Reconsidering Age

Adjustment

Procedures:

Workshop Proceedings

Series 4:

Documents and Committee Reports

No. 29

This report contains papers presented at the Workshop on

Age-Adjustment, March 7, 1991. Presentations are made by

representatives of selected Federal, State, and international

institutions dealing with issues in the use of the 1940 U.S.

population as a standard for the age-adjustment of official

statistics.

Manning Feinleib, M.D., Dr.P.H., and Alvan O. Zarate, Ph.D., Editors

Hyattsville, Maryland

October 1992

DHHS Publication No. (PHS) 93-1466

Vital and

Health Statistics

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Public Health Service

Centers for Disease Control

National Center for Health Statistics
Copyright Information

All material appearing in this report is in the public domain and may be

reproduced or copied without permission; citation as to source, however, is

appreciated.

Suggested Citation

Feinleib M, Zarate AO, eds. Reconsidering age adjustment procedures:

Workshop proceedings. National Center for Health Statistics. Vital Health Stat

4(29). 1992.

Library of Congress Cataloging-in-Publication Data

Reconsidering age-adjustment procedures : workshop proceedings.

p. cm.—(Vital and health statistics. Series 4, Documents and committee

reports ; no. 29) (DHHS publication ; no. (PHS) 92-1466)

ISBN 0-8406-0464-5

1. Epidemiology—United States—Statistical methods—Congresses. 2.

Epidemiology—United States—Mathematical models—Congresses. 3. Age

distribution (Demography)—United States—Congresses. I. National Center for

Health Statistics (U.S.) II. Series. III. Series: DHHS publication ; no. (PHS)

92-1466.

RA652.2.M3R43 1992

614.4’273—dc20

92-25492

CIP
National Center for Health Statistics

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

Jack R. Anderson, Acting Deputy Director

Jacob J. Feldman, Ph.D., Associate Director for Analysis

and Epidemiology

Gail F. Fisher, Ph.D., Associate Director for Planning and

Extramural Programs

Peter L. Hurley, Associate Director for Vital and Health

Statistics Systems

Robert A. Israel, Associate Director for International

Statistics

Stephen E. Nieberding, Associate Director for

Management

Charles J. Rothwell, Associate Director for Data

Processing and Services

Monroe G. Sirken, Ph.D., Associate Director for Research

and Methodology

David L. Larson, Assistant Director, Atlanta

Office of the Center Director

Alvan O. Zarate, Ph.D., Special Assistant to the Director
Preface

This report contains papers presented at a workshop

held at the National Center for Health Statistics (NCHS)

on March 7, 1991. The workshop was held in order to

address concerns arising from the use of the 1940 U.S.

population as a standard for age adjustment of vital rates

and to review issues surrounding the use of alternative

standards from a variety of perspectives. Participants included representatives of selected Federal Government

agencies, the National Committee on Vital and Health

Statistics, the State of Michigan, and present and former

NCHS staff.

iii
Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Part I Introduction

Chapter 1. A reconsideration of age adjustment by Manning Feinleib............................................. 3

Part II Historical and methodological perspectives

Chapter 2. A short history of standardization for vital events by Lester R. Curtin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Chapter 3. Inadequacies of age adjustment as illustrated in cancer mortality by Katherine Gray-Donald. . . . . . . . . . . . . . . . . 17

Part III Advantages and disadvantages of age adjustment

Chapter 4. The use of multiple standards by Joel C. Kleinman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Chapter 5. Choosing a standard population: Some statistical considerations by Harry M. Rosenberg, Lester R. Curtin,

Jeffrey Maurer, and Katherine Offutt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Park IV Areas of application

Chapter 6. The choice of the standard for age adjustment by Richard Rothenberg and Robert Hahn . . . . . . . . . . . . . . . . . . . . 49

Chapter 7. Use of age-adjusted disease rates for cancer by Edward J. Sondik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Chapter 8. Age adjustment for the year 2000 health objectives by Michael A. Stoto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Part V Considerations for the future

Chapter 9. Age adjustment in National Center for Health Statistics mortality data: Implications of a change in

procedures by John E. Patterson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Chapter 10. Summary and recommendations by Manning Feinleib . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Part VI Appendixes

Appendix I. Workshop participants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Appendix II. Age-specific weights of age-specific percent changes assumed in the computation of percent changes

based on age-adjusted death rates by Jeffrey Maurer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

v
Symbols

- - - Data not available

. . . Category not applicable

- Quantity zero

0.0 Quantity more than zero but less than

0.05

Z Quantity more than zero but less than

500 where numbers are rounded to

thousands

* Figure does not meet standard of

reliability or precision

# Figure suppressed to comply with

confidentiality requirements

vi
Part I

Introduction
Chapter 1

A reconsideration of age

adjustment

by Manning Feinleib, M.D., Dr.PH.

National Center for Health Statistics

Introduction

During the last 150 years, there have been at least

eight formal occasions that I have been able to identify on

which experts have come together under the auspices of

official statistical agencies or societies to discuss the issue

of age adjustment. Undoubtedly, there have been innumerable other discussions that have not found their way into

the literature. The need for this current workshop came

about because of some adjustment issues that arose in

formulating the health objectives for the Nation for the

year 2000. We hope this workshop will provide a collegial

discussion of these issues.

From another point of view, however, it is almost a

court case with plaintiffs and defendants. The plaintiffs

arguing that the current method has to be changed and

offering reasons why, and the defendants arguing that the

current method has been successful, or at least adequate,

for its intended uses and should be maintained. At the end

of this workshop, we will deliberate the claims and make

recommendations as to how to adjudicate between them.

The crux of the issue is that we want to find out to

what extent the current standard is truly inadequate—

either not representing the true trends in a statistical

sense or not communicating the intended message in a

psychological or political sense. While age adjustment is

used to enhance understanding of masses of data, it also

conceals details; and we need to see examples of how this

works in practice to the disadvantage of public health

information, which will guide policies and inform the

public.

As an introduction to these discussions, I would like

to review briefly a few issues that have come up repeatedly

in previous deliberations on age standardization and put

them into a more modern perspective.

The distribution of the population and

deaths by age

Figure 1 shows the number of deaths, the estimated

midyear population, and the calculated death rates by age

for the United States in 1986. Ten-year intervals are used

except for ages less than 1 year, 1–4 years, and 85 years

and over. Many demographers have been struck by the

exponential nature of the mortality curve (for example,

Gompertz), which ranges from 236 per million at ages 5–9

years to 153,989 per million at ages 85 years and over.

Most age adjustment methods weight the individual agespecific death rates by a standard distribution of the

population or of the number of deaths in order to provide

a single index, which is to serve as a summary indicator of

this broad range of death rates (See part II). Some causes

for concern are immediately obvious. Since most deaths

occur over 70 years of age, age adjustment methods based

upon numbers of deaths will give great weight to this

portion of the age range. Those based on population

distributions will give more weight to the younger age

groups.

The current practice is to calculate a single death rate

for the 85 years and over population. Currently, nearly

20 percent of all deaths are included in this single group.

As the population ages and the mortality curve becomes

flatter into older ages, more and more deaths will occur in

this terminal open-ended stratum. A single death rate

calculated for this group will not be sensitive to any

changes in the age-specific death rates over the age of 85.

Historical perspective

The earliest reference to age adjustment that I have

been able to find comes in an 1844 paper by F.G.P. Neison

(1) presented to the Statistical Society of London (now the

Royal Statistical Society). Neison was responding to an

issue raised by Sir Edwin Chadwick concerning the

Figure 1. Number of deaths and death rates: United States, 1986

3
measurement of the health of different communities. Chadwick had recommended the examination of the average

age at death of those who had died during a particular

year in the relevant communities as an indication of the

comparative health conditions in those communities. Neison pointed out that the average age at death would

depend to a large extent on the age distribution of the

residents of the communities. While Chadwick’s method

may have had some merit if the populations of the

communities had been closed and stable, Neison recognized that differing migration and birth patterns produced

differences in the age distributions, which would have a

profound effect on the average age at death.

In his paper Neison compared the mortality of two

communities, Bethnal-green and St. George’s Hanoversquare, and proposed that in order to overcome the

differences in age structure, ‘‘. . . we shall suppose that the

population of Bethnal-green is actually transferred to St.

George’s . . .’’ and subject it to ‘‘. . . exactly the same rate

of mortality as that prevailed in St. George’s Hanoversquare’’—the only change being a change in population.

Bethnal-green is taken here to be the standard population.

Here we have—almost fully blown—the direct standardization method. As a matter of fact, later in the paper

the indirect method of standardization is presented as

well. One year later, Neison presented an even more

detailed report using the same method comparing occupational classes (2).

A more complete historical review is presented in part

II in a paper presented by Lester Curtin.

Age patterns of mortality by cause

As I have indicated, mortality rates vary considerably

with age. The trends in mortality rates over time also vary

by underlying cause of death. Figure 2 shows changes

between 1940 and 1988 in age-specific mortality rates from

accidents, cancer, and heart disease.

For all causes combined, there has been tremendous

improvement at virtually all ages. At the youngest ages,

mortality is only 20 percent of what it was 50 years ago—a

tremendous improvement by any standard. Even at the

older ages, mortality is only about 60 percent of what it

was in 1940.

In the case of heart disease there has, at the youngest

ages, actually been an increase due to better diagnosis of

congenital conditions. At 5–14 years of age, mortality from

heart disease is only about 10 percent of what it was. With

advancing age, the improvement is progressively less impressive so that at age 85 years and over the 1988 rate is

only 20 percent less than the rate for 1940.

In contrast, trends in mortality from accidents indicate that while young adults have just about the same rate

they had 50 years ago, there have been great improvements among children and even more dramatic improvements among the elderly. We have made the environment

much safer for elderly people. There have been great

improvements in industrial and occupational accidents,

and people who fall and break a hip will usually survive,

whereas, 50 years ago it was virtually a death sentence.

The situation with cancer is more complicated. Significant progress has been made in infancy but we have not

done as well among those 5–14 years of age. I imagine that

some of the increase in mortality at these ages represents

a postponement of deaths from younger ages. We have

done well among young adults but after the age of 55

there has actually been a 20 percent increase since 1940.

The effect of using different standard

populations

To show the effect of using different standard populations when employing the method of direct standardization, we have examined trends for six different populations

(figure 3):

U.S. 1940. This population has been used for nearly 50

years in the United States. It emphasizes the age group

15–24 years, and then it has a nearly linear decline to ages

85 years and over.

WHO WORLD. WHO has estimated the world population for 1975, which is even younger than the U.S. 1940

population but has essentially the same structure (3).

WHO European. This standard is based upon the

populations of the European countries in 1975 and is quite

older than the WHO WORLD population. Between the

ages of 5 and 64 years, there is virtually a uniform

distribution of population and then a linear decline through

the remaining ages (3).

U.S. 1990. Because of its recency, this has been

proposed as a new standard. It is similar to the WHO

European standard, except for distortions due to the baby

boom.

World Bank 2020 and 2050. Using World Bank projections for nine industrialized countries (4), Dr. Alvan O.

Zarate and I have developed standard populations for the

year 2020 and 2050. The idea being that since the choice

Figure 2. Age-specific mortality rates from selected causes, 1988

as percent of 1940

4
of the standard population is somewhat arbitrary, instead

of looking back into the past we might look ahead to the

standard population that we are aiming toward and that if

the population changes as predicted, the age-adjusted rate

might approximate the crude rate. The 2020 and 2050

standards are similar except that the 2020 standard reflects the lingering effects of the post-World War II baby

boom while that for 2050 eliminates the baby boom and

allows for some improvement in mortality at the older

ages.

Now let us examine the effect of applying the six

different standards to the three disease conditions mentioned previously.

Cancer

Using the U.S. 1940 population, the trend in cancer

mortality shows about a 10 percent increase (figure 4).

Because the other standards are based on older populations (the WHO WORLD is excluded), they all result in

higher rates but the trends are quite similar, with increases ranging from 10 percent using the U.S. 1940

standard to 15 percent using the 2020 or 2050 populations. The unadjusted rate increased by 64 percent.

From the point of view of the long-term trend in

cancer then, these rates are essentially equivalent—

although the absolute rates themselves are quite different.

None of the standard populations can reveal the differences in trends among the younger and older adults and,

indeed, because of the grossly different time trends among

these two different age segments, a single age-adjusted

rate should probably not be used to summarize these

trends.

Heart disease

Figure 5 shows that the unadjusted mortality rate for

heart disease appears to have peaked sometime around

1960, while use of the 1940 standard results in a peak at

about 1950. Again, all of the standards show similar trends

Figure 3. Age distribution of six standard populations

Figure 4. Age-adjusted death rates for cancer using alternate

standards

5
with declines ranging between 57 (1940) and 66 (World

Bank 2050) percent. Whatever standard is used, the interpretation would be quite similar.

