Revenue Service. (Edited by Beth Kilss and Wendy

Alvey. ) Many of the citations in the present paper come

from this volume, which contains the proceedings of the

Workshop on Exact Matching Methodologies, held May

9–1 O, 1985, in Arlington, Virginia.

Statistical Working Paper Series ( 1977–85), Federal Committee on Statistical Methodology. (Produced under the

general editorial guidance of Maria Elena Gonzalez.) See

especially No. 5, on “Exact and Statistical Matching,”

and No. 6, on the “Statistical Uses of Administrative

Records.” Some of the publications in the series were

prepared by the U.S. Department of Commerce; more

redently the publications have been issued by the U.S.

Office of Management and Budget.

Statistics of Income and Related Administrative Record

Research (198 1–84), U.S. Internal Revenue Service.

(Edited by Beth KiLss and Wendy Alvey.) This annual

publication series contains numerous papers on record

linkage topics and is a successor to the Social Security

publications: Statistical Uses of Administrative Records

With Emphasis on Mortality and Disability Research

(1979) and Economic and Demographic Statistics (1980),

which also may be useful.

Statistical Uses of Adtnini.mratitse Records: Recent Research and Present Prospects (1984), U.S. Internal Revenue Service. (Edited by Thomas Jabine, Beth Kilss, and

Wendy Alvey, ) This handbook of recent work includes

many papers on data linkage, most of which are also

found in the series listed previously.

St[idies From Interagency Data Linkages (1973-80), U.S.

Social Security Administration. (Produced under the general editorial supervision of Fritz Scheuren. ) Of special

interest may be the bibliography by F. Scheuren and

W. Alvey ( 1975), “Selected Bibliography on the Matching

of Person Records From Different Sources.”” which will

be found in Report No. 4 in the series, pages 127-136.

. Policy Analysis ~tidl Social Security Research Files

( 1978). U.S. Social Security Adrninistmtion. (Edited by

Wendy Alvey and Fritz Scheuren. ) Most of the research

files described are based on data linkage methodologies.

. Accessing Itldii%idualRecords from Personal Data Using

Non-Unique ldentijiers, National Bureau of Standards.

NBS Special Publication 500-2.

Additional citations to the recent Iitemture on disclosure which

may be of value are given below. Some of these are of

interest as general background: others focus specifically on

disclosure barriers to data linkage.

Crank, S.: Evaluation of privacy and disclosure policy

in the Social Security Administration. .kial Security Bu/-

Ietin. U.S. Social Security Administration, 1985.

Dalenius. T.: Privacy and Confidentiality in Censuses

and Surveys, in Proceedings, Section on Sutnzy Re.wwrl+

Methods. American Statistical Association. 1985.

Hansen. M.: The Role and Feasibility of a National Data

Bank, Based on Matched Records and Alternatives. Federal Statistics. Report of the President’s Commission.

VOI. 11,1971.

.Spruill, N.: The Confidentiality and Analytic Usefulness

of Masked Business Microdata. in Proceedings, Section

on Sur\’ey Research Methods. American Statistical Association, [983.

Spruill. N.: Protecting Confidentiality oj’Busine.w Microdata by Masking. Alexandria. Va. The Public Research

Institute, 1984.

Young, P.: Legal anti Administrate\’e ltnpediments to the

Conduct of Epidemiolo,qic Re.yeat-ch. Washington, D.C.

Task Force on Environmental Cancer and Heart and Lung

Disease, 1984.

89
Appendix B

Taxpayer opinion question on sharing

Internal Revenue Service data

Yankelovich, Skelly and Whtie, Inc. (1984)

1984 General Pwpose Tax~yer Opinion Survey

60a. As you may know, the IRS has been required by law

to keep all of their records confidential. However, some

people feel the IRS should share this information with

other government departments in order to save money

and reduce bureaucratic waste since those departments

also need this information to do their work. Others feel

that the taxpayer’s right to privacy is more important.

For which, if any, of these departments or purposes

do you think it would be all right for the IRS to provide

information?

a. The Census Bureau . . . . . . . . . . . . 24%

b. Major criminal investigations (such as drugs

and orgdnized crime) . . . . . . . . . . . . 43%

c. Investigations of illegal aliens . . . . . . . 34%

d. Welfare fraud investigations . . . . . . . . 489o

e. Draft Boards or Selective Service . . . . . 17%

f. Other U.S. Federid departments . . . . . . 12%

g. State governments . . . . . . . . . . . . . I370

h. Child support investigations . . . . . . . . 38%

i. Freud and embezzlement investigations . . 43%

j. Other . . . . . . . . . . . . . . . . . . . . 1%

k. None (should keep records private) . . . . 31%

1. Don’t knowhm answer . . . . . . . . . . . 4%

90
Appendix C

Record matching information for

the National Health Interview Survey

1984 Supplement on Aging

Question 16

Read to respondent -In order to determine how health practices and conditions are related to how long people live, we would like

to refer to statistical records maintained by the National Center for Health Statistics.

UT 69

3 -T

,

)0. I have your date of birth as (birtfrdate from item 3 on HIS- 1 Househo/d I

5–11

Composition page). Is that correct? I Date of birth

.— 1 1

I Month Date Year

1

I

1

b. In what State or country were you born? 1

99 ODK 1...?2I I

I

Write in the full name of the State or mark the appropriate box if the I State

sample person was not born in the United States.

1

I 01 u Puerto Rico 05a Cuba

1

I 020 Virgin Islands 060 Mexico

1

r 030 Guam gaa All other countries

I

1 040 Canada

I

c. To verify tha spelling, what is your full name, including ILaat M

middle initial? I

1

I First Q

I

I

1

I Middle initial ~

I

I

I

Verify for males; ask for females, I IEk!z

d, What was your father’a LAST name?

I

I

Verify spelling. DO NOT write “Same.” 1

1.

Father’s LAST name

Read to respondent – We also need your Social Security Number. \

- ‘lEz

This information is voluntary and collected I

wewgasw ❑ D K

under the authority of the Public Haalth Sar- I

vice Act. There will be no effect on your

benefits and no information will be given to ~

any other government or nongovernment

agency.

,

Social

cm-m-mm

Security Number

f?ead if necessary – The Public Haalth Servica Act is title 42, j

United States Code, section 242k. I Mark if number obtained from —————+I m Memory

E

. . What is your Social Security Number? I 2 ❑ Records

I

Instructions 3. Enter the full State name on the line in 16b; do not

1. Read the introductory statement above item 16 to explain

the purpose of obtaining the information.

2,’ When iisking 16a, insert the birth date from the HIS-1,

Household Composition Page. If the birth date recorded

in the HIS- I is in error, make no changes to the HIS-1

entry, but enter the correct birth date in the answer space

in 16a and note “Date verified.” If you determine that

the person is actually under 55 years of age, footnote

the situation and continue the interview. Do not make

any changes to the HIS- I(D 16-2) or to the supplement.

Mark Check Item S2 in Section S based on the original

HIS-1 age.

‘RcvismlFebruary1984.

use abbreviations. If the sample person was not born

in one of the 50 States or the District of Columbia,

mark the appropriate box in 16b, leaving the State line

blank.

4a. If questions arise in 16c, we want the name the sample

person is legally known by. If the person has more than

one middle name, enter the initial of the first one given.

Some women use their maiden name as a middle name;

accept the response as given. Be sure to verify the spelling

and record the last name first in this item.

4b.* It is acceptable to record an initial as the first name

in 16Cif this is how the person is legally known. Even

if such a person uses their full middle name, only the

91
5a.

5b.

middle initial is necessary. For example, G. Watson Levi

would be recorded as Levi, G., W. in 16c. Do not record

name suffixes such as “Sr. ,“ “Jr. ,“ “HI,” etc.

When verifying 16d for males. ask “Was your father’s

last name ?“ Always ask the question for t’emales. regardless of their marital status. Be sure to verify

the spelling.

Enter the last name of the sample person’s father in the

answer space, whether it is the same as the person’s

name or not. Always verify the spelling, even if the

names sound alike. If it is volunteered that the person

was legally adopted, record the name of the adoptive

father.

NOTE: Take special care to make the entries in 16b-d legible.

Printing is preferred.

6. Read the introduction to 16e to all respondents. If you

are asked for the legal authority for collecting social security numbers, cite the title and section of the United

States Code, as printed below the introduction. If you

are given more than one number, record the first 9-digit

number the respondent mentions, not the first one issued.

If the number has more than 9 digits, record the first

9-digits. Do not record alphabetic prefixes or suffixes.

7. After recording the social security number, mark the appropriate box indicating whether the number was obtained

from memory or records.

Sensitive questions in the 1984 Supplement on

Aging

There are no questions considered to be sensitive on either

the core series of items or the supplement. However, certain

information may be considered sensitive and the following

explanation of the need for the data is provided regarding

social security number and the subject of incontinence.

. Social Security Number and National Death Index Match

So that in the future the National Center for Health Statistics (NCHS) may investigate the relationship between the

results of the “Supplement on Aging” data and causes of

death, the supplement collects the appropriate information

(items 11a-1le of questionnaire Section 3, Occupation/Retirement), particularly the social security number, that will enable

monitoring the National Death Index records for sample persons.

The cost-effectiveness of this supplement is enhanced

by the availability of the National Death Index (NDI). Data

on the future mortality of the survey population will be available with minimum expenditures by means of a computer

search of the NDI. Information on age at death, cause of

death, residence at time of death, and place of death can

be easily ascertained from a copy of the death certificate

obtained from the appropriate vital records office. This additional information can be integrated with data from the original

survey to greatly enrich the scope of the analysis. Extensive

information on the health status of the elderly is being collected

on the original survey. Information obtained from death certificates will allow investigators to relate these health status measures to longevity and cause of death. It will also be possible

to determine whether selected behavioral and socioeconomic

factors collected at the time of the original survey, such as

living arrangements, affect the relationship between health

characteristics and mortality.

Several years after the data collection and prepmatir)n

are completed, a list of all survey respondents will be submitted

to the NDI and a search made to determine which respondents

had died during the interim period. Additional searches of

the .NDI will be carried out on a periodic basis. In order

to optimize the successfulness and reduce the cost associated

with these searches, the following information must be collected as part of the original survey: social security number,

full (legal) name, date of birth, State of birth, race, sex,

and mariml sratus. Ascertainment of social security number

is most essential. A search of the NDI which uses social

security number should produce only one match if the subject

is deceased. The other information is then used to verify

the match. The result of such a match identifies a death

certificate which can be obtained from the State with reasonable “

certainty that it is in fact for the subject. If a social security

number is not available, multiple matches within the age

range established will occur, especially for common names.

This would necessitate obtaining death certificates from several

States and attempting to determine whether any of them is

for the subject. These false positives would add both acquisition costs and staff” costs to the death search process, as

well as introducing error.

Interviewers will verify the person’s name and birth date

(which may have been provided by the household respondent

on the core questionnaire), and obtain the last name of the

person’s father. The social security number will also be requested and if the person is unable to recall the number,

he or she will be asked to check the card. This information

is not thought to be sensitive; however, respondents will be

reminded of the voluntary and confidential nature of the survey,

the purpose of the data collection, the legislative authority

under which the information is being collected, and the absence

of any penalty for refusal. Nonresponse to any of these items

will not affect most of the analyses planned for the supplement;

however, provision of social security numbers allows for future

epidemiologic research for this population without the necessity of conducting a separate longitudinal or followback survey.

. Incontinence

NCHS’S and the National Institute on Aging’s interests

in general physical problems of older people, which relate

directly to their quality of life, include questions on urination

and bowel control (Pretest Questionnaire Section V, Items

6a-6e, 7a–7e). One issue is the relationship of incontinence

to the aging process. In this case, incontinence can be viewed

as a health problem, independent of other illnesses. In order

to examine this issue, it will be necessary to collect data

from all persons in the 55-and-over age group (so that their

effects can be examined) and from people both with and

without other illnesses.

In addition, a substantial part of the interest in the problem

of incontinence results from the relationship between incontinence and institutionalization. It is the view of some experts

92
consulted that incontinence is one of the main reasons for

the decision to institutionalize an older person.

Considerable effort went into wording these questions

both to minimize sensitivity and to assure comparability with

similar items proposed for the 1985 National Nursing Home

Survey, Analysis of comparable data for both the institutionalized and noninstitutionalized populations from the

two surveys is planned.

93
Appendix D

Record matching information for the

Survey of Income and Program Participation

Question 33

CARD B-Continued

COMMON QUESTIONS AND SUGGESTED ANSWERS

I thought that the Bureau of the Census operated only every

10 years, when they counted people. What is the Bureau

of the Census doing now?

In addition to the decennial census, which is conducted every

10 years, the Bureau collects many different kinds of statistics.

Other censuses required by law are conducted on a regular

basis including the Census of Agriculture, the Censuses of

Business and Manufactures, and the Census of State and Local

Governments. In addition, we collect data on a monthly basis

to provide current information on such topics as labor force

participation, retail and wholesale trade, various manufacturing

activities, trade statistics, as well as yearly surveys of business,

manufacturing, governments, family income, and education.

Why does the Census Bureau want to know my social securi@

number?

We need to know your social security number so we can

add information from administrative records to the survey

data. This will help us avoid asking questions for which

information is already available and help to ensure the completeness of the survey results. The information we obtain

from the Social Security Administration and other government

agencies will be protected from unauthorized use just as the

survey responses are protected.
~ C06W2+ WMR M m

0 M 8 MO 0W7 0425 4DW..9l E. D,,.. h w ,U

nt :gm

WAVi ExTRA UNIT w... 10( pm” glpp~ol u s cfwm#fMyuww::M:lJc,

ADORf SS , u A661.s, 1 2 3 4 6 ● j7, #)s ~m~r

!0

mw”.1 m, ,.0.! .vhnch

u ~~,, “’”3’

Coof mu USowf4N !s.Em0M:5Nd ls-

! 9uilr*

Yowl

,,, 3❑ Pam.! :&-’- c.r.o.cd CONTROL CARO

,,.

,,, . ❑ Am, 8URVET OF INCOME ANO ● #OORAM PARTICIPATION

,1,

I I I I I I

,1 5cls+9cl#@cce - ova, ❑ Y*, ❑ V*, ❑ IT.* E Y*S

I :s 1814 I bnwMm ❑ lw. ElM. ❑ M. ❑ N, 13N.

mm. vw-. *titi. d- bb. mht3ustilh

.t+. *bl —ti — udl!mb9d&* M#nIldc.fc4m

HOUSEHOLD RECORD lCard _

FILL (“R “FUA [t .4S APPRO,

==

I

—.

●m

17A1ftM1EflEDul11t11 mrllll UAIFAGE

of_

m

‘m‘f RSOh

luMBU

‘A%N I

!7,1,, ,.

18,.4

19b it

..”,

,,(J91

J i

11

.).1

L 1.

11

[1

I.1

11

11

.L-l.

11

&&

E

FILL ITEMS 17-20 SOB All fflWONS LW,&

f18SI lNIERWEWAT ORIGINAL ADDRESS

,.,, ,02, Wh,,mm (lw”,muol ~Pu80n#U,kIS

~0,, ~Oz, lmmm,mnld thh hwna. ?hm., lndud.

OR STA V#NG HERE ——

.ELA,,ox%iri,

~RSON lRPI

d,k ,f w, JJ,p,,en,

Whst h,. ..s tddomhip to

f~ti”,nw of 8P,?

E.mw+ Refer,,,,,,,,,.,,.

!+i, 0( h.,t!,”d ,~ d,,qh,,,

SC+ m I,w. ,?,(,,. /.$,,( ,.”.

p,”.,<, I@?,, Ia##e# ,, Wfe

se, WC+, m !,8!0,”,,!,. 9,?4!,

S 23 33f0f

.s!(,, ,5”

‘m

MARITAL

STATUS

. ..”.w -

1.,,,..

!.s, r,.,

2W5,%OVO:

D1..,c.d

p“v.<.,ti

.N...r

r“.,.l.d?

. .

~

!dc Up,,g

—

.

?3

{OUSfllOLD MEMBERS ONL v Ask .&ch ,,en I., ●,,,, h.

;, 07”- *m ‘Bil -m m

c

CIESIGNAIEO SEX RACE ORIG,N

‘AnEN1 ‘R A,koIIw, Snow Silo w

:%% ‘#GuAROIAN ,W ~“,, wvare.t :AMII FLAW

W:lfsc .~,~ w, 1. .. ~h,, ,, :,nh ,h,

&;J: ;y,,y I M*~ ,ht ,.., ** .

welt. .,8 ~:m,,,t 01 ,.ch A,,,., 01

#“d 26, ~~o ~ ,~, P*11O. .=h m

,:%,7 ~o”,- ~, Ill Ihls s.. !.7thl.

r.sm.slk4. ho”,.. h..,..

999, ,.,. . .? hold? h.!df

[ma x,,.. . se <c++, S,, cod,,

.u,nbe, ~r~ be,ow ~,,ow

—.. .—L. -—— -

II ;.1. ‘W ._. .1

11’ I .-1- ‘!YJ _... ..1.

LL .L l– !&n . _.. .-.1

II .1-L- ‘U:’!J_ .J

11 L- L_ ‘[w ..__ 1

11 I .l– !!!2U ‘.-L

[.1..1-.1. ‘!w -. 1.

L.1 L. 1–.![2+! _. . -1..

J .1 .~ J_ 1~[ 1

...- .1..

L1. -L .1 _ ‘!-!!?B _._. .J_

Ll l.-L- –

ILJ&J ..J -

,flldJ ‘- I I I I I

CODESFOR30

tmld M“,,? .,,,,,$,,,.,,,.,,,

4W ,1 !6 ())?0,[1<8 1

I

Housl m

HOLO

MEM8E

EOUCAIION ARMEDfoncfs - c

*EEH wRJg m !i% “,,2,,*, 1

—-. .—

hst h th. hlsh,,! Oid Did

●d, ., “,.,.1 m H ,

c.ampl.t. .“.,

,U4., school ,h., ,.,,, O“ Whm * I*, . . (

1. ..W .I1..d.d? Sr.d. ,,,1.. --*T y,- ~

s,, ,.,+,< {,..0? d.,? in s.:$:~ ~,m,d ,

,!.,0,+

; :,- :,”% F.,.,,? 1

F.,..*1 I t’,, 1

1 v,, 2 No h

2 No

-#~ ~:

‘efl ‘? ‘el ?.o .-

,1 1;2[ I ,( 1;,!) _. <ml

1! 1;,1 I ,1 111{1 ,rJ12[I

l—. . —. .-,——.

II112[Il[_y! .._ ,[Jy

.....

,11:71I,11:?[!. “- ,[I;21-I

—.—

,!J:?II,[1:211 II 11,( I

—,—.

,[ I;21J ,1 1:2!1 ‘“ “- ,[ 1,2[:1

-- .—,. —

II 112[1 ,[.l; ?[1 ,ITII

---- --4——

,1 1:2! I,!”Jj,lI In&i

,!Ij?ll ,[~fi!l Irlldl

—.. .—

,11;2[I ,q,lj ----- d ]prt

..—

-‘- J

-- —

II1:?!! dj:2L! _ -_,_ ,[ ?&n

,[ 1:/[I ,[”l~dI 1[1:2(J

SOCIALSECU81rY

;%%:: s”;;!:,,,y *h,,*,,~,~ -

r

Nm,,,Pw,,e

cm:A5:,.1:/#,,, ,,,, ,,”nt,,,“.**d “,,”,m.n,)~mkl ~,,,,he

..,?., fr,!,, ,fdnn,knmv.,k .EWOC4W.b.

,,,9,,.,~,”.,,,,.,

,..,.”

Cm!,,!,,, ,I”>sfm’-,,’d l,, z,,,,, . w,,, {, .,,+.,,., IJ,,hl

?1 m,., .,*, .“drl,,e

,..i”ui. ,., 20 19!33

s,., cod., M,!,, 1701 t924

D..,

.,”,II”

t!,. h,, I ,.4 %..,,,880<,,),,,8,,,,,,

I v,,

2 No

P/C&

10,

, Unf

I

b .1* “on dnm.80.1,..8s old. h

m., ..,..., ? I I=.:2 IL?*I,frmrwwe. nl,de.

1

flllSTINTEW!f W AT MOVER S NEW

AOORfSS lot m“,,., ww ,dd,e,,

F,lllf,rns 17 19,, 24. 33, M0,, the

~ ___ :,,8”,,$7

————-

ii.’

;;6,

1

.:

:.

.;..

I

,

.: -

,

t

,

I

L.

.;

1

1

,

{

;; -h

. —1 -

,g y

—-

_- tIJ1A — $.-

,:~j~

—

ILI 12r

-— ,..

J! p!

,Lj ;?!

,[1 :2[

-_—

IQ p[

.-.

l[~!

11-l:2j

_ ,[1 12[

.-L.

l[J 12[

SING ———-UNIT CO

FRVICW

A,

.

v

.

——— .

——— .-

---——

—.

——..

—.——.

—.— .

-—. ---

.——

_-— .- K: ——.—

J- —–__...__

— -L. -

-1. .J_ —— —-

–z ––.

-u —. ..__ .

_J-L_ -. —.

— —.

L-L —

—-—

-L-L .—.___..

—..

I I I I

HOUSEHOLD ROS1tR COVERAGE

●FIP.5rlN1ElWlEW ——--7 I “- Ask,, ,,.,,;..,.,,! mm.”

c“,,,,.

,,,,,,

f,.”..

,!.,,”

ply

y:;,,h

.,.,.d ,’3

0,4,,, r’I h,,. H,,,dftle,d”m, f,ms,e,n 19,). h,,. I

=lz

“mticom+,,!ems 17-20xwve d– ‘es ~=

INIERvIEwER CHCCK ,lCM

----r ‘“””-

0.,s . . . e!h,r

mh.d -11. r., ,L?an” of the I.uw,,”g. ,,h Im”,,hold on ,h. war..,

~.,,% n l..,Decmalola.,SKIP,.24,1,.,, ~ou,tho,dr IVlb.OR .stwith ,hi.

-Anvluk4w8nu@ch kkrn?............... ~ltl 2fl [1 No, a“●,cu,,I d,,, ASK 22b

I I:L,,j:;;:&::ti A,; ,,,10, ,FW,,.,

- A.” mm, bo”dt,,, or PUB..* ,0. W@.” %Xr, m . 0. ,U PO,,.., in ,hl, lmu,, hold Ii”, OR b, ,18“U,om who 1,,,0

,“.. “u,, .!,,! #!,.r!, , I “,

,,,

5 D.<..,.”

G !.,,,,.,.,,,,,,,,

) L,”mg !!, Arm<. }..,,,, ,,.,,,’,

~ MWe6(>”l, (de d ,.””,,,

5“”.!,,44,” “r (!,..(, ,

0 P#!m” !1,!1.,.! 201 I !!. ,,}.”,,

,,.,!>s W!,h ,.”}.!, ,,.,s.”

I O,hea

m

V6 W? WS W9

118I 0r54~j 0.;s-!] ooi~

1, IIJY ,IIY IL],

IN ,IIN 2[]N 2[IN

..

— .C.T , ,,,0.

com $08 3ia I “n’-”bu”””’u’”hh’smw” d=r%i”‘-’k?E3!i2’s% , ,F:;’’2SI

+U”ollucl . . . . . . . . . . . . . . . . . . . . . . . . ..$3. U 2L_l ,,l,o~,,h,rl

IWVJ-1,.,,IW otschool,w Ins hc.,ti,l? .,_

&q w ,2(W W,h RP

— x .21:[.% ::; ,L%2. ?

Icws :W21W31W41W5

. .

m N...# .,,,”,<, ., ,m,.,g,!,e,(

0, on E,.tn,.,,, r

09 12 “#oh .,...,

2 I 26 Cd!,., ,Ac.dem,c ,

-bvndu:tl,k ti ?. .. . . . . . .

,~oq,u I JJ I

●rlSST INTC7VIEWA7 MO#EH SNt,W ADDRESS

——

I k,. Mild.. .(R,uimws I<m,!,,n 19,) a, now

;.-—? E * sU8SE0tlfN; iiiiiiiii

,,bq,[ 1 ~r ]

ml I h,.oll,lcd lRcadn.wm, fkm,,,m,

N,* ●t Ihk *,,. 1, !hls CWI,ctr 19,). A,, ,11.1 ,h.,. w,,.. ,,811li,lng

; O!z !?026 ] @l 00?;

I: No, ##k -Wl&hpmlen *U rat rim., h.,,? , c+,u,I., h,,,? IIIIY ,[IY ,IJY ,1 IY

—.

Wk ad marchIn,,? En,”,Dplm”,e,.ade,r.#;

If(’0 ●sk Who no 10”s,, 8,,,, h,,.>

Wh,n d,d I,,,,?

;2 I IN 21 IN ,1 iN ,1 114

dateml,t,m23 —. t ----- (M,, awoP!,,t, <tit and 0,,, kl,,,,,,,<”23 /

1,lh,m .o,m, ,1,.11”1.0w SI.”1.Shownow? ., . . . . ‘ 00201,1 I 2[ 1

Pm>, <1“,,,,,*W IO, ,,,s00 h,, ,,,,,,,,.

Iws,bdd !nov*a <, fl,det, ,,,”,21 d,,,,,,””, ;

II YES, ,,k “Mw Mdcmc&,,8,,ms I 7 19,. mm ,Sk ,

who” did ,, bqinn”in, ko?

,.. .14,$s (,,,,7) 34, ,,,d end “l,e,.,,,v

.—. — _—. —_

If mowed., Ml,,, ,ti W,ool’1 ,“,,, .06, 4 m ; ; 00241 002? [ 0032 I 003G I

,,,”, 23 WJ mI, d,,,

If .llOv.d Ill ,, tti ‘,”1. ml, m .11,, ,*”DIO ,.,,s””,,) ,

I“,tudi”a“.w be.,”b,bu, P ,,1 1, ,1 Ir ,[ 1“ ,1 i“

,“,,, ,PmoPaaf, code ..d date m ,te”I 23 ,! ),s ,,, ,!,n,e ,,.,,,,,,,”,,.,,,,,,,, , /

2(1.4..”0 ,74,,!,!. WI,.” d,d

: /1 ;N ,’ I *J ,1 ‘t. ,1 i.+

WM.; 01 ti.1. p.;,xla.n;,; ,;1, h.m.. f ‘– ; bmm km h,., ? f,,,,, q,...<.

A,,ag” a, r, fmen,, mt,c+”,,,mn 19b ,O,,,!s! 1,1,”,’,,,,,, ?1 An,m, .(,, ?

..-— —_

- ‘C; DES FOR 29

I w.,,

1 88,’.

1

1 A“U,.,. ,rd.,, E,,-* A!.”,

4 A,,,. w F.,,,,’ ,3,,*!

5 OUeea s“<<,,, &b.

k::.. pxz:-

— cows f0ii2b –

[m.,....m .=...,,,,,,,”

0,4,,.3,,,,0,,,, r,,., d

1,,,,.,, s.,... ,,, V,.,!,.,.

.“, m ..,., .,,,,, ,

11 -----

II

II ---

II .._ lJJ‘

140 ] Q044 ] 00:8 J o-o~~j

lY, li i! 111” ,1;”

IN /1 N ,ilN 7i!N )’”.’ ,,, ;’ 1

I I I

lt, lt Il\!tv;l ,1 ,.. ,, ,,, ,, .,,,.. !!”,.,, !.. ,. ,, !. r ,,.. I

w

Ln
Statistics for an aging

population, dealing with

uncertaintya’b

by Michael A. Stoto, Ph.D.,

Associate Professor of Public Policy, John F. Kennedy School

of Government, Harvard University

Policymakers often ask questions that are hard for statisticians to answer. Someone concerned with policy for an aging

population, for instance, might ask: “In 2015, what will be

the health needs of the population 65 and over in the United

States?” Because of uncertainties that arise from definitions,

from data sources, and from the difficulty of extrapolation

into the future, it is difficult to answer any such question

with a high degree of confidence. Uncertainties matter, however. In 1982 the possibility that the deficit in the Social

Security Trust Funds could average more than $91 billion

over the next 75 years helped to convince Congress to make

major changes in the system.

This paper describes the kinds of uncertainties that appear

in statistical data used to guide policy and discusses some

methods that statisticians and policy analysts can use to deal

with uncertainty. The first section lays out common sources

of uncertainty and, in the process, illustrates the broad meaning

of the term. The next section discusses ways to reduce the

uncertainty from these sources. The third section discusses

the importance of recognizing and explicitly dealing with the

uncertainty that is inherent in policy problems. The next section

explores four approaches for measuring and expressing uncertainty in quantitative terms. The paper closes with four recommendations for improving our ability to deal with uncertainty.

Throughout, the paper uses statistics on aging populations

as examples, but the issues and methods are more general.

Sources of uncertainty

“Uncertainty” in policy data comes from a number of

sources. For the purpose of discussion, let us consider them

in four groups:

1. Definitions. Uncertainties arise in reconciling multiple

data sources, each employing different definitions, and

in using data for policy problems that do not exactly

correspond to the original definitions.

2. Sampling errors in surveys.

3. Nonsampling errors. Sometimes known as “bias,” this

kind of uncertainty includes problems such as age misstatement and reporting errors.

“Cuntents arc the opinions of the author and not thnsc of’ the Dcpdrtmcnt

uf Health and Human Services or the organization hc works for.

‘G) 1985 National Academy of Sciences.

4. Scientific uncertainty. This arises from the use of scientific

theory, whether simple or sophisticated, to fill in the

gaps left by missing data or to use data for policy purposes

for which they were not originally intended. It includes

errors due to extrapolation, especially into the future,

and the use of models.

The remainder of this section illustrates the different kinds

of statistical uncertainty by reviewing a series of policy issues

that exemplify the different sources. Although not exhaustive,

the list does identify most of the major sources of uncertainty

in statistics for an aging population. There is overlap among

the categories, but the list should serve well as a vehicle

for discussing approaches to reducing and dealing with

uncertain y.

Question 1. In 1980, how many people 65 and over

were there inthe United States?

Two kinds of uncertainty arise in the answer to this question: problems with definitions and two kinds of nonsampling

errors.

Place of residence—What does “in the United States”

mean? The decennial census uses a concept of “usual residence” to answer this question. Although the precise definition

is complex, the goal is to include everyone residing in the

country on the day of the census, including U.S, residents

temporarily abroad on business or vacation travel and excluding

foreign residents temporarily in the United States. This definition does not involve citizenship or legal or tax residence.

Depending on their purposes, the Social Security Administration, the Internal Revenue Service, and the Republican National Partyj for instance, employ different definitions and

come up with different population figures. As an example,

in 1979 the population covered by the Social Security system

(which includes U.S. citizens living abroad, residents of U.S.

territories, and an estimate for census undercount) was 2 percent larger than the resident population (Faber and Wilkin,

1981).

The same uncertainty takes on even more significance

for smaller geographic areas. In recent years, it has become

common for retired people to winter in places with warm

climates. The concept of “place of usual residence” used

in the decennial census, however, was developed before such

regular migration became common. Because of uncertainty

about how these

about where they

people should answer the census question

live, and because the census date, April 1,

96
is close to the time that many of them move back north,

the census contains substantial uncertainty about the size of

the aged population in places with high seasonal migration.

Age misstatement—In most data sources, including the

decennial census, age data come from the individuals involved

or other informants. Ages are not verified against independent

sources, Comparison of individual records from different

sources shows that the inconsistency in age data increases

with age, and that age tends to be overstated (Siegel and

Davidson, 1984). The amount of age misstatement varies

in an unknown way from one data source to another and

contributes a major source of uncertainty. In 1970, for instance,

perhaps 2 percent of the total population reported to be 65

or older was actually under 65 and the proportion for males

of races other than white was 9 percent (Siegel and Davidson,

1984).

Population undercount—in the decennial census an unknown proportion of the population fails to be enumerated.

Based on comparisons with other statistical records, such as

Medicare enrollment, past censuses, and independent surveys,

the Census Bureau has estimated that 1.4 percent of the population 65 and over was not counted in the 1980 census. This

estimate, however, is itself subject to error. Furthermore,

the proportion not counted varies substantially with age, race,

and sex. For the population aged 75 and older, for example,

the estimate ranges from a O.6-percent undercount for white

males to a 4,7-percent overcount for black females (Siegel

and Davidson, 1984).

Question 2. In 1985, how many people 65 and over

~ therein the United States?

It is substantially harder to estimate the population in

a noncensus year than in a census year. Such estimates must

depend either on data (often vital statistics) to fill in the

time gap, or on mathematical assumptions about how to extrapolate the available data, or both. Each of these brings

additional sources of uncertainty.

Intermediate estimates—If births, deaths, and immigration

figures are to be used to bring population estimates forward

from a census to a current year, they must be timely. In

fact, final versions of vital statistics are often delayed a few

years, and intermediate estimates involve substantial uncertainties, Immigration and emigration data are even more problematical. Since the 1950’s, the United States has not kept

statistics on the number of residents leaving the country to

live abroad, Passel (1985) has estimated that more than

133,000 former immigrants leave the country every year.

Moreover, 1 to 4 million Mexicans alone entered the country

illegally each year during the 1970’s (Warren and Passel,

1983;Bean, King, and Passe], 1983).

EWapolatiott-If timely vital statistics or other data are

not available, demographers must resort to extrapolating trends

from past years. This could mean simply extrapolating the

total population 65 and over; extrapolating the population

by age, sex, and race components; or extrapolating past trends

in vital statistics and migration and applying them to the

last census population figures. Different extrapolation methods

often lead to quite different estimates, and it is usually not

clear which of the many possible methods is most appropriate.

Question 3. In 2015, how many people 65 and over

willthere be in the United States?

.Extrapo/ation-The problems associated with extrapolating population data from a census year to the current year

are compounded when making population forecasts. The data

must be extrapolated over a longer period so that the specification of the functional form and interrelationships of the variables becomes more important.

Unknown ~uture—All projections rely on (sometimes hidden) assumptions about what remains constant. In forecasts

of the population 65 and over, for instance, assumptions about

future mortality rates are crucial. Various analysts have assumed that age-specific mortality rates would remain constant,

that the rate of change in the mortality rates (over the last

so many years) would remain constant, that the rate of change

in age-specific mortality rates would remain constant, and

so on. In making short-term extrapolations from a past census

to the present, even when we cannot get accurate data on

death rates, sometimes we can assess through indirect nonsystematic means whether the trends are holding. When projecting

into the future, obviously we cannot.

Question 4. In 2015, what will be the health needs

of the people 65 and over inthe United States?

Definition of health needs—’’Health needs” are neither

easily nor well defined. First, they include many dimensions.

Need can be measured by the number of people with various

types of health problems; by demands on health care providers

for various sorts of hospital, outpatient, and nursing home

care; or in other ways. Second, the needs themselves can

be defined by absolute level of care deemed medically desirable, by comparison with the needs of presumably well served

populations, or by other means. Each definition leads to different estimates of health demands. In projecting health needs

from 1980 to 2040, Rice and Feldman (1983) have found

that the number of elderly (65 years and over) with limited

ability to perform the “activities of daily living” will increase

by 233 percent, the number of elderly nursing home residents

will increase by 319 percent, and nursing home expenditures

for this same group will increase by 160percent. /

Conceptual problems with definitions arise in other areas

as well: we tend to think of retirement, for instance, as a

binary choice, but many intermediate steps lie between fully

employed and totally unemployed.

Survey error—Sample surveys of individuals provide

many measures of health status and health needs. These data

have two types of uncertainty. The first, and simplest to

deal with, is sampling error, which arises because the selected

random sample might differ from another that might have

been selected. An important determinant of sampling error

is sample size. Because the aged population makes up a

relatively small proportion of the overall population, nationally representative samples that provide a reasonably small

97
sampling error overall may have an unacceptably high sampling error for questions asked of the aged population. Second,

the other sources of error are known collectively as nonsampling error (Mosteller, 1978). This category includes, for

instance, differential nonresponse by particular subpopulations

and errors due to self-reporting. Measures of absolute health

needs often rely on individuals’ self-reports of their health

status, which may be more difficult to interpret than more

expensive measures based on physician examinations.

Interactions—Estimates of future health needs are often

“ obtained by multiplying estimates of future population by

per capita utilization rates. The work by Rice and Feldman

(1983) mentioned previously is a prominent example. These

utilization rates (the average number of hospital days per

year for a 70-year-old man, for example) are based on current

values or extrapolation of current trends. But complex interactions may cause utilization rates to change. For instance,

because of their increased political influence, the per capita

level of services provided or demanded may be higher in

places where there is a large concentration of elderly. Additionally, the definition of need may change with the introduction

of new services. Many health policymakers are concerned,

for instance, that the introduction of home health care as

a substitute for nursing homes will drastically increase the

demand for such services (General Accounting Office, 1982).

Reduction

One way, and

with uncertainty is

of uncertainty

in some respects the best way, to deal

to get better data. Gathering more data,

taking more care in its collection, or more thorough analysis

can help reduce uncertainty from each source mentioned previously. For some problems it is relatively feasible and inexpensive to gather more data or perform more analyses, and this

is a reasonable short-term solution. For other problems, however, such responses are at best long-term solutions; often

little can be done in the short term. The next section describes

methods for living with uncertainty that cannot be removed.

Definitions-If uncertainty arises from differing definitions, our first response is to seek agreement on a single

definition. In practice, however, this is very difficult, First,

in most policy questions the relevant data come from a variety

of different organizations, each with its own data needs. Regarding the definition of the U.S. population discussed earlier,

the Social Security Administration and the Internal Revenue

Service will not be easily convinced to redesign their statistical

systems to accommodate health planners. Second, even if

agreement on a common definition were achieved, it often

is not possible to readjust data for past periods.

Some problems with definitions are conceptual and hence

difficult to resolve by any means. The different definitions

of health needs, for instance, reflect truly different concepts,

and a single answer would be misleading. Two major Census

Bureau surveys, the Cuqent Population Survey (CPS) and

the Survey of Income and Program Participation (SIPP), use

different definitions of personal income—SIPP includes income from government programs that CPS excludes—because

98

they have different purposes. Furthermore, statisticians and

policymakers must choose between relatively simple definitions that are easy to operationalize and more complex definitions that may be more relevant but more difficult to

implement.

Sampling errors—In theory, uncertainty due to sampling

errors can be resolved very simply by increasing sample size.

In order to get better statistics on the aged population, it

may be necessary to oversimple the aged in nationally representative surveys or to arrange for special surveys of the

elderly. In 1980, 17 percent of the adult population of the

United States was 65 or older. These numbers alone suggest

that, to equal the sampling error of the population as a whole,

the elderly must be oversampled by a factor of about 6.

In some instances, sampling error can also be reduced by

using more complex sampling procedures.

Nonsampling errors—This residual category of sources

of uncertainty in survey and census data, which includes problems of nonresponse and self-reporting, offers no easy sohltions. In particular, these errors do not decrease as sample

sizes increase. Furthermore, there is a tradeoff between sample

size and nonsampling error. In larger samples, especially if

they involve more personnel and data from multiple institutions, coordination problems often lead to greater nonsampling

errors. In the long run the development of better survey instruments and more careful collection of statistical data are necessary. (See, for instance, Kruskal, 1981.) A large-scale national

longitudinal survey of the elderly population suggested by

Branch et al. (“Health indicators for an aging population”

in Chapter II of this report) would go a long way toward

reducing nonsampling error. In addition, as with the problem

of definitions, relevancy often competes with simplicity of

operation.

In the short run, statisticians can sometimes adjust data

by building a model to describe nonresponse or self-reporting

problems or by using ancillary data. However, such approaches

usually depend on unverifiable assumptions about the structure

of the data or the problem. For instance, statisticians sometimes

address nonresponse problems by assuming that the people

who dld not respond are the same as the respondents, are

similar to some other group, or have a particular reason for

not responding. In some problems, the assumptions in the

adjustment method may introduce more uncertainty than is

removed by the adjustment itself. The estimates of census

undercount mentioned previously may be an example (Stoto,

1983b).

Scientijk uncertain~—Social scientists often try to reduce

uncertainty by building formal models or by carrying out

social experiments. In estimating future health needs, demographers disaggregate by age, race, and sex, as well as by

components of change such as cause-specific mortality rates,

and look for simpler trends than are present in the aggregate

data. Econometricians build regression-based models and bring

in variables that they think determine health behaviors and

the supply and demand of medical care. Biometricians build

models that attempt to realistically represent disease processes.

Social experiments, in which individuals or entire groups are

actually exposed to an innovation, are more difficult to perform

but may give more realistic answers (Rivlin, 1974; Gilbert,
Light, and Mos.teller, 1975; Fienberg, Singer, and Tanur,

1985).

Formal models are essential for both forecasting and interpreting the results of social experiments. All forecasts require

some theory about what remains constant. Social programs

are complex and change quickly; experiments based on one

version of the program need some theory for translating their

findings to a new situation. Formal models, however, have

intrinsic limitations. All models involve difficult to check

judgments and assumptions about theory and data. Important

interactions, resulting from complicated social and political

accommodations, are often unforeseen. Investigations have

shown that more complex models do not necessarily result

in more accurate forecasts of aggregate measures (Armstrong,

1978; Stoto, 1982, 1983a). These factors suggest that formal

models offer only a limited potential for reducing uncertainty.

Living with uncertainty

In most important policy questions, decisionmakers press

for quick answers. Once the policy options are on the table,

it is rare for a statistician or policy analyst to have a year

or more to study a problem before a decision is made; more

frequently, answers must be available in a matter of months,

weeks, or even days. In such short timeframes, the techniques

for reducing uncertainty described in the previous section

cannot be employed; policy analysts and decisionmakers must

somehow live with the information that is available. Most

decisions, however, are subject to revision as more facts develop. Living with uncertainty involves making the most of

available data and being conscious of the opportunities for

getting better data and revising policies.

The situation facing the Social Security Administration,

the President, and the Congress in 1982 illustrates the potential

contribution of being specific about uncertainty. The actuaries

of the Social Security Administration had prepared three projections of the income and costs of the Old Age and Survivors

Insurance and Disability Insurance (OASDI) Trust Funds under

idtcrnative sets of demographic assumptions shown in figure 1. In monetary terms, the difference between them was

greut, more than 50 percent of outlays in some years (Congressional Budget Office, 1982). Over the 75-year period, the

shortfall could average as much as 6.5 percent of the taxable

payroll (Koitz, 1982). In 1982, 1 percent of payroll equaled

$14 billion, so the average annual deficit under the pessimistic

assumption would have been $91 billion.

A proponent of the status quo, relying on the optimistic

assumption, could argue that no changes were needed. But

the lesson of the figure is that there is a good chance that

action is needed now. If we wait until the next century to

decide whether to increase payroll taxes or reduce retirement

benefits, we will have missed the opportunity to build up

a surplus in the trust funds in the intervening years. Projections

like the one summarized in figure 1 played a large part in

convincing the Congress in 1982 to make major changes in

future Social Security taxes and benetlts.

The simplest and most common way that decisionmakers

deal with uncertainty is to ignore it (Fischhoff, SIovik, and

Liechtenstein, 1977). In the game of bureaucratic politics, “he

who hesitates loses his chance to play at that point and he

who is uncertain about his recommendation is overpowered

by others who are sure” (Allison, 1971, p. 171). Ignoring

uncertainty takes a variety of forms. At one extreme, an

advocate for a particular course of action could seize upon

the most extreme projection that favors his or her approach

regardless of the plausibility of the projection’s assumptions.

At the other extreme, a decisionmaker could consider the

range of forecasts of population growth, employment and

retirement patterns, and so on, and make his or her best

honest judgment about future needs. Neither approach leads

to good policymaking.

The Social Security example illustrates the danger in

selecting the most favorable projection for a particular policy

approach. Policymakers regularly consult demographic and

economic forecasts in determining the Social Security payroll

tax, benefit levels, and other matters. The 1982 projections

suggested that the next few decades offered an opportunity

to build up the Social Security Trust Fund to help support

the retirement benefits of the Baby Boom population in the

next century. But since increasing payroll taxes and building

up the tmst fund have implications for today’s economy and

individual welfare, too much investment also has serious costs.

Selecting an extreme estimate maybe convincing to an analytically unsophisticated audience, but it is bound to lead to

too much or too little current investment. Unfortunately, the

adversary system of decisionmaking common in many policy

areas fosters such extreme estimates. The Social Security Commission that was established by President Reagan in 1982

was fortunately able to deal with the inherent uncertainty

and forge a compromise (May and Neustadt, 1986).

Relying on an individual expert’s single best judgment,

even if an unbiased expert can be found, is still problematical.

For example, retirement policies have differential costs and

benefits for individuals through time. Furthermore, individuals

differ in their aversion to risk, the amount they are willing

to pay now to reduce uncertainty in the future. Decision

theory tells us that if individuals are to make informed decisions, they need information on the range of possible outcomes and their relative likelihood (Raiffa, 1968; Stokey and

Zeckhauser, 1978; Behn and Vaupel, 1982). A single best

estimate, no matter how thoughtful and honest, is not enough.

Faced with the uncertainty in future income and outlays of

the OASDI Trust Funds, highly risk averse policymakers would

increase payroll taxes now to ensure against the “pessimistic”

forecast, even if the “intermediate” forecast were thought

to be more likely.

Another problem with basing a policy decision on a single

best estimate is that it ignores the possibility of gathering

more information. In spite of the short timeframe for policy

decisions discussed earlier, it is always possible to decide

to delay or to review and revise in the future. Retirement

security policy decisions, for instance, have a long time horizon

and their major source of uncertain y is the extrapolation

of demographic and economic trends into the future. In such

situations, knowledge of the nature and magnitude of

99
.

/-

/

\

10 \

MA

If

\

\

Optimistic

0

.e.—.~.

\

\

-10 - \

“\’\

. \

– 20 -

\

\

\, Intermediate

. \

_-— —

\

--- ——

-30 -

.

\

-40 -

“\.

Pessimistic

\

-50 -

“1.

\.

“~.

-60

1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060

SOURCE: Congressional Budget Office, baaed on 1982 Annual Report of the Board of Trustees, Federal Old Age and Suwivora

fnsurance and Oisabifify Insurance Trust Funds, Table 29, pp. 67-68,

NOTE.% Optimiafic aasumptiona = Alternative I Intermediate assumptions = Altemafive II-B. Pessimistic assumptions = Alternative Ill.

Figure shows the difference between revenues and costs in each year, rather than OASDI balances. tfifferances are on a“ a“nuaI basis,

and are not cumulative from year to year.

“L

F@Jra 1. OASDI surplus or defioit as a percentage of outleya under three alternative sets of assumptions 1985-2080

forecasting uncertainty should help determine the need for

and the frequency of periodic scheduled policy reviews.

If short-term consequences are dominant, it may be advisable to delay a decision if a critical piece of information

can be made available shortly. With an accurate assessment

of the nature and magnitude of the uncertainty and the feasibility and cost of gathering new information, decision theory

can help determine whether the benefits of more information

exceed the costs of delay and the additional expense of data

gathering (Raiffa, 1968: Behn and Vaupel, 1982).

Thus, the way that statistical estimates and forecasts are

used in the policy arena has two implications for statisticians

and decisionmakers in dealing with uncertainty. First, we

need to understand the sources of uncertainty so we can deter100

mine the possibility of reducing it in the long run or estimating

it in the short run. Second, in order to employ formal or

informal decision theory, we need quantitative measures of

the amount of uncertainty in our estimates in terms of subjective

probability.

Estimating and reporting on

uncertainty

Many people regard uncertainty either as a quality that

is present or absent (“these data are uncertain”) or a matter

of degree (“these data are highly uncertain”). But the language
of subjective probability can and should be used to convey

aclear and precise message to policymakers about the degree

of uncertainty in a statistical estimate.

For instance, suppose the problem involves estimating

the proportion of the U.S. population that will be 65 or over

intheyear 2015. Inthemost recent Census Bureau projections,

the “middle” assumptions of mortality, fertility, and migration

lead to an estimate of 15.4 percent, but everyone would regard

this as “uncertain.” A simple measure of uncertainty is a

range of estimates. For instance, the Bureau has constructed

a “high population growth” estimate of 14.9 percent and a

“low population growth” estimate of 15.7 percent. Without

knowing something about the likelihood of the assumptions,

it is hard to know what to make of this range. It would

be more informative to have a statement such as:

“There is a 50 percent chance that the proportion will

be between 14.7 and 15.8 percent, and a 90 percent

chance that the proportion will be within 14.2 and 16.4

percent.”

At the end of this section, I will return to this example and

describe how I calculated these figures.

One alternative to quantitative estimates is to use words

like “very likely,” “probably,” and “rarely.” Research has

shown, however, that these “probability words” mean different

things to different people. Behn and Vaupel (1982), for instance, found that in a group of 163 students, the numerical

interpretations of the phrase “It is probable that.. .“ ranged

from 20 to 98 percent. One-fourth of the students interpreted

it to be less than 61 percent, and another one-fourth interpreted

itto be greater than 80 percent.

Explicit quantification of uncertainty is important for two

reasons. First, in order to employ the kind of formal decision

analysis described in the previous section, quantitative estimates of subjective probabilities are needed. Formal decision

analyses are rarely the deciding factor in a complex public

policy decision. Such methods are, however, employed by

policy analysts in synthesizing background information and

making recommendations to decisionmakers. Second, even

if there is to be no formal decision analysis, decisionmakers

still need an accurate picture of the amount and nature of

uncertainty.

Some people object to the use of numerical probabilities

to express uncertainty. They say that using any numbers at

all implies a false sense of precision. But the point is accurate

communication. An expert who makes a statement about percentiles such as the one in the example above may or may

not be correct, but at least we know what he or she intends.

The remainder of this section describes approaches for

making formal or informal estimates of the amount of uncertainty associated with statistical estimates. None is completely

satisfactory and some are controversial, but they offer a start.

1. Sampling theory

Of all sources of uncertainty, sampling error is the simplest

to quantify and communicate. Introductory statistics books

describe methods for calculating confidence intervals, and

they need not be repeated here. The interpretation of these

intervals is simple in theory, but in practice there is sometimes

confusion.

One problem with using confidence intervals to measure

and communicate uncertainty arises from their relative simplicity. Sampling error frequently contributes only a small part

of the total uncertainty in statistical estimates. In good surveys,

sampling error is often less than 10 percent of the quantity

being estimated; there is often more uncertainty associated

with differences in definitions, nonsampling error, and scientific uncertainty. But since there are no equally simple and

objective measures of other sources of uncertainty, some

analysts only report confidence intervals based on sampling

error. Even if these analysts make it clear that these confidence

intervals are lower bounds on the true uncertainty, the numerical results tend to be remembered and repeated by others

and their limitations ignored or forgotten.

Statistical measures sometimes contain more uncertainty

than sampling theory would suggest because of hidden

heterogeneity. For instance, if individuals differ in mortality

risks, the variance formulas for the binomial distribution

@(1–p)/N) will understate the true variance because of the

formula’s assumption that p is the same for all individuals.

