The Use of Preventive Services by the Elderly

January 1989

NTIS order #PB91-192880
FOREWORD

Interest in health promotion and disease prevention strategies for the elderly has grown in

the last ten years, partly as a result of advancing knowledge in these areas and partly due to the

search for ways to moderate the rising cost of health care in this growing segment of the population. Reflecting this interest, the House Committee on Ways and Means requested that OTA

analyze the effectiveness and costs of providing selected preventive health services to the elderly

under the Medicare program. The Senate Labor and Human Resources Committee has also requested that OTA provide information on the value of preventive services for the American

people.

This Staff Paper, The Use of Preventive Services by the Elderly, is the second in a series of

papers being prepared in response to these requests. Understanding the use of preventive services by the elderly is an important component of assessing their effectiveness as Medicare

benefits. In this paper we review both new and previously published data on the proportions of

elderly currently receiving a variety of preventive health services; we examine factors associated

with whether the elderly receive these services; and we analyze the likely implications for

Medicare if preventive health services were offered as covered benefits.

The first paper in this series on “Preventive Health Services Under Medicare” examined

glaucoma screening as a potential- Medicare benefit. Subsequent papers will assess screening for

cholesterol, cervical cancer, and colorectal cancer, and will analyze broad issues related to

Medicare financing of preventive services for the elderly.

u JOHN H. GIBBONS

Director
The Use of Preventive Services by the Elderly

by

Michael E. Gluck

Judith L. Wagner

Brigitte M. Duffy

Health Program

Office of Technology Assessment

Congress of the United States

Washington, D.C. 20510-8025

January 1989

A Staff Paper

in OTA’s Series on

Preventive Health Services Under Medicare

Carol Guntow prepared this Staff Paper for publication.

The views expressed in this Staff Paper do not

necessarily represent those of the Technology

Assessment Board, the Technology Assessment

Advisory Council, or their individual members.
CONTENTS

Chapter Page

1. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ....... . 1

2. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 5

Types of Preventive Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Preventive Services and Medicare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3. Recommendations of Professional and Expert Groups for the Use of

Preventive Services by Older Adults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

4. What Percentage of the Elderly Use Preventive Services? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Sources of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Estimates of Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Use of Multiple Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Time Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

5. What Factors Promote or Inhibit Elderly People’s Use of

Preventive Services?● . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Theoretical Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Evidence on Patient Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Evidence From Studies of Health Care Provider Behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

6. Implications for Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Potential Medicare Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Delivery of Preventive Services for the Elderly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Appendix

A. Advisory Panel-- Project on Preventive Health Services Under Medicare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

B. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

C. Empirical Studies of the Use and/or Determinants of Use of Preventive

Services by the Elderly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

D. Empirical Studies of the Use and/or Determinants of Use of Preventive

Services by the Non-elderly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

E. OTA Analysis of Preventive Service Use By the Elderly Using Data from

the 1982 National Health Interview Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

F. Analysis of Preventive Service Use By Older Adults in a Health

Maintenance Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ......... 59

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ......... 60

Tables

Table Page

1. Selected Potential Clinical Preventive Services for the Elderly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2. Published Recommendations for the Use of Selected Preventive Services

by Older Adults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3. Percent of Elderly People Receiving Preventive Services Within Specified

Periods of Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4. Percent of Persons Over 65 Using Multiple Preventive Services from

the 1982 National Health Interview Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

ii
CONTENTS (cent’d)

5.

6.

7.

8.

9.

10.

11.

12.

13.

14.

Trends in the Percent of Adults or Older Adults Ever Having

Received Selected Preventive Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Selected Factors Hypothesized to Affect Use of Preventive Services by

the Elderly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Significant Predictors of Use from OTA Multivariate Logit Analysis of

1982 National Health Interview Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Periods of Time Used by OTA and Two Studies to Measure Older Adults’ Use

of Preventive Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

1982 National Health Interview Survey: Selected Descriptive Statistics

for Persons Over 65 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Elderly Use of Five Screening Services: Logistic Regression Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Correlation Matrix for Variables in Logistic Regression Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Effect of Statistically Significant Binary Variables in Logistic

Regressions on Elderly Use of Five Screening Services: Predicted

Probabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Sample Sizes for Each Measurement Period in OTA’s Analysis of Preventive

Service Use in One HMO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Percents of Continuously Enrolled HMO Members Receiving Eight Preventive

Services During Specified Periods of Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Figure

1. Effect of

2. Effect of

3. Effect of

4. Effect of

Page

Age on Use: Predicted Probabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . , 54

Income on Use: Predicted Probabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Education on Use: Predicted Probabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Bed Days on Use: Predicted Probabilities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

iii
1. SUMMARY

In recent years, clinicians, academics,

and policy makers have begun to examine the

potential benefits of services to promote

health or to prevent disease, disability, or

death in the elderly. Although Medicare, the

Federal program responsible for paying the

bulk of the noninstitutionalized elderly’s

health care bills, currently pays for few preventive services, Congress has several

proposals pending to expand Medicare

coverage of these procedures. In this paper,

OTA examines the implications of potential

Medicare coverage for the use of preventive

services by analyzing current use and the

determinants of that use.

How Many Elderly Use Preventive

Services?

OTA found few sources that measure the

use of preventive services by the elderly.

The data that are available (summarized in

table 3 in the text of the paper) suggest two

main

■

•

conclusions:

The use of preventive services by the

elderly varies according to the type of

service from a low of 20 to 30 percent

for routine fecal occult blood testing in

some sites to a high of 93 percent for

blood pressure measurement. These differences cannot be explained by differences in the periods of time over

which use is measured.

Rates of use of specific services show a

high level of consistency across studies,

despite differences in methods.

Trends in available data suggest that the

use of these procedures has increased over the

last 15 years. Data also indicate that if an

elderly person receives any preventive services, he or she is likely to receive multiple

services.

Which Elderly Use Preventive

Services?

Studies to isolate factors associated with

the use of preventive services fall into two

categories:

• those that focus on the behavior of

patients, and

● those that focus on the behavior of

health care providers and organizations.

Most of the studies in both of these categories examine preventive service use among the

nonelderly. An analysis of data for the over65 population in the 1982 National Health Interview Survey (NHIS) found that, controlling

for other factors, the probability that an

elderly person used each of five selected preventive services--glaucoma screening, eye exams,

ares,

to:

•

●

■

•

■

■

■

blood pressure measurement, breast exand Pap smears-- was consistently related

being male (for the three services available for both men and women),

being younger (although still over-65),

having more education,

having greater family income,

having some health insurance in addition

to Medicare,

living in a metropolitan area, and

having spent more days in bed during

the previous year.

OTA found that receiving health care through

a prepaid health plan was not related to the

use of any preventive service. However, so

few people in the study sample belonged to

prepaid plans that it may not have been possible to find a statistically significant effect.

Race, living alone, and having some limitation in activity had no clear or consistent effect on the use of the five services studied.
2 • The Use of Preventive Services by the Elderly

Other studies of the relationship between

patient characteristics and the use of preventive services have had similar findings.

Among health care providers, physicians

play a key role in the provision of preventive

services. The evidence suggests that gaps exist between physicians’ knowledge and experts’ recommendations on the use of preventive services as well as betwee n

physicians’ knowledge or beliefs and actual

practice. These gaps may be more prominent

in relation to elderly patients. While they

may suggest a shortcoming in physicians’ performance, they could also indicate that

physicians take individual patients’ situations

into account when ordering preventive services.

Other insights into the importance of

health care providers in determining whether

the elderly receive preventive services come

from trials designed to improve compliance

with expert recommendations. These studies

indicate that health care organizations can

organize themselves to affect the percentage

of individuals receiving such services.

Strategies suggested in the literature worthy

of further study include:

targeting groups in need of prevention,

using non-physician medical professionals to deliver services, and

generating reminders to physicians and

patients about the periodic need for preventive services (especially with the aid

of computerized record-keeping systems).

Although OTA’s analysis of preventive service use showed that health maintenance organizations (HMOs) had no discernible effect

on elderly enrollees’ preventive activities, the

review of the literature on provider behavior

indicates that HMOs and other group practices with centralized administration and

record-keeping may have potential for increasing the use of such services.

Implications of Medicare

Coverage for the Use of

Preventive Services by the Elderly

The findings of this study have three

main implications for potential Medicare

coverage of preventive services:

■ Reducing patients’ out-of-pocket expenses for preventive services through

Medicare would probably increase the

percentage of elderly receiving preventive care. For four of the five services examined in detail by OTA, having some insurance coverage beyond

Medicare is associated with about a 10

percent increase in the

receiving each service.

However, there are three

finding:

(1) OTA’s analysis measured the

likelihood of

caveats to this

(2)

(3)

presence of insurance tha t

reduced patients’ total out-ofpocket health care expenditures,

not direct coverage of preventive

services. The effect of direct

coverage on use may be different

from the effect observed in

OTA’s analysis.

The association between insurance

and use may not always reflect a

direct cause and effect. Rather,

some people may be likely both to

buy supplemental insurance and

use preventive services out of

concern for their own health.

OTA’s analysis suggests that insurance coverage alone would not be

sufficient to induce many elderly

to avail themselves of preventive

services.
The Use of Preventive Services by the Elderly Ž 3

Medicare coverage of preventive services

may indirectly increase the use of preventive services by raising interest in

preventive care among non-Medicare

health care consumers, providers, and

payers. Such coverage would, in effect,

place the authority of the Federal Government behind the covered services.

OTA found no existing data to estimate

the existence or magnitude of this

potential effect.

Because large numbers of elderly people

already use preventive services, expansion of Medicare to cover preventive

procedures will represent an immediate

boost in the program’s financial obligations even if increases in use are minimal or nonexistent. While Medicare

may already pay for some screening services incorrectly labeled as diagnostic

procedures, Medicare coverage would

still transfer a large portion of the current costs of preventive services from

patients or other payers to the Federal

Government.

Other Implications for Policy

Among other factors important in

determining whether the elderly receive preventive services, a few such as gender, age,

education, income, rural or urban residence,

and bed days could be useful in helping

policy makers target educational efforts on the

need for preventive services to those elderly

at highest risk of not complying with expert

recommendations. The relationship between

use and educational level suggests that

policy makers should carefully consider the

media they employ to promote preventive

service recommendations, benefits, and other

programs they undertake. Pamphlets or other

materials that rely heavily on the written

word are not as effective for the less welleducated who also have a relatively higher

risk of not receiving preventive procedures.

Policy makers could consider using visual

media to communicate their messages to such

groups.

Some of the factors important to the

elderly’s use of preventive services, such as

income or educational level, are unlikely to

be the focus of policy efforts designed solely

to increase the use of preventive services.

However, changing these factors for some

other purpose might result in increases in use.

The analysis in this paper concentrates

on those services most often raised in congressional discussions of prevention under

Medicare--screening and immunizations. The

conclusions presented above may have limited

applicability to consideration of other preventive services such as health risk appraisals,

health education, counseling services, or prevention of disability among elderly suffering

from chronic disease.
2. INTRODUCTION

In 1984, personal health care expenditures for the 28 million Americans over the

age of 65 totaled $120 billion, nearly all of

which went toward the treatment of existing

conditions rather than to screening for or

preventing health problems (85). Recently,

however, policy makers, health advocates, and

medical practitioners have begun to focus

greater attention upon the potential of preventive medicine for the elderly. As the

elderly population has grown, physicians and

decisionmakers have looked to preventive services as a possible means of extending life,

reducing morbidity and disability, and controlling health care costs (67,50). Congress

has recently mandated studies of communitybased preventive health service programs for

the elderly and expanded Medicare coverage

of certain services, including screening mammography and some immunizations (34).

This paper has three purposes:

to summarize existing professional

recommendations for older adults’ use of

preventive health services,

to estimate the percentage of elderly

who currently use such services, and

to identify the factors related to elderly

individuals’ use of preventive care with

particular attention to the potential effects of Medicare coverage.

The information brought together in this

paper has two major policy implications.

First, in order to estimate the impact of

Medicare coverage of preventive services on

Medicare program expenditures, one must

know the number of potential users. While

current rates of use alone may not adequately

predict use under expanded third-party

financing of preventive services, examination

of existing literature and data provides insight into factors associated with use. In particular, such analysis reveals the relative importance of Medicare coverage in removing

barriers to use for elderly Americans.

Planners and administrators of disease

prevention for the elderly also benefit from

an analysis of current use. By understanding

those factors that affect whether older people

accept and receive preventive services, Congress may be able to target initiatives where

they will be most effective or most needed.

Where supported by the evidence, this paper

points out such implications for public policy.

Types of Preventive Services

The traditional taxonomy of prevention

distinguishes among primary, secondary, and

tertiary prevention (38,56). Primary prevention refers to activities designed to avoid

disease or other conditions that adversely affect health. Immunizations are one example

of primary prevention. Secondary prevention

includes efforts to identify existing conditions

that could cause illness and disability before

the appearance of clinical symptoms, or to

minimize the progression of disease. Disease

screening is one form of secondary prevention. Tertiary prevention refers to efforts

to control irreversible chronic conditions in

order to avoid disability or death. Kane, et

al., have suggested that this typology does not

adequately distinguish among preventive services, especially those targeted toward the

chronic conditions common among the elderly. For example, while diet change can be a

means of primary prevention of hypertension,

treatment of existing hypertension is also primary prevention of stroke.

To avoid such ambiguities, this paper

simply distinguishes among immunizations,

disease screening, and educational or counseling services. Table 1 lists specific examples

of each category of prevention. While the list

of services in table 1 is not exhaustive of all

preventive services applicable, it does include

the procedures examined in this paper.

5
6 ■ The Use of Preventive Services by the Elderly

Table 1.--Selected Potential Clinical

Preventive Services for the Elderly

Immunizations

■ Influenz a

■ Tetanus

■ Pneunococcua

■ Hepatitis B b

Screening

■

■

■

■

■

■

■

■

■

■

■

■

■

■

Cancer screening:

-Breast cancer (clinical examination ;

mammography)

-Colorectal cancer (occult blood stool;

sigmoidoscopy)

-Cervical and uterine cancer (clinical

examination; Pap smear; endometrial biopsy)

-Prostate cancer (clinical examination ;

ultrasound )

-Skin cancer (clinical examination )

Blood pressure measurement

Vision examination

Glaucoma screening

Hearing tes t

Cholesterol measurement

Mental status/dementia

Osteoporosis (standard x-ray; quantitative CT;

other radiological techniques )

Diabetes screening

Asymptomatic coronary artery disease (exercis e

stress test )

Dental health assessment

Multiple health risks appraisal/assessmen t

Functional status assessment

Depression screening

Screening for hyperthyroidism

Education and Counseling

■ Nutritio n

■ Weight control

m Sinking cessation

■ Home safety/injury prevention

■ Stress management

■ Appropriate use of medications

■ Alcohol use

■ Exercis e

Abbreviation: CT =computed tomography.

a

Currently covered by Medicare.

Currently covered by Medicare for high risk

patients .

SOURCE: Office of Technology Assessment, 1989.

Preventive Services and Medicare

In defining preventive services and

measuring their use, this paper focuses on the

implications for their potential coverage under Medicare.

This perspective limits the preventive interventions analyzed to personal health services

offered to individuals. This review does not

examine mass media education programs

targeted toward the elderly.

As enacted in 1965, Medicare covered no

preventive services. It paid for procedures on

a “diagnostic” basis only--that is, when the

patient has a symptom or a previous diagnosis

for a condition. However, because treatment

of most diagnosed conditions is covered,

Medicare does pay for much tertiary prevention designed to control existing chronic

conditions. In addition, some physicians

probably receive payment for screening services they incorrectly label as “diagnostic.”

The extent of this de facto coverage of prevention has gone unmeasured.