Accidents

Accidents present a paradoxical picture because as we

indicated, mortality was extremely high among older age

groups in 1940, and the most dramatic declines have

occurred in these groups (figure 6). Yet, because the 1940

standard population is young compared with the other

standards under examination, the smallest improvement in

overall mortality results from its use.

While the trends are similar no matter which standard

is used, the order of magnitude is quite different. Using

the 1940 population, 1988 accident mortality is 48 percent

of that for 1940. Using the 2050 standard, mortality is

32 percent of what it was in 1940, but again, there is an

unmistakable decline.

This demonstrates some of the varied results that can

be obtained by using different standards. While the trends

are sometimes more dramatic using the older standards,

the results are essentially the same.

The key dilemma that arises in using an age-adjusted

rate is the impossibility of a single index being able to

reflect nonhomogeneous effects. As long as the trends

over time for different age groups are nearly the same, any

reasonable choice of standard population will serve to

reflect the overall trend adequately and permit comparison of different populations. The greater the disparity in

trends by age, the more unsatisfactory will a single summary measure be. There is general consensus that serious

investigations of trends should consider the trends in

age-specific rates. But because such analyses become very

detailed, it is still desirable to provide some simplifying

summary. If a single measure is deemed unsatisfactory and

detailed age-specific trends too complex, is it possible that

a compromise can be struck by using a relatively small

number, say 5 or 6, of broader age groups that will reflect

the general trends, show major inhomogeneities, and yet

be readily understandable by most data users?

For example, the following segmentation of the age

range (with age adjustment within each strata, if necessary) may serve to accomplish these ends:

1. Infant mortality

2. Ages 1–14 years

3. Ages 15–34 years

4. Ages 35–64 years

5. Ages 65–84 years

6. Ages 85 years and over

It will be useful in our discussion to examine this

concept and see if a consensus could be reached about

appropriate age strata.

Charge to the workshop

With this as an introduction, I wish now to have the

workshop participants review and make recommendations

concerning the following:

1. The potential confusion among some data users in

understanding the difference between crude and ageadjusted rates and the disparity between the magnitude of crude and age-adjusted rates.

2. The adverse ‘‘psychological’’ effect of current rates

being adjusted by the 1940 population.

3. Differences in trends for certain causes of death when

different standards are used.

4. The appropriateness of adjusting at all when trends

differ markedly across age groups.

5. The impact of using different standards by different

Federal agencies or in different reports.

6. The lack of information concerning the rationale for

use of Federal standards.

Figure 5. Age-adjusted death rates for heart disease using

alternate standards

Figure 6. Age-adjusted death rates for accidents using alternate

standards

6
Some of these issues I have already briefly touched on

but I hope that in our presentations and discussions, we

will give adequate consideration to all of these concerns

and develop a set of recommendations for dealing with

them.

References

1. Neison FGP. On a method recently proposed for conducting

inquiries into the comparative sanatory condition of various

districts. London J Royal Stat Soc 7:40–68. 1844.

2. Neison FGP. Contributions to vital statistics, especially

designed to elucidate the rate of mortality, the laws of

sickness, and the influences of trade and locality on health,

derived from an extensive collection of original data, supplied by friendly societies, and proving their too frequent

instability. J Royal Stat Soc 8:290. 1845.

3. World Health Organization. World Health Statistics Annual

Geneva:World Health Organization. 1991.

4. Zachariah KC, Vu MT. World population projections 1987–88

ed.: standard populations represent weighted averages of

the projected populations of Australia, Canada, Hungary,

Israel, Japan, Norway, Poland, the United Kingdom, and

the United States. Baltimore: Johns Hopkins University

Press. 1988.

7
Part II

Historical and methodological

perspectives
Chapter 2

A short history of

standardization for vital

events

by Lester R. Curtin, Ph.D., Office of

Research and Methodology, National Center

for Health Statistics

Introduction

In 1662, John Graunt published Natural and Political

Observations Mentioned in a Following Index and Made

Upon the Bills of Mortality (1). This analysis of the Bills of

Mortality for London signaled the beginning of the development of analytic methods, such as the life table method,

for using vital statistics and mortality data to examine

public health issues.

A large variety of summary indexes for mortality have

been proposed since the time of John Graunt. Most of

these indexes consider the problem of standardization for

differential age distributions. In the following, a brief

history of two such summary indexes is presented that

draws heavily on some previously published work.

Lilienfeld and Lilienfeld, in examining the history of

epidemiology, took note of beginnings of age standardization for mortality data (2). That article, plus a later one by

Lilienfeld (3), are the sources of much of the following

information. In addition, more detailed history and discussion of mortality indexes can be found in chapter 4 by

Woolsey in Vital Statistics Rates in the United States, 1900–40

(4) and in the chapter by Pearl in Medical Biometry and

Statistics (5).

Background

The development of analytic methods for vital statistics coincided with many of the initial developments in the

field of mathematical statistics. Some time after Graunt,

DeMoivre published his ‘‘Doctrine of Chances’’ in 1718

and is often credited with the discovery of the normal

curve in 1733 (6). Laplace published ‘‘Theorie Analytique

des Probabilites’’ in 1812, the same year Gauss published

the ‘‘Theory of Least Squares’’ (5). Naturally, these theoretical developments were soon followed by new applications of the methods.

The Frenchman Pierre Charles-Alexandre Louis is

attributed with the first vigorous applications of statistical

methods to medical data, starting around 1830. Louis

popularized the use of numerical methods in the study of

medicine and was ‘‘responsible for the development of

current concepts of epidemiologic reasoning’’ (3). Two of

Louis’ students, William Guy and William Farr, were

responsible for the development of the English school of

statistics in the mid-1800’s. William Guy, a professor of

medicine at King’s College, London, used statistical methods to study occupation diseases (7) and was probably the

first person to employ Monte Carlo simulation to empirically examine statistical theories (8). William Farr’s contributions to epidemiology were numerous (2,3); his work

has been collected in a memorial volume (9).

The growing science of statistics was still in its infancy

when, at the suggestion of the British Association for the

Advancement of Science, the Statistical Society of London

was formed in 1834 (10). The Royal Actuarial Society was

founded 14 years later, in 1848, and the London Epidemiological Society was formed March 6, 1850 (2). Thomas

Malthus, Benjamin Gompertz, Auguste Quetelet, and Sir

Edwin Chadwick were among the notable founders of the

Statistical Society of London (10). Thomas Malthus is, of

course, famous for his theories of population growth.

Benjamin Gompertz developed the first mathematical

model for mortality data. Auguste Quetelet, a Belgium

statistician who trained briefly in France with Fourier and

Laplace, was influential in the application of statistical

thought to the social sciences (6). Sir Edwin Chadwick, a

sanitary reformer, was indirectly responsible for the development of standardized rates. Because of the influence of

these statistical pioneers, about one-third of the early

papers presented at the Statistical Society of London were

concerned with vital statistics (10).

During this period, English Public Health Practitioners, sometimes referred to as ‘‘sanitary physicians,’’ recognized the importance of mortality data as a means to

describe and compare the health of various communities.

These early public health practitioners also began to

realize that the crude death rates were not appropriate to

compare the health conditions for various small geographic areas. It was the issue of geographic comparisons

of mortality that lead to the development of the present

methods of direct and indirect standardization.

The beginning of the use of standardization

By the 1840’s, comparisons of crude rates were recognized to be inappropriate when the age distributions of

the geographic areas differed greatly. Public health analysts wanted an index of mortality that would be free from

the effect of age differences; they also wanted an index

that would provide for an economy of expression and a

simple summarization of mortality. While many different

11
summary measures of mortality have been proposed over

the last 150 years, two particular indexes remain as the

most used.

These two widely used summary indexes for mortality

data have become known as the direct standardized (ageadjusted) death rate and the indirect standardized death

rate. Indirect standardization is often interpreted as an

approximation to the method of direct standardization.

That is, when data needed to compute a direct measure

are not available, analysts may still have enough information to compute an indirectly standardized measure. However, the indirect standardization has intrinsic value and

should be considered on its own merits, not solely as an

approximation to direct standardization (4). Although

demographic textbooks will often denote the direct measure as the preferred measure, this viewpoint is definitely

not shared by everyone.

Interestingly, although the direct measure is now the

preferred measure for vital statistics in the United States,

it appears as though the indirect standardized death rate

may have appeared first in (British) official government

statistics. Benjamin and Pollard state:

The first reference to a standard rate occurs in Farr’s

report of 1856 (Sixteenth Annual Report of the Registrar General for 1853) . . . The concept was later

used (Twentieth Annual Report) to calculate a standard ‘‘natural’’ death rate for London in order to

assess the excess mortality of the Metropolis. Essentially it represented ‘‘indirect’’ standardization. It appears that the direct method was due to Ogle who

read a paper on the subject to the International

Statistical Institute in 1891, recommending the use of

an international standard population (though the direct method had in fact been employed in the Annual

Report of the Registrar General for 1885) (11).

However, Benjamin and Pollard may be incorrect on

both the first appearance of the direct standardized rate

and as to who suggested the direct method. Several papers

have attributed the first use of a standardized rate to

Neison in 1844 (3,4).

Neison read a paper before the Statistical Society of

London (now the Royal Statistical Society) on January 15,

1844. This paper was a response to a paper read by Sir

Edwin Chadwick at the previous meeting of the Society.

Subsequently, both papers were published in the Journal

of the Royal Statistical Society (12,13).

Sir Edwin Chadwick was one of the early public health

reformers in England. In particular, he was interested in

the sanitary conditions of the laboring poor and the

subsequent differences in health between urban and rural

areas (14). Chadwick had written a report in 1842 that

emphasized the link between disease and ‘‘dirt’’ due to

insanitary conditions and overcrowding. His report stressed

both the economic cost of ill-health and the social cost in

terms of morals and habits (15,16). To support his conclusions, Chadwick examined mortality data for the different

sanitation districts.

In a paper read before the Royal Statistical Society,

Chadwick proposed the use of a mean age at death as an

index of health that could be used to compare the health

of the various ‘‘sanitation districts’’ around London. He

argued that the mean age reflected a true summary of the

age-specific risks of dying. Neison, a practicing actuary,

took exception to the logic behind Chadwick’s proposal

and in a very brief period put together a rather remarkable article that introduced both the concepts of direct

and indirect adjustment as well as introducing the term

‘‘standard population.’’

Basically, Neison recognized that the age distributions

for the various community populations were very different.

He reasoned that, because mortality increased with age,

Chadwick’s mean age at death for those communities with

a relatively older population would tend to overstate the

excess of death.

In order to account for the difference in age distribution of the communities to be compared, Neison states:

We shall suppose that the population of Bethnalgreen is actually transferred to St. George’s, Hanoversquare, but influenced by exactly the same rate of

mortality (12).

That is, the age-specific rates for each community

could be assumed to apply to exactly the same population

distribution by age. Neison then used the expected deaths

by age to compute an adjusted, or corrected, mean age at

death; the method proposed is clearly the method of

direct standardization.

Neison compared the crude mean age at death with

the mean age computed by a method of direct standardization to illustrate the fallacy of Chadwick’s comparisons.

At that time, mortality rates were given as ‘‘mortality

percents,’’ and Neison’s table included a mortality percent

for ‘‘deaths transferred’’—that is, Neison computed an

age-adjusted rate by the direct method with the population of Bethnal-green as the standard. Neison even called

the population a standard, viz. ‘‘what would have been the

result provided they had been under the Bethnal-green

standard of population’’ (12).

Neison goes on to state ‘‘another method of viewing

this question would be to apply the same rate of mortality

to different populations’’ (12). This, of course, is the

method of indirect standardization. Neison again used the

indirect method to compute an adjusted mean age at

death; but he did not carry through on the method to

compute what we would now call a standardized mortality

ratio.

A short time after Neison had read his paper, William

Farr entered the debate in a curious manner. Farr had

been appointed compiler of abstracts in the office of the

Registrar General in 1839. According to Lilienfeld (3),

after Neison’s paper had been presented, Chadwick had

shown his analyses to Farr and Farr had apparently agreed

with Chadwick. Chadwick then published his analysis

along with Neison in the Journal of the Royal Statistical

Society. But the Lancet published an editorial critical of

12
Chadwick’s analysis. Much to Chadwick’s surprise, the

(unauthored) editorial was apparently written by Farr

(3,17).

If this was indeed the sequence of events, then it is

reasonable to assume that Farr was more in agreement

with Neison; this is borne out by the subsequent use of the

indirect adjustment procedure by Farr in 1853. Again,

Neison had proposed both a direct and an indirect adjustment procedure, but Farr was the first to implement the

indirect adjustment procedure. After the initial debate on

standardization came to a close, Neison went on to become one of the founders of the Royal Actuarial Society;

Chadwick continued to fight for reform of laws affecting

the poor, and Farr became the leading epidemiologist of

his time.