More realistic estimates can be obtained by examining the

variation in p across different subgroups of the population

(Mosteller and Tukey, 1977, Chapter 7) or by modeling the

distribution ofp in the population and estimating its parameters

(Manton, Stallard, and Vaupel, 1981).

“ Some statistical estimation problems are too complex to

allow the mathematical derivation of standard errors, and many

analysts in this situation do not report any confidence intervals

at all. A relatively new set of statistical techniques known

as resampling plans provides a means for calculating variances

in this situation. Suppose X is the statistic that is to be estimated

from the available data. One technique, the jackknife, works

by dividing the available data into groups and examining

how X varies as each group is sequentially left out of the

calculation (Mosteller and Tukey, 1977). Another technique,

the bootstrap, draws samples with replacement from the available data and recalculates X a large number of times (Efron,

1982). In each method, the observed variability in the recalculated X values yields an estimate of the sampling variability

and can be used to calculate confidence intervals. Like traditional confidence intervals, these methods estimate only sampling variability and not the other sources of uncertainty discussed in this paper.

2. Assessment of experts’ subjective distributions

Statisticians have developed techniques for assessing and

quantifying the uncertainty in expert estimates in the form

of a subjective probability distribution (Raiffa, 1968; Spetzler

and Stael von Holstein, 1975). These techniques, based on

Bayesian updating and decision theory, have been used extensively in business problems, weather forecasting, and other

areas. In essence, the methods offer the experts a series of

choices between lotteries with known probabilities and lotteries

dependent on the subjective distribution that is being elicited.

101
The difficulty with these methods lies not so much in

the techniques but in the experts themselves. Investigations

in a wide range of fields show that experts tend to be overconfident in their own estimates or predictions; that is. they tend

to have subjective distributions that are too narrow (Behn

and Vaupel, 1982; Tversky and Kahneman, 1974). A review

of population projections made by the U.S. Census Bureau

and the United Nations since World War II, for instance,

shows that actual population growth often falls outside of

the low-to-high range given by the forecasters. The traditional

range corresponds, roughly, to a two-thirds confidence interval

(Stoto, 1983a). Furthermore, because experts’ views are really

opinions (though perhaps based on extensive experience) rather

than hard scientific data. the results are often controversial.

3. Sensitivity analysis

Sensitivity analyses are commonly used to assess the

amount of uncertainty due to different definitions and different

methods of extrapolating into the future. The Census Bureau’s

presentation of its population projections provides a good

example of the strengths and weaknesses of the approach.

For concreteness, let us focus on the proportion of the population that will be 65 years or olderin2015.

In the latest set of national projections, the Bureau presents

27 separate projections of the U.S. population by age, race,

and sex (Spencer, 1984). These correspond to the conjunction

of three fertility assumptions (ultimate number of births per

woman equal to 1.6, 1.9, or 2.3 births), three mortality assumptions (life expectancy in the year 2000 equal to 71,

73, or 75 years for men and 78, 80, or 82 years for women),

and th;ee immigration assumptions (total net immigration of

250,000,450,000, or 750.000 people).

Traditionally, the Bureau has presented “high,”

“medium,” and “low” population projections. In 2015, for

instance, the population aged 65 and over is projected to

be 14.9 (high), 15.4 (medium), or 15.7 (low) percent. In

these projections, “high” corresponds to high population

growth, that is, the high assumptions for fertility and immigration and the low assumption for mortality (high assumption

for life expectancy); and “low” is the opposite. But as Siegel

and Davidson ( 1984) have pointed out, this approach gives

an unrealistically narrow range for the proportion of the population aged 65 or older. The highest proportion results from

a combination of low fertility, low mortality, and low immigration, and the lowest proportion results from the opposite combination. Taking this approach, the proportion ranges from 13.5

to 17.5 percent.

A range alone is not enough; users need to know the

probability that the quantity projected will be within the range

given. For instance, the “traditional” range (14.9 to 15.7

percent) is probably too small. Similarly, if the assumptions

about the range of uncertainty in the components are reasonable, the alternative range ( 13.5 to 17.5 percent) may be

too large. For either of the extremes to be achieved, each

of three variables (fertility, mortality, and immigration) has

to take on its own extreme value.

One solution is to have the experts who prepare the forecasts give a range and their best estimates of the probability

associated with it. The Census Bureau does not do this but

instead takes “the longstanding projections approach of showing a ‘reasonable high’ and a ‘reasonable low’ selected on

a judgmental basis” (Spencer. 1984). They are “reasonable”

only in the sense that their assumptions are clearly presented.

To the Bureau’s credit, the latest report presents a table comparing the differences between the highest, middle, and lowest

series (in terms of population growth) with errors in past

projections.

Making quantitative statements about the likelihood that

the projected value will fall within a given range need not

be difficult. The proportion of the population 65 and over

in 2015 provides a simple example. As a start, assume that

the future courses of fertility, mortality, and immigration are

independent of one another. Further assume that there is a

50-percent chance that the middle value of each will turn

out to be true, and a 25-percent chance that each of the

extreme values will turn out. 1These two assumptions allow

us to calculate a probability, ranging from 1/64 to l/8, for

each of the 27 projections. The results are shown in table 1; for each scenario there is both a projected value and

an associated probability. The next step. shown in table 2,

is to sort the scenarios by their projected values and cumulate

the probabilities. The calculations result in an empirical

cumulative distribution that reflects the probabilistic assumptions about the input values. Interpolating in this table gives

the following quantiles:

Proportion ofpopulation

Percentile 65andover(krpercent) s

5 13.9

10 14.2

25 14.7

50 15.4

75 15.8

90 16.4

95 16.7

In actuality, the confidence intervals based on these calculations may be too narrow. First, they reflect only the uncertainty covered in the Bureau’s sensitivity analysis. There are

many assumptions about the age-specific rates that add up

to the aggregate fertility, mortality, and immigration figures

and the time pattern of change that are not reflected in the

sensitivity analysis. Second, this calculation neglects the possibility that actual fertility, mortality, and immigration may

go beyond the assumed range. Although it is not possible

to adjust formally for these factors without running different

projections, the distribution provides a good base for judgmental adjustments.

Other analyses offer opportunities for sensitivity analyses

expressed in quantitative terms. According to Scheuren

‘Donald Shepard (personal communication) has calculated that it’ a normal

distribution is divided into three segments at the 25th and 75th Perccmi[cs.

the means of the outer two segments are approximately equal to the 1t)th

and 90th percent points of the distributions. Thus. a normal distribution

can be roughly approximated by a three-point distribution with probability

1/4 on the 10th percentile, 1/2 on the mean, and 1/4 on the 90th percentile.

If the uncertainty about the Bureau’s mortality, fertility, and migration assumptions can be represented by a normal distribution with 10th and 90th percentiles

at the “low” and “hig~ values, the calculation in the text may be a reasonable

approximation of the uncertainty in the proportion of the popu]a[im a@

65 and over.

102
Table 1. Reautfs of a sensitivity snalysis of mortelii, immigration, and fertility on the proportion of the population 65 and over in 2015, with aaeoeieted

subjective probabilities

Percent 65

Series Mortality Immigration Fertility Probability and over

l . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

lo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

11 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

12 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

13 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

14 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

16 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

18 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

20 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

21 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

22 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Low

Low

Low

Low

Low

Low

Low

Low

Low

Middle

Middle

Middle

Middle

Middle

Middle

Middle

Middle

Middle

High

High

High

High

High

High

High

High

High

Low

Low

Low

Middle

Middle

High

High

High

Low

Low

Low

Middle

Middle

Middle

High

High

High

Low

Low

Low

Middle

Middle

High

High

High

Low

Middle

High

Low

Middle

High

Low

Middle

High

Low

Middle

High

Low

Middle

High

Low

Middle

High

Low

Middle

High

Low

Middle

High

Low

Middle

High

1/64

2/64

1/64

2/64

4/64

2/64

1/64

2/64

1/64

2/64

4/64

2[64

4/64

6164

4/64

2/64

4/64

2{64

1/64

2/64

1164

2/64

4164

2164

1/64

2164

1/64

17.5

16.5

15.5

17.3

16.3

15.4

16.7

15.8

14.9

16.5

15.6

14.7

16.3

15.4

14.6

15.6

15.0

14.2

15.7

14.9

14.0

15.6

14.7

13.9

15.1

14.3

13.5

Table 2. Cekuletion of the subjective probabfity distribution for the

proportion of the population 65 and over in 2015, baaed on the sensitivity

●nalyaia in table 1

Percent65 Cumulative

and over Series Probability probability

13.5 27 .0156 .0156

13.9 24 .0313 .0469

14.0 21 .0156 .0625

14.2 16 .0313 .0936

14,3 26 .0313 .1250

14.6 15 .0625 .1675

14.7 12 .0313 .2166

14.7 23 .0625 .2613

14.9 9 .0156 .2969

14.9 20 .0313 .3261

15.0 17 .0625 .3906

15.1 25 .0156 .4063

15.4 6 .0313 .4375

15.4 14 .1250 .5625

15.5 3 .0156 .5761

15.6 11 .0625 .6406

15.6 22 .0313 .6719

15.7 19 .0156 .6675

15.6 6 .0313 .7166

16.8 16 .0313 .7500

16.3 5 .0625 .6125

16.3 13 .0625 .6750

16.5 2 .0313 .9063

16.5 10 .0313 .9375

16.7 7 .0156 .9531

17.3 4 .0313 .9644

17.5 1 .0156 1.0000

(“Methodologic issues in linkage of multiple data bases” in

Chapter 11 of this report), for instance, statistical ideas such

as information theory, contingency tables, and error-inwu-iable approaches could help quantify uncertainties due to

assumptions intheanalysis oflinkeddata sets.

4. Multiple estimates

In many policy problems, statistical estimates are the

result of a complex process and involve numerous extrapolations, adjustments, assumptions, and other judgment calls.

In such situations, alargepart of the uncertainty comes from

the factthatanalysts takedifferentapproaches andmakedifferent assumptions. One way to measure the uncertainty in this

process istohave agroupofanalysts makeestimatesindependentlyandexamine thedistribution ofresults.

The sensitivity analysis described previously comes close

to this approach. The difference is that most sensitivity analyses

use a common model and vary its parameters. When the

model itself is uncertain, we need to go beyond it to measure

the inherent uncertainty (Stoto, 1982).

One more general method, described by Mosteller ( 1977),

is “triangulation. ” In making order of magnitude calculations,

he recommends making two or three different estimates of

the same quantity, using different data and methods. If the

resulting estimates are close to one another, we can be confident in the results. Ifnot, we have some idea ofthe magnitude

oftheuncertainty.

In this respect, Rice and Feldman’s (1983) estimates of

nursing home needs for the elderly, mentioned earlier, shed

some light on the uncertainty of the estimates. Two of the

estimates, for the growth in nursing home residents and nursing

home expenditures, ought to be parallel. In fact they are,

respectively, 319and 160 percent. The difference might reflect

a projected decrease in cost per resident, but this seems unlikely. The first estimate was based on utilization rate data

from the National Center for Health Statistics, the second

on financial estimates from the Health Care Financing

103
Administration. The difference in the estimates probably arises

from differences in the data used for the projections, and

thus reflects the uncertainty in the projection.

Light and Pillemer (1984) discuss the importance in policy

analysis of combining evidence from separate evaluation

studies. They stress the fact that the various approaches taken

by different investigators result in different answers. Upon

closer analysis, however, the results often turn out to be

estimates of different targets: variations of the programs are

being tested on various subpopulations. Rather than being

confused by the range of answers, Light and Pillemer recommend that analysts take the opportunity to learn something

about the influence effect of the program variations and subpopulations that might lead to more efficient policies.

The approaches discussed so far tend to be informal and

thus do not lead to explicit measures of uncertainty in terms

of subjective probabilities. In certain circumstances, however,

quantitative measures are available, including the “empirical

Bayes” methods developed by Efron and Morris ( 1975). In

comparing a group of independent studies of different surgical

innovations, Gilbert, McPeek, and MosteHer (1977) developed

a method for measuring the distribution of likely success

of future innovations. Similarly DerSimonian and Laird (1983)

studied a group of independent studies of the effect of coaching

on Scholastic Aptitude Test scores and developed a subjective

measure of the likely gain from coaching.

Stoto (1982) describes a less formal method for quantifying the uncertainty reflected in a small number of estimates

made by experts working independently on estimating the

same quantity. The idea is that if the estimates can be regarded

as a random sample from a distribution of expert opinion,

the mean and variance of the sample reflects the mean and

variance of the underlying distribution.

Suppose that two demographers independently make projections of the proportion of the U.S. population that will

be 65 or older in 2015, and that their results are 14.2 and

15.4 percent. We might be tempted to think of the range

defined by these two estimates as an extreme, but if we

regard the two estimates as a sample of two chosen at random

from the distribution of possible estimates, there would be

a two-thirds chance that the next estimate chosen from that

same distribution will be outside the range defined by the

first two. For a sample of size two from a normal distribution,

the standard deviation can be estimated as 0.886 times the

range, in this example 1.06, and the mean is 14.8. This

calculation suggests the following distribution:

Percentile

5

10

25

50

75

90

95

Proportion of population

65 and over (h percent)

13.1

13.4

14.1

14.8

15.5

16.2

16.5

This method leans heavily on the assumption that the

estimates are independent. Advocates for different sides of

a policy debate, for example, would not make independent

estimates but would tend to bias their estimates in dpposing

directions. If the method were applied to two estimates made

by opposing advocates, ”the resulting confidence intervals

would be too wide. At best, the method provides a rough

estimate that can be modified by informed judgment. The

method also relies on the assumption that the distribution

is normal, but can be modified for other distributional

assumptions.

Bayesian analysis provides more formal methods for constructing subjective probability distributions based on the estimates of one or more experts (Morris. 1974, 1977: Winkler.

1967; Agnew, 1985). All these methods begin with the decisionmaker’s a priori subjective distribution and also subjective distributions about what the experts will say conditional

on the true state of the world, both expressed in quantitative

terms. These methods will be difficult to use in public policy

matters, however, because (1) decisionmakers are not likely

to be able to develop the relevant subjective distributions

in quantitative terms, and (2) there is frequently more than

one decisionmaker.

Recommendations

1. Pay more attention to uncertainty.

All statistical measures have some degree of uncertainty,

and efforts to reduce that uncertainty can have only limited

success. In developing public policy, this uncertainty must

be taken into consideration. Statisticians, however, for different reasons, tend to shy away from considering it. These

facts suggest that both statisticians and policymakers should

pay more attention to measuring and communicating uncertainty, perhaps as much attention as they pay to getting better

estimates.

2. Develop uncertainty profiles for major

statistical data series.

As a first step, statisticians ought to give serious attention

to developing uncertainty profiles of some of the most important data series on aging. This should include comparisons

of different definitions and the implications for analysis, and

studying the magnitude of nonsampling errors and scientific

uncertainty. With the current state of the art, sensitivityy

analyses and multiple independent estimates offer the most

hope for quantifying uncertainty.

3. Develop more sophisticated methods

for quantifying uncertainty from all sources.

Because statisticians have paid so little attention to quantifying uncertainty, the methods described in this paper are

still relatively primitive. They all involve a considerable

amount of judgment and tend to be controversial in policy

settings. Further statistical research is necessary to advance

the state of the art.

Progress can be made in a number of areas. Better formal

models of heterogeneity, more disaggregation of data to learn

104
about hidden heterogeneity, and more use of resampling plans

could improve estimates of uncertainty due to sampling and

heterogeneity among individuals and population subgroups.

Experts in various areas can be trained to be better probability

assessors. Methods for representing sensitivity analyses in

quantitative terms, and Empirical Bayes and other methods

for representing multiple estimates, are in their infancy but

offer great potential.

4. Develop more effective methods for

communicating uncertainty to poliiyrnakers.

Part of the reason that policymakers have difficulty dealing

with uncertainty is that scientists do not have good methods

for communicating it. We need to know more about how

policy makers use statistical information. Drawing upon this,

we need to develop better techniques for graphical and numerical presentation of uncertainty, both general and developed

for specific policy issues. To the extent that the uncertainty

is presented in terms that are familiar to policy makers (payroll

taxes and deficits, not birth rates and death rates), the more

likely it is to be understood.

Conclusion

ticians, who are comfortable with precise methods for dealing

with the simple problem of sampling errors. are reluctant

to deal with the messier, and often larger, problems of nonsampling errors. As the initial discussion shows, statistical data

for an aging society are not immune to broader kinds of

errors and uncertainties.

Once we understand the sources of uncertainty, there

are two ways to deal with it: reduce it or live with it.

In most situations, decisionmakers must learn to live with

uncertainty. Statisticians, however, have not yet developed

simple, reliable, and objective methods for the measurement

and reporting of uncertainty. Except for methods for measuring

sampling errors, those methods that do exist tend to be subjective and often controversial. Statisticians must develop better

ways to measure and express the full range of uncertainty

in quantitative terms. The methods described in the last section

of the paper are a modest start, but much more work is

necessary.

As this paper shows, reducing uncertainty is not easy,

even in the long run. Unfortunately, the sources of uncertainty

that are the hardest to measure and discuss quantitatively

are the ones that are the hardest to reduce. But in many

situations some reduction in uncertainty is possible and may

well be worth the cost and effort. Being precise about the

extent and sources of uncertainty is the first step in targeting

efforts to reduce it.

Uncertainty in statistical data for policy is a difficult

problem to resolve. Policy makers would rather ignore it. Statis105
References

Agnew, C. E.: Multiple probability assessments for dependent experts. Journal of the Americun Stutisticul Association 80:343–347,

1985.

Allison, G.: Essence of Decision. Boston. Little-Brown, 1971.

Armstrong, J. S.: Long Runge Forecu.sting From Crys!ul Bull to

Computer. New York. Wiley, 1978.

Bean, F. D., A. G. King, and J. S. Passcl: The number of illegal

migrants of Mexican origin in the United States, Scx ratio based

estimates for 1980. Demoiqraphy 20:99-109, 1983.

Behn, R. D., and J. W. Vaupcl: Quick Analysis for Busy Decision

Makers. Ncw York. Basic Books, 1982.

Crsngrcssional Budget Otllcc: Financing Sociul Security, [ssues and

Option.sfiw the Long Run. Washington. U.S. Congress, 1982.

DcrSimonian. R., and N. M. Laird: Evaluating the effect of coaching

on SAT scores. A recta-analysis. Hurvurd Educution Review 53: 1– 15,

1983.

Efron, B.: The Juckkn~/2, the Bootstrup, und Other Resumplitrg

Plutrs. Philadelphia. Soclcty for Industrial and Applied Mathematics,

1982.

Efron, B., and C. Morris: Data analysis using Stein’s estimator

and its general izations. Journul of the Americun Statistical Association

70:311-319, 1975.

Faber, J. F., and J. C. Wilkin: Sociol Security Areu Population

Projections, /981. Actuarial Study No. 85. Bakimorc. Social Security

Administration. 1981.

Fienberg, S. E.. B. Singer, and J. M. Tanur: Large Scale Social

Experimentation in the United States, in A Ce/ebru/ion of Sturisrics:

The ISI Centenury Volume. Ncw York. Springer-Vcrlag, 1985.

Fischhoff, B., P. Slovic, and S. Lichtcnstcin: Knowing with certainty,

The appropriateness of cxtrcmc confidence. J. E.rp. Psychol. (Hum.

Percept.) 3:552–564, 1977.

General Accounting Office: The Elderlv Shou/d Benej7t From E.rpunded Honre Heol!h Cure but Increus;ng These Sersices Will Not

Insure Cost Reductions. Washington. U.S. Congress. 1982.

Gilbert. J. P.. R. J. Light, and F. Mostcllcc Assessing Social Innovation, An Empirical Base for Policy, in C. A, Bennett and A. A.

Lumsdainc, cds., E\wluution und E.rperimen(: Some Criticul l.ssues

in Assessin%q.$ociul Progrum.s. Ncw York. Academic, 1975.

Gilbert, J. P.. B. McPcck, and F. Mcrstcllcn Progress in Surgery

and Anesthesia, Benefits and Risks of Innowtivc Therapy, in

J. P. Bunker, B. A. Barnes. and F. Mostcllcr, cds., Cos/s. Risks.

und Benefi/s ofSur.qery. Ncw York. Oxford University Press, 1977.

Ho@in, D. C., R. J. Light. B. McPcck, ct al.: Du/u for Decisions:

Itrjbrmotion Strategies ji)r Policymukers. Cambridge, Mass. Abt

Books. 1982.

Koitz. D.: A Siunmurv of the 19&? .$ociu! Security Trtis(ees’ Report

utul S[tI]l~let~l~jtttllryH;.storicul Infh-muiion. Rcp(rrt No. 82–75 EPW.

Washington. Congressional Research Scrvicc, 1982.

Kruskal, W.: Statistics in society, Problems unsolved and unformulated. Journul of the Americun Statistical Association. 76:505-515,

1981.

Light, R. J., and D. B. Pillemcn Summing Up: The Science of

Reviewing Research. Cambridge, Mass. Harvard, 1984.

Manton, K. G., E. Stallard, and J. W. Vaupel: Methods for comparing

the mortality cxpericncc of hctcrogcncous populations. Demography

18:389410, 1981.

May, E. R., and R. E. Ncustadt: Thinking in Time, The Uses qf

History for Decision Makers. New York. The Free Press, 1986.

Morris, P. A.: Decision analysis expert USC. Management Science

20:1233-1241, 1974.

Morris, P. A.: Combining expert judgments, A Baycsian approach.

Munugement Science 23:679-693, 1977.

MostcIler, F.: Assessing Unknown Numbers, Order of Magnitude

Estimation, in W. B. FairIcy and F. Mostellcr, eds., Statistics and

Public Po/icy. Reading, Mass. Addison-Wesley, 1977.

Mostcllcr, F.: Nonsampling Errors, in W. H. Kruskal and J. M.

Tanur, cds., International Encyclopedia of Statistics. New York.

The Free Press, 1978.

Mostellcr, F., and .!. W. Tukcy: Da(a Analysis und Regression,

A Second Course in Statistics. Reading, Mass. Addison-Wesley,

1977.

Passel, J. S.: Methodological Changes in Population Estimates. Paper

presented to the Census Bureau Joint Advisory Committee Meeting.

Rosslyn, Va., Apr. 1985.

Raiffa, H.: Decision Analysis: lntroductor.v Lectures on Choices

Under Uncertainty, Reading, Mass. Addison-Wesley, 1968.

Rice, D. P., and J. J. Feldman: Living Iongcr in the United States,

Demographic changes and health needs of the elderly. Mi/bunk Mere.

Fund Q. 6 I:362–396, 1983.

RivIin, A. M.: Allocating rcsourccs for policy research, How can

cxpcrimcnts bc more useful’? American Economic Review 64:346-

354, 1974.

Siegel, J. S., and M. Davidson: Demographic and socioeconomic

aspects of aging in the United States. Curretr! Popuhltion Reports.

Series P–23, No. 138. Bureau of the Census. Washington. U.S.

Government Printing Office, 1984.

Spctzlcr, C. S., and C. StacI von Holstein: Probability encoding

in decision analysis. Journul of Munu,qemetu Science 22:34G358,

I975.

Spcnccr. G.: Population projection of the United States, by age,

scx, and race, 1983 to 2080. Current Population Reports. Series

P–25, No. 952. Bureau of the Census. Washington. U.S. Government

Printing Office, 1984.

Stokcy, E., and R. Zcckhauscr: A Primer ,Jbr Policy Anulysis. Ncw

York. W. W. Norton. 1978.

106
SIOIO, M. A.: What to Do When the Experts Disagree. Working Warren, R., and J. S. Passel: Estimates of Illegal Aliens From

Paper WP–82-65. Laxenburg, Austria. International Institute for Mexico Counted in the 1980 United States Census. Presented at

Applied Systems Analysis, 1982. the 1983 Meeting of the Population Association of America.

Stoto, M. A.: The accuracy of population projections. Journu/ of

Pittsburgh, Pa., 1983.

IheAmericun StutisticalAssociulion 78: 13–20, 1983a. Winider, R. L.: The quantification of judgment, Some methodological

Stoto, M. A.: Affidavit submitted for defendants in Cuomo v.

suggestions. Journal of the American Statistical Association 62:I IOS–

IMdridge, U.S. District Court, Southern District of Nc.w York, 1120, 1967.

80 Div. 4550 (JES), 1983b,

Tvcrsky, A,, and D. Kahncman: Judgment under uncertainty, Heuristics imd biases. Science 185:1124-1131, 1974.

107
Health indicators for an

aging populationa’b

by Laurence G. Branch, Ph. D., Boston University; Julius B.

Richmond, M. D., Harvard Medical School; David E. Rogers,

M. D., Cornell University Medical College; Ronald W. Wilson,

M.A., Division of Epidemiology and Health Promotion,

National Center for Health Statistics; and Mary M. E. Adams,

Ph.D., Harvard Medical School

It was the best of times,

It was the worst of times, .

--Charles Dickens, A Tale

1. Introduction

Are the closing years

. .

of Two Cities

of the 20th century the best of

times for elderly people in the United States? Or the worst

of times? Or perhaps a bit of both? Unfortunately, the existing

Federal statistical base will not help us with the answer.

The fundamental problem is that, although life expectancy

continues to increase in the United States and people who

survive to age 65 can expect more additional years of life

than at any other time in the history of the world, no one

knows to what extent these additional years of life are years

of independence and vigor, or years of dependency and frailty.

One of the perennial challenges facing health professionals

is to define a yardstick against which one can measure the

health of the population (in this instance, the health of the

elderly population). Is it improving, staying the same, or

failing? Few of us disagree with the goal of improving the

health of older citizens, but many might disagree about how

to measure it, or, more practically, how to recognize improvement if we in fact achieve it.

One response to the challenge of finding the appropriate

measures is to throw up one’s hands in defeat. The New

York Times recently reported that the Administration has proposed a sharp reduction in the Federal Government’s efforts

to gather and distribute statistics about all aspects of American

life (March 31, 1985). However, wholesale reductions might

be shortsighted (Committee on Government Operations, 1984).

Another response is to evaluate critically the current statistics for an aging population, consider what additional information might be necessary for assessing advances or declines

in the health of older Americans, and offer recommendations

for improving the accuracy and appropriateness of health indicators for an aging population in the United States.

The goal of this paper is to suggest constructive approaches

to measuring the health of older Americans. To this end,

we first reconsider the importance of the topic; second, review

the strengths and weaknesses of traditional indicators of health;

third, suggest criteria for appropriate health indicators for

older Americans; and fourth, propose specific alternative health

“Contents arc the opinions of the authors and not those of the Department

ot’Health and Humtin Scrviccs or the organizations they work for.

‘(Q 19x5 Nati(mol kadcmy of.%iwrccs.

indicators for older people during the remaining years of the

20th century.

IL The importance of the topic

Demographics-Changes in demographics and longevity

in the United States have converged to force a reexamination

of the measurement of the health status of older Americans.

The demographic facts are well known (Rice and Feldman,

1983; Soldo, 1980; American Council of Life Insurance,

1982). At the turn of the 20th century, there were only 75

million Americans, and one out of 25 was aged 65 or older.

By 1980, one out of nine Americans was aged 65 or over,

and the population had expanded more than threefold to over

225 million. By the year 2000, it is estimated, there will

be over 36 million people aged 65 years or older; they will

represent over 13 percent of the U.S. population (Rice and

Feldman, 1983). In addition to the sheer numbers, it is helpful

to recognize that since the beginning of time, one-half of

all the people who have ever survived to age 65 are alive

today.

The challenges for both older individuals and society

at large are different now, during the closing years of the

20th century, than they were during the previous decades.

At the turn of the century, few older people entered a stage

of life we could call “healthy retirement,” a stage characterized

by complete or near complete disengagement from one’s life

work, good health and functional robustness, and anticipation

of a steady (if not substantial) pension for the duration of

one’s lifetime. By the latter part of the 20th century, most

people aged 65 years or older have been able to enter this

state of healthy retirement. However, for the robust pensioner

the glamour of a leisurely retirement loses its luster when

income or health fails. At an individual level, it is still easier

to assume responsibility for one’s financial future than for

one’s future health. Notwithstanding the laments of brokers

concerning the uncertainty of financial planning and the boasts

by medical researchers about the near certainty of preventing

certain diseases, the fact is that maintaining the buying power

of one’s retirement funds is still more predictable than maintaining one’s health and vigor.

108
Lon,gevity-The longevity statistics are as compelling as

the demographics (Rice and Feldman, 1983; Soldo, 1980;

American Council of Life Insurance, 1982). In 1900, a man

who reached the age of 65 could expect to live 11 more

years, and a woman aged 65 could expect to live 12 more

years, By 1980, men turning 65 enjoyed a 3-year increase

in life expectancy on the average, to 14 additional years.

Women turning 65 in 1980 could anticipate an average of

6 additional years of life expectancy, as compared with the

1900 cohort, for a total of 18 additional years. The important

issue, however, is the quality of the additional years of life

expectancy for the elders of the 1980’s. Are the additional

years ones of vigor and independence, or of frailty and dependence’?The debate among health professionals has begun,

but with a conspicuous lack of data on both sides.

Katz, Branch, and their colleagues (Katz et al., 1983)

provided a conceptual basis for assessing the quality of life

during the later years of life and presented preliminary findings

based on small-scale demonstration data. Adapting the concepts and methods developed for quantifying life expectancy

that measure the age at which one-half of a group or cohort

will experience mortality, Katz and colleagues measured the

to obtain an estimate of the average number of years of dependency prior to death that a person of a given age is likely

to experience. Based on the pilot data, people aged 65-69

years could expect that 6.5 (approximately 40 percent) of

their remaining 16.5 years would be in a state of dependency.

People aged 85 years or older could expect 4.4 (approximately

60 percent) of their remaining 7.3 years to be spent in a

state of dependency.

Federal statistics have documented the changes in life

expectancy during the 20th century. Unfortunately, there are

no national data showing whether the active life expectancy

has also changed. Therefore, we do not know whether either

the actual number or the proportion of the later years spent

in a state of dependency has increased, stayed the same,

or decreased during the period when the number of additional

years of life has increased.

Ill. Review of traditional public

health indicators

age at which one-half of the initially independent older people MortaMy-For many years mortality statistics have carsubsequently lost their independence in one of three ways:

by death, by entering a nursing home, or by relying on another

ried the burden as the major indicator of health. How people

die tells us something about how they live. Mortality statistics

person for help in performing activities of daily living (e.g.,

bathing, dressing, eating, transferring from a bed to a chair)

have helped to establish public health priorities. The reliance

on mortality as an indicator of a population’s health status

while remaining at home. Figure 1shows how one can subtract is warranted for a variety of reasons.

the estimate of active life expectancy from total life expectancy

20-

18 -

16-

14-

12-

10-

8-

6-

4-

2-

= - _ Active life expectancies,

16.5 Massachusetts, 1974-76

\ 141

— Life expectancies,

Massachusetts, 1974

I

(6.51)k\ 11.6

(6.0)

I 8.9

10.0 --+

-i

“. ---

(4.8)

1

.

6.8

2.9

65-89 7C-74 7.5-79 60-64 85+

Age

F~ure 1. Life expectancies and actiie fife expectancies in Massachusetts

109
First, the fact of death is certified by a licensed physician

and uniformly recorded. This record-based system benefits

from nearly 100 percent compliance. National fact-of-death

mortality statistics are readily available and have provided

an invaluable base for the assessment of the health status

of the population.

Second, mortality statistics have an extremely valuable

inherent quality that is often unrecognized: they provide individually based information at two points in time. Each person

who dies during a specific interval of time was by definition

alive during the previous interval, and the individual’s age

indicates exactly how many previous intervals the individual’s

life encompassed. The ability to measure change for the individual is an extremely important component of health status

indicators, one which we will discuss in greater detail at

a later point.

Third, death is irreversible. Thus each person can be

counted as dead once and only once. There are no interpretive

difficulties, such as whether one person getting pneumonia

twice during a year is better, the same, or worse than two

people getting pneumonia once a year.

Fourth, mortality statistics reflect a clear dichotomy. An

individual is either dead or alive at any instant; there is no

grey area of judgment in between. In some circumstances

there are benefits to having graded outcomes (such as high,

medium, and low). However, monitoring the health status

of the population has been facilitated in the past by the reliability resulting from the unambiguous dichotomous measure of

“dead” or “not dead.”

Fifth, in addition to fact of death, cause of death is

provided on the death certificate. Cause of death is based

on a uniform and well-established classification system, the

International Class@cation of Diseases, which is periodically

updated, most recently in 1979 (Public Health Service and

Health Care Financing Administration, 1980). Consequently,

both all-cause mortality (i.e., all mortality regardless of cause)

and cause-specific mortality (i.e., mortality attributable to

a specific cause such as heart disease, cancer by various

sites, and so forth) can be monitored over time. For example,

in 1980 in the United States the suicide rate for men rose

sharply after age 74, and for men aged 85 years and over

it was alarmingly high at 52.5 deaths per 100,000. For persons

aged 85 and over, neither white women nor men or women

of other racial groups had suicide rates anywhere near that

of white men, who thus contribute disproportionately to the

overall rate. In fact, motor vehicle accidents ranked only

slightly ahead of suicide as a cause of death for white men

aged 85 or older; data for 1980 show that motor vehicle

accidents claimed the lives of 356 men in this age group

and that 328 were victims of suicide. Without knowing that

suicide was the cause of death for these white males 85

and over, some of us might have assumed that their deaths

represented a triumph of the health system because they died

after attaining the age of 85, after reaching a point many

consider to coincide with the end of the lifespan for humans.

The specific cause-of-death information, however, suggests

that it is not a triumph but a tragedy, perhaps resulting either

from the 10SSof social role and social contacts that is often

experienced by very old men in our society or from an individual de~ision to escape the ultimate consequence of a fWd

disease.

Sixth, all-cause mortality in general and cause-specific

mortality in particular have undeniable associations with the

health status of the population. For young and middle-aged

individuals, we have assumed that most deaths are preventiible.

Consequently, many statements of public health goals for

the Nation have been expressed in terms of reduced mortality

(usually cause specific) for specified age groups (usually the

young and middle-aged). For example, table 1 indicates a

summary of the health goals for the six age groups as articulated

by the Surgeon General in the 1979 report Healthy People

(Office of the Assistant Secretary for Health and the Surgeon

General, 1979). Notice that the goals for healthy infants,

healthy children, healthy adolescents and young adults, and

healthy adults were each stated in terms of reduced all-cause

mortality. Notice also that the goal for healthy older adults

was the only one not stated in terms of reduced mortality,

a point to which we will return later. For older adults. some

might argue that substantial reductions in age-specific, allcause mortality rates are probably unattainable because older

adults by definition are reaching the end of the lifespan.

The underlying logic of such arguments is often based on

a variant of the logic of competing risk, which would contend

that mortality at the end of the lifespan is inevitable, tmd

any success in reducing one type of cause-specific mortality

would be offset by an increase in another. A critical assumption

within this argument is that the end of the lifespan is not

Table L The 1990 heafth goala for the United Statea

Group Goal

Healthy infants . . . . . . . . . . . . To continue to improve infant health,

and, by 1990, to reduce infant mortality

by at Ieaet 35 percent, to fewer than 9

deaths per 1,000 live births.

Healthy children. . . . . . . . . . . . To improve child health, foster optimal

childhood development, and, by 1990,

reduce deaths among children aged

1-14 years by at least 20 percent, to

fewer than 34 per 100,000.

Healthy adolescents and

young adults . . . . . . . . . . . . . To improve the health and heallh habits

of adolescents and young adults, and,

by 1990, to reduce deaths among

people aged 15-24 by at least 20

percent, to fewer than 93 per 100,000.

Healthy adults . . . . . . . . To improve the health of adults, and, by

1990, to reduce deaths among people

aged 25-64 by at least 25 percent, to

fewer than 400 per 100,000.

Healthy older adults . . . To improve the health and quality of life

for older adults, and, by 1990, to

reduce the average annual number of

days of restricted activity due to acute

and chronic conditions by 20 percent,

to fewer than 30 days per year for

people aged 65 and older.

SOURCE: Office of the Assistant Secretary for Health and the Surgeon General r-fea/#ry

People, The Surgeon Generaf’s Ffepoti on Health Promotion and Disease Prevent!orr, 1979

DHEW Pub. No. (PHS) 79-5507. Public Health Service Waahinglon U S Government

Printing office, July 1979.

110
many years away for those who have reached the age of

6S, For centuries the consensus usual end of the lifespan

for humans was declared to be approximately 70 to 80 years.

In the 10th century before Christ, King David stated that

“the days of our years are threescore and ten, and if by

reason of strength they be fourscore years . ..” in Verse 10

of Psalm 90. Zoology texts also suggested that “the Biblical

‘threescore years and ten’ is still a fair approximation” of

Iengthof life forhumans (Storer, 1943). Fries has suggested

the mid-eighties for average lifespan under normal circumstances(Fries, 1980).

However, some have begun to challenge this consensus

and point out that insufficient information exists on the issue.

By definition, mortality at the end of the lifespan would

hiive to occur in the absence of a preventable cause of death.

Present conventional medical wisdom suggests that major portions of the second through sixth leading causes of death

for people aged 65 years and over are eminently preventable

(some cancers, stroke, influenza and pneumonia, some arteriosclerosis, and diabetes mellitus). Furthermore, many types

of heart disease, the leading cause of death, are also preventable. Although the vast majority of deaths among this age

group may be due to preventable causes, we have insufficient

information to speculate on the average age at death due

to nonpreventable causes. Furthermore, recent years have witnessed substantial reductions in all-cause mortality in two

age groups: those aged 75–84 years and those aged 85 years

and, older. From 1980 to 1982, all-cause mortality for those

aged 75-84 years decreased by 5 percent, and it declined

by 4 percent for those aged 85 and older. These rates support

the claim that lifespan for humans is not limited to “threescore

and ten. ”

We have discussed the strengths of the mortality statistic:

record based. 100-percent coverage, an individual longitudinal

indicator, an irreversible state, a judgment-free dichotomy,

augmented by specific cause-of-death information, and undeniably associated with a fundamental dimension of health. What

are the liabilities of the mortality statistic? The fundamental

liability is that the absence of death is not all there is to

health. Therefore, mortality indicators are necessary and helpful, but not exhaustive, indicators of the health status of

an aging population.

Morbidi@—Morbidity patterns (i.e., the distribution of

diseases in a population) historically have been useful adjuncts

to mortality statistics and have proved to be another sensitive

gauge of the health of certain populations.

Implicit in the monitoring of the incidence of disease

(new cases during some time interval) and prevalence (total

number of cases at any time within the population) is the

assumption that these rates can be reduced with certain interventions, Interventionscan includeprimaryprevention(avoiding the disease completely), secondary prevention (reducing

the duration and intensity of the disease), and tertiary prevention (reducing the consequences of the disease).

Some diseases are more amenable to counting than others.

The more easily counted include acute conditions of serious

nature, illnesses with rapid onset, diseases with classical presentations-all those whose identification is necessary for the

available effective treatments. These are more accurate] y

counted than chronic conditions for which most treatments

are ineffective (e. g., chronic lower back pain), illnesses with

insidious onset (e. g., glaucoma, cataracts, or senile dementia),

or diseases with atypical presentations (e.g., myocardial infarctions among the elderly). Consequently, procedures for monitoring changes in patterns of disease among populations have

emphasized rapid-onset, classically presenting, life-threatening

acute conditions over insidious-onset, atypically presenting

chronic conditions that are debilitating but not fatal. It is

these chronic conditions that actually may influence the day-today lives of the elderly more than the acute conditions. However, methods of reliably, validly, and accurately measuring

these chronic conditions are underdeveloped. Current morbidity statistics often reflect the same choice as that made by

the drunk looking for lost keys under the street lamp where

the light is better rather than in the alley where he lost them.

It is easier to monitor reliably and validly the rates of those

conditions with recognizable onset and predictable progression

than other conditions, even though some of the latter might

be more important to the individual and to the health status

of the population.

Also germane to the reconsideration of heaIth indicators

for an aging population is this question: To what degree should

contact of individuals with the medical health care system

be necessary for the accurate counting of their morbid. conditions? Recall that mortality data are uniformly available for

virtually 100 percent of all decedents. The same claim of

virtually 100-percent counting cannot be made for most diseases; one of the few exceptions would be tuberculosis, for

which mandatory reporting to a State registry is required.

Consequently, record-based morbidity information is often

an undercount. Self-reported morbidity information (for example, the kind reported in the National Health Interview Survey)

is thought generally to be an underestimate also. Some people

may hesitate to report specific illnesses or conditions if not

confirmed (and labeled) by a physician. However, access

to physicians is not uniform. Furthermore, even elders in

regular contact with physicians often have unrecognized (either

by the patients or by the physicians) conditions, many of

which are treatable. The accurate reporting of morbid conditions is necessary but difficult to achieve.

The interpretation of measured rates and changes in rates

over time is complicated further by improvements in diagnostic

methods, improvements in interviewing techniques, and increases in public awareness and understanding which influence

changes in reported rates of disease.

The weaknesses of morbidity statistics as indicators of

the health status of a population are similar to the weaknesses

of mortality statistics. First of all, there is more to health

than the absence of disease. Second, the connection between

current health status and the history of a specific condition

at a point in time is sometimes tenuous, particularly when

the condition is no longer of consequence (e.g., a successful y

repaired hernia). Furthermore, the hope that the average

number of reported conditions can serve as an indicator of

function and/or as a harbinger of premature mortality is unwarranted. Morbidity status, health status, and functional status

111
simply are different concepts (largely overlapping, perhaps,

but nevertheless not interchangeable).

DL$abi/i@-The third traditional health indicator is disability. Fortheolder person, thepresence ofadisease orcondition

is not nearly as important as its consequences. Some diseases

cause disability only during the acute phase and have no

lasting influence on the long-term functioning of the individual

(e.g., influenza). Other diseases vary not only in the degree

of disability that they cause during the acute phase, but also

in their contribution to the long-term decrease in functional

ability. A cerebral vascular accident, or stroke, is an example

of a disease with a substantial compromise of function during

the acute phase and significant declines in the long-term functional ability of the victim relative to levels of function before

the stroke. On the other hand, arthritis is an example of

a disease with minimal disruption of function during an identifiable acute phase, but it causes long-term functional limitations

among its victims.

Compromised physical function is a final common pathway for many of the diseases affecting older people. Compromised physical function also is assumed by some to be

a consequence of aging that is independent of specific diseases.

The importance of physical function in understanding the health

status both of individuals and of populations is readily agreed

upon, but its measurement is not.

Two common measures of self-reported disability incorporated into the National Health Interview Survey (NHIS) are

days of disability and limitations of activity. Days of disability

are measured in four ways: ( 1) bed disability (days spent

in bed for all or most of the day for reasons of health);

(2) work loss (days lost from work); (3) school loss (days

lost from school); and (4) restricted activity (days during

which a person reduces normal activities for all or most of

the day because of an illness or injury). Bed-disability, work10SS, and school-loss days each would be included in the

fourth category. Statistics on days of disability per person

per year are further refined by the percentages attributable

to acuteversus chronic conditions.

The concept of limitation of activity in NHIS refers to

the long-term reduction in activity attributable to chronic disease and impairment. The measurement of this concept in

NHIS permits four distinctions: (I) those unable to carry on

their usual major activity, (2) those limited in the amount

or kind of their usual major activity, (3) those limited but

not in their usual major activity, and (4) those not limited

at all. Before 1982 the major activity of an individual was

detined by the usual activity for each age-sex group, for

example, ordinary play for preschool-aged children, going

to school for people of school age, keeping house for females

who are not in the paid labor force, and working for all

others including retired males. An obvious defect in the measurement of limitations in major activities prior to 1982 was

the undetlned nature of major activity for males who were

retired from the paid labor force. How was a retired individual

to respond to the question of whether he is unable to work

for reasons of health when his company enforced a mandatory

retirement policy predicated on an assumption of reduced

work capacity among older workers. but the retiree thinks

he is still capable? Since 1982, limitations in major activities

were measured among all people aged 60 or older by independence or dependence in activities of daily living (such as eating,

dressing, bathing, shopping, or everyday household chores);

all individuals aged 18-69 were asked also about possible

limitations in their ability to work for pay. Usual activities

remained the same for preschool children, school-age children,

and those keeping house (up to age 70). The 1982 revisions

are a definite improvement.

The primary strength of the use of disability as a health

indicator for an aging population is its focus on function—the

person’s capacity or ability to perform appropriate activities—

which presumably is closely related both to the individual’s

quality of life and the aggregate health status of the population.

In general, the limitations of disability status as a health

indicator for an aging population are weaknesses of application,

not limitations of the concept. Specifically, additional improvements in the measurement of the concept of disability are

necessary, as is a consistent approach to measurement in

those areas that reflect consensus. The refinement of the concept of “major activity” for retirees not keeping house is

an example of a recent advance in measurement.

One nonobvious implication of advances in measurement

deserves mention. What do we do with the less accumte

data? The temptation is strong, and not resisted by all researchers, to disregard the inherent unreliability of the undefined

concept as it existed prior to 1982 and to present the agespecific rates of limitations in major activity over time as

an indicator of a changing health status of the elderly population. Professionals differ on whether compromised data are

better than no data at all.

IV. Five criteriafor appropriate

health indicators for an aging

population

The criteria for,generating appropriate indicators of health

status for an aging population are discussed in the context

of the following five issues: ( 1) study design, (2) sample

selection, (3) instrument development, (4) data merging, and

(5) data analysis.

Study design—A longitudinal panel study design is essential (that is, a design calling for repeated interviews with

the same individuals over an interval of many years). Repeated

cross-sectional data (that is, data from different individuals

interviewed at different times) invite difficulties in interpretation because observed changes over time can be attributed

either to real changes in the population or to selective mortality

in the population. For example, consider the hypothetical

(and quite impossible) case of a country with a total population

of 1,000 elders, 750 of whom thought their health was great

and 250 of whom thought their health was terrible. Suppose

we did an accurate sample survey last year, and found the

correct proportion of 75 percent in great health and 25 percent

in terrible health. Suppose further that during the course of

112
the following year, the 250 who thought they had terrible

health in fact did, and they all died. Then we do a new

sample survey, it is accurate, and we estimate that 100 percent

of the population has terrific health. The simplest interpretation—that the health of the elderly population in this country

has improved this year compared to last year—is misleading;

none of the individuals with initial poor health experienced

improved health status.

This example is used to demonstrate at an intuitive level

that repeated cross-sectional data can be misleading in certain

circumstances, and the aging population frequently embodies

those circumstances.

Consider another hypothetical situation in the country

with 1,000 elders, 600 of whom demonstrate independent

physical function and 400 of whom demonstrate compromised

function. Suppose further that one-half (200) of those with

compromised function regain independence during the following year, and the other one-half (200) remain dependent.

Suppose further that 200 who were initially independent become dependent. Our second sample survey would then estimate that 60 percent of the population was independent in

physical function at the second year, and 40 percent had

compromised function, just as we found at the first year.

Would the public health czar of this country suggest different

programs if he realized there is one group who continues

with physical disabilities, a second group whose function improved, and a third group whose function recently deteriorated?

Would the czar recommend still different services if there

were only one group of disabled individuals: those who once

became disabled and remained disabled?

Sample selection-A second criterion of appropriate health

indicators for an aging population is that the participants in

the longitudinal panel study design must be both a large and

a representative sample. The aspect of sample size is important

because public policy discussions about options for the health

care delivery system frequently require specific information

on relatively small subgroups of the population. For example,

in the context of capitated prospective payment systems in

which the provider of health care shares in the financial risk

that a group of enrollees might require more health care services

than initially budgeted, health planners are fi-ustrated by the

lack of national representative data which specify the probabilities with which subgroups of older people historically

have entered long-term care institutions. Does hospitalization

history alter the probability that a person over age 70 will

enter a nursing home within 5 years? Do those with functional

deficits which produce dependency in basic activities of daily

living require longer lengths of stays in acute care hospitals

compared to their agemates without the physical disabilities?

Are those who usually use walkers and/or wheelchairs more

prone to use home care services than their agemates? What

characteristics distinguish those who will use the less expensive

home-based long-term care services from those who will use

the more expensive institutional long-term care support?

The most rudimentary information concerning issues like

these requires a longitudinal panel design with a sufficiently

large number of people to have adequate numbers of those

with the relatively infrequent characteristics of concern (in

these examples, dependency in activities of daily living or

using ambulatory aids).

Our national health statistics system has been unable to

provide data on these fundamental issues of health policy.

In 1983 the national health expenditures of $355 billion approached 11 percent of the gross national product (Gibson

et al., 1984). Perhaps it finally will seem reasonable to devote

a most modest fraction of that amount to obtaining an appropriate repository of national health indicators for an aging

population. The right health indicators can clarify and thereby

rationalize the over $100 billion business of health care for

the elderly by providing the basic descriptive and actuarial

information that is currently unavailable.

Concerning the issue of representativeness of the large

sample participating in a longitudinal panel study, it is important to recognize the need to generalize the information obtained from the sample to the population as a whole. The

issue of representativeness is intertwined with both the previous

issue of sample size and the subsequent issue concerning

the type of information collected. Concerning the interactin~

issues of sample size and representativeness, the sample shouid

adequately represent important subgroups such as black elders,

rural elders, Hispanic elders, and Native American elders,

and these subgroups of elders do not live in approximately

equal proportions throughout the United States. Therefore,

samples clustered in one geographic area or even a few geographic areas run the risk of inappropriate representation of

important minorities. Consequently, the large representative

sample should be drawn from many diverse places across

the Nation.

Instrument development—The third criterion for appropriate health indicators deals with the type of information obtained

during the interview. Self-reported information has had a useful

history in research on health indicators. Methodological studies

have demonstrated the reliability and validity of most types

of self-reported health information, including the utilization

of health services; functional status (physical, cognitive, emotional, and social); the knowledge, attitude, and practices of

individuals relative to their own health care: and, to a lesser

degree, their own medical histories. Obviously, self-reported

information would be a part of the information collected.

In addition to self-reported information, other studies have

demonstrated the utility of physical examination and laboratory data as health indicators. The merits of examinations

such as blood tests, measurements of lung function and blood

pressure, oral health examinations, and a variety of others

have been demonstrated repeatedly. The incorporation of any

of these kinds of examinations or tests would be a valuable

addition to the health indicators information base. However,

their inclusion would preclude conducting the interview by

telephone and likewise would be very difficult to perform

in the participant’s home without appropriate portable

equipment.

Actual performance testing of physical function would

be another valuable addition to the health indicators information base. The concordance between self-reported capacities

and actual performance abilities has been an issue of recurring

professional debate. A battery of performance tests should

113
be developed as part of the health indicators for an aging

population. Among the candidates for inclusion would be

stair climbing, walking one-half of a mile on a treadmill,

tests of the range of motion of the arms and legs. grip strength.

torque strength as measured by removing the cover from a

jar. manipulator ability as measured byopening the current

“safe” medicine containers. and a variety of other tests.