In recent years, however, Congress has

incrementally added coverage of some immunizations and screening services. These

include hepatitis B immunizations for beneficiaries at high risk of contracting the disease

and pneumococcal pneumonia vaccinations

for all beneficiaries. The Medicar e

Catastrophic Coverage Act of 1988 (Public

Law 100-360) includes coverage of up to $50

for biannual screening mammographies beginning in 1990. In addition, Congress has

mandated that the Health Care Financing Administration (HCFA) fund demonstrations of

influenza immunization coverage, and of

therapeutic shoes for diabetics, and several

community-based demonstration projects to

analyze health outcomes and costs associated

with the provision of screening, health risk

appraisals, education, and counseling to

Medicare beneficiaries,

Additional proposals brought before the

100th Congress included coverage of Pap

smear screening for cervical cancer and a

physical examination with medical history

upon enrollment in Medicare or on a periodic

basis.
3. RECOMMENDATIONS OF PROFESSIONAL AND EXPERT

GROUPS FOR THE USE OF PREVENTIVE SERVICES

BY OLDER ADULTS

One way to measure the use of preventive services is to compare the actual behavior of individuals with the frequency of

use recommended by expert groups.

Numerous groups have provided recommendations about the periodicity with which

the elderly should receive particular immunizations and screening services. In interpreting the medical evidence on frequency of

use, these expert groups vary in the criteria

they employ in developing recommendations.

Table 2 summarizes several selected sets

of recommendations made by professional or

expert groups for older adults, primarily for

those over 65 years old. The summary is not

comprehensive; rather it includes a range of

views on the use of preventive services analyzed in this paper.

The most comprehensive guidelines come

from the two governmental task forces. Over

the last ten years, the Canadian Task Force

on the Periodic Health Examination has analyzed medical evidence about the effectiveness of preventive services and made recommendations for Canadian citizens ( 18). The

U.S. Preventive Services Task Force has

engaged in a similar exercise and recently

published some of its findings. The task

force will publish its full report in 1989

(39,43,79).

Among other U.S. governmental organizations, individual institutes within the National Institutes of Health (NIH) have made

recommendations for cancer and coronary

heart disease screening (37,77,78). Some of

these recommendations result from intramural

efforts within NIH, while others are the product of consensus development conferences

that bring together experts and interested organizations. Additional guidelines come from

professional societies such as the American

College of Physicians (4,5), the American

Medical Association (68), the American

Academy of Ophthalmology (2), the American Optometric Association (9), the American College of Obstetrics and Gynecology

(42), and the American College of Radiology

(6) as well as health consumer organizations

such as the American Cancer Society (3), the

American Society to Prevent Blindness (10),

and the American Heart Association (8). As

table 2 indicates, there is nearly complete

agreement among the included groups making

recommendations for immunizations for the

elderly. For screening services there is a high

degree of consistency among groups, but

some disagreement does exist.

7
Table2. --Published Recommendations for the Use of Selected Preventive Services by Older Adults

Preventive Professional

service CDCa

Consumer

ACPbNIHc

CTFd

USPSTFe societiesf

organizations9

Tetanus Booster every 10 Booster every 10 Booster every 10 Booster every

immunization years if primary years years 10 years

series has been

done

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

Pneumococcal Over age 65--once Over age 65--once High risk Over age 65--

immunization patients--once once

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

Influenza Over age 65-- Over age 65-- Over age 65--

immunization every year every year every year

---- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ---,.. ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- -

Occult blood in NCI: over age ACS: over age

stool 50--every year 50--every year

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

Sigmoidoscopy NCI: over age ACS: over age

50--every 3-5 50--every 3-5

years years after 2

negative tests

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

Digital rectal NCI: over age Not recommended ACS: over age

exam 40--every year for prostate 40--every year

cancer; no

recommendation

for enlarged

prostate

screening

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

Clinical breast Considered in NCI: over age Every year from Over age 40-- ACR: over age 35-- ACS: over age

examination conjunction with 50--every year age 50 to 59 every year every year (with 40--every year

mammography monthly breast self- (with monthly

examination) breast selfACOG: advises examination)

following ACS

guidelines

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

Mammography Says screening NCI: over age Between ages 50 Over age 50-- ACR: over age 50-- ACS: over age

with mammography 50--every year and 59--every every year every year 50--every year

is effective; year ACOG: advises

does not specify following ACS

frequency or when guidelines

to start; says AMA: between ages 40

screening women and 49--every one to

aged 50 to 59 two years; age 50

saves lives and over--every year
Table 2. --Published Recommenda tions for the Use of Selected Preventive Services by Older Adults (Continued)

Preventive Professional Consumer

service CDCa

ACPbNIHc CTFd

 USPSTFe

societies f organizations

Pap smear MCI: over age 18 Every 5 years ACOG, AMA, ANA, ACS: supports

or if sexually from age 35 to AAFP, AND AWJA: NCI guidelines

active--3 age 60; screening support NCI

consecutive should continue guidelines

annual Pap smears if prior smears

and pelvic exams have been

with negative abnormal

results, then

less frequently

at discretion of

physician

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

Cholesterol NHLBI: over age

screening 20--every 5 years

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

Serum glucose Not recommended

without family

history of

diabetes or

previous

circulatory

problems

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

Blood pressure NHLBI: over age Over age 65--

18--at least every 2 years

every 2 years,

depending

previous reading

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

AHA: supports

NHLBI

recommendations

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

ADA: people at

risk should be

screened (no

frequency

specified)

AHA: every 5

years from age

20 to 75;

optional after

age 75 if

baselines are

uell-documented

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

AHA: every 5

years starting

at age 20

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

EKG Recommended for AHA: at ages 20,

symptomatic 40, and 60

adults only

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

Vision Not recommended AOA: over age ASPB: over age

examination 40--every year 35--every 2 years

including MO: over age

glaucoma 40--every 2 to 5

screening by

tonometry

years

---- ---- ----- ----- ----- ---- ----- --.-- ----- ----- ---- ---- ---- ---- ----- ----- ----- ---- ----- ---- ----- ---- ----- ----- ---- ---- ----- ----- ---- ---- ---- ---- ----- ----- -
10 H The Use of Preventive Services by the Elderly

,,

II

a

L

0
4. WHAT PERCENTAGE OF THE ELDERLY USE

PREVENTIVE SERVICES?

While a large empirical literature exists

on the use of medical services in general, few

studies concentrate on preventive services and

fewer still analyze use of these services by

the elderly .

1 Apart from the analyses presented for the first time in this paper, only

nine studies offer empirical evidence about

the use of preventive services among older

adults. Appendix C summarizes the scope

and methods of each of these studies.

Because of the small volume of research

examining preventive service use by the

elderly, this paper also draws upon empirical

investigations of use by the non-elderly. Appendix D describes 35 studies in this

category. Several of these studies examine

how age affects the use of preventive services

and offer insight into the behavior of older

patients in seeking out such care.

Sources of Data

Table 3 presents comparative estimates of

the percentages of elderly people using 17

preventive services within specified periods

of time. Three of the seven sources contain

national estimates. OTA analyzed data from

the 1982 National Health Interview Survey

(NHIS). (See appendix E for a description of

the NHIS.) Results of this analysis, showing

the percentage of the elderly who have

received five preventive services--glaucoma

screening , eye exams, blood pressur e

measurement, breast exams, and Pap smears --

within periods of time roughly similar to the

intervals suggested by expert groups are

shown in the sixth column of the table.

1 Many studies have been carried out on the use and

correlates of use of non-preventive medica[ services and dental care by the elderly. However,

because the purpose, nature, and likel y

determinants of use of these services diffe r

markedly from those of preventive health services,

such studies are not reviewed in this paper.

The second source of national estimates

in table 3 comes from a survey conducted by

the Gallup Organization every 3 or 4 years

for the American Cancer Society (ACS). This

household, mail survey examines individuals’

knowledge of cancer risk factors and the frequency with which they receive certain

screening tests (28). Gallup publishes results

by gender, age, and selected demographic

variables. Although the study does not present findings for Medicare-eligible respondents as a separate group, it does give results

for individuals over 50 years old.

These two studies rely on respondents’

self-reported behavior, which may affect the

accuracy of the estimates. The direction of

this potential bias is unclear. On the one

hand, lack of familiarity with medical services may cause respondents not to know that

they had received a given service, and hence,

to underreport use. On the other hand,

respondents may perceive preventive behavior

to be socially desirable and may inflate the

use they report to the interviewer. The relative importance of each of these biases in affecting the estimates is unknown.

The third national data source is the U.S.

Immunization Survey conducted annually by

the Centers for Disease Control (CDC) until

1985. A household survey, it provides data

on the percentages of individuals immunized against influenza and pneumococcal

pneumonia, broken down by age (including

people over 65)(26).

Another set of estimates, found in the

seventh column of table 3, comes from a

large, urban “closed-panel”* health maintenance organization (HMO). This HMO pro2 In a ‘closed panel” HMO, enrollees must receiv e

health care from a physician employed directly b y

the HMO usually in a clinic run by the organiza -

tion .

1 1
12 Ž The Use of Preventive Services by the Elderly

Table 3--- Percent of Elderly People Receiving Preventive

Services Within Specified Periods of Time

Studya

Servic e ACSb

BrowncCDCd

ChaoeLazaro f OTAg

OTAh

Rundall i Winawerj

Blood pressure

Men 93 91

Women 93 9 2

-------------------------- ........ ....... w

k

..... ...-...... :

2

.!:?. . . . . . . . . . . -------- -

. . . . . . . . . . . . . . . . . . . . . . . .

Breast exam

Women 48 53

(I<yr )

(<2 yr)

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

Breast self-exam

Women

Monthly or more frequently 3 7

Less frequentl y 3 9

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

Cholesterol

Men 75

Women 73

(5yr )

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Complete check-up By age, not sex:

Men 62 60-74=45 4 9

Women 6 7 75+=24 51

(<l yr) (WA ) (1 yr)

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

Eye examination

Men 73 72

Women 76 75

(<3 yr) (2 yr)

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

Fecal occult blood

Men 20 30 4 9

Women 19 29 52 70-8 0

.. ......-...............!!. !!?.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... .. -:: -![!...: N

!

A

?... ------ -

Total

. .

Glaucoma screening

Men 64

Women 70

(<3 yr)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Influenza vaccin e

Men 23 58

Women (both sexes; 5 7

1 yr) (1 yr)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Mammography

Women 6 11

(ever) (1 yr)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Pap smear

Women 60 63 50 71

(<=3 yr) (1 yr) (<4 yr) (3 yr)

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

Pneumococcal vaccine

Men 11 3 8

Women (both sexes; 3 0

1 yr) (ever)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

(Cont’d)
The Use of Preventive Services by the Elderly Ž 13

Table 3--- Percent of Elderly People Receiving Preventive

Services Within Specifed Periods of Time (Cent’d)

stud y

a

Service A C S

b

BrowncCDCdChaoeLazarof OTAg

OTAhRundall i Winawerj

Procto exam

Men 15

Women 12

(1 yr)

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

Rectal exam

Men 28

Women 28

(1 yr)

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

Sigmoidoscopy

Total 95

(N/A)

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

Swine flu vaccine

Sample estimate 72

Population est. 6 3

(N/A)

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

Tetanus vaccine

Men 30

Women 26

(10 yr)

Abbreviations: HMO = health maintenance organization; NHIS = National Health Interview Survey .

~Fu(( descriptions of methodology of each study can be found in table 4.

Ga((up Organization, IiThe 1987 survey of Public Awareness and Use of CanCer Detection Tests: SU’IWM ry of Findings,ll Conducted for the American Cancer Society (princeton, NJ: Gallup Organization, January 1988); n=952;

age=50+.

CJ.T. Brown and B.S. Hulka, ItScreening Mamgra@y in the Elderly: A Case-Control Study,” J. Gen. Intern. h4edicine

d 3:126-131, 1988; n=309; age=60+.

D.S. Fedson, llInf[uenza ad pnemcoccal Invrnmization Strategies for Physicians,it Chest 91:436-443, 1987; n=not

given; age=65+.

‘A. Chao, A. Paganini -Hill, R.K. Ross, et al., Illjse of preventive Care by the Elderly,” Preventive Medicine

~ 16:710-722, 1987; n=ll,888; age range=48--100; mean age=74.4.

C.M. Lazaro, D.N. Logsdon, and R. Meier, IIljti(ization of preventive Health Services by the Elderly,” Insure

Project, Lifecyc(e Preventive Health Services, New York, NY, presentation to the American Pyschological Association Convention, Aug. 31, 1987, New York, NY; n=713; age=60+. Use rates from Lazaro study are proportions

of ail persons invited to receive checkup uho actually received the service. Proportions of persons accepting the

invitation who actually received the checkup are as follows: ages 60-74=.65; ages 75+=.53.

goffice of Technology Assessment/NHIS, 1988a; n=11434; age=65+.

‘Office of Technology Assessment/HMO, 1988b; n=5394 for checkup, influenza, and fecal occult blood; 3371 for eye

exams and Pap smears; 2322 for cholesterol; and 894 for pneunococcal and tetanus; age=65+.

‘T.G. Rundall and J.R.C. Wheeler, ~lFactors Associated Uith Utilization of the Swine Flu Vaccination program Among

Senior Citizens in Tompkins County,” Medical Care 17:191-200, 1979; sample n=232; population N=5000; age=65+.

js.J. Uinawer, M. Baldwin, E. Herbert, et al., IIScreening Experience With Fecal Occult Blood Testing as a Function of Age,n in Prospectiveson Prevention and Treatment of Cancerin the Elderly, R. Yancik (cd.) (Neu York, NY :

~ Raven Press, 1983); n=21,961; age=40+.

Notation in parentheses indicates period of time over which use was measured.

Key for parenthetical notations:

y r = year or years

< = up to but not including

<= = up to and including

N/A= not applicable; study is a single trial conducted over a finite period.

SOURCE: Office of Technology Assessment, 1989.

L ,—~ ,,, - 4 -
14 Ž The Use of Preventive Services by the Elderly

vialed OTA with data on the percentages of

adults in various age categories who received

each of nine preventive services within periods of time specified in table 8. (See appendix F for a more complete discussion of the

data and estimation methods.)

Unlike the three national surveys, these

estimates come directly from the provider’s

records, thus avoiding the potential inaccuracies of self-reported data. However,

the population from which the HMO data are

drawn is probably not representative of the

national experience or even of other HMOs.

The elderly enrolled in this single prepaid

plan may be different from the total elderly

population in the HMO’s market area as well

as the elderly population of other areas. In

addition, HMOs in general tend to provide

better coverage of preventive services than do

other insurance plans (46). ‘ This HMO in

particular engaged in activities to promote the

use of some preventive procedures. All of

these potential distortions suggest that

estimates from this HMO are probably indicative of the upper bound of use attainable

under Medicare coverage rather than national

estimates of current use.

In the four remaining sets of data presented in table 3, estimating use was not the

authors’ primary objective. One paper was a

case-control study of breast cancer in elderly

women (15). Another looked at the relationship between screening and disease prognosis for colorectal cancer. The third

examined factors associated with swine flu

vaccination during the predicted epidemic of

1977 and 1978 (59), and the fourth presents

self-reported data from a retirement community about respondents’ most recent use of

five preventive services (19).

Estimates of Use

Because of some overlap in the services

examined in the seven studies discussed

above, one can compare different estimates of

use of the same services. These procedures

are general examinations, fecal occult blood

screening, mammography, breast examinations, Pap smears, eye examinations, and

blood pressure checks. For four services, the

estimates of use are consistent across data

sources. About 92 percent of the elderly

report having their blood pressure checked

within a l-year period and 74 percent report

eye examinations within the previous 2 years.

Although estimates for Pap smear use show a

bit more variation across studies, the range

runs only from about 50 percent of elderly

people in the NHIS sample to 71 percent in

the HMO data.

Differences in the periods of time over

which researchers measure use do not account

for the variation in estimates that does exist.