Use of direct standardization in England

Various mortality indices were published in reports of

the Registrar General of England and Wales during the

late 1800’s (4). As mentioned, the indirect standardized

rate was introduced in 1853. Woolsey, along with Benjamin and Pollard, states that the first use of the direct

standardized rate was in 1883 in the Registrar General’s

report. The English reports continued to use direct standardization up to 1938. In 1946 the comparative mortality

index was adopted, and then in 1958 the standardized

mortality ratios were adopted (18,19).

When the first ‘‘official’’ direct standardized death

rates were published in 1883, the standard population was

based on the most current census population available,

namely, the 1881 census population. The initial practice

was to change the standard population every 10 years

using each new census population. This was found to be

problematic due to considerations of recomputing historical rates to assess current trends.

Because of the problems of changing standards every

10 years and the need for international comparisons as

well, Ogle, one of Farr’s successors, proposed the use of

an international standard population (20). This standard

was based on a composite of seven European country

populations. However, the idea of one international standard never gained acceptance, even in England and Wales.

Around the turn of the century, England and Wales

decided to adopt the 1901 population as ‘‘the’’ standard

population and to use this standard even after a new

decennial census. Note that at the time of its adoption, the

1901 population was the latest, most current census population.

Use of direct standardization in the United States

In the early 20th century, methods for the analysis of

vital statistics in the United States often followed the lead

of England and Wales. In Mortality 1911, standardized

death rates were published for States and cities having

populations of 100,000 or more (21). From 1921 to 1924 a

similar form of the age-adjusted rate was shown for

specific causes and for geographic areas. In Mortality Rates

1910–20, published by the Bureau of the Census in 1923, a

number of ‘‘adjusted’’ rates were shown (22).

In these first United States mortality reports, the

age-adjusted death rates were based on the England and

Wales standard population of 1901. At that time, the

standard population was chosen to provide comparability

between the mortality measures produced by the two

countries. For Vital Statistics Rates in the United States,

1900–40 (23) as well as for Vital Statistics Special Reports,

1900–53, it was decided that the United States population

was different enough from 1901 England that a new

standard should be used. At the time, the 1940 U.S.

population was the latest census population available and

was thus used as the standard. Since that time, all ageadjusted rates produced as part of the official vital statistics that are published in the Vital Statistics of the United

States, have used the 1940 population as the standard.

Except for life tables, no other mortality summary rates

have been published on an annual basis in the Vital

Statistics of the United States.

Selection of standard population

In the early 1930’s the Committee on Forms and

Methods of Statistical Practice of the American Public

Health Association

. . . has addressed itself to the task of determining

what, if anything, might conveniently be done to

reduce the distortion and incomparability of rates due

to varying proportions of young and old in the populations of different times and different geographic

areas (24).

This committee presented two reports (4). The first

report states ‘‘. . . is it not unsound to confuse the health

administrator with crude death rates and possibly to cause

the public erroneously to infer that mortality is in general

rising, at a time when health appropriations are greater

than ever before?’’ (25). The second report contained

many recommendations, three of which Woolsey specified

as:

a. That the use of the unadjusted total death rate be

minimized... (and) referred to always as the ‘‘crude

death rate’’;

b. that age-specific rates be used in place of the crude

death rate whenever possible, even if broad age

groups be used; and

c. that before any single death rate for all ages be

adopted as standard, there should be further study

(26).

Thus, the committee argued strongly in favor of adjusted

rates but provided no recommendation for which index or

which standard to use.

The issue of what standard population to use in direct

standardization has been addressed by a number of authors in journals and in textbooks. One of the best

summaries of the issues in selection of a standard population was given by Wolfenden:

13
In connection with this problem of choosing an appropriate standard population, it is essential to remember

that directly standardized rates are really index numbers which are constructed only for the purpose of

comparison, so that no significance is to be attached to

their absolute magnitudes; the main characteristic of

the standard population consequently should be that

it is not unnatural or clearly abnormal. In practice it

will be found that various standard populations chosen within reason have insignificant effects upon the

inferences to be drawn from comparisons of directly

standardized rates for different geographical areas at

all ages from all causes... Care, however, must of

course be taken in drawing inferences from comparisons of standardized rates in respect of subdivisions of

a general population, e.g., for certain causes of death

or occupations, in which the age distributions of the

populations exposed to risk may show peculiar characteristics sharply different from any normal general

population (27).

There are three main points in this statement. First,

the age-adjusted rate is an index number. Its magnitude

has no meaning, and it should be used only for comparisons. Thus, in examining age-adjusted rates based on

different standard populations, it is not really a valid

argument to state that a particular standard population

yields an age-adjusted death rate that looks ‘‘too low.’’

The only valid consideration is whether the comparisons

between geographic areas, or between population subgroups (such as males compared with females), are valid.

Second, although selection of a standard is somewhat

arbitrary, the standard selected should not be clearly

abnormal. Thus, in the current debate, the issue of whether

the current United States census population is significantly

different from the 1940 population is a valid point of

contention.

Third, when age-adjusted rates are computed and

published in quantity, such as in the Vital Statistics of the

United States, and a valid standard is chosen, there is still a

certain amount of caution that needs to be exercised in

the use of the age-adjusted rates. An analyst must still

confirm that for any particular analyses, the subdomain

comparisons are appropriate and the use of a summary

measure is valid.

Returning to the issue of selecting a standard population, it should be noted that in examining official mortality

statistics, it seems as though the initial selection of a

standard population has almost always been the most

current census population. Once used, however, the standard may remain in place for some time.

From time to time the question of changing the

standard population is asked. Woolsey examined some

alternative standards and concluded that there would be

no significant change in age-adjusted rates among the

standards examined (4).

When the American Public Health Association prepared its monographs around the 1950 census, Speigelman

and Marks examined the questions of whether to change

to the 1950 population as standard (28). They concluded

that there was little reason to change.

Since that time, there have been periodic challenges

to the use of 1940 population as the standard. These

challenges seem to coincide with each new decennial

census. For example, the issue was again examined by

Curtin, Rosenberg, and Maurer in 1980 (29). They examined the effect of the 1940, 1970, and a life table population as a standard on such measures as black-white and

male-female comparisons for selected causes of death.

They also looked at overall mortality by State. They found

that the ranking of States by age-adjusted death rate was

not greatly affected by choice of standard population. The

issue of selecting a standard population was then revisited

by Robert Johnson at the 1990 annual meeting of the

American Statistical Association (30).

Again, it seems that after every census, the question of

changing the standard population arises. Perhaps it is a

more important issue today because the 1940 census

population is 50 years old and because of the increased

emphasis on the analysis of chronic diseases and health

issues for the elderly population.

Discussion

Although developed and widely used in England during the 19th century, the age-adjusted death rate was

never accepted by everyone. In a discussion before the

Royal Statistical Society, the age-adjusted rate was termed

the ‘‘despised standardized rate’’ (31), and, 50 years later,

the direct-standardized age-adjusted death rates are still

despised by some illustrious persons (32). In Greenwood’s

time, such displeasures with the age-adjusted rate led to

discussions of alternative summary measures, such as

using the life table death rate (33), the equivalent average

death rate (34), or measures developed by Yerushalmy

(35), Kerridge (36), and others. Woolsey (4) examines

some alternative summary measures as do numerous review articles (37–43).

Throughout the history of the use of the directstandardized or age-adjusted death rate, the utility of the

measure has often come into question. Nevertheless, the

age-adjusted rate continues to be an integral part of the

analysis of mortality trends and differentials.

Accepting this, the need for a summary index must be

balanced by recognition of the limitations of summary

measures. One hundred years after the founding of the

Royal Statistical Society, Major Greenwood, in his discussion of Yule, stated:

. . . the numerical statistical method, as distinct from

the tabular statistical method of our ancestors, has

been introduced precisely because the power of the

human mind to grasp a number of particulars is

limited, . . . It was not until the seventeenth century

that it was realized that in seeking to grasp everything,

one tended to grasp nothing (34).

14
This statement, either quoted directly or in a slightly

different form, has continued to be prevalent in the

multitude of published work on uses and limitations of

summary measures of mortality.

Assuming the usefulness of the age-adjusted rate, the

question of selection of the standard population remains.

Clearly, the age-adjusted rate is a summary index, an

index number whose magnitude has no meaning and

should be used for comparisons only. Also, there can be

delineated instances where it is inappropriate to use a

standardized measure. This leads to a rather simple conclusion: If it is appropriate to use age adjustment, then the

results should not be affected by the selection of a standard population; but if the results can be affected by the

choice of a standard population, then it is not appropriate

to use standardization at all.

In closing this historical overview, certain recurring

issues may be mentioned:

• Standardization is not a substitute for the examination

of age-specific rates, the age-adjusted rate in an index

measure, whose magnitude has no intrinsic value, that

is to be used for comparison purposes only.

• The standard population should not be considered

‘‘abnormal’’ or ‘‘unnatural’’ relative to the populations

under study.

• Comparisons between alternative standards generally

produce insignificant differences, but if significant differences do occur, one probably should not be using

standardization in the first place.

• When a decision is made to change standards, the

most current census population is usually selected.

Basically, there are few statistical reasons to guide the

selection of a standard population for mortality data. In

comparing several different populations, the variance of

the direct-standardized measure is minimized when a

‘‘pooled’’ standard is used (46). If the interest is in

comparing trends over time, the decomposition formula of

Kitagawa seems to imply the use of the base-year population as a standard (47). That is, if the trend between 1950

and 1990 is of interest, then the difference between the

crude rates for 1950 and 1990 can be decomposed into a

difference of age-specific rates weighted by the 1950

population (thus a difference in two standardized rates

with the 1950 population as the standard), a difference

between the populations in the two years, and a ratepopulation interaction term.

In addition to the lack of a statistical reason to change

standard populations, there are many practical reasons for

official government statistics not to change the 1940 standard. The possibility of enormous resources spent to

recompute historical figures and the confusion resulting

from the publication of a set of numbers that would no

longer be comparable to figures previously published are

two reasons not to change the 1940 standard.

However, throughout the history of its use, the emphasis on selecting a standard population has been that the

standard should not be greatly different or ‘‘abnormal.’’

With the 1940 population already 50 years out of date, the

conceptual, not statistical, issue is whether the 1940 population should be considered abnormal when compared

with today’s United States population, and, if so, can the

costs resulting from changing the standard population be

justified.

References

1. Graunt J. Natural and political observations mentioned in a

following index and made upon the bills of mortality. London, 1662. Republished by Johns Hopkins Press, Baltimore.

1939.

2. Lilienfeld DE, Lilienfeld AM. Epidemiology: A retrospective study. Am J Epidemiol 106:445–59. 1977.

3. Lilienfeld DE. The greening of epidemiology: Sanitary physicians and the London Epidemiological Society. Bull Hist

Med 52:503–28. 1979.

4. Woolsey TD. Adjusted death rates and other indices of

mortality. In: Linder FE, Grove RD, eds. Vital statistics

rates in the United States, 1900–40. Washington: U.S. Government Printing Office. 1959.

5. Pearl R. Medical biometry and statistics, 1st ed. Philadelphia:

WE Saunders. 1923.

6. Stigler SM. The history of statistics: The measurement of

uncertainty before 1900. Massachusetts: Belknap Press of

Harvard University Press. 1986.

7. Guy WA. Statistical development with special reference to

statistics as a science. J Royal Stat Soc 50:72–86. 1885.

8. Guy WA. On the values of the numerical method as applied

to science, but especially to physiology and medicine. J Stat

Soc 2:45. 1839.

9. Farr W. Vital statistics: A memorial volume of selections

from the reports and writings of William Farr. In: Humphries

NA. ed. London: Office of the Sanitary Institute, 1885.

Republished by the Library of the New York Academy of

Medicine. New Jersey: Scarecrow Press, Inc. 1975.

10. Mouat FJ. History of the statistical society of London. J Stat

Soc 50:14–71. 1885.

11. Benjamin B, Pollard JH. The analysis of mortality and other

actuarial statistics. London: Heineman. 1980.

12. Neison FGP. On a method recently proposed for conducting

inquiries into the comparative sanitary conditions of various

districts. London J Royal Stat Soc (now the Royal Stat Soc)

7:40–68. 1844.

13. Chadwick E. Best modes of representing the duration of life

and the pressure and progress of the 6-causes of mortality

amongst different classes and different districts and countries. J Royal Stat Soc 7:1–40. 1844.

14. Lewis J. The origins and development of public health in the

United Kingdom. In: The Oxford textbook of public health,

2nd ed. vol I. Influences of Public Health 23–33. New York:

Oxford University Press. 1991.

15. Finer SE. The life and times of Sir Edwin Chadwick.

London: Methuen. 1952.

16. Finn MW. Introduction to Sir Edwin Chadwick’s the sanitary condition of the laboring population of Great Britain.

Edinburgh: Edinburgh University Press, 1–73.

17. Glass DV. Numbering the people. Franborough: Washington Public Health Services. 1973.

15
18. Wolfenden HH. Population statistics and their compilation.