Any consideration of a large. nationally representative

sample undergoing performance tests and selected examinations emphasizes the need for sophisticated mobile equipment

to support the collection of the type of information necessary

for health indicators for an aging population. A group of

interviewers with long-distance telephone lines is insufficient.

Data merging-The fourth criterion of health indicators

for an aging population is the availability of refined

mechanisms for merging appropriate information from other

record sources for the large representative national sample

participating in the longitudinal panel study. The ability to

merge the individual’s Medicare utilization history with data

from his or her self-reports, performance, and examinations

is necessary. If record-based information of reasonable accuracy already exists, it is less reliable to ask the individual

to duplicate or summarize the existing information. Merger

of panel study information with information on decedents

from the National Death Index is also appropriate.

Other papers commissioned by this panel of the National

Academy of Sciences address the issues involved in merging

dispersed data files.

Data atzalysis—A fifth criterion of health indicators for

an aging population concerns the analytic capability to develop

composite indicators that would supplement single items and

to examine an individual’s patterns over time rather than just

a snapshot at one point in time. In some instances, single-item

measures from one point in time on some aspects of the

health of the elderly are certainly appropriate indicators of

health. For example, cross-sectional prevalence rates for certain things like disability days or the post-1982 limitations

in major activities are valuable. In other instances, composite

indicators from cross-sectional surveys are useful. For example, some investigators have tried to develop composite indices

of physical disability by combining information from Katz’s

activities of daily living items (Katz and Akpom, 1976), some

items from Resow and Breslau’s functional health scale

(Resow and Breslau, 1966), and some items from Nagi’s

disability surveys (Nagi, 1976) into a cumulative disability

score (Pinsky et al., 1985). In still other instances, accurate

representations of the functional histories of individuals are

critical, and these historical patterns require longitudinal information. In the case of the example repeatedly used in this

paper, any increase in the years of life without a delay in

the onset of disability would yield an increase in the proportion

of dependent years at the end of life, a dubious public health

goal. The longitudinal history of physical function among

individuals is necessary in order to examine this phenomenon.

V. Recommendations for

specific health indicators for an

aging population from 1988 to

2000

The context of the following recommendations is that

health care is second only to defense in its proportional contribution to the gross national product of the United States.

In 1983, $355 billion were spent on health care in the United

States: this sum represented 10.8 percent of the gross national

product in that year. In light of this magnitude of health

care expenditures, it is surprising that basic information necessary to clarify some of the most fundamental issues of health

policy is still lacking. For example, the national statistical

base does not have the data that would tell us whether the

years that have been added to life expectancy in the United

States during the 20th century are additional years of independence and vigor, or additional years of dependency and frailty.

The national statistical base is insufficient to implement rationally what has been described as the most significant change

in the health care system since the introduction of Medicare

and Medicaid in 1965, namely, the conversion of Federal

Medicare reimbursements for acute hospital stays for older

people from a retrospective cost-reimbursable basis to a prospective payment system based on uniform payments for diagnostically related groups of illnesses requiring hospitalization.

Within the context of this type of prospective payment system,

the Federal Government is trying to entice providers of health

services to share the financial risk of care for Medicare beneficiaries. Appropriately, the providers have requested some

elementary information, such as whether those with greater

physical disabilities are more likely to be hospitalized regardless of the diagnosis. This basic information is simply unavailable. A rational and planned health care system would require

information about the characteristics distinguishing those who

need institutional long-term care from those who need longterm care or support services provided in their homes. This

information is also unavailable.

At this time, our national health statistics system cannot

answer these kinds of basic questions. Unfortunately, our

present national health statistics system will not be able to

answer those kinds of questions in the 1990’s either.

How the absence of such fundamenvd] information might

have contributed to the inefficiencies of the health care system

in the past is not our concern now. Rather, our present concern

is to ensure that such essential information concerning health

indicators for an aging population will be available in the

future.

We recommend that the National Center for Health Statistics participate in a joint public and private effort to establish

a longitudinal panel study, with a large representative national

sample, which would provide self-reported information as

114
well as data from performance tests and physical examinations,

collected on a biannual basis. The reasons for recommending

that the National Center for Health Statistics coordinate the

public participation stem from its long and laudatory history

of ensuring precision and rigor within national data files.

The reasons for recommending private foundation participation

in this effort stem from a desire to increase support and

funding stability for a project of indefinite duration and to

fticilittite a public/private partnership in analyzing the data

and churting national health goals.

Furthermore, we recommend that mechanisms for merging

the information from the panel study with existing recorded

information for the participants be developed and become

part of a national system for health statistics. Specifically,

the National Death Index and Social Security files should

be merged with information from survey participants. Legislative initiatives will be required to overcome certain existing

obstucles to record linkage.

In order to collect information about the transition to

retirement and to capture an accurate assessment of the knowledge, iittitudes, and practices of late middle-aged individuals,

we recommend that the age of eligibility for the participants

in the national longitudinal panel study be 55 years. Furthermore. both institutionalized and noninstitutionalized participants should be enrolled. In addition, new cohorts should

be enrolled periodically into this panel study of indefinite

dumtion.

The mechanisms of data collection should incorporate

self-rcptwted information, performance tests, and data from

htxdth examinations. Accordingly. mobile testing and laborahwy equipment would be needed to support this longitudinal

pimclstudy ofhwdth indicators among an aging population.

The feasibility of employing students from schools of

medicine, nursing, social work, and other allied health professions to ussist in data collection should be explored. It would

be a valuable experience for these students to participate in

the collection of a national statistical data base that provides

the health indicators for an aging population, and which in

turn is used to modify our approach to delive~ of health

services. In addition, these students would soon become the

professionals whose fields would benefit from the information

obttiirwd. The overall goal of a national system of health

indictitors for an aging population would be to assess our

progress toward our targets and to modify our health care

systcm, This goal would be facilitated by an informed cadre

of health service providers. the nucleus of which could be

formed by these student participants.

These recommendations build on the accomplishments

of (~ther successful cross-sectional and longitudinal studies.

Among the cross-sectional studies which have successfully

implemented parts of our recommendations are the National

Health Interview Surveys, the National Health and Nutrition

Examination Survey (NHANES), and medical expenditure surveys including the National Medical Care Expenditure Survey,

the National Medical Care Utilization and Expenditure Survey,

and the National Expenditures Survey. NHIS has pioneered

work on self-reported health indicators for older people.

NHANES has developed portable examination equipment suitable for older respondents. The several expenditure surveys

have demonstrated the abilities of respondents to provide complex information to interviewers.

Successful longitudinal studies which have demonstrated

the feasibility of parts of our recommendations include the

Framingham Heart Study (FHS), the Longitudinal Study of

Aging (LSOA), and the Health and Nutrition Examination

Survey I Epidemiological Foliowup (HANES I EFS). FHS

has demonstrated an ability to maintain high participation

rates for over 30 years among a panel of community participants subjected to laboratory tests, interviews, and examinations on a biannual basis. LSC.)Ahas demonstrated the ability

to enroll and maintain a national sample of elderly participants

over a series of interviews. HANES I EFS has demonstrated

the ability to enroll and maintain a national sample of elderly

participants in an examination and performance testing study.

The feasibility of merging data files from dispersed record

sources has been demonstrated by some small-scale projects.

In summary, our recommendations for improving the

health statistics for an aging population require the establishment of a new national statistical effort with the following

characteristics:

A continuous longitudinal panel study, with precision

and rigor comparable to current surveys of the National

Center for Health Statistics.

A large representative national sample of institutionalized

and noninstitutionalized adults aged 55 and over.

Periodic enrollment of new cohorts.

Obtaining self-reported, performance, and examination

information on a biannual basis.

Ability to merge the study data with existing record information for the participants.

Use of students of medicine, nursing, social work, and

other allied health professions to assist in data collection.

Data available for public use in a timely fashion.

The information from this national statistical effort can

enable the Nation to monitor the health status of our aging

population, to set health goals for our elders, and to chart

our progress toward those goals.

The monetary costs of mounting this kind of national

statistical effort are considerable, but the human costs of not

undertaking this challenge are even greater. For the sake of

current older people and future groups, we cannot afford

nor to undertake this national statistical effort.

115
References

American Council of Life Insurance: Dututruck 9: Older Americuns.

Washington, D.C. American Council of Llfc Insurance, 1982.

Committee on Government Operations: The Federal Slu(istical $vstem: 1980 to1985. Washington. U.S. Government Printing Office,

NOV. 1984.

Fries, J. F.: Aging, natural death and the compression of morbidity.

N. ,5rgl. .J. Med. 303(3):13G135, 1980.

Gibson, R. M., K. R. Lcvit, H. Lazenby, and D. R. Waldo: National

health expenditures, 1983. Hea/th Cure Financing Review. Volume 6, No. 2. Office of Research and Demonstrations, Health Care

Financing Administration. Washington. U.S. Government Printing

Office, 1984.

Katz, S., and C. A. Akpom: A measure of primary sociobiological

functions. [trt. J. Health Serv. 6:493-508, 1976.

Katz, S,, L. G. Branch, M. H. Branson, et al.: Active life expectancy,

N. Engl. J. Med. 309(20):1218–1224, 1983.

Nagi, S. Z.: An epidemiology of disability among adults in the

United States. Milbank Mere. Fund Q. 439-465, Fall 1976.

Office of the Assistant Secretary for Health and the Surgeon General:

Healthy People, The Surgeon Gerwrul’s Report on Health Promotion

and Disease Prevention. 1979. DHEW Pub. No. (PHS) 79–5507.

Public Health Service. Washington. U.S. Government Printing Office, July 1979.

Pinsky, J. L., L. G. Branch, A. M. Jette, et al.: Framingham

disability study: Relationship of disability to cardiovascular risk factors among persons free of diagnosed cardiovascular disease. Am.

J. Epidemiol. 122(4):644-656, 1985.

Public Health Service and Health Care Financing Administration:

ln~ernational Clu.@Ication of Diseases, 9th Revision, Clinicul Mod-

[Ycation. DHHS Pub. No. (PHS) 80-126o. Public Health .%rvicc,

Washington. U.S. Government Printing Office, Sept. 1980.

Rice, D. P., and J. J. Feldman: Living long in the United States,

Demographic changes and health needs of the elderly. Mi/bank Mwn.

Fund Q. 61(3):362–396, 1983.

Resow, I., and N. Breslau: A Guttman Scale for the aged. J. Geron/o/.

21:556-559, 1966.

Soldo, B. J.: America’s elderly in the 1980’s. Population Bu//erin

35(4):3–36, 1980.

Storer, T. 1.:General Zoology. New York. McGraw-Hill, 1943.

116
Chapter #11

Issues in improving data resources
Research for tomorrow’s

elderlyayb

by George C. Myers, Ph. D.,

Duke University Center for Demographic Studies

Introduction

The purpose of this report is to review the state of current

etlwts to forecast the nature of our future aging population

and some major issues that are likely to emerge from these

developments. Recent reviews of substantive findings from

existing projections of the aged population and some of its

complwitiorral characteristics have recentty been made (U.S.

Burewt of the Census, 1984; Myers, Manton, and Bacellar,

19/35;Manton and Soldo, 1984). In addition, an examination

of computer data bases was completed in 1982 by the U.S.

General Accounting Office for the Senate Special Committee

on Aging. The scope of this report will not duplicate these

efforts, but rather will assess the methodological aspects of

various forecasting activities and raise issues intended to make

these endeavors more meaningful.

The major areas of concern for policy decisionmaking

relate to demographic factors, health, family structure, and

economics. In this report particular attention is given to the

first two factors. An emphasis on various dimensions of health

is justifiable not only because of the significant impact that

population aging has upon this dimension, but also in recognition of the importance that health and its endpoint, mortality,

have upon the growth and changing composition of the aged

population. Therefore, it is both an input to and an outcome

of prtljccticmefforts.

The aged population is a dynamic segment of the population thut is constantly being modified by both new entrants

retiching the older ages (e.g.. age 65) and exits through dying.

The “natural increase” between these two forces modifies

not only the size of the aged population, but also its composition. At the same time, there may be changes among the

population of older persons that occur through the normal

aging process and other changes that result from behavioral

factors (remarriage, movement, etc. ). Thus, the aged population is tiltered both in size and composition by the new cohorts

entering the older ages and the internal dynamics taking place

during this period of life. The high rates of turnover in the

+@ population (nearly 60 percent of the persons in the population 65 years of age and over are not found among this

suhpopulation 10 years earlier) give support to this view.

In this report, attention is really being given to the population

45-64 years of age, who will become the new aged in the

next 20 years—up to the year 2005. While subsequent cohorts

of the new aged after the year 2005 will be larger in size.

reflecting the Baby Boom generation, those persons currently

45-%4 years of age represent cohorts which are very different

in educational, marital, income, and perhaps health characteristics from cohorts of persons who are presently aged. They

merit, therefore, special research attention.

The dynamic aspects of population change among the

older population must be viewed within the context of an

aging society whose social and economic structures are also

undergoing change. To assess major issues arising from these

demographic and societal developments requires forethought

along multiple dimensions. Demographic projections per se

provide only one facet for our consideration. We also are

interested in examining how other societal structures and policies would be affected and, in turn, how various policy options

can influence the projections. Thus, the aim of projection

activities can be viewed as at least threefold. One, they can

create a new awareness of emerging issues. as in the case

of the growth of the aged population. Two, they enable us

to assess the relative importance of different parameters; for

example, how the relative health status of the population

relates to mortdit y, which also affects the growth and structure

of population. Three, projection models enable us to examine

existing policies, their implications, and how alternatives may

be needed to address emerging issues.

In this report, we will attempt to examine the basic national

population projections, focused projections that often rely directly on these efforts, and other types of projection modeling

that are intended either to probe in greater depth input or

outcome dimensions or to incorporate different models into

integrative frameworks.

Projecting the aged population

Most projections of the aged population are derived as

simply one age segment in a total population projection. While

this should not cause any problems from a theoretical point

of view, it does raise some practical issues that have arisen

in many of these projection efforts. One, inadequate attention

is given to age detail at older ages, i.e., truncation at ages

75, 80, or 85. Two, the absence of detailed data at the extreme

older ages limits the attention that can be given to many

of the factors that may be of particular interest to the policy

analyst (e.g., institutionalization). Three, little consideration

is given to problems that may arise with respect to the quality

119
of data at older ages. These problems can involve age misstatement, underenumeration, inaccurate reporting of characteristics, etc.

Perhaps a greater concern derives from the fact that the

rates of transition between relevant states at older ages are

often much greater than at younger ages. Therefore, greater

sensitivity is implied for purposes of adequate estimation.

For example, mortality rates after age I tend to increase

throughout life and reach much higher levels at extreme ages.

The 1982 death rate for males was 546.1 per 100,000 at

ages 45-49 but nearly 20 times greater at ages 80-84. Thus,

the margins of error in estimation are often greater at older

ages, and unless special care is given to these issues, inappropriate findings for the aged population can result. High transition rates among the aged also apply to morbidity, hospitalization, retirement from full-time employment, and the like.

In contrast, the fact that some transition rates are much lower,

as for geographic migration, marriage and divorce, and housing

mobility, should make the estimation task relatively easier

for older persons.

In short, there are a number of particular issues that

are raised in dealing with the aged population which suggest

that specific efforts are called for in projecting their future

states.

Projection shortfalls

The past record with respect to the accuracy of projections

of the aged population has not been an enviable one for

the most part. An examination of 23 major sets of projections

made by the U.S. Bureau of the Census since 1947 shows

the projections (more recently, the medium variant set) to

have consistently underestimated the subsequent census enu-,

rnerations, even over the short term (Myers, 198I). This is

true for the aged population as a whole (65 years of age

and over) and for both sexes, both white persons and persons

of other races, and even for different age groups within this

subpopulation.

The main reason for these shortfalls primarily concerns

the mortality component and specifically the survival ratios

that have been used, both at adult ages under age 65 and

at the later ages. It also should be pointed out that some

of the inadequacy rests on the apparently inadequate base

populations that are used, as well as unexpectedly more complete enumerations, such as in 1980, in which much larger

numbers of older persons than expected were reported. These

are due to highly technical matters involved in the projection

procedures followed, which will not be discussed in this paper.

Yet another part of the problem can be traced to the ad

hoc basis on which projections are prepared.

In reviewing these forecasts, one becomes aware of the

lack of regularity in issuing official sets of productions. The

23 different sets prepared by the Bureau of the Census since

1947 have been issued at many different time points (e.g.,

only years ending in 6 and 9 are apparently omitted). So

too, the series have beginning dates that are not systematic.

Surprisingly, few begin with census dates, although clearly

base populations for these dates (or estimates for July of

census years) would seem most reliable, especially after postenumeration adjustments are made. One is tempted to conclude

that the use of varied beginning dates offers a hedge against

subsequent questions of accuracy (Myers, 1983). In addition,

the periods covered by the projections have tended to vary

widely, with more recent sets extending farther into the future.

For example, the most recent Census Bureau projections extend

to the year 2080. It might be suggested that this lack of

regularity simply implies that new sets (or additional series)

are prepared on]y when they are needed, but the recent gap

between 1977 and 1984, when the last official version was

issued, belies this assertion. Certainly there was strong evidence that the 1977 forecasts were deficient in many respects,

particularly in the mortality assumptions. A delay in preparing

a new set of forecasts was no doubt due to the decision

to wait until the 1980census figures were available.

With respect to the single-factor projections of other characteristics, a similar situation exists as with the basic demographic forecasts. They are issued at irregular intervals, have

varied beginning dates, and cover different time horizons.

However, very few of these projections extend for more than

25 years. In addition, while the projections of social and

economic characteristics are generally based on the so-called

official demographic forecasts, these specialized forecasts

often use selective series and, at times, somewhat divergent

methodological approaches (Long, 1981). As Long notes,

“Perhaps the most striking results of this overview of projections methodologies are the lack of a mechanism for assuring

consistency among projected variables and the apparent arbitrariness of many of the assumptions used....“(1981, p. 3 17).

Types of projections

It is important at the outset to address the various ways

in which the future states of the population can be examined.

(1) Basic population projections, such as those issued

by the U.S. Bureau of the Census and often considered “official” because they are so widely used, are the most commonly

available sources of information about the future age, sex,

and race composition of the national population. Moreover,

the set of Bureau of the Census State and regional projections

is developed to be consistent with these basic projections.

The Office of the Actuary of the Social Security Administration

prepares long-term population projections with a comparable

cohort-component approach (Social Security Administration,

1984b). In addition to age and sex characteristics, the projections also treat marital status in an endogenous manner. It

should be noted that race is not treated in these projection

series. These projections utilize cohort-component/demographic accounting procedures in which each of the components is independently estimated. Land (1986) and Long ( 1985)

have recently reviewed the various methods that have been

used to forecast components.

The basic national projections form the basis for a range

of single-factor projections prepared by the Bureau of the

Census and other Federal agencies (Long, 1981; U.S. Bureau

of the Census, 1983, appendix). They relate to educational

attainment, household and marital smtus, labor force status,

120
MC. A ~h;l~it~t~~i~ti~ f~iiturc ot’ these national prf)jecti(ms is

that they employ fuirly c(mven[ional rittio procedures by upply -

ing fixed ratios or ratios changing in some prcdcternlined

pattern [o the distiggmgated base figures found in the projection

wits [usuully the middle varitint. although sometinms with

;dtetmutivc sets (W even special base distributions). So too.

Mmw range (Jr ratio values may be introduced. In al I cases.

these projections are single -fwtot’ projections in the sense

that (hey do m~t incorporate interactions between the outcome

facttw and the dynamic fwtors that “drive” the basic projecti(ms-fertility. mortality. and migration. For example.

chtinges in wiuctitionirl levels would not affect fertility outctmws. although they might be expected to in reality.

(2) There are a number of more ,q/oha/ models that attempt

tt~ intcgmte different subnmciels or modules into i comtnon

project ion tk.unework. In particular. there are numerous aggregate ect)tli)ll~ic-delll{~graphic models that have mainly treated

the dcrm~:raphic components as exogenous for purposes of

shtwt-tm-tn forecasting. Ahlburg (to be published) htis reviewed

ttcent attempts to incorportik a number of endogenously determined ctmlp[ments, For example, the Brook ings. Chase. and

Whirrt(m econometric models now hove developed modules

m accomplish this in a variety of ways. Although most of

these mmkls have not been used extensively to project specifically the aged population or econotnic outcomes related to

the gr(wh ot’ the older population. a t’ew models of this

t} pe hove been developed or modified m provide longer term

pr~~jegiw-ts ofthe tiged.

The National Institute on Aging Macroecm-mmic-Demo-

.gmphic Model is a globol, long-term model that is intended

tt~ tisscss how the changing age structure of the population

will tiffcct the income level of the elderly population. as

well us productivity, consumption. saving. and investment

(Natiorml Institute on Aging. 1984). The model treats all

popul;ltionft ictorsexogenously within the comprehensive, integrated model oflong-tertn economic growth and labor markct. These. in turn. are related to major features ot’natiomrl

pensi(m systems and trunsfer programs. This means that

economic fticturs contained in the other modules have no

impoct (m the population dimensions,

An[~ther large-scale macrueconomi cmodeldevelopedby

Dtitu Rcstu-ces, Inc., incorporated a Demographic-Economic

Mtdcl t}fthc Elderly for25-year projection so ftheeconomic

i]l~plicoti~)ns ()fantiging p()pulati()n (Olsen, Cat()n. and Duft’y.

198 l). The demographic variables are determined exogenously

in this tmxiel also.

Yet another-strategy of global modeling involves microsirnulutitm rather than macrosimulation. Although models of

this type seem to have fallen from favor somewhat. they

still hold promise if adequate estimation procedures can be

introduced. These types of models show probabilistic estimates

(Monte Curio procedures) of transitions fordifferent pammekrs t~~opcrute on a sample population over time, thus changing

their status distributions (e. g., marriage, childbearing, and

emplt~ynwnt). An early mitt-osimulation model developed by

the Research Tritingle Institute. called POPSIM, was pmticuIat-ly useful in eviduating the effects ~~fdifferent patterns of

childbearing anti ctmtmceptive regimes upon population

(>rowth. Another widely utilized micros imuitition tTlOdel. .S

DYNASIM. was prepared by the Urban Institute (W;rtheimer

and Zedlewski. 1980). It has been used to exwnine labor

force dynirmics such as employment. hours worked. retirement, and the impact on these dimensions of vw-ious changes

in Federal legislation (Hendricks and Storey. 1981: Anderson

w-td McNuught. 1982). A preliminary research project using

a microsimulation model [o examine future health status was

conducted by the Duke University Center fur Demographic

Studies (Myers et al.. 1977). Exercises were conducted using

the model based on transitions among 10 disease states and

subsequent death for the elderly population to the year 2000

and for the population of U.S. male vetetms. Although these

efforts were successfully completed. some serious shortcomings in the ptmuneter estinmti(m. due mainly tu insufficient

and inadequate data. were enc(wntered.

(3) A third type Of projection can be identified that deals

with tnore ,/iKw.wd objectives. These endpoints often are intended to explore the implications ofcertain population changes

on matters of potential national importance. such as service

and F~cility demand’s. labor force requirements. and expenditures. Like the single-factor projections of population characteristics. they may be limited to simple application of fixed

ratios to the population counts or mtiy involve u chain of

variables directed toward certain endpoints, For example, the

Health Care Financing Administration has examined health

expenditures through such procedures (Free land and SchendIer, 1983). The Socitil Security Administration ( 19840) has

used its projections tu assess the future requirements of the

Old Age. Survivors. and Disability Insurtuwe (OASDI) system

and its fiscal health. The income projections of the Bureau

of the Census and sevet-d efforts at the National Center fur

Health Statistics to assess future levels of health status. utilization of services. and health expenditures also are of this type

(National Center for Health Statistics. 1983). It should be

noted that these focused projections may involve alternative

series from a baseline population projection or tilternative

assumptions dealing with the factors to be estimated.

Moreover. ench of the outcomes may entoil more complex

modeling as well. Thus, on both the input side. in terms

of components of change affecting the population characteristics. and on the outcome side. in terms of structural ktors.

there may be quite sophisticated treatment of estimation equations for forecasting.

This brief review of projection techniques is not intended

to be exhausti~re. but rdther to be indicative of the variety

of approaches that characterizes the field of forecasting. In

the sections to follow. we will examine projection efforts

applied to older persons that illustrate these approaches in

terms of certain important demographic, heulth, social. and

economic dimensions.

121
Data available

Population characteristics

The two most general and, no doubt, widely used projection series are those issued by the Bureau of the Census

and the Office of the Actuary of the Social Security Administration. There are similarities between these two activities in

at least three respects. One. they are issued on a recurring

basis—the Census Bureau projections were last released in

final form in May 1984 and the previous set was released

in 1977, whereas the Social Security projections have been

issued on an annual or biennial basis since 1974. the most

recent being issued in May 1984. In both cases, theprojectiorrs

are long term. extending to the year 2080. with data for

selected intermediate years reported. Tn’o, the projections are

made according to age and sex in both efforts. but race is

included onlyinth eCensusprojectio ns.Since1980, the Social

Security projections also include marital status. Three, the

projections involve conventional cohort component methods

and even share alternative series assumptions. Thus. the Census

Bureau utilizes the mortality forecast assumptions of Social

Security as a starting point for their projections, while the

reverse exchange between the agencies is true of fertility

and net migration assumptions.

Nonetheless, there are important differences between the

two agency efforts. The major difference is in the population

coverage. In published reports, the Census Bureau projections

cover the resident population of the 50 States and Washington,

D.C., along with Armed Forces overseas. The so-called “Social Security Area” is broader and also includes the civilian

residents of Puerto Rico, the Virgin Islands, American Samoa,

and Guam; Federal employees and their dependents overseas;

and other citizens overseas. The intent of the Social Security

Area is to reflect those covered by the OASDI system. In

1982 it was estimated that an additional 9 million persons

are found in this enlarged Social Security Area compared

with the Census coverage, with 681,000 of these persons

65 years of age and over.

A second difference between the two projections relates

to the fact that the Census Bureau reports results of 30 alternativeseries of projections, based on three alternative assumptions for fertility and mortality and four assumptions for net

international migration. The Social Security published reports

contain information on only three alternatives. The casual

user of these alternative series must be made aware that the

two extreme Census alternatives (called Highest and Lowest)

do not correspond to Social Security Alternatives 1 and HI.

The Census extremes reflect highest population growth (low

mortality and high fertility) and lowest growth (high mortality

and low fertility); whereas the Social Security extremes reflect

the lowest fiscal burden on the OASDI system (high mortality

and high fertility) and the highest (low mortality, low fertility).

Moreover, the middle series also differ in terms of fertility

(completed fertility of 1.9 children in the Census projections

and 2.0 in the Social Security series). Mortality assumptions

also differ in the two series, as can be noted from the levels

of life expectancy at birth forecast for the terminal year 2080:

Male Female

Social Social

Mortality Census Security Census Security

Low . . . . . . 80.83 83.8 90.98 91.8

Middle . . . . . 76.75 77.6 85.22 85.5

High . . . . . . 74.46 74.4 81.22 61.9

These differences result from the assumptions, mainly those

made for the low series, and the time trajectories of change

adopted.

The general procedures involved in the mortality forecusts

bear directly on our concern with the growth of the aged

population and health smtus characteristics, The need for developing alternative assumptions for mortality evolved at Social Security in the 1970’s along with the general procedures

for forecasting this component. The adoption of alternative

mortality assumptions by the Census Bureau in its latest projections reflects the Pactthat mortality forecast errors had become

a significant proportion of overall error in the short as well

as long term, especially for projected estimates of the older

population. The U.S. Bureau of the Census report noted that:

“Mortality accounted for more than 40 percent of the (i-yew

aggregate error in the lasttwo projections” ( 1984. p. 17).

The general procedure followed by Social Security is

to begin by examining past trends in cause-specitlc mortality

by age and sex for 10 major cause-of-death groups. For the

most recent Social Security projection (1984b), trends for

ages under 65 were examined for the 1968–80 period. For

data on ages 65 and over, Medicare program records are

used to derive rates by age and sex. Age-sex-cause rates

for this group are determined by the distribution of the number

of deaths by cause from vital statistics sources. Average annual

improvements in the rates are calculated over the period by

age, sex, and cause. Judgmental procedures are then applied

to determine ultimate annual percentage improvements that

would apply to the year 2080 at three alternative levels for

each sex. For 1983, the first year of the most recent projection,

the 1968–80 annual improvements were arbitrarily assumed

at 50, 100, and 150 percent of the change for the three

alternatives 1, II, 111. They were then interpolated by

logarithmic function to the ultimate annual improvements over

the period 1983–2008. From 2008 to 2080, the ultimate annual

improvements are applied on a constant basis. The result

of these multiple procedures is the derivation of a gradually

declining rate of improvement up to 2008 compared with

the period 1968–80 (dampened extrapolations), and then a

steady but lowered improvement through the rest of the forecast

period. Thus, there is a combination of both extrapolation

and judgmental procedures employed, with rather strong assumptions made about the pace of change for the different

causes.

The latest U.S. Bureau of the Census projections (1984)

are based on somewhat earlier Social Security forecasts, but

they are generally based on the same procedures. An exception

is the modification in the high improvement assumption to

only 125 percent of the 1968–80 annual improvement. This

more conservative assumption through the year 2005 produces

lower life expectancy figures at subsequent points in time.

122
The Social Security forecasts also show greater divergence

in the sex mortality differential than is true in the Census

Bureau forecasts. In addition, forecasts of mortality by race

were made independently by the Census Bureau using fairly

arbitrary assumptions that black male mortality would reach

white rates by 2080 and that black female levels would converge earlier with white rates.

The data for both major sets of basic population data

are published for selected years to the year 2080. In the

Census report, for the Middle Series, single year ofage data

are reported up to age 84 and 5-year data are reported to

age 99 on an annual basis to the year 2000 and every

5 years from 2000 to 2080. Five-year age data are given

for the Lowest and Highest Series for the same years. For

the other series, 5-year age data are provided at 5-year intervals. In the Social Security report, 5-year age data up to

age 95 are presented for 1985, 1990, 1995, and 2000, and

then data are presented at 20-year intervals to 2080. Intermediate year data and more detailed information on the values

for components are generally available from the agencies in

unpublished form.

Family and household structure

There are a variety of different aspects of family and

household structures that are of considerable importance for

older persons and their social, economic, and health wellbeing. These include marital status, living relationships of

older persons, kin networks (e.g., multigenerational families),

and sources of support, The complexity involved in describing

these structures at even a single point in time presents some

major obstacles in the way of forecasting. It is not surprising

that a number of different projection approaches have been

applied to further our knowledge in what has come to be

recognized as a crucial dimension of public policy for the

aged.

The Census Bureau has periodically issued projections

of marital status; households, families, and unrelated individutils; and families by type. The last projection report on these

dimensions was issued in 1979 for the period 1979–95. These

projections are of the fixed-ratio type, in that changes in

the proportions of persons in each marital status who are

heads of households or families are applied to projections

by ever-married status, Multiple series were generated in the

last projection to represent four patterns in the rates of change

in marital status, and thereby household headship, as reflected

in the changes between various periods over the years 1964

to 1978. No change in the four sets of rates was assumed

over the projection period. All of these projection series used

only the 1977 population Series 11 figures. The published

tables include annual data to 1995 of households by 10-year

age intervals and data for marital status in 1985, 1990, and

1995 by the same 10-year age intervals.

The projections of marital status prepared by the Social

Security Administration also involve constant transition rates

over the entire projection period derived from the 12-month

period ending June 1983. Incorporated into their procedures,

however, are differentials by previous marital status and mortality variations by marital status (the latter are based on

data available only for the period 1959–6 1). No alternative

series were developed. There are numerous comments in the

1984 report indicating the relative crudeness of these procedures and suggestions that further improvements will be made

in future projections. The reporting detail is the same as

for the age and sex characteristics.

Several projections are currently underway that adopt

multistate demographic procedures to project future marital

status distributions. Essentially, these involve the calculation

of increment-decrement marital status life tables derived from

data on either the retrospective experiences of cohorts over

time or period information. At the Research Triangle Institute,

Griffith and associates have prepared female marital status

projections on successive birth cohorts from 1906-10 to 1946-

50 through age 70 (Griffith, Suchindran, and Koo. 1985).

For estimation of future cohort experience, four assumptions

are made, including a static assumption based on period transitions in the period preceding 1980. Data are drawn from

the June 1980 Current Population Survey, in which a sample

of 66,000 females 15–75 years of age were asked about timing

of past marital events for their first, second, and most recent

marriage. Only limited data have been presented to date from

this activity.

Espenshade at the Urban Institute has used the same data

to project female marital status distributions for the period

1980 to the year 2080 ( 1985). Fixed-rate projections were

made under two assumptions—the rate of transitions in the

period 1975–80 (low family stability) and that of the 1950’s

(a high family stability scenario). It should be noted that

these projections incorporate their own fertility and mortality

assumptions relevant to the two periods. Sensitivity tests were

run to assess the relevant effects of fertility, marital status

transition rates, and mortality. Although the reported projections are not broken down by age, they illustrate the use

of so-called integrated population systems that make use of

disaggregated state transitions. For example, fertility levels

may best be forecast in terms of the childbearing patterns

for each marital class of females. In general, these approaches

make considerable demands for suitable input data that contain

age- and status-specific transition rates. Moreover, in nearly

all applications of these procedures for projection purposes,

fixed-rate transitions over time have been applied.

Wolf ( 1985a) at the Urban Institute has used a semiMarkov process model and microsimulation to examine lifecycle marital dynamics. Projections of marital status distributions by different birth cohorts to the year 2025 have been

made under two simulation variants. This is an interesting

exercise in that, unlike the multistate life table approaches,

it explicitly treats duration of time or sojourn time in a given

marital state in estimating the probabilities of moving between

states. Although the tables published to date start only with

cohorts of persons 40-45 in 1985, the analysis has been made

for the earlier cohorts (Wolf, 1985b). The analysis is also

based on the June 1980Current Population Survey data set.

Housing

The projecting of future housing requirements relies quite

heavily on household and marital status forecasts. At the

123
Joint Center for Urban Studies of the Massachusetts Institute

of Technology and Harvard University (1982), projections

of housing consumption have been prepared to the year 2000

under a set of assumptions relating to marital and household

structures for different birth cohorts. (See also Pitkin and

Masnick, 1980.) The reports of this research do not contain

detailed age data. but certainly such materials are available

inasmuch as age groups through 85 and over are examined.

Furthermore, households are projected by owner and rental

tenure and for regions of the United States. It should be

noted that this projection effort is self-contained and not based

on Bureau of the Census projections, although comparisons

are made between the two efforts.

Although some attention is paid in these reports to type

of housing (e.g.. public housing, trailers), the specit’icdemands

arising from the growth of the older population are not addressed. The U.S. Senate Special Committee on Aging ( 1983)

examined a range of such issues dealing with housing trends

and programs concerned with the aged, but no projections

are reported. The variety of different types of housing used

by older persons and the marked changes in living arrangements would seem to be a research area in which projections

are needed. At the same time. the potentially growing need

for institutional facilities should command greater attention.

(See, for instance. Rice and Feldman, 1983.)

Education

Projections of educational attainment have not been

routinely prepared by Federal agencies or other groups. The

last Census Bureau projections were released in 1972 and

contain projections at 5-year time points from 1975 to 1990.

They are fixed-ratio projections based on two sets of age

and sex educational attainment rates determined by trends

for prior periods applied to official projection figures. The

procedures implied that rates would remain fixed after ages

30-34 and that mortality for a given age group is the same

regardless of level of attainment. In spite of the relatively

nontechnical procedures that would be required for this type

of projection, official series have not been prepared by the

Bureau of the Census in recent years. For the older population,

at least 40 years into the future, educational attainment levels

can be determined in a crude way by using existing data

on attainment and simply carrying them forward (see, for

example, Uhlenberg, 1977). Such an effort also has been

reported as unpublished projections prepared by the Bureau

of the Census up to the year 2000 and the year 2014 (chart

presented in Federal Council on the Aging, 1981) for males

and females aged 65 and over.

In light of the important changes that are anticipated

in educational attainment levels for cohorts entering older

ages up to at least the year 2000, it is surprising that more

attention has not been paid to this issue. More sophisticated

projections would need to include both differential survival

by educational level (where differences are known to exist,

but for which national data are sparse) and estimates to account

for different forms of adult educational achievement, especially

at older ages.

Health status and health care

Although health status, health care utilization, and costs

emerging from these services constitute a matter of great

national importance, it has been treated only sporadically by

projection efforts. It is, of course, a complex and difficult

area to examine, particularly at older ages, where chronic

diseases and related conditions become more manifest. Chronic

diseases are complex for a variety of reasons. One, they

evolve over long periods of time; two, they tend to produce

various symptoms related to the quality of life (e.g., varied

types and levels of functional limitations); three, they are

etiologically multifaceted, involving both specific risk factors

and physiological changes due to the aging process; and,

four, joint prevalence and interactions between these diseases

frequently exist (Manton, 1985). Thus, in spite of clearly

charted trends in causes of death and overall mortality, the

underlying health dimensions present patterns of change that

are only starting to be considered in forecasting efforts on

a national scale.

In addition, the forecasting of various aspects of the health

care system rests not only on patterns of health status for

the population and its subgroups, but intrinsically on intervention strategies (e.g., primary prevention, surgical and medical

treatment, and technological developments) and the degree

of commitment taken to aIter the course of disease processes

and their related elements. We are dealing with a complex

interplay, therefore, between what might be called natural

processes and social, economic, and political factors. Let

us examine some of the forecasting efforts that have been

made to capture varied aspects of the situation.

The Social Security projections make use of several strategies to forecast mortality. As noted earlier, they involve empirical trend analyses of broad categories of causes of death

to determine age- and sex-specitlc rates of change and judgments regarding the ultimate rates of change and the patterns

of change over the projection period. Judgments also are

made to create alternative series. While such procedures are

certainly an improvement over earlier efforts at simply extrapolating age- and sex-specific mortality rates, they still

involve rather arbitrary assumptions about the future course

of terminal conditions rather than any profound consideration

of the biological process involved (Manton, to be published).

While these forecasts provide a basis for making general population projections, they do not enable us to examine in any

great depth the underlying dimensions of health status.

The estimates of future mortality resulting from these

efforts have served as a basis for the Social Security and

Bureau of Census projections and a number of other forecasting

efforts. In the way of aggregate forecasts, the Health Care

Financing Administration has developed projections of national

health care expenditures and types of expenditures to the

year 1990 using the Social Security projections, Alternative II (Freeland and Schendler, 1983). No specific costs

connected with the aged are reported, but clearly these demographic trends are involved in the projected figures on expenditures and their share of the gross national product.

124
The National Center for Health Statistics also has reported

on prtnjections of various aspects of health services utilization

and health care expenditures for the year 2003 (National Center

for Health Statistics, 1983). These projections involved assumptions for both a constant mortality (1976) and declining

mortality extrapolated from the trend from 1953–78 to project

the age structure of the population in 2003. Fixed ratios of

limitations of activity, utilization of health services, and health

care expenditures were then applied to determine the overall

impuct of the population change, the contribution attributable

to specitlc age groups (including older persons), and the effect

of the two mortality assumptions. Rice and Feldman ( 1983)

updated these earlier projections by using the 1981 Social

Security projections (Alternative II) and more recerlt health

utilization and related data. Using the same fixed-ratio procedures, they carry projections out to the year 2040. These

reports pointedly note the limited nature of these exercises,

but emphasize that they do provide general indications of

how population aging can affect various dimensions of health.

For the 1981 White House Conference on Aging, the

Federal Council on the Aging also reported in graph form

some fixed-ratio projections related to health care and needs

for long-term care. Derived mainly from the current estimates

of various agencies and the 1977 Bureau of the Census projections, estimates are made of the use of health services by

the elderly in the year 2000. Estimates also are made of

the percent of elderly who will be childless and average

childbearing, by race, for those turning 65, and at ages 65

and over through the year 2008. Finally, the percent of persons

in the Iubor force to the year 2000 is estimated by sex and

i.tges 45–54. 55-64, and 65 and over in order to gain a perspective on persons potentially unavailable to serve as caregivers

to the elderly. While these are fairly crude efforts at estimating

future needs and currently are outdated, they do indicate some

sociut dimensions that warrant further attention. It may be

noted that the U.S. Senate Special Committee on Aging and

the American Association of Retired Persons presented more

current charts on various health utilization dimensions to the

year 2050 using similar procedures ( 1984).

Mantort and Soldo ( 1985) have prepared projections of

the noninstitutimtalized and institutionalized long-term care

population for married and unmarried persons of both sexes

for the years 2000 and 2040. Three groups of older persons

are identified, those 65-74, 75–84, and 85 and over. In addition. four different levels of functional limitations are assessed

for the noninstitutionalized—three activities of daily living

Ievcls and one instrumental activities of daily living level.

Fixed ratios of rates that are age, sex, and marital status

specific. estimated from the 1982 National Long Term Care

Survey, were applied to the Social Security projected populations (tidjusted to U.S. resident level). The 1977 National

Nursing Home Survey provided ratio estimates of the

institutionalized.

In tiddition, they derived further estimates of total hours

per week spent in providing informal care for functionally

Iimitcd persons 85 years of age and over where the helper

wus u spouse, offspring, relative. or nonrelative (Manton and

Soldo, 1985). These ratios were derived from the National

Long Term Care Survey and the National Opinion Research

Center Survey of Caregivers. An alternative scenario was

developed by examining the age- and sex-specific disability

levels compared to the projected 1981 Social Security mortality

rate declines. Thus, they have examined some interesting

issues of both future forecasts of disabilities and certain kinds

of service requirements that such levels would generate, drawing upon varied surveys for the fixed estimates. Finally, they

determine the volume of informal services implied by current

levels of care and the expenditures that would be involved.

A more focused effort at developing biomedically informed forecasting models has recently been prepared by Manton (1985). Incidence and prevalence of cancer morbidity

by age groups to age 90 and over are projected to the year

2000 under assumptions of a changing population structure

and one fixed from 1977. Projections for lung cancer deaths

in 2000 under both assumptions also were made. What is

interesting about these forecasts is that Manton employs

stochastic compartment modeling techniques to estimate health

state transitions for persons subject to specific diseases. Multiple sources of data are used to deal with age cohort differences

in risk, changes in risk exposure over the lifespan, individual

differences in risk, and both independent and dependent competing risk assumptions about interactions among diseases.

A second aspect of this research uses more complex random process modeling strategies that are particularly suitable

for use with data from longitudinal studies of chronic diseases.

These models are shown to have considerable use in assessing

particular intervention strategies. In the application examined,

lost future lifetime income is estimated for risk variable controls (e.g., smoking) at different ages. Prevalence costs for

lung cancer to the year 2000 also were determined from the

stochastic compartment model under different population

structures.

Labor force

A variety of different projection approaches have been

taken to forecast the participation of women and men in the

labor force (i.e.. employed or seeking work). The Bureau

of Labor Statistics prepares a series of widely used projections,

the most recent of which was issued in 1984 to cover the

period to 1995 on an annual basis. Three series are included

(high, medium. IOWeconomic growth) that are based on extrapolation of age- sex- and race-specific participation rates

for the period 1962–8 1 or subperiods, and assumptions relating

to change over the projection period in both demographic

and economic conditions. The changing schedule of rates

is then applied to the Bureau of the Census Middle Series

projected populations. It should be noted that the scenarios

are mainly determined judgmentally, but empirical trends and

related modeling are also taken into account. One of these

econometric models, which involves a marital status model,

has been used to forecast labor force participation among

older persons (Duggan, 1984). The U.S. Bureau of the Census

( 1984) also has published labor force participation ratio figures

for the year 2000. but these are now superseded by the later

modified set of projections by the Bureau of Labor Statistics.

125
The Social Security Administration, as noted earlier, prepares economic projections to assess the financial status of

the OASDI system (1984). Estimates of labor force participation over the projection period enter into these efforts. Projections are made to the year 2060, by ages up to 70 and over

and by sex, according to four long-range economic assumptions. The ratio estimates are based on previous trends with

judgments made as to their future course.

Although the Bureau of Labor Statistics and Social Security projections do not consider part-time employment, an

earlier study by Wertheimer and Zedlewski (1980), using

the Urban Institute’s DYNASIM microsimulation model, included detailed examination of work status by annual hours

worked for various older age groups of males and females

to the year 1990. Two alternative scenarios were examined,

depending on early or late departure from the labor force.

The DYNASIM model takes into account a variety of individual characteristics—age, sex, race, marital status, education, disability status, employment history, children of the

elderly, etc. Mortality changes were also introduced into the

model, for this application focused on an aging society.

Another simulation model based on retirement decisions has

been utilized by Hendricks and Storey (1981) at the Urban

Institute to investigate the effects of alternative mandatory

retirement policies on labor force participation of persons

around retirement ages for the years 1990 and 2000.

A Macroeconomic-Demographic Model of the National

Institute of Aging ( 1984) includes a labor force integrated

supply and demand model, which produces labor force participation rates for each age-sex group. The MDM has mainly

been used for economic policy analyses to date and, in this

regard, three pension system models have been developed

and two Federal transfer models. Some aggregated projections

have been prepared to the year 2055 for employment, compensation, private pension benefits, and other economic dimensions (National Institute of Aging, 1984). Anderson and

McNaught of ICF, Incorporated, the developers of the model,

also report labor force participation rates to the year 2050

(1982). Interesting alternatives are provided for both high

and low productivity growth in the economy.

Income

Estimates of income and sources of income for the aged

population in the future have been the goals of a number

of projection efforts using the MDM and other models. The

1984 volume describing the model provides some breakdowns

of income by selected sources for older age and sex groups

through the year 2055. Anderson and McNaught (1982) also

provide aggregate compensation over the projection period

to 2050. It is likely that more detailed age and sex information

is available from the analyses reported for the MDM model.

The U.S. Bureau of the Census (1980) has published

illustrative projections of income distributions for different

types of households by broad age categories of householders

for 1980 through 1995 at 5-year time points. Income levels

from the 1978 Current Population Survey were applied to

the two intermediate household projections to derive those

estimates. Seven annual growth rates of income, ranging from

O to 4.0 percent, were assumed to operate in a fixed manner

over the projection period, thus yielding 14 series of

projections.

The Data Resources, Inc., Macroeconomic Model and

Demographic-Economic Model of the Elderly form the bases

for the analyses reported by Olsen, Caton, and Duffy in their

work The Elderly tmd the Future Economy ( 198I ). Data for

limited age categories for each sex are reported on the distribution of income through the year 2005 under a series of economic

scenarios involving different government programs. These data

are based on simulations involving increased labor force participation of older persons, income guarantees, increases in

personal savings, and investment-oriented tax cuts. A baseline

projection series provides a means of assessing the different

program implications. The macroeconomic models are dynamic econometric formulations, but the population base used

in each series is the 1977 Middle Series from the Bureau

of the Census.

Evaluation and suggestions

Even the brief review of the projections that have been

made about tomorrow’s elderly indicates that a bewildering

array of data exists from a variety of different sources. Dissemination of the results of these exercises has been in many

different formats. It is clear that a wide range of projection

strategies has been applied, but with little overall assessment

of efficacy. There appears to be only limited coordination

among these efforts, even among Federal agencies in which

some common forecasting parameters are used. Although efforts have been made from time to time to bring some of

the evidence together from these projections (e.g., U.S. Bureau

of the Census, 1984; Myers, Manton, and Baceller, 1986;

Siegel and Davidson, 1984), there has been a lack of systematic

evaluation. The recently held Workshop on Forecasting Life

Expectancy and Active Life Expectancy, sponsored by the

National Institute on Aging and the American Council on

Life Insurance, suggests that recognition of this situation is

emerging. A fully developed program of evaluation of the

theoretical, methodological, and data requirements for forecasting of the aging population seems in order, perhaps under

the aegis of the National Research Council and the National

Institute on Aging.

It should be emphasized that we are not suggesting that

a rigid framework for projections should be introduced, for

there are advantages in diversity, but rather that greater attention be given to a more coordinated effort. It also is clear

that forecasts are intended for different purposes and therefore

require varied approaches. Nonetheless, the independent efforts may lead to quite different conclusions that require systematic evaluation. With these thoughts in mind, a number

of more specific suggestions can be made in terms of the

organization of these efforts, areas of needed research, and

data requirements.

126
Organization

( 1) It wtmld he desirable if the ad htw timing of the

basic set of projections prepared by the Bureau of the Census

ctwld he repli.uxl by tin integrated proytam effort mude every

1() yc:ws, lmswl on the decennid census results d related

lklt:l sets. Pcrhups a shorter term projection could also be

prepared at [k midcensal point as well. This would replace

the sporadic ef~orts that have been unctertken in the past.

These projections would ideally covet- all the charticteristics

th:u huve ken included in the past—age. sex. race. marikd

status. h(~uscholds. educationalat tainlllent.”

(2) The office of the Actuary of the .!hcii.tl Security

Administrti(ion has been issuing projection series on an annwtl

(w biennial basis for some time. There seems to be ]i[(]e

whantage realized by making long-range projections so repc~tediy—~ greater range of alternatives would certainly

:wwm]plish the stimc end. The important contribution made

hy Stwial Security is to forecast mortality. the results of which

;Irc used in p~rt by the Bureau ot’ the Census. While these

torwwsts incorporate cause-of-death trends. this effort could

pr{~tlt from exposure to a wider range of expert judgments

rcluting tt~ variations in the future. Incorporation of more

hi(mledicully inf[wmed actuarial procedures also seems useful.

especially in developing morbidity and disability t’orecasts.

Cooperation with the Health Care Financing Administration

und the Notitmtil Center fur Health Statistics in these efforts

might prove beneficial. In addition, if mortality forecasts were

mwk” by ruce. this wtmld relieve the Census Bureau of the

ncwl to prepare such estimates. Finally. the inclusion of marital

skttus projecti[ms by Social Security since 1980 appears useful.

but coordirmtion of these efforts with other agencies in improving the projection procedures is desirable. especially in devch~ping alternatives.

(3) The National Institute on Aging’s MacroeconomicIlmmyywphic Model would seem to be an excellent aggregateintqated mode] fur examining a whole range of economic

issues relating to older persons. However. there is a need

ftw developing more endogenous modules for the demographic

c[mlponent with respect to family formation and dissolution,

fertility. und especially health status tind mortality. In this

rcgurd the pi(meering work performed by the Australian group

wtwking on the IMPACT model would seem to be particularly

relevant, especially with respect to their endogenous population

suhm(du]e (Sams, 1979). The MDM seems (o have been

used :dmost exclusively for assessment of employment decisitms and income considerations. Research is currently under

wily f~w the development uf modules to assess the demand

f~w health insurance and services and another one on health

expenditures, which seemingly would require further detail

(m htitlth sttitus ofhwtschold members,

(4) Developments are called for that would lead to more

sophist icated modeling of the components of population. particularly the mortality component that su affects projections

(~t’the uged population. In this regard. integrated efforts are

nccesstiry tt) forecast hetilth status. functional Iimitatiuns, and

supp[wt systems available for older persons (Mantun, 1984).