For example, the ACS estimate of Pap smear

use within a 3-year period is actually higher

than the NHIS estimate that examines a period of up to 4 years. Hence, these differences

reflect either different populations or different survey methods.

Despite some consistency across studies

for the same service, there is little similarity

in rates of use across different services. For

example, while less than 15 percent of the

elderly report having had annual rectal exams, 92 percent report an annual blood pressure check. Estimates for the remaining services fall within this wide range. These

drastic differences in rates of use suggest that

preventive services are more different from

one another than they are alike. Several

studies discussed later in this paper have examined these differences.

Use of Multiple Services

Measuring the percentage of elderly individuals who receive multiple preventive

services provides a slightly different profile

of individuals’ preventive behavior than is

revealed by examining one service at a time.

As indicated in table 4, a majority of elderly

persons report receiving all three services that

both sexes can receive (glaucoma screening,

eye exams, and blood pressure measurement).

One-quarter of men and one-fifth of women
The Use of Preventive Services by the Elderly Ž 15

report receiving one or fewer of the three

services. The extremely small percentages

who report using no services reflect the almost universal measurement of blood pressure. Looking only at women and including

the two additional services they can receive

(Pap smears and breast exams) reveals that

only 13 percent report using none or one service. However, only 30 percent report using

all five services. These data indicate a great

deal of variation in the number of services

elderly people receive. In the only other

study to examine multiple preventive service

use, Calnan found that among middle-aged

women, the probability of using one service

does not predict whether an individual uses

others (17).

Time Trends

The differences in trends between the

ACS

sible

●

■

and NHIS data sources have several posexplanations:

Only Pap smears and breast exams overlap the NHIS and ACS surveys. The

differences between the two data sources

could be due to different trends in the

particular services each survey examined.

NHIS estimates are for individuals over

65 years old, while the ACS data are for

wider age ranges. If the trend in the

NHIS applies only to the elderly, the inclusion of non-elderly people in the

ACS samples might obscure this trend.

Table 4--- Percent of Persons Over 65 Using

Multiple Preventive Services (From the 1982

National Health Interview Survey)a

The ACS and NHIS data allow examination of time trends in self-reported use of

several services over the period from 1973

through 1987. As shown in table 5, the percentage of older Americans who report ever

having received these preventive services

grew over the periods measured. Using

identical questionnaires, the NHIS showed

substantial increases in the use of seven services between 1973 and 1982 (72,75).

The trends in the ACS data are not quite

as dramatic (28). Some procedures show little

change between 1980 and 1983 with five services showing a decline in use. The declines

between 1980 and 1983 most likely reflect

sampling error. While all of the tests except

digital rectal exams for women increased between 1980 and 1987, the jumps are less

dramatic than those suggested by the NHIS

data.

Glaucoma, Breast exam, Pap smear

eye exam and glaucoma, eye exam

Number of blood Pressurea

and blood pressure

services Men Women Women

Zero 4 3 3

One 22 19 10

Two 16 13 11

Three 58 65 26

Four N/A N/A 21

Five N/A N/A 30

Total 100 100 100

Abbreviation: N/A = Not applicable.

a

For men and women, table presents proportions o f

the noninstitutionalized, civilian, over-65 population using none, one, two, or three of th e

following services--glaucoma screening, eye ex -

amination, and blood pressure measurement--within

~the periods of time listed in table 8.

For women only, the table also presents the pro -

portions of this same population using none, one,

two , three, four , or five of the followin g

services--glaucoma screening, eye examination ,

blood pressure measurement, Pap smears, and breast

examination--within the periods of time listed in

table 8.

SOURCE: Office of Technology Assessment, 1989.
16 Ž The Use of Preventive Services by the Elderly

Table 5.--Some Trends in the Percent of Adults or Older Adults

Ever Having Received Selected Preventive Servicesa

National Healthb American Cancer Society/Gallup

Interview Survey Organization survey

Year Year

Servic e 1973 1982 1976 1980 1983 1987

Check-up 45 42 4 6 4 7

(annual) a

(All adults )

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

Breast exam

(Women 58 74 79 8 9 81

only ) (Ages 65+ ) (All adult women)

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

Pap smear 54 79 8 6 84 8 7

(women (Ages 65+ ) (All adult women)

only )

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

EKG 6 7 82

(Ages 65+)

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

Eye exam 94 

(Ages 65+ )

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

Glaucoma 56

(Ages 65+ )

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

Fecal occult bloo d

Men 17 29 43

Women 20 2 7 4 7

(Ages 50+)

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

Mammography 43 41 62

(women only) (Ages 50+)

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

Proctosigmoidoscopy

Men 3 7 32 43

Women 35 31 42 \

(Ages 50+)

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

Rectal exam

Men 54 56 53

Women 52 4 7 5 8

(Ages 40+)

Abbreviation: EKG = electrocardiogram

With the exception of the medical check-up, this table presents data on the proportion of individuals who

having received each service. For medical check-ups, the statistics refer to the proportion

~who report having a regular, annual exam.

National Center for Health Statistics, U.S. Department of Health, Education, and Uelfare, Wse of Selected

Preventive Services U.S--- 1973,N Vital and Health Statistics, Series 10, No. 110 (Washington, DC: U.S. Govermnent Printing Office, March 1977); and National Center for Health Statistics, U.S. Department of Health

ad Human Services, Wse of Selected Preventive Services U.S--- 1982,11 Vital and Health Statistics, Series 10,

No. 157 (Washington, DC: U.S. Govemnent Printing Office, August 1986).

cGallup Organization, llThe 1987 survey of public Awareness and Use of Cancer Detection Tests: SLm’Iary of

Findings,ll conducted for the American Cancer Society (Princeton, NJ: Gallup Organization, January 1988).

SOURCE: Office of Technology Assessment, 1989.
5. WHAT FACTORS PROMOTE OR INHIBIT

ELDERLY PEOPLE’S USE OF PREVENTIVE SERVICES?

Theoretical Approaches

In addition to providing estimates of the

percentage of the population receiving preventive services, the literature laid out in appendixes C and D offers insight into factors

associated with use. These studies represent

at least two theoretical approaches to explaining the use of preventive services: 1) an

preach that emphasizes patient behavior,

2) an approach that emphasizes provider

havior.

Patient Behavior

apand

beUnderlying this approach is the assumption that the decision to use a preventive

health service is made by the recipient.

Receipt of these services results from factors

that influence the decision to seek preventive

care and the patient’s ability to carry out that

decision. There are two main versions of this

approach: 1) a model of medical service

utilization first proposed by Andersen and his

colleagues (11,12,36), and 2) the Health Belief

Model (57,58).

The Andersen Model.--According to this

model, three types of factors determine an

individual’s probability of using medical services as well as the volume of use:

Predisposing variables includ e

demographic factors and the individual’s

beliefs about the services.

Enabling variables that affect the

patient’s ability to gain access to services

include the individual’s financia l

resources, the availability of the services

in the individual’s community, and insurance coverage.

Need variables include practitioners’ and

patients’ own perceptions of the patient’s

health status. Poor health status may indicate a need for better health care, including preventive services. Alternatively, variables that measure health

status may actually be proxies for the

need for nonpreventive health services.

To the extent that the need for these

other services increases contact with the

health care system, individuals may be

more likely to receive preventive services that require some health care intervention. Hence, health status variables may enable or predispose individuals to receive preventive services by

increasing their contact with the health

care system.

The Health Belief Model. --This behavioral model arose from an attempt by medical

sociologists during the early 1970s to understand patterns of preventive health and health

maintenance (48). It is similar to Andersen’s

model in its focus on the patient. However,

it posits that patient beliefs and attitudes are

the most direct determinants of the decision

to receive preventive care. Sociodemographic

factors, characteristics of the health care system, and other exogenous variables (such as

public education or illness of a family member) all indirectly affect preventive behavior

by influencing the individual’s beliefs and attitudes (57,58).

These attitudinal factors include:

■

■

■

■

the patient’s perceived susceptibility to a

given disease or condition;

the perceived potential severity of that

disease;

the perceived benefit of preventive action

in reducing susceptibility or severity;

cues to taking the action such as public

education programs, reminders and

physician recommendations; and

the perceived barriers to taking the action including cost, inconvenience, and

embarrassment.

One major limitation of the Health Belief

Model in explaining the use of preventive

services is the lack of data measuring individual attitudes and perceptions. Only data

sets constructed specifically for Health Belief

Model analyses are likely to contain the requisite information (23,31 ,54).

17
18 Ž The Use of Preventive Services by the Elderly

However, some researchers have used the underlying relationships suggested by the Health

Belief Model to design experiments to improve preventive behavior among patients

(70).

Provider Behavior

While patient behavior models focus on

the consumers of health, provider behavior

models focus upon providers of such services

(86). They suggest that patients receive preventive services as the result of their providers’ decisions to offer, encourage, and

enable their uptake. While the patient behavior models see patients as active decisionmakers, the provider behavior approach sees

patients as more passive, less important than

their providers.

Explanatory studies that use the provider

behavior approach examine the effects on use

of health care organization, patient contact

with the health care system, or with different

types of health personnel, and providers’

knowledge of preventive services (55,92).

This approach also underlies experiments and

demonstration projects that try to determine

how the manner in which services are provided can maximize their use. Mass screening programs or trials that employ physician

education are examples of these types of

studies (20,24,62,74).

Combining the Patient and Provider

Approaches

The patient and provider approaches

need not be mutually exclusive. At least

three studies have attempted to combine the

provider and patient approaches into a single

model (21,29,86). Although each approach

places emphasis upon different groups of

potential determinants of preventive behavior,

they may be valid in explaining different

parts of the variation in use. In addition,

there is some overlap among the two approaches. Andersen’s enabling variables

represent the same basic ideas that the provider behavior models focus upon. However,

in the patient behavior models, characteristics

of the health care system affect individual

patient decisionmaking. Provider behavior

studies implicitly assume a more passive role

for patients who respond largely to actions of

health providers.

Evidence on Patient Behavior

This section describes the results of

OTA’s analysis of the 1982 NHIS data set (see

appendix E for detailed discussion of methods) and examines how these results compare

with results of studies listed in appendixes C

and D. While many of the studies in appendix D are limited in their implications for

elderly use of prevention, they provide a

general context within which studies of

elderly populations can be interpreted.

OTA’s Analysis of the 1982 NHIS

Although the 1982 NHIS does not contain

all of the variables described in Andersen’s

approach and in the Health Belief Model, it

is the most comprehensive existing source of

information about the determinants of preventive behavior among the noninstitutionalized elderly. Table 6 lists potential explanatory variables included in the NHIS data set.

Table 6. --Selected Factors Hypothesized

To Affect Use of Preventive Services

by the Elderly

Predisposing factors

■ Gender

■ Age

■ Race (white/non -white)

■ Education

Enabling factors

■ Family income

■ Having health insurance in addition to

Medicarea

■ Receiving health care through a prepaid plan

■ Living alon e

■ Living in a metropolitan area

Measures of health status

■ Bed days in the previous 12 months

■ Having some limitation on activity

a

Potential insurance coverage for the elderly i n

addition to Medicare includes privately purchased

health insurance, Veterans Administration or military health insurance, or means tested publi c

assistance health benefits including Medicaid.

SOURCE : Office of Technology Assessment, 1989.
The Use of Preventive Services by the Elderly Ž 19

The major category of variables missing from

these data are the health belief variables:

patient perceptions of disease threat, perceived benefits of preventive services, and

perceived barriers to their use.

OTA used weighted logit models to

estimate the independent effects of each of

Table 7--- Significant Predictors of Use From

OTA Multivariate Logit Analysis of 1982

National Health Interview Surveya

Glaucoma Eye Blood Pap Breas t

Variabl e screening exam pressure smear exam

Predisposing factors

1. Sex (male)

2. Age

3. Race (non-white)

4. Education

Enabling factors

5. Family income

6. Having health

insurance in

addition to

Medicare

7. Receiving health

care through

a prepaid plan

8. Living in a

metropolitan area

9. Living alone

. .

. .

++

++

++

NS

++

NS

Measures of health status

10. Bed days in

the previous

12 months ++

11. Having sane

limitatio n

on activit y NS

. .

NS

NS

++

+

++

NS

++

NS

++

. .

++

NS

+

NS

++

NS

NS

NS

++

+

N/A N/A

. . . .

NS NS

++ ++

++ ++

++ ++

NS NS

+ ++

NS ++

++ ++

NS NS

Abbreviations: N S = Estimated coefficient on variable not statistically significant ;

N/A = Not applicable; variable no t

included in model.

a

Use measured according to standards described in

appendix E and in table 8.

Key to symbols:

+ =Difference in proportions using service sig -

nificant at 0.05 level (2 tailed), variabl e

positively associated with us e

++ =Difference in proportions using service sig -

nificant at 0.01 level (2 tailed), variabl e

positively associated ~wth use

. =Difference in proportions using service sig -

nificant at 0.05 level (2 tailed), variabl e

negatively associated with use

. . =Difference in proportions using service sig -

nificant at 0.01 level (2 tailed), variabl e

negatively associated with use

SOURCE: Office of Technology Assessment, 1989.

the variables listed in table 6 on the use of

each screening test or examination included

in the NHIS. These models posit that the

probability that an individual uses a preventive service within a specified period of

time is a function of the variables listed in

the table. Appendix E describes each model

specification in greater detail and presents

actual estimates. Table 7 summarizes the

statistically significant predictors of use.

Despite the substantial variation that exists across the five services in the percentage

of elderly receiving the specified levels of

prevention, the estimated models show a great

deal of consistency across services in the significant predictors. Only the use of blood

pressure checks appears different. The analysis suggests that fewer variables are important in predicting blood pressure checks than

in predicting the other services. This is consistent with the relative lack of variation in

the use of this service; over 90 percent of the

elderly report having had their blood pressure

measured within the previous 2-year period.

Almost every medical visit includes blood

pressure measurement, and individuals can

use machines found in many supermarkets

and restaurants to screen themselves for hypertension.

As expected, OTA’s analysis found that

younger age, more education, and higher income are all consistently associated with a

higher probability of using the five preventive services measured in the NHIS. For

the three services applicable to both genders,

men over 65 are less likely than women of

similar age to receive them.

More bed days are consistently related to

use of the five services, suggesting that sicker

individuals have greater contact with the

health care system, and hence, a greater opportunity to be offered preventive services.

The analysis showed no relationship between

limitation in activity and the use of any of

the preventive services except for eye exams,

where the direction of the association is neg-
20 ■ The Use of Preventive Services by the Elderly

ative, and blood pressure measurement, where

the direction is positive.l

For all services except blood pressure

measurement, living in one of the Census

Bureau’s Standard Metropolitan Statistical

Areas (SMSAs), which are defined by geography and population density, is positively

and significantly related to the use of preventive services. In urban communities with

a large number of health facilities and personnel, individuals are likely to live closer

and have more ways to get to appropriate

health facilities than their rural counterparts.

Having some health insurance in addition

to Medicare is also associated with use. Although such “Medigap” coverage (held by 71

percent of the elderly in the NHIS sample) is

unlikely to pay for preventive services, it

does lower patients’ out-of-pocket expenses

for medical care, thus making preventive services more affordable . In addition, a

patient’s willingness to buy such insurance

may indicate a certain concern for his or her

own health also found in individuals likely to

use preventive services.

Among variables not associated with the

use of preventive services, membership in an

HMO or other prepaid health plan is the most

unexpected. Published literature indicates

that such health care providers are more likely than others to offer preventive services to

] It is possible that bed days and limitations ar e

measuring health status in the same way. If the

two variables are measuring the exact same idea,

the logit estimation procedure would not be able to

distinguish the independent effects of each variable. This could lead to the insignificant coefficients found for the limitation variable. To

test for this potential problem (multicollinearity), we examined the correlation between

the bed days and limitations variables. Ue found a

correlation coefficient of 0.31 suggesting that

uhile the estimated standardemm of the two logit

coefficients may be somewhat biased downward (thus

creating potential non-significance), the two variables largely measure different notions of health

status.

their patients in hopes of lowering treatment

expenses (46,54). While OTA’s analysis suggest that this relationship may not exist, it is

also possible that the small number of elderly

NHIS respondents enrolled in HMOs did not

provide enough statistical power to detect an

actual effect of prepaid membership. In 1982

only 2.3 percent (or about 573,000) of the

elderly belonged to HMOS,

2 and the NHIS

sample reflects this relatively small number.