University of Chicago Press. 1954.

19. The Registrar-General’s Statistical Review of England and

Wales for the Year 1937. New annual series, no. 17, text.

HM Stationery Office, London. 1940.

20. Ogle MW. Proposal for the establishment and international

use of a standard population,. . . Bulletin de l’Institut International de Statistique, tome VI, livre 1:83–5. Rome. 1892.

21. U.S. Bureau of the Census. Mortality 1911. Washington:

U.S. Department of Commerce. 1913.

22. U.S. Bureau of the Census. Mortality rates 9111–20. Washington: U.S. Department of Commerce. 1923.

23. Linder FE, Grove RD. Vital statistics rates in the United

States, 1900–40. Washington: U.S. Government Printing

Office. 1943.

24. Report of the Committee on Forms and Methods of Statistical Practice of the American Public Health Association.

A.P.H.A. Yearbook. 1936–37.

25. Report of the Committee on Forms and Methods of Statistical Practice of the American Public Health Association.

A.P.H.A. Yearbook. 1937–38.

26. Report of the Committee on Forms and Methods of Statistical Practice of the American Public Health Association.

A.P.H.A. Yearbook. 1939–40.

27. Wolfenden HH. On the methods of comparing the mortalities of two or more communities, and the standardization of

death rates. J Royal Stat Soc 86:399–411. 1923.

28. Spiegelman M, Marks HH. Empirical testing of standards

for the age adjustment of death rates by the direct method.

Hum Biol 38:280–92.

29. Curtin LR, Maurer J, Rosenberg HM. On the selection of

alternative standards for the age-adjusted death rate. Proceedings of the Social Statistics Section. American Statistical Association. 1980.

30. Johnson R. Proposed new standard population. Proceedings

of the Social Statistics Section. American Statistical Association, 176–181. 1990.

31. Greenwood M, et al. Discussion of the value of life tables in

statistical research. J Royal Stat Soc 85:537–60. 1922.

32. Tukey JW. Statistical mapping: What should not be plotted.

In: Proceedings of the 1976 Workshop on Automated Cartography and Epidemiology, 18–26, DHEW Publication No.

79–1254, Hyattsville, Maryland: Public Health Service. August 1979.

33. Brownlee J. The relationship between ‘‘corrected’’ death

rates and life table death rates. Massachusetts: Cambridge

University Press. J Hyg 13:178–90. 1913.

34. Yule GU. On some points relating to vital statistics, more

especially statistics of occupational mortality. J Royal Stat

Soc 97:1–84. 1934.

35. Yerushalmy J. A mortality index for use in place of the age

adjusted death rate. Am J Public Health 41:907–22. 1951.

36. Kerridge DA. New method of standardizing death rates, Br

J Prev Soc Med 12:154–55. 1958.

37. Kitagawa EM. Theoretical considerations in the selection of

a mortality index and some empirical comparisons. Hum

Biol 38:293–308. 1966.

38. Chiazze L Jr. Indexes of mortality. Discussion. Hum Biol

38:320–22. 1966.

39. Elveback LR. Indices of mortality and tests of their statistical significance. Discussion. Hum Biol 38:322–4. 1966.

40. Kilpatrick SJ. Mortality comparisons in socio-economic

groups. Appl Stat 12:65–86. 1963.

41. Kitagawa EM. Standardized comparisons in population research. Demography 1:296–315. 1964.

42. Kleinman JC. Age-adjusted mortality indices for small areas: Applications to health planning. Am J Public Health

67:834–40. 1977.

43. Inskip H, Beral V, Fraser P. Methods for age-adjustment of

rates. Stat Med 2:455–66. 1983.

44. Lilienfeld DE, Pyne DA. On indices of mortality: Deficiencies, validity and alternatives. J Chron Dis 32:463–8. 1979.

45. Hickey RJ, Clelland RC, Clelland AB. Epidemiological

studies of chronic disease: Maladjustment of observed mortality rates. Am J Public Health 70:142–50. 1980.

46. Kalton G. Standardization: A technique to control for

extraneous variables. Appl Stat 17:118–36. 1968.

47. Kitagawa EM. Components of a difference between two

rates. J Am Stat Assoc 50:1168–94. 1955.

16
Chapter 3

Inadequacies of age

adjustment as illustrated in

cancer mortality

by Katherine Gray-Donald, Ph.D.,

Department of Epidemiology and

Biostatistics, McGill University

Introduction

Over the years several different forms of age adjustment have been proposed as means of summarizing agespecific mortality rates for the purpose of comparing

differences among groups or trends over time. Summary

statistics allow comparisons among population groups or

time periods whose populations have differing age structures, but they reflect age-specific differences reliably only

if the differences are consistent across age groups. In the

case of trends over time, if the age-specific mortality

trends vary across age groups (that is, an age and calendar

time interaction exists), summary statistics may conceal

more than they reveal. Disease trends are, unfortunately,

often subject to different patterns in different age groups.

For example, this is true of cross-sectional data that are

affected by birth cohort effects (such as lung cancer) and

in the case where a period effect may decrease death rates

in some age groups but not others (such as cervical

cancer).

Each of the many summary statistics uses a different

weighting scheme to summarize the age-specific mortality

rates, and each of these different summary statistics is

affected by any change in the choice of the standard

population. This chapter concerns the weights used in

several different standardization techniques. The need for

homogeneity of age-specific mortality ratios to summarize

age-specific death rates adequately is emphasized. An

example of the different patterns of the summary statistics

obtained by using different methods is illustrated.

Notation used in this chapter

In the notation, suffix i indicates the ith age interval

(here 0–4, 5–9, . . . 80–84, 85 and over). Suffix j indicates

the jth year, in the example 1950, 1955,. . . 1985. For each

year j, the data consist of the i-specific population pij,

deaths dij, and death rates rij. Capital letters are used to

indicate the reference values. Pi and Di are used for the

reference population and number of deaths in the reference population (in the ith age interval); Ri indicates the

death rate in the standard population. The ratio of the

age-specific rate in the jth year to that in the reference

population—that is, rij/Ri

—is termed λ ij in accordance

with Liddell’s usage (1).

Methods of standardization

Direct and indirect standardization are well known, but

there are also several other methods of standardization, including the equivalent average death rate (2) and methods by

Yerushalmy (3), Kerridge (4), and Liddell (1). These methods

were reviewed by Liddell (1) in 1960.

Each of these methods of summarization can be seen as a

weighted average of age-specific death rates. The similarity of

the direct and indirect methods has been pointed out by

Rothman (5). The weights for the age-specific death rates for

direct and indirect standardization are in fact the distribution

of the standard and specific-year populations, respectively (5).

As a result, the direct and indirect methods show extremely

similar trends in most instances. The equivalent average

method, in contrast, weights death rates in each age group

equally (2).

Conceptually one can think of standardization techniques

as yielding a weighted average of either absolute death rates in

each age group or as a weighted average of the ratios of

age-specific death rates in a specific year to those of a standard

population. The latter are termed mortality ratios, or λ ij

(λij = rij/Ri). Age-specific mortality ratios are useful, as they

compare each age-specific death rate in the specific population

with the corresponding rate in the standard year to show the

relative changes over time in each age group.

The weights that are applied to the age-specific mortality

ratios can be obtained directly from the usual formula for the

adjustment of age-specific death rates (1,6). For example, the

comparative mortality figure (CMF), obtained from direct

standardization, can be expressed as either a weighted average

of age-specific mortality rates or age-specific mortality ratios

(λij).

Equations used to obtain selected statistics are shown

below.

Directly standardized death rate ∑i rij Pi

∑i Pi (1)

Comparative mortality figure ∑i rij Pi ∑ Di ∑i

rij Pi

( ) (2)

∑i

Pi ∑ Pi

= ∑ D / i

17
Substituting λij = rij / Ri

∑i rij Pi ∑i λij Ri Pi ∑i λij Di

= = (3)

∑i

Di ∑ Di ∑ Di

The directly standardized death rate (equation (1)), is a

weighted average of age-specific death rates, and the weights

are proportional to the population distribution. The CMF is

this rate divided by the crude death rate in the standard

population to form a ratio of rates. The CMF is also a

weighted average of age-specific mortality ratios (λij) where

the weights are deaths in the standard population, as seen in

equation (3). In a similar manner, one can calculate the

weights used to average age-specific mortality ratios by the

indirect method and by the Kerridge and person-weighted

ratio (PWR) method presented by Liddell (1).

In the case of the indirect method, the age-specific

mortality ratios are weighted by the deaths expected in the

specific population. In the case of the methods of Kerridge and

Liddell, the mortality ratios are weighted by the population

distribution of the specific and standard populations, respectively (1).

Ideally, if we standardize only age-specific mortality

ratios that are consistent over the age groups for any one year,

the method of weighting of these ratios is not of major

consequence. (The ratios in any one year are all similar and

any summary statistic will suffice.) If mortality

ratios in a particular year differ by age, the choice of

method of standardization will affect the resulting summary statistic.

Cancer mortality trend for U.S. white males

As an example of the striking differences obtained by

different methods of standardization, all cancer deaths of

white males in the United States for the period 1950–85

have been analyzed using the CMF (directly standardized

rate) and the Liddell method. Five-year age groups were

used, and death rates for every fifth year were used to

arrive at summary statistics for these years (figure 1). The

standard used was a composite of the eight 5-year intervals during the period 1950–85. The trends are strikingly

different. The CMF shows a steady increase up to 1980,

whereas the PWR by Liddell shows a decline. The agespecific death rates for 1950 and 1985 are shown by

10-year age groups in table 1. The changes in these death

rates over time are provided in the two right-hand columns. Both the absolute and relative change from the

beginning to end of the time period are shown. Cancer

mortality rates have decreased among those under 50 and

increased for persons above this age. Thus the age-specific

mortality ratios are not homogeneous across age groups,

and any summary statistic will be a reflection of the

particular weighting scheme used. For direct standardization, because the mortality ratios (λij) are weighted by

Figure 1. Age-adjusted ratios of cancer mortality for white males: United States, 1950–88

18
deaths in the standard population, the summary measure

will reflect the pattern at the ages where the highest

proportion of deaths occur. In contrast, the Liddell method

weights the death ratios by the population distribution of

the standard, and hence reflects the changes in death rates

in the largest population groups. Clearly in this example

the message concerning the trend depends on the choice

of method of standardization. Other examples of such

varied mortality ratios are not difficult to find (6,7).

Aside from the method of standardization, the choice

of standard population also may affect the comparison of

summary indicators from year to year. The weights for the

age-specific mortality ratios for both the direct method

and Liddell method using two different standard populations for 1950 and 1985 are shown in table 2. For the

direct method the weights are the number of deaths in

each age group attributed to cancer in U.S. white males,

and for the Liddell method the weights are the population

distribution for U.S. white males. Using the direct method,

37 percent of the weight for the age-specific mortality

ratios was given to those 70 years of age and over in 1950,

whereas 50 percent of the weight was attributed to this

age group in 1985. Using the Liddell method, the weights

attributed to those 70 years of age and over for these two

years were only 4.5 percent and 6.5 percent, respectively.

Although the weights are different as a result of a change

in the standard year, this difference is small compared

with the difference resulting from the choice of the method

of standardization.

The mortality ratios are weighted by deaths in direct

standardization. Thus, where mortality ratios are not homogeneous, the summary statistic will reflect the mortality

ratios in the age groups with the largest number of deaths.

In the case of white male cancer mortality, the proportion

of deaths in the oldest age groups continues to increase

over time. Therefore, the use of a recent year as the

standard (for instance, 1985) leads to an even greater

emphasis on the trend in death rates among the elderly.

Conclusion

When age-specific death ratios are heterogeneous, the

use of any summary statistic is not appropriate. In such

cases, the choice of method of standardization, and to a

lesser extent the choice of standard used, inevitably affects

the pattern of the summary statistic. If mortality patterns

vary by age, a closer examination of age-specific rates in

both birth cohort and cross-sectional analyses can reveal

far more pertinent and useful information.