These forecasts not only can have utility for probing important

policy issues related to health care expenditures and welfare

proyrams. but also can serve to intorm improved mortality

[(wccasts in :cnerid populttti(m projecti(ms. Projections in this

:uwl sh(m[d bc aided as datu t’ixm] the National Health and

Nutriti(m Exiiminiltion Survey I-Epidemiologic Followup Survey. the 1984 Supplement t)n Aging (Jt’the Nati(mal Health

Interview Survey. ancl the National Long Term Ctwe Survey”s

1984 round btx$(mw avai l;iblc.

Needed research

(5) There tire several ureas of importance [or increasing

our understwding of the future elderly that htive been relatively

neg]e~ted M far as projecti(ms are concerned. One Uf these

is ethnicity. both in tern~s of’ f(weign-born persons of non-U .S.

purents and foreign ancestry. Uhlcnb@ ( 1977) reported some

changes in foreign-born older persons using static-component

procedures. but we are unaware OF tiny projections relating

to Americon Indians. for example. There is a lack of data

on mortality risk for different types of immigrants. but this

perhaps could be overcome with existing information on records. Of course. estimation would be difficult to assess fmgroups in which undocumented mi:ration is extensive. A .sccond area is living arrangements. This is a particularly difficult

dimension to forecast in view of the strong economic, health.

tind value determinants that operate in conjunction with demographic fac((~rs such as age. sex. tind me. %me expkmd!ory

work has been undertaken in this area. but more vigorous

efforts would seem desirable.

(6) Another dimension that has been largely omitted in

the discussion (o this point concerns the lack of geographic

detail in many of the m.ttionol projections. The Bureau of

the Census provides periodic State projections that agree in

aggregate with the national figures. but these are limited (o

only age. sex. and race. Long tind McMillen (to be published)

have reviewed recently various demo: mphic-economic models

for projecting State and metropolittin tireti populations. Masnic!i and Pitkin ( 1982). at the Joint Center for Urban Studies

of the Massachusetts Institute of Technology and Harvard.

have prepared projections for States and regions for 1970

through 2000, but the data are limited at older ages. Of

course. States often issue their own projections. often for

counties. county groupings. and metropolitan areas. but these ‘

are not prepared on a periodic basis and the procedures may

be quite variable from State to State. This is particularly

true for migration estimates. There are currently underway

several programs of research on the migration of the elderly.

but these have nor led to projection efforts as yet. Clearly.

there is a need for greater attention to forecasting of migratory

flows, perhaps the major elements of uncertainty in State

and local area migration.

(7) There is a continuing need to develop approaches

for presenting estimates of uncertainty associated with projections. It is often suggested that confidence limits should be

a built-in feature of projections. just as in the case of sampling

error estimates with different types of surveys. Several approaches have been suggested for determining confidence

limits. They include estimates based un post fiicto empirical

evaluations of previous projections (Key fitz. 198 I ); systematic

use of alternative series fur this purpose; techniques dealing

127
with assumed likelihoods of certain alternative combinations,

as suggested by Stoto (“Statistics for an aging population,

dealing with uncertainty” in Chapter II of this report); and

Delphi approaches.

(8) Dissemination of data from projections has tended

to be mainly in the form of published tables showing limited

age and characteristic information for only a few points in

time. Detailed information for alternative series is often omitted. Moreover, the input schedules for the change components

are seldom presented in published reports in adequate detail.

One means of overcoming such shortcomings is to release

information in tape form or through interactive computer systems. The Bureau of the Census has been moving ahead

in making dissemination in this form possible, but it should

be more widely encouraged. In this regard, it is likely that

future projection developments may involve more user involvement, in which standard methods programs and input data

can be used by those interested in order to prepare their

own altemative projections. There is an effort underway by

the American Council of Life Insurance that would create

a computer program that is interactive and allows changing

mortality assumptions for specific diseases to be introduced.

This appears to be a most interesting communication

development.

Data requirements

(9) A sound fundamental premise of forecasting is that

the issues to be addressed and the methodological strategies

adopted should dictate data requirements. Although data are

seldom gathered or processed with the intent of forecasting

in mind, it is clear that certain types of data are particularly

valuable in promoting more sophisticated forecast modeling.

In general, data that capture the process nature of change

are more useful than static measurements, or even retrospective

historical data, for estimating transition values. Data of this

type can come from longitudinal studies that are based on

repeated surveys and/or record linkages (Migdal, Abeles, and

Sherrod, 1982). The latter sources may prove to be more

effective in that larger numbers of cases and more representative samples are possible, although the intensity of the behavioral or underlying conditions for state transitions may

be lacking. The recent development of methodologies for

analyzing life history events suggests that steps are underway

to utilize data that can be quite effective in forecasting efforts.

In addition, the developments in multistate demography should

lead toward more disaggregated efforts for estimation of transitions between states, such as in the case of marital status

modeling. The same may be said of modeling of the sicknessdisability-death processes. These methods have only begun

to be introduced in forecasting efforts, but will no doubt

come into wider usage.

(10) More specific to the older population, there is a

need for assembling and reporting basic data on an age-cohort

as well as an age-period basis. There clearly is greater need

for more detailed data at the extreme ages. Many projections

are truncated at age 75 or age 80, largely because of the

absence of further detail or unreliability that prevents its use.

Systematic efforts to encourage research on the oldest old

by the program at the National Institute on Aging should

stimulate greater data accumulation, which can then be incorporated into projection models.

128
References

Ahlburg, D. A.: Aggrcgtitc Economic-Demographic Mrxlcls, in

K. C. Land wrd S. H. Schncidcr, cds., Foreca,uing in the Social

undivamnd Sciences. Boston. D. Rcidcl, to bc published.

Anderson, J. M., and W. McNaught: Projecting A1/ernafi!,e Fu/ures

jbr the R@tirenuwt Income System. Final Report to the National

Institute on Aging. Washington, D.C. lCF, Inc., 1982.

Burwtu of Labor Statistics: Employnlen/ F’rojer/ionsfor / 995. Bulletin

No. 2197, Mar. 1984,

Duggan, J. E.: Labor force participation of older workers. /ndus/ria/

und Labor Relations Reviett,. 1984.

Espcnshadc, T. J.: hfultistate Projections of Population By Agc

und hfarital Status, in Internu[ional Populurion Conference, Florence,

/YA’5, Vol. 4. Liege, Belgium. International Union for the Scientific

Study of Population, 1985.

Federal Council on the Aging: The Need for Long-Term Cure: A

Chartbook qf the Federal Council on the Aging. DHHS Pub. No.

(OHDS) 81-20704, 1981.

Frecland, M. S., and C. E. Schendlec National heakh cxpcnditurc

growth in the 1980’s, An aging population, new technologies, and

increasing competition. Health Cure Financing Review. Volume 4, No. 3. Office of Research and Demonstrations, Health Care

Financing Administration, Washington. U.S. Government Printing

Office, 1983.

Griffith, J. D., C. M. Suchindran, and H. P. Koo: Cohort Change

in the Marital Life Course, An Analysis of Marital Life Histories

Llsing Multistate Life Table Techniques. Paper presented at the Annwd Meeting of the Population Association of America. Boston,

tvtitss., 1985.

Hendricks, G,, and J. R, Storey: The Long-Run EffecIs of Alternative

Mundu/ory Retirement Policies. Washington, D .C. The Urban Institute, 1981.

Joint Center for Urban Studies of the Massachusetts Institute of

Technology and Harvard University: Trends in Housing and Demo-

,Wpllic Factors. Rcport submitted to The President’s Commission

on Housing, 1982.

Kcyfitz, N.: The limits of population forecasting. Popu/u/ion and

De\wlopmerrt Review 7(4):579-593, 1981.

L~nd, K. C.: Methods for national population forecasts, a review.

Jwrnul ofthe Americun StutisticulAssociation 81, 1986.

Long, J. F.: Survey of federally produced national level demographic

projections. Re)”ie)t’of Public Da:u Use 9:309–3 19, 1981.

Long, J. F., and D. B. McMillen: A Survey of Census Bureau

Popukstion Pmjcction Methods, in K. C. Land and S. H. Schncidcr,

cds., Forecus/in,q in the Sociul and Nulural Sciences. Boston. D.

Rcidcl, to be published,

Long, J. F.: The Utility of Demographic Economic Models in Projecting State and Major Metropolitan Area Populations in the United

Stutcs. Ptipcr presented at the International Population Conference.

Florcncc, I%35.

Manton, K. G.: Forecasting Health Status Changes in an Aging

U.S. Population, Assessment of the Current Status and Some Proposals, in K. C. Land and S. H. Schneider, eds., Fm-ecas~ing in

ihe Social and Na/ural Sciences. Boston. D. Reidel, to bc published.

Manton, K. G.: Methods and issues in the projections of population

hcaith status. World Health Sta/. Q. 37:294-305, 1984b.

Manton, K. G,: Models for Forecasting the Health Status of the

Elderly. Paper presented at the Workshop of Forecasting Life and

Active Life Expectancy. Bethesda, Md., June 25–26, 1985.

Manton, K. G.. and B. J. Soldo: Long-Range Planning for the

Elderly, An Integrated Policy Perspective. Report prepared for the

U.S. Senate Special Committee on Aging. Duke University Center

for Demographic Studies, 1984.

Manton, K. G., and B. J. Soldo: Dynamics of health changes in

the oldest old, Ncw perspectives and evidence. Milbank Mere. Fund

Q. 63(2):206-285, 1985.

Masnick, G., and M. J. Bane: The Nation’s Families: 1960–1990.

Cambridge, Mass. Joint Center for Urban Studies of the Massachusetts Institute of Technology and Harvard University, 1980.

Masnick, G., and J. Pitkin: The Changing Population of Stares

und Regions: Analysis und Projections 1970-2000. Cambridge, Mass.

Joint Center for Urban Studies of the Massachusetts institute of

Technology and Harvard University, 1982.

Migdal, S., R. P. Abeles, and L. R. Sherrod: An /n~~en~oryof

Longi[udinul Studies of Middle and Old Age. New York. Social

Science Research Council, 1982.

Myers, G. C.: Future Agc Projections and Society, in A. J. J.

Gil more ct al., cds., Aging: A Chullenge and Social Policy,

Vol. II. Oxford. Oxford University, 1981.

Myers, G. C.: The Current State of Population Projections. Paper

presented at the Annual Meeting of the Southern Regional Demographic Group. Chattanooga. Term., Oct. 1983.

Myers, G. C., K. G. Manton, and H. Baceliec Sociodemographic

Aspects of Future Unpaid Productive Roles, in Committee on an

Aging Society, ed., Productive Roles in an Older Society. National

Research Council. Washington, D.C. National Academy Press, 1986.

Myers, G. C., A. M. Pitts, R. Hillson, and E. Stallard: A MicrosimuIation Model for Health Status Projections, in Proceedings of [he

Social Statistics Section of the American Statistical Association,

Part 11, 1977.

National Center for Health Statistics: Changing mortality patterns,

health services utilization, and health care expenditures, United

States, 1978–2003, Vi/u/ and Health Statistics, Series 3, No. 23.

DHHS Pub. No. (PHS) 83-1407. Public Health Service. Washington.

U.S. Government Printing Office. Sept. 1983.

National Institute on Aging: The Nu/ional Ins/i/u/e on Aging

Macroeconomic-Demographic Model. NIH Pub. No. 84-2492, 1984.

129
Okn. L.. C. Caton. and M. DutYy: The Eldmly mtl the FWLIW

hmomy, Lexington. Mass. Lcxin,gtcm Books. D.C. Heath. 1981.

Pitkin, J.. und G. Mtssnick: Projections of housing consumption

in the U. S.. 1980 to 2000. Anmcd Hous;niq SnrIKy .%rie.$. No. 9.

U.S. Dcpiirtmcnt of Housing and Urban Dcvclopmcnt, 1W-+().

Rice. D. P.. turd J. J. Fcldmtm: Living longer in the United States.

Demographic chwrgcs and health needs of the elderly. Milhmk Mm.

FmfQ. 61(3):362-396. 1983.

Sams, D. B.: The Demographic Core of the IMPACT Project. Working Paper No. BP-18. Mclbmrrnc, Australia. impact Rcscarch Ccntcr,

1979.

Siegel. J.. and M. D~vidson: Demographic and socioeconomic aspects

of aging in the United States. Cut-rent Pophtinn Reports. Series

P–23. No. 138. U.S. Bureau ofthc Census. Aug. 1984.

Socitil Security Administration. Office of the Actuary: Economic

Projec(inns fiw OASD1 Cost Estinwcs. 1983. Actuarial Study

No. 90, 198zks.

Social Sccurit y Administration, Office of the Actuary: Social Swnrity

Area Popnktion Projections, 1984. Actuarial Study No. 92, 1984b.

Stoto. M.: The accurticy of population projection. Jourttd ofihe

Atnerican Smisticul Assnciutinn 78: 13–20, 1983.

Uhlenbcrg. P.: Changing structure of the older population of the

U.S.A. during the twentieth century. Gerrtn/o/ogisf 17:197-202,

1977.

U.S. Bureau of the Census: Projections for the United States. Cwrent

Popltluliotl Rep[)rt.v. Series P–25. No. 476. Washington. U.S. Government Printing OtYicc, 1972.

U.S. Bureau nf the Census: Projections of the number of households

and families. 1979 to 1995. Current Poptdafion Reports. Series

P-25, No. 805. Washington. U.S. Government Printing Office, 1979.

U.S. Bureau of the Census: IIlustrativc projections of money income

size distributions for households, 1980 to 1995. Cnrren/ Popddion

Reports. Series P-60. No. 122. Washington. U.S. Government Printing Office. I980.

U.S. Bureau of the Census: America in transition. An itging society.

Cnrrent Pqmhtion Reports. Series P–23. No. 12X. Washington.

U.S. Government Printing Office. 1983.

U.S. Bureau of the Census: Projections of the population of the

United States by age, SCX.and race, 1983 to 2080. Cmwl/ Popt(htim

Reporfs. Series P–25, No. 952. Washington. U.S. Government Printing Office. 1984.

U.S. Scrmtc Special Committee on Aging: Dew/opnwnts in Agin,g:

/983, Vol. 1. Chapter IV, Housing. 1983.

U.S. Senate Special Committee on Aging and the Amcricwr Associtition of Retired Persons: Aging Anwim: Tretuls (ml Projection.i,

1984.

Wcrtheimcr, R. F., and S. R. Zcdlcwski: The Aging of Amcricff:

A Porttwit of the Elderly in 1990. Washington. D. C., The Urbim

Institute, 1980.

Wolf, D. A.: Marital Status as a Scnli-Marktrv Process. P~pcr prcscntcd at the Annual Meetings of the Population Assocititirrn of

America. Mar. 1985a.

Wolf, D. A.: Simulation Methods for Analyzing Continuous Time

Event-History Models. Unpublished paper. Washington. D.C. The

Urban Institute, 1985b.

130
Data needs for policy

analysis of Iong=term carea

by Joan F. Van Nostrand, M. P.A., Division of Health Care

Statistics

Introduction

Policy analysts itnd decisionmakers lament the lack of

policy-relevant data to address key issues in long-term cm-e

(LTC) for the elderly. Although some data on LTC exist.

major data needs of policy analysts often are unmet. The

need for policy-relevant data about LTC is increasing as the

population at greatest risk. those aged 65 and older, grows.

What data are needed to address key policy issues in LTC

twcr the next decade? This paper examines the Federal data

system vis-a-vis data needs for policy analysis of LTC. It

also recommends ways to fill the major data gaps.

LTC for the elderly is defined broadly in this assessment

of ditt~ gups. LTC irwlurlcs health care. socitd services. and

supportive housing for elderly who w-e functionally disabled.

The functionally disabled arc those with a chronic physical

or mental condition thtit impairs them so that they require

the help of imother person in performing everyday activities

(Doty. Liu, ml Wiener. 1985: Federal Council on the Aging.

198 I: Interagency Statistical Committee on Long-Term Care

~or the Elderly. 1980). Everyday activities fall into two general

catcgorics:

. Activities of daily living (ADL’s)—These are personal

cure tasks of bathing. dressing. eating. toiieting. and

widking.

● Instrumental tictivities of daily living (lADL’s)—These

arc tasks necessary for living independently in the community, such as shopping. prep~ring meals. cioing household

chores. mwttiging money. using the telephone, and taking

mwiicine.

Although this paper focuses on LTC for the elderly. it

should he noted thot the elderly are not the only LTC population. Other LTC populations include the mentally retarded.

physically handicapped. and chronical] y mentally ill. Data

nwls for policy wralysis of these LTC populations are beyond

the scope of this paper.

Not all discussions of LTC cover such a broad array

of services. In some ctises. LTC is defined as solely nursing

h~mw care (Meincrs. 1983). This paper addresses the entire

service continuum from medical care to housing services.

It does so to provide as broad a framework as possible for

identit’ying data gtips in conducting policy analysis about the

“Contwrts we the opinions of the authur and nut those of the Department

tfHculth wt Hurnim ,%rviws.

functionally disabled elderly. The purpose of this paper is

to:

. Describe the LTC policy issues of major consequence

for the next decade.

. Identify major gaps in data when addressing these issues.

. Recommend efficient ways to fill the gaps according to

priority needs.

An assessment of data gaps in LTC for the elderly was

conducted in 1980 (Interagency Statistical Committee on LongTerm Care for the Elderly. 1980). It focused on identifying

data gaps through analysis of available national data. Srrme

recommendations from this assessment, most notably those

on analyzing existing data (Scankm et al., 1983) and collecting

data on informal supports. were implemented in the early

1980’s. Much of the data resulting from these recommendations is “in the pipeline”’ today. In other words. data have

been collected but are still being processed. Most of the data

will be released publicly within the next 2 years. The LTC

data collection efforts of the early 1980’s represent a “spurt”

in the Federal data pipeline, not a continuous flow. They

were designed as single-time surveys. Although one survey

has been repeated. none is on an established schedule. Hence,

the need for periodic data to trdck trends in LTC remains

largely unmet. This paper comes at an appropriate time to

examine the contributions of this data “spurt” and to address

the need forper-iodic d;ta forLTC policy analysis.

The assessment of various surveys. inventories. and administrative records of the Federd data system was conducted

by examining in detail the content of items. overall design.

sample size. and collection procedures. The major policy issues

are presented here as a framework for assessment of the available data. They were based on existing LTC policy analyses

(Meltzer. Farrow. and Richman. 198 I; Callahan and Wallack.

198 I: Scankm and Feder. 1984: Kemper et al.. 1986: Office

of the Assistant Secretary for Planning and Evaluation, 1981;

Vladeck, 1980). To ensure that issues were relevant to future

policy development. LTC policy analysts in the Federal Government. Congress. and private research organizations were

interviewed. Each analyst was asked to reflect cm key policy

issues and on data needs forthe next decade. The LTC policy

issues identified by this process fall into four areas:

● Use of LTC services.

. Supply of LTC services.

● Role of informal support.

● Cost of LTC.

131
The remainder of this paper synthesizes the policy issues,

assesses the relevant data systems and recommends ways to

fill priority data gaps for each policy area. Recommendations

build on existing data systems whenever possible so that available resources are used most efficiently.

Use of LTC

Introduction

The use of LTC—as determined by needs, attitudes toward

seeking care, and availability of services and third-party payments—is a priority area for LTC policy analysis. Because

functional disability increases with age. the elderly are at

greatest risk of needing LTC and comprise the majority of

the population using LTC services. The elderly LTC population

included 2.6 million persons living in the community, according to 1979–80 data (Van Nostrand, 1985), as well as 1. I

million in nursing homes, according to 1977 data (National

Center for Health Statistics, 1979). Of those in the community,

12 percent were disabled in ADL functioning only, 33 percent

in IADL functioning only, and 55 percent in both. The majority

of the elderly LTC population-75 percent—received LTC

from informal supports only. A minority-only 10 percent—

received LTC solely from formal sources (Van Nostrand,

1985). Because of the major growth projected for the elderly

population. the future need for LTC and its cost are major

concerns to policy makers.

There are diverse views on the future need for LTC.

One view, based on demographic changes, forecasts major

increases in the number of elderly who are functionally disabled

and in their use of nursing home care (Rice and Feldman,

1983). Another view is that the need will decrease due to

a compression of morbidity within a fixed lifespan (Fries,

1980). A third view is that projections of future need for

LTC must also incorporate changes in qualitative variables

such as living armngements, economic s~atus, and social networks (Manton and SoIdo, 1985).

Policy issues

Policy issues concerning the use of LTC focus on four

themes:

●

132

Need ((ml usc ,jbr LTC—What is the need for LTC today

and in the future”? What is the degree to which toddy’s

needs are met’? What is the use of LTC services in relation

to need’? What is the use by type of service’? What is

the lifetime use of nursing home care’? What are the

transitions in the use of LTC services as needs change”?

What is the use of acute care, especially hospital care,

in relation m the use of LTC? What are the outcomes

of various LTC services’? How do outcome+ vary by level

of functional disability’? What modes of care best meet

the needs of subgroups of the LTC population’? Do ,the

need for LTC and its use change for different cohorts

of the elderly’?

●

●

●

Determimwts (!fu.w-How do type of disability and extent

of its severity intluence use of LTC? How does change

in disability lead to change in use of LTC? How do

qualitative aspects (such as living arrangements, mwitul

status, social network, income/assets. and housing) impact

on use of LTC’? How do changes in these qualitative

aspects lead to changes in use of LTC’? What uttitudes

of the elderly lead to the decision to seek LTC services’?

Can the LTC population be classified into subgroups ttccording to their service needs’? What is the impact of

technological innovations on the use of LTC’? HOW do

exogenous factors. such as cold climate and rural area.

impact on the use of LTC? How does the supply of

institutional and community-based LTC impact on use’?

Fwzclional di.w{hilify-What biological, psychological.

and social factors increase the risk of functional disability’?

What impact do health promotion and disease prevention

have on delaying the onset of disability or minimizing

its severity’? What are the effects of stress on functional

disability’? Is there a compression of morbidity occttrring

at older ages’?

Impact ofpublic polic~-How do recent changes in public

policy for Medicare and Medicaid impact on the use

of LTC? How do policies for retirement, housing. i.tnd

taxation interact with those cm Medicare and Medicaid

to influence the use of LTC’? Can case management control

costs, satisfy unmet needs, and improve the well-being

of the LTC population’? What are the differential impacts

of public policies on subgroups of the LTC population’?

Existing Federal data

Brief descriptions of the major Federal systems which

provide national data on the use of LTC are presented next.

The descriptions begin with surveys covering elderly living

in the community and end with surveys of those in institutions.

Indepth descriptions are available elsewhere (U.S. Senate.

19?)6).

● 1982 and 1984 National LTC Surveys (NLTCS’s ), conducted by the Health Care Financing Administration and

the Office of the Assistant Secretary for Health—The

1982 NLTCS collected data on persons 65 and older

who were functionally disabled and lived in the community. The sample was identified by telephone screening

of persons on the Medicare Health insurance enrollment

file. Those who were disabled in ADL or IADL functioning were eligible for the sample. Sample size was ubout

6,000 elderly persons. The NLTCS collected data on

the degree of disability in ADL and IADL functioning,

cognitive functioning, the informal support system, use

of medical services. out-of-pocket costs for some LTC.

insurance coverage, and income. The 1982 NLTCS collected indepth data about informal supports who provided

assistance with ADL’s. The 1984 survey was a followup

of the 1982 sample; the questions asked were virtually

identical except that the indepth survey of informal supports was excluded. It included persons who. when telephoned in 1982, were reported by their families as living
in nursing homes. The NLTCS is i.Iparticularly rich sow-cc

of inf(mnation on functional distihility. use ot’ LTC services. charwteristics of the infortmid support system. and

dtiys ~~fhelp received. The potential exists for additional

followup of the original sample to identify chtmges that

htive occurred. A public use tape is available.

● 19S4 Supplement on Aging of the National Health lntervimv Survey (SOA/NHIS ). conducted by the National

Center for Health Statistics—The 1984 SOA/NHIS collected dots on o sample of persons aged 55 and older

who lived in the community. The core NHIS collected

data [m health status and hospital and physician care.

The SOA/NHIS collected data on availability of informal

support. retirement income. barriers in housing. home

invnership, disability in ADL and IADL functioning.

chronic c(mditions causing disability, and use Of social

services, nursing homes. and hospice cm-e. Social security

number was collected to link to the National Death Index.

This linkage will provide cause-of-death data for those

in the sumple who die in later years. Also built into

the survey was the potential to recnntact the respondents

tt> determine what changes have occurred. (An initial

recontact effort-the Longitudinal Survey on Aging—is

described next. ) Sample size was 9,000 elderly: 3.500

of them were age 75 and older. The SOA/NHIS is a

rich source of data on availability of informal support,

functional disabilities, use of social services. and chronic

conditions causing functional disability. A public use tape

is available.

● Longituditud Study of Aging (LSOA). conducted by the

Natiotwl Center for Health Statistics and the National

Institute on Aging—The LSOA was conducted in 1986.

It was a brief telephone interview of those persons 70

and over who were in the 1984 SOA/NHIS sample. The

LSOA collected data on changes since the 1984 interview

in functional disability and on the use of hospital, physician, and nursing home care in the interim. For those

members of the 1984 sample who died. cause of death

was determined by using the National Death Index. The

LSOA is a particularly rich source of data for the oldest

old on chm-tges in functional disability, risk factors leading

to institutionalization and death, and use of acute and

LTC services. The potential exists for further followup;

a 1988 LSOA is scheduled.

. Ntitiontil Health and Nutrition Examination Survey 1

Epidemiologic Fo]lowup Study (NHANES I EFS), conducted by the National Center for Health Statistics and

the National Institutes of Health—This survey is a folknvup in 1982-84 of the sample of persons ages 1-74

who participated in the 1971-75 National Health and

Nutrition Extimination Survey (NHANES 1). The

NHANES I EFS collected data on Family history, risk

factors associated with morbidity and mortality. changes

in ability to perform ADL’s and lADL’s, and use of

hospital nnd nursing home care during the interim. The

weight and blood pressure of each respondent were measured, Continued followup of the elderly was conducted

in 198S. The size of the sample of elderly was 3,900.

Because current dtita cm be compared to data from the

indepth physical examination conducted in NHANES I.

the EFS is a rich source of data on change in health.

It provides data on mortality and changes in morbidity.

along with associated risk factors such as chronic conditions. diet. and alcohol consumption.

● The 1977 and 1985 National Nursing Home Surveys

(NNHS’S). conducted by the National Center for Health

Statistics—The NNHS’s collected data on the characteristics of the nursing home, its costs. staff. current residents,

and discharges. Persons of all ages are included in the

samples. Data on residents and discharges include functional disability. chronic conditions. services received,

charges for care. and sources of payment. Data on discharges also include duration of stay and outcome. These

data have been used to develop a synthetic admission

cohort (Liu and Manton, 1983). The 1985 NNHS included

items on use of hospital care before. during, and after

stay in the nursing home. It also added a component

on the next of kin of residents and discharges. This component collected data on lifetime use of nursing home care.

changes in sources of payment over time, functional disability prior to admission. caregiver stress. and the family’s

view of the reason for institutionalization. Like the i 984

SOA/NHIS. the 1985 NNHS collected social security

number for matching to the National Death Index. In

both surveys, items on functional disability were structured

to collect similar data to permit comparison between the

LTC population in nursing homes and in the community.

The sample size of the 1985 NNHS was about 12,000,

equally divided between the resident and discharge samples. The NNHS is a rich source of data about use and

cost of nursing home care, the functional disability of

current and discharged residents. and charges and sources

of payment for care. Because the NNHS’S have been

conducted periodically since the early 1960’s, they provide

trend data for assessing the impact of public policy on

use of nursing home care. Public use tapes for the 1985

NNHS are available.

Assessment

Because most of the policy issues on use of LTC concern

changes over time, the need for longitudinal data is paramount.

Data are needed for extended time periods—5 to 10 years+n

major changes in the characteristics of the LTC population,

their need for and use of services, and their informal supports.

Data are needed on n-ansition.s: what major changes occurred

and when they occurred. Collection of data to identify transitions requires a contact, possibly by telephone, once or (preferably) twice a year. For example, take the situation where

some elderly received social services that resulted in improvement in ADL functioning for a year. Then, when services

were no longer received, they declined back to the original

level of disability. A survey with a 2-year followup (like

the NLTCS or the LSOA ) would describe this subgroup incorrectly as having no change in functional disability. Furthermore, because the reference period for receipt of LTC is

133
usually 3–6 months prior to the interview, such a survey

would describe the group as having received no LTC services

in the interim.

The availability of longitudinal datti which identify major

transitions in functioning and tie them to risk factors and

service use is limited. Although the LSOA is collecting some

data on changes which occurred since the 1984 SOA/NHIS

via a brief telephone followup, it is limited in the amount

of data collected and in the population covered (i. e., those

70 and older). It does not provide data on what transitions

occurred during the 2-year period or on when they occurred.

The situation is similar for the 1982 and 1984 NLTCS’S.

Although they provided data on the status at the first interview

and at the second. they did not provide data on the transitions

occurring in the interim. The NHANES I EFS has a similar

limitation in describing transitions in morbidity because of

the 10-year gap between the baseline and the followup survey.

Although it is a rich source of data on use of hospitals and

nursing homes, the EFS does not cover receipt of LTC social

services. The NNHS covers only persons in nursing homes

and excludes elderly in other long-term institutions, such as

rehabilitation hospitals and facilities for the mentally ill and

mentally retarded. Although the NNHS provides data on

changes in functional disability and sources of payment between the date of admission and the date of discharge, it

does not provide data on transitions occurring during the stay.

Recommendations

1. Collect longitudinal data about the LTC population and

its use of services. The longitudinal data should be transitional in nature, providing data on each major change

and on when it occurred. (Telephone interviews may be

a cost-efficient method of collecting transitional data. )

Appropriate models to examine for developing a longitudinal survey of LTC are the NLTCS, the SOA/NHIS, the

LSOA for the elderly in the community, and the NNHS

for the elderly in LTC institutions. Collect transitional

dard on:

Disability in ADL and IADL functioning and, for

use in health promotion activities, the chronic diseases

causing disability.

Mental health status, including not only cognitive

functioning, but depression and behavior problems.

Qualitative factors such as living arrangements, marital status, housing, income, assets, and informal

supports.h

Attitudes of the functionally disabled (and their

families) that impact on the decision to seek, or not

to seek, LTC.

Use of LTC and of acute care.

Use of nursing home care over the lifespan.

Costs and sources of payment for LTC~

Availability of LTC services in the community~

7-. Expand the NNHS to collect longitudinal data from all

types of LTC institutions and conduct it more frequently.

Information on admission cohorts is one approach to pr[)-

vide longitudinal data for studying trmsitions in the use

of services and outcome. Because of increases in the

costs of nursing home care and frequent changes in policy.

the NNHS should be conducted more often thwt every

6-8 years. Several policy analysts have indicated that

a 3-year interval would be appropriate for tiddressing

issues of changes in health of the elderly, outcomes of

care. targeting/substitution, and policy impact. The expanded NNHS and the longitudinal survey of the LTC

population in the community should have some questions

which are similar in content. Such similarity will permit

basic comparisons between the LTC population in institutions and in the community. The sample size of the expanded NNHS should be large enough to produce reliable

data for the major types of LTC institutions and for important resident subgroups (e.g., the oldest old, minorities ).

3. Include in the longitudinal survey of persons in the community both the elderly aged 65 and older and persons

nearing retirement. i.e., those aged 55–64. Comparisons

of the functionally disabled in these two age groups will

address issues of entitlement versus need and the impuct

of different entitlement ages for Medicare, the Older

Americans Act. and retirement benefits.

4. Include in the longitudinal survey of persons in the community those persons who are not functionally disabled.

Comparison of the disabled and nondisabled will provide

information on the differential use of acute care. Data

on persons who become disabled during the course of

the survey will provide information on risk factors tmd

the early progression of functional disability.

5. Link the data from the longitudinal surveys of the elderly

in the community and in institutions to the National Death

Index and to Medicare and Medicaid tape files. These

linkages require not only collection of the appropriate

identification numbers and assurance of confidentiality,

but also coordination and cooperation among various agencies responsible for the tape files. Such linkages will

provide, at very low cost, detailed information for tmalyzing cause of death and use of publicly funded health

care.

Supply of LTC services

Introduction

The supply of LTC services covers a broad continuumhealth care, social services. and residential care. These services

are provided in institutions, in community-based settings. or

in the client’s own home. The supply of LTC services by

type includes:

. Health care—nursing homes, home health care, rehabilit~-

tion hospitals, rehabilitative adult day care, respite care.

‘Detailed recommwrcktiwis on informalsupports, costs and sources of payment

for LTC, and availability of LTC services are presented in later sections

of this paper.

134

social health maintenance organizations, psychiatric hospitals, psychiatric treatment centers, and day-only or nightonly psychiatric facilities.
. !3wial services—homemaker/chore services. social adult

dtiy care, income maintenance. congregate and homedclivcred meals. transportation. counseling (legal.

economic, housing), escort services, protective services.

guardians/conservators.

. Housing with support services (i.e.. residential care)—

congregate housing. life-care communities. board and care

homes, domiciliary homes, psychiatric halfway houses,

psychiatric community residential facilities.

Although there is consensus that certain LTC services

belong in a particular type (e.g., home health in health care

und homemaker/chore in social services), the assignment of

st~mc services is debatable. For example. board and care homes

A domiciliary care homes. though classified here as residential cure. could possibly be health or social care. This depends

(m the type and level of services provided beyond the basic

scrvicc t~f protective oversight. The lack of consensus illustrutcs the need for further efforts in clarifying the boundary

lint between residential care and institutions providing either

htxdth or social care.

Policy issues

Pt]licy issues in the area of supply of LTC services fall

under several themes:

. C~/paciry-What is the supply of each type of LTC service’? What are the basic characteristics of each type’? What

is the capacity to provide care and how is it being used’?

Whtit are the standard kinds of care provided (e.g.. nursing

services, rehabilitation therapy, protective oversight)’?

● Qmtlity-What is the quality of care provided’? How can

quidity be maintained at an acceptable level’? How are

outcomes related to the kinds of care provided and type

of functional disability’? What are appropriate outcomes

of LTC’?

● T((r,qetit~,ql.~t{ l>.~tittltio\lof .wrt,ice.w-—What is the appropriate service mix for different LTC subpopulations? What

types of services are substitutions for others (e,g., home

health and adult day care for nursing home care)? For

which subpopulations are the substitutions most effective?

For which subpopulations does housing with support

services mitigate the need for health and social LTC

services’? 1s case management a viable, cost-effective process for targeting of services’? Are approaches such as

sociti] health maintenance organizations and vertical integration of hospital and LTC services viable, cost-effective

services for certain LTC subpopulations?

● Policy impac&_What changes are occurring in the supply

of LTC providers based on changes in program benefits

tmd in reimbursement and regulatory policy’? What is

the impact of policy which focuses on providing care

in the least restrictive setting? What are the trends in

nursing home bed supply in relation to reimbursement,

targeting of care. and regulatory efforts at controlling

growth’?

Existing Federal data

Federal data systems about the supply of LTC exist for

certain service types:

●

c

●

●

Nursing homes-The National Master Facility Inventory

(NMF1 ). mairmdined by the National Center for Health

Statistics.

Psychiatric facilities—The Inventory of Mental Health

Organizations (IMHO). maintained by the National Institute of Mental Health.

Medicare- and Medicaid-certified nursing homes, home

health services. hospices—Master Providers of Service

File. maintained by the Health Care Financing Administration.

Drug abuse centers—National Alcohol and Drug Abuse

Program Inventory, maintained by the National Institute

on Drug Abuse.

Because information about these data systems has been available to the public for some time. they are not described

here in any detail. Indepth descriptions are available (U.S.

Senate, 1986).

The major Federal data system to address the issues of

targeting and of substitution of services is the Channeling

Demonstration (Kemper et al., 1986). It is a large-scale demonstration of case management in 10 sites. Though limited in

geographic coverage, it provides detailed information to identify subgroups for targeting and for substitution of community

services for institutional care.

Assessment

Most of these inventories provide basic data on service

characteristics and use. Not all LTC services are covered

by these inventories. Coverage is best for health care services,

especially those certified for Medicare and Medicaid. It is

poor for social services and for housing with support services.

The inventories do not provide comparable data; even a basic

item such as an “inpatient bed’ has differing definitions. Comparisons of the same kind of care across service types cannot

be made because there are no common definitions of the

standard types (and units) of care. For example, although

care from a physical therapist is provided in many LTC settings, data about such care are not measured or recorded

in any uniform way. This lack of common definitions for

care precludes analysis of the potential substitution of one

LTC service for another. Although the quality of care is

a major issue in LTC, the definition and measurement of

high-quality care are not as well developed as for short-stay

hospitals. Work in defining and measuring quality for LTC

providers has been concentrated on nursing home care. The

latest effort (Institute of Medicine, 1986) shifts the emphasis

from characteristics of the facility to those of the residents.

Outcome is also seen as an important factor in assessing

quality. Most inventories are not structured to provide data

to assess quality of care or to measure outcomes because

135
they do not include claw about individual clients. Although

the impact of public policy on the supply and use of LTC

services is an importmt issue. no ongoing data systems provide

such information. The major gap is not as much with data

on supply and use as it is with data on major policies. Data

which summarize and quantify Federal and State policies on

reimbursement, benefits, coverage, quality. etc. m-e not recorded on a consistent basis and are not linked to data on

service supply and use.

Recommendations

1. Conduct inventories so that all health and social LTC

services, are covered. With the exception of nursing homes.

psychiatric facilities. and Medicare- or Medicaid-certified

providers. inventories are needed for the health care and

social care services listed previously. The NMF1 could

be expanded to include these uncovered LTC services.

Considerable work had been done in 1980–81 to develop

specifications to include these services in the NMFI but

was discontinued due to lack of resources.

-.7 Develop a sampling frame of the various types of residential care through the decennial census. This entails the

development of definitions that distinguish (a) among the

various types of housing with support services and (b)

between housing and health care institutions as defined

by the NMFI and the lMHO. It is essential that the

Bureau of the Census develop this sampling frame through

the 1990 census so that a nationwide list of residential

LTC is available for conducting sample surveys. Such

surveys can provide data on the living arrangements and

the characteristics of the occupants. At present. some

natioha] data systems include persons in some types of

residential care (e. g., the National Health Interview Survey and the Survey of income and Program Participation),

but the surveys are not able to identify them separately.

Once a nationwide sampling frame is available, such surveys can produce data specifically for this subgroup of

the LTC population.

3. Include the LTC Minimum Data Set (National Committee

on Vital and Health Statistics. 1980) in all LTC administrative. survey. and research data collection activities. The

LTC Minimum Data Set, developed by the National Committee on Vital and Health Statistics. is a basic set of

data about persons receiving LTC services. It includes

data items about the person, services, and reimbursement.

The items have standard definitions and reporting

categories. Use of the LTC Minimum Data Set will provide

a core of data to make valid comparisons not only among

different LTC services but also among different Fedeml

data systems.

4. Implement a standard classification of kinds of care provided by LTC services. This classification should be included in the inventories of LTC services and in the

sample surveys of the LTC population in nursing homes

and the community. In addressing the issues of capacity

and substitution of services, comp&jsons of kinds (and

units ) of care among providers ari.. essential. The list

5.

6.

7.

of direct services in the LTC Minimum Data Set is an

excellent starting point for definitions of major classes

of care. It requires additions, particularly in the areas

of assistance with IADL’s and protective oversight. Because the National Committee on Vital and Health Statistics and an interagency task force currently are reviewing

the LTC Minimum Data Set, they may be appropritite

groups to be charged with expanding its list of direct

services.

Develop criteria for defining and measuring quality of

care in LTC services and include them in inventories

and surveys of these providers. Special attention should

be given to defining the criteria for community-based

LTC services, especially where care is provided in the

home. The work of the Institute of Medicine in defining

quality for certified nursing homes should be the basis

for the actual items to measure quality. These items should

be included in the NNHS in order to assess the quality

of all the Nation’s nursing homes, not just those certified

by Medicare or Medicaid. Longitudinal surveys should

include items to determine both the immediate and longrange outcomes of LTC in order to assess quality. The

data on quality from the 18-month Channeling Demonstration can be used to address the issue of maintaining quality

at acceptable levels.

Identify items needed to address issues of targeting i.md

substitution from the experience of the Channeling Demonstration. Once items are identitled, incorporate them

in the longitudinal surveys of persons in the community

and in institutions.

Create a policy-impact data system’ for LTC services.

Such a system can be developed by linking inventory

data to policy data. For example, inventory data on growth

and use of LTC services can be linked to data about

Federal and State changes in legislation, regulations. reimbursements, benefit packages, quality assessment. and

other policy. A possible model for this system is a project

of the General Accounting Office, Nursing Home Data

by State (U.S. Senate, 1986), which examined the impact

of policy on changes in the nursing home bed supply

during 1976-80.

Informal support

Introduction

Informal support is assistance in performing ADL’s and

IADL’s provided by family, friends, and neighbors (Interagency Statistical Committee on Long-Term Care for the Elderly, 1980). Support is classified as informal as long as

it is not provided by members of a formally organized group.

For example, a lunch prepared and delivered by a neighbor

is informal support unless the neighbor is a volunteer in an

organized meals-on-wheels program.

Informal supports, especially from spouse and family.

provide the greatest amount of assistance to the elderly LTC

population (Shanas, 1982). Although informal support plays

a dominant role in LTC, the contribution of formal care,

136
especially for elderly without family, should not be overlooked.

One systems model of social care emphasizes the everchanging, interactive nature of the relationship between informal supports and formal care (Cantor and Little, 1985 ). Because of the dominant role of informal support in providing

LTC and in preventing or delaying institutionalization. it is

an important topic for policy analysis,

Policy issues

Policy issues ohout informal supports Fdll into five

categories:

A\wild)ilify and }t,illit?(qt?(’.s.s—Whois available to provide

int’orrntil support to the functionally disabled elderly’? Who

is willing to provide informal support’? How does willingness to provicic care change as needs for care intensify’?

Does the provision of informal support differ by the characteristics of the recipient (e. g., level of impairment,

type and frequency of assistance needed. relationship to

curegiver. mtirital status. income)’? Does the provision

of informal support differ by the characteristics of the

provider (e. g.. employment. income. health status, ability

to provide tippropriate care, responsibilities to other members of the family)? What changes in informal support

will occur for future cohorts of the elderly”?

lt)~pwt of it@t)wl .wpport-What is the impact of informal support on the physical and mental well-being of

the functiomdly disabled elderly’? Does impact differ by

the type and severity of need? 1s the care provided of

high quality? What types of informal support deter or

delay admission to a nursing home’?

Strc.w-What is the capacity of the informal system to

provide continued assistance’? What are the tactors that

lead to the breakdown of the informal support system’?

How can this breakdown be prevented or minimized?

How does providing informal support affect the caregiver

physically, emotionally. and financially’? What are the

opportunity costs borne by the caregiver (i. e., the cost

to the cw-egiver of opportunities foregone because time

is spent on providing care)?

Sdl.vtitl{tiott of f?)rmal c(ire jbr it!jbrtna[ support-Does

irwretised availtihility of formal care lead to its substitution

for informal support’? What factors about the care needed,

formal services, and informal support are related to substitution’? How rapidly does substitution occur’?

P(dic’y i)}l\)li((itiotl.v-What financial incentives can encourage families to cure for functionally disabled elders?

Whtit service incentives are needed to prevent breakdown

Of the informal support system and maintain its viability’?

How con high qu~lity (;f informal

Con informal networks be “created”

is not availahlc?

Existing Federal data

support be assured’?

when Family support

Duta about informal support are from Fedemi systems

that w-e “in the pipeline. ” In other words, data have been

collected. but much of the data has not been released yet,

The data are described briefly here. More detailed descriptions

of the data systems were presented previously.

●

●

●

●

Informal support for impairments in ADL’s for the LTC

population in the community—The 1982 NLTCS included

a special component on caregivers who provided assistance

with ADL’s. It collected detailed data on the characteristics of the caregiver, including the assistance provided,

associated stress, and opportunity costs (Stone, Cafferata,

and Sang], 1987).

Informal support for impairments in both ADL’s and

IADL’s for the LTC population in the community—The

1984 SOA/NHIS collected basic data on availability of

persons who might serve as informal supports, their willingness to do so, and their kin relationship to the elderly

recipient of care.

Informal support received prior to admission to a nursing

home-The 1985 NNHS collected data on availability

and willingness of persons who might serve as informal

supports, on assistance for impairments in ADL’s, and

on reason for “breakdown” of the informal support.

Changes in the receipt of informal support over time—The

Channeling Demonstration collected data for 10 sites at

the initial assessment and at 6- and 12-month intervals.

Data were collected on the amount and type of assistance

provided, associated stress, and opportunity costs. This

is the only source that provides data to address the issue

of substitution of formal services for informal support

(Kemperet al., 1986).

Assessment

The lack of data about informal support for the elderly

who need LTC was identified in a 1980 assessment as a

major data gap for LTC (Interagency Statistical Committee

on Long-Term Care for the Elderly, 1980). Data were lacking

on both the characteristics of the care provided and the characteristics of the caregiver. The Federal da~d systems described

previously have made major efforts to fill this data gap. (The

1982 NLTCS. in particular, has collected some data about

the care provided and the caregiver. ) Their success in filling

this data gap is difficult to assess until all the data are available

for analysis by the research community. The cross-sectional

data from the 1982 NLTCS and the 1984 SOA/NHIS have

provided important information about informal support.

Nevertheless. it is only longitudinal data that can address

the bulk of the policy issues described earlier. This is because

the concept of change underlies most issues. For example,

issues of continued willingness to provide care and caregiver

stress require data over time to address the dynamic nature

of the situation. Lack of national data on the dynamics of

informal support restricts analysis of interventions to strengthen

the informal support system. It also restricts the ability to

model changes in informal support for future cohorts of the

elderly.

Recommendations

1. Collect longitudinal data about informal supports by using,

if possible, the longitudinal surveys on use of LTC recom137
2.

3.

4.

mended previously. If the same longitudinal survey collects data on use of LTC and on informal support, it

will provide a rich data base for analysis of dynamics

of the relationship between formal and informal care.

It is important that longitudinal data be collected for a

sample of all elderly, rather than only those disabled

in ADL’s or IADL’s. Information on elderly who may

become disabled is crucial to understanding what factors

influence the willingness of persons to provide informal

support. Relevant questions from the Federal data systems

described previously provide a starting point for development of items for the longitudinal survey.

Include in the longitudinal survey the following data:

Availability of a potential network of persons who

might serve as informal supports. (The questions from

the 1984 SOA/NHIS area useful model. )

Factors that determine the willingness of available

persons to provide informal supports when the need

arises.

Changes in the assistance provided by the informal

support system as type and severity of disability

change.

Changes in stress and the identification of events

precipitating the breakdown of informal support.

(Questions from the 1982 NLTCS on the caregiver’s

health, emotions, finances, and ability to work are

a useful model. )

Use of formal care (i.e., nursing home care, community-based LTC, and hospital care) in relation to

changes in disability and in informal support provided. Include in the survey questions about factors

that impact on substitution of formal care for informal

support. (Analysis of data from the Channeling Demonstration should identify some factors. )

Quality of informal care from an assessment of

changes in the care recipient’s physical, mental, and

emotional functioning.

Conduct research and, when necessary, demonstrations

to determine:

. What financial and service interventions could prevent

the breakdown of informal supports. Research should

build on the existing work done on incentives for

family caregivers (Shanas and Sussman, 198 I). Data

from the Channeling Demonstration and the 1982

NLTCS also maybe useful.

● How to “create” informal supports when family are

not available through mechanisms such as church

groups, volunteer organizations, and block

associations.

Develop models for forecasting the informal support networks of future cohorts of the elderly. An approach to

such modeling has been proposed (Manton and Soldo,

1985 ) which addresses not only demographic variables

(e.g., marriages, births. divorces, labor force participation, income) but also health status and disability data

from the cross-sectional surveys described earlier.

Cost of LTC

Introduction

A major concern of policy makers is the growth in costh

of LTC and the substantial proportion paid by the public

sector. The cost of nursing home care, for example. has

increased fourfold from $7.2 to $28.8 billion over the period

1973–83 (Gibson et al., 1984). Economists identify nursing

home care as the most rapidly growing sector of the health

care delivery system. [n 1983. public funds accounted for

nearly one-half of nursing home costs. Costs for LTC w-e

projected to increase in the future (Freeland and Schendler.

1983 ). This is because of the great increases projected for

the group that is the major user of LTC—the oldest old.

Increasing attention is being given to various approaches for

controlling the cost of LTC. These run the gamut from Federal

LTC insurance to incremental changes in existing programs

to private sector initiatives such as LTC insurance packages

and life-care communities (Doty, Liu. and Wiener. 19X5).

It is the costs of LTC for both the aggregate and the individual

that are addressed in this section.

Policy issues

Policy issues in the area of costs of LTC fall into the

following categories:

. E.rpendituresfor LTC—What are the national expenditures

for LTC by type of service and source of payment? Whut

are the historical trends in national expenditures and what

key factors (e.g., inflation, service intensity, popukuion

growth) account for these trends’? What are the costs

and payment sources for particular LTC subpopulations

as defined by disability, income, amount of informal support, and program eligibility? How do costs and payment

sources vary for different LTC services’? How do costs

of LTC services vary based on the types of care provided

(e.g., nursing care, physical therapy, protective

oversight )’?

. Financing and reimbursement i.wlie.~-What would be the

design of a comprehensive package of Federal LTC insurance’? How viable are private sector initiatives (such M

social health maintenance organizations, life-care communities, and home equity conversions) in meeting needs

for LTC? How can the role of the private sector in fintincing LTC be expanded’? What is the extent of asset transfer

and “spend down” in meeting Medicaid eligibility requirements’? What are the costs of LTC over the lifetime of

a cohort o; for a specific LTC subgroup? How can prospective payment successfully be applied to various types

of LTC services? What are the incentives and disincentives

of prospective payment for institutional and communitybased care? How do the disabled finance LTC over time

in relation to their income and assets’?

. Cost cotztrol-Are community-based services less costly

than institutional care’? Is channeling (i.e.. case manage138
ment) ti viable mechanism for controlling costs of LTC?

What impact would coinsurance or deductibles have on

the use of LTC services’! How can the catastrophic nature

t>fnursing home costs be minimized?

Existing Federal data

Existing Federal systems which provide data on costs

t~f LTC are described briefly below. Information on macrokvcl systems is provided first. followed by information on

the micro. or individual. level.

National Health Accounts (NH A). prepared by the Health

Core Financing Administration—The NHA provide

mticro-kvel data on aggregate health care expenditures

(Gibson et al., 1984). The only LTC service for which

costs are presented separately is nursing home care. Home

health care. though included, is subsumed in the category

“t~ther professional services. ” Costs for social services

funded by the Social Services Block Grant and the Older

Americans Act are excluded from the NHA.