The remaining two variables in the logit

models are not consistently associated with

use: race is positively associated with glaucoma screening; living alone is positively associated with breast examinations.

Summary of Evidence on the Determinants of

Use of Preventive Services

Age. --Age has generally been found to

be a negative predictor of the elderly’s use of

preventive services (16,19,69). Studies of

younger adult populations have also found

such an association (32,44,80,81,81,88). In

the OTA analysis, all services except blood

pressure followed this pattern.

Two studies have examined the relationship between age and immunization behavior; neither found any strong association

between age and swine flu immunization

(22,59). Of the other studies that look at age,

one found a positive correlation with the

likelihood and volume of preventive visits to

a single HMO (40). Another study that

looked only at fecal occult blood screening

within a well-defined trial also found no age

effect .

2 Since 1982, the number of Medicare beneficiaries

has grown due to risk- and cost-based Medicare

demonstration programs. Under contract with the

Health Care Financing Administration, each HMO

participating in these programs agrees to provide

Medicare benefits to eligible enrollees. As o f

January 1988, over 1.7 million elderly were partic -

ipating in Medicare/HMO demonstration program s

(71) .
The Use of Preventive Services by the Elderly ■ 21

Despite these exceptions, the bulk of

available evidence suggests that use of preventive services falls with age, especially

among the elderly. There are at least three

explanations for this observation:

As an individual gets older, he or she

may perceive fewer benefits and more

barriers to receiving commonly recommended levels of prevention.

The observed difference may reflect a

tendency of individuals who were elderly in the early 1980s not to use prevention -- a tendency that will disappear

among future groups of elderly. 3 T h e

benefits and availability of most preventive services emphasized today have

been known for only the past generation. By the time that these services became widespread, older individuals may

already have established patterns of

health care that did not include prevent ion. According to this reasoning,

succeeding generations may have more

uniform rates of preventive care over

the adult age spectrum.

As individuals age, they are more likely

to visit the doctor for diagnostic and

therapeutic services. While they still

may receive preventive services, they

may not remember that the clinician

performed these procedures. Prevention

becomes obscured by treatment.

Education .- - All studies of use that have

examined education as an explanatory variable have found it to be a statistically significant predictor of the use of preventive

services (22,40,6 1,8 1). The more education a

person has, the more likely he or she is to use

preventive services. OTA’s analysis of the

NHIS data set conforms to this observation.

There are two possible explanations for the

association between education and use:

■ Education may affect the decision to use

preventive services by altering patients’

3 This is sometimes cat led a “cohort effect. ” The

t rend data presented ear 1 i er i n this paper are

consistent with this hypothesis.

perceptions about disease and potential

services. It increases their general

knowledge and ability to evaluate health

risks and the net benefits of prevention.

In addition, it may increase their knowledge of specific diseases, recommendations for preventive services, and

sources of care.

Education and prevention are both investments with expected future payoffs

(27). Individuals with more education

may be more oriented toward the future

than less educated people. Hence, these

people tend to seek both education and

prevention.

Gender. - - The evidence on the effect of

gender on the use of preventive health services is conflicting. Several studies, including

OTA’s analysis, found a strong significant association between being female and engaging

in preventive health measures (31,40,41). An

analysis of swine flu vaccinations, however,

indicates only a weak correlation between

being a woman and use (22). On the basis of

two services for the elderly (from OTA’sq

analysis) and a few other studies of the whole

adult population, one cannot conclude that

elderly women have a consistently greater

predisposition toward the use of preventive

services than do their male counterparts.

Even if such a conclusion were empirically

justified, no explanation for this finding is

readily apparent.

Race .- - The relationship between race

and the use of preventive services by the

elderly or other adults is ambiguous. OTA’s

multivariate logit analysis revealed that elderly whites are more likely to receive glaucoma

screening than are elderly members of other

racial groups. However, race was not a significant predictor of any of the other services

studied.

Other studies that have looked at the relationship between race and the use of preventive services by the non-elderly present

inconsistent results. Four studies found a

statistically significant negative association
22 Ž The Use of Preventive Services by the Elderly

between being black or non-white and using

preventive services (60,62,66,82), but three

other studies found no significant relationship

(15,31,44). In one study, the results varied

according to the preventive service (91). In a

review of studies of participation in fecal occult blood screening, Blalock and colleagues

report similarly inconsistent results of the effect of ethnicity on the use of this one cancer

screening test and draw no conclusions (14).

Income--- Income is a reflection of the

availability of financial resources to purchase

health services. Economic theory suggests

that the consumption of most goods rises with

income. Preventive health services for the

elderly may be particularly sensitive to income for two reasons. First, unlike acute illness care and even some types of chronic illness care, preventive care can be put off

without short-term consequences. Thus, preventive care may receive a lower priority

than other types of health care or other

necessary consumption. Second, Medicare,

the primary health insurer for the elderly,

does not cover most preventive services.

Hence, to use such services, the elderly must

have private health insurance (discussed be10W), wealth, or income to pay the out-ofpocket expenses.

Almost all multivariate studies of preventive use, including the OTA analysis,

found that income has a significant positive

effect in predicting the use of preventive services (17,22,31,32,40,41,44). Studies examining only the bivariate relationship between

prevention and income have also consistently

found such an association between prevention

and income (54,81,91).

Using Michigan survey data, Rundall and

Wheeler examined the relationship between

income and the use of preventive visits in

greater detail. Their analysis indicated that

although income has little direct effect on

preventive use, it indirectly increases the

likelihood of use by altering perceptions

about health and susceptibility to illness, and

by increasing the probability that individuals

have a regular source of care (60).

Insurance and Price--- Except for OTA’s

analysis, which found that insurance coverage

beyond Medicare had a consistently positive

significant effect on the use of preventive

services, the effect of insurance has not been

studied in elderly populations. The published

literature on adults’ use of preventive services

generally supports the contention that the

out-of-pocket price is a significant negative

predictor of use (16,32,47,61,63,86,91).

However, no study has examined the relationship between actual cost to the patient

and the use of services.

The potential impact of insuranc e

coverage on use has important policy implications for consideration of Medicare coverage

of preventive services. In assessing the benefits and costs of such a decision, one would

want to know the number of new users of

covered services as well as the total number

of users. The OTA analysis and other

studies (16,45) suggest that while insurance

coverage does increase use, a substantial percentage of individuals do not receive recommended levels of preventive care, even in the

presence of generous health insurance. A

recent study that compared the use of three

preventive services-- blood pressure measurement, breast exams, and Pap smears--in Canada, where preventive services are covered by

national health insurance, and in the United

States found little difference in rates of use

by elderly individuals in the two countries

(76). Only breast exams were used with

statistically significantly greater frequency in

Canada.

Enrollment in Prepaid Plans.--Except

for OTA’s analysis, which found no evidence

that enrollment in HMOs increases the use of

preventive services by the elderly, only one

other study has compared preventive care in

HMOs with that of traditional insurance plans

(66). The researchers in that study found

that employed adults in a prepaid group

practice had utilization rates for preventive

services no different from those of similar

individuals in a Blue Cross plan.
The Use of Preventive Services by the Elderly Ž 23

Living Arrangements and Logistical Barriers. --In addition to the financial costs of

preventive services, these procedures also

entail time and transportation costs. One

would expect the use of preventive services

to decline as the distance between services

and an individual’s home or job increases.

Two analyses of adult demand for preventive

care in an HMO found that distance to a

source of medical care was negatively (but

nonsignificantly) related to the probability of

use (40,41 ). Two other multivariate analyses

that included indexes of time, transportation,

and perceived difficulties in obtaining preventive procedures also found no significant

relationship between these logistical barriers

and the likelihood of using preventive services (16,22).

A less perfect measure of logistical barriers to access that may be especially relevant

for the elderly is whether or not the individual lives alone. Living with another person

could either raise or lower the logistical barriers to preventive services. An additional

household member could assist an individual

in overcoming immobility or distance; on the

other hand, if the additional household

member is in some way limited in mobility or

function, the effect on the healthy member’s

use of preventive services may be negative

since it may be difficult to leave a dependent

partner to receive preventive care. In OTA's

analysis of the NHIS data, living alone was a

statistically significant, positive predictor of

use for breast exams only.

Geographical Location. --The community

in which an older person lives may affect his

or her access to prevention. In a multivariate

analysis of the effect of geographic location

on use based on the same data set that OTA

used (i.e., the 1982 NHIS), Woolhandler and

colleagues found that among middle-aged

women, nonrural residence had a negative effect on the likelihood of having a glaucoma

test but had no effect on the use of hypertension screening, Pap smears, and clinical

breast examinations (91 ). These results conflict with OTA’s analysis which found that

people in urban communities use more preventive care than do those in non-urban

communities. The differences between the

two studies may be due to several factors:

■

■

Woolhandler, et al., estimated a logit

model with fewer explanatory variables

and a slightly different distinction between urban and rural residence from

that used by OTA;

Woolhandler, et al., used a less sophisticated method of estimating variances

from the complex NHIS sample design

than OTA did;

living in a rural area may be less of a

barrier for middle-aged women than for

the elderly population in obtaining preventive services.

Health Status.--The evidence on the effect of health status on preventive health service use is equivocal. Most multivariate

analyses have found no significant effect of

health status on use (19,40,41,44,91 ).4 Except

for OTA’s analysis, which found a strong

positive significant relationship between

number of bed disability days and use, only

Rundall and Wheeler found that reporting

relatively poor health has a direct positive effect on the likelihood of receiving preventive

care (60). However, variation in measures of

health status, model specifications, and

samples make it difficult to draw conclusions

from these studies.

A few other researchers have measured

health status by the presence of chronic disease. Blalock, et al., reported that having a

chronic condition increases the likelihood of

receiving fecal occult blood screening for

colorectal cancer ( 14). Warnecke, et al.,

found a similar association with the probability of a regular check-up in Illinois adults

(86).

d In their multi variate models of the use of prevent i ve services by middle-aged women using the

1982 NH I S data, Wool handl er, et a 1., found that

being healthy was significant ly and positively related only to blood pressure screening.
24 Ž The Use of Preventive Services by the Elderly

Limitations of the Patient Behavior Analyses

of Preventive Service Use

The literature and analysis reviewed in

this section suggest at least two major limitations of the patient behavior approach to understanding the use of preventive services:

■

■

Existing studies do not account for many

factors specific to a particular preventive intervention such as patients’

perceptions of pain, discomfort, embarrassment, or complexity of administration that may impinge on the willingness

to use of a particular procedure. Inclusion of variables that measure these perceptions would be consistent with the

Health Belief Model.

The models of use examined in this section assume that patients themselves decide whether to receive preventive care.

OTA’s analysis and most of the published patient behavior literature do not

directly examine the role of the primary

care physician and the health care organization in the decision to use services.

Evidence From Studies of Health

Care Provider Behavior

The Physician

Physicians must perform, supervise, or

prescribe most preventive services in order

for a patient to receive them. In fact, many

adults may depend on their primary care

physician to tell them what types of prevention they should receive and how often

(92). The literature examining the role of

physicians in determining the use of preventive services includes three types of analysis:

■

■

■

comparisons of physicians’ knowledge

about appropriate prevention with published sets of recommendations;

analyses of actual physician performance; and

experiments to increase physician compliance with recommended procedures.

Because almost all of the trials designed to

narrow the gap between published recommendations and actual practice focus on

changes in health care organization or management rather than just physician behavior,

this paper considers studies that fall into the

third category in the section on health care

organization below. None of the published

studies analyzed the elderly as a group separate from the general patient population.

Physician Knowledge and Actual Practice. -- Woo and her colleagues asked 83

physicians in a hospital-based teaching ambulatory care practice about the frequency

with which they recommend 16 screening

procedures to different age groups (92).

Across all patient age groups and procedures,

physicians with less training recommended

with greater frequency. Doctors with a history of cancer in their families recommended

more frequent sigmoidoscopies and mammograms. The mean physician recommendation for preventive use was more frequent

than the mean of published guidelines in 48

situations and less frequent in 18 situations.

The researchers found close agreement among

the respondents on Pap smears, blood pressure checks, physical exams, and medical histories, but wide variation in glucose and

cholesterol measurement and mammography.

Almost half the physicians reported that

they knew they recommended preventive services more frequently than published

guidelines and cited as reasons patient desires

and the belief that the guidelines are insufficient. Woo suggests that despite recommendations published by the Canadian Task

Force and others, the physicians in this study

may believe it better to err on the conservative side by recommending services for which

the supporting medical evidence of effectiveness is inconclusive.

5 A llsi tuationll is a particular screening service

for a particular age group.
The Use of Preventive Services by the Elderly Ž 25

An analysis of patient records in the

same study found a higher level of physician

compliance with recommended guidelines for

those services that doctors could order other

health personnel to perform, such as laboratory or radiological tests, than with services

that require labor by the primary care

physician, such as sigmoidoscopy and breast

exams. Woo and her colleagues infer that because the patients they surveyed desire

screening with appropriate regularity, the gap

in performance must be due to doctors failing

to offer prevention according to their own or

published recommendations. They also suggest that rates of use are partially related to

the ease with which physicians can provide

them. Other studies support this hypothesis

that variation in the use of different preventive services is a function of characteristics of the services themselves (e.g., patients’

pain, discomfort, and embarrassment) (57,

63,65).

Another study compared the preventive

care recommendations of 31 physicians practicing general internal medicine in North

Carolina with three sets of published

guidelines (55). The participating doctors

listed procedures they considered essential to

a periodic examination for three age groups

(30-39; ages 40-49; and ages 50-59). Among

the procedures recommended in published

guidelines but not chosen as essential or

routinely recorded in the medical record by

at least two-thirds of the sample physicians

were hearing exams, vision exams, fecal occult blood tests, lipid profiles, mammography

for women over 50, immunizations, and exams for hypothyroidism. The North Carolina

physicians also recommended services not

recommended in the published guidelines, including thorough examinations of the major

organ systems, measurement of blood urea

nitrogen, white blood counts, chest x-rays,

and microscopic urinalysis.

Examining the records of 334 patients

visiting the 31 North Carolina internists for

general examinations, the researchers found

that, on average, 59 percent of the procedures

recommended by expert groups were found in

the record. Compliance was greater for

younger patients, a result consistent with the

estimates of use of several services reviewed

earlier in this paper. The researchers also

found that compliance was greater for

laboratory and physical examination procedures than for medical history and counseling

services and was inversely related to the

number of expert groups recommending each

procedure.

Noting that the lowest compliance occurred with procedures identified by the Canadian Task Force on the Period Health Examination, one of the recommending bodies,

as having strong scientific validity, Romm

and colleagues suggest that improving compliance requires physician education. In discussing ways to improve compliance, they did

not consider the possibility that physicians

may take into account individual patient

characteristics and circumstances in deciding

not to provide recommended preventive services.

In another study, McPhee, et al., found

that discrepancies exist between American

Cancer Society (ACS) recommendations for

the use of seven preventive services and

physician performance and that physicians

tend to overestimate their own provision of

these procedures (51 ). The researchers report

that physicians cite four reasons most frequently for not providing recommended services: forgetfulness, lack of time, inconvenience or logistical difficulties, and

patient discomfort or refusal.