Table 1. Death rates for all cancers in white males, by age: United States, 1950 and 1985

Age

1950

(1)

1985

(2)

Absolute change

column (2) minus

column (1)

Relative change

column (2) divided by

column (1)

Rate per 100,000 persons

Under 10 years ................. 11.64 4.67 –6.97 .40

10–19 years ................... 8.66 5.00 –3.66 .57

20–29 years ................... 13.47 9.15 –4.32 .68

30–39 years ................... 28.59 22.19 –6.40 .78

40–49 years ................... 87.79 80.99 –6.80 .92

50–59 years ................... 269.69 309.62 +39.93 1.15

60–69 years ................... 593.36 758.96 +165.60 1.28

70–79 years ................... 1,064.04 1,445.88 +381.84 1.36

80 years and over ............... 1,598.25 2,259.62 +661.37 1.41

Table 2. Percent distribution of relative weights used for comparative mortality figure and Liddell methods of age adjustment: United

States, 1950 and 1985

Age

Proportion of deaths (all cancers) Proportion of population (white U.S. males)

1950 1985 1950 1985

All ages ..................... 100.00 100.00 100.00 100.00

Under 10 years ................. 1.60 0.31 19.98 14.63

10–19 years ................... 0.87 0.34 14.66 15.00

20–29 years ................... 1.44 0.76 15.47 18.25

30–39 years ................... 2.92 1.64 14.89 16.41

40–49 years ................... 7.72 4.08 12.82 11.08

50–59 years ................... 19.22 14.03 10.38 9.06

60–69 years ................... 29.67 29.30 7.28 8.49

70–79 years ................... 25.68 32.06 3.51 4.77

80 years and over ............... 10.88 18.12 0.99 1.76

19
References

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2. Hill AB. A short textbook of medical statistics. 9th ed.

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3. Yerushalmy J, Wolfenden HH. On the methods of comparing the mortalities of two or more communities and the

standardization of death rates. Am J Public Health 41:907.

1951.

4. Kerridge D. A new method of standardizing death rates.

Brit J Prev Soc Med 12:154. 1958.

5. Rothman KJ. Modern epidemiology. Boston: Little, Brown

and Company. 1986.

6. Tsai SP, Wen CP. A review of methodological issues of the

standardized mortality ratio (SMR) in occupational cohort

studies. Int J Epidemiol 15:8–21. 1986.

7. Colton T. Statistics in medicine. Boston: Little, Brown and

Company. 1974.

20
Part III

Advantages and disadvantages of age

adjustment
Chapter 4

The use of multiple

standards

by Joel C. Kleinman, Ph.D.,

Division of Analysis, National Center for

Health Statistics

Age-adjusted death rates have been used extensively

in mortality analyses for more than a century. Because the

crude death rate is heavily influenced by the age composition of the population, it is not a useful measure for

monitoring changes in mortality. For example, if there had

been no changes in the age-specific death rates between

1980 and 1988, the aging of the population would have

produced an 8 percent increase in the crude death rate.

In fact, between 1980 and 1988 there was a 0.4 percent

increase in the crude death rate even though every agespecific death rate declined.

To avoid the problems associated with crude death

rates, age-adjusted death rates are often used to summarize mortality trends. However, as with any summary

measure, essential details are often lost. In this presentation I will cover the main advantages and disadvantages of

age adjustment, especially as they relate to the choice of

the standard population. I will concentrate on the direct

method of adjustment and the assessment of trends,

although many of the points are relevant to indirect

adjustment and the comparison of subpopulations or geographic areas.

The major advantage of age-adjusted rates is their

simplicity as a summary measure of the set of age-specific

rates. (In this presentation I will assume that the agespecific rates are expressed in the standard 11 age groups:

under 1 year, 1–4 years, 5–14 years,. . . ,75–84 years, and

85 years and over.) When all the age-specific rates move in

the same direction at the same relative magnitude, the

age-adjusted rate is a valid summary measure in the sense

that it will reflect that trend accurately—no matter what

standard population is chosen.

Another advantage of the age-adjusted rate is that it

has a smaller relative standard error than any of the

age-specific rates; this is an important advantage when

comparing subpopulations or geographic areas.

The disadvantages of age adjustment occur primarily

when the age-specific rates move in different directions or

at different relative magnitudes. When this occurs, the

trend in the age-adjusted rate will reflect some sort of

weighted average of the age-specific trends, where the

weights depend upon the standard population chosen. In

order to illustrate this problem, let us consider U.S.

standard populations for three different years, 1940, 1980,

and the projected population in 2050. Figure 1 shows that

the population over 65 years accounted for about 5 percent

of the total in 1940, 11 percent in 1980, and nearly

25 percent in 2050. Therefore, the 2050 standard will give

the greatest weight to mortality changes among the elderly

while the 1940 standard will give the least.

To illustrate the sensitivity of the adjusted rate to

changes in age-specific rates, I started with the 1980 all

causes death rate and tried four simple scenarios. Each

scenario doubled the age-specific death rate for a portion

of the age range while keeping the remaining rates constant. Figure 2 shows that for any standard population,

doubling the rates at the older ages has a much larger

effect on the age-adjusted rates than doubling at the

Figure 1. Age distribution of United States population: United

States, 1940, 1980, and 2050

23
younger ages. However, as the standard population becomes older this effect is magnified. If middle-aged (45–64

years) death rates are doubled, the 1940 adjusted rate

would increase by 31 percent but the 2050 adjusted rate

would increase by only 12 percent. On the other hand, if

elderly (65 years and over) death rates are doubled, the

1940 adjusted rate would increase by 52 percent compared with an 84 percent increase for the 2050 adjusted

rate. Thus, changes in mortality among the elderly have

the largest impact on age-adjusted rates, even when using

the standard population that gives least weight to the

elderly (1940). This is due to the relatively large magnitude of the death rates among the elderly compared with

those at younger ages.

One method for avoiding this emphasis on mortality

among the elderly is the use of Years of Potential Life

Lost (YPLL). YPLL weights each death according to the

number of years of life lost to age 65. Death rates for the

elderly are not used so that the YPLL rate summarizes

mortality among those under 65 years of age. YPLL rates

can be age adjusted in the usual way, which also requires

the choice of a standard population. Let us consider the

1980 and 1988 age-specific death rates for the United

States.

Figure 3 shows that there have been substantial reductions in most age groups. Yet the reduction in ageadjusted rates or YPLL differs considerably (figure 4).

The reduction ranged from 5.5 percent for the ageadjusted rate using the 2050 standard to 12.4 percent for

YPLL using the 1940 standard. Note that there is very

little difference among the YPLL rates, including the

crude. However, the age-adjusted rates differed much

more and the crude death rate even showed a 0.4 percent

increase.

The situation is even more complex when trends run

in different directions as they do for cancer mortality.

Figure 5 shows that cancer death rates have declined for

those below 55 years of age while they have increased for

those over 55 years of age. Figure 6 shows the resulting

age-adjusted trends. The YPLL rates declined for all three

standards chosen but among the age-adjusted death rates

only the 1940 standard population shows a hint of a

decrease.

Another difficulty with age-adjusted death rates is that

the magnitude of the rates is totally arbitrary and depends

upon the standard chosen. This can lead to confusion

when comparing rates for different causes of death. For

example, figure 7 shows the age-adjusted death rates

using the 1940 standard for heart disease, cancer, stroke,

and lung cancer. One might mistakenly conclude that lung

cancer became a ‘‘more important’’ cause of death than

stroke because of the crossover in 1982. Yet this is entirely

a function of the standard population chosen. Figure 8

shows the same trends based on crude rates. Note that

there were still more stroke deaths than lung cancer

deaths in 1988, and that the gap between heart disease

and cancer was substantially greater than was suggested by

the age-adjusted rates. Because crude rates reflect the

aging of the population as well as mortality trends, the

declines in heart disease and stroke mortality are much

less marked, and the increases in cancer much greater

than was evident with the age-adjusted rates. Figure 9

shows the same trends using the 2050 population as the

standard (the scale has been multiplied by 2.5 because of

the greater magnitude of these rates; the relative scale is

comparable to the previous figures). With this age distribution, stroke mortality appeared to be higher than cancer

(and lung cancer) mortality until 1970 and considerably

higher than lung cancer mortality in 1988. The trends in

stroke mortality using the 2050 standard do not begin to

decline until after 1960, compared with a noticeable decline between 1950 and 1960 when using the 1940 standard.

A reasonable compromise to the difficulties of using a

single age-adjusted rate is to present more than one rate,

but fewer than 11 age-specific rates. For example, one can

compare trends in mortality for young people (under 25

years), middle-aged (25–64 years), and older persons (65

years and over). Figure 10 shows trends in age-adjusted

rates for these three categories using both the 1940 and

2050 standard populations. Over these narrower age bands,

the choice of standard population becomes much less

critical. For the youngest age groups, the two standards

are indistinguishable. The largest difference in magnitude

occurs among the elderly but even here the trends are

quite similar. Figure 11 shows the three sets of rates for

cancer mortality. Both the magnitude of the rates and the

trends are quite similar, regardless of which standard is

used.

In summary, the choice of standard populations depends upon the purposes for which the age-adjusted rate

Figure 2. Increases in age-adjusted death rate after doubling

age-specific death rates in selected age groups: United States,

1940, 1980, and 2050

24
is being used. Since the choice of standards is arbitrary,

there is nothing inherently wrong or misleading about the

1940 standard—despite its being 50 years old. In fact, the

large difference between the crude rates and the ageadjusted rates based on the 1940 standard is probably

beneficial because it is less likely to lead to confusion than

a more recent standard. The presentation of several ageadjusted rates based on different standard populations

could lead to confusion and erroneous comparisons. A

reasonable compromise is to present tables of ageadjusted rates for three broad age groups (under 25 years,

25–64 years, and 65 years and over). The choice of

standard population for these rates is less critical since

both the trends and magnitudes will be similar over a wide

range of standards.

Figure 3. Percent decrease in age-specific death rates: United States, 1980–88

25
Figure 5. Percent change in age-specific cancer death rates: United States, 1980–88

Figure 4. Percent decrease in age-adjusted YPLL and death rates: United States, 1980–88

26
Figure 7. Mortality trends, age-adjusted death rates (1940

Standard): United States, 1950–88

Figure 8. Mortality trends, crude death rates: United States,

1950–88

Figure 9. Mortality trends, age-adjusted death rates (2050

Standard): United States, 1950–80

Figure 6. Percent change in age-adjusted YPLL and death rates for cancer: United States, 1980–88

27
Figure 10. Age-adjusted death rates in three age groups, all

causes of death: United States, 1950–88

Figure 11. Age-adjusted cancer death rates in three age groups:

United States, 1950–88

28
Chapter 5

Choosing a standard

population: Some statistical

considerations

by Harry M. Rosenberg, Lester R. Curtin,

Ph.D., Jeffrey Maurer, and Katherine Offutt,

Division of Vital Statistics, National Center for

Health Statistics

Introduction

The issue of alternative standard populations for age

adjusting death rates surfaces into open debate from time

to time. This may even have been an issue in the 19th

century, when age-adjusted death rates were first regularly

published in the English official reports of vital statistics.

It certainly was in the 1930’s when G. Udny Yule presented a paper on standardization at the meeting of the

Royal Statistical Society (1) and, again, in the early 1940’s

when Linder and Grove prepared their classic compilation

of historic vital statistics rates (2).

In discussions of which population standards to use

for England, it was argued that the 1901 population of

England would give too much weight to the younger

population, which at the time was experiencing rapid

reductions in mortality. In current discussions, it is often

argued that the 1940 population also gives too much

weight to the younger population in comparison with the

1990 population that is more heavily weighted by the

elderly. In her review of the early history of age adjustment, Klebba notes that the 1901 population of England

and Wales was initially adopted as a standard by the U.S.

Bureau of the Census (3). But the 1940 population was

selected as the standard by Linder and Grove for vital

statistics, and it has remained the standard for almost 50

years in presenting mortality data by the National Center

for Health Statistics (NCHS), its predecessor agencies,

and by the States.

Use of the 1940 standard by NCHS has not gone

without challenge in recent years. The issue was raised in

the media in 1979 when Harry Schwartz of the Columbia

University College of Physicians and Surgeons wrote in

the Wall Street Journal (4) that the increase in cancer

mortality between 1968 and 1978 was due in large measure

to the aging of the U.S. population rather than to increases in age-specific mortality risk. In challenging

Schwartz’s conclusions, Samuel Epstein of the University

of Illinois School of Public Health wrote that had the 1970

population instead of the 1940 population been used as a

standard population, the change in mortality during the

period would have been 5.5 percent rather than 2.5 percent

(5).

The issue of which population standard to use for

age-adjusting death rates has been raised in recent years

within the Federal Government. A recent instance resulted

from the use of a standard population other than 1940 in a

series of articles on chronic diseases that appeared in the

Morbidity and Mortality Weekly Report, a widely read publication of the Centers for Disease Control (CDC). Rates

presented in the articles for selected chronic diseases by

State differed from rates prepared by NCHS and by the

States, which were based on the 1940 standard. The issue

was also raised in designing CDC’s ‘‘State Profiles,’’ which

present a number of health indicators for individual States.

Again, the issue was addressed in developing the

statistical methodology for the Nation’s health objectives

for the year 2000 (6). It was recognized that the methodology for the Year 2000 Health Objectives would have

broad implications for the statistical measures used at

both the National and State level for many years; the 1940

standard was selected.

In the future, which standard to use will no doubt be

raised time and time again in not only a national but also

an international context, as efforts are made to promote

international comparability in health statistics methodology, following the model of international comparability in

cause-of-death classification through the International Classification of Diseases of the World Health Organization

(7).