The 1982 and 1984 NLTCS’s—They collected crosssectional data on out-of-pocket costs and third-party payments far some LTC services. These services include

ci.tre from visiting nurses, therapists, and mental health

professionals, as well as assistance with IADL’s. Data

were also collected on the out-of-pocket expenditures paid

by caregivers to cover services. medicine, and special

equipment for the disabled elderly.

The 1980 Ntitional Medical Care Utilization and Expenditure Survey (NMCUES)—It collected cost and source-ofpoyment data over a 15-month period for home health

care. The 1980 NMCUES also provided data on LTC

services funded by Medicare and Medicaid by linking

to provider and claims files.

The 1987 National Medical Expenditure Survey

(NMES)—This survey is being developed by the National

Center for Health Services Research. In concept, the

planned 1987 NMES will collect data on medical expenditures for persons in households, nursing homes, and facilities for the mentally retarded, The design of the survey,

its questionnaire content, and procedures will be determined in the Pall of 1986 based on pretest experience

gtiined earlier in the year. Because this survey is in the

developmental phase as of this writing, assessment of

its utility is premature. If implemented as originally designed, it has the potential to make an important contribution in addressing issues on the cost of LTC.

The 1985 NNHS—Data were collected for nursing home

residents and discharges on costs, sources of payment,

chimges in sources of payment since admission, and the

sequence of payment for previous nursing home stays

(i.e.. the h~edicaid spend-down phenomenon).

Assessment

Avuilable data on aggregate costs for LTC services are

fragmented. Although the NHA cover nursing homes and

home health care. costs for social services funded by the

Social Services Block Grdnt and the Older Americans Act

are excluded. It is left to the analyst to compile LTC costs

by tracking down the various data sources and reviewing

their descriptions in the hope of aggregating comparable

information.

Data on expenditures at the micro level are also limited.

Neither the NLTCS nor the NMCUES provided cost and

source of payment data on the complete spectmm of LTC

services received by functionally disabled elders living in

the community. Although the NNHS covers costs and sources

of payment for nursing home care. comparable data me not

available for other LTC health institutions (e.g., rehabilitation

hospitals; inpatient hospice service; facilities for the mentally

ill. for the mentally retarded. and for the physically handicapped) or for housing with support services. It is also importmt for cost surveys of institutions to collect cost data on

services provided to residents outside the walls of the institution

and not included in their bill (e. g., hospital care, communitybased care). Inclusion of such data will provide a more complete picture of the cost of an episode of institutional care.

It should be noted that the NLTCS and the NNHS (and

to a lesser extent the NMCUES) are rich data systems for

information on functional disabilities. demographic variables.

and caregiver characteristics. This richness is important to

cost analysis because it permits examination of cost data in

relation to health and demographic characteristics of specific

LTC subpopulations.

Several of the policy issues deal with changes in cost

over time (e.g., issues about Medicaid spend down. cost

of LTC insurance, and the economic viability of social health

maintenance organizations). Data to address these issues range

from severely limited to nonexistent. The NMCUES covered

only home health costs for a 15-month period. The NNHS

covered costs from the most recent admission. Neither of

these time periods is long enough to provide data to address

these policy issues. A period of 3-4 years would be more

appropriate.

Recommendations

1. Prepare aggregate national LTC expenditures for health

and social services. The NHA should be used as a model.

The expenditure data should be presented by type of

service and source of payment, as was done in the Urban

Institute analysis of LTC data (Scanlon et al., 1983).

The national LTC expenditures should include data about

historical trends and an analysis of key factors accounting

for cost increases (e.g.. intlation, service intensity, and

population growth).

2. Conduct periodic surveys on the costs and sources of

payment for all LTC services received by elders disabled

in ADL and IADL functioning who live in the community.

It is essential (a) that total costs, as well as out-of-pocket

expenditures, be collected and (b) that expenditures for

each sampled person be collected for each and every

type of LTC service. Such a survey will provide comprehensive cost information on the entire spectrum of

139
LTC services. The survey should also include data on

demog%dphics, disability. income. and receipt of informal

support so that analyses can be done for special LTC

subpopulations. The NLTCS. the NMCUES, and the

NMES can be used as the basic model. Once a sampling

frame of housing with support services is developed (see

previous section on supply of LTC services), elderly in

these living arrangements can be specitlcally sampled

for this cost survey and analyzed as a special population.

3. Conduct periodic surveys on the costs of care and sources

of payment for persons in (//1 types of LTC institutions.

Cost data on care received outside the walls of the institution (e.g.. from acute care hospitals or community-based

services) should be included. The NNHS and the NMES

could serve as the basis for developing a periodic survey

covering all LTC institutions.

4. Add a longitudinal component to the periodic LTC cost

surveys of the community and institutions recommended

previously. This component, perhaps only a subsample

of the total survey sample, would be followed for 3-4

years (possibly by telephone) to provide data on changes

in costs, in sources of payment, and in use of LTC.

Such longitudinal data are essential to estimating the costs

of program benefits, LTC insurance packages, life-care

communities, and care for specific subgroups of the LTC

population.

140
References

Callahan, J., and S. Wallack, eds.: Reforming rhe Long-Term Cure

.Sysfem. Lexington, Mass, Lexington Books, 1981.

Cantor, M., and V. Little: Aging and Social Care, in R. Binstock

and E. Shanas, cds., Hundbook of Aging und the Sociul Sciences,

2d cd. New York. Van Nostrand Reinhold Co., 1985.

Doty, P., K. Liu, and J. Wiene~ An overview of long-term care.

Heal/h Cure Financing Review. Vol. 6, No. 1. Office of Research

tind Demonstrations, Health Care Financing Administration.

Washington. U.S. Government Printing Office, Spring 1985.

Federal Council on the Aging: The Need for Long-Term Care, Informu/ion und /s.wes. DHHS Pub. No. (OHDS) 8 I–20704, 1981.

Frccksnd, M,, and C, Schcndlec National health expenditure growth

in [hc 19804s, An aging population, new technologies and increasing

competition, Hea/th Cure Financing Review. Vol. 4. No. 3. Office

of Rcseurch and Demonstrations, Health Care Financing Administration. Wtishington. U.S. Government Printing Office, March 1983.

Frks, J.: Aging, natural death and the compression of morbidity.

N. Eng/, J, Med. 303(3): 130-135, 1980.

Gibson, R., et al.: National health expenditures, 1983. Heal/h Care

Fimmcing Ret,ie\t!. Vol. 6. No. 2. Office of Research and Demonstrations, Health Care Financing Administration. Washington. U.S. Govcrnmwrt Printing Office, Winter 1984.

Institute of Medicine: Improving the Quality of Care in Nursing

Hmnes. Washington, D.C. National Academy Press, 1986.

Interagency Statistical Committee on Long-Term Care for the Elderly:

Dutu Cot’eruge of the Functionally Limited Elderly. NTIS No. PB80-

212558, 1980.

Kcmper, P., et al.: The Ewduution of/he National LTC Demonstration: Fired Sutnmury Report. NTIS No. PB86-2291 I9, 1986.

L]u, K., and K. Manton: The characteristics and utilization patterns

of tin ximission cohort of nursing home patients. Geron(ologisr

23( i ):92-98, 1983,

Munton, K,, and B, Soldo: Long-Range Planning for the Elderly,

An Integrated Policy Perspective. Mimeographed document. Paper

prepturd for U.S. Senate Special Committee on Aging, 1985.

Mcincrs, M.: The case for long-term care insurance. Health Ajjairs

2:55-78, Summer 1983,

Meltzer, J., F. Farrow, and H. Richman: Policy Options in Long-Term

Cure. Chicago. University of Chicago Press, 1981.

National Center for Health Statistics, J.F. Van Nostrand. A. Zappolo,

E. Hing, et al.: The National Nursing Home Survey, 1977 Summary

for the United States. Vita/ and Heahh Smristics. Series 13, No.

43. DHEW Pub. No. (PHS) 79–1 794. Public Health Service.

Washington. U.S. Government Printing Office, July 1979.

National Committee on Vital and Health Statistics: Long-Term Health

Care Minimum Dara Sel. DHHS Pub. No. (PHS) 80-1158.

Washington. U.S. Government Printing Office, 1980.

Office of the Assistant Secretary for Planning and Evaluation: Working Papers on Long-Term Care Prepared for the 1980 Undersecretary’s Task Force on Long-Term Cure. Washington, D.C. Department

of Health and Human Services, Oct. 1981.

Rice, D., and J. Feldman: Living longer in the United States, Demographic changes and health needs of the elderly. Miibank Mere.

Fund Q. 61: 362–396. Summer 1983.

Scan Ion, W., and J. Fedec The long-term care marketplace, An

overview. Journal of Health Care Financial Management. Jan. 1984.

ScanIon W., et al.: Project to Analyze Existing Long-Term Care

Data. Mimeographed document. Washington, D.C. Urban Institute,

1983.

Shanas, E.: Nutiona/ Survey of the Aged. DHHS Pub. No. (OHDS)

83–20425. Washington. U.S. Government Printing Office, 1982.

Shanas, E., and M. Sussman: The Family in Later Life, Social

Structure and Social Policy, in R. Fogel et al., eds., Aging: Stability

and Change in the Family. New York. Academic Press, 1981.

Stone, R., G. Cafferata, and J. Sangl: Caregivers of the frail elderly:

A national profile. Gerontologist 27(5):616-626, 1987.

U.S. Senate: Statistical Policy for an Aging America. Senate Hearing

99-858. Washington. U.S. Government Printing Office, 1986.

Van Nostrand, J.: Social Supports and Health Care of the Elderly.

Paper presented at the International Congress of Gerontology, New

York, 1985.

Vladeck, B.: Un/oving Care. New York. Basic Books, 1980.

141
Data requirements for longterm care insurance

by Mark R. Meiners, Ph. D.,

National Center for Health Services Research

Introduction

The potential of insurance for long-term care (LTC) is

beginning to receive widespread attention. However, this kind

of coverage is currently available on only a limited basis.

Only recently have insurers begun to seriously explore the

market. One of the primary barriers to their efforts has been

the lack of reliable data on virtually all the key elements

necessary for product development and marketing. Nonetheless, the state of knowledge has progressed rapidly in recent

years and a variety of insurers are committed to exploring

the market. To a large extent, these efforts reflect the belief

that the market for LTC insurance is viable, growing, and

profitable. Progress in testing this belief, however, will be

slower and more conservative than is desirable until insurers

are comfortable with the data available for actuarial pricing

of such products. The purpose of this paper is to examine

data needed for LTC insurance as a basis for stimulating

further data base development.

In thinking about the kinds of data that will be required

to evaluate and further develop the market for LTC insurance,

it is quite easy to feel overwhelmed by the wide array of

information to consider. A broad range of covered services

is theoretically possible. The care can range from intensive

skilled services to custodial care and can be provided in a

variety of settings including hospitals, nursing homes, board

and care homes, in a person’s own home, as well as in

the community.

The delineation of covered services is a crucial step in

the design of such coverage and will dictate the data that

are needed to fulfill that design. Ideally, service coverage

will be precisely defined in terms of what services are covered,

who may provide the service, and under what conditions

the services may be provided. Precise definitions are important

for the prediction of utilization, as well as for administering

the claims payment process.

Background’

To understand how data requirements are driven by policy

specifications, consider the seemingly straightforward goal

of insuring long-term nursing home and home health needs.

The first step is to recognize that, to accomplish this goal,

the policy must cover more than what is currently covered

by Medicare. Medicare was not designed to address the need

for long-term care. Nursing home care is covered up to 100

days, with the first 20 days completely covered and a daily

copayment equal to one-eighth of the hospital deductible for

the remaining 80 days. In order to qualify for this coverage,

a person must have been hospitalized for at least 3 days

for the same sickness or injury; the patient must be transferred

to a skilled nursing facility (SNF) within 30 days after discharge

from the hospital and the transfer certified by a doctor; the

SNF must be certified by Medicare; and the patient must

need and receive daily skilled nursing care or daily skilled

rehabilitation therapy to improve his or her condition.

In practice, users of Medicare nursing home benefits receive only about 27 days of covered care per year (Muse

and Sawyer, 1982). Medicare claims criteria are often complex

and restrictive; also, they are subject to the individual judgments of claims reviewers, which can vary substantially (Smits,

Feder, and ScanIon, 1982).

Also, only a portion of all nursing home beds are certified

for Medicare, limiting access to covered care. Medicare certification has lost its appeal for nursing home operators for a

number of reasons. Some of these are the fear of retroactive

denials of claims, strict Federal requirements for Medicare

SNF certification, and the paperwork burden of a reimbursement system that encourages rapid turnover and is different

from the system used for private-pay and Medicaid patients

(Feder and Scanlon, 1982).

Home health care is covered under both the hospital insurance (Part A) and supplementary medical insurance (Part B)

portions of Medicare. Benefits are payable if a beneficiary

needs part-time skilled nursing, physical therapy, or speech

therapy. Although the number of visits is unlimited under

Medicare Parts A and B, the program requires that the needed

services be documented by a physician as primarily skilled

care and that the patient be “homebound.” These requirements

effectively limit use. The average user has only about 23

visits per year (Muse and Sawyer, 1982). Personal care services

provided by home health aides on a part-time or an intermittent

basis are covered if the patient is receiving one of the qualifying

‘Contents are the opinions of the author and not thow d’ the Depmtmurt

of Health and Human Services.

‘Portions of thissection appeared earlier (Meiners. 1984).

142
skilled services. Homemaker services can be provided if they

are incidental to personal care and do not substantially increase

the time spent by the home health aide.

To be meimingtld for long-term care, nursing home and

home cure benefits must cover a broader range of services

than is covered by Medicare. In nursing homes. coverages

hch)w the skilled level are typically referred to as intermediate

and custodial care. The concept of intermediate care, which

comes from the Medicaid program. addresses coverage of

what amounts to personal care with skilled nursing services,

but on u less frequent basis than would be the case with

skilled care. In practice, the intermediate care definition varies

grwttly by State, with many viewing it simply as a cheaper

version of skilled care. The definition of custodial care also

wtries, ranging from personal care with some nursing, to

(rely personal care, to simply sheltered living with no specific

provision for personal assistance. People with chronic illness

requiring long-term care need coverage h- personal care along

with nursing cure and will benefit most if covemge continues

even when personal cure is the predominant service they

rcccivc.

For home health care, further liberalization of Medicare

means removing the homebound requirement and/or increasing

the coverage to include personal care and homemaker services

without the strict ties to skilled care. Removing the homebound

requirement involves expanding into community care benefits

that are considered important services in the LTC continuum.

A broad array of services such as day care, transportation,

nutrition services, respite services, and chore services can

be imagined.

The problem insurers have with expanding the insurance

package beyond Medicare’s nursing home and home health

benefits is that long-term care needs are complex and definitions are not uniform. Services tend to overlap in their purpose

and there is little consensus as to how services should or

could be packaged, staffed, and provided. In addition, the

best approach to providing care often depends on the living

circumstances of the individual, including the availability of

family care and other informal support.

Many of the services are nonmedical and are required

to tissist with health status deficiencies associated with the

normul aging process. Insurers seriously question whether

such services are insurable. They are concerned about having

to pay for the care of a person who no longer wants to

live independently and are inclined to rely on medical needs

to define insurable events.

Traditionally, services are considered insurable if they

would not be affordable without signitlcant financial difficulty,

if the probability of needing them is low enough to allow

the risk to be spread, and if the need is random enough

that users cannot be identified in advance. Many LTC services

do not satisfy these criteria without further clarification. For

example, nursing home care is often a catastrophic expense

because it is needed for an extended spell of illness rather

than because it requires intensive services. Many home and

community services meet the criteria only indirectly. The

services are considered insurable only because they are believed to be lower cost substitutes for insurable services.

Ideal data base

It should be clear from this discussion that insuring longterm care is not straightforward. Ideally we would like to

have a longitudinal data set that tracks the utilization patterns

of a large representative population insured under provisions

similar to those being considered in the product design. It

would contain the date the policy was issued; basic demographic information on the insured; and dates of admission

and discharge for all hospital, nursing home, home, and community care utilization by skill level required. It would also

include the date and location of death.

Such a data set is ideal in several respects. Because the

utilization patterns would have been established under a known

insurance plan, the effect of insurance on the demand for

care would be taken into account. Also, the comprehensiveness

of the data base would allow consideration of the effect of

service availability on utilization patterns. Furthermore, the

longitudinal aspect of the data would provide information

on how those patterns change as the cohort of insured persons

ages.

In its broadest sense, the ideal data base for developing

long-term care insurance is essentially similar to what

policy makers and planners would like to have to guide their

decisionmaking for long-term care. The data are not easily

obtained for several reasons. In a recent overview of international perspectives on LTC, Laura Reif noted three important

problems with obtaining the ideal data set: “(1 ) most nations

have no systematic and universal scheme for data-reporting

that would make it possible to obtain information about care-recipients and service use in different service sectors, and across

government programs and jurisdictions (local, regional, national); (2) implementing such a scheme is very problematic

because it is difficult to track the movement of care-recipients

among assistance programs, and from one service setting to

another; and (3) even if an accurate picture of current care-recipients, service use, and costs could be obtained, planners

would still lack adequate information about the extent of unmet

need, and what service utilization and expenditures would

look like if services were matched to need, rather than being

constrained by inadequate funding and limited availability y

of services” (Reif, 1985). It is clear that the United States

shares these problems with other countries.

In reality, insurers would be satisfied with much less

than the ideal data set. To understand just how much less,

the next section will review the approach and data used to

develop workable LTC insurance in the face of these difficulties. The discussion is suggestive of areas where marginal

improvements in data can be especially helpful.

Stepping back from the ideal

In spite of having much less than ideal data, insurers

can begin to provide useful LTC benefits, minimizing many

technical concerns through careful policy design. To demonstrate this, the National Center for Health Services Research

143
(NCHSR) undertook the specification and pricing of a prototype LTC policy (Meiners, 1983; Meiners and Trapnell,

1984). The prototye was designed to avoid policy features

which required data that were unlikely to be available. At

the same time, an effort was made to address considerations

important to buyers and sellers of such coverage.

The policy focuses on nursing home care. However, in

recognition of data weaknesses, no effort is made to distinguish

length of coverage or amount paid on the basis of the level

of care provided. Nursing home coverage is emphasized over

home care because such payments are the largest single out-ofpocket health care expense faced by the elderly (Fisher, 1980)

and there is a need for improved insurance coverage for this

risk. Private insurers are more likely to be willing to provide

extended coverage for care at both the skilled and personal

care levels in nursing homes than in the home. They know

that the elderly have a strong preference for home health

care and fear that such insurance would encourage increased

utilization due to lower out-of-pocket expenses. In addition,

the data necessary for setting insurance rates for home health

care are more limited than those for nursing home care. Home

and community care benefits are available in exchange for

nursing home benefits once nursing home use has been established. This benefit is included to help relieve the bias toward

remaining institutionalized by providing the beneficiary the

option of being discharged to home or community care.

Benefit payments are limited to a fixed amount for each

day of covered service to encourage patients and their families

to search for efficient providers who can supply the necessary

care within the budget. It is also a simple way to limit the

insurer’s liability and reduce the risk of providing insurance.

Policies paying the “reasonable and customary” charge for

specific services are much more open ended and have been

criticized as one of the reasons for the rapid inflation of

health care costs because there is little incentive for patients

or providers to hold down costs.

To minimize insurers’ concerns about an open-ended

liability, the prototype recognizes the need for time limits

on eligibility. A 90-day elimination period is required before

benefits begin. The 90-day elimination period was chosen

to provide protection from catastrophic long-term care expenses. A period of about 3 months in a nursing home tends

to be the critical break between whether the patient is going

to be a long stayer or a short stayer (Meiners, 1983). It

also represents a significant deductible which should help

limit insurance-induced demand. A 3-year limit on coverage

was chosen to encompass a reasonably large portion (about

85 percent) of all nursing home stays. For those few beneficiaries whose stay exceeded the benefit period, Medicaid would

serve as the payer of last resort after private resources are

exhausted.

The prototype policy is a hybrid of features modeled

after other types of insurance. Included are elements of whole

life insurance (level payments based on expected lifetime risk),

term insurance (annual premium payments in return for guaranteed continued protection), and disability/health insurance

(payment of benefits on the basis of the onset of a covered

event). The key data elements for estimating premiums are

the frequency of establishing a benefit period (the admission

rate) and the utilization per benefit period (the length of stay)

of potential purchasers. Other necessary data are actuarial

data related to mortality. The estimates also required experience data on withdrawal rates of policyholders, financial dard

(interest and inflation rates) for estimating investment returns

on reserves, and operating expense factors (commissions, profit and risk allowances) reflecting the insurer’s mode of operation. It is the utilization data, however, that distinguish a

long-term care premium calculation from other insurance products. The following discussion focuses on those contingencies

to illustrate how available data were used to estimate utilization

and to highlight those areas where better data are needed.

Utilization data and assumption

The National Nursing Home Discharge Survey of 1976

was used for calculating both frequency of benefit periods

and the average number of covered days per period. The

data were analyzed according to a number of characteristics

that it was believed might disclose significant variations in

the shape of the distribution of people across differing lengths

of stay, including age, sex, and marital status. In the final

analysis, utilization patterns were distinguished only by age.

Frequencies were determined for three age groups (65–74,

75–84, and 85 years and over) and the intermediate frequencies

were determined by interpolation.

Although marital status appeared to have a major impact

on the average length of stay, marital status was not used

in classifying the data since the status reported relates to

the time of a claim rather than to the time at which a policy

is issued. Thus, an insurer could not use this information

in setting separate rates for married and nonmarried persons.

Sex was also not used as a basic classification variable.

The patterns of length of stay did not appear to be sufficiently

different tc require use of separate tables by sex. In reality

we know, however, that women, in general, live longer than

men. Increasing age and loss of spouse are thought to be

associated with increased need for LTC. One of the practical

difficulties in using these data to understand utilization patterns

among different population groups is the sample sizes. The

more detailed the analysis, the thinner the data and the more

suspect the conclusions.

Though the Discharge Survey data were the best available

for the necessary utilization estimates, adjustments were

needed to overcorhe several problems. The Discharge Survey

was not designed to track patients through the various transitions that may occur duting a covered stay. For example,

a transfer between nursing homes will continue a covered

stay for application of the elimination and benefit periods

of the policy, but may result in the duplication of cases according to the Discharge Survey. Similarly, brief transfers home

or to a general hospital and then back to a nursing home

would be part of one covered stay, but may be recorded

2This section draws on material that appeared Aier (Mcincrs ml Tmpncll.

1984; Traprrell et al.).

144
as multiple discharges. In addition, adjustments were necessary

due to classification problems that occured because some of

the survey responses did not provide the information that

allowed for classification. For example, it was not always

known whether a patient died after being discharged to a

hospitul.

The discharge data needed to be classified into one of

the following three categories of nursing home discharges:

● Death causing an end to the benefit period, either as

the reason for the discharge from the nursing home or

following a hospital or other institutional stay without

readmission to a nursing home.

● Discharge home or to another setting in which home

care benetlts would be paid,

. Transfer to another nursing home, or transfer to a hospital

or other noncovered institution followed by readmission

to u nursing home.

Each category has a different effect on the benefit period.

Policyholders in the first category are not eligible for further

nursing home or health benefits. Those discharged home are

eligible for home care benefits. The last group represents

stays that are continued. In the final tally, only the discharges

classitled as recoveries or death counted in the admission

rates.

The transfers were estimated to be about 23 percent of

all discharges. To evaluate the reasonableness of this estimate,

longitudinal data from a study of admissions to proprietary

skilled nursing homes in San Diego were examined (National

Center for Health Services Research, 1983). About 28 percent

of the admissions were transfers. The shorter timeframe of

the San Diego data and more limited coverage of facility

types relative to the national data limit the applicability of

this check, However, it is clear that multiple admissions occur

to a significant extent and estimates of lifetime admission

rates need to be adjusted for this phenomenon.

The transfers, although dropped from the frequency count,

had their days added to the days of the discharge groups.

Since only limited information was available for matching

transfers to the continuation stays, each nontransfer stay was

assumed to be equally likely to represent a continuation. The

transfers were in effect randomly assigned to nontransfer discharges in the same age group. Data on the length-of-stay

patterns of patients with multiple admissions are needed to

extimine this approach,

The adjustments significantly shifted distributions toward

longer stays. In the 75–84 age group, for example, the preconvohttion distribution shows only 10.5 percent remaining after

3 years compared to 15.8 percent remaining in the convoluted

distribution. At the lower end of the distribution, the preconvolution distribution shows 52.4 percent discharged within

90 days compared to 43.4 percent for the convoluted distribution. Nearly all of this difference occurs within the 30-day

interval.

Finally, life table techniques were used to convert the

discharge data to an admission cohort. Because discharges

in 1976 were used to estimate admissions in 1976, when

the admissions in reality occurred prior to 1976, adjustments

to the distribution of admissions were necessary. The discharges for stays longer than 6 months originated, on the

average, in years before 1976, when the population from

whom these admissions arose was somewhat smaller. This

biases downward the proportion of discharges for longer stays.

If there were no restraints on utilization (e.g., an unlimited

supply of beds) and if the admission rates for each age-sex

group did not change, the relevant growth rate would be

that of the corresponding population group. But since the

bed supply constrains utilization, the bias will be proportional

to the number of beds (Scanlon, 1980). The problem may

then be restated by saying that the number of discharges

admitted in some past period must be increased to reflect

the number that would have been admitted if the bed supply

had always been as large as it was in 1976.

According to the National Master Facility Inventory, the

bed supply was growing at approximately 2.24 percent annually from 1973 to 1976, a period which includes the admission

dates for most of the discharge sample (National Center for

Health Statistics, 1979). Thus, the bias can be approximately

offset by increasing the stays by 0.18 percent per month.

The number of people in each interval was then weighted

by the average length of stay for the interval to derive the

necessary utilization estimates.

In the absence of data concerning the average number

of post-nursing-home-confinement home or community care

visits that would be reimbursed by the policy, utilization was

proxied by assuming that all persons discharged home used

all the visits per week allowable under the policy as long

as they were alive and not reinstitutionalized. The mortality

and reinstitutionalization rates were set higher (250 percent)

than those for the general population to reflect their higher

health risk. The maximum for home health benefits is the

number of weeks remaining in the lifetime benefit period

after the discharge from the nursing home. The estimates

implicitly assume that formal home or community care will

be substituted for family care.

Areas for improvement

It is clear that the utilization estimates involved numerous

assumptions and adjustments to compensate for weaknesses

in the data. In particular, data are needed that do a better

job of tracking transitions over an extended period so that

estimates of admission rates and length of stay accurately

reflect the fact that a spell of illness can involve multiple

admissions. The data are particularly weak for estimating

the projection of those who die after discharge to another

institution, also for understanding the frequency of discharge

from a nursing home to home or community care as well

as the circumstances that make this transition feasible, Clarification of any of these transitions from virtually any data

source would be usefid at this early stage of data development.

Another weakness of the data is that they reflect general

population utilization patterns that are influenced in an unknown way by numerous factors, most notably the supply

145
of nursing home beds and the availability of other LTC services 

that might serve as alternatives to using those beds. To some 

extent, this is as much an analytic problem as a data problem. 

Further analysis ot available data sources is needed to help 

clarify the determinants of LTC utilization patterns. 

Faced with uncertainty about the determinants of utilization, insurers apply a range of risk-management technique:, 

to help them select among applicants. Typical risk-management techniques to avoid adverse selection include screening 

applicants for health status and requiring waiting periods before 

preexisting conditions are qualified for coverage. Data and 

analysis are needed to help assess the importance of thc\c 

practices. For example. how many prospective insurance purchasers are likely to be screened out and whether screens 

are justified are important issues for determining product design 

as well as whether the market can be widespread. 

Insurers are also faced with the unknown effect of insurance-induced demand (“moral hazard”). Besides drawing inferences on this problem from other insured products. there 

is little information to guide adjustments for this problem. 

Typical risk-management techniques used by insurers to limit 

insurance-induced demand include specific definitions of qualifying facilities and care, prior hospitalization requirements, 

physician recommendation and review of care, and the use 

of copayments and deductibles. Data and analysis are also 

needed to understand the feasibility and implications of applying these techniques. As noted earlier. distinguishing benefits 

on the basis of definitions of facilities and care is likely 

to be controversial and difficult to administer. How important 

is it to require a j-day hospital stay before benefits begin’? 

Does the answer change if there is an elimination period 

deductible of 30-90 days‘? How frequently, if at all. does 

utilization review need to be done given changes in the health 

status of chronic care patients’? Better answers to these questions will further the development of LTC insurance that provides consumers with meaningful coverage and keeps insurers 

solvent. 

litilization estimates from general population data are 

not likely to reflect the patterns faced by a particular insurer. 

However. they pt-ovide a convenient benchmark that Insurers 

can use as a basis for their own estimates. An insurer’s own 

risk-selection factors will dictate the necessary adjustments. 

If an insurer carefully “underwrites” a policy (i.e., selects 

applicants in good health or with characteristics that make 

utilization less likely), the average admission rates may be 

expected to be lower than in the general population. In underwriting policies. insurers are essentially trying to eliminate 

those persons for whom the risk is predictably higher than 

what they had estimated in their premium calculations. Further 

data and analysis are needed to help establish conclusively 

how long-term care benefits can be structured to meet criteria 

of insurability for broad segments of the population. 

Premium calculations also require mortality rate estimate5 

for all insured persons whether or not they are institutionalized. 

One of the interesting facets of the need for nursing home 

care is that most people die without ever having been admitted 

to a nursing home and a significant number of those admitted 

jie after only a short stay. usually following an acute care 

episode of illness. These people use little or no nurstng home 

care. At the same time, we know that utilization of health 

care In general ix disproportionately higher in the last year 

of lit’e. The Implications of this for long-term care insurance 

are unclear. Further data and analysis on the interrelationship 

of mortality and morbidity arc needed to help clarify the 

potential role of LTC benefits (e.g.. hospice) as lower cost 

substitutes for acute care services. 

Other models of LTC insurance 

The freestanding type of coverage which has been the 

focus of the discussion is one of three basic LTC insurance 

models which have been emerging. The others are the social 

health maintenance organization (S/HMO) and continuing care 

retirement communities (CCRC). Each approach has different 

degrees of benefit comprehensiveness w&h determine the 

data that are needed to insure the LTC risk, Each approach 

IS similar. however. in the sense that they all require essentially 

the same types of data described earlier. with the specific 

requirements determined by the detail of the health care guarantee and the administration of the risk. 

Continutng care retirement communities are perhaps the 

oldest example of LTC insurance. They are organized communities for the elderly which provide permanent apartments 

along with health care guarantees in exchange for entry fees, 

most typically in the range of$ I3.000 to $65.000. plus monthly 

fees, most typically ranging from $300 to ‘$900.3 LTC in 

a nursing cam unit is available on either a k3TIpOrA~ or 

a permanent basis when residents can no longer live in their 

apartment\. Different health care guarantees exist. Some communities have no extra charges beyond the monthly fee. while 

others make only partial health care guarantees. Though there 

are a variety of types. in their most comprehensive form 

thcsc communities offer the full range of !,TC services, including sheltered livtng arrangements, meals, care in their own 

apartment\. and nursing home care. all paid for on the basis 

of pooling the risk and cost among community residents. 

This form of LTC insurance is the most comprehensive 

because it explicitly considers the resident’s home in the community as one of the factors in estimating the costs of the 

benefit package. As Winklevoss and Powell (1981) point out, 

“communtty sponsors must determine the associated (health 

care) liability and develop their pricing structure accordingly 

to insure the long-term financial viability of the community” 

(p. IO). The estimates must balance the fact that, while permanent transfers from the community to the nursing home will 

result in increased health care costs, the resale of the permanently vacated apartment will also generate increased revenue. 

The components of the Winklevoss and Powell model 

of health care utilization in CCRC’s include both permanent 

and temporary transfers from the community to the health 

care center. ax well as death rates for persons residing in 
each of those settings during a particular year and in subsequent

years, To operationalize this model, a separate schedule of

mortality rates for community residents and nursing home

residents is needed, along with a schedule of mortality rates

applicable to permanent transfers and a schedule of morbidity

rates applicable to temporary use of the nursing home.

Winklevoss and Powell ( 1981)elaborate further:

Because mortality and morbidity rates vary by age, a

schedule of rates is needed instead of simply one rate

for each assumption. Since mortality rates tend to improve

over time, a mortality improvement scale also is desirable,

producing lighter rates for each future calendar year. In

addition, such rates vary by sex, requiring eight schedules,

four for women and four for men. Finally, the mortality

and morbidity schedules are generally dependent on each

person’s length of stay in the community. In other words,

the death rate applicable to a female age 80 who entered

the community at age 75 is generally higher than the

rate applicable to a female age 80 who just entered the

community. Consequently, eight schedules (along with

a mortality improvement scale and select factor) should

be established for each age at which individuals enter

the community (pp. 10-1 1).

The data requirements for insuring the LTC risk in CCRC’S

are essentially the same as described for freestanding LTC

insurance. In fact, some CCRC’s are exploring the possibility

of purchasing such insurance as a way to cover their nursing

home liability. Such contracts need to reflect the fact that

people who join such communities tend to be older than

the average elderly population and live longer than the average

for people their age, The contracts also may need to be structured in a way that shares the risk among the community,

the insurer, and the resident so that the utilization incentives

are balanced among the key participants in the insurance

contract,

Sharing the risk among providers, insurers, and consumers

is the cornerstone of the S/HMO model, an innovative attempt

to extend the health maintenance organization acute care model

of case management and prepaid cavitation to include LTC

benefits. Four essential features of the S/HMO model are

described by Greenberg and Leutz ( 1984):

First, a sponsoring agency (or agencies) takes responsibility for bringing the full range of acute and chronic care

services into a single system, Second, this new S/HMO

entity serves a membership that is representative of the

community. Most importantly, it enrolls both disabled

and able-bodied elders. Third, the S/HMO is paid on

a premium or “prepaid cavitation” basis—by both individual members and third parties (Medicare and

Medicaid). The S/HMO pools these premiums to pay

for member services. Fourth, the S/HMO is “at risk”

for service costs. That is, the organization takes responsibility for meetingits own budget, and it standsto experience some level of profit or loss for its efforts (p. 58).4

d’t’hc most comprehensive work to date on the S/HMCt is by l-em et al.

[1985).

The goal of the S/HMO model is to provide comprehensive

health benefits including LTC. Conceptually the model is

between freestanding LTC insurance and the CCRC approach

in terms of comprehensiveness. It attempts to integrate the ~

entire continuum of health care services into its benefit package

rather than just focusing on LTC but does not include the

use of the insured’s home in the calculation of benefit costs.

In practice the experimental nature of the S/HMO and

market pricing considerations have required a more conservative approach. For LTC the emphasis is on comprehensive

home care for chronic or disabling conditions which require

rehabilitation, support, and maintenance care that may not

meet Medicare criteria. This home care includes medical transportation, home-delivered meals, adult care, and homemaker

services, as well as nurse, therapy, and home health aide

services. Utilization of these benefits will be controlled by

case management, copayments, and benefits ceilings set at

the discretion of each participating site.

The value of the “chronic care benefit” ranges from $6,500

to $12,000 per member per year and the copayment rates

vary from 10 percent to 20 percent, depending on the site

(Greenberg and Leutz, p. 61). The chronic care benefits are

renewable annually at all sites for the community and homebased benefits, but are not necessarily renewable for institutional care. The S/HMO benefit package is likely to cover

only part of the costs for the most impaired, especially those

needing nursing home care (Greenberg and Leutz,

p. 61).

Although the basic data requirements for estimating the

LTC risk in the S/HMO are similar to the data needed by

the other LTC models, the comprehensiveness of the cavitation

approach introduces some additional complexity. The pooling

of Medicare, Medicaid, and private insurance dollars requires

estimates of the average acute and LTC liability of all three

payers rather than just the risk of LTC expenses for the insurer.

The Greenberg and Leutz discussion of data required for the

S/HMO estimates strikes some familiar themes.

Data on long-term care utilization and costs are skimpy,

and even the best had to be interpreted in light of differences in such areas as program, benefits, population

served, and reporting format. HMO data on acute-care

costs had to be interpreted to account for both the S/HMO’s

older, poorer, and more disabled population and for the

lack of HMO experience at some sites. Furthermore, existing data and methodologies gave little guidance on two

key issues: ( 1) how full consolidation would affect acutecare utilization and (2) what proportion of the membership

would use chronic-care service (p. 72).

They go on to point out that in the face of these data gaps

most of the sites made relatively conservative assumptions

in estimating their risks.

The S/HMO has recently begun at four sites: The Metropolitan Jewish Geriatric Center in Brooklyn, New York; Kaiser

Permanence in Portland, Oregon; the Ebenezer Society in Minneapolis, Minnesota; and the Senior Care Action Network

in Long Beach, California. The demonstration is scheduled

to run for 3 years with an outside evaluation, funded by

147
the Health Care Financing Administration (HCFA), that will

focus on issues such as cost-saving potential, health outcomes,

and selection bias in enrollment. In addition, Brandeis University, in cooperation with the four sites, is currently seeking

private funding to conduct its own evaluation of the S/HMO,

focusing more on the internal dynamics. The intent of the

Brandeis effort is to “become part of the intervention itself

by using research to detect problems, make timely mid-course

corrections, and improve the model” (Research Consortium

of Social Health Maintenance Organizations, 1985). To accomplish its goals, the Brandeis consortium has identified the

following key elements of a core data set to be standardized

across the four sites:

●

✎

✎

●

●

✎

✎

✎

●

✎

✎

●

●

●

Social/demographic.

Health status.

Functional status.

Mental status.

Personal adjustment.

Informal support system.

Care plan elements.

Hospitalization/SNF utilization.

Ambulatory visits.

Care in an intermediate care facility.

Home care services.

Enrollment/case mix/targeting data.

Medicaid statuslspend-down experience.

Marketing data.

The S/HMO experiment and evaluations will provide valuable information about costs and utilization of LTC services

in the context of a prepaid capitated environment where the

providers have incentives to be cost conscious in the management of the clients’ care. The significant role of public funding

and support for the S/HMO experiment should provide a rare

opportunity for disseminating these valuable data to wider

audiences than is normally the case when individual entrepreneurs accept the risks and rewards of such innovative efforts

on their own.

Other areas for data

development

The last two items listed in the proposed S/HMO care

data set (Medicaid status and marketing) deserve some additional attention. So far the discussion has focused on the

data needed to define the risks associated with the need for

LTC as a basis for specifying and pricing meaningful insurance

protection. The supply side of the issue tends to dominate

the discussion because until recently LTC was generally not

perceived to be insurable, and without a product to sell there

is little basis for discussing the demand and impact aspects

of the market. With LTC insurance beginning to emerge as

a viable product, consumer demand and the potential impact

of such coverage on public as well as private budgets become

essential topics for further analysis.

That a market cannot exist without consumer demand

is obvious. However, the details of consumer demand for

LTC insurance are only beginning to be understood. Questions

of consumer knowledge, preferences, ability to pay, and willingness to pay are all part of what insurers need to know

as they go forward in their product development. It is becoming

recognized that consumers generally do not have a good understanding of Medicare and their private supplemental insurance.

Their knowledge of the risks of LTC and how such care

is financed is inadequate for wider market development. They

have a strong preference for the more difficult to insure home

and community benefits and have an aversion to nursing home

care, for which LTC insurance is more feasible. Even when

there is interest, there remain questions about affordability.

For the market to be widespread, insurers will have to carefully

examine their product design and actuarial assumptions to

bring prices down from current levels. It is a process that

requires better data on the demand side as well as the supply

side of the market. In particular, better data are needed on

income and assets along with analysis of lifetime savings

and consumption patterns and how those might be changed

to help finance LTC for an aging population.

Market development is also influenced by the significant

role currently played by Medicaid in financing LTC. It has

been suggested that the availability of Medicaid is a major

barrier to private market development because it is viewed

by consumers as acceptable protection against LTC risks.

Whether this is true is open to question. We need a clearer “

understanding of the process by which people become eligible

for Medicaid, particularly for people who end upon Medicaid

because of their need for LTC (the “spread-down” process).

The relationship of private and public payment for LTC is

also important because there is a potential for public savings

if LTC insurance is widely marketed. The significance of

this potential needs to be examined to determine whether

public intervention or subsidies (such as tax incentives) should

be used to stimulate the market and, if so, which approaches

are likely to produce the best results.

Summary and discussion

The development of insurance coverage for

care is currently receiving considerable attention.

long-term

There is

widespread interest among consumers, insurers, government,

and providers of services in such coverage. A number of

insurers are now selling such policies, but questions remain

relating to the scope and type of benefits as well as the

premium costs. Part of the problem in designing long-term

care insurance is the availability of an adequate data base

for actuarial pricing. This paper addresses a number of important data issues relating to the design, pricing, and marketing

of long-term care insurance.

It is clear that the development of LTC insurance is in

its infancy and there is a need for improved data on virtually

all aspects of that development, including information on utilization, costs, risk management, marketing, and the impact

of such coverage. Policy design and service definitions play

148
un important role in determining specific data needs. The

most important data are utilization data on covered services.

Ickdly, insurers would like to base their planning and development on a data set containing longitudinal utilization patterns

for u Iurge number of persons insured under provisions similar

tt] those being contemplated.

In reality, insurers can begin to make the necesstuy estimutes with much less than the ideal data set. As an example,

the features of a beginning LTC insurance prototype and the

data used to estimate the necessary utilization patterns are

described. The example is illustrative of the kinds of policy

fctitures, data assumptions, and analytical techniques that can

help insurers to begin with available information. The prototype features and estimates are also indicative of the compromises insurers must make without better data.

Freestanding LTC insurance is only one of the models

thut are emerging. Insurance for LTC is also an important

part of continuing care retirement communities and social

health mtiintenance organizations. The basic data requirements

of these alternative models are also examined. Each model

represents different levels of comprehensiveness: the S/HMO

merging LTC benefits for chronic care with the more standard

ucute cure coverage; the CCRC’S (in their most comprehensive

form) considering virtually all aspects of daily living for an

elderly person, including a sheltered living environment, in

their benefit package calculations. For each model, however,

the btisic data needs for insuring LTC risk are similar, with

the specifics determined by the benefit design and administration of risk.

The review of alternative models suggests that we know

enough to begin insuring LTC but that there is ample room

for improvement, In particular, we need to do a better job

t~f tracking health care utilization patterns over time. Fortunutcly there is H growing recognition of the importance of

understanding the dynamics of long-term care utilization patterns. The 1985 National Nursing Home Survey not only

includes a much needed update of the 1976 National Discharge

Survey but makes a concerted effort to gather data on a

cl~mplete history of nursing home utilization of both residents

tind discharges by doing followup with the patient, a relative,

or other knowledgeable person. The survey is also designed

to ctipture data on where patients were admitted from (including

length of stay in a hospital) and their discharged destination.

Also included is information on whether there were short-term

hospittil stays that did not require a formal discharge from

the nursing home. The survey will again try to determine

if death occurred following a discharge to another facility.

These data will be extremely useful for reexamining the nursing

home utilization estimates made with the 1976 National Discharge Survey and for understanding how those patterns may

be changing as the population ages and the impact of changes

in public policy interventions such as hospital diagnosis-related

groups.

Other valuable data gathering efforts that are in the planning stages are the HCFA and Brandeis evaluations of the

S/HMO (described earlier) and the 1987 National Medical

Expenditure Survey of the National Center for Health Services

Research. This survey is an update of the 1977 National

Medical Care Expenditure Survey. It will give special attention

this time to LTC data collection. A detailed set of questions

is planned regarding home health care events, including the

content of the care given (i.e., skilled rehabilitation, assistance

in activities of daily living), the relationship of the home

health event to medical provider events, charges and sources

of payment, and the frequency of events over the year. The

survey will also include an institutional component to collect

data on current residents and admissions of nursing homes,

personal care homes, facilities for mentally retarded, and psychiatric hospitals. This survey will provide valuable longitudinal data needed for LTC insurance by tracking the utilization

patterns for noninstitutionalized as well as institutionalized

persons.

These data bases hold much promise for better data for

LTC insurance esimates in the future and deserve support.

In the short term, however, we will need to rely on existing

data, which may be useful only for understanding smaller

pieces of the big picture. Data from the channeling demonstration and the 1982 and 1984 Long-Term Care Surveys of

disabled persons in the community are examples of forthcoming data sets that hold promise for further insights concerning

the development and pricing of LTC insurance. Other possibly

useful data sources that need further exploration may be available from the States. Perhaps insurance companies and CCRC’S

can be encouraged to pool their data through independent

private organizations such as the National Association of Insurance Commissioners or the Society of Actuaries or through

a government agency such as the National Center for Health

Services Research so that all can share in an improved state

of knowledge and key data elements regarding the insurance

of LTC risks. LTC insurance is potential y an important piece

of the LTC financing puzzle. How such coverage emerges

will be greatly influenced by the availability of reliable data

on which to base further development.

149
References

Fmlcr. J.. and W. ScanIon: The unclcr-used benefit. Mcdicarc’s

covcmgc of nursing home care. Mi/bmA A4em. Fmd Q. 4:604-636.

1982.

Fisher, C. R.: Ditkrcnccs by agc groups in health care spending.

Hcd//I C’wv Fimincin,q l?e~’ie]!’.Volume i, Issue 4. HCFA Pub.

No. 03045.” OtYicc of Research. Dcmonstmtimrs. and Statistics.

Hcuith Car-c Financing Administmtion. Washington. U .S. Government Printing Office. Spring 1980.

Grccnbcrg. J. N.. und W. N. Lcutz: The Social/Health Maintcnancc

Organization and Its Role in Reforming the Lon.g-Term Care Systcm,

in Lotl,q-Tetw CWYI Fitwncing wd Delitwy Sy~ten?s, E.~pkvin,q Some

A//ernwil’e.$. Cent’crcncc pmcccdings. HCFA Pub. No. 03174. June

1984.

Lcutz. W.. C[ al.: Chmging HWIIII Care ,fiv m Agin,q .!h)iefy.

Lexington. Mass. Lexington Books, 1985.

Mcincrs, M, R.: The case of Iong-term care insumncc. Hed(b Afkir.s

2:55–78. Summer 1983.

Mcincrs, M. R.: The State of the Art in Lcmg-Term Care lnsurancc,

in Lon,q-Tern? Cm-e Financing and Deli\wy Sy.Y/enLs,hplorin,y Sane

Ahernati]v.s. Confcrcncc pmcccdings. HCFA Pub. No. 03174,

June 1984.

Mcincrs, M. R.. wrd G. R. Trapncll: Lon.g-term care insurance,

Premium estimates for pmtotypc policies. Med. Care 90 I–9 1I,

Oct. 19X4.

Muse, D. N., and D. .%twycc The Mcdicarc and Medicaid Data

Book. 198 [. Health Care Financing Administration. HCFA Pub.

No. 03128. Apr. 1982.

National Center for Health Scrviccs Research: San Diego Nursing

Home Inccntivc Rcimburscmcnt Study. Unpublished data, 1983.

Viral and Hcwlth Statistics. Series 14, No. 23. DHEW Pub. No.

(PHS) 80-1818. Public Health Scrvicc. Washington. U.S. Govcmmcnt Printing Office, Jan. 1980.

Rcif, L.: Long-term care. Some Icssons from cross-national cmnptirisons. Home HealIh Care Ser~’ice.rQiiwterly 5(3 14):329–34 1, 1985.

Research Consortium of Social Health Maintenance Organizations:

Draft proposal by 13randcis University Health Policy Center.

Waltham, Mass.; Eldcrplan, Inc., Brooklyn. N. Y.: Kaiser Pcrmancntc Center for Health Research, Portland. Ore.: Mcdicarc Partners, Minneapolis, Minn.; and SCAN Health Plan, Inc., Long Beach.

Calif., Mar. 1985.

ScanIon, W. J.: A theory of the nursing home market. /nqr/ir,v

17:25, 1980.

Smits, H. L., J. Fcdcr. and W. ScanIon: Mcdicarc”s nursing home

benefit, Variations in intcrpretatimr. N. Eug/. J. Med. 307( 14):855-

862, Sept. 30, 1982.

Trapncll, G. R., J. P. Bmwnlcc. H. Chang, ct al.: Private Hctdth

lnsurancc for Long-Term Care Manual of Study Pmccdurcs. UnpubIishcd rcprn-t prepared for the National Center for Health Scrviccs

Research by Actuarial Research Corporation under Contract 233-XI3033. Available from National Technical lnfrn-mation Scrvicc, 5285

Port Royal Rd., Springfield. Va. 22161, No. PB 84- I87699.

Winldcvoss, H. E., and A. V. Powell: Rctircmcnt communities.

Assessing the liability of alternative health care guarantees. The

Jmrncd ofLon{q Tertn Cure Adnlini.$/ra[ion 1X(4):8–33, Winter 19X1.

Winklcvoss, H. E., and A. V. Powell: Ccvzfim{in,q Cwe i7e/irenwz~

Conwumities, An Empirical, Finmcial md Letqld Analysis. Homcwood, Ill. Richard O. Erwin, Inc., 19/34.

National Center for Health Statistics, J. F. Sutton and A. Sirrocco:

Inpatient hculth facilities as reported from the 1976 MFI Survey.

150
Coordination of Federal

statistics related to the

elderlyasb

by Katherine K. Wallman,

Council of Professional Associations on Federal Statistics

Introduction

The U.S. Federal statistical system, by design and in

practice, is decentralized; responsibility for the collection,

processing. analysis, and dk+semination of statistics which

relate to the economic, physical, and social well-being of

vtirious groups in the population is spread among many agencies and departments of the Federal Government. More than

60 years ago, a report prepared for the Congress by the Bureau

of Efficiency noted:

Practically every bureau in Washington collects or disseminates statistics of one kind or another; and there

is much confusion in the public mind concerning the

work done by the various offices.

The statistics collected by the Government relate to nearly

every aspect of our economic and social life. Statistics

of agriculture begin with the seed and follow through

to the marketing of the ripened product. Statistics of

manufacturers extend from the mining of crude ore to

the production of the manufactured article; those of commerce, from the lighting of rivers and harbors to the

consumption of imported commodities; and those of social

relationships from a mere enumeration of population to

elaborate data regarding the incidence of disease (U.S.

Bureau of Efficiency, 1922, p. l).

The trend described in 1922 continues. In 1985, the Office

of Management and Budget provided budget and program

information on more than 70 agencies which reported outlays

of at least $500,000 for statistical activities in any of fiscal

years 1984-86 (Office of Management and Budget, 1985).

Debates over the relative merits of centralized versus

decentralized organizational structures for the production of

Federal statistics have occurred with regularity during the

20th century. (See, for example, Duncan and Shelton, 1978,

and Bonnen et al., 1980.) However, the United States has

continued to follow a decentralized model. Proponents of

thu decentralized organizational structure have argued that

certain benefits derive from such an arrangement for gathering

and distributing Federal statistical information. In particular,

they have suggested that the statistical programs are, as a

consequence, more relevant to poiic y and program concerns.