One study suggests that physicians may

differ by specialty in their performance of

some preventive services. In a study of Pap

smear use by physicians in Maryland, Teitelbaum, et al., found that specialists in obstetrics and gynecology (OB/GYN) were more

likely than general practitioners and internists

to encourage patients to receive Pap smears,

to remind patients by mail or telephone to get

a Pap smear, and to achieve compliance with

their recommendations (69).
26 ■ The Use of Preventive Services by the Elderly

Finally, one study currently underway

may shed additional light on the role and

motivations of the physician in providing

preventive services. Schwartz and Lewis in

cooperation with the American College of

Physicians (ACP) recently surveyed ACP

members about preventive practices (64). In

addition to examining the frequency with

which physicians say they perform a number

of services, Schwartz and Lewis will look for

relationships between preventive practices for

patients and demographic characteristics, the

physicians’ experience, and the physicians’

preventive practices for themselves.

Health Care Organization

Because physicians work within a larger

health care system with other practitioners

and administrators, it is possible that health

personnel and characteristics of the office,

hospital, clinic, group practice, or HMO

providing patients’ care could affect whether

or not older individuals receive preventive

services. This section considers the relationship between the use of preventive procedures and the health care organizations that

provide them.

Health Maintenance Organizations

(HMOs) and Other Prepaid Plans. --Some researchers have claimed that HMOs, in general, may promote the use of preventive services (46). To the extent that HMOs stand to

gain from potential savings in health care

costs resulting from preventive services, these

organizations would have an economic incentive to offer more preventive services. In addition, since visits to HMOs are either free or

very inexpensive, HMO patients may demand

more such visits.

Data from a single HMO that provided

OTA with estimates of preventive service use

suggests that these health care providers may

have the ability to organize themselves to

provide more preventive services than is now

generally received by patients. As table 3

above indicates, the rates of use in the HMO

were at least as high as or higher than comparable rates of use reported in other studies.

Not only has this HMO made preventive care

a stated organizational goal, but it has developed management tools to achieve compliance

with some preventive recommendations, including computer-generated reminders to

both patients and physicians for immunizations. However, these relatively high rates of

use may be achieved by recruiting patients

who already have characteristics that make

them more likely to use preventive care. If

the high levels of use found in the single

HMO for which OTA obtained data are related to its organization, it is not clear

whether less centralized prepaid health plans

such as independent practice associations

(IPAs) would be able to do the same.

OTA’s multivariate analysis of the 1982

NHIS found that the elderly enrolled in an

HMO are no more likely to use prevention

than their unenrolled counterparts. Given the

small number of elderly in HMOsq, however,

the NHIS sample may not have been large

enough to detect an actual difference. With

respect to preventive services other than

screening, Riddiough, et al., reported mixed

evidence about the relative likelihood of

HMOs to provide immunizations (54).

Organizational Factors Related to Use---

Other characteristics of health providers may

also affect the use of preventive procedures.

For example, the use of non-physician personnel, cues to compliance such as reminders

and media, or health fairs are all organizational strategies that have been employed to

increase the use of preventive services.

One potential mechanism for increasing

the use of preventive services is the health

screening fair in which participants can

receive selected procedures at a publicized

time and place. In an analysis of the costeffectiveness of this screening method, Berwick concluded that fairs work best when the

target population is clearly defined, the

screening tests are appropriately chosen, reliable and accurate, and the fair provides appropriate guidelines for abnormal results,
The Use of Preventive Services by the Elderly Ž 27

follow-up, referral, and treatment (13). Although fairs may increase the ability to detect

and prevent illness, significant risks may exist

if screening in the fair setting is relatively

insensitive or unspecific or does not provide

adequate follow-up.

Among interventions designed to increase

patient compliance with recommended services, Thompson, et al., studied the value of

combining talks by physicians and nurses,

postcard reminders to patients, and phone

calls to comply with a fecal occult blood test

(70). While those receiving any one of these

interventions had an average compliance rate

of 89 percent versus 68 percent among the

control group, the reminder postcard was especially cost-effective, raising compliance

about 25 percent to an overall rate of 93 percent at a relatively small cost. The talk by

health personnel, which was somewhat more

labor intensive, increased compliance about

13 percent.

McDonald and colleagues found similar

results in another randomized trial. Interns

and resident physicians who received computer-generated reminders provided 49 percent of the preventive services suggested to

them, while physicians in the control group

provided only 29 percent of the services (49).

Among physicians who received the reminder

intervention, the researchers found that overall attitudes toward the reminder system and

whether the physician read and signed the

reminder were statistically significant predictors of use, while years of training and faculty assessments of the physicians were not.

The researchers conclude that noncompliance

with recommendations is an error of omission

that can be mitigated by technological aids.

In another study, Satarino and colleagues

retrospectively asked patients receiving free

breast cancer screening at two clinics in New

York City how they learned of the service

and their need to be screened (62). Most

black screenees with less than a high school

education learned of the clinic through television ads followed by word-of-mouth and

private physician referrals. While this paper

suggests that television may be a useful cue

in promoting use, it does not indicate how

one might reach individuals who were n o t

screened at one of the two clinics.

Two studies have tested strategies to induce the provision of preventive services to

patients. In one study, four clinics were randomly assigned either to participate in a program that combined physician education with

a checklist of services due each patient on the

medical record or to a control group. Over a

4-month period, the researchers measured

rates of mammography and influenza and

pneumococcal immunizations among eligible

patients in each group. The intervention

group had significant increases in the use of

these services, ranging from 2 to 40 percent

over the control group. The researchers also

found significant increases in tests of

physician knowledge and attitudes about prevention among physicians (20).

In another randomized trial, nurses who

already routinely reviewed patient charts in a

university-based internal medicine practice

reminded physicians when a patient was due

for particular screening services and immunizations (24). For patients receiving the

nurse-reminder intervention, the researchers

found statistically significant increases in

rates of use for fecal occult blood screening

(32 to 47 percent), breast exams (29 to 46

percent) and influenza immunizations (18 to

40 percent), but not for Pap smears (13 to 14

percent). The study represents the only randomized experiment to analyze the role of

non-physician personnel in providing or

boosting the use of preventive services.

Physicians believe that such organizational strategies would improve the use of

preventive services. In two studies of

physician attitudes, researchers conclude that

while most physicians see themselves as ineffective in improving patient compliance

with their recommendations for preventive

care, they believe that they could be much

more effective if they had more resources at
28 Ž The Use of Preventive Services by the Elderly

their disposal including better training for

themselves and their support staff, improved

reimbursement for preventive services, and

better educational materials for patients

(84,87).

Other research currently underway may

enhance our understanding of the impact of

organization on use. The Health Services Research Center at the University of North

Carolina, with funds from the National Center for Health Services Research and Health

Care Technology Assessment (NCHSR), has

recently surveyed administrators, medical

directors, and staff physicians in 150 large

medical practices of different types (25).

While most of the study seeks to identify organizational characteristics that contribute to

physician satisfaction with his or her work

environment, it will also focus on the participating organizations’ preventive care practices.

Limitations of Evidence About Provider

Behavior

Compared to studies of patient behavior,

there are relatively few studies of the structure of the health care system or the role of

physicians in the use of preventive services

(54), and none focuses exclusively on prevention for an elderly population. A new

study of preventive practices among physicians currently underway may shed more

light on these issues. However, no current or completed studies to date have examined the role of other potentially important

factors, including:

potential revenue obtainable from preventive services,

potential liability associated with offering or withholding preventive services,

and

the degree of management control within

the health care organization.

The evidence about whether or not

HMOs provide greater levels of prevention is

ambiguous. While HMO enrollees may receive more preventive care than enrollees of

other health plans, other predisposing and

enabling factors such as gender, education,

and income may explain this differential.

Controlling for these factors, OTA’s analysis

of the NHIS data revealed no effect of HMO

membership on the probability of using five

preventive services. The data gathered by

OTA from one closed-panel HMO suggest

that prepaid health plans may have the

potential to increase the use of preventive

services among older adults. No data exist

about whether other prepaid plans achieve a

level of preventive care comparable to the

one OTA examined.

The literature contains several studies of

interventions within clinics, ambulatory care

practices, and HMOs designed to promote the

use of preventive services. They suggest that

provider-based strategies can increase the use

of preventive strategies. However, the narrowness of these studies indicates the need

for more research. In particular, the existing

literature does not adequately address:

the generalizability of particular interventions to other settings,

the most effective means of informing

the public about the need for and

availability of screening programs,

the role of non-physician personnel in

affecting patient use of preventive services, and

the potential of technological advances

(e.g., the introduction of computerized

medical records and new screening technology for the physicians’ offices) in affecting patient use of preventive services.
6. IMPLICATIONS FOR POLICY

Potential Medicare Coverage

Medicare represents the Federal Government’s major financial and policy commitment to health care. In 1986, Medicare

represented 58 cents of each Federal dollar

spent on health care and was the source of

payment for 29 percent of all expenditures

for hospital care and 21 percent of expenditures for physician services (73). The use and

correlates of use of preventive services for

the elderly have several important implications for the Medicare program.

First, covering preventive services under

the Medicare program would probably bring

about increases in the percentage of elderly

receiving preventive care. However, current

evidence suggests that reducing out-ofpocket expenses for patients is not sufficient

to assure compliance with published preventive recommendations. OTA’s analysis of

five preventive services suggested the

presence of insurance beyond Medicare is associated with about a 10-percent increase in

the percentage of elderly receiving each service during the period of time examined.

With the exception of blood pressur e

measurement, which almost all elderly already

receive on a routine basis, substantial portions

of the elderly with additional coverage that

defrays out-of-pocket expenses do not use

each of the preventive services OTA examined. In addition, because the additional insurance coverage held by Medicare recipients

in most instances excludes preventive services, the OTA analysis is not a direct test of

the impact of coverage of specific procedures

on the rates of use of these services.

Other factors enter into the physician’s

decision about whether to offer or provide

the service and the patient’s decision about

whether to seek or use it. Some of these factors may be amenable to change through public policy, while other characteristics describe

groups of elderly patients at relatively high or

low risk of not receiving adequate preventive

care.

Second, coverage of preventive services

for Medicare beneficiaries could affect preventive use beyond the Medicare population

itself. Medicare payment may raise interest

in preventive care among health care providers and payers by placing the authority of

the Federal Government behind it. In addition, consumers of health care may put more

weight on preventive services in managing

their own health because of the public discussion and attention focused on Medicare

coverage. None of the data or literature currently available allows OTA to estimate the

existence or magnitude of this potential indirect effect. A recent analysis of preventive

services in Canada where such procedures are

paid for by the government revealed rates of

use comparable to those in the United States,

suggesting that both the direct and indirect

effects of government coverage may be small

(76). The Medicare Catastrophic Coverage

Act of 1988 (Public Law 100-360) may offer

an opportunity to assess the full impact of

Medicare coverage of breast cancer screening

for the elderly by “analyzing trends if the use

of this service is monitored.

Third, expansion of Medicare to cover

preventive procedures will represent an immediate boost in the program’s financial obligations even if increases in use are minimal

or nonexistent. Although gaps exist between

experts’ recommendations and current levels

of use, substantial numbers of elderly still

receive a variety of preventive services at

recommended frequencies. OTA’s analysis

indicated that at least one-half of the noninstitutionalized elderly receives each of the

five services examined on a regular basis.

For three of these services (glaucoma screening, eye examinations, and blood pressure

measurement), rates of use were even higher.

Estimates of costs attributable to expanded

Medicare coverage of preventive services

must take account of the program’s obligation

to pay for procedures whose costs are currently borne by other payers. However, some

portion of these services are for patients with

a related medical history or symptoms and are

29
30 Ž The Use of Preventive Services by the Elderly

“diagnostic” and already reimbursable under

Medicare. It is also probable that some

physicians may categorize some examinations

and screening services as “diagnostic” so that

the procedure will be covered by Medicare.

No existing data from published literature or

the records of the Health Care Financing Administration (HCFA), the Federal agency that

administers Medicare, indicate what portion

of all procedures reimbursed by Medicare are

actually for screening purposes.

Fourth, the conclusions of this paper

about the use of medical services such as

screening and immunizations may have

limited applicability for policymakers considering Medicare coverage of other preventive

services such as health risk appraisals, education, counseling services, or tertiary prevention of disability among elderly suffering

from chronic disease. Many of the services

listed in table 1 could rely on non-physician

personnel to a greater degree than do screening and immunization, and patients could

receive them in a wider array of settings than

they receive most medical services. These

characteristics suggest that use of preventive

services not examined in this paper may be

markedly different from those explored here.

Hence, an understanding of the implications

of Medicare coverage of services other than

screening and immunizations use would require additional study.

Delivery of Preventive Services

for the Elderly

Although one of the major focuses of

this paper is the potential impact of insurance

coverage on the use of preventive services by

the elderly, the data and literature reviewed

in this paper suggest that other factors are

strongly related to use. This information

would be useful to public policy makers who

seek strategies for altering the elderly’s use of

preventive services. Of the patient and provider characteristics related to use, a few are

immutable, some are amenable to change

through policy, and others are theoretically

amenable to change, although the policy interventions to accomplish these changes are

unlikely to prove cost-effective.

Public policy cannot affect age, gender,

rural versus urban residence, and usually,

days spent in bed during the previous year,

three factors correlated with the use of preventive services. However, these demographic and health status characteristics do

identify segments of the population particularly at risk of not receiving adequate screening or immunizations. Knowing that on

average more women than men receive such

care or that recommended prevention appears

to decline with age and good health may help

policy makers target some of their preventive

care efforts toward the more vulnerable

groups. Because Medicare is an entitlement

program available to all persons over 65 who

receive Social Security, it is an unlikely

means of focusing efforts on demographically

defined subsets of the elderly population.

Nevertheless, other government investments

in prevention such as mass media campaigns

and screening fairs may be able to narrow

their target. Policy makers who want to bolster use among elderly groups unlikely to

receive preventive services may wish to study

the potential costs and effectiveness of such

programs in detail.

Other factors related to the elderly’s use

of preventive care do seem amenable to

policy interventions. Studies that examine

influences on physician behavior suggest that

better or more frequent physician education

may bring about better compliance with preventive recommendations. In addition, evidence suggests that record-keeping systems

and reminders to physicians (possibly aided

by computer technology) have positive effects

on use. This paper has discussed the potential for insurance coverage to increase somewhat the percentage of elderly receiving prevention. Government and providers could

design policies to bring about these changes

where they do not already exist.

The relationship between use and provider characteristics is not clear. For exam-
The Use of Preventive Services by the Elderly Ž 31

pie, conflicting published literature and the

small number of elderly enrolled in HMOs

makes it difficult to determine if the enrollment of older adults in prepaid health plans

increases the amount of preventive care they

receive. Additional research is also needed to

establish the potential of nurses and other

non-physician health professionals in providing or promoting the appropriate use of preventive services. Previous research suggests

the substantial contact they have with patients

in an ambulatory care setting, the availability

of new screening technologies (e.g., instruments that can measure cholesterol in a

physician’s office from a finger prick), and

the growth in “health fairs” that provide some

preventive services in alternative settings may

enhance the role of these professionals.

The remaining variables affecting older

individuals’ use of screening and immunizations - -educational level and family

income --are also potentially susceptible to

government interventions. However, public

policies designed to change these characteristics are so much more broadly construed

that they would never be implemented simply

to affect the use of preventive care. If, however, the government decides for some other

reason to promote education or supplement

income among the elderly, long-term increases in the use of preventive procedures

may be an additional benefit.
Appendix A: ADVISORY PANEL

Marianne C. Fahs

Department of Health Economics

Mt. Sinai Medical Center

New York, NY

Gordon De Friese, Panel Chair

Health Services Research Center

University of North Carolina, Chapel Hill, NC

John Frank

Department of Preventive Medicine &

Biostatistics

University of Toronto

Ontario, Canada

Gary D. Friedman

Epidemiology and Biostatistics Division

Permanence Medical Group, Inc.