Nonstatistical considerations

A review of the literature on age standardization

reveals the complexity of the issue of alternative standards, which involves not only statistical but also nonstatistical considerations. While the present paper focuses on

statistical aspects of the issue, it may be useful to review

some nonstatistical considerations in choosing population

standards for age-adjusting death rates.

Among these are the uses to which age-standardized

measures are put, that is, whether principally for in-depth

epidemiological research, or whether, instead, for routine

presentation of data. In the former case, there are opportunities for augmenting summary measures such as ageadjusted death rates, where necessary, with more in-depth

analyses of age-specific rates, and even introducing controls for demographic characteristics such as race, sex, and

geographic area. In-depth research provides unique opportunities for exploring alternatives and for examining factors contributing to change over time and to demographic

differentials in mortality. In contrast, in routine data

29
production and dissemination, the requirements of continuity and uniformity are powerful and, indeed, may be of

the overriding considerations in the selection of population standards for age adjustment.

Policy considerations can play a role in the type of

statistical index chosen for data presentation. For example, a public health program whose focus is youth may be

inclined to select measures that give greater emphasis or

statistical weight to the younger population than a program whose target is the adult or elderly population. An

example of a standardized index that emphasizes youth is

the ‘‘Years of Potential Life Lost’’ (YPLL), a measure

widely favored in injury prevention programs. This is a

measure in which age-specific mortality is weighted by a

factor representing the difference between an arbitrary

end point age, usually 65 or 70 years, and the midpoint of

a population group. Under such a scheme, the age group

20, for example, has a weight of 50—the difference between 70 years and 20 years—while the age group 50

would get a weight of only 20—the difference between 70

years and 50 years. The ratio of these weights is 50 to 20,

or 2.5 as compared with a ratio of 1.5 when the weights of

the standard 1940 population are used. The YPLL is

widely used in injury research, whose program targets tend

to be the younger population.

Another nonstatistical consideration in selecting a

standard population is purely administrative, such as the

initiation of a new public health program. Thus, the

selection of the 1970 population standard for cancer data

is roughly coincident with the beginnings of the cancer

tumor registry program of the National Cancer Institute.

The 1970 population also coincidentally gives greater

weight to the older population than the 1940 standard,

with the statistical result, shown below, that the ageadjusted death rate for all cancers combined shows a later

inflection point signaling the beginning of a downward

trend than when the 1940 standard is used.

Once a population standard becomes identified with a

particular series of statistical data as, say, in the case of

cancer data, widely used and accepted, the standard

assumes a kind of ‘‘verity’’ and historic momentum as is

the case with the 1940 population as the standard for

mortality data in the United States and the 1901 population used for England and Wales.

An important nonstatistical consideration is ease of

use and interpretation, particularly by the public and lay

users. Thus, a single standard is less likely to confuse than

multiple standards, and the retention of a standard is less

likely to confuse than changing standards. Changing standards, moreover, means that statistical results prepared

under one regimen may not be comparable with those

prepared under a different set of population standards,

giving possible rise to confusion. While Johansen has

argued that the statistical discontinuities associated with

revisions in cause-of-death classification are already part

of the accepted statistical apparatus in mortality presentation (8), the problems created by these revisions—technical, administrative, and interpretive—should not be

minimized. It is not clear, moreover, that the issue of

age-standardization is comparable to that of cause-ofdeath classification which must capture as accurately as

possible medical terminology that is rapidly evolving as a

result of changes in medical technology, medical knowledge, and medical practice.

If multiple standards were used to avoid the problem

of discontinuing a time series but yet initiating a new one,

the multiple standards would also have the potential of

confusion in both the presentation and interpretation of

data. Data users find consistency and simplicity of presentation appealing and intelligible. Multiple choices, as in

population projections prepared by the U.S. Bureau of the

Census (for example, high, medium, and low series) can

put an unnecessary burden on the data user, while a

change in statistical practice and standards—without a

compelling justification—may raise questions in the minds

of the public as to whether the change is motivated by

other than technical considerations.

Statistical considerations

The purpose of this paper is to provide some statistical perspective on the question of alternative population

standards. As such, the paper approaches the problem in

much the same way as a number of earlier papers, such as

Spiegelman and Marks (9), Curtin, Maurer, and Rosenberg (10), Metropolitan Life Insurance Company (11),

and most recently Johansen (8). All these papers have in

common the empirical exercise of comparing age-adjusted

death rates using alternative population standards. They

differ mainly in the types of standards used and in the

cause-of-death categories and demographic groups to which

the alternative standards are applied.

The nature of age-adjusted death rates

As an introduction to the empirical comparisons, it

may be helpful to review the meaning of an age-adjusted

death rate. It is a weighted average death rate, where the

weights wi of the corresponding age-specific death rates ri

range from 0.0 to 1.0. The crude death rate, under this

conceptualization, is an average rate whose population

weights are those representing the current population.

Clearly, a variety of weights can be used, including the

1940 standard that is now widely used by NCHS and the

States in their vital statistics programs, or the 1970 standard used in much cancer research, or the YPLL weights,

or the 1980 population used by parts of CDC other than

NCHS.

Once the population weights have been selected, they

are multiplied by the age-specific rates under consideration and the results summed to achieve a weighted

average. In this paper, the age-adjusted death rate for the

first population group is R1 and for the second group it is

R2.

The characteristic that distinguishes one group from

another may be cause of death, that is, the rate for cancer

30
as compared with the rate for heart disease; or it may be

race, that is, white persons as compared with black persons; or it may be sex, males as compared with females; or

it may be time, that is, 1980 as compared with 1988. And,

finally, the comparison may represent any combination of

characteristics, such as the cancer death rate for females

in 1980 as compared with their rate in 1988.

The comparison of two age-standardized rates R1 and

R2 using the same standard population as a percent

change

R2−R1 ×100

R1

or as ratio R2 to R1 would be a measure of the comparison

of the entire set of age-specific rates for the two population groups. Under ideal circumstances, the percent change

or ratio for R1 and R2 should reflect exactly the same

percent change or ratio for every age group.

Graphically, as shown in panel A of figure 1, this is

presented by parallelism in the two lines. In practice,

however, age-specific ratios in the death rates of two

population groups are rarely the same. They differ from

one age group to the next, sometimes extremely, such that

the rate for one population group may be higher for one

age group but lower for the adjacent age group, or there

may be other more general patterns such as those illustrated in panels B and C. In panel B, the general pattern is

one in which the age-specific death rates tend to ‘‘converge’’

with increasing age. This is a widely observed general

pattern for many causes of death; also shown is a less

common diverging pattern of age-specific rates. In panel

C, the age-specific rates are depicted in a pattern of

‘‘crossover,’’ where the relationship of the rates, age by

age, crosses over from higher to lower as has been observed in age-specific comparisons between the white and

black population groups. Under the circumstances of

convergence and crossover, comparisons based on a single

average measure such as the age-adjusted death rate, may

not represent well the complex pattern of age-specific

variation, although they do show the ‘‘average’’ ratio

between the two groups.

As comparisons depart increasingly from parallelism,

the less appropriate it is to use age-adjusted death rates

for making comparisons; and the comparisons of ageadjusted death rates become highly dependent upon the

particular standard selected. Thus, if the comparisons are

about the same for each age group, it does not make much

difference if the standard is 1940 or 1990. But if the

age-specific ratios are not uniform, the choice of standards

is likely to make a difference.

Figure 1. Comparison of hypothetical age-specific death rates for two population groups

31
Methods

Nature and sources of data

To illustrate the sensitivity of age-adjusted death rates

to alternative population distributions, several causes of

death were selected with contrasting age structures of

mortality. These causes were as follows: (1.) All causes of

death combined. (2.) Malignant neoplasms, including neoplasms of lymphatic and hematopoietic tissues, ICD–9

Nos. 140–208, hereinafter referred to as ‘‘all cancers.’’ (3.)

Malignant neoplasms of respiratory and intrathoracic organs, ICD–9 Nos. 160–165, referred to as ‘‘respiratory

cancer’’; Cerebrovascular diseases, ICD–9 Nos. 430–438,

referred to as ‘‘stroke; and Homicide and legal intervention, ICD–9 Nos. E960-E978, referred to as ‘‘homicide.’’

For each of these causes of death, age-specific death

rates were estimated separately for the white and black

population and for males and females for each of the years

1968, 1980, and 1988. Age-adjusted death rates were

calculated for each of these groups using two alternative

standard populations, that of 1940—the standard currently used in mortality time series of NCHS—and that for

1988, referred to, for convenience, as the standard for

1990, which it closely approximates. For each of these

groups, percent changes in mortality were calculated for

the periods 1980–88 and 1968–88. For the latter period,

the classification of the selected causes of death were

sufficiently comparable between the Eighth and Ninth

Revisions of the International Classification of Diseases to

calculate percent change without adjustment for comparability. Mortality sex ratios and mortality race ratios were

also calculated.

These rates, ratios, and percent changes are shown in

detail in tables 1–5. In addition, for each of the cause-sex

or cause-race groups, estimates were made of standard

errors and relatively standard errors for the alternative

age-adjusted death rates. These are shown in detail in

tables 6–10.

To simplify the presentation in this paper, patterns of

mortality for the total population are approximated by

using the white population, whose deaths in 1988 accounted for 87 percent of all deaths.

Age patterns of mortality

The diversity of the age patterns of mortality for the

selected causes of death is illustrated in figure 2, using

death rates for 1988, which are similar to those of other

years. For all causes of death combined, the age schedule

of mortality shows the well-known ‘‘J-shaped’’ pattern

with slightly elevated rates for infants, the lowest rates at

ages 5–14 years and gradual increases beyond that age

group. Indeed, this general pattern is characteristic of

most ‘‘natural’’ causes of death, in contrast to those causes

by external trauma—such as accidents, homicides, and

suicides whose age pattern of mortality reflects predominantly social and behavioral factors rather than biological

factors related to aging.

The pattern for all causes is heavily influenced by, and

indeed reflects, the pattern for heart disease, which in

1988 accounted for about one-third of all deaths. For all

cancers and cancers of the respiratory system, the pattern

of age-specific mortality follows this general pattern, although with later onset and somewhat attenuated increase

with increasing age. The pattern for stroke is similar but

with a higher proportion of deaths at advanced ages.

In contrast to the characteristic increasing death rates

with increasing age for the natural causes, the age pattern

of mortality for homicide is essentially flat; that is, rates

are relatively uniform throughout the age distribution,

although with somewhat higher rates for young adults.

Figure 2. Death rates by age for selected causes of death: United

States, 1988

32
Not only do causes of death have somewhat different

patterns of age-specific mortality for 1988, they also show

different age-specific patterns of change over time, illustrated by percent change during 1968–88. For all causes of

death combined, the greatest reductions in mortality were

all cancers and respiratory cancer, there were decreases

for age groups under 55–64 and 45–54 years, respectively,

and increases thereafter, examples of the ‘‘crossover’’ that

make age-adjusted death rates for all cancers and respiratory cancer especially sensitive to alternative standard

populations.

The age pattern of mortality change for stroke differs

from the other causes in having relatively uniform reductions in mortality at each age. This pattern should result in

little difference in change based on either of the alternative population standards. For homicides, the pattern of

change is a mirror image to that for all causes, that is, the

greatest increases at the younger age with generally smaller

increases at the older ages.

Age distribution of standard populations

The alternative populations used as standards have

somewhat different age structures, as illustrated in figure 3.

The 1940 population is ‘‘younger,’’ that is, it has a greater

proportion of the population concentrated at younger

ages. This reflects both the effects of higher fertility, which

tend to spread out the population base, as well as the

higher mortality at almost every age than the 1990

population standard. In contrast, the 1990 population is

‘‘older,’’ and appears to be moving toward the European

model of population structure with an ever-increasing

proportion of the population at the older ages.

The alternative structure of the two standard populations can be expected to have consequences for the

age-adjusted death rates in which the population at each

age group serves as a weight. Accordingly, the 1940

population weights give relatively greater emphasis to the

younger population and to the causes of death that are

more closely associated with the younger population,

namely, the external causes of death; while the 1990

standard will give greater emphasis to the natural causes,

for which the highest rates are at the advanced ages.

Results

Causes of death

The effect of alternative population standards on

death rates for selected causes is shown in table A. The

top two panels show, respectively, absolute and relative

levels of mortality in 1988 under the assumptions of either

a 1940 or a 1990 standard population, while the lowest

panel shows the effect of alternative standards on changes

in mortality for the selected causes during 1968–88.