“C~mtcntsw tht opinions or’the author and not those ot’ the Department

(If }krlth d Human ,%rviccs or the or.gsnizstion she works krr.

II,C,tq~5 National A~adcmy Of.%imlox.

At the same time, both proponents of decentralization and

those who favor a more centralized system have recognized

that there are costs attributable to such an organizational structure. For example, difficulties in ensuring the availability

of a comprehensive and consistent body of data to explore

the condition and problems of particular populations may arise

when no single agency has lead responsibility for meeting

information needs related to that population group.

Attendant to the decentralized structure of U.S. statistical

activities have been initiatives, particularly within the past

50 years, to provide mechanisms for coordination of the Government’s statistical programs. Sustained efforts to coordinate

Federal statistics began in 1933 with the formation of the

Central Statistical Board, which was later merged, in 1939,

with the Bureau of the Budget (BOB). By 1947, the Division

of Statistical Standards in BOB had become a 69-person unit

managing statistical policy coordination and forms clearance

under the authorities of the Budget and Accounting Procedures

Act of 1921] and the Federal Reports Act of 1942. Essentially,

these acts gave the Statistical Policy Division authority to:

. Develop programs and issue regulations and orders for

the improved gathering, compiling, analyzing, publishing,

and disseminating of statistical information for any purpose by the various agencies in the executive branch

of the Government.

. Prevent use of unapproved forms; designate a single

agency to perform the collection when more than one

agency required specified information; prevent agencies

from collecting unnecessary information; and require

agencies to disclose information they had gathered to

other agencies (under prescribed conditions for maintaining confidentiality).

Through a variety of mechanisms, including the review of

fiscal budget requests, the clearance of proposed requests

for information, and the formation and leadership of interagency committees and working groups, the statistical policy

staff served to coordinate activities among various involved

and affected agencies.

During the ensuing years, while Government statistical

programs expanded, the size of the staff for statistical policy

and coordination functions decreased. By 1977, the staff of

‘Rdwant provisinrrs were later incnrpomtu.i in .$xtimr 103 d’ the Budget

and Accounting Prnccdurw Act of 1950.

151
the Statistical Policy Division in the Office of Management

and Budget (OMB) numbered 29. Nonetheless, that staff (and

its successor, the Office of Federal Statistical Policy and

Standards, located from 1977 to 1981 in the Department of

Commerce) carried on efforts to coordinate statistical programs

in order to prevent unwarranted burden on respondents, to

prevent duplication, and to assure adequate coverage of important issues.2

In the view of the President’s Commission on Federal

Statistics, the central statistical office served the decentralized

Federal system well by:

Designating one agency to obtain and process data of

interest to several agencies;

Encouraging agencies to use-or make available for the

use of others—administrative data that can substitute for

survey data;

Clarifying the wording of questions and eliminating unnecessary questions on both survey and administrative

forms;

Encouraging the use of probability sampling in place of

judgment sampling or complete coverage;

Increasing comparability of data from different sources

by requiring the use of standard definitions and classifications, standard reporting periods, and standard reporting

populations;

Improving the quality of analysis of problems by calling

attention to ways in which data obtained from different

sources could fruitfully be used to modify both survey

questions and administrative records so that they can be

used in complementary fashion to provide information

on a common problem; and

Sparking considerable reexamination of individual surveys

by the smaller producer agencies with the result that some

of those series have been eliminated (President’s Commission on Federal Statistics, 1971, pp. 139–140).

Whether or not the statistical coordination functions performed during the period from 1933 through 1980 constitute

a complete, or necessarily proper, set of activities, an understanding of the types of initiatives undertaken historically

is important both to appreciating the views of statistics producers and users with respect to the current environment and

to formulating possible recommendations for the future. This

background paper explores current mechanisms for oversight

and coordination? of Federal statistics relevant to the elderly

population, provides an assessment of those existing activities,

and presents possible recommendations for future efforts.

~For cxmnptc. in 1979 [he office ot’ Fcdcrttl Sttitisticd Policy and SWrdmls

furnwcl the Inwragcncy Stotisticid Cmnmittcc on Lung-Tcrnl Grc t’or the

Elderly. This cmnmittec. which incluclcd rcprcscntativcs km] every &partmurt concerned with [he ckkrl y popukit ion. prepared u series of rccommcndiitiuns on nucdcd impnwemcnts IU dtito in urctis such us bnwd and cm

und infbrmol suppnrts. When the responsibility for s~~tisticd policy wos

returned to OM B undo’ ihc Papcrww+ Reduction Act ut’ 19X(). however.

the Cmnmittcc cwtwxt to timct ion.

Responsible

agencies

departments and

Responsibility at the Federd level for the collection, processing, analysis, and dissemination of statistics of interest

to those involved in research, policy-setting, and programmat ic

decisions affecting the aging population is dispersed among

almost two dozen executive branch agencies. Among the data

of greatest interest to users of information or] the elderly—located not only in the statistics-producing agencies but in other

Federal and non-Federal organizations as well—are general

population statistics and statistics on income and wealth, health

and nutrition, labor force participation and retirement, and

housing and living arrangements.

At the present time, the Federal executive branch agencies

with complete or partial responsibility for production of statistics related to these areas include the following:

Department of Agriculture

Economic Research Service

Food and Nutrition Service

Department of Commerce

Bureau of the Census

Department of Health and Human Services

Administration on Aging

Alcohol, Drug Abuse, and Mental Health

Administration

Assistant Secretary for Planning and Evaluation

Centers for Disease Control

Health Care Financing Administration

National Center for Health Services Research

National Center for Health Statistics

National Institute on Aging

Office of Human Development Services

Social Security Administration

Department of Housing and Urban Development

Office of Planning, Development, and Research

Department of Labor

Bureau of Labor Statistics

Employment and Training Administration

Office of Pension and Welfare Benefit Programs

Department of the Treasury

Statistics of Income Division, Internal Revenue Service

Veterans’ Administration

To gain a fuller understanding of current authorities. policies, and activities related to oversight and coordination of

3Within this paper. “’cnnrdinatimt”’ generally refers [u interagency autivitiw

intcndwl to increase the quality and utility ofstatistical intixmatimr t’or multiple

users. whik minimizing the cnst and the burden ot’ datti cnlktimr on the

public. Such activities incltrdc. for example. thnsc rckttcd to the dctcrmirwtiort

of which agency will collect the required information: the design of purtimrltw

stiitistical prugmms (e. g.. dccisimrs on content and cuvcragc): the cstablishmwrt ut’ standard mrccpts and detlnitimts: and the dcvcloprncnt of rcquircmmrts fbr dimsanalysis. prcscnuation. and disscmimttiun.

152
statistics on the elderly population, and views on the effectiveness of these mechanisms, interviews were conducted with

representatives of each of the agencies currently involved

in the production of information in major areas of interest.

In addition, interviews were conducted with representatives

of two units with assigned responsibility for oversight of the

statistical programs of concern—the Office of Management

and Budget (Office of Information and Regulatory Affairs

and the Special Studies Division) and the Office of Health

Planning and Evaluation in the Public Health Service. To

complement the views provided by those involved substantially

in the production of statistics, representatives of Federal legislative branch organizations-the Congressional Budget Office,

the General Accounting Office, the Office of Technology

Assessment, and the Senate Special Committee on Aging—and

of selected non-Federd organizations—the American Association ctf Retired Persons, the Employee Benefit Research Institute, and The Gerontological Society of America—were asked

to provide a “user’s” perspective. (A listing of the individuals

interviewed is provided as appendix A.)

Questions posed to obtain background information on

current activities and suggestions for the future were as follows:

What formal or informal authorities does your agency

have, if any, for inter- or intra-agency oversight and

coordination of Federal statistics related to the elderly?

What general policies (formal or informal) does your

agency follow in matters related to oversight and/or coordination of statistics on the elderly?

What formal or informal activities does your agency undertake to encourage or execute inter- or intra-agency oversight or coordination of statistics related to the elderly?

What formal or informal policies or practices do you

believe enhance or inhibit fruitful oversight and/or coordination of statistics on the elderly?

What mechanisms and practices have produced improvements to the coverage, quality, relevance, and timeliness

of statistics related to the conditions and problems of

the elderly?

Where has the lack of inter- or intra-agency oversight

or coordination resulted in failure to produce needed information, or in less than optimal products to inform decisions

related to the elderly?

What alternatives might be considered for improving the

oversight and coordination “of statistics on the elderly?

What would be involved in making such changes? What

factors might inhibit the success of such initiatives?4

The responses provided by those interviewed are summarized

in the following sections of this paper and form the basis

for the evaluation of current mechanisms and suggestions

for the future.

4NOIC: Inhxvicws with individuals in organizations not rcsponsibk for the

production ofstutistics tkuml nn the lost four qucstinns.

Current mechanisms for

oversight and coordination

At the present time, legislative authority to coordinate

Federal statistics of interest to those involved in research,

policy-setting, and programmatic decisions affecting the aging

population is embodied in two statutes. The first of these,

the Paperwork Reduction Act of 1980, provides to the Office

of Management and Budget broad responsibility to:

. Develop long-range plans for the improved performance

of Federal statistical activities and programs;

. Coordinate the functions of the Federal Government with

respect to gathering, interpreting, and disseminating statistics; and

. Develop and implement government-wide policies, principles, standards, and guidelines concerning statistical collection procedures and methods, statistical data classifications, and statistical information presentation and

dissemination.

The Paperwork Act also assigns to OMB responsibility for

clearance of information collection requests, i.e., the authority

to review and approve information collections proposed by

agencies, and to determine whether the collection of information by an agency is necessary for the proper performance

of the functions of the agency. Both the statistical policy

and reports clearance functions are delegated, by the act,

to OMB’s Office of Information and Regulatory Affairs.

The second legislatively prescribed authority to coordinate

Federal statistics related to the elderly population is far more

specific. Under the Older Americans Act of 1965, as amended,

the Administration on Aging is assigned the duty and function

to “gather statistics in the field of aging which other Federal

agencies are not collecting, and take whatever action is necess~ to achieve coordination of activities carried out or assisted,

by all departments, agencies, and instrumentalities of the Federal Government with respect to the collection, preparation,

and dissemination of information relevant to older individuals. ”

Further, the Act assigns to the Commissioner of the Administration on Aging the responsibility to advise, consult, and

cooperate with the head of each Federal agency or department

proposing or administering programs or services substantially

related to the purposes of the Older Americans Act, and

requires the head of each Federal agency, in turn, to consult

and cooperate with the Commissioner. Among the programs

cited in this context are the demographic statistics and analysis

programs conducted by the Bureau of the Census.

Despite die existence of these legislative authorities to

oversee and coordinate statistics related to the elderly population, neither the Office of Management and Budget nor the

Administration on Aging is aggressively pursuing such activities. The Statistical Policy Office in OMB’S Office of

Information and Regulatory Affairs (OIRA)-which currently

has a staff complement of six professionals—is not focusing

153
attention on statistics related to the elderly. OIRA desk officers

responsible for clearance of information requests may, in the

interest of minimizing respondent burden, question the need

for proposed collections of information or suggest the use

of alternative data collection sources. But active efforts to

coordinate or improve the scope and quality of information

on the elderly population are not being undertaken by OMB’S

Office of Information and Regulatory Affairs.

Likewise, while the Administration on Aging currently

is reviewing plans for the statistical programs and publications

of a few agencies (e.g., the aging supplement to the Health

Interview Survey, the National Mortality Followback, and

the Standard Death Certificate programs of the National Center

for Health Statistics, and the Decennial Census and the Survey

of Income and Program Participation conducted by the Bureau

of the Census), such reviews are conducted largely in response

to requests from the data collecting agencies rather than at

the initiative of the Administration on Aging. Although some

leadership to coordinate statistics related to the aging was

taken by the Administration on Aging during the mid- 1970’s

(i.e., an interagency group on statistics related to the elderly

was established, and an inventory of existing statistical programs was compiled and published in 1979), such efforts

have not been pursued in more recent years, primarily because

of a lack of staff resources.5 In sum, while present efforts

by the Office of Management and Budget and the Administration on Aging may serve to eliminate duplication or to improve

a particular survey, the current coordination activities of these

agencies are not directed to identifying gaps in data on the

elderly, or to ensuring that such gaps are filled.

While the two Federal agencies with specific legislative

authority to oversee and coordinate statistical programs related

to the elderly population are not currently undertaking efforts

to meet these mandates, two other Federal agencies, within

the past year, have initiated interagency activities to improve

the coordination of such information. Noted most often by

those interviewed was the “Forum for Statistics on Aging,”

an informal group instituted at the initiative of the Census

Bureau. Composed of statisticians from various Federal agencies that develop statistics on the elderly population, the Forum

for Statistics on Aging was established as a means to:

. Exchange information;

. Coordinate the approach to the collection and tabulation

of data on the elderly population;

. Keep the Federal agencies informed about needs for dat%

and

. Identify data gaps in the Federal statistical system relevant

to the elderly.

The second mechanism established during the past year to

enhance interagency coordination of statistics on the elderly

population is a Subcommittee on Data Sources under the

aegis of the Interagency Committee on Research on Aging,

led by the National Institute on Aging (NIA). The overall

‘A! the prcwrt time. only onc individual

is assigned hl work on stNisticul wwlysis.

in the Administration on Aging

Committee, created some 10 years ago, includes as members

representatives of approximately 30 Federal agencies within

and outside the Department of Health and Human Services.

Various subcommittees address topics such as research

priorities, technology and aging, and noninstitutional care.

The recently formed Subcommittee on Data Sources, established primarily to promote the exchange of information on

existing data sources and to assess how such sources have

been influenced as research priorities have changed, reviewed

potential changes for the 1990 decennial census and provided

feedback on concerns and priorities. The subcommittee also

has had some preliminary discussion on developing an inventory of research on aging, including statistics, and may in

the future look at data gaps. In general, the NIA-sponsored

Subcommittee focuses on dard sources as they relate to research

issues, whereas the Census-sponsored Forum is concerned

with data fqr a broad spectrum of policy and programmatic

uses.

Beyond these two “government-wide” efforts, agencies

responsible for statistics related to the elderly population indicated that current activities to coordinate with other Federal

units were largely limited to one-to-one efforts. For example,

the National Center for Health Services Research has shared

research plans for the proposed 1987 National Medical Expenditure Studies with agencies providing financial support for

the program, and the National Center for Health Statistics

(NCHS) coordinates with specific agencies on plans for parti&-

Iar data collection activities. The Food and Nutrition Service

at the Department of Agriculture will provide drafts of its

plans for nutritional monitoring to interested agencies for comment, and the Health Care Financing Administration now

has about 30 cooperative agreements to meet information requests from other organizations such as NCHS, the National

Cancer Institute, the Census Bureau, and the Centers for Disease Control.

Within Health and Human Services, the Federal department housing the largest number of agencies responsible for

research and service programs affecting the elderly population,

efforts to coordinate statistical activities are fragmented. For

example, both the National Institute on Aging and the Administration on Aging have reviewed plans for certain NCHS programs (e.g., the aging supplement to the Health Interview

Survey, the Standard Death Certificate, and the National Mortality Followback). The National Center for Health Services

Research has undertaken activities to coordinate with the

Health Care Financing Administration and with NCHS, but

has not worked in cooperation with the Administration on

Aging or the Office of Human Development Services. Two

offices which have specific coordination responsibilities review

the plans of some, but not all, of the departmental units

which provide data on the elderly population. The Office

of Health Planning and Evaluation (Public Health Service),

largely in the context of reports clearance activities, provides

some coordination of programs sponsored by the National

Institute on Aging, the National Center for Health Statistics,

the Alcohol, Drug Abuse, and Mental Health Administration,

and the Health Care Financing Administration, while the Office

of the Assistant Secretary for Plannihg and Evaluation reviews

154
planned activities of the Health Care Financing Administration,

the National Center for Health Services Research. and the

Office of Human Development Services.

Effectiveness

mechanisms

of current

As a means to achieve interagency coordination on issues

and problems and to improve the statistics available on the

elderly, the Census-sponsored Forum received high marks

from those who are participating, While the relative newness

of this initiative precluded an assessment of its ultimate effectiveness, the sense of the agencies was that the Forum for

Statistics on Aging has great potential for increasing awareness

of available statistics, for sharing information on gaps, and

for suggesting solutions to meet such needs. As a case in

point, the signatories to the Forum’s memorandum on proposed

changes in the 1990 census hope that, by presenting a modest,

unified proposal on suggested modifications, their recommendations will carry greater weight with the Bureau of the Census

and the Federal Agency Council, and will meet with more

succcss in the clearance process at the Office of Management

and Budget.

Also cited as examples of “good practice” were selected

one-to-one efforts that have been undertaken by some of the

data-producing agencies. In particular, close coordination with

other agencies by the National Center for Health Statistics

on the Mortality Followback, and by the Health Care Financing

Administration and the Bureau of the Census in preparing

special tabulations from existing data sources, were viewed

as having positive outcomes,

A number of individuals interviewed noted that recent

budget stringencies, which have necessitated more frequent

joint funding of data collection programs, have led to greater

interagency coordination. The Survey of Income and Program

Participation and the National Nursing Home Survey were

key examples mentioned in this context.

Although some cases of positive outcomes from coordination were cited by agency and other respondents, examples

of losses occasioned by the failure to coordinate were more

frequent.h Virtually every individual interviewed noted at least

one instance of a data collection program or analytic activity

that might have been improved through greater interagency

coordination. In some cases, these “lost opportunities” resulted

in failure to meet a particular need, or “data gaps,” while

in other cases, the consequence was a lack of comparable

information from sources in various agencies.

Among the data gaps noted by producers andlor by users

of data on the elderly population were the following:

Information on the “old old,” i.e., those 85 and over.

Information on age segments of the elderly population

by, for example, 5-year breaks of the population 65 and

over.

““l-hetililul-c [() c.()(mlirrotc tmiy. ot” cwursc. bc only purtidlly responsible

tor w+ d:Ilu y;ips. f:iscd d burden tsudgc( cnnstrctints may bc contributing

tiwor~ in Mmw caici.

Information on subgroups of the elderly population—those

with serious health problems, women living alone, and

minorities.

Longitudinal information on utilization and financing of

health care facilities and services.

Information on “out-of-pocket” expenses, e.g., for overthe-counter drugs, by income group and by race.

Information on housing and alternative living arrangements for the elderly.

Information on family support systems.

Information on the types of long-term care that are available and on needs for long-term care which are not being

met under present arrangements.

Information on the nursing home population, including,

among other things, diagnostic data.

Information on functional abilities and chronic diseases

of the elderly.

In addition to data gaps which result from the lack of

efforts to coordinate between and among agencies responsible

for collecting and using information related to the elderly

population, those interviewed suggested a number of instances

where the absence of coordination by the data-producing

agencies— in particular, failures to integrate appropriate content and to use comparable concepts and definitions in designing data collection programs—has led to noncomparable or

inconsistent information from Federal data sources.7 For example, several respondents noted the dit%cuky in obtaining useful

data relating income level to the use of and methods of payment

for health care services. The Current Population Survey, which

has good data on income, lacks information on use of health

services and methods of payment, while health data sources,

e.g., the Health Interview Survey, generally lack good information on income. Other examples of cases where insufficient

interagency coordination has led to noncomparable or less”

than optimal products included inconsistencies between questions in Census Bureau surveys and the Health Interview

Survey, differences between Census and Social Security projections of birth and mortality rates, and the failure to match

Social Security Retirement Histories with Medicare files.

Beyond the citation of particular data gaps or instances

of failure to produce comparable or integrated information,

several cases of inattention to the appropriateness for the

elderly population of survey coverage or of specific questions

asked in general surveys were noted. For example, the Current

population Survey (CPS) queries the noninstitutionalized

civilian population; as a consequence, other surveys tied to

the CPS frame suffer by failing to include the institutionalized

population (e.g., those in nursing homes, who are likely to

have the highest health care costs) in examining questions

such as total costs of medical care. In the case of the 1980

decennial census, it was noted that the “disability” items,

which were related to the ability to work and the ability

to use public transportation, were of little relevance for major

‘This is not to suggest. of cwrrw. that wcry survey should include all pntcntidly LIsct’ul content arws: intcywd planning with rcspwt to ky areas.

howwr. could wbstmtti:i]ly crrh:mw ongoing ml proposwl prq!mms.

155
segments of the elderly population. A third example in this

general category was the weakness of reporting in the CPS

with”respect to rents, dividends, and interest, an increasingly

important source of income for some sectors of the older

population.

At a more general level, the individuals interviewed

suggested a number of reasons why coordination and cooperation among agencies have not always been successful. Among

the problems cited were:

Lack of a “structure” that leads to coordination—incentives currently favor agencies acting independently, in

their own perceived best interests.

Lack of a clear, formalized decision process within various

agencies for developing particular data collection programs, making it difficult for other data producers and

users to interact with the collecting agency.

Absence of information which would enable data producers and users to know what other agencies are doing.

Lack of information from data users inside and outside

the statistics-producing agencies about information requirements, and about how the information is or could

be used.

Failure to reach out to see what others are doing (i.e.,

to examine existing data sources) or to obtain the views

of counterparts when initiating or modifying data collection programs.

Lack of exchange of information early enough in the

process to influence design of data collection or tabulation

programs. (For example, coordination on the National

Nursing Home Survey did not take place until the time

of OMB clearance. )

Failure to take advantage of special expertise of kindred

agency personnel in designing survey programs.

Underlying these factors which inhibit the coordination

of Federal statistical programs of interest to those involved

with research, policy formulation, and programmatic decisions

affecting the aging population are several fundamental causes:

the historic lack of mechanisms, formal or informal, to foster

coordination; current fiscal budget stringencies; present-day

philosophy and regulations governing Federal information collection, which require adherence to the “burden budget” and

demonstration that data have “practical utility” for the agency

sponsoring the information collection request; the tendency

for data collection activities to mirror program structures (e.g.,

how the elderly are housed is the responsibility of the Department of Housing and Urban Development, whereas determining what kinds of services the elderly receive is largely the

responsibility of the Department of Health and Human Services—in turn, the agencies collect information in their respective domains);s reluctance to open information collection programs to the broader community, with the concomitant poten-

‘Incki. even within an area, such as health care, the delivery systcm is

fmgmcntcd. and the Federal data smrrccs mirror that fragmentation.

tial for “loss of control” of the survey; and the fact that

cooperation generally benefits the “user” agency far more

than the “producer” agency.

Suggestions for the future

In spite of the frequent citation of specific examples where

coordination among the statistics-producing agencies might

lead to more useful or usable information products, the individuals interviewed expressed little support for any substantial,

formal mechanism to engender cooperation.9 As one respondent noted, there is no need for a “grand guru of aging

data.” Reasons given by the many who shared this opinion

included:

The current policy of “less data:’ which could lead to

a situation where coordination would be used as a means

to further cut back on Federal information collection

activities.

The delays in implementing data collection programs

which result from undertaking coordination activities.

The lack of human resources to lead or to participate

in coordination activities.

The fact that there are too many groups of special interest

to establish formal mechanisms to coordinate data on

each.

The view that the interest and desire of technical personnel

to cooperate, rather than formal mechanisms, prove most

effective in achieving coordlntaion.

While little enthusiasm was expressed for the establishment of a formal mechanism to coordinate statistical activities

related to the elderly population, there remain nonetheless

various problems which must be resolved through cooperative

efforts among Federal agencies. Based on the interviews conducted with agency representatives, a list of improvements

to be gained through further cooperative efforts among agencies

would include the following:

The establishment of mechanisms to educate users and

producers of statistics on the elderly, both within and

outside government, about existing sources of

information.

The establishment of mechanisms to improve the flow

of information about data needs and problems with current

data sources between users of information and those responsible for the collection, tabulation, and dissemination

of Federal statistics.

The establishment of a means to provide early information

on plans for new data collection programs or for modifying

existing systems.

% is interesting to note, however, that scvcrai inquiries have bmr mwle by

individuals wishing to coordinate proposed activities through the dctirnct interagency Statistical Committee on Long-Term Care for the Elderly.

156
The establishment of efforts to merge existing data files

(from both statistical survey and administrative record

sources).

The establishment of mechanisms to develop and exchange

methodological knowledge on techniques for obtaining

information from the elderly population, and to examine

the appropriateness of survey questions for older

respondents.

In an environment characterized by staff resource and

response burden constraints, the incentive to coordinate, and

the success of such efforts, will depend in large measure

on the assurances provided to the Federal agencies that the

products of such efforts will have status and acceptance

throughout funding and clearance processes. Lacking evidence

of such “clout,” initiatives such as those related to the 1990

decennial census, and others currently envisioned by the Forum

on Statistics for the Aging, will have little chance of achieving

ongoing support from either working-level personnel or agency

leuders.

Although it might seem appropriate, in the context of

the mission of the Panel on Statistics for an Aging Population,

to strive for solutions unhampered by current policies. trends,

and constraints, more modest proposals, taking into account

current and foreseeable realities, are offered in the balance

of this paper. These proposals recognize

Current formal and informal coordination authorities and

‘ activities.

The decentralized character of Federal assistance and

statistical programs.

Federal domestic spending limitations.

Present philosophy and practice with respect to the approval of Federal information collection and dissemination

activities.

Given the breadth of Federal agencies involved in the

production of statistics related to the elderly population, it

would seem perhaps self-evident that the logical focal point

for coordination of the statistical programs of interest would

be the Office of Management and Budget. OMB clearly possesses the strongest set of authorities over the many agencies

tind departments involved; it has responsibility not only for

executive branch decisions on fiscal budget matters, but also,

by law, for statistical policy and information clearance decisions. Nevertheless, at least two factors argue against the

ikmibility of substantially improved efforts by OMB, in the

currently constituted Office of Information and Regulatory

Affairs, to coordinate Federal statistics on the elderly population: the extremely limited staff available for statistical policy

and coordination activities, and the current organizational arrangement for information clearance activities, which are conducted by desk officers essentially on an agency-by-agency

basis.

In spite of the fact that OMB is unlikely either to accept

or to be able to exercise abroad mandate to coordinate statistics

on the elderly, there are at least two steps that might be

taken by the Office of Information and Regulatory Affairs.

1. At the present time, the Office of Information and Regulatory Affairs requires of each department an Information

Collection Budget, which details all planned activities

for the current year. To address the need cited by many

producers and users of Federal statistics on the aging

for more advance notice of plans to institute new data

collection programs or to modify existing activities, OIRA

could include in the Information Collection Budget request

to Federal departments and agencies a requirement for

information on planned activities during the coming 3

to 5 years. While those wishing to make use of this

resource would still be required to look at the plans of

several agencies, the information base to do so would

exist. Further, a group such as the Subcommittee on

Data Sources of the NIA-sponsored Committee on Research on Aging could accept responsibility for collating

across-agency synthesis of planned data collections related

to the elderly.

2. The Federal Committee on Statistical Methodology, under

the sponsorship of the Office of Information and Regulatory Affairs, has served in the past to focus attention

and conduct research on crosscutting methodological problems in Federal statistics. For example, the Committee

has been responsible for two studies related to statistical

uses of administrative records. In view of the fact that

the Committee’s agenda is driven primarily by issues

suggested by the participating agency representatives, it

would be appropriate to ask the Committee to address

the need for the development and exchange of methodological knowledge on techniques for obtaining information

on the elderly population, and for an examination of

the appropriateness of survey questions for older

respondents.

Under the assumption that OMB is unlikely to shift either

the orientation (to reduce data burden) or the organization

(by agency) of its current information collection oversight

activities-or to experience a substantial increase in staff resources for statistical policy activities—it is necessary to look

elsewhere for a mechanism to improve coordination of Federal

statistics related to the elderly population.

The second agency with existing, but currently unused,

legislative authority to coordinate statistical programs related

to the elderly population is the Administration on Aging

(AOA). As noted in earlier sections of this paper, AOA has

declined to exercise this authority in recent years, reportedly

because of a lack of staff resources. Nonetheless, it may

be appropriate, in looking toward the future, to suggest one

or more specific roles that could be played by the Administration on Aging. For example, OMB could be asked to delegate

to the Administration on Aging certain responsibilities for

reviewing agency budget proposals calling for modifications

to existing data collection activities, or for the implementation

of new statistical programs, and request that an assessment

of such proposals be provided to OMB. Such a review process

would provide an early opportunity to identify whether priority

data gaps were being addressed in agency budget proposals.

Similarly, if the Information Collection Budget process were

157
modified to include plans for a 3- to 5-year period in the

future, the information collection plans could be reviewed

by AOA. This procedure would provide a considerably more

timely opportunity for ensuring that appropriate interagency

linkages were developed as an integral part of the design

of new or revised statistical programs.

While proposals for strengthening the coordination efforts

of OMB andlor the Administration on Aging have the advantage of resting on an established legislative authority, they

suffer from not insubstantial problems: the time it will take

to put such activities in place, the resources necessary to

make them more than “paper exercises ,“ and the largely negative view currently held toward formal coordination

mechanisms. These factors (and others, no doubt) must be

weighed against the inherent appeal of legislatively founded,

formal coordination mechanisms.

In the interim, past experience coupled with current attitudes seem to suggest that the type of mechanism most

likely to succeed in the near term is one constituted by concerned agency representatives willing to devote the time and

energy to share information and problems, and to work

cooperatively toward solutions. Although still in its infancy,

the Forum for Statistics on Aging, initiated by the Bureau

of the Census, appears to hold great promise for operating

under these requirements. Both the goals of that entity and

specific topics suggested for its agenda, as outlined in a March

1985 memorandum to the members (appendix B), point toward

the Forum’s potential for addressing needs to educate producers

and users about existing data sources, to provide a means

for improving the flow of information about data gaps, and

to merge existing and planned data sources in more useful

ways.

At the same time, it must be recognized that the Forum

is a “volunteer” effort without dedicated resources; that its

effectiveness will rest on the “good will” of the participants;

that the ultimate adoption of its recommendations may largely

be a function of “peer pressure”; and that its sphere of influence

will depend heavily on the involvement of all appropriate

agencies. As a consequence, consideration should be given

to:

1. Instituting a formal agenda for the work of the Forum,

outlining specific activities to be undertaken and anticipated products.

2. Enlarging the membership of the Forum to include appropriate representatives of the Department of Housing and

Urban Development, the Department of Labor, the Statistics of Income Division at the Internal Revenue Service,

and additional agencies in the Department of Health and

Human Services (e.g., the National Center for Health

Services Research, the Office of Human Development

Services, the Alcohol, Drug Abuse, and Mental Health

Administration. and the Centers for Disease Control. )

3. Inviting as observers at Forum meetings representatives

of the Office of Management and Budget’0 who are respon4.

5.

6.

sible for budgetaty and information collection decisions

affecting statistical programs on the elderly population.

Requesting formal agency signoff on Forum recommendations, with a concomitant pledge to implement suggested

changes.

Seeking staff support from the participating agencies for

activities that require background research or the development of products for use by the Forum.

Instituting at an early date a program to share with users

of statisti& in the legislative bra~ch agencies and outside

organizations information on current statistical sources,

and to obtain feedback from data users on needs for

and problems with information related to the elderly

population.

While the Forum may serve quite effectively to improve

interagency coordination and cooperation with users outside

the executive branch, some further progress might also be

made through greater emphasis on coordination among the

many agencies within the Department of Health and Human

Services responsible for programs and activities that provide

statistical information relevant to the elderly population. The

Administration on Aging, which has in place legislative authority to perform this function, should be called upon to enhance

its efforts in this area. Although limitations on staff resources

may continue to be a problem, the failure to coordinate may

in the end lead to considerably greater expenditures—not only

for statistics, but also, perhaps, for program services.

In the foregoing proposals related to possible expansion

and strengthening of the interagency and interdepartmental

coordination roles and activities of the Administration on Aging

and the Forum, the question of “clout” remains largely unaddressed. Several possibilities—most notably the provision of

funding for particular activities to a single agency (as was

done in the case of the Survey of Income and Program Participation) or the assignment of an information collection clearance

authority to a lead agency-exist. But each of these

mechanisms brings problems. In the case of providktg funding

to single agencies, the issue of who controls content is bound

to arise, and needs of particular agencies may be met even

less well than at present; in the case of requiring clearance

by a lead agency, which could result in the addition of another

layer to the process, the benefits might well be outweighed

by the costs in time and effort required. In the end, the

importance of improving statistics on the elderly population,

and the interest of data users and producers in addressing

current problems and needs, will prove most critical in achieving better cooperation and coordination.

1‘)ltshould be nntd thdt t+wncy initiativcstn ti~stcrsuch cuordi nationhuvc

rcccivd tkmabk reviews from the otl’icc d’ Managenwvrt and Budget.

For cxwnplc. 131RA currently pm’ticipates in the Intcrugorcy Committee on

Dissemination d’ Statistics. formwi umkr the Icwkrship of a std’t’ rrwmbcr

at the Energy Int’orrnation Administmtion when OMB disbdrrdd its lntcragurcy Committee on I)m Access and Use.

158
References

Bonncn, J. T., et al.: Improving the Federal Statistical System, Government—Fiscal Year /986. Statistical Policy Office, Office of

Report of the President’s Reorganization Project for the Federal Information and Regulatory Affairs, June 1985.

Statistical System. Statistical R;por[er, No. 80. 8: 197–2 19, May

1980. President’s Commission on Federal Statistics: Fedwo/ Sro/i.~/irsReport of(hePrcsickw’s Commission, Vol. 1.1971.

Duncan, J, W,, and W. C. Shelton: Revolution in United States

Government Statistics, 1926-1976, Chapter 5. U.S. Department of U.S. Bureau of Hficicncy: Report on the S!aii.s(icd Work of the

Commerce, Office of Federal Statistical Policy and Standards, 1978. United Sta/e.s Golww?en/. Submitted to Congress Sept. 7, 1922.

Office of Management and Budget: Federal Statistics—A Special

Report on the Statistical Programs and Activities of the United States

159
Appendix A

Organizations/individuals

interviewed

Federal executive branch agencies

Office of Management and Budget

Ojfce of Information and Regulatory Affairs

Dorothy Tells

Richard Elsinger

Special Studies Division

Gaby Lupo

Department of Agriculture

Food and Nutrition Service

Robert Rizek

Economic Research Service

Calvin Beale

Department of Commerce

Bureau of the Census

Cynthia Taeuber

Department of Health and Human Services

Administration on Aging

Donald Fowles

Howard White

Alcohol, Drug Abuse, and Mental Health Administration

David Larson

Assistant SecretaWfor Planning and Evaluation

Paul Gayer

Centers for Disease Control

Norman Axnick

Health Care Financing Administration

Jim Lubitz

Glenn Martin

John Parmigiani

National Centerfor Health Services Research

Corbin Llu

Dan Walden

National Centerfor Health Statistics

Mary Grace Kovar

Joan Van Nostrand

National Institute on Aging

Ronald Abeles

Shirley Bagley

Richard Suzman

Public Health Service, Oftce of Health Planning and Evaluation

Gooloo Wunderlich

Social Security Administration

Jane Ross

Melinda Upp

Department of Housing and Urban Development

Ojjice of Planning, Development, and Research

Duane McGough

Department of Labor

Bureau of 1,.abor&atistics

Sandra King

Philip Rones

Employment and Training Administration

Paul Mayrand

Of/ice of Pension and Weljare Benejit Programs

John Turner

Department of the Treasury

Internal Revenue Service, Statistics of Income Division

Dan Holik

Marvin Schwartz

Veterans’ Administration

Phyllis Thorburn

Federal legislative branch organizations

Congressional Budget Office

Nancy Gordon

General Accounting Office

Janet Schickles

Office of Technology Assessment

Robert Harootyian

Senate Special Committee on Aging

Betsy Vierck

Other organizations

American Association of Retired Persons

John Rother

Theresa Vainer

Employee Benefit Research Institute

Emily Andrews

The Gerontological Society of America

John Comman

Robett Binstock (Case Western Reserve)

Marilyn Moon (Urban Institute)

160
Appendix B

UNITED STATES DEPARTMENT OF COMMERCE

Bureau of the Census

Washington, D.C. 20233

March 14, 1985

MEMORANDUM FOR: Members of Forum for Statistics on Aging

From: Cynthia M. Taeuber Cwr

Chairperson 64

Subject: Meeting, March 19, 1985

Per our telephone conversation, you are invited to participate in the Forum

for Statistics on Aging (FSA) which will meet March 19, 1985, 1:30 p.m., Room

1169 (lst floor), HHS North Building at 330 Independence Avenue.

The FSA will be made up of statisticians from federal agencies who develop

statistics on the elderly population. The FSA will serve as a means of:

● exchanging information (i.e. , a network) ;

● coordinating the approach to the collection and tabulation of data on the

elderly population;

● keeping the federal agencies informed about the needs for data; and

● identifying data gaps in the federal statistical system relevant to the

elderly.

The agenda for the first meeting is to discuss specific recommendations for

data needs for the elderly from the 1990 census. Come prepared with ideas.

Dorothy Guilford from the National Academy of Sciences will discuss their

project m aging and explain the inventory of data sets they will be

producing.

The list below will give you an idea of the types of subjects we may want to

discuss in the future:

level of age detail that is available and creative ways we could make

statistically reliable data available for the very old;

use of data on the institutional population; enumeration problems;

commonalities/conflicts with types of questions asked in various surveys;

areas of overlap; suggestions about where money can be saved;

bring in outside data users for presentations of problems;

intergenerational issues; data availability; methodological

problemsisolutions;

ability to do international comparisons;

data timeliness;

frequency of data collection;

opportunity for combining data for interagency joint reports;

report on SIPP, use of longitudinal data;

report by NIA on statistical programs and research they are currently

funding;

161
● reports by other agencies on their work;

● policy issues from the Senate Committee on Aging; etc.

This, along with the ideas you bring, should keep us entertained for awhile.

I look forward to meeting with you.

CTaeuber:ja

cc : Joan Van Nostrand, NCHS

Rosalyn Bass, NIMH

Dorothy Guilford, NAS

Philip Roans, DOL

Barbara Torrey, Census

Don Fowles, AOA

Richard Suzman, NIA

Jane Ross, Social Security

Cal Beale, Agriculture

Jack Feldman, NCHS

John Mather, VA

Paul Gayer, HHS

Uarian Gornick, HCFA

162
Lessons from longitudinal

studiesa’ b

by Clifford H, Patrick, Ph. D., Veterans Administration Health

Services Research Program and Duke University

L Introduction

For over four decades, the government has supported

numerous longitudinal sample surveys on a wide range of

topics related to aging. Yet there continues to be a call for

new longitudinal studies of aging whenever research priorities

are discussed (DeFriese, Hersh, and McManus, 1985; National

Center for Health Statistics, 1980). DeFriese, Hersh, and

McManus ( 1985), summarizing priority research issues for

elderly health status and risk factors, included the following:

“Longitudinal cohort studies are needed of the aging process,

including natural histories of diseases, the loss of ADL functional abilities and the relationship of health and functional

status by disease to health services utilization. ” Before launching new studies, we should determine what existing studies

have provided that has been useful to the research community

and policymakers. While these data are useful for describing

and explaining aging phenomena or processes important to

policymakers, they are also appropriate for predicting events

tind circumstances of the elder]y. Longitudinal studies incorporate invaluable information on dynamic processes of aging

and changes in individual circumstances over defined time

periods. As such, they should be considered national scientific

and policy resources to be fostered and shared for maximum

benefit to society.

Do we really need new longitudinal studies of human

populations to investigate scientific issues concerning aging

processes and outcomes? To answer this question, we need

to determine the uses of existing longitudinal surveys and

to evaluate the possibility of using other methods to reach

definitive conclusions. There are several uses of temporal

data that need to be identified. Longitudinal data are used

to examine the aggregation of experiences over time, as

suggested by Meiners (Chapter III) in terms of duration of

episodes of confinement in hospitals and nursing homes, which

in fact are one continuous experience of long-term care in

different settings, Perhaps the most common use of longitudinal

data is to make etiological inferences about the incidence

or onset of an event based on characteristics at the beginning

of the period and subsequent changes. Another common use

is to examine the natural history of a condition or illness.

Studies looking at the impact of specific treatment, such as

“C~mtcnts we (he opinions of the aathor and not those of the Department

t)fHdth d Hamwr Services or the organization he works for.

‘{CI19X5Ntitionol Acwicmy of ,%iurces.

the channeling demonstration, represent another use of temporal data on an aging population group. Fhally, randomized

trials to measure the impact of alternative treatments represent

longitudinal data on individuals available for analysis. The

Rand health insurance study is a classic experiment of this

type (Morns, 1983; Newhouse, 1974). Each of these types

of longitudinal studies has contributed valuable information

on aspects of aging in American society.

Within this brief paper, such an enormous undertaking

as analyzing each of these uses is not possible; clearly, longitudinal data are appropriate to examine such policy issues

as the natural history and onset of conditions, including disease

entities, activities of daily living, widowhood, and pove~y.

By examining a policy-oriented hypothetical longitudinal sample survey based on general principles, an overall impression

of the issues cart be gleaned and valuable lessons on planning

fiture longitudinal surveys can be learned. This paper begins

where such inventories as those of Migdal, Abeles, and Sherrod

(1982) end. It discusses in broad terms survey planning and

execution, analytical issues, and the lessons we should have

learned from past experience with surveys. Several of the

key issues are also discussed in papers by Fienberg and Tanur

(1986) and by Fienberg, Martin, and Straf (1985). The paper

attempts to indicate the advantages and disadvantages of longitudinal surveys compared to other methods of acquiring

knowledge of the aging process and life cycle events such

as time series of cross-sectional data. The paper concludes

with recommendations regarding fi,tturelongitudinal surveys.

IL Defining longitudinal sample

surveys

Conceptually, longitudinal data are used to study individual change in a dynamic sense. This may be done prospectively or retrospectively. Because we are interested in the

possibility of new funded surveys, a longitudinal sample survey

is defined narrowly in this paper as a prospective field study

in which the characteristics of interest of each person in a

sample population are surveyed over time to determine what

changes in selected variables have occurred and why (Nesselroade and Baltes, 1979). As Flenberg and Tanur ( 1986) indicate, “questions of process can only be answered with longitudinal data.”

163
Longitudinal survey data may be derived from cohort

studies, from rotating panels, or from field experiments. [n

each type, the sample subject is resurveyed to elicit repeated

measurements on selected items of interest. This narrow definition precludes such valuable data sets as the Continuous Work

History Sample and Medicare public use files, which are

administrative records, as well as aggregate time series from

the Census Bureau. National Center for Health Statistics, and

Bureau of Economic Analysis, which are relatively inexpensive

to collect and analyze compared to true longitudinal sample

surveys. This paper also excludes clinical trials, a type of

longitudinal study that is not a true survey.

Existing sample surveys generally fall into two classes.

First, longitudinal surveys of nationally representative samples

attempt to indicate how the U.S. population has aged and

its characteristics changed. The longitudinal surveys of social

and economic phenomena such as education, income, wealth,

employment, and retirement decisions typically have been

based upon national samples which are representative of the

U.S. population. These are frequently based on the sampling

frame of the Current Population Survey (CPS). Examples

of nationally representative longitudinal studies focused on

social issues include the Retirement History Survey, the Michigan Panel Study on Income Dynamics, the National Longitudinal Survey (NLS). the Survey of Low Income Aged and

Disabled, and the Survey of Income and Program Participation

(SIPP) (Duncan and Morgan; IreIan et al., 1976; IreIan and

Schwab, 1979, 1981, and 1982: Kasprzyk and Herriot, 1984;

Nelson, McMillen, and Kasprzyk, 1984; Parries, 1979; Riccobono et al., 198 I; Tissue and McCoy, 1981).

These surveys have been planned and directed primarily

by social scientists. Directed at social issues, their analyses

delve into a wide range of topics, including demographic,

health, and other characteristics which are thought to influence

income, work, and retirement patterns. The health data are

based on self-assessment and are considered by some to be

less reliable than those found in health surveys. The results

have been invaluable in broadening understanding of social

issues and aging. In addition to these, the First National

Health and Nutrition Examination Survey (NHANES I)

Epidemiologic Followup Study is creating a longitudinal data

base. This study is picking up an existing nationally representative population observed originally in the NHANES I sample

(Cornoni-Huntley et al.. 1983). The followup study will gather

recent information on survivors and determine cause and date

of death of decedents, as is done in many of the longitudinal

sample surveys.

Second. longitudinal surveys of people in a given setting

have not attempted to be representative of the U.S. population

so much as to uncover underlying risk factors and parameters

of the aging process which might be replicated elsewhere.

These studies for the most part focus on the natural history

of disease. Examples of this type of longitudinal study include

the Framingham Heart Study, the Alameda County Study,

the Tecumseh Study, the Evans County Study, the Baltimore

Longitudinal Study. the Duke Longitudinal Survey, the Honolulu Heart Study, the Washington County Study, and others

supported by the National Institutes of Health (Alexander,

1974; Brdnch and Jette, 198 l; Dawber, 1980; Gordon et al.,

1974; Greulich, 1985: Kagan et al., 1974; Kannel and Gordon,

1980: Manton and Woodbury, 1983; McGee, 1973; Napier

et al., 1972; Phillips et al., 1974; Rogot, 1974; Scheuren,

Chapter 11).

While the health surveys usually contain some information

on socioeconomic and demographic characteristics of subjects,

the key hypotheses and bulk of the data are health related

and include physiological and related biomedical parameters.

Though they are not nationally representative, analyses of

these studies are a major factor in the growth of prevention

programs. The previously mentioned surveys are only a few

of the existing studies and indicate the tremendous resources

which have been provided for longitudinal studies of the social

and health characteristics of the aging population at both local

and national levels. Whether future longitudinal studies will

start with a national sample, as social surveys have traditionally

done, or begin with local samples which are nonrepresentative

of the U.S. population but which can be replicated at other

sites depends on the goals of the study and involves tradeoffs

to be considered early in planning.

111.Longitudinal survey planning

and execution

Let us assume that we intend to initiate a new longitudinal

sample survey. Initial planning strategies for the survey include

goals, study design, sample size, sampling frame, survey

instrument, response rates, and analyses of whether the study

is to be done only once or repeated over time to create a

longitudinal data base (Nelson, McMillen, and Kasprzyk,

1984). Therefore, only those planning and execution strategies

which are concerned with the longitudinal nature of the survey

are discussed herein.

Probably the most critical planning decision involves the

goal of the survey. This requires clear articulation of the

hypotheses to be tested by data which the study genertites.

Is the goal of the study to describe changing behavitr or

conditions associated with aging, such as retirement. financitd

resources, and health changes: to explain such dynamic behavior, e.g., declitie in activities of daily living (ADL’s)

based on changing household and individual characteristics:

or is it to predict changes and future distributions of individuals

in certain states, such as admissions to nursing homes or

hospital intensive care units as a consequence of declining

ADL or disabling morbidity, all of which have policy mmifications’? IS information needed which cannot be collected, tit

possibIy much less expense, through ongoing Federal activities

such as the Current Population Survey, the National Health

Interview Survey (NHIS), or departmental administrative records’? (See Rogot et al.. 1983: Scheuren. Chapter II.) The

answer may be no if the goal involves behaviors or other

relationships which are believed to change overtime. especially

as a result of the individual’s aging (Damon, 1965; Duncan

and Morgan; Manton and Woodbury. 1983; McGee, 1973;

MotIey. 1978: Nesseh-oade and Baltes. 1979). The paper by

164
Meiners (Chapter 111)offers an excellent example of the types

t]f temporal changes in individual circumstances involving

uhernative long-term care arrangements of individuals and

insurance coverage which have policy ramifications.

The goals of the survey to a large extent dictate the

sampling methodology which will be employed. To study

rare events will require a larger starting population, longer

and more meticulous followup, and greater efforts to reduce

attrition, all of which serve to increase the cost of the study.

To study the incidence and risk factors of a chronic condition,

one must consider the timeframe of development of the disease

in individuals with diverse characteristics. To study retirement

decisions and adjustments, information on individuals before

and after retirement is required. The experiences of past studies

should be used to determine design considerations such as

sample size, response rates in any one wave, and rates of

loss from wave to wave as well as over the entire study.

Policymakers need analyses of longitudinal tracking on

crucial aging issues such as work, income, and retirement

conditions; living and social support arrangements; meanstested program participation; health and disease dynamics;

tind individual environmental factors which are subject to

temporal fluctuations. If our goal is simply to look at changes

in aggregate levels or average changes in some set of variables

for the population and if the changes occurring over time

in individual circumstances are not crucial to the hypotheses

tested or goals of the study, a longitudinal sample survey

is probably a costly, inefficient method to use to collect needed

data. However, if we are interested in the individual dynamics

of sequential episodes and natural histories of events, then

a longitudinal sample survey is the most appropriate vehicle

for analytic input to aging policy.

In the current political environment, a new longitudinal

study of the elderly which has multiple goals or outcomes

probably has a better chance of surviving Federal review

processes, since several agencies with their particular interests

tind objectives for the study probably will be needed to provide

funds and justification, Such a study would need to specify

clearly why separating differences in individual aging patterns,

intercohorr differences in risk factors and other characteristics,

tind period effects is crucial to the goals and policy ramifications of the survey. A longitudinal health survey of the elderly

would probably want to combine the goals and funds of the

National Institutes of Health, the National Center for Health

Services Research, and other Public Health Service units,

the Health Care Financing Administration, and office of

Human Development Services (OHDS), all within the Department of Health and Human Services (DHHS), and the Veterans

Administration and would be carried out by the National Center

for Health Statistics (NCHS) using either the NHIS or

NHANES sampling frame. A longitudinal study of income,

work, and retirement would probably want to combine the

interests and goals of the Social Security Administration (SSA),

other DHHS agencies such as HDS, and the Department of

Labor, with the Census Bureau conducting the survey using

the CPS sampling frame.

Ideally, the multiple goals of a new longitudinal survey

would be determined as differential outcomes influenced by

individual behavior patterns and risk factors amenable to policy

changes. The design would utilize an existing sample such

as is found in the SSA Continuous Work History Sample,

the Census Public Use Sample or CPS, the NCHS NHIS

or NHANES, though NHANES has no subjects over age

75. The sample would be linked to their existing administrative

records, e.g., SSA and Medicare, and tied into the National

Death Index for followup of lost cases. Subject nonresponse

rate and attrition would be minimized through interwave contact via postcard and the administrative record linkage. After

a followup call to overcome initial nonresponse on any item,

a hot deck estimation procedure would be utilized to derive

a value to replace the missing response. Items which consistently showed high nonresponse rates across waves must be

considered for deletion from the survey instrument and analysis

because of the likelihood of biased responses. Special care

must be taken in long-term surveys where the interpretation

of questions and responses can change over the course of

the study due to technological or societal factors, especially

in the health field, with the adoption of new diagnostic methods

and changing concepts of disease entities.