Oakland, CA

Lawrence Gottlieb

Clinical Guidelines Program

Harvard Community Health Plan

Brookline Village, MA

Mary Knapp

John Whitman and Associates

Philadelphia, PA

Risa Lavizzo-Mourey

Geriatrics Program

University of Pennsylvania

Philadelphia, PA

M. Cristina Leske

Department of Preventive Medicine

SUNY at Stony Brook

Stony Brook, NY

Donald Logsdon

INSURE Project

New York, NY

Mildred B. McCauley

American Association of

Retired Persons

Washington, DC

Peter McMenamin

Chevy Chase, MD

Meredith Minkler

Center on Aging

University of California, Berkeley

Berkeley, CA

Marilyn Moon

Public Policy Institute

American Association

of Retired Persons

Washington, DC

George Morley

Department of Obstetrics/Gynecology

University of Michigan Medical Center

Ann Arbor, MI

Gilbert Omenn

Dean, School of Public Health &

Community Medicine

University of Washington

Seattle, WA

George Pickett

Department of Public Health Policy

School of Public Health

University of Michigan

Ann Arbor, MI

Donald Shepard

Department of Health Policy

and Management

Harvard School of Public Health

Boston, MA

Barry Stults

Division of General Internal Medicine

University of Utah Medical Center

Salt Lake City, UT

Advisory Panel members provide valuable guidance during the preparation of OTA reports.

However, the presence of an individual on the Advisory Panel does not mean that individual

agrees with or endorses the conclusions of this particular paper.

33
Appendix B: ACKNOWLEDGMENTS

The OTA staff would like to express our appreciation of the following people for their

valuable guidance. This acknowledgment should not be construed in any way to imply that the

individuals agree with or endorse the conclusions of this paper.

Ronald M. Andersen Charlotte Muller

The University of Chicago Mt. Sinai Medical Center

Chicago, IL New York, NY

Robert Burack Annlia Pajanini-Hill

Wayne State University University of Southern California

Detroit, MI

Morris F. Cohen

Kaiser- Permanante

Oakland, CA

Alan Garber

National Bureau of

Stanford, CA

Pearl S. German

The Johns Hopkins

Baltimore, MD

Robert L. Kane

Los Angeles, CA

Penelope Pollard

Medical Care Program National Health Policy Forum

The George Washington University

Washington, DC

Economic Research, Inc. John A. Sawyer

University of Texas

Health Science Center

San Antonio, TX

University

F. Douglas Scutchfield

American College of Preventive

Medicine

University of Minnesota

Minneapolis, MN

Mary Grace Kovar

National Center for Health Statistics

Washington, DC

David R. Lairson

Health Science Center

Houston, TX

Angela Mickalide

U.S. Preventive Services Task Force

Washington, DC

Washington, DC

Stephen J. Williams

San Diego State University

San Diego, CA

Steven H. Woolf

U.S. Preventive Services Task Force

Washington, DC

Steffie Woolhandler

The Cambridge Hospital

Cambridge, MA

Steven Moore

Office of the Surgeon General

Rockville, MD

34
Appendix C: - EMPIRICAL STUDIES

DETERMINANTS OF

BY THE ELDERLY

OF THE USE AND/OR

USE OF PREVENTIVE SERVICES

35
36 ■ The Use of Preventive Services by the Elderl.y

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The Use of Preventive Services by the Elderly ■ 37

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Appendix D: EMPIRICAL STUDIES OF THE USE AND/OR

DETERMINANTS OF USE OF PREVENTIVE

SERVICES BY THE NON-ELDERLY

— —
The Use of Preventive Services by the Elderly 39

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Appendix D. --cont'd

Services Period of time in Sample description

study examined which use is measured and design Analyses

Cummings et al., Swine flu vaccine Lifetime (albeit Telephone survey of 286 adults Uptake of swine flu vaccine as a function

19797 only offered randomly drawn from all of Health Belief Model variables using

within finite households in Oakland City, regression and path analyses

period) Michigan

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

David and Boldt, 19808None; examined patient Not applicable 92 responses to mailed survey of Descriptive statistics of patient

attitudes toward preventive random 10% of active patients of attitudes toward prevention, physician

care University of Kentucky Medical preventive function, time and cost of

Center preventive care; bivariate relationships

between attitudes and social position

----- ------ ------ ------ ------ ------ ------ ------ ------ ------ --- ,----- ----- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ . . . . . . . . . . . . ------ -

Davidson et al., Pap smear 3 years Patients receiving outpatient Effect of nurse-initiated reminder system

19849 Fecal occult blood 1 year care through university based of preventive services due; outcome

Breast exam 1 year general internal medicine measure is the difference in rate of

Influenza immunization 1 year (for 65+) practice during two l-year compliance with recommenda tions between

(or 1 year for periods; randomized trial; n=450 experimental and control periods

all services:

not clear)

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

Harris and Guten, Health protective Not applicable Interview With 842 randomly Relationships between constructed scales

197910 activities including selected adults from Cleveland, of health protective behavior and Health

regular medical and dental Ohio area Belief Model variables (bivariate)

checkups

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

Hayward et al., Pap smear 1 year and 3-5 4659 women from random national Estimates of proportions of women

198811 years telephone survey (1986 Access to receiving cancer screening within

Breast examination 1 year Care Study) recommended periods of time; bivariate

Mammography 1 year and multivariate analysis of correlates

Periodic health exam 1 year of use

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

Howard, 197912 Mammography Date of last use Not applicable Reviews four studies on use of

mammography and three studies of major

deterrents to physician use of

mammography

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

Larison and Swint, Preventive and non- 1 year 5% random sample of 3892 Compared likelihood and volune of use of

1976. 197813 preventive physician visits individuals enrolled in large preventive andnonpreventive visits in an

prepaid plan (Kaiser Portland) HMO as a function of health status,

demographic, insurance, and other

economic variables

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

McDonald et al., Variety of screening Not applicable 115 resident physicians in a Estimated the effectiveness of ccmputer198415 services, immunizations, teaching hospital general generated reminder system in achieving

and weight reduction medicine practice (61 study and compliance with medical indications for

54 control, randomly assigned) preventive and other procedures;

studied over a 2-year period analyzed the correlates of physician use

and reasons for nonuse

---- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- . . . . . . . . . . ----- ----- ---
The Use of Preventive Services by the Elderly • 41

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Appendix D. --cent’d

18pub(ic Health Service, U.S. Department of Health and Hunan Sef’ViCeS, ‘use of Mammography for Breast Cancer Screening -- Rhode Island, 1987,” Morbidity

and Mortality Ueekly Report 37(23):357-360, 1988.

19p~[ic Health Service, U.S. Department of Health ad Human Services, I!provisima[ Estimates From the Nati~l Health Interviw Survey S~[WIWlt Otl

Cancer Control -- United States, January-March 1987,” Morbidity and Mortality Ueekly Report 37(27):417-425, 1988.

20L-A. Li[[ard, H-G. Manning, C. petersofl, et al., Preventive medical Standards, Usage, and Efficacy (Santa Monica, CA: The Rand Corporation, Care:

1986); and N. Lurie, U.G. Manning, C. Peterson, et al., I$preventive care: Do ue practice what we Preach,ll Am. J. Public Health 77(7):801-804, 1987.

21M.A. Riddiouqh. J.S. Uil(ems, C.R. Sanders, et al., I*Factors Affecting the Use of Vaccines: Considerations for I mnunization Program Planners,” Public

Health Repor~s.96(6):528-535j 1981 .

22F-J. R-, S.u. Fletcher, ad B. Hulka, ~The per

Journa[ 74(3):265-271, 1981.

23T-G. Rundal[ ad J-R-C. wheeler, ~The Effect of

Social Behavior 20:397-406, 1979.

24D.s- Sa[kever, l)Accessibi lity and the Demand for

25U.A. Satariano, A.G. s~h~artz, ad G.M. s~anson,

odic Hea~th Examination: Comparison of Rec onwnendations and Internists’ Performance,” Southern Medical

ncom on Use of Preventive Care: An Evaluation of Alternative Explanations,w Journal of Health and

Preventive Care,w Social Science ard Medicine 10:469-475, 1976.

ItScreening for Cervical Cancer: Results From Several Intervention Strategies,w in Issues in Cancer

Screening arid Communications (Neu York, NY: Alan R. Liss, Inc., 1982).

26J.A. Sawer, J. Earp, R.H. Fletcher, et at., IIpap cjmars in Rural Black u~n,ll fr~ the Department Of Med

University of North Carolina at Chapel Hill, Chapel Hi(l, NC, unpublished manuscript.

27D-P. slesinger, R.C. Tessler, and D. Mechanic, llThe Effects of Social Characteristics on the utilization 0<

Health Care programs,~ Medical Care 14(5):392-404, 1976.

2~.A. TeiteLba~. C-S- Ueisman, A.C. K[assen, et al., ~pap Testing Intervals: Specialty Differences in Phys

cine and School of Public Health,

Preventive Medical Services in Contrasting

cians’ Rec cmnnendations in Re[ation to

Uomen’s Pap Testing Behavior, ~ Medical Care26(6):607-618, 1988.

29R.S. Thonpson, M.E. Michnich, J. Gray, et al., llMaximizing c~liance Uith Hemoccu[t Screening for colon Cancer in clinical praCtiCe,” MediCa[ care

24(10):904-914, 1986.

30c.M. Valente, J. Sobal, H.L. MunCie, et ai., llHealth pr~tion: Physicians I Beliefs, Attitudes, and Practices,” AM. J. Preventive Medicine 2(2):82-88,

1986.2

31R-B. uarnec k e , p.L. Hav[icek, and c. Hanfredi, !IAHareneSS ad use of screening by Older-Age perSOnS,ll in prOSPt? ctives on Prevention and Treatment of

Cancer in the Elderly, R. Yancik (cd.) (New York, NY: Raven Press, 1983).

32H. uechsler, S. Levine, R.K. Idelson, et al-, llThe physicians Role in Health prOMOtiOn ‘-

A Survey of Primary-Care Practitioners,” N. Eng[. J. Med.

308(2):97-100, 1983.

33p.s. Uilli-on, C.E. Driscoll, L.D. Dvorak, et al., llHea\th Screening Examinations: The patient’S %?Wective8 II The Journa{ of Family Practice

27(2):187-192, 1988.

34R.A. wi~sor and J. Morris, Ucms-r particivti~ ad parent for Health pr~ti~ programs at an IJrban Primary care Center,” The Alabama Journal O f

Medical Sciences 21(2):209-213, 1984.

35B. uoo, 6- u~o, E.F- cook, et al., IiScreening proc~ures in the Asypt~tic Adult: Comparisons of Physicians’ Reccmnendations, patients’ Desires,

Published Guidelines, and Actual Practice,ti J.A.M.A. 254(11):1480-1484, 1985.

SCIJRCE: Office bf Technology Assessment, 1989.
Appendix . . E: OTA ANALYSIS OF PREVENTIVE SERVICE USE BY

THE ELDERLY WITH DATA FROM THE 1982

NATIONAL HEALTH INTERVIEW SURVEY

Methods

The Data

The National Health Interview Survey

(NHIS) is a representative household survey

conducted annually since 1957 by the U.S.

Department of Health and Human Service’s

National Center for Health Statistics (NCHS).

In addition to a core questionnaire that

measures the self-reported prevalence of various medical conditions, the use of health

services, general health status, disability, and

demographic characteristics, NCHS adds supplemental questionnaires on specific topics

that vary from year to year. In 1982, the

NHIS contained supplemental questionnaires

on the use of preventive health services and

the types and degree of health insurance

coverage. Although NCHS has published

some data from the preventive services supplement in tabular form (75), no published

work to date has attempted to use these data

to understand what factors are associated with

the use of preventive services by the elderly.

OTA decided to conduct such an analysis.

OTA obtained magnetic tapes of the core

and supplemental questionnaire data for the

1982 NHIS from NCHS. These data files

contained 103,923 observations reflecting

respondents of all ages with 11,434 observations for individuals 65 years or older. In

addition to responses to survey questionnaires,

each observation contained a uniqu e

identifier, variables identifying a stratum and

cluster from which it was drawn, and the

weights necessary to produce representative

estimates.

Among the variables on the data set,

OTA was interested in:

■ the amount of time elapsed since the

respondent last received each of five

screening services (glaucoma screening,

blood pressure measurement, eye examination, breast examination, and Pap

smear); and

■ factors potentially associated with the

use or nonuse of these five services.

OTA converted each of the five variables

measuring elapsed time since use of preventive services to a binary variable that

measures whether or not the individual used

the service within a specified period of time.

These periods of time are based on the

recommendations of expert groups presented

in table 2 in the text of this paper. Because

there is some variation across the different

sets of expert guidelines listed in table 2,

OTA summarized the published recommendations in the composite measures presented in

table 8. These composite measures do not

represent a set of recommendations themselves; rather, they are merely one benchmark

for comparing actual use to what is generally

considered adequate by recommending

groups. Where there is disagreement among

recommending groups, the composit e

measures tend toward longer intervals between screenings in order to measure compliance with minimal recommended levels of

prevention. These composites of expert

recommendations pertain only to the primary

analyses conducted by OTA. As a source of

comparison, table 8 also includes the periods

used by two other studies of the use of preventive procedures. In one of these papers,

the authors measured recommended periodicity as the mean of published recommendations (92). The other paper formed a

consensus based on their own review of

relevant literature (45).

Because of the coding scheme of the

NHIS, use within an ‘x” year period really

means that the individual had used the service within a period of less than but not including “x+1” years. For example, consider

the case of breast examinations. Table 2 suggests that one should measure use within the

previous year. However, under the NHIS

coding scheme, one would consider an elderly

46
The Use of Preventive Services by the Elderly Ž 47

woman who had her last breast examination on the data set:

21 months ago to have been adequately

screened; a woman whose last breast exam- ■

ination was 24 months ago would not. Table

9 lists all variables used in the analysis and

also includes appropriately weighted descriptive statistics. ■

Methods of Analysis

OTA used PC-CARP R software (on an

80286 personal computer) to analyze the data.

PC-CARP

R was developed by the Statistical

Laboratory at Iowa State University especially

for analyzing data from surveys with complex

designs like the NHIS. It makes use of the

sampling information in the data files to produce appropriate point and variance estimates. OTA performed two separate analyses

a descriptive summary of the percentages of elderly individuals who reported

using each of the five screening services

within the specified time; and

a multivariate weighted logistic regression analysis of the use or nonuse of

these services.

In addition, OTA:

■ examined whether observations dropped

from the multivariate analysis because

they contained some missing information

differ in any important ways from observations included in the analysis,

potentially biasing our estimated parameters;

Table 8--- Periods of Time Used by OTA and Two Studies to Measure

Older Adults’ Use of Preventive Services

Period of time to measure use employed by:

Service OTAa

Lillard, et al., 1986b woo, et al., 1985C

■ Initial or periodic physical exam 1 year . . . . 1.4 years

■ Blood cholesterol level 5 years . . . . 4.5 years

■ Fecal occult blood test 1 year 1 year 1.0 year

■ Pap smear 3 years* 3 years 4.1 years

■ Glaucoma screening 2 years* . . . . ----

■ Optometry/ophthalmology exam 2 years* ---- ----

■ Pneumococcal immunization L i fet i me Lifetime ----

■ Influenza immunization 1 year 1 year . . . .

■ Tetanus immunization 10 years 10 years -.. .

■ Hypertension screening 1 year* ..-. 1.4 years

■ Breast examination 1 year* ..-. ... -

Abbreviation: HMO= health maintenance organization.

~Intervals listed in this colum represent composites of the expert recommendations surrnarized in table 2.

L.A. Li[lard, W.G. Manning, C. Peterson, et al., Preventive Medical Care: Standards Usa~e and Efficacy (Santa

Monica, CA: The Rand Corporation, 1986).