In terms of comparisons between causes of death, use

of the 1990 standard tends to increase the absolute level of

mortality for the natural causes (deaths from diseases

rather than from trauma) and all causes of death combined, since the 1990 standard gives more weight to rates

in the older age categories. As a result, the age-adjusted

death rates based on the 1990 population more closely

resemble the unadjusted death rates for these causes of

death. The greatest differences in magnitude are for those

causes with the greatest concentration of mortality at the

older ages such as stroke, whose age-adjusted death rate

more than doubled when the 1990 standard is used as

compared with the 1940 standard. Age-adjusted death

rates for the other natural causes are from 40 to 70 percent higher using the 1990 standard. In contrast, the

age-adjusted death rate for homicide, whose rates are

relatively evenly distributed throughout the age range, is

about the same under the two standards.

While the absolute levels of mortality tend to be

affected by the choice of standards, the relative dispersion

of the rates, shown in the first panel (columns 3 and 4), is

not highly affected. For example, cancer risk is about

one-quarter that of all causes of death combined under

either assumption. More generally, comparisons of risk

among the causes seem to be only marginally affected by

the choice of population standards.

The middle panel shows changes in age-adjusted death

rates during 1968–88, based on the alternative population

standards. For both the white and black populations, there

are differences in the magnitude of change when different

Figure 3. Comparison of age distributions of the 1940 and 1988

populations of the United States, expressed as percents

33
standards are used but not in direction. Under the 1990

standard, decreases in mortality by cause of death are

reduced in size while increases tend to be amplified,

except for homicide which remains about the same.

The largest differences in percent change between the

two standards are for white mortality from all causes of

death combined, from a decrease of 10.5 to one of

7.9 percent; and for all cancers, an increase from 2.4 to

6.0 percent. While the absolute magnitudes of rates are

different for the black population, the effect of the alternative standards is proportionately similar to that of the

white population.

The bottom panel of table A shows the effect of

alternative standards on trends for the selected causes by

sex. Again, absolute magnitudes of change differ between

the two standards, but the direction of change and the

general order of magnitude of change are the same.

Sex comparisons

Because mortality analyses often examine patterns

and trends by sex, the effect of alternative standards on

death rates of males and females was examined, as shown

in table B. The ratio of male-to-female age-adjusted death

rates in 1988 is slightly but not consistently affected by the

choice of the standard population. When the 1990 standard population is used, the resulting ratio is smaller for

all causes of death combined and for stroke; but it is larger

for all cancers and for cancer of respiratory system; and it

is relatively unchanged for homicides. However, the trend

in the ratio, which represents the convergence or divergence in mortality between the sexes over time, appears to

be about the same regardless of the choice of standard.

Race comparisons

Race comparisons of mortality are of great interest

and are widely used to monitor and evaluate the health of

the minority populations. Table C compares mortality

between the two race groups in 1988 in terms of the ratio

of black to white age-adjusted death rates based on the

standard populations for 1940 and 1990. Ratios by cause

of death differ depending on which standard is used. The

1990 standard tends to reduce the race differential,

Table B. Comparison of the ratio of male to female age-adjusted death rates based on the standard populations of 1940 and 1990, for

selected causes of death: United States, 1988; and comparison of percent change in ratios, 1968–88

Cause of death

Ratio, 1988 (male/female) Percent change in ratio, 1968–88

1940 1990 1940 1990

All causes ..................... 1.72 1.62 21.9 21.1

Malignant neoplasms, total........... 1.46 1.57 2.2 3.4

Malignant neoplasms, respiratory....... 2.44 2.62 −54.8 −53.5

Cerebrovascular diseases ........... 1.18 1.11 −2.3 −3.3

Homicide and legal intervention ....... 1.27 1.26 −18.1 −18.8

Table A. Comparison of age-adjusted death rates based on the standard populations of 1940 and 1990, for selected causes of death:

United States, 1988; and expressed as ratio to All causes of death combined; and comparison of percent change in rates, by race and

sex, 1968–88

Cause of death

Age-adjusted death rate

(rate per 100,000 population) Ratio of rates (All causes=1.00)

1940

(1)

1990

(2)

1940

(3)

1990

(4)

All causes ....................... 509.8 841.6 1.00 1.00

Malignant neoplasms, total............. 130.0 191.4 0.26 0.27

Malignant neoplasms, respiratory......... 39.4 55.0 0.08 0.07

Cerebrovascular diseases ............. 27.5 57.5 0.05 0.07

Homicide and legal intervention . . . . . . . . . 5.3 5.2 0.01 0.01

Percent change in rates, 1968–88

White Black

All causes ....................... −10.5 −7.9 −5.2 −1.7

Malignant neoplasms, total. . . . . . . . . . . . . 2.4 6.0 9.8 17.9

Malignant neoplasms, respiratory......... 49.6 58.8 63.7 79.2

Cerebrovascular diseases ............. −58.4 −57.4 −59.0 −56.4

Homicide and legal intervention ......... 25.6 23.8 −19.3 −19.4

Male Female

All causes ....................... −27.8 −25.1 −7.5 -5.0

Malignant neoplasms, total. . . . . . . . . . . . . 4.5 9.6 2.2 6.0

Malignant neoplasms, respiratory......... 24.6 35.1 175.7 190.7

Cerebrovascular diseases ............. −58.4 −58.1 −57.8 −56.6

Homicide and legal intervention ......... 60.5 +60.6 2.3 3.1

34
although the extent of the reduction varies by cause of

death. Thus, for all causes of death combined, the mortality race ratio using the 1990 standard is 1.36 compared

with 1.55 for the 1940 standard; for cancer, 1.27 compared

with 1.32; for respiratory cancer, 1.26 compared with 1.21;

and for stroke, 1.50 compared with 1.87. For homicide, the

ratio is virtually unchanged, 6.42 compared with 6.45.

Trends in mortality race ratios are shown in the

second panel of the table. For some of the causes of death,

the 1990 ratio increases somewhat the percent change in

the ratio. This represents a greater widening in the mortality

differential between the white and black population than

depicted when the 1940 population is used as a standard.

For stroke and homicide, the rate of change in the ratio is

about the same, regardless of which standard population is

used.

Stochastic variation

Another issue examined in comparing alternative population standards is statistical significance. Because the

numbers of deaths reported through the vital statistics

system are a complete count of events, they are not subject

to sampling error—although they are subject to errors in

the registration process. However, when the figures are

used for analytical purposes, such as comparison of rates

over time or among groups and geographic areas, the

number of events that actually occurred may be considered as one of a large series of events that could have

arisen under the same circumstances, as indicated by

Chiang (12). The probable range of values may be estimated from the actual figures according to certain statistical assumptions.

In general, distributions of vital events may be assumed to follow the binomial distribution. Estimates of

standard error and tests of significance under this

assumption are described in most standard statistics texts.

When the number of events is large, the standard error

expressed as a percent of the number or rates is usually

small.

When the number of events is small (perhaps less

than 100) and the probability of such an event is small,

considerable caution must be observed in interpreting the

conditions described by the figures. This is particularly

true for infant mortality rates, cause-specific rates, and

death rates for small areas such as counties. Events of a

rare nature may be assumed to follow a Poisson probability distribution.

For aggregate mortality indexes such as crude death

rates and age-adjusted death rates, the variances can be

thought of as a weighted average of the variances for the

individual age groups, where the weights reflect the square

of the proportion of the standard population at each age.

In general, the larger the number of deaths on which an

age-adjusted death rate is based, the smaller the variance,

and, therefore, the standard error.

To determine if the standard errors and relative

standard errors are affected by the choice of the standard

population, these statistics were estimated for the selected

causes of death by race and sex for 1988. It could be

predicted that the standard errors would increase using

the 1990 population roughly in proportion to the increases

that occur in the age-adjusted death rate. It could also be

expected that the relative standard error would change

relatively little if the change in the standard error and the

rate were about the same.

The results shown in table D are generally consistent

with expectations, that is, the standard errors for the

natural causes increased by up to 50 percent, except for

homicide, for which the standard error did not change. In

Table D. Comparison of standard error and relative standard error of the age-adjusted death rates based on the standard populations of

1940 and 1990, for selected causes of death: United States, 1988

Cause of death

Standard error Relative standard error (in percent)

1940 1990 1940 1990

All causes . . . . . . . . . . . . . . . . . . . . . . . 0.4 0.6 0.1 0.1

Malignant neoplasms, total. . . . . . . . . . . . . 0.2 0.3 0.2 0.2

Malignant neoplasms, respiratory. . . . . . . . . 0.1 0.2 0.3 0.3

Cerebrovascular diseases . . . . . . . . . . . . . 0.1 0.2 0.3 0.3

Homicide and legal intervention . . . . . . . . . 0.1 0.1 1.0 1.0

Table C. Comparison of the ratio of black to white age-adjusted death rates based on the standard populations of 1940 and 1990, for

selected causes of death: United States, 1988; and comparison of percent change in ratios, 1968–88

Cause of death

Ratio, 1988 (black/white) Percent change in ratio, 1968–88

1940 1990 1940 1990

All causes .................... 1.55 1.36 5.9 6.7

Malignant neoplasms, total.......... 1.32 1.27 7.2 11.3

Malignant neoplasms, respiratory...... 1.26 1.21 9.4 12.9

Cerebrovascular diseases .......... 1.87 1.50 4.0 3.9

Homicide and legal intervention ...... 6.45 6.42 −35.8 −34.9

35
contrast, the relative standard errors for all the causes of

death were similar for both the 1990 and the 1940 standard

populations.

Also examined was the effect of reducing the number

of observations on the variance under alternative assumptions of the standard population. For each of the causes of

death, the number of deaths and population were reduced

successively to 10 percent, then 1 percent of their observed

size to assess the effect on the respective variances. The

results of this exercise are shown in table E for all causes

of death combined, results that are essentially the same

for the other causes of death.

With successive reductions in the size of the sample,

the standard error increased as did the relative standard

error; the reduction in the sample to 1 percent of the

original size resulted in tenfold increases in standard

errors and relative standard errors. The increases were the

same in the standard errors and relative standard errors as

sample size was reduced for the two population standards.

Discussion

This paper has examined the effect of using alternative population standards on age-adjusted death rates

using methods similar to those of earlier studies by Spiegelman and Marks; Curtin, Maurer, and Rosenberg; Metropolitan Life Insurance Company; Johansen; and others.

The results of this study are generally consistent with

those of the previous studies, while use of the more recent

(1990) standard greatly affects the absolute levels of mortality, bringing them into close alignment with the observed unadjusted death rates for the current period.

Their effect on relative levels and on trends are far less

marked, with a few exceptions, similar to those noted in

previous studies.

Mortality sex ratios are somewhat affected by the use

of the more recent population standard but trends in these

ratios are not. Mortality race ratios are affected, as are

trends in the ratios for a number of the selected causes of

death, a finding that definitely needs to be considered if a

change in standards is contemplated. The impact of alternative standards on stochastic variation was also examined, as it was in the earlier study by Curtin (10). It was

shown that while standard errors were considerably larger

when the 1990 standard was used, relative standard errors

remained the same.

While the effect of alternative standards on geographic comparisons was not explored in the present

study, it was by Curtin and his colleagues (10). They found

that the ranking of States in terms of their average

mortality levels tended to remain relatively stable regardless of whether the 1940 or the 1970 population was used

as the standard. Further, the rankings of the States in

terms of their age-adjusted death rates were highly correlated with rankings based on life expectancy. In the few

instances where the State rankings changed considerably,

the age-specific mortality patterns or age structures of the

States (Alaska and Hawaii) were unusual, suggesting the

need to augment aggregate measures with more detailed

measures.

The conclusions drawn by this study are also generally

consistent with those of earlier studies, namely, that there

is no compelling statistical basis for selecting one standard

population over another. Instead, the arguments pro and

con tend to emphasize, instead, symbolic or nonstatistical

criteria. These are exemplified by the opposing views of

Johansen on the one hand and Chiazze on the other. In

arguing for a more current standard, Johansen states ‘‘we

should want to know what is happening in terms of our

current population, not what would have happened to a

population living 50 years ago’’ (8). Chiazze has taken an

altogether different view of using an old versus a current

standard population. He notes than an earlier standard

helps remind us that age-adjusted death rates are constructs

or indexes that are useful for comparative purposes, ‘‘but

whose absolute magnitude are not indicative of the actual

situation,’’ (13) and have the additional advantage of

retaining continuity with historic time series.

The arguments we hear today regarding alternative

population standards remind us of the arguments in the

1930’s over the appropriate population standard for England and Wales. The issues and the considerations have

not changed much. The complexity of the issue, the

importance of the issue for promoting uniformity and

comparability of major health indicators domestically and

internationally, and the consequences of making changes

for the production of tabular data suggest the wisdom of

proceeding on the matter in a deliberative and cautious

way, seeking consultation and consensus to either stay

with the existing standard or to embrace a new standard

when both the statistical and nonstatistical considerations

for change are sufficiently compelling.

References

1. Yule GU. On some points relating to vital statistics, more

especially statistics of occupational mortality. J Royal Stat

Soc 97:1–84. 1937.