There are several studies which provide alternative design

strategies for conducting such studies. The Retirement History

Survey (RHS) and SIPP provide two examples in the social

area. The two surveys differ in several important aspects

which will be discussed, as will examples from other studies,

throughout the paper. While both were developed from the

CPS sampling frame, the goal of RHS involves the answer

to questions with a longer timeframe of action, such as determining why individuals retire early, what proportion of their

income before retirement is replaced after retirement, and

how laws governing retirement and pensions affect people’s

behavior (Irelan et al., 1976; IreIan and Schwab, 1979 and

1981). Thus, the RHS sample interviews at 2-year intervals,

the same as in the Framingham Heart Study (Dawber, 1980),

seemed appropriate when the RHS study was being planned

in the 1960’s.

The RHS involved an initial sample of 11,153 men and

unmarried women ages 58–63 in 1969 who were to be interviewed every 2 years until 1979. While it now appears to

have been a strategic mistake not to include married women

in the initial sample, wives of married respondents were asked

a battery of questions in the initial wave. The survey was

linked to administrative records on individual earnings to provide a rich source of economic data on this aging sample.

Unfortunately, the National Death Index did not exist for

followup until the final year ( 1979) of the study. The matched

information has been provided on data tapes through the National Archives to the research community.

An unusual feature of the five followup waves is the

replacement of a decedent by the surviving spouse as a full

participant in the survey from the wave following the death

of the husband until the end of the study. As a result, in

any wave following the initial survey, each observation may

take on one of six indicators: ( I) original sample person responding in this wave; (2) died, surviving spouse responding

as replacement of original subject in the survey: (3) died,

no surviving spouse; (4) institutionalized; (5) noninterview

165
for reason other than IA; and (6) disappeared or lost to

followup. These varying categories provide detailed temporal

information on aging couples and widows which would not

otherwise exist. The RHS samples must be analyzed with

care to take account of these dynamic features of the sampling

strategy, however.

The goal of SIPP, on the other hand, involves short-term,

means-tested program information such as monthly income

flow, wealth, and program eligibility and utilization (Kasprzyk

and Herriot. 1984). Because of this, a more complicated sampling procedure was initiated to reach this goal. Basically,

about 20,000 households drawn from Current Population Survey samples representing the U.S. noninstitutional ized population are interviewed every quarter over the 2-year life of

each SIPP panel (Kasprzyk and Herriot, 1984; Nelson, McMilIen, and Kasprzyk, 1984). The panel is considered the key

feature of SIPP, with a new panel established each January.

Each sample panel has been divided into four rotation groups,

one to be interviewed in each month of the quarter to enhance

the sample size. Since each interview asks for recall information over the past 4 months, there are 7 months of recall

data in total with each quarterly wave of the survey. By

matching individual wave data, monthly analysis of the full

sample provides results needed to achieve the study goals

of SIPP (Kasprzyk and Herriot, 1984). SIPP”has had roughly

an 80 percent attrition rate in the 1984 panel, with losses

of about 5 percent in the first two waves and about 2 percent

in the following waves. Item nonresponse is estimated using

the hot deck procedure.

Unfortunately, the planning of SIPP also involved several

administrative moves of the project before the study was

housed in the Census Bureau. This may partially account

for what I consider a major problem in the study. Two years

(eight quarters) per respondent appear to constitute an extremely short period for observing income and program participation, especially since most of the questions asked are not

repeated from one wave to the next. Since the elderly comprise

a large proportion of the government’s means-tested programs

and their income and health can change rapidly, a larger

sample of elderly households and a longer period of observation, with repeating questions on their health status within

a panel across waves, also would appear from the research

and policy perspectives to have been advisable in planning

SIPP.

Longitudinal surveys conducted in one location have involved goals and planning that have required long-term commitments of Federal resources. For example, the Framingham

Heart Study has collected detailed medical examination and

other data every 2 years since 1948. The goal of this study

has been to determine the natural history and risk factors

for heart disease. The study population was composed of

a sample of over 5,000 volunteers ages 30-59 years as of

January 1, 1950, in Framingham, Massachusetts. To date,

about 4 of every 10 in the original population have died.

Spinoff studies of children of the original sample members

have also been undertaken which examine the relationship

between genetic and environmental factors in the development

of heart disease (Branch and Jette, 1981; Dawber, 1980; Gordon et al., 1974; Kannel and Gordon, 1980; McGee, 1973).

Among the policy-relevant questions related to aging

which can be addressed with the Framingham data base are

the development of medical conditions. health care arrangements, changes in economic and social characteristics, and

changes in family composition over time. The socioeconomic

data are not the strength of the study because this information

is not as detailed as in the social surveys. The original Framingham sample, in conjunction with other longitudinal surveys

sponsored by the National Institutes of Health (NIH). provided

much of the programmatic information used to determine the

major risk Factorsfor cardiovascular diseases, including smoking behavior, cholesterol levels. and high blood pressure (Kannel and Gordon. 1980). These analytical outcomes were possible because the longitudinal observation of individual changes

in health and concomitant factors was dictated by the original

goals of the study.

The Baltimore Longitudinal Study of Aging (BLSA), an

intramural project of the National Institute on Aging, presents

an interesting contrast to these studies. Begun in 1959. the

study has followed 650 healthy male volunteers ages 20-96.

The goals of the study were to determine what older adults

are like physically and mentally and to determine what can

be done to keep them healthy and productive as they age.

The sample of volunteers was not intended to be representative

of the U.S. population, but rather the upper end of the health

spectrum. Each subject visits the Gerontological Research

Center in Baltimore to have a medical examination and to

be asked survey questions. Those age 70 and over do so

annually; those aged 60-69 do so every IS months: and the

remainder do so every 2 years. The study has added 500

women to try to develop information on the aging problems

of females. A significant finding from the BLSA is: many

physical functions, including cardiac functions and electrolyte

balance, remain constant over time in the aging adult, while

ability to metabolize glucose declines (Greulich, 1985). The

finding could be obtained only through the long-term observation of healthy individuals.

Not every longitudinal survey has been successful. however. The Departments of Housing and Urban Development

(HUD) and Health, Education, and Welfare (DHEW) longitudinal study of the housing adjustments of older people never

progressed beyond the pilot study. Newman ( 1981) states

the primary issue of this discussion clearly: “The sampling

plan, survey design, and analysis strategies for this study

of housing adjustments are driven by the principal goal of

the research—to examine the relationship between major

changes in people’s lives as they grow older and the adjustments they make in their housing environments in response

to these life events.” Yet the proposed study failed, apparently

for lack of funding beyond the pilot stage, a serious blow

to research on the life cycle demand for housing.

Having seen possible alternatives, assume we decide to

initiate a 5-year longitudinal survey of the population age

55 and over in six annual waves, using NHIS as the nationally

representative sampling frame, but with a sample of long-term

166
care patients included also. Our objective is to determine

the natural history and costs of disability. Our survey instrument combines questions taken from existing studies as well

as those suggested by expert consultants from academia and

g~wernment agencies,

By starting with a sample of both healthy and morbid

subjects. we are able to trace movement into and out of

the disability state of interest by individuals of different initial

stwi~mconomic and health characteristics. including different

types and levels of insurance coverage. which allows us to

appreciate the range of individual heterogeneity and the asstxiated dynamics of the processes of becoming sick, then

distibled, and recovering or dying. Each new wave in the

study would find that individuals would either remain in the

sume state M in the previous wave, change states, or drop

(w. Using the National Death Index and administrative

rccwrds, we could trace decedents and other nonrespondents.

Charticteristics of the subjects such as ADL level or work

hist~wy could be used to determine if there were consistent

c(wrelates (w predictors of the likely move into and out of

[he disabled state from one wave to the next.

An integral part of the planning must involve the disseminateion of information. The information maybe either published

results or raw data on computer tapes. Data rapes of the

national surveys, especially the social surveys, have been

mtide available to the research community through such distribution networks as the Inter-University Consortium at the

University of Michigan, the Duke University Aging Center,

und the National Archives.

While the nationally representative surveys have generally

provided data tapes to the research community through archival

distribution networks, the longitudinal health surveys conducted in local areas through NIH funding have not generally

provided public data tapes for distribution through an archival

system. which would have enabled the wider research community to antilyze the study information. Even though these

survey data are federally funded and maintained in most cases,

the program managers often seem to treat the information

os if it were proprietary. This situation needs to be corrected

to achieve both systematic exploration of the data on aging

tmd a replication of existing studies to obtain maximum societal

benefit from the Federal investment in these studies.

Because the original researchers have invested so much

t~fthemselves in the data and know the collection and recoding

procedures, they should have an opportunity to review and

analyze the data as soon as they are available. However,

within a year of the production internally of an initial data

ttipe from each wave, there should be a public use tape and

ct~de book provided to a national archival system. In addition,

the Federal agencies funding such surveys should set up a

small grtints program to encourage full exploration of the

dtita in a timely Fdshion, as the Department of Education

(then DHEW) did with the National Longitudinal Survey (Ricct~bonoet al.. 198I). In the past, data have been made available

(m a wave-by-wave basis without linking sampled individuals

acrt)ss waves; such linkage of responses from the same individual across waves is necessary if the research and policy

communities are to derive the full benefits of the longitudinal

nature of the study.

Publication beginning with the baseline cross-sectional

data from these surveys can serve as the beacon for studies

of the ongoing temporal changes. For instance, a concerted

effort was made to present the initial information from the

RHS in a single volume which could be used to plan and

execute future studies using additional years of data as they

became available (Irelan et al., 1976). These data have provided the basis for numerous studies over the last decade,

both within the Social Security Administration and by academic

researchers (Anderson and Burkhauser, 1983; Clark and

Sumner, 1985; Irelan and Schwab, 1982; Keith, 1985; Motley,

1978). From the success of this program, it is clear that

both the presentation of the baseline data and the preparation

of public use data tapes need to be included in planning

longitudinal surveys.

The SIPP survey, for example, has plans for crosssectional and longitudinal data files and publications (Nelson,

McMillen, and Kasprzyk, 1984). Quarterly and annual crosssectional reports and data files of each wave will be issued

along with special topic reports which explore only crosssectional relationships. Six types of longitudinal reports are

also being planned. A longitudinal microdata file will provide

monthly response data over a calendar year for each person

in the sample. Such elaborate early planning of the data products is to be commended and will facilitate the planning of

research by outside groups which contemplate using SIPP.

For instance, the information requested by Congress, under

Public Law 98–2 1 Sec. 20 I(d), on the adverse impacts of

raising the retirement age might be provided using the SIPP

longitudinal data.

The goals of each survey tend to define the nature of

the sampling methodology, including the sample design, instruments, and periodicity which each uses. These alone cannot

guarantee the success of the study. however, as indicated

by the fate of the HUD-DHEW study mentioned earlier. There

are surveys that sample the same individuals every 2 years

for 10 or more years. those that sample quarterly for 2 years,

and those that sample annually. Some follow only the original

sample, one picks up a new sample every year with a rotational

design, and one replaces decedents in the original sample

with their surviving spouses. One has failed to survive the

pilot stage; another has been transfemed among agencies in

the last decade and has begun with a sampling design which,

on the surface at least, does not appear to provide sufficient

temporal data on elderly individuals to provide policy makers

with adequate information.

Clearly, the planning process must identify well-defined

goals to ensure the support necessary for completion of data

collection, preparation, and dissemination. This has been the

experience of RHS and other successful surveys. The example

of an ideal sample survey was meant to convey broad issues

to be considered in planning a new longitudinal study.

167
IV. Analytical issues including Markov models, need to be applied to these data

sets to take advantage of the individual heterogeneity of the

While data collection and dissemination are extremely observations and the dynamic structure of the information

vital to these studies, data analysis must be an integral part

(Goldstein, 1979; Stasny and Fienberg, 1985; Hoem, 1983;

of the study planning as well. The value of longitudinal data Kalbfleisch and Prentice, 1980; Singer, 1980). For example,

on individuals needs full appreciation. There is a tendency

in their analysis of 5 years of long-term care data, Lane

for analyses to deal with information from these surveys as

et al. ( 1985) found that the Markov model yielded a superior

either means or aggregates rather than as individual observa- forecast of long-term care services than either the moving

tions and as if the information were static rather than dynamic average growth model or regression analyses.

(Fienberg and Tanur, 1986; Manton and Woodbury, 1985).

The gain in knowledge of aging processes and related

It is not enough to report what proportion of subjects became

factors to be derived from longitudinal sample surveys comdisabled or institutionalized, changed jobs, or retired from

pared to other available information sources goes beyond the

one period to another.

purely statistical, methodological issues. Though aggregate

Such information is often available in repeated crosstirne-series analysis may provide statistically accurate information on trends in the mean values of certain measures of

sections. This use may negate the value of the longitudinal

nature of the study (Damon, 1965; Goldstein, 1979; Manton

interest, it masks such crucial information as: Who might

have entered or left the sample over time’? What are the

and Woodbtrry, 1983). Rather, the analyst must look at the

individual diversity and inherent time dimension in the data

characteristics of the extremes in the distribution? Are factors

set and how changes in one characteristic or condition are

of interest normally distributed? Are the relationships between

associated with changes in another. It is unclear whether this

the independent and dependent variables consistent across pertype of analysis occurs so infrequently because the methods

centiles in the distribution?

While cross-sectional microanalytic studies can answer

are too sophisticated for many analysts or because the methods

are inadequate to test the hypotheses dealing with individual

some of these questions, they cannot indicate the dynamics

changes over time for which these studies were designed

of who survives or how the relationships of factors of interest

and funded (Cole and Grizzle, 1966; Cook and Ware, 1983;

change over time for those survivors and how they are different

Darby and Fearn, 1979; Elbers and Ridder, 1982; Heame,

for decedents. These are key questions which make individual

Clark, and Hatch, 1983; Hoem, 1983; Kalbfleisch and Prenheterogeneity seen in longitudinal surveys and analyzed using

tice, 1980; Kalton, 1983; Lancaster and Nickell, 1980; Manappropriate models irreplaceable from both research and policy

sour, Nordheim, and Rutledge, 1985; Singer, 1980; Stasny

perspectives. Alexander ( 1974)succinctly points out the advanand Fienberg, 1985; Ware, 1985). Nonetheless, more analyses

tages of these studies of individuals over time. Longitudinal

need to look systematically at the temporal changes in individu- studies permit analysis of individuals which can be used to

als in different strata to analyze the concomitants of aging

indicate if there are patterns which differ among individuals.

and major risk factors.

These studies also enable one to identify the survivors or

Several statistical issues are important to consider in the returning examinees and compare them to the nonreturnees

analyzing longitudinal sample surveys. Obviously, this paper

to identify differences and potential biases of survivorship,

cannot explore them in detail. Longitudinal studies can provide

which is not possible in cross-sectional studies and aggregate

needed information about individual patterns of change over

time series (Gehan and Siddiqui, 1973; Laird and Olivier.

time which are critical to aging. Generally, these cannot be 1981; Manton and Woodbury, 1983). Longitudinal studies

determined using only cross-sectional samples because of the also make it possible to identify the cohort effects which

confounding of age, period, and cohort effects (Fienberg and

occur in succeeding generations, such as health. As Damon

Mason, 1985; Ware, 1985). Analyses can take the form of ( 1965) noted, “Longitudinal studies in adult life, though expendescriptive statistics, explanatory analyses, or predictive equasive. laborious. and long-lasting. are.. indispensable. It is

tions, for which there are a multitude of statistical techniques. no longer a matter of refining or confirming what we think

These studies are a form of repeated measures design for we know about aging from cross-sectional studies. What we

think we know may be in serious error.’” which an array of statistical methods have been developed.

These methods are designed to take into account the problems Modeling appears to be useful in analyzing individual

longitudinal survey sample data because it can deal with attri- associated with individual and item or measurement response

errors (Cole and Grizzle, 1966; Ghosh, Grizzle, and Sen, tion and nonresponse (Kagan et al., 1974). The analysts of

1973; Goldstein, 1979; Greenlees, Reece, and Zieschang, the health surveys appear to have a tradition of using the

1982; Griliches, Hall, and Hausman, 1977; Hearne, Clark, individual data to derive results based on the dynamic nature

and Hatch, 1983; Hunyh and Feldt, 1980; Jensen, 1982; Koch of the study (Damon, 1965; McGee, 1973). Social scientists

et al., 1980; Mansour, Nordheim, and Rutledge, 1985). Lon- are beginning to take advantage of these temporal data (Andergitudinal samples may provide better estimates of covariates son and Burkhauser, 1983; Elbers and Ridder, 1982; Manton

within subjects than can be derived from cross-sectional studies and Woodbury, 1985; Stasny and Fienberg. 1985). Some

with the same total number of observations (Cole and Grizzle, examples may help to illuminate several important analytic

1966). A major concern for both the.. researcher and the concepts. For instance, Pugh and Neibauer (1980) tested a

policymaker is the ability of the survey to represent the true

microsimuiation model using mortality data from RHS to valipopulation (Fienberg and Tanur, 1986). Stochastic techniques, date parameters of the model that originally had been estimated

166

.“ .

.;
using cross-sectional data. Lancas[~r and Ni~k~ll ( I~X()) used

&t[a (m individuals over time to analyze factors associated

\vith the duration of unemployment. Using a likelihood model

with a hazard function. they demonstmte a key point: “It

sores in p-mice very difficult to distinguish between the

effects of heterogeneity and the effect of pure time variation . . . A more promising direction of research is a detailed

study of a cohort of individuals with an attempt to determine

h(nv their policies tind experiences change as their unempk)ynwnt extends. ” Elbers and Ridder (1982) take this analysis

further by showing that “it is possible to distinguish between

time dependence and sample heterogeneity” when the model

has observed explanatory variables in the hazard function.

Analysts of existing longitudinal surveys, SIPP for instance.

might consider models of this genre to take advantage of

the individuality of the participants in these surveys.

When analyzed as temporal observations of heterogeneous

individuals using appropriate techniques, these surveys are

the richest source of information on aging in man (Manton

and Woodbury, 1983; Singer, 1980). The results provided

to policy makers and researchers cannot be acquired by using

either aggregate time-series analysis or cross-sectional

methods. While other types of studies maybe cheaper initially,

the potential gains through savings in lives and program costs

from the analytic results derived from longitudinal studies

of aging and related factors clearly indicate that these longitudinal sample surveys have been productive and have provided

unparalleled knowledge of the behavior and conditions of

the aging American population (Dawber, 1980; h-elan and

Schwab, 1982: Kagan et al., 1974; Kannel and Gordon, 1980;

McGee, 1973; Phillips et al., 1974; Rogot, ]974). Each is

a major policy issue with tremendous personal and social

costs .

V. Conclusions and lessons

future longitudinal studies

for

The future of any longitudinal sample survey is precarious

in a period of high Federal deficits, domestic program budget

cuts, and laissez-faire attitudes. The trials and tribulations

of SIPP as it migrated from one organization to another before

finding a permanent home at the Census Bureau and the

aborted HUD-DHEW longitudinal survey should be sufficient

proof of that. Higher level administrators who do not appreciate

issues such as sample heterogeneity may argue for the use

of aggregate data. even though such data may result in misleading analyses and misdirected policies. The availability of crosssectional surveys such as the National Health Interview Survey

und the Current Population Survey ensure that data will be

at hand on many issues that a dynamic survey would address.

Therefore, it is essential to utilize existing longitudinal surveys,

including RHS, NLS, SIPP, BLSA, and Framingham, as fully

as possible to study facets of aging which are relevant to

research and policy. It is also important that, when temporal

data are necessary in the future, the samples from such studies

as NH IS, NHANES, and CPS continue to be given strong

consideration as the basis. as is being done in the NHANES

I Followup Survey (Comoni-Huntley et al., 1983).

Since longitudinal studies involve costly surveys which

require large initial samples, considerable resources for followup, and an appreciation of analytic techniques, the lessons

to be learned from past studies are extremely important. Longitudinal sample surveys have both relative advantages and

disadvantages compared to other data sets. In the data collection and dissemination phase. they are similar in many respects

to cross-sectional surveys except for the temporal loss of

respondents; this loss requires starting with a larger baseline

sample, holding the study team together, and taking changing

technical and social measurements into account in the questionnaire and in the analysis. This is necessary to ensure that

the terminal sample size is adequate for statistical estimation

purposes and that the measured items are equivalent.

Longitudinal surveys provide precise details of individual

changes in behavior and other characteristics over time, but

this greater specificity of information than is possible through

a single study with recall questions is exacted at a cost of

maintaining the original sample minus dropouts. The population lost to followup also can provide invaluable information

on the heterogeneity of the survivors and decedents that is

not available in other data. The information provided. can

be used with a great deal of confidence for research and

policy-related issues in aging where individual distinctions,

such as risk factors for certain diseases or characteristics which

determine program eligibility, are important. Among the important issues for which longitudinal information is now available to researchers and policy makers are streams of income,

wealth, and assets; health status, disability, and long-term

care arrangements; retirement and employment; living arrangements under changing family and household structure: risk

factors for death and illness. especially from chronic diseases;

and adjustments to changing personal circumstances and social

conditions (Alexander, 1974; IreIan and Schwab, 1982; Kagan

et al., 1974: Kannel and Gordon, 1980: Manton and Woodbury, 1983; McGee, 1973; Newhouse, 1974: Phillips et al.,

1974; Quinn, 1979; Rogot, 1974: Tissue and McCoy, 1981).

Given the success of previous studies and limited resources, what lessons can be learned from the existing studies

with regard to committing more resources to new longitudinal

survey samples? Perhaps the most critical element in the planning of a study is identifying the goal of the survey in the

context of an important, long-term policy issue which will

justify the commitment of funds and personnel over an extended period. In the past, numerous health and social programs have been able to do this. Yet, one recently planned

longitudinal sample survey (the HUD–DHEW study of the

housing adjustments of the elderly) has failed in the pilot

stage because of inadequate support to continue the study.

Another (SIPP) has gone through administrative purgatory

and appears to have a design that does not utilize information

on the behavior of individuals as fully as it might have.

The heterogeneity of individuals should be emphasized

as important to poIicy. The analysis must take advantage

of the inherent time dimension and individual diversity of

169
the observations. The survey instrument must be planned with

the analysis of the collected data and the goal of the project

in mind. For example, SIPP has a very elaborate analysis

plan. The baseline analyses of RHS suggest that a wide array

of papers was anticipated. Even though there were plans for

analyses of RHS. there have been more papers published

than would have been planned because the public data files

have been analyzed by the wider research community. The

external benefits derived from making the data available to

the public cannot be estimated beforehand, but should not

be minimized. Public use data sets should be a part of the

data preparation and analysis plans for every longitudinal

survey.

Alternatives to initiating new longitudinal studies, such

as using cross-sectional or aggregate time-series data, do not

provide information on the same level as longitudinal sample

surveys do. As discussed earlier, static analyses of such data

can be misleading regarding dynamic events and behavior.

For most policy-related issues, agencies need to know the

specific characteristics of individuals to determine “who wins”

and “who loses” when a policy change is considered.

The possible alternative of using linked administrative

and census records has a number of positive features, including

a much lower program cost, and could be used effectively

if the variables of interest are found within an administrative

data set (Rogot et al., 1983). For the elderly population,

this could involve Medicare data sets and Social Security

files. However, to provide public use data files maybe impossible if the data set contains certain legally restricted administrative information (e.g., Internal Revenue Service data) or does

not contain information essential to the study (see Scheuren,

Chapter 11). Thus. it would seem that there may continue

to be a need for these surveys as long as the Federal Government has programs which deal in different ways with persons

of different Characteristics.

The future of longitudinal studies for aging policy is

not as bright as it’should be for several reasons. These studies

are seen as extremely costly, which they are in the short

term. However, the potential benefits in terms of the potential

program savings often far outweigh these costs. The political

time horizon in a period of severe budget constraints would

tend to prohibit planning and executing any new longitudinal

sample survey. Nonetheless, the results of past surveys have

provided invaluable data for health and social policies and

programs after years of planning, fieldwork, and analysis.

In summary, there are six lessons we should have learned

from past longitudinal sample surveys:

1.

7-.

.3.

Longitudinal surveys should be used to examine dynamic

events where changes in individual characteristics and

outcomes associated with aging are the issues under study.

New longitudinal sample surveys should start with an

existing data base such as CPS or NHIS, be linked to

administmtive records, and use the National Death Index

for followup of nonrespondents.

Expert consultants from academia and government agencies should be involved early in the planning process

to ensure that the survey instrument and methodology

are appropriate to achieve the goals of the study.

4.

5.

6.

Analyses should use methods, including stochastic models, which deal with the temporal and heterogeneous nature

of the observations in such surveys.

When used appropriately, longitudinal sample surveys

provide information for the research community and

policymakers which cannot be acquired by other types

of studies. Therefore, these data sets should be considered

as valuable research and policy resources and be made

available in well-documented public use tapes through

a national archival system.

Cross-sectional data and aggregate time series are useful

for comparative static analyses, especially where mean

and total values are the measures of interest; to use longitudinal data for such studies is inefficient and may

be inappropriate when the numbers are too small.

In the final section. recommendations for future kmgitudinal studies are presented.

VI. Recommendations

There are several recommendations which naturally flow

from the discussion in this paper. These include the excellent

recommendations on data sharing made by the Committee

on National Statistics (Fienberg, Martin, and Straf, 1985).

These recommendations are intended to stimulate further discussion of the value of longitudinal sample surveys in relation

to other methods used to obtain statistics needed to analyze

problems in an aging society.

Recommendations in the area of planning and execution

include:

1.

2.

3.

4.

5.

6.

Any new longitudinal sample survey should justify its

longitudinal nature and hence higher cost by showing

how temporal dynamics of heterogeneous individuals are

necessary to the research or policy issues which otherwise

might be addressed by either aggregate time-series or

annual cross-sectional data to attain the goals of the study.

Ongoing cross-sectional studies, such as the CPS, NHIS,

and NHANES, should be explored as sources for establishing new longitudinal studies of aging and the aged.

All federally funded surveys should routinely be linked

to appropriate administrative records and the National

Death Index. This will require legislative mandate to make

such records available for research purposes.

SIPP and the NHANES I Followup Study should follow

the same individual for longer continuous periods to capture more behavioral and health changes due to aging

that are likely to be seen by following each cohort longer.

The socioeconomic risk factor data in ongoing longitudinal

health surveys should be expanded if it can be done

within the goals of the study and without disrupting established relationships between the subjects and the survey

staff.

Longitudinal studies should be well documented and computer-readable copies and codebooks archived in a nation170
7.

8.

1.

2.

.3.

ally recognized distribution network within a year of the

first data runs of the study.

The w.xdemic research community should be involved

early as advisors in the planning of any new longitudinal

surveys.

Administrative records expunged of personal identifiers

should be linked to survey data on a continuing basis

to strengthen the subjective data and extend the temporal

coverage of individuals in the survey, as RHS has done.

Recommendations in the area of analysis include:

Analyses of longitudinal data should employ methods

appropriate for exploring hypotheses concerning temporal

dynamics of heterogeneous populations, such as the application of models involving hazard functions and Markov

processes.

Statistical techniques involving repeated measures should

be utilized and refined for longitudinal surveys involving

rmnrandom nonresponse.

Dissemination of public dara sets to the research community should be given a higher priority by the Federal agencies

responsible for maintaining longitudinal local studies so

that fuller exploration of this information can proceed

4.

5.

in a manner similar to that which has occurred with the

information from national]y representative surveys.

Analyses should compare results of longitudinal sample

survey data collected in the different local studies to demonstrate the consistency of the results of these surveys

as input to social and health policies.

Analyses should compare results of longitudinal sample

survey data and similar cross-sectional and aggregate tinleseries data to demonstrate the value of the longitudinal

sample survey data for policy and research.

In summary, longitudinal sample surveys have been extremely valuable in expanding knowledge of social behavior,

health changes, and other individual responses to aging. Longitudinal studies should not be considered either obsolete or

too expensive to be used now. The recommendations in this

paper are broad and should be widely applicable, though

they do not attempt to address the specific details of a given

study. In today’s environment, we must have information

to determine the value of policy-related issues such as retirement age changes, pension and insurance proposals. disease

prevention activities, and health service utilization; we must

have longitudinal sample surveys.

171
References 

Alcxandcr. R.: Phyhlcal Fitness. Cardiovascular I*unction and Age. 

in A. Ostfeld and 1). Gibson. cdx. Epldcmiology of Aging. DHEW 

Publication No. (NIH) 77-71 I. 1974. 

Anderson, K.. and R. Burkhauser: The Effect of Actual Mortalit> 

Experience Within a Rctircment Dcclsion Model. Working Paper 

No. X3-WOX. Vanderbilt University. IYX3. 

Branch, G.. and A Jctte: The FramIngham disability study. I 

Social disability among the aged. A~jr. J. Plrhlic Hrtrlth 7 I: I 202m 

121(1. 19x1. 

Clark. K., and D. Sumner: Inflation and the real income of the 

cldcrly. Rcccnt evidence and expectations for the future. G(‘).oIz- 

/o/0~;.s/25(2):14~l52. IYXS. 

Cole, J., and J. Grirzle: Applications of multivarlatc analysis oi 

variance to repeated measures cxpcriments. L)io/l~c,/~ic~.s 22:X1&X2X. 

I Y66. 

Cook. N., and J. Wart: Design and analysis methods for longitudinal 

rcycarch. Anructrl RrCec~ off U/J/~ Nan/r/~ 4: I-24. I YX3. 

Coinoni-Huntley. J.. H. Barbano. J. Brody. et al.: Natmnal Health 

and Nutrition Examination I-Epidemiologic Followup Survey. I’rrhlrc, 

Hed/h Rep. 9824%2.51. 198.3. 

Damon, A.: Discrepancies between findings of longitudinal and cro’rsectional studlcs in adult life, Physique and physiology. H~tru Ill I 

X:l6.lYhS. 

Darby. S., and T. Fearn: The Chatam Blood Pressure Study, An 

application of Bayesian growth curve models to a lonpltudinal study 

ofbloodpressure inchildren. /r7/. J. Epirlen7iol X: 15-21, 1970 

Dawber, T.: 7’17~ Frw777~~/7rr~n Sfnd~. Tl7e ~;/~jd~777;0/0,~~. I)/ 

Atl,c,~o.sc,/~rori~, DI’.YP~I.s(,. Cambridge. Mass. Harvard Univcrsi(y 

Press. IYXO. 

DeFricse, G . A. Harsh. and M. McManus: A proposed rcscarch 

agenda for health promotion and disease prevention for children 

and the elderly. He&h .Sm~. Rrs. 19: 1033-1041, IYXS. 

Duncan. G.. and J. Morgan: pi~,c, TI~orrstrnd Ftrmilir.5 (Analyqcs 

of the first x years of the Panel Study of Income Dynamics). Ann 

Arbor. Mich. Institute for Social Research. annual volumes. 

Elbers, C., and G. Ridder: True and spurious duration dependcncc, 

The identifiability of the proportional ha;rard model. Rcvic,,t, c~/ 

E~~oNolr7ic Sfudir.\ 49 :403i109. 19x2. 

Ficnbcrg, S.. and W. Mason: Specification and Implementation ot 

Age, Period, and Cohort Mod&, in Mason and Ficnbcrp. cda., 

Cohor/ Anul~sis in Sociul Research. Beyond tire Ider7~rfic~tr7ion Proh- 

/c,r,r. New York. Springer-Vcrlag, IYXS. 

Ficnbcrg, S., and J. Tanur: The Design and Analyslj of Longitudinal 

Surveys, Controversies and Issues of Cost and Continuity. In K. 

Boruch and K. Pearson. cds.. fksi,yn of K~wrt+7 Whfv7 Krvo7trf~c.c 

Arr S~urc~c. 19x6. 

Ficnberg, S.. M. Martin. and M. Straf, cds : .97tr7-i77,q Kc.,cwrc-h 

DUIO. Washington. National Academy Press. 19X5. 

(ichan. ts.. .~nd \I. S~dd~qu~. Simple rcgrcssion methods for survival 

time data ./A.5;\ hX~X4X-X56. Dee. lY7i. 

(ihosh, M J (;rl//lc. and P Scn: Nonparamctrics in longitudinal 

\rudlL!\ ./A.\4 hX.79 36. IY73 

(Gordon. ‘I . cl al I)lftcrcnccs in coronary heart*diseasc In braming11;17n, Honolulu and I’ucrto RICO. J. (‘hro/tcc~ fli\. 27:32Y-344, lY73. 

Grecnlec\. J W t&cc. and K. Zicschang: Imputation of missing 

\aluc\ when the prohablllty of response depends on the variable 

bclng 17npu1ed JA.SA 77:251 -160. IYX2. 

Greullch. K Phc B,lltimorc Longitudinal Study on Aging. NIA 

Tenth Annl\cr\;q Syn~po~~um on Agmg. July IYXS. 

Grlliche\, % B. Hall. and J Hauxman: Missing Data and Self 

Sclcctmn 10 I .arge Panel\ INSEE Confcrcncc on Economics ot 

Panel Data. Pm\. 1077. 

Hcarnc. t’ , (i (‘lark. and J. Hatch. A text for serial correlation 

In univariatc repcatcd measures analysis L(io777rlric..s 39237-243. 

iYX3. 

tiocm. J. I%‘clphtlng. Ml\classlficatlon. and Other Issues m the 

Analysis ot I,ltc Historich. Stockholm Research Reports in Dcmopraphy No. I I I’n:vcr\ity ~~IStockholm. 10X3. 

Hunyh. f1 .md I. !.cldt, Performance of traditional F tests in 

repcatcd mcasurcs dc\igns under covarlancc hetcropcncity. (‘or77777. 

.S/cr/ 777cor .Mc7/7. ,A 9 hl 73. 10X0. 

Irclan. I.. and K Schwab: Research Potential of the Social Security 

.4dministratlclrl’\ Kctircmcnt History Stud! Paper presented at the 

Confcrcncc on Dcmopraphic and Health Information for Aging Rcstarch. NIH/NI\, June Z-27, IY7Y 

Irclan. I> .~nd K. Schwab: The Social Security Administration’s 

Rctircmcnt History Study Rrptrrc.lr cm Ag7/7g 3:3X l-386, 19X I 

Irclan, I... and K. Schwab. Thcorctical and policy-related findings 

from the Social Security Administration’s Rctircment History Survey 

K~~~icwof flrh/rr~Ikrrrr ~!SP IO:22 I-225. lYX2. 

Irclan. I. I>. Motley. K. Schwab. et al.: Almost 65. Basclinc 

Ihta From the Rctircmcnt Histoq Study. Rcscarch Report 

No. 49 Social Security Administration. IY76. 

Jcnscn. D : ~fficicncy and robustness in the USC of repcatcd measurcmsnt\. Himr7c,/rir~.c 5 I :X I3 X25, 1082 

Kqan, 4.. OI al.: Epidemiologic studies of coronary heart disease 

and stroke in Japanese men living in Japan, Hawaii, and California, 

Demographic. physical. dietary. and biochemical characteristics. 

J. C’hronic D1.5. 27:Wm364. 1074. 

Kalhtlci>ch. J . and R Prcnticc. 7710 .S~trri.stic~trl Anuluis of Ftrrlurc 

IC~rrc,Otrrtr New York. Wiley. 19X0. 

Kalton, (i.: hlodels rn the practice of survey sampling. 1171. Star/. 

Ret, 51:17S-GIXX, 1933 

172 
I&m, R., R. Kane, and S. Arnold: Prevention and the elderly.

Risk factors. HedIh Set-v. Re.\. 19:945-1006, 1985.

Kimncl, W.. anti T. Gordon: Cardiovascular Risk Factors in the

Aged, The Framingham Study, in S. Hayncs and M. Fcinlcib, cds.,

Sctond C’on@rence on fhe Epidemioh~,q.v of A,ging. NIH Publication

No. 80-969, JUIY 1!)80.

ILisprzyk, D.. and R, Hcrriot: The Survey of Income and Program

Participation. Vic*rtt’~ttllr.s}l(~fi(’xr Wir/.s<”ll~$!,$[]r.~chutlaq 4:439451,

IWM.

Keith, P.: Work, rctircmcnt, and well-being among unmarried mcn

und women. Gwwnro/ogis/ 25:410-416, Aug. 198S.

Koch. G., 1. Amara, M. Stokes, and D. Gillings: .%smc views

on parametric and rmnparamctric analysis for rcpcatcd mcasurcmcnts.

lnt. SIut. Rc\”. 48:249-265, 1980.

Luird, N., and D. Olivicc Covariancc analysis ofccnsorcd survival

dtita using Iog-linear analysis techniques. JASA 76:231–240, June

11)X1,

Limcastcr, T., and S. Nickcll: Thcanalysis ofrc-cmploymcnt prob-

~hilitics fm-thcuncmploycd .JRSS(A) 143:141 –165, l~~o.

L~nc, D., D. Uycno, A. Stark, ctal.:Forccasting dcmandfrn’lcmgtcrnlcure scrviccs. H(’f//tll Ser\’. Rs.20:435460,O, 1985.

Mtinsour. H., E. Nordhcim, and J. Rutledge: Maximum Iikclihcmd

cstimtition of variance comprnrcnts in rcpcatcd measures designs

ussumingautt~rcgrcssivc cmors. Bi{~n~etric.~4l:287-294, 1985.

Manton, K.: Changing concepts of morbidity and mortality in the

cldcrly population .Mi/btt/~k Mer?l, Fund Q.6O:l83-244, 1982.

Mtutton, K., and M. Woodbury: A mathematical model of the physiological dynamics of aging and correlated mortality selection, Part

II, Application to the Duke Longitudinal Study, ./. Gercmtol. 38:406-

413, 19X3.

Mtinton, K.. and M. Woodbury: A Continuous Time Multiv@atc

Gtiussian Stochastic Pmccss Model of Change in Discrctc and ContinumtsStutcVariablcs, in M. Tuma, cd., S~~c”if~/r~<qic(// Me/hf~df~/f~<qy,

1985, 1985,

MCGCC. D.: The Probability of Developing Certain Cardiovascular

Disctiscs in Eight Years at Spccificd Values of .%mc Characteristics,

Section 28 in W. Kanncl and T. Gordon, cds., The Frumitr,@am

S/m/y. Washington. U.S. Government Printing Offlcc, 1973.

Migdal, S., R. Abclcs, and L. Shcrrtsd: An Inventory ofLmr,gi/udinul

SnuIies of Middk* und Old A,qe. Ncw York. SSRC, 1982.

Morris, C.: Nonrcsponsc Issues in Public Policy Experiments, With

Emphasis on the Health Inst.umcc Study, in W. Madow and I.

Olkin, cds., Incomplctc Data in Sample Surveys, Vol. 3, Proceedin,qs

tlf[//t,Syt}Ip~~.y[ll/t].Ncw York. Acadcmic Press, 1983.

Motley. D.: Awsilability of Retired Persons for Work, Findings

From the RHS. Rctircmcnt History Study Report No. 12, Apr.

1978.

Napier. J., ct al.: Limitation of morbidity and mortality data, obtained

~rom thmily histories-A report from the Tccumseh Community

Hwdth Study. Am. J. Public Heuhh 62:30-35, 1972.

National Ccntcrfor Health Statistics: Lwrg-TermHeal/hCare, h4initnum Da/a Se/. DHHS Pub. No. (PHS) 80-1158, 1980.

Nelson, D., D. McMillcn, and D. Kasprzyk: An overview of the

Survey of Income and Program Participation. S/PP Working Paper

Series. No. 8401. U.S. Bureau of the Census, 1984.

Ncssclroadc, J., and P. Baltcs: Longitudinal Research, in The SU@

offlehu~’ior utril Development. Ncw York. Academic Press. 1979.

Ncwhtsusc, J.: A design for a health insurance experiment. Inquiry

11: 5–27, 1974.

Newman, S.: Exploring housing adjustments of older people, The

HUD-HEW longitudinal study. Research on Aging 3:417427, Dec.

1981.

Pamcs, H.: Frotn the Middle to the Lu!er Years, Longituditrui Studies

ofthe Pre- and Post-Retirement E.rperietrce ofMen. Ohio State

University, 1979.

Phillips, R., ct al.: Frequency of coronary iwart disease and cerebrovascukir accidents in parents and sons of coronary heart disease

index cases and controls. Am. J. Epidemio/. 100:87-100, 1974.

Pugh, R., and A. Ncibaucr Evaluation of a Mortality Model With

Longitudinal Data. Paper prcscntcd at the Institute of Management

Scicncc meeting. Virginia Beach, Vs., Oct. 9, 1980.

Quinn, J.: Longitudinal Analysis of Rctircmcnt Decisions. Problems

and Prospects. Paper prcscntcd at the Confcrcncc on Demographic

and Health Information for Aging Research, NIH/NIA, June 25–27,

1979.

Riccobono, J., L. Henderson, G. Burkhcimcr, ct al.: Na/ionul .Longitudinul .Vudy, Buse Year (1972) Through Fourth Follo}ls-Up, 1979

Data File Users Manual. Research Triangle Park. N.C. Research

Triangle [nstitutc, 1981.

Rogot, E.: Stnoking utrd General Mortulity Among U.S. Veterans,

/954-/969. DHEW Pub. No. (NIH) 74-544, 1974.

Rogot, E.. ct al.: On the feasibility of linking Census samples to

the National Death Index for epidemiologic studies. Am. ./. Pub/it

Health 73: 1265–1 269, 1983.

Sirrgcr, B.: Individual histories as the focus of analysis in longitudinal

surveys. Jourrud of Economics and Busine.s.s 32:126-151, 1980.

Stasny, E., and S. Ficnbcrg: Some Stochastic Models for Estimating

Gross Flows in the Prcscncc of Nonrandom Nonrcsponse. Proceedings

of a Confcrcncc on Gross Flows in Labor Force Statistics,

Washington. BLS. 1985.

Tissue, T., and J. L. McCoy: Income and living arrangements among

pooragcd singles. SOC.Secur. Bu//. 44(4):3-13. 1981.

Ware. J.: Linear Models for the Analysis of Longitudinal Studies.

Technical Report No. 5. Department of Biostatistics. Harvard School

of Public Health. Apr. 1985.

173
State data issues for an

aging populationa’b

by Charlene A. Barrington, Ph. D., Institute for Health &

Aging, University of California at San Francisco

The collection and analysis of data for an aging population

depend heavily on activities of agencies at the State and Federal

level. States are both producers of data and users of data.

States have considerable discretion in setting policies for collecting, analyzing, and disseminating administrative data. At

the same time, States are heavily involved in cooperative

relationships with Federal agencies in producing and using

data. Federal statutes, regulations, contracts, informal agreements, and financial support define, to some extent, State

data efforts. Federal relationships vary by agency, but generally

provide guidelines, technical assistance, and, in some instances, financial support. This paper reviews the availability

and collection of current State data and examines the quality,

timeliness, and special data problems. Needs for additional

data and necessary changes in the process of data collection

and analysis at the Federal and/or State levels are identified.

States are also consumers of State data, but primarily utilize

the Federal reports of State and substate data. Where State

officials have identified data needs and Federal activities that

would be of value, these are noted within each section of

the paper.

This paper examines State data needs and activities in

five areas: population, vital statistics, social services, aging,

and Medicaid health services. These areas were selected because they are the primary State program activities related

to the aged. Population data centers and vital statistics programs produce data about the aged in the population from

administrative records and other sources. Social services,

aging, and Medicaid are the three largest State programs which

provide services to the aged, as well as other groups, and

consequently have administrative data relative to their programs. States also have data on health and social service

providers and facilities, but these were not included in this

paper because of limitations of time and scope.

Methodology

This paper is based on materials from a number of sources.

First, the paper is based, in part, on findings from an eight-State

study of State Medicaid, Social Service, and Supplemental

Security Income programs that was completed by the Institute

“Contwmarc [hc opininns of the uuthnr and not [how of the Department

of }{calth wrd }Iummr Scrviccs nr ofthc organization shc works for.

bc 1985 National Academy of.%icnccs.

NOTE: The fiss]wmcc o!’Leslie A. Grunt, Ph. D.. is gratct’ully acknowledged.

for Health and Aging (formerly the Aging Health Policy Center) in 1983 for the Health Care Financing Administration.

This study collected data from interviews with State officials

in eight States: California, Florida, Massachusetts, Missouri,

Pennsylvania, Texas, Washington, and Wisconsin. These

States have about one-third of the Nation’s elderly and are

believed to represent a cross section of States in terms of

geographic, administrative, political, and historical characteristics. Second, telephone interviews were recent]y conducted

with State officials in five of these same States (California,

Florida, Missouri, Texas, and Wisconsin) to identify current

State activities and problem areas. Finally, members of other

State, Federal, and private agencies were interviewed, and

documents and reports on data problems were collected. The

analysis of the issues and recommendations are the responsibility of the author and are drawn from a synthesis of these

materials.

Need for State data

relative to the aged

The needs for State data are extensive. States are the

primary users and producers of their own State and substate

data. Such data are needed by State governments to make

estimates of revenues and expenditures in preparing budget

allocations. Most State agencies (e.g., those responsible for

Medicaid, social services, Supplemental Security Income,

aging, mental health, health planning, income maintenance,

housing, employment, and transportation programs) use State

population data in preparing budget estimates and program

planning. Health and social service programs must take into

account previous patterns of utilization and expenditures and

those changing sociodemographic and economic factors that

affect annual or monthly rates of eligibility, utilization, and

expenditure. Since the number of individuals who are 65

years and over (and especially the number 85 and over) is

a major predictor of utilization and expenditure, there is a

vital need for detailed State and substate data by age and

disability status.

County and city governments need the same type of data

for their own planning and budgeting purposes. Not only

do State and substate agencies need data for planning and

budgeting, but also State data are needed for monitoring State

trends and program efforts. Vital statistics and program service

data are used to measure program outcomes.

174
Muny (~ther users of State data. especially population

estimates and ptxljecti~ms, are found in the public sector and

private sectors. These public agency users include: the regional. Sttite, and loco] pltinning commissions: health planning

agencies; commissions on aging; area ugencies on aging:

c{wnty health tind social service agencies; transportation agencies; und many l~thers. State data are used by the private

wctlw in pltinning and development of health care facilities,

housing pro.gmrns, and other programs that serve special popuIati{m groups such as the aged. These agencies are vitally

interested in age-specific data, particularly for the older age

grtmps.

State government generally is interested in data about

its own State. To the extent that Federal agencies such as

the Bureau of the Census produce data about a State or its

substate divisions. States make use of such data. Unfortunately,

many of the national studies of the aging population do not

provide data on individual States. Generally, the sample sizes

for the national surveys are not large enough to allow for

estimates on a State-by-State basis, This includes the National

Nursing Home Surveys, the National Health Interview Survey,

und others. States may use national statistics collected from

such surveys to compare with their own State data. Unfortuntitely, most States do not have comparable data on their

own State to compare with national survey data, and for

States to collect such data through State surveys would be

prohibitively expensive. Federal data that are not disaggregate

by State are of only limited use to States.

States are also users of across-State data on an aging

population and programs that provide services to the aged.

The national sources that compile State data on population,

vital statistics, and programs for the aged are used by States

to compare their own State ranking with others. Generally

Stutes compare their own State with other States of similar

size or within the same geographic region because of the

wide variations across States.

Since States are given discretion in establishing their social

services, aging, and Medicaid programs, there are 50 different

Sktte programs that represent natural experiments that can

he examined. Each State has its own program data, and the

various State statistics represent the only prima~ data source

which must be utilized to build a data base for the 50 States.

Nutional surveys of the population are not a substitute for

dtita on the State programs and their target populations produced by the State programs. Because of the many needs

for State data, there is a strong rationale for maintaining

and strengthening the existing State data efforts.

Population data by age

Federal and State population data activities

The States and Federal Government are actively involved

in population data activities. The Bureau of the Census (BOC)

hw been actively collecting population statistics for the country

since 1790. The BOC reports population data by age, sex,

race, living arrangement, education, geographic mobility, fertility, voting patterns, and other characteristics. The BOC

reports data from the decennial census by age categories up

to age 85 for the total population in the United States and

for States. counties. and cities. During the periods between

the decennial censuses, ihe BOC prepares annual State population estimates and periodic projections which provide detail

on age and other characteristics. The BOC estimates State

and substate data by age category up to age 65; however,

further age detail is not given for the category 65 and over.

In 1969. a Federal-State Cooperative Progrtim (FSCP)

for Population Estimates was established and has continued

to operate since then. Each State designated a single State

agency (State data center) to carry out the responsibility for

making statewide population estimates. The establishment of

the FSCP had the effect on local agencies of eliminating

some of their efforts and/or shifting their emphasis to subcounty

areas such as census tracts (U.S. Bureau of the Census, 1978).

The FSCP was designed so that State agencies would

work jointly with the BOC in preparing population estimates

for small areas such as counties and metropolitan areas on

an ongoing basis. These estimates are computed using uniform

BOC procedures for data collection and standardized methods

for estimation. The figures are accepted by the Federal Government as the State’s official population estimates. State data

centers are also involved with data collection activities for

the BOC decennial census. The role of the BOC under the

FSCP is to provide technical assistance, computer assistance,

and training to State agencies as appropriate.

Many State agencies make special estimates and projections for State and substate areas in addition to the estimates

prepared with the BOC. State agencies may also develop

estimates for subcounties (e. g., cities, incorporated and unincorporated areas, and census tracts). In 1975, a survey by

BOC found that almost 70 percent of the State agencies that

were involved in population estimates or projections made

estimates for counties. and 75 percent made estimates for

cities. In addition to State programs, other agencies are involved in making population estimates and projections. Over

140 agencies nationwide were estimating the population down

to the census tract and 156 agencies were making estimates

for other small geographic subdivisions, such as traffic zones

or planning areas, which are often composed of census tracts

(U.S. Bureau of the Census, 1978).

Methods for population estimation

The BOC and the designated State agencies participating

in the FSCP have developed a number of methods for making

estimates of the State and substate population. Most States

have adopted standardized methods recommended by the BOC

(U.S. Bureau of the Census, 1985a).

Most States use a combination of the Ratio-Correlation

Method, used in 46 States, and the Administrative Records

Method, used in 48 States (U.S. Bureau of the Census, 1985a).

The Ratio-Correlation Method uses multiple regression equations for variables that are transformed by calculating ratios

of percentage shares in a later year to an earlier year (U.S.

Bureau of the Census, 1985b). The Administrative Records

Method looks at Federal tax data to measure the net intercounty

migration of the population under 65 years of age, birth and

death statistics, and Medicare enrollees (U.S. Bureau of the

17.5
Census, 1985b). Other States (14 in number) use a Component

Method whereby the figures are averaged with the estimates

made by one or both of the two other methods, depending

upon their accuracy relative to the most recent census (U.S.

Bureau of the Census, 1985a). The Component Method II

examines net migration through school enrollment or differences between the actual school-age population and the population predicted on the basis of births. Another technique which

is used for making population estimates is the Housing Unit

Method, which makes estimates of residential housing based

on building and demolition permits; certificates of occupancy;

electric, gas, or water meter connections; or field surveys

(U.S. Bureau of the Census, 1978).