CB. Woo, B. Woo, E.F. Cook, et al., IIScreening procedures in the Asymptomatic Adult: Comparisons of

Physicians’ Recommendations, Patients’ Desires, Published Guidelines, and Actual Practice,ti J.A.M.A.

254(11):1480-1484, 1985.

*As described in greater detai[ in appendices E and F, OTA eStiIMtd the Use Of preVefltiVe SerViCeS aMOn9

the elderly with two different data sources-- a single HMO and the 1982 National Health Interview Survey

(NHIS). The asterisk indicates services included in the NHIS analysis. Because of the coding scheme of

the NHIS, use within an ‘xM year period really means that the individual has used the service within a

period of less than but not including ‘X+lU years. Under this scheme, we would consider an elderly woman

who had her last breast examination 21 months ago to have been adequately screened; a woman whose last

breast examination was 24 months ago would not. For the HMO data, use within ‘xM years carries a literal

definition .

Sources: Office of Technology Assessment, 1989.
48 ■ The Use of Preventive Services by the Elderly

■ considered whether multicollinearity in

the models might reduce precision; and

■ examined how the probabilities of use

predicted by the models varied with

each significant variable.

The multivariate analysis uses Taylor

series techniques to estimate a weighted

logistic regression model of the form:

is the probability of elderly person i

using service j. Use is measured by

binary variable Yij (=1 if person i

had service j within the specified

period of time; Yij O otherwise).

is a vector of predisposing characteristics describing elderly person i.

Ei is a vector of enabling characteristics

.

Hi

Uj

&j

describing elderly person i.

is a vector of health status characteristics describing elderly person i.

is an estimated parameter, and 6 j,

8 j* Yj are vectors of estimated

parameters for service j.

is an individual, service specific error term.

PC-CARP

R produces estimated coefficients

that are consistent and appropriately

weighted. Estimates of asymptotic variances

also appropriately reflect the complex survey

design.

PC-CARP

R requires that no observation

in the data matrix contain missing values.

For the logistic regression analysis, OTA used

SPSS-PC+ to create two data files. OTA

purged both files in a listwise fashion of observations containing missing data on any

variable in the models. 1 OTA used one of

the data sets to estimate models for preventive services potentially used by both

sexes--glaucoma screening, eye examinations,

and blood pressure measurement. OTA used

the other data set, which contained only the

observations for women, to estimate models

for breast examinations and Pap smears.

The data set for both sexes contained exactly 9,000 out of the original 11,434 observations. The remaining 2,434 observations,

which had missing data, represented a

weighted 21.5 percent of the over-65 population. The single variable with the most missing observations was family income. This

variable alone had missing observations

representing 15.3 weighted percent of the

elderly population. Of each of the other variables containing missing values, none lacked

data on observations representing more than 4

weighted percent of individuals over-65. The

data set containing only women had 5,040

observations out of a possible 6,655. The

1,615 observations with missing dat a

represented 19.6 weighted percent of all

women over 65.

Results

The estimates of the national proportions

of elderly using each of the five screening

services within the specified time are presented in table 3 in the text of the paper.

Additional descriptive statistics are presented

in table 9. Table 10 below presents the parameters of the estimated logistic regressions

that attempt to explain the use or nonuse of

each service. Table 7 summarizes these

results, and the text of the paper discusses

their significance.

1 To estimate the descriptive statistics presented

in table 9 and the national percentages of elderl y

receiving the five screening services, OTA use d

data sets that contained all observations for which

any data is available for the particular variabl e

in question. The Listwise deletion of missing values

described here only applies to the logisti c

regression analysis.
The Use of Preventive Services by the Elderly Ž 49

Table 9--- 1982 National Health Interview Survey:

Selected Descriptive Statistics for Persons Over 65

Number of observations: 11,434

Weighted number of observations: 25,391,02 3

Weighted Means and Standard Deviations for Continuous Variables

Variabl e Mean Standard deviation

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

AGE -73.39 6.6 3

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

INCOME 15,217.9 7 13,853.5 6

Weighted Frequency Distributions for Categorical Variables

Variabl e Proportion Variabl e Proportion

GLAUCOMA EDLEVEL Highest educational level attained:

l=screened for glaucoma within O=none or kindergarten 0.02

previous 2 years, 11 months 0.6 6

O=otherwise 0.34

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

EYE

1=received eye examination within

previous 2 years, 11 months 0.75

O=otherwise 0.25

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

BLOODP

l=had blood pressure measured within

previous 1 year, 11 months 0.93

O=otherwise 0.07

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

BREAST

l=had clinical breast examination

within previous 1 year, 11 months 0.50

O=otherwise 0.50

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

PAP

1=had Pap smear within previous 3

years, 11 months 0.52

O=otherwise 0.48

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

1=1 to 8 years (elementary ) 0.3 8

2=9 to 11 years (some high school) 0.1 6

3=12 years (high school graduate) 0.2 6

4=1 to 3 years colleg e 0.09

5=college graduate 0.05

6=post-graduate education 0.04

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

SMSA

1=resides in a Census Bureau Standard

Metropolitan Statistical Area (urban area) 0.6 4

0=otheruis e 0.3 6

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

PREPAID

1=enrolled in HMO or sane other prepaid

health plan 0.02

0=otherwise 0.9 8

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

HLTHINSR

1=has some health insurance coverage

or health benefits beyond Medicare

including prepaid, Veteran s

i

, military,

or means tested public assistance

health benefit 0.7 8

0=otherwise 0.2 2

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

ALONE

1=lives alon e 0.30

0=otherwise 0.70

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

0.4 1

0=female 0.59

BEDDAYS days in bed during previous

12 months:

O=none 0.6 5

1=1 to 7 days 0.1 6

2=8 to 30 days 0.1 2

3=31 to 180 days 0.0 5

4=181 to 365 days 0.0 2

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

NONWHITE

l=nonwhite

0=white

LIMITED

0.1 0 1=limited in some activit y

0.9 0 0=otherwise

0.70

0.30

SOURCE: Office of Technology Assessment, 1989.
50 ■ The Use of Preventive Services by the Elderly

Table 10.--Elderly Use of Five Screening Services: Logistic Regression Results

GLAUCOMA BLOOOP EYE -BREAST PAP

Independent Mean Estimated Estimated Estimated

variable value coefficients coefficients coefficients

INTERCEPT 1.0000000 0.4160073 -0.3433352 0.8399042

Predispose ng factors:

MALE 0.4162490

AGE 73.2137000

NONWHITE 0.0945106

EDLEVEL 2.3429000

Enabling factors:

INCOME 15276.8000000

SMSA 0.6336510

PREPAID 0.0229561

HLTHINSR 0.7896040

ALONE 0.3067190

Health status measures:

BEDDAYS 0.6316600

LIMITED 0.2992070

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

(0.2927390) (0.7538013) (O.296341O)**

-0.3009192 -0.3259628 -0.2439690

(0.0534922)** (0.0957841)** (0.5874069)**

-0.0091917 0.0264826 -0.0061867

(0.0038157)* (0.0095720)” (0.0039844 )

-0.3735570 -0.0368144 -0.0887188

(0.0838741)** (0.1647689) (0.0923361)

0.1601070 0.1038493 0.1147214

(0.0223536)** (0.0436750)* (0.0229877)**

0.0000114 0.0000106 0.0000066

(0.0000026)** (0.0000058) (0.0000028)*

0.2167839 0.0064913 0.2054843

(0.0571966)** (0.1049219) (0.0589708)**

0.2349249 0.6391572 0.1612398

(0.2200813) (0.5023571) (0.2554439)

0.4306881 0.5616778 0.3853976

(0.0625027)** (0.1248533)** (0.0628222)**

0.0062441 -0.1136007 0.0406232

(0.0589261) (0.0944282) (0.0638674)

0.1227310 0.8908110 0.0776005

(0.0304799)** (O.1O5O414)** (0.0281800)**

0.0003354 0.3276151 -0.1233437

(0.0585122) (0.1317948)* (0.5909522)*

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Model statistics:

N 9000.0 9000.0 9000.0

Dependent variable mean 0.6635 0.9319 0.7432

F-statistic 32.41** 15.14** 15.12**

Average design effect=

1.32 1.53 1.28

Mean Estimated Estimated

value coefficients coefficients

1.0000000 -0.0166337 2.1542170

(0.39609n) (0.3889979)**

. . - . . .

. . . . . .

73.4815000 -0.0167258 -0.0400128

(0.0051317)** (0.0049593)**

0.0937475 0.0263338 0.1506788

(0.0982817) (0.1056188)

2.3367600 0.1132536 0.0962961

(0.0255863)** (0.0249885)**

4059000000 0.0000119 0.0000102

(0.0000027)**(0.0000028)** (0.0000028)**

0.6364580 0.3110662 0.1555873

(0.0694007)** (0.0700830)*

0.0199337 0.3845290 0.4433711

(0.2405225) (0.2723756) (0.2723756 )

0.7834470 0.3761862 0.3581028

(0.0884009)**(0.0861343)** (0.0861346)**

0.4099680 0.1971576 -0.0397508

(O.O74391O)**(O.O665113)** (0.0651125)

0.6405870 0.3337425 0.1943092

(0.0327194)**(0.0335717)** (0.0333572)**

O.3O8W1O 0.0448802 0.0331326

(0.0703522) (0.0762676) (0.0762676 )

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

5040.0 5040.0

0.4985 0.5243

23.09** 25.87**

1.20 1.23

a

byAs totic standard errors are in parentheses belou each estimated coefficient.

Variable means uere calculatd from data matrices used to estimate logit models (i.e., purged of

observations ~ith missing values).

cAverage “effectU of coaplex survey design on variances of estimated coefficients. This ‘effect” is measured

as the nunber of times greater the variance from the complex design is than the variance from a simple

random design.

*Estimated parameter significant at the 0.05 level, t~o-tailed test.

**Esti~ted ~r~ter significant at the 0.01 level, tW-tailSd test.

Variable Key:

~~v~

GLAUCOMA --l=sc~eened for glaucoma within previous 2 years, 11 months; O=otheruise

EYE --l=received eye examination within previous 2 years, 11 months; O=otherwise

BLOCX)P --l=had blood pressure measured uithin previous 1 year, 11 months; O=otheruise

BREAST --l=had clinical breast examination within previous 1 year, 11 months; O=otheruise

PAP --l=had Pap smear within previous 3 years, 11 months; O=otherwise

~tv~

MALE --l=male~ O=female

AGE --respondent’s age in years

NONWHITE --l=nonwhite; O=white

EDLEVEL --highest educational level attained; O=none or kindergarten; 1=1 to 8 years (elementary); 2=9

to 11 years (sp,e high school); 3=12 years (high school graduate); 4=1 to 3 years college;

5=college graduate; 6=post-graduate education

INCOME --family income in dollars

SMSA --l=resides in a Census Bureau Standard Metropolitan Statistical Area (urban area); O=otherwise

PREPAID --l=enrolled in HMO or some other prepaid health plan; O=otherwise

HLTHINSR --l=has some health insurance coverage or health benefits beyond Medicare including prepaid,

Veterans’, military, or means tested public assistance health benefit; O=otherwise

ALONE --l=lives alone; O=otherwise

BEDDAYS --days in bed during previews 12 months; O=none; 1=1 to 7 days; 2=8 to 30 days; 3=31 to 180

days; 4=181 to 365 days

LIMITED --l=limited in some activity; O=otherwise

SOURCE: Office of Technology Assessment, 1989.
The Use of Preventive Services by the Elderly Ž 51

Table 11. --Correlation Matrix for Variables in Logistic Regression Modelsa

NON- ED- HLTH -

GLAUCOMA BLOODP EYE BREAST PAP MALE AGE WHITE LEVEL INCOME SMSA PREPAID INSR ALONE BEDDAYS LIMITED

GLAUCOMA 1.00

xx

BLOODP 0.19 1.00

xx xx

EYE 0.69 0.18 1.00

xx xx xx

BREAST xx xx xx xx

xx xx xx 1.00

PAP xx xx xx xx xx

xx xx xx 0.49 1.00

MALE

AGE

NONWHITE

EDLEVEL

INCOME

SMSA

PREPAID

HLTHINSR

ALONE

BEDDAYS

-0.06

xx

-0.05

xx

-0.09

xx

0.15

xx

0.12

xx

0.07

xx

0.03

xx

0.12

xx

-0.01

xx

0.04

xx

-0.04

xx

0.03

xx

-0.02

xx

0.05

xx

-0.05 xx xx 1.00

xx xx xx 1.00

-0.03 xx XX -0.06

XX -0.06 -0.15 XX

-0.03 xx xx 0.00

xx -0.02 0.00 xx

0.10 xx xx 0.01

xx 0.11 0.11 xx

0.04 0.07 xx xx 0.10

xx xx 0.09 0.10 xx

0.01 0.06 XX xx -0.01

xx xx 0.09 0.05 xx

0.02 0.02 xx xx 0.03

xx xx 0.04 0.04 xx

0.07 0.09 xx xx 0.02

xx xx 0.10 0.09 xx

-0.01 0.01 xx XX -0.26

xx XX 0.01 -0.06 XX

0.13 0.02 xx xx -0.02

xx XX 0.15 0.08 XX

1.00

1.00

-0.01

-0.01

-0.14

-0.14

“0.11

-0.09

-0.00

0.01

-0.03

-0.03

-0.09

-0.08

0.18

0.21

0.06

0.06

1.00

1.00

-0.16 1.00

-0.18 1.00

-0.13 0.39 1.00

“0.13 0.32 1.00

0.04 0.09 0.12 1.00

0.02 0.08 0.11 1.00

-0.00 0.04 0.03 0.10 1.00

-0.01 0.03 0.01 0.08 1.00

-0.12 0.18 0.11 0.01 0.08 1.00

“0.11 0.18 0.07 0.00 0.08 1.00

0.01 -0.02 -0.31 -0.00 -0.04 -0.01 1.00

“0.01 -0.00 -0.36 -0.01 -0.03 0.03 1.00

0.04 -0.05 -0.03 -0.01 -0.02 -0.01 0.01 1.00

0.04 -0.05 -0.04 -0.00 0.00 -0.02 0.01 1.00

LIMITED -0.00 0.06 -0.03 xx xx -0.04 -0.07 0.06 -0.12 -0.07 -0.04 -0.00 -0.02 -0.03 0.31 1.00

xx xx xx 0.04 0.03 xx -0.04 0.07 -0.12 “0.05 -0.03 0.01 -0.01 -0.04 0.45 1.00

‘Pearson correlation coefficients. First row in each cell gives correlation in data set used to estimate

models for GLAUCOMA, EYE and BL~P’(n=9000). Second row gives correlation for data set used to estimate

mode(s for BREAST and PAP (n=5040).

symbol Key: XX=Not applicable (Both variables not contained on that data set)

Variable Key:

DependentVariables:

GLAUCOMA --l=screened for glaucoma within previous 2 years, 11 months; 0=othewise

EYE --l=received eye examination within previous 2 years, 11 months; O=other~ise

BL00DP --l=had blood pressure measured within previous 1 year, 11 months; O=otherwise

BREAST --l=had clinical breast examination within previous 1 year, 11 months; O=otherwise

PAP --l=had Pap smear within previous 3 years, 11 months; O=otherwise

Independent Variables:

MALE --l=male; O=female

AGE --respondent’s age in years

NONWHITE --l=nonwhite; O=white

EDLEVEL --highest educational level attained; O=none or kindergarten; 1=1 to 8 years (elementary); 2=9 to

11 years (some high school); 3=12 years (high school graduate); 4=1 to 3 years college;

5=college graduate; 6=post-graduate education

INCOME --family income in dollars

SMSA --l=resides in a Census Bureau Standard Metropolitan Statistical Area (urban area); O=otherwise

PREPAID --l=enrolled in HMO or some other prepaid health plan; O=otherwise

HLTHINSR --l=has some health insurance coverage or health benefits beyond Medicare including prepaid,

Veterans', military, or means tested public assistance health benefit; O=otherwise

ALONE -l=lives alone; O=otherwise

BEDDAYS --days in bed during previews 12 months; O=none; 1=1 to 7 days; 2=8 to 30 days; 3=31 to 180 days;

4=181 to 365 days

LIMITED --l=limited in some activity; O=otherwise

SOURCE: Office of Technology Assessment, 1989.
52 Ž The Use of Preventive Services by the Elderly

To examine the possibility that multicollinearity among the independent variables

in the model might preclude precise estimation, OTA estimated the weighted first-order

Pearson correlation matrices for the two data

sets. Table 11 presents the correlation

statistics. Only three pairs of independent

variables had correlations greater than 0.25

(or less than -0.25): EDLEVEL and INCOME (0.39 in the two-gender data set and

0.32 in the women only data set), ALONE

and INCOME (-0.31 and -0.36 respectively),

and the two measures of health status,

LIMITED and BEDDAYS (0.31 and ().43

respectively). In addition, ALONE and AGE

have correlations of 0.18 and 0.21 respectively in the two data sets. However, despite the

potential effect of this collinearity on the

estimated variances, the conclusions are unlikely to change. In all models except blood

pressure measurement, EDLEVEL and INCOME are both already significant predictors

of preventive service use. ALONE is significant in three out the five (with blood

pressure measurement and Pap smears being

the exceptions). Although there is a high degree of correlation between BEDDAYS and

LIMITED, at least one of them is statistically

significant in all of the models except glaucoma screening, thus supporting the notion

that health status is associated with preventive

service use among the elderly.2

OTA excluded a substantial proportion of

observations because data were missing for

one or more variables in the model. In order

to examine if these exclusions could have

biased the results of the multivariate models,

OTA compared the characteristics of the included and excluded groups. In both data

sets, the included respondents were significantly different from those eliminated beZ However, because mul ticol 1 i neari ty reduces the

precision of the estimator, the stsndard error o f

these two variables’ estimated coefficients may be

biased in the glaucoma screening model.

cause of missing data for only two variables:

HLTHINSR (the presence of any health insurance beyond Medicare) and INCOME. 3

For each of the other variables (including the

dependent variables), the mean for the observations with missing data did not differ

statistically from the mean for observations

included in our analyses. This analysis suggests that the exclusion of observations with

missing data is unlikely to introduce bias into

the multivariate models, but OTA cannot rule

out the possibility.

In order to examine the effect of each

significant variable in the estimated models,

OTA simulated, one independent variable at a

time, how the probability of using each

screening service varied with each possible

value of the independent variables. In these

simulations, all independent variables, except

the one whose effect was being simulated, assumed their mean values.

Table 12 and figures 1 through 4 present

the results of this analysis for each significant

variable in our models. Among the independent variables, holding other factors constant,

age, education, and health insurance appear

to have the greatest overall effect on the

probability of receiving each of these services. This analysis also supports the notion

that blood pressure measurement is different

from other services. Since almost everyone

receives it, there is less variation to explain.

Hence, the variables in the model appear less

important in predicting its use than they do

for the other services.

3 The group of observations excluded from the

analysis had a louer mean income ($14,475 versu s

$15,276 in the two gender data set; pcO.01) and was

less likely to have any insurance coverage beyond

Medicare (0.70 versus 0.79 in the two gender data

set; P<o. os) than was the included group. The in -

come statistic may not accurately reflect th e

entire group of observations with missing dat a

since three-quarters of the observations missin g

any data at al 1 did not have income data.
The Use of Preventive Services by the Elderly ■ 53

Table 12--- Effect of Statistically Significant Binary Variables a in Logistic Regressions on

Elderly Use of Five Screening Services: Predicted Probabilitiesb

MALE

= 0

= 1

. . . . . . . . . . .

NONWHITE

= 0

❑ 1

. . . . . . . . . . .

SMSA

= o

= 1

. . . . . . . . . . .

HLTHINSR

= 0

= 1

. . . . . . . . . . .

ALONE

= o

= 1

....-- . . . . .

LIMITED

= 0

= 1

GLAUCOMA

Predicted

probability

0.70

0.63

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

0.68

0.59

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

0.64

0.77

. . . . . . . . . . . .

0.59

0.69

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

. .

. .

. . . . . . . . . . . .

. .

. .

BLOOOP

Predicted

probability

EYE

Predicted

probability

0.96

0.94

. . . . . . . . . . . . . .

. .

. .

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

. .

. .

. . . . . . . . . . . . . .

0.92

0.96

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

. .

. .

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

0.95

0.96

0.77

0.72

. . . . . . . . . . . . . .

. .

--

. . . . . . . . . . . . . .

0.73

0.77

. . . . . . . . . . . . . .

0.69

0.77

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

. .

. .

. . . . . . . . . . . . . .

0.76

0.74

BREAST

Predicted

probability

xx

xx

. . . . . . . . . . . . .

. .

. .

. . . . . . . . . . . . .

0.45

0.53

. . . . . . . . . . . . .

0.43

0.52

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

0.48

0.53

. . . . . . . . . . . . .

. .

. .

PAP

Predicted

probability

xx

xx

. . . . . . . . . . . . .

. .

. . . . . . . . . . . . .

0.50

0.54

. . . . . . . . . . . . .

0.46

0.55

. . . . . . . . . . . . .

. .

. .

. . . . . . . . . . . . .

. .

. .

. . .

. . .

. . .

. . .

. . .

a

Effect of significant non-binary variables shown in figures 1 through 4.

b

Predicted probability is estimated as 1/[1 + e-x~l where ~is the vector of

estimated coefficients and X is the vector of individual characteristics. Of these

characteristics (a[l independent variables included in the estimated model), eac h

takes on its mean value except the one designated in that row of the table above;

it takes on the va(ue shown in the row header.

Symbo l Key:

XX=Independent variable not included in model

--=Estimated coefficient on independent variable not significant at 0.05 level, two-tailed test

Variable Key:

Dependent Variables:

GLAUCOMA --l=screened for glaucoma within previous 2 years, 11 months; O=otherwise

EYE --l=received eye examination within previous 2 years, 11 months; O=otherwise

BLOOOP --l=had blood pressure measured within previous 1 year, 11 months; O=otherwise

BREAST --l=had clinical breast examination within previous 1 year, 11 months; O=otherwise

PAP --l=had Pap smear within previous 3 years, 11 months; O=otherwise

Independent Variables:

MALE --l=male; O=female

NONWHITE --l=nonwhite; O=white

SMSA --l=resides in a Census Bureau Standard Metropolitan Statistical Area (urban area); O=otherwise

PREPAID --l=enrolled in HMO or some other prepaid health plan; O=otherwise

HLTHINSR --l=has some health insurance coverage or health benefits beyond Medicare including prepaid,

Veterans!, military, or means tested public assistance health benefit; O=otherwise

ALONE --l=lives alone O=otherwise

LIMITED --l=limited in some activity; O=otherwise

SOURCE: Office of Technology Assessment, 1989.
54 The Use of Preventive Services by the Elderly

0

0

*

w

0

0

0

z

E

.

*w

4

’ +
APPENDIX F: ANALYSIS OF PREVENTIVE SERVICE USE

BY OLDER ADULTS IN A HEALTH

MAINTENANCE ORGANIZATION

Methods

The Data

OTA contracted with a health maintenance organization (HMO) to provide data

on the use of eight preventive services by

their

■

■

■

■

■

■

■

■

over-65 year old enrollees:

Check-up visit,

Cholesterol measurement,

Eye examination,

Fecal occult blood test,

Pap smear,

Influenza immunization,

Pneumococcal immunization, and

Tetanus immunization.

OTA chose these services in consultation with

the HMO to meet the following criteria:

they are services often included among

discussions or recommendations for

elderly preventive health; and

the HMO’s data system routinely records

their use as distinct services.

To examine how use varies with age, the

HMO also provided comparable data for enrollees between the ages of 40 and 64. The

HMO measured the proportions of enrollees

using each service within the periods of time

presented in table 8.

The HMO is a large, urban, staff model

health maintenance organization located in

the Northeastern United States. It serves enrollees through private employers, government agencies, and individual accounts.

Since January 1976, the HMO has served

Medicare beneficiaries, initially under a plan

where the HMO billed Medicare fo r

Medicare-covered procedures on a fee-forservice basis. The HMO provided noncovered procedures, including the preventive

services examined in this study, through a

“wraparound” or “Medi-gap” policy purchased

by or for the enrollee.

Beginning in July 1985, the HMO entered into a Medicare demonstration risk contract with over 80 percent of its 2500 existing

Medicare enrollees transferring into this plan

within the first three months. All of the services covered under the HMO’s basic benefit

package, including preventive services, were

included in the risk contract plan.

The HMO has traditionally encouraged

the use of preventive services by at-risk populations through clinical guidelines for preventive care and coverage of regular checkups. Before October 1987, the monitoring of

compliance with these guidelines was limited

to pediatric screening and immunization,

prenatal screening, and influenza immunization. Since that date, the HMO has adopted a

program to monitor and inform clinicians at

each visit of a patient’s compliance with the

HMO’s preventive guidelines. Since OTA

believed that this program is not typical of

most HMOs, this HMO used October 1987 as

the endpoint for measuring rates of use for

each preventive services studied. Hence,

during the periods of time examined, only

influenza immunizations reflect any monitoring by the HMO, and for that service,

clinicians only received information on aggregate rates of compliance among all enrollees.

Methods of Analysis

The base population for this study is all

present and former HMO enrollees who were

age 40 or older as of October 1, 1987. The

HMO identified the base population through

a computerized search of enrollment records

and separated the population into four subgroups on the basis of age:

■ 40 to 49 years old,

• 50 to 64 years old,

■ 65 to 74 years old, and

■ 75 years and older.

55
56 ■ The Use of Preventive Services by the Elderly

Because the HMO calculated age at the end

of the observation period, some of the enrollees in each group fell below the low age

threshold at the time they actually used a

specific service.

Through computer searches of this base

population, the HMO defined a “denominator

population” for each age group and observation period over which the use of a specific

preventive service was to be measured. Each

“denominator poplation” consisted of all persons of appropriate age continuously enrolled

in the HMO during the observation period.

Since two of the HMO’s ten clinics did not

have computerized records at the level of

specific clinical services, enrollees from these

sites were excluded from the analysis. Enrollees excluded from the denominator files because they came from one of the noncomputerized sites or because they were not

continuous members represented 20 percent

of the base population in each age group.

The “denominator” file for the over-65

age groups consists of all continuously enrolled individuals from the eight sites. The

denominator population for the 40 to 64 age

groups were so large that the HMO used a

random sample of these groups for the analysis. They chose a 10-percent random sample

for all but the 10-year observation period,

where they chose a 20-percent random

sample. Table 13 presents the number of observations in each “denominator” file used to

calculate the rates of use.

In order to measure the use of each service for each age group, the HMO searched

the base population to form “numerator” files

consisting of persons who met both of the

following criteria:

■ the individual was enrolled in one of the

eight sites at the time the analysis was

conducted (June through September

1988); and

■ the individual received the specific preventive service within the observation

period.

To calculate rates of use, each “numerator” file was compared to its corresponding

“denominator” file. Individuals in the numerator file who did not appear in the denominator file were discarded. Stratifying by

gender, the HMO tallied the number of individuals remaining in each “numerator” file

and divided that number by the number in

the corresponding “denominator” file to calculate a rate of use for each service and agegender group. Table 14 presents the results

of this analysis.

It is possible that a few continuously enrolled members transferred from one of the

two excluded sites to one of the eight included sites before October 1, 1987. While

such individuals would be included in the

“denominator” files, they would not appear in

the “numerator” file if they received a preventive service at the excluded site. This

would deflate the use rate. However, because

the two excluded sites serve geographically

distinct communities with most members

living in close proximity to the clinic, transfer to another site is relatively rare. Therefore, OTA and the HMO concluded that the

potential undercounting in the use rates is

minimal.
Table 13--- Sample Sizes for Each Measurement Period in OTA’S Analysis of

Preventive Service Use in One HMO

3 yr. and 2 yr. **

10 yr. period 5 yr. period** ( 10/84-10/87) 1 yr. period**

Age ( 10/77-10/88) ( 10/82-10/87) and (10/85-10/87) (10/86-10/87)

I I 1 1

Male Female Total Male Female Total Male Female Total Male Female Total

40-4P 250 260 510 455 507 962 725 797 1522 995 1068 2063

50-64’ 246 227 473 282 331 613 466 518 984 654 784 1438

65-74** 329 307 636 849 ,956 1805 1265 1440 2705 1902 2219 4128

75+** 113 145 258 204 313 517 271 395 666 514 752 1266

\ I I I I

● The n for 40-49 and 50-64 age groupa represent a 20 percent sqle of menbers continuously enrolled during each period at the eight sites

studied. The 65-74 and 75+ age groups represent all mwbers.

● *The n for 40-49 ad SO-64 age groups represent a Fpercent sanp~e of menbers continuously enrolled durirw each period at the eight sites

studied. The 65-74 and 75+ age groups represent all merbers. —

Table 14--- Percents of Conti nuously Enrolled Members Receiving Eight Preventive Services

During Specified Periods of Time

Pneunococcal

Check-up visit Cholesterol Eye exam Fecal occult blood Pap smear Influenza vaccine vaccine Tetanus vaccine

Age (1 year period) (5 year period) (2 year period) (1 year period) (3 year period) (1 year period) (lifetime) (10 year period)

Male Female Total Male Female Total Male Female Total Male Female Total Male Female Total Male Female Total Male Female Total Male Female Total

40-49 yrs. 24% 39% 32X 68% 67% 67% 39% 50% 44% 21% 32% 27% N/A 80% N/A 4% 5% 4% 1% 1% 20% 17 18%

50-64 yrs. 38 49 44 80 77 78 51 55 53 36 46 41 N/A 73 N/A 14 12 13 7 5 6 13 13 13

Subtotal

40-64 29 43 37 73 71 72 44 52 48 27 38 33 N/A 77 N/A 8 8 8 3 2 3 17 15 16

65-74 yrs. 48 52 50 77 76 76 70 74 72 49 53 51 N/A 75 N/A 55 55 55 29 27 28 47 40 44

75+ yrs. 52 47 49 69 64 66 80 79 79 51 46 48 N/A 60 N/A 67 63 64 47 42 44 40 35 37

Subtotal

65+ 49 51 50 75 73 74 72 75 73 49 52 50 N/A 71 N/A 58 57 57 33 30 31 45 38 42

,

Abbreviation: N/A = Not applicable.

SOURCE: Office of Technology Assessment, 1989.

■
ACRONYMS

AAO

ACOG

ACP

ACR

ACS

ADA

A HA

AMA

AMWA

ANA

AOA

ASPB

CDC

CPS

CTF

EKG

HCFA

HMO

IPA

MMWR

NCHS

NCHSR

NCI

NHIS

NHLBI

NIH

OTA

SMSA

USPSTF

--American

--American

--American

--American

--American

--American

--American

--American

--American

--American

--American

--American

Academy of Ophthalmology

College of Obstetrics and Gynecology

College of Physicians

College of Radiologists

Cancer Society

Diabetes Association

Heart Association

Medical Association

Medical Women’s Association

Nurses Association

Optometric Association

Society to Prevent Blindness

--Centers for Disease Control

--Current Population Survey

--Canadian Task Force on the Periodic Health Examination

--Electrocardiogram

--Health Care Financing Administration

--Health maintenance organization

--Independent practice association

--Morbidity and Mortality Weekly Report

--National Center for Health Statistics

--National Center for Health Services Research and Health Technology Assessment

--National Cancer Institute

--National Health Interview Survey

--National Heart, Lung, and Blood Institute

--National Institutes of Health

--Office of Technology Assessment

--Standard Metropolitan Statistical Area

--United States Preventive Services Task Force

59
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0