The methods States use to make estimates and projections

for State, county, and subcounty areas vary considerably.

They include the different standard approaches described previously with additional modifications. For example, in three

States, a unique method is used to calculate a figure which

is then averaged with that derived using one of the methods

described earlier. California uses a Component Method based

on changes in the addresses listed on driver’s licenses. Changes

in the number of housing units are included in Florida. Individual income tax returns are used to estimate the population

18 and over in Maryland (U.S. Bureau of the Census, 1978).

While significant improvements have been made in standardization, State agencies have a great deal of discretion in the

decentralized system for population estimates.

Components of population estimates

The components of State and substate population estimates

also vary across State jurisdictions. Each State makes policy

decisions about what to include based, in part, upon the needs

of users, the financial costs, and other factors.

Age detail—Age is a critical factor in population estimates.

As noted earlier, BOC does not make population estimates

with age detail within the category 65 and over for counties

or cities except at the time of the decennial census. Some

States develop their own data, which include each year of

age up to the age of 85, while other States develop an age

detail using 5-year or 10-year increments. Other States compute

the total population for State and substate areas but do not

make detailed estimates by age.

In some States, the data are disaggregate (i.e., broken

down) into a number of components which include age, sex,

and race. Income, education, place of residence, household

size, marital status. living arrangement or household status,

occupation, and other factors may also be specified. These

characteristics may also be cross-tabulated by age, sex, and

race. Unfortunately, such data are not generally prepared in

the population estimates.

Changes in the patterns of mortality and morbidity in

the aged population make the category of 65 years and over

less meaningful than one that is more precise, such as reporting

for each year of age or in 5-year categories. Age-detailed

data at the State and substate levels are critical for planning,

budgeting, and making policy decisions. In order to improve

the availabiiit y of data, age detail for State and substate population estimates and projections should certainly be developed

voluntarily. Strong consideration should be given to mandating

that such data be compiled by both State and Federal agencies

on a yearly basis. Such action would require a statutory change’

atthe Federal level and financial support for these activities.

Disability data—A major gap in the population data is

the lack of statistics on disability rates for State and substate

areas. Although the decennial census collects data on the

number of individuals who are institutionalized, data on disability levels for the institutionalized and noninstitutionalized

elder]y are not collected. The National Center for Health

Statistics has conducted the National Health Interview Survey

since 1957. This survey collects data on disability by gathering

information about the numb~r of restricted-activity days for

individuals during the year and the number of days individuals

are confined to bed. These indicators can be used as a proxy

measure for the rate of disability in the general population.

These data were collected in 1969–70, 1973–74, 1980-81,

and most recently in 1984 (National Center for Health Statistics, 1984b). Unfortunately, the sample size of the survey

is too small to make estimates or projections of disability

in State or substate areas. The most recent State data on

disability rates were available from the National Health Interview Survey conducted by the National Center for Health

Statistics (1978), where the sample size was large enough

to make State estimates.

Data on disability are needed on a State-by-State basis

and for localities for the planning of health, social services,

and aging programs. Age alone is not an adequate predictor

of the need for health and social services because there is

no abrupt change in functional status at any given age. A

better approach would be to collect data on age, sex. and

disability in the general population. Disability can be measured

through self reports. The incidence of disability changes over

time and varies across States and geographic regions (National

Center for Health Statistics, 1978). Such data are essential

in making projections of future needs for and costs of health

and social service programs to the public and private sectors.

Such data are also basic in planning income maintenance

programs. Data on disability are needed nationally as well

as for State and substate areas (General Accounting Office,

1979). Careful consideration should be given to how such

disability data can be collected along with information about

age, sex, race, income, and other characteristics. Questions

about the level of disability should be included in the decennial

census so that such information is collected on a regular basis.

Other data—Better State data on several demographic

categories, particularly from the 1990 census, would be valuable for policy analysis and research on the aged by States

and other agencies. The National State Data Center Steering

Committee (NSDCSC) recommends that a question with combined race/Spanish origin be included in the decennial census

and that greater detail be provided for ethnic groups within

race groups (National State Data Center Steering Committee,

1984). More specific information on household relationships,

especially for nonrelatives who may or may not be related

to household members other than the householder, is important

in predicting caregiving needs, utilization, and expenditures

for the aged. Information about the number and patterns of

marriages and divorces of the aged and their potential care176
giver children would also be useful. The NSDCSC also recommended including an indicator for full- or part-time employment, and adding greater detail on migration by asking about

the number of moves during a 5- and 10-year interval. Greater

detail should be provided on income, especially nonmoney

income (National State Data Center Steering Committee,

1984). Data on total health and social service utilization and

expenditures would be valuable. These data would be extremely useful to States if available from the decennial census

on a State-by-State basis. States could then make detailed

age estimates and projections for the population by these

critical factors.

Accuracy of methods

The accuracy of State and substate estimates is an important concern. In 1975, the BOC was concerned about the

high number of agencies below the State level that were

not using ratio-correlation techniques in making their estimates

and the number using extrapolative methods that had been

discredited (U.S. Bureau of the Census, 1978). The BOC

evaluated the different estimation methods used by States

during the 1970’s and found that the accuracy of the methods

was similar, but that error rates were high when compared

to the 1980 census. For 1980, the methods for State estimates

were refined considerably, and the new procedures improved

prediction nationally. The Component Method H was found

to be the least accurate, while the new Composite Method

had the lowest percent error of all the methods (U.S. Bureau

of the Census, 1984).

While the accuracy of estimates appears to be improving,

the State and substate variations continue. A basic policy

question concerns why these parameters are not standardized

to a greater degree across States. One argument is that since

resources and technical capability for making estimates vary

across States, some variation in method should be allowed

to Pakeinto account these factors and other unique characteristics of State data availability. On the other hand, an argument

can be made that the comparability and reliability of data

should be enhanced across the States by mandating that each

State use an identical method which is selected on the basis

of accuracy and financial feasibility. Although State prerogatives have already been established, the BOC should enforce

greater standardization in methods and components at the

State level or the Congress should mandate more uniform

procedures,

Timeliness of data

Another major concern is the timeliness of data from

the BOC, The BOC has developed a system for provisional

county estimates which achieves a substantial improvement

in the timing of the estimates by using a variation of the

Administrative Records Method. The subcounty reports on

population were prepared on an annual basis in the 1970’s;

however, these are now prepared every 2 years (Melnick,

1982). While officials in State government report that the

delivery of data from the 1980 census was an improvement

over the 1970 census, the need for substantial improvements

still remains. Numerous revisions were made in the schedules

to distribute data from the 1980 census. As a result, some

confusion was created and public distrust was reportedly

aroused (National State Data Center Steering Committee,

1984).

Because of the great need for more frequent data, Congress

authorized the Census Bureau to conduct a mid-decade census

beginning in 1985 (Storey, 1985). While plans were made

to do this in 1985, financial resources to complete such a

survey were not made available by either the Carter or Reagan

administration (Storey, 1985). The methods and technologies

are available for more frequent survey activities. Clearly,

the time required to prepare data on State and substate areas

could be shortened by giving greater financial resources to

the BOC, and resources for a mid-decade census are needed.

A mid-decade census would be one of the most valuable

Federal efforts for States because it would reduce the workload

of State data centers and would improve the quality and accuracy of State estimates and projections. This would benefit

all State users of data, who depend heavily upon population

estimations for planning and budget development.

Financial resources

The lack of adequate financial resources limits the activities of Federal and State agencies making population estimates. The amount of data available, their timeliness, how

often they are collected, and the inclusion of details on age,

disability, and other elements all are dependent upon financial

resources and personnel.

The demand is increasing for State and substate population

data of improved volume, type, and level of sophistication

(National Governors’ Association, 1985; U.S. Bureau of the

Census, 1985c). At the same time, the capacity of the Federal

and State agencies to produce such data and respond to requests

is hampered by real financial constraints. The tinancial resources for data on population are a political problem at both

the Federal and State levels, since data activities are invariably

viewed as less important than providing direct services to

the population. In periods of time when human service programs are threatened or actually reduced, political and public

attention first is directed to protecting service programs, and

less attention is given to data issues. Nonetheless, population

data are fundamental to the effective planning and management

of direct service programs.

The Bureau’s current, periodic, and reimbursable budget

for 1983 was estimated at $236.3 million, compared to $257.8

million in 1982 and $334.2 million in 1981. This was a

decline of 29 percent from 1981 to 1983 (Melnick, 1982).

The BOC also laid off an estimated 300 employees in 1981-82

due to budget cuts. Delays in the data analysis and publication

of reports from the 1980 census created many delays for

data users. These delays caused a ripple effect on the rest

of the statistical system (Melnick, 1982).

Some BOC reports from the 1980 census (e.g., the 1980

census report on the older population with detailed local area

data on the aged) have been canceled (Storey, 1985). The

mid-decade census planned for 1985 was canceled because

177
of the budget cuts. This census was strongly supported by 

State officials, who need more up-to-date data because of 

the dramatic increase in the pace of social and economic 

changes occurring in specific localities. This population survey. which would have included data on age. sex. and other 

demographic characteristics, would have improved the accuracy of data for State and local planning efforts (Storcy. 

1985). Thus. these cutbacks have created further problems 

for State agencies who are already having difficulty in obtaining data for making population estimates. 

State agencies have also experienced problems in having 

adequate resources to carry out their responsibilities in the 

Federal-State cooperative program. Some States have reduced 

their data programs. Many have reduced their outreach activities, training, and dissemination of reports. The National 

Governors’ Association staff and officials from selected States 

have reported problems resulting from the cutbacks. Some 

States have attempted to respond to the cutbacks by becoming 

more entrepreneurial, through selling their information to private clients. 

A National State Data Center Steering Committee consisting of nine members from State agencies was established 

in 1983. This Committee is designed to foster communications 

between individual State data centers and the BOC. The Committee meets periodically to address common problems for 

the States such as funding levels and standardization. It publishes a newsletter and sponsors data conferences (U.S. Bureau 

of the Census, 1985~). These activities represent a positive 

direction for improving communications and formulating policy issues. 

Developing adequate financial resources for collecting 

and analyzing population data is a major impediment in improving the quality, quantity, timeliness, and usability of data, 

especially data on the aged population at the State and substate 

levels. Some type of political action will be needed if substantial improvements are to be made, and the political decisions 

will, by necessity, require an analysis of cost effectiveness 

to justify this need. 

Vital statistics 

Vital statistics are data on births, deaths, marriages, divorces, abortions, and other demographic parameters. These 

data are important to the national data base, and collecting 

such data is an important activity at the State level. 

Statistical activities of State health departments first started 

with their responsibility for vital statistics. In 1969, the National Center for Health Statistics (NCHS) developed a 

Cooperative Health Statistics System (CHSS) with the States 

(National Center for Health Statistics, 1980). The idea of 

the CHSS was to develop uniform definitions and standards, 

to collect data at the State level, and to lower costs by using 

a decentralized system for data collection. NCHS negotiated 

contracts with State agencies to collect data on vital statistics, 

health facilities. health manpower and ambulatory care, and 

health interview data (National Center for Health Statistics, 

1980). 

In 1978. almost all of the States had a State center which 

collected data on some or all of the following three programs: 

vital statistics. health manpower. and health facilities. A study 

of State statistical programs in 1978 showed that the 50 State 

health agencies spent $47 million (National Center for Health 

Statistics, 1980). Eighty-seven percent of the funding was 

from State sources, IO percent from contracts with NCHS 

for the CHSS activities, and 2 percent from Public Health 

Service funds. 

Guidelines defining the characteristics and functions of 

the State agencies were developed by NCHS in 1980 (Federal 

Register. 1980). Under the cooperative program, States designated a State agency to be responsible for State health statistics 

and these agencies are called State Centers for Health Statistics. 

During the same year, the Federal funds were eliminated 

for the State Centers for Health Statistics. In spite of this 

development. some States continued to collect data on health 

manpower and facilities (National Center for Health Statistics, 

1982a). Delaware just designated a cente? in 1985 (National 

Center for Health Statistics, 1985a). 

States have continued their vital statistics activities and 

the National Center for Health Statistics has continued funding 

for this activity. At the same time, NCHS continues to provide 

technical assistance to the States to support their program 

activities in vital statistics. The States are also asked to send 

representatives to periodic meetings to evaluate the standards 

for vital statistics. 

A study by the Department of Health and Human Services 

(National Center for Health Statistics, 1980) found that 43 

States furnished data on births and deaths, and a smaller 

number of States produced other types of vital statistics, including marriages and divorces, in 1980. Other States provided 

copies of vital records in photographic form, which were 

then used to prepare NCHS computer tapes. No recent study, 

however. has been conducted on the extent of State activities 

on vital statistics. 

The study by the Department of Health and Human Services recommended that future work in vital statistics concentrate on improving data quality on births and deaths (especially 

fetal death records) and more timely reporting (National Center 

for Health Statistics, 1980). Because the current system is 

voluntary and decentralized, problems of data quality and 

reporting have persisted. NCHS must wait until the States 

submit their statistical data before publishing the State mortality 

and birth rates. For example, the final mortality statistics 

for 1982 were not published by NCHS until 1984 (National 

Center for Health Statistics, 1984~). In 1984, the lag time 

for most State vital statistics data was expected to be 2 years 

(Woolsey, 1985). Methods to speed reporting by States are 

needed to make the data more useful. 

Because of the need for more timely data, NCHS conducts 

a IO-percent sample study each month from the death statistics. 

This provides monthly estimates of State death rates, and 

corrections to the estimates are made on an annual basis 

(National Center for Health Statistics, 1985a). While this sample is used to make U.S. estimates of deaths by age, sex, 

and race, these estimates are not made for each State. If 

the sample size were larger than IO percent, it would be 

178 
possible to make State estimates for sociodemographic characteristics. This is especially needed to make State estimates

o!’death rates by age.

NCHS recently has begun to analyze multiple causes of

detith from the death certificates. This is especially important

for the study of chronic illness patterns in the older age groups,

who tend to have multiple illnesses. The National Center

f{w Health Statistics ( 1984a) reported that in 1978 nearly 75

percent of the death certificates had more than one condition

listed and 15 percent had more than four conditions listed.

Based upon these data, a valuable report was developed for

the United States describing multiple causes of death by age,

sex, and race (National Center for Health Statistics, 1984~).

Such reports should also be prepared and analyzed by States

identifying multiple causes by age, sex, and race, since the

data are available on existing birth certifications and are important for both scientific analyses and policy decisions.

While there is general support for continuing the present

Federal and State cooperative arrangement for vital statistics,

the major issue is the extent of Federal financial support

for the collection of State vital statistics. The activities in

the area of vital statistics, as in other areas of data collection,

are heavily dependent upon financial resources at both the

Federal and State levels. A 1981 study of State vital statistics

activities found that the cost of operating the vital statistics

programs was approximately $53 million (National Center

for Health Statistics, 1982b). Of this total, approximately

$4.5 million, or 8 percent, was paid by NCHS. The $4.5

million paid by NCHS covers approximately 29 percent of

the costs that were determined to be within the scope of

the Federal-State vital statistics program (National Center for

Health Statistics, 1982b).

States have argued that they should receive 50-percent

Federal funding for vital statistics activities. The 1980 study

of the Department of Health and Human Services mentioned

curlier recommended that the Federal share be at least one-third

of the in-scope costs. In spite of this recommendation, the

Fedeml share of funding was estimated to be 26 percent for

fiscal year 1986, the same level as in the previous year

(Woolsey, 1985). While it is consistent with cost-containment

et’forts to attempt to control the budget, it is also necessary

to maintain a minimal level of funding for these program

activities. The Federal Government should pay its fair share

of the costs, at least one-third of the Federal-State vital statistics

pr(~gram, and the budget should keep pace with inflation if

the program is to remain viable, and certainly if the program

is g[~ingto be improved,

Social services

Social services have been provided by government for

man y years in the United States. The Federal Government

assumed a greater role in the early 1960’s and then, in 1975,

the Title XX Social Service program was established (Gilbert,

1977; Lindeman and Pardini, 1983). This program was organized as a Federal and State partnershipuntil major changes

were made in the program in 1981. The 1975 legislation

required States to provide at least one service for each of

five program goals: economic self-support; personal self-sufficiency; protection from abuse, neglect, and exploitation; prevention of inappropriate institutionalization; and arrangement

for appropriate institutional care (Lindeman and Pardini,

1983). States were given wide discretion in the number and

scope of services provided, but the services were targeted

to those with low incomes. States were required to conduct

specific planning and reporting activities, including development of a Comprehensive Annual Service Program Plan

(CASP) and preparation of quarterly and annual reports for

the Social Services Reporting Requirements (SSRR).

With passage of the Omnibus Budget Reconciliation Act

(OBRA) of 1981 (Public Law 97–35), the program changed

to the Social Service Block Grant (SSBG) and no State matching funds were required. Federal funds were reduced from

$2.9 million in 1981 to $2.4 million in 1982. In 1983, the

Federal allocation based on State population was limited to

$2.5 billion, and it was raised to $2.7 billion for 1984 (Federal

Register, 1983). Thus, Federal funding did not keep pace

with inflationary increases during the period 1981–84. As

the level of funding was reduced, Congress also removed

various requirements, including those for the CASP and the

SSRR. States were also allowed to select the types of social

services provided, and the requirements for information and

referral or protective services were removed. Only minimal

requirements for overall planning and community involvement

in the planning process were maintained by the legislation

(Lindeman and Pardini, 1983).

Lindeman and Pardini (1983) and others at the Aging

Health Policy Center conducted a survey of the social service

programs in eight States (noted earlier) to examine the impact

of these changes in 1982 and 1983 after passage of OBRA

in 1981. Only two of the eight States maintained their social

service programs at 1981 funding levels during 1982 and

1983; the remaining six states reduced the budget for their

programs. One State reduced the number of eligibles, two

reduced the number of services, three reduced the units of

services, and two increased the client fees.

The Title XX program was criticized prior to the OBRA

1981 changes because data about its programs and clients

were so poor and incomplete. In many States, the program

planning and reporting of expenditures and recipients were

simply viewed as a bureaucratic exercise. The program did

not require States to submit detailed data on the number of

recipients or provide breakdowns by age, sex, race, or income.

Utilization data by service were also not requested or required.

The Federal reporting requirements were not specific enough

to bring uniformity to the program. Uniform definitions of

program services were never established at the Federal lev;l

and States developed their own unique approaches to the

definitions and provision of services. No penalties were imposed on States for failure to comply with reporting

requirements.

After the adoption of the SSBG, the data reporting became

more problematic, although, to some extent, State planning

activities may have improved as States struggled to make

179
budgetary decisions that would target the use of their limited

resources (when the Federal program budget was reduced

by about 20 percent) (Lindeman and Pardini, 1983). Some

of the States in an eight-State study sample were in the process

of upgrading their fiscal and program data systems. Others,

however, placed no emphasis on attempting to correct a problem situation (Lindeman and Pardini, 1983).

Some States such as Texas have developed sophisticated

client and provider information systems for social services

that include all recipients. The Texas system provides detailed

data on the number of services and costs for each individual

client. It also provides data by age and other demographic

characteristics by county. This system has been accomplished

because all private provider agencies under contract are required to report this information in order to receive payment

for services. Other systems, such as California’s social service

program, use the county as the primary service delivery and

data reporting agency (Barrington et al., 1983). It is more

difficult to manage such a decentralized system and to assure

uniform and accurate data.

Even though some individual States have developed

reasonably good reporting and evaluation systems for their

State social service program, reporting to the Federal Governmentcontinues to be poor. States are no longer reporting

SSRR data, but are now required to prepare pre- and postexpenditure plans for the Federal Government in a format of the

State’s own choosing. These expenditure plans must provide

two components: (1) a description of services and attributes,

and (2) a description of the characteristics of persons served

(Office of Human Development Services, 1983). In 1985,

the Office of Policy Coordination and Review in the Office

of Human Development Services, Department of Health and

Human Services, reported that a few States were reporting

detailed data, but many States were just sending in brief

lists of the types of services offered (Office of Human Development Services, 1985).

The Office of Human Development Services (1983, 1984)

prepared an analysis of the preexpenditure reports submitted

by States. The preexpenditure data vary considerably by State

in terms of the types of information contained, the level of

specificity, and the range and definitions of services offered.

Cost-of-service data were available for 42 States. Although

some general data were provided by the States, these data

were much too general to provide a basis for any reliable

or accurate conclusions.

In 1982, the American Public Welfare Association

(APWA) developed a proposal for the Office of Human Development Services to establish a voluntary program for reporting aggregate State data on social service expenditure programs

for three groups: developmentally disabled and mentally retarded, child welfare, and social service block grants. The

APWA was funded for an exploratory design and study of

the feasibility of developing such a national data-base system.

Aging was not included because the Administration on Aging

had a contract with the National Association of State Units

on Aging to collect data on aging programs.

State data from the Voluntary Cooperative Information

System (VCIS) was analyzed by the American Public Welfare

Association ( 1984). APWA found that only 43 States provided

data on services and recipients for 1981-82. Data from only

31 States were usable; the remainder was not included because

the data were for points in time rather than cumulative and

data from some small States were reported to represent less

than 90 percent of the total SSBG-funded program. VCIS

data were derived from a combination of actual and estimated

figures. Few States reported unduplicated counts of recipients.

While the VCIS did provide a useful summary of reported

data, the problems noted before are serious.

In summary, the need for a standardized minimum aggregate data set to be collected and reported by the States is

evident. The most critical aspect of this would be to develop

a standard set of service definitions that could be used across

States and across programs (e.g., social services, aging, and

Medicaid).

Although States are willing to provide some information

to the Office of Human Development Services and APWA,

the problems in quality and accuracy are so overwhelming

that the data become meaningless. On the other hand, a mandate for improved State reporting systems would require a

statutory change in the SSBG legislation. It probably would

also necessitate providing Federal funds for States to make

improvements in their data information systems. As long as

Congress and the Administration continue to constrain the

SSBG program budget (which has been declining in terms

of real dollars), resources will not be available to develop

adequate reporting systems for the program. Because data

are not available on what the program provides, who the

beneficiaries are, and what the impacts and outcomes of the

SSBG program are, funds for social services are extremely

vulnerable to future Federal and State cuts. The issue is whether

or not the need for data about the program outweighs the

cost of designing and implementing a reporting system to

the Federal and State Governments.

State aging services

The Older Americans’ Act (Public Law 89–73) was established in 1965 to provide Federal funds for social and recreational programs for the elderly. In 1972, the Federal program

added funds for social and nutritional services. The Social

Security Act amendments of 1967 expanded the definition

of eligibility and prohibited tying eligibility to income (Lindeman and Pardini, 1983). Since these programs were intended

for all of the aged, State and area agencies which administer

these programs have minimal and sometimes nonexistent reporting on recipients, utilization, or expenditures. No Federal

data reporting system was required and a voluntary system

was not established initially.

A survey of States in 1982 and 1983 found the State

programs and their data systems to be in disarray (Lindeman

and Pardini, 1983). No Federal data system for State and

area agencies on aging was required nor were such data available. The only data available were in the States, and the

State data were primarily for expenditures by fiscal year.

Some States such as Missouri eliminated their data reporting

system on the aging program because of financial reasons.

180
The problems of data are similar to those in the social service

system, where no uniform definitions of services and providers

have been developed and used by States. Thus, even available

data collected by State surveys were not useful because of

problems in different State service definitions and incomplete

information (Lindeman and Pardini, 1983).

In 1981, the National Association of State Units on Aging

(NASUA) established a voluntary data reporting system. The

NASUA system collected data from State units on aging

(SUA’S) and from 660 area agencies on aging (AAA’s). The

data collected include service and client information from

service providers (usual]y contractors). Data are being compiled for fiscal years 1980-84. The data include the services

and the dollar amounts spent by type of service from Federal,

State, and private sources. In 1981, NASUA had a 94-percent

response rate from the SUA’S and an 84-percent response

ri.tte from the AAA’s. Since 1982, the survey has used a

rotating stratified random sample which includes one-third

of the AAA’s per year but all of the SUA’S are surveyed

tmnually (National Association of State Units on Aging, 1985).

In addition to data on administration, staffing, policies, and

revenues, data are also collected on the types of services

provided, the units of service, the percentage of funds spent

on the service, and the recipients’ characteristics in terms

of sex, race, and the proportions who are age 75 and over,

low income, and living alone. No data on actual expenditures

by type of service or by client characteristics are available

(National Association of State Units on Aging, 1985).

The NASUA survey conducted in 1983 (National Association of State Units on Aging, 1983) categorized services into

30 different groups, In regard to client characteristics reported

by the type of service, 69 percent of the agencies surveyed

reported some statistics for the number of recipients age 75

and over. Seventy-seven percent of the agencies reported on

the proportion of low-income clients. In general, on]y about

60 percent of the agencies had client-based data (National

Association of State Units on Aging, 1985). As noted earlier,

the NASUA data base reports on only one-third of the AAA

agencies each year.

The data compiled by NASUA are valuable for examining

issues related to program policy and budgets for State and

area aging agencies. This data information system should

be maintained because it is the only source for data of its

type. This data base, however, is extremely limited in providing a description of aggregate client characteristics across

States, and does not allow for a comparison of client characteristics to program utilization and expenditures.

A basic aggregate data base for an aging program should,

at a minimum, recognize the importance of collecting data

for the different ages within the overall category of aging.

The data on the program should also include demographic

data by age, sex, race, income, education, marital status,

and other factors. Service definitions should be developed

for every different type of service that may be used and

these services should be grouped into categories where aggregation is desired. The service definitions should not be limited

to only 30 categories unique to the aging program. Uniform

definitions of service providers should also be developed that

would be used across programs (i.e., social services, Medicaid,

and others) and data should be reported by service and by

providers of services. The data should include services provided, providers, utilization, and expenditures, as well as

quality outcome measures for the program.

The findings from the voluntary NASUA survey show

how inadequate the current data collection activities of State

and area agencies are. As long as the Federal Government

does not require mandatory client reports on utilization by

type of service, the data reporting activities will, no doubt,

remain problematic. Historically, the States have had weak

data reporting systems for the aging programs. These activities

have deteriorated even further with the reductions in reporting

requirements. Only additional Federal funds and mandatory

requirements are like]y to change the current patterns.

Medicaid data

The Medicaid program was enacted in 1965 to provide

health care services to the poor. The program provides Federal

matching funds for State programs. The Medicaid program

requires the States to provide certain mandatory services and

allows for some optional services. Services are provided to

those who are eligible for Aid to Families with Dependent

Children and those aged, blind, and disabled who are eligible

for the Supplemental Security Income program. The legislation

gives the States a great deal of discretion in managing the

programs as long as the minimum benefits and eligibility

requirements are met.

In 1972, Congress authorized funding to provide incentives to States to design, develop, and install Medicaid Management Information Systems (MMIS‘s) and provided 90-percent Federal funding for this activity. Congress also provided

75-percent Federal funding to operate the systems after they

were established (General Accounting Office, 1982). The

MMIS programs provide data for claims processing to providers and detailed data on recipients, services, utilization, and

expenditures based on billing data. The general system design

established for the MMIS programs allowed considerable variation in its characteristics across States, and that approach

is still used to maintain a decentralized system (Sawyer et

al., 1983).

In 1978, a General Accounting OffIce report about

MMIS’S found that the Department of Health and Human

Services was approving State systems that were not meeting

the program’s need for data (General Accounting Office,

1982). Congress passed the Schweiker amendment in 1980,

which required most States to install a computerized MMIS

program. In 1981, 35 States operated approved systems at

a cost of $214 million and 21 States (12 of which already

had approved systems) spent $48 million on system design,

development, and implementation (General Accounting Office, 1982). Since 1980, the Health Care Financing Administration (HCFA), which is responsible for the Medicaid program,

has developed performance standards for the MMIS program

and has careful]y monitored State progress.

The Federal Medicaid reporting requirements are complex

and include several different types of data. States must submit

181
Form 2082 annually and Form 120 monthly to report the

number of recipients by specific eligibility category (including

the aged, blind, and disabled) and by service category.

States still employ nonstandardized coding, processing,

and file structures (Sawyer et al., 1983). These problems

are compounded in States that have decentralized Medicaid

programs. such as New York, which has allowed county-based

MMIS systems with cross-county variations (Sawyer et al.,

1983 ). Some States have problems in the accuracy of their

reporting of recipients by county. For example, in California,

when a recipient moves from one county to another, the

recipient generally receives a new identification number (Barrington et al., 1983), leading to duplicate counting of recipients. If States were required to use social security numbers

as identification numbers, then there would be less chance

for duplicate counts of recipients.

In 1983, six States still were not filing annual reports

on Medicaid and two did not file monthly reports. Thus,

HCFA has had to estimate the missing data using a weighted

linear extrapolation method and aggregate data from other

sources (Sawyer et al., 1983). Problems with missing and

unreliable data are extensive (Heinberg and Adler, 1982).

Data on the number of individuals who are eligible for

the Medicaid program are generally not available. Since States

are required to report only on the number of recipients (users),

they have not reported the number of eligibles to HCFA.

Although some States do have data on the number of eligibles,

such data are not reported by the States to the Federal Government. Information about the rates of utilization for the number

of individuals who are eligible are not available by State

from a national source.

One problem with Medicaid data on the aged is that

many aged individuals are also in the disabled category. Those

individuals who are categorized as disabled before they reach

age 65 generally remain in that category after age 65 because

the benefit level under the Supplemental Security Income

(SS1) program is higher for the disabled than for the aged.

According to the Social Security Administration, 21.8 percent

of the 2 million disabled in the SS1 program are also 65

and over (Social Security Administration, 1983). Thus, the

number of aged Medicaid recipients is undercounted. State

Medicaid programs are required to report data on actual age

of recipients (for those aged 65 and over) on the Federal

2082 forms. Unfortunately, these data have been found to

be incomplete and inaccurate for many State reports for the

period 1978-83.

An added problem is that State Medicaid data on the

number of days of care in hospitals and nursing homes are

not reported accurately, especially for Medicaid recipients

who are also Medicare beneficiaries. These data are critical

in understanding patterns of utilization. In 1985, data about

the days of care are going to be made available by the Health

Care Financing Administration only after a substantial period

of cleaning and editing of the State data from the 2082 reports.

These data are still not considered to be very accurate. In

the future, the States should make greater efforts to improve

the accuracy of data on days of care.

Another problem is that State Medicaid data systems generally are not linked with the Medicare billing data, which

are produced by fiscal intermediaries under a Federal system.

Since 12 percent of the aged who use Medicare also are

on Medicaid, this information is critical in determining the

total expenditures for individual Medicaid recipients (Sawyer

et al., 1983). The total days of care and expenditure data

for these individuals (called crossover cases) frequently are

inadequate because a large portion of the services and expendimres per illness episode are actually reported only by the

Medicare program. Claims data for crossover cases generally

are sent to Medicare for payment, and then Medicaid may

pay the remainder not covered by Medicare. Medicaid data

for these individuals are frequently incomplete.

Medicaid data on the MMIS are generally reported as

the bills are submitted. Thus, data are reported at the time

of payment rather than when the services are rendered. This

makes analysis of the policy changes difficult and makes

certain types of analyses difficult.

More importantly, State Medicaid data are reported to

the Federal Government only in an aggregate form rather

than for any individual or group of individuals, such as the

elderly. Thus, while arithmetic means can be calculated, measures of central tendency and dispersion cannot be calculated

and many important questions cannot be answered (Heinberg

and Adler, 1982).

HCFA established a project for five States during 1980

and 1981 that would create uniform person-level data by grouping all billing data for individual recipients. This project created

a National Unit Record Medicaid Data Base (tape-to-tape

data) with uniform files from 100 percent of all State claims.

Such data allow for tracking of utilization and expenditure

patterns for individual recipients rather than simply reporting

totals for the State by service or eligibility categories (Heinberg

and Adler, 1982). Unfortunately, such data are not otherwise

available.

One approach toward solving some of these Medicaid

data problems would be to require all States to use a standardized format that specifies a minimum data set from its

MMIS program similar to that created for the National Unit

Record Medicaid Data Base on the tape-to-tape data. Such

data from the billing records could then be sorted for each

recipient and actual utilization and expenditures reported for

unduplicated counts on a monthly and annual basis. This

would eliminate the need for the 2082 and 120 forms and

would put data into a more usable format. The advantages

of such a system would be the ability to: ( 1) take a comprehensive look at special groups such as the institutionalized aged;

(2) conduct distributional analyses; (3) look at Medicare crossover recipients; (4) make accurate cross-State comparisons;

(5) analyze provider participation and recipient access: (6)

link Medicaid with Medicare and other data sources; (7) conduct small-area studies or special studies by services; and

(8) analyze cross-tabulations by age and other demographic

characteristics (Heinberg and Adler, 1982). By merging

Medicaid with Medicare data for individual recipients, a greater

understanding of utilization and expenditure patterns of the

aged could be gained. This system could also include the

actual birth date of all recipients so that age patterns could

be studied in relation to utilization and expenditures. Finally,

if such data reporting systems were required, the extensive

182
activities undertaken by many State agencies to produce estimates and projections for Medicaid planning and budgetary

purposes would be substantially reduced.

Summary and conclusions

State data systems for population, vital statistics, social

services, aging, and Medicaid are all designed as decentralized

State programs. The Federal Government has primary responsibility for technical assistance, coordination, analysis, leadership, and dissemination of data. It also has some financial

responsibilities. States are given a great deal of discretion

not only in the design of their health, social service, and

aging programs but also in the development of data reporting

systems for those programs. Even in the area of population

estimation and projection at the State level, which involves

highly technical methodologies, State agencies are given considerable latitude in the methods used and the components

included. Medicaid MMIS is the most highly developed data

reporting system in any of the health and social service programs at the State level due to its large payment system

to private providers; however, even it has many problems.

The quality of State data on the population, Medicaid, and

vital statistics has improved over time, while the social service

data have deteriorated and the aging data remain poor as

a direct result of the change to Federal block grants to States

and the failure to employ detailed program reporting requirements. While the voluntaty systems (e.g., the APWA and

the NASUA surveys) can provide some useful information,

these systems are not satisfactory for aggregate client data

or for utilization and expenditure data.

Two major consequences of the current State data systems

are that data are frequently not comparable across States and

quality is variable. Definitions of service categories, utilization, eligibility groups, and client characteristics all vary within

and between the State programs. The element definitions,

formatting conventions, timeframes, and data types all vary.

Some States may even have decentralized systems at the county

level that make data collection and analysis even more difficult.

The programs are, for the most part, not mandated to report

client-based data. When client-based data are available, these

are almost invariably in the form of aggregate statistics, thereby

Iimiting their usefulness,

The limitations in the current data systems for basic popuhttion and program data for all age groups pose a serious

problem which grows as the demand for data by policy makers,

purchasers, consumers, government analysts, researchers, program managers, and private businesses increases. The technology in data information systems has improved exponentially .

during the last decade as the country has had an information

revolution. The data capacity of the public sector has not

kept pace with the growth in technology.

A more rational and effective data system can be achieved

through the development of a national data base for all States

using administrative records (vital statistics, social services,

aging, and Medicaid) and other sources (e.g., population estimates using standardized methods). No other source is available to replace the type of valuable information that States

are able to produce from administrative records. National

surveys generally do not have adequate funds to collect data

from a large enough sample to provide State and substate

data.

States are the producers of State administrative data and

the primary users of the State data. States probably use Bureau

of the Census State and substate data from the decennial

census more than other data sets because most State agencies

need such data, especially those on age and income, for program planning and budgeting. While States occasionally use

data from national sources that are not disaggregated by State,

these are not considered to be as valuable as State data.

States use their own program data for describing, explaining,

and predicting their program activities and budgets. States

also compare their own State program data with those of

other States. The data produced by States on social services,

aging, and Medicaid must be improved before such cross-State

data will be very valuable to States. Unless improvements

are made in national data on States, States will probably

not be extensive users of Federal data.

States have the technical capacity to improve their State

data reporting systems. In order to improve the State systems.

a number of changes are needed. Such systems must be uniform

and standardized in terms of their data elements, definitions,

formatting, timeframes, and types of data (Rice, 1983).

Methods for collecting, cleaning, coding, calculating, and

preparing the data must also be standardized. Ideally, data

systems should be linked across programs (i. e., population,

social services, aging, Medicaid, and vital statistics) at the

State level and with other data at the Federal level (i.e.,

Social Security and Medicare data). The data systems should

not only serve as payment mechanisms but be effective as

tools for management and evaluation. Standard methods should

be developed to ensure access and privacy. Methods for making

corrections and updating are important. More importantly,

some ways to assure quality and timeliness are vital.

The ideal administrative data system would have both

aggregate and individual client data. The systems described

have focused primarily on aggregate cross-sectional data because they are available. Individual client data would allow

for different types of policy analysis and research over time.

Such data could build a continuous history for individuals

over time and allow for distributional studies of eligibility,

utilization, access, and expenditures by programs. If such

data could be linked across programs (e.g., for social services,

aging, Medicaid, and Medicare), studies of cross-program

and policy issues could be conducted. These data would be

of great value for describing, explaining, and predicting program activities. It would be difficult to build such a detailed

data system by any other method. This type of system would

not necessarilyy cost more than the data systems already established in the Medicaid and Medicare programs, but would

require uniform definitions, coding, formatting, and data collection across programs and across States. Costs would be

incurred in establishing data systems for social services and

aging in States which do not have such systems.

Voluntary State data systems have not proven effective.

The mandatory reporting system established by Medicaid,

in spite of its many deficiencies, is far more effective than

183
the social service and aging data systems. If States are not

mandated to report, they will not have a real incentive to

improve their systems. If mandatory requirements are established, some type of real penalties would have to be developed

for States that fail to comply with the basic requirements.

Such penalties should have both intermediate and severe sanctions that can be invoked for noncompliance. Mandatory reporting requirements by the Federal Government would require

statutory changes within each program.

The most difficult aspect of designing a rational system

is in obtaining the political support from the President and

Congress to make these changes. Data systems do not have

natural organized political constituencies, except perhaps those

who are directly involved in data collection activities.

Policymakers would be more sympathetic to imposing mandatory Federal reporting requirements on the States if States

could be provided the financial resources for such activities.

Given the current fiscal crisis and the large budget deficit

at the Federal level, the likelihood of giving substantial financial resources to States to develop improved data systems

is low. State governments continue to have their own financial

limitations and generally do not view the establishment of

a national data base for State data as a high priority, and

certainly not one they would be willing to finance without

Federal support.

The most important factor relative to data quality, timeliness, accuracy, and detail is to provide adequate financial

resources for the programs at the Federal level. In order to

justify additional Federal expenditures to improve State data

systems, some cost effectiveness would have to be demonstrated. No study has looked at the direct and indirect costs of

the voluntary, decentralized, and chaotic systems that currently

exist. The amount of waste, duplication, and ineffectiveness

resulting from the complexity and fragmentation in the existing

systems has not been documented. The financial costs and

personnel hours consumed by the use of manual systems or

inadequate computerized systems also need to be examined.

The indirect costs to the public and private sectors due to

poor planning and management, waste, and inefficiency are

another consideration. Although it is clear that the current

data systems at the State level are not satisfactory, an analysis

of the cost effectiveness of redesigning and improving the

systems is needed before policy recommendations for change

will be taken seriously.

184
References

American Public Welfare Association: A Statistical Summary of/he

VCL$ Social Services Block Grant (SSBG) Data. Washington, D.C.

American Public Welfare Association, July 1984.

Ft’dt*r(ilRe<qi.~ter:Vol. 45, No. 142, July 22, 1980.

Federal Re,qis!er: Vol. 28, No. 228, Nov. 25, 1983.

Gmwral Accounting Office: Conditions of Older People, National

Information System Nccdcd. Report to the Congress. Washington,

D. C., Sept. 20, 1979.

General Accounting Office: Federal Oversight of State Medicaid

Management Information Systems Could be Further Improved. Report

to the Secretary of Health and Human Scrviccs. Washington, D. C.,

hdy 30,1982.

Gilbert, N.: The transformation of social service. Social Service

Review, 51(4):629-41, Dec. 1977.

Hwrington, C., et al.: cidl~ornia. SIate Discretionary Policies utul

.$(v-}~itesin the Medicaid, Social Sert~ice.v, and SupplementuI Security

Imwne Pro,qtwn.s. San Francisco. Aging Health Policy Center, University of California, San Francisco, 1983.

Hcinbcrg, J., and G. Adlcc Evuluufion Options for Medicaid, Technical Appendix, Review of HCFA Data Sources. Urban Systems Research and Engineering, Inc. and Systcmetrics, Inc. DHHS Contract

No. HH5–1OO-8 1-0026, NOV. 1982.

Lindcman, D. A., and A. Pardini: Social Services, The Impact

of Fiscal Austerity, in C. L. Estcs ct al., eds., Fiscal Aus/erity

und,4gintq. Beverly Hills. Sage Publications, 1983.

Mclnick, D.: Recent C’hunges in the Federal Government’s Statistical

Pro,qrums, An 0~’erview’ of (he President’s Budget for Fiscal Year

1983 und Analysis of the Departments of Energy, Labor and the

Bureuu of (he Census. Washington, D.C. Congressional Research

Scrvicc, the Liberty of Congress, Apr. 8, 1982.

National Association of State Units on Aging: Funding Sources

und E.rpenditure Patterns of Stute and Areu Agencies. Washington,

D,C. National Association of State Units on Aging, July 1983.

National Association of State Units on Aging: Telephone interview

with staff, June 4, 1985.

Nutional Center for Health Statistics: Slate Es/itnates of Disabi/ify

and Utilization of Medical SerL’ices, United States, 1974–76. DI-H%

Pub, No. (PHS) 78-1241. Hyattsville, Md. Public Health Service.

1978,

Nationid Center for Health Statistics: Directions of fhe /980’s, Final

Report of the Ptmt4 to E\’aluute the Cooperative Health Statistics

System. DHHS Pub. No. (PHS) 80-1204. Hyattsville, Md., Aug.

1980.

Nistiomd Center for Hcidth Statistics: Directors” Meeting, Dcsignistmi State Centers for Health Statistics. Sponsm-cd by the Office

of Program Planning. Evaluation. and Coordination of the National

Center for Health Statistics. Unpublished paper. Atlanta, Oct. 26-

X). 19~=)~.

National Center for Health Statistics: Report of the Working Group

on the Completion of Vital Statistics Cooperative Program. Unpublished paper. Hyattsville, Md., May 21, 1982b.

National Center for Health Statistics: MuItiplc causes of death in

the United States. Monthly Vital Statistics Report. Vol. 32,

No. 10. Supp. 2. Public Health Service. Hyattsvillc, Md., Feb.

17, 1984a.

National Center for Health Statistics: Health characteristics by geographic region, large metropolitan areas, and other places of residence, 1980-81. Vilal and Heahh S~atistics. Series 10, 146. DHHS

Pub. No. (PHS) 84-1574. Public Health Service. Washington. U.S.

Government Printing Office, Sept. 1984b.

National Center for Health Statistics: Advance report of final mortality

statistics, 1982. Monthly Vital Statistics Report. Vol. 33, No. 9.

Public Health Service. Hyattsville, Md., Dec. 20, 1984c.

National Center for Health Statistics: Telephone interview with staff,

June 19, 1985a.

National Center for Health Statistics: Births, marriages, divorces,

and deaths for March 1985. Monthly Vital Statistics Report.

Vol. 34, No. 3. Public Health Service. Hyattsville, Md., June 21,

1985b.

National Governors’ Association: Telephone interview with staff,

June 7, 1985.

National State Data Center Steering Committee: Position Paper on

1990 Census and State Data Centers. Unpublished draft. Washington,

D. C., Sept. 10, 1984.

National State Data Center Steering Committee: National State Data

Center Memorandum, No. 3. National State Data Center Steering

Committee, Apr. 1985.

Office of Human Development Services: Analysis of Fiscal Year

1983 Social Services Block Grant Pre-expenditure Reports. Unpublished report. Washington, D.C. Office of Policy Coordination and

Review, May 1983.

Office of Human Development Services: Summary of Information,

Social Services Block Grant Pre-expenditure Reports, Fiscal Year

1984. Unpublished report. Washington, D.C. Office of Policy Coordination and Review, May 1984.

Office of Human Development Services: Telephone inverview with

staff of the Office of Policy Coordination and Review, June 5,

1985.

Rice, D. P.: Health care data in the United States. Revie}i’ of Public

Da[a Use. 11(22):79-84, 1983.

Sawyer, D., M. Ruther, A. Pagan-Berlucchi, and D. N. Muse:

The Medicare and Medicaid Data Book, 1983. Office of Research

and Demonstrations, Health Care Financing Administration. HCFA

Pub. No. 03156. Baltimore. Dec. 1983.

Social Security Administration: Current operating statistics. Sot.

Secur. Bull. 46 (6), June 1983.

185
Storey, J. R.: Recent Changes in the Availability of Federal Data

on the Aged. W ashin.gton, D. C. The Gerontological Society of Ameri- ‘

ca, Feb. 11, 1985.

U.S. Bureau of the Census: Demographic aspects of aging and the

older population in the United States. Current Population Reports:

Special Studies. Series P–23, No. 59. Washington. U.S. Government

Printing Office, May 1976.

U.S. Bureau of the Census: State and local agencies preparing population estimates and projections, Survey of 1975-76. Current Population Reports: Population Estimates and Projections. Series P–25,

No. 723. Washington. U.S. Government Printing Office, June 1978.

U.S. Bureau of the Census: Census Catalog 1982-83. Washington.

U.S. Government Printing Office, Sept. 1983.

U.S. Bureau of the Census: Estimates of the population of States,

1970 to 1983. Current Population Reports: Population Estimates

186

and Projections. Series P–25, No. 957. Washington. U.S. Government Printing Office, 1984.

u .S. Bureau of the Census: Methods Used in County Estimates

for the 1980’s. Unpublished table, 1985a.

U.S. Bureau of the Census: Estimates of the population of Missouri

counties and metropolitan areas, July 1, 1981, 1982. and 1983.

Current Population Reports: Local Population Estimutes. Series

P-26, No. 83-25-C, Jan. 1985b.

U.S. Bureau of the Census: Tclcphonc interview with staff, June

17, 1985c.

Woolsey, T. D.: Questions and Answers on NCHS Programs. Unpublished memo. Bethesda, Md., Mar. 1, 1985.

*.IJ. S. GoVERNMENT PRINTING Off ICE :19 E8- 241-259 :EIO03

k
Vitaland HealthStatistics

series descriptions

SERIES 1

SERIES 2.

SERIES 3.

SERIES 4.

SERIES 5,

SERIES10

SERIES 11,

SERIES 12.

SERIES 13

Programs and Collection Procedures-Reports descnbmg the

general programs of the National Center for Health Statrsbcs

and its offices and divisions and the data collecbon methods

used. They also include defmmons and other material necessary

for understanding the data.

Data Evaluation and Methods Research-Studies of new statlstIccIl methodology including experimental tests of new survey

methods, studies of wtal statistics collection methods, new analytical techniques, ob)ective evaluations of rehablhty of collected

data, and contributions to statistical theory, Studies also include

comparison of U.S. methodology with those of other countnes.

Analytical and Epidemiotogical Studies-Reports presenbng

analytical or mterprebve studies based on wtal and health statlstms, carrying the analysis further than the expository types of

reports m the other series.

Documents and Committee Reports-Fmal reports of malor

committees concerned with wtal and health statisbcs snd documents such as recommended model wtal registration laws and

revised birth and death ceriiflcates.

Comparative International Vital and Health Statistics Reports-Analytical and descriptive reports comparing U.S. vital

and health statistics with those of other countnes.

Data From the National Health Interview Survey-Statmtlcs

cm tllness. accidental mjunes. dlsab!hty, use of hosp!tal. medcal,

dental, and other serwces, and other health-related topfcs, all

based on data collected m the contmumg national household

mtetvlew survey.

Data From the National Health Examination Survey and the

National Health and Nutrition Examination Survey-Data from

direct examination, testing, and measurement of national samples

of the chdian noninstitutionalized population provide the basts

for (1) estimates of the medically defined prevalence of spectic

dmeases m the United States and the d!stnbutlons of the population

with respect to physical, physiological, and psychological characfenstlcs and (2) analysis of relationships among the various measurements wdhout reference to an exphc!t finite umverse of persons.

Data From the Institutionafized Population Surveya-Dscontmued m 1975. Reports from these surveys are Included m Series

13.

Data on Health Resources Utilization-Statlstlcs on the uthzatlon of health manpower and fachtles prowdmg long-term care,

ambulatory care, hosptal care, and famdy planning serwces.

SERIES 14

SERIES 15

SERIES 20

SERIES 21

SERIES 22.

SERIES 23

Data on Health Resources: Manpower and Faci!ffes-Statjs

t!cs on the numbers, geographic dlstnbutlon, and charactenst[cs

of health resources mcludmg physicians, dentists, nurses, other

health occupations. hosplals, nurs!ng homes, and outpabent faC]lltles

Data From Special Sumeys-Stattstlcs on health and healthrelated topics collected in special surveys that are not a part

of the cormnumg data systems of the National Center for Health

Statlstlcs

Data on Mortafiiy-Var!ous statmtm on mortality other than

as included m regular annual or monthly reports Special analyses

by cause of death. age, and other demographic variables: cJeographlc and time series analyses, and Sta!lst!cs on charactemstrcs

of deaths not available from the wtal records based on sample

surveys of lhose records

Data on Natafifiy, FMarriage, and Divorce-Var[ous stat[stlcs

on natallty, marriage, and dworce other than as included m regular

annual or monthly reports Spec[al analyses by demographic

variables: geographic and f!me series analyses; studies of fertlllty;

and stat! stlcs on charactensbcs of brths not available from the

wtal records based on sample suweys of those records.

Data From the National Mortality and Nata!ity Survey+Dtscontmued In 1975. Reports from lhese sample suweys based

on wtal records are included m Ser[es 20 and 21, respectwely

Data From the National Survey of Famiiy GrowtkStatmt[cs

on femhty, famdy formatmn and d!ssolutlon, fam!ly planmng, and

related maternal and infant health toptcs derived from a penod!c

survey of a natromwde probab!hty sample of women 15-44 years

of age

For answers to questions about th!s reporl or for a list of t!tles of reports

pubhshed m these series, contact

Sclentiflc and Techmcal Information Branch

National Center for Health Statmtlcs

Centers for Disease Control

Pubkc Health Serwce

Hyaftswlle, Md. 20782

301-436-8500
U.S. DEPARTMENT OF HEALTH AND

HUMAN SERVICES

Public Health Service

Centers for Oisease Control

National Center for Health Statistics

3700 East-West Highway

Hyattsville, Maryland 20782

OFFICIAL BUSINESS

PENALTY FOR PRIVATE USE, $300

PRESORTED

SPECIAL 4TH CLASS RATE

POSTAGE & FEES PAID

PHSINCHS

PERMIT NO. G-281

DHHS Publication No. (PHS) 89-1488, Series 4, No. 25