Table E. Comparison of standard error and relative standard error of the age-adjusted death rates based on standard populations of

1940 and 1990, for different sample sizes: United States, 1988

Size of sample

Number of

deaths

Standard error Relative standard error (percent)

1940 1990 1940 1990

100 percent ................. 1,876,979 0.4 0.6 0.1 0.1

10 percent .................. 187,697 1.3 1.9 0.3 0.2

1 percent .................. 18,770 4.1 6.0 0.8 0.7

36
2. Linder FE, Grove RD. Vital statistics rates in the United

States, 1900–40. Washington: U.S. Government Printing

Office. 1943.

3. Klebba AJ. Effect of changing age composition on measures

of mortality from malignant neoplasms, for white and all

other races: United States, 1950–75. In: Proceedings of the

Social Statistics Section. American Statistical Association,

594–7. 1977.

4. Schwartz H. A look at the cancer figure. Wall Street J. Nov

15, 1979.

5. Epstein S. Letter to the editor: The cancer mortality rates.

Wall Street J. Dec 10, 1979.

6. Office of the Assistant Secretary for Health. Healthy people

2000: National objectives for health promotion and disease

prevention. Washington: Public Health Service. 1990.

7. World Health Organization. Manual of the International

Statistical Classification of Diseases, Injuries, and Causes of

Death, based on the recommendations of the Ninth Revision Conference, 1975. Geneva: World Health Organization. 1977.

8. Johansen RJ. Proposed new standard population. Paper

presented at the annual meeting of the American Statistical

Association. 1990.

9. Spiegelman M, Marks HH. Empirical testing of standards

for the age adjustment of death rates by the direct method.

Hum Biol 38:280–92.

10. Curtin LR, Maurer J, Rosenberg HM. On the selection of

alternative standards for the age-adjusted death rate. In:

Proceedings of the Social Statistics Section. American Statistical Association. 1980.

11. Standardized mortality rates. Stat Bull, Apr–June 1981.

New York: Metropolitan Life Insurance Company.

12. Chiang CL. Standard error of the age-adjusted death rate.

Vital statistics: Special reports, vol 47 no 9. National Office

of Vital Statistics. Washington: Public Health Service. 1961.

13. Chiazze L Jr. Indexes of mortality. Discussion. Hum Biol

38:320–22. 1966.

37
ates based on the 1940 and 1990 standards and death rates by 10-year age groups for All causes, by selected characteristics: United States, 1968,

Age-ajusted death rate

1940

standard

1990

standard

Under

1 year

1–4

years

5–14

years

15–24

years

25–34

years

35–44

years

45–54

years

55–64

years

65–74

years

75–84

years

85 years

and over

569.5 913.9 1,169.2 61.2 31.5 111.6 114.9 206.3 559.2 1,318.5 2,945.8 6,734.8 15,761.1559.4 906.3 1,099.9 57.9 29.1 112.0 118.4 197.2 531.6 1,276.7 2,921.1 6,664.9 16,220.0509.8 841.6 832.0 45.7 23.9 95.1 116.2 188.0 438.8 1,173.0 2,667.6 6,282.9 15,875.6

(10.5) (7.9) (28.8) (25.3) (24.1) (14.8) 1.1 (8.9) (21.5) (11.0) (9.4) (6.7) 0.7

(8.9) (7.1) (24.4) (21.1) (17.9) (15.1) (1.9) (4.7) (17.5) (8.1) (8.7) (5.7) (2.1)

831.8 1,162.6 2,596.1 104.6 43.2 136.1 262.5 508.2 1,105.3 2,159.9 3,876.0 6,569.2 12,550.7842.5 1,201.3 2,356.6 97.6 39.0 138.3 269.5 489.9 1,087.6 2,146.6 3,932.9 7,382.6 13,610.8788.8 1,142.6 1,996.6 80.8 36.0 145.2 275.4 499.3 924.6 1,923.9 3,649.7 7,440.9 13,482.5

(5.2) (1.7) (23.1) (22.8) (16.7) 6.7 4.9 (1.8) (16.3) (10.9) (5.8) 13.3 7.4(6.4) (4.9) (15.3) (17.2) (7.7) 5.0 2.2 1.9 (15.0) (10.4) (7.2) 0.8 (0.9)

1.46 1.27 2.22 1.71 1.37 1.22 2.28 2.46 1.98 1.64 1.32 0.98 0.81.51 1.33 2.14 1.69 1.34 1.23 2.28 2.48 2.05 1.68 1.35 1.11 0.81.55 1.36 2.40 1.77 1.51 1.53 2.37 2.66 2.11 1.64 1.37 1.18 0.8

5.9 6.7 8.1 3.4 9.8 25.2 3.7 7.8 6.6 0.1 4.0 21.4 6.62.7 2.4 12.0 4.9 12.4 23.6 4.1 6.9 3.0 (2.5) 1.6 6.9 1.2

965.2 1,482.1 2,562.6 98.2 52.3 182.8 214.0 406.0 988.7 2,344.1 5,049.1 10,215.2 21,732.0777.2 1,217.7 1,428.5 72.6 36.7 172.3 196.1 299.2 767.3 1,815.1 4,105.2 8,816.7 18,801.1696.7 1,110.7 1,113.7 56.5 30.9 151.0 196.7 301.4 629.0 1,606.9 3,573.8 8,223.2 18,370.8

(27.8) (25.1) (56.5) (42.5) (40.9) (17.4) (8.1) (25.8) (36.4) (31.4) (29.2) (19.5) (15.5)(10.4) (8.8) (22.0) (22.2) (15.8) (12.4) 0.3 0.7 (18.0) (11.5) (12.9) (6.7) (2.3)

437.4 723.3 1,222.7 58.8 25.6 59.6 77.5 168.6 431.7 948.2 2,125.3 5,444.1 14,245.0432.6 723.7 1,141.7 54.7 24.2 57.5 75.9 159.3 412.9 934.3 2,144.7 5,440.1 14,746.9404.4 687.2 897.7 45.0 20.4 52.1 74.0 140.0 350.9 904.7 2,056.1 5,173.3 14,508.1

(7.5) (5.0) (26.6) (23.5) (20.3) (12.6) (4.5) (17.0) (18.7) (4.6) (3.3) (5.0) 1.8(6.5) (5.1) (21.4) (17.7) (15.7) (9.4) (2.5) (12.1) (15.0) (3.2) (4.1) (4.9) (1.6)

2.21 2.05 2.10 1.67 2.04 3.07 2.76 2.41 2.29 2.47 2.38 1.88 1.531.80 1.68 1.25 1.33 1.52 3.00 2.58 1.88 1.86 1.94 1.91 1.62 1.271.72 1.62 1.24 1.26 1.51 2.90 2.66 2.15 1.79 1.78 1.74 1.59 1.27

(21.9) (21.1) (40.8) (24.8) (25.9) (5.5) (3.7) (10.6) (21.7) (28.2) (26.8) (15.3) (17.0)

(4.1) (3.9) (0.8) (5.4) (0.1) (3.3) 2.9 14.6 (3.5) (8.6) (9.2) (1.9) (0.7)
ates based on the 1940 and 1990 standards and death rates by 10-year age groups for Cancer, by selected characteristics: United States, 1968, 1980,

Age-adjusted death rate

1940

standard

1990

standard

Under

1 year

1–4

years

5–14

years

15–24

years

25–34

years

35–44

years

45–54

years

55–64

years

65–74

years

75–84

years

85 years

and over

.. 126.9 180.6 4.9 8.4 6.6 8.3 16.8 58.0 175.2 402.0 742.2 1,142.5 1,509.7.. 129.6 187.8 3.1 4.5 4.5 6.3 13.5 46.0 170.8 422.2 807.2 1,227.6 1,600.8.. 130.0 191.4 2.2 3.8 3.2 5.1 11.5 41.6 152.3 437.1 833.8 1,305.6 1,640.2. . 2.4 6.0 (55.1) (54.8) (51.5) (38.6) (31.5) (28.3) (13.1) 8.7 12.3 14.3 8.6. . 0.3 1.9 (29.0) (15.6) (28.9) (19.0) (14.8) (9.6) (10.8) 3.5 3.3 6.4 2.5.. 155.9 205.5 4.7 6.2 4.9 7.8 21.9 89.8 266.7 546.6 845.5 1,035.9 1,079.3.. 172.1 236.0 3.7 4.5 3.5 6.8 16.4 73.7 276.3 612.0 981.1 1,352.5 1,571.1.. 171.3 242.2 3.0 3.6 2.9 5.6 15.8 69.7 243.8 588.1 1,029.2 1,542.8 1,720.1

. . 9.8 17.9 (36.2) (41.9) (40.8) (28.2) (27.9) (22.4) (8.6) 7.6 21.7 48.9 59.4.. (0.4) 2.6 (18.9) (20.0) (17.1) (17.6) (3.7) (5.4) (11.8) (3.9) 4.9 14.1 9.5

.. 1.23 1.14 0.96 0.74 0.74 0.94 1.30 1.55 1.52 1.36 1.14 0.91 0.71

.. 1.33 1.26 1.19 1.00 0.78 1.08 1.21 1.60 1.62 1.45 1.22 1.10 0.98

.. 1.32 1.27 1.36 0.95 0.91 1.10 1.37 1.68 1.60 1.35 1.23 1.18 1.05

. . 7.2 11.3 42.2 28.4 22.1 16.8 5.4 8.2 5.2 (1.0) 8.4 30.3 46.7

.. (0.7) 0.7 14.3 (5.3) 16.5 1.7 13.1 4.6 (1.0) (7.2) 1.6 7.3 6.9

.. 155.5 227.4 4.7 8.8 6.9 10.0 17.3 53.7 183.0 497.9 998.2 1,520.1 1,936.1.. 165.5 249.2 3.7 5.2 4.9 7.8 13.4 44.0 188.7 520.8 1,093.2 1,790.5 2,369.5.. 162.4 249.2 2.3 3.8 3.6 5.9 11.7 39.7 166.3 526.7 1,072.7 1,861.0 2,527.9

. . 4.5 9.6 (51.1) (56.8) (47.8) (41.0) (32.4) (26.1) (9.1) 5.8 7.5 22.4 30.6.. (1.8) (0.0) (37.8) (26.9) (26.5) (24.4) (12.7) (9.8) (11.9) 1.1 (1.9) 3.9 6.7

.. 108.8 149.3 5.1 7.3 5.7 6.5 17.2 67.9 183.0 337.2 553.1 869.4 1,223.6.. 109.2 152.5 2.7 3.7 3.6 4.8 14.0 53.1 171.8 361.7 607.1 903.1 1,255.7.. 111.2 158.2 2.3 3.7 2.7 4.2 12.2 48.5 154.9 376.6 659.2 982.6 1,292.8

. . 2.2 6.0 (54.9) (49.3) (52.6) (35.4) (29.1) (28.6) (15.4) 11.7 19.2 13.0 5.7. . 1.8 3.8 (14.8) 0.0 (25.0) (12.5) (12.9) (8.7) (9.8) 4.1 8.6 8.8 3.0.. 1.43 1.52 0.92 1.21 1.21 1.54 1.01 0.79 1.00 1.48 1.80 1.75 1.58

.. 1.52 1.63 1.37 1.41 1.36 1.63 0.96 0.83 1.10 1.44 1.80 1.98 1.89

.. 1.46 1.57 1.00 1.03 1.33 1.40 0.96 0.82 1.07 1.40 1.63 1.89 1.96

. . 2.2 3.4 8.5 (14.8) 10.1 (8.7) (4.7) 3.5 7.4 (5.3) (9.8) 8.3 23.6.. (3.6) (3.6) (27.0) (26.9) (2.0) (13.6) 0.2 (1.2) (2.3) (2.9) (9.6) (4.5) 3.6
ates based on the 1940 and 1990 standards and death rates by 10-year age groups for Cancer of respiratory system, by selected characteristics:

d 1988

Age-adjusted death rate

1940

standard

1990

standard

Under

1 year

1–4

years

5–14

years

15–24

years

25–34

years

35–44

years

45–54

years

55–64

years

65–74

years

75–84

years

85 years

and over

26.3 34.7 0.2 0.0 0.1 0.2 1.0 10.2 41.2 106.6 168.3 161.3 117.435.6 48.2 0.1 0.0 0.0 0.1 0.7 9.0 53.6 140.4 242.7 252.9 185.639.4 55.0 0.1 0.0 0.0 0.1 0.7 7.0 47.6 160.0 279.7 325.7 228.349.6 58.8 (43.2) (100.0) (100.0) (43.2) (31.9) (31.3) 15.4 50.1 66.2 101.9 94.510.6 14.1 0.0 0.0 0.0 0.0 0.0 (22.2) (11.2) 14.0 15.2 28.8 23.030.4 37.1 0.2 0.0 0.0 0.1 1.4 18.5 66.3 124.5 149.5 130.2 98.946.5 59.0 0.4 0.1 0.0 0.2 1.3 16.5 89.7 198.3 263.9 243.3 172.949.8 66.5 0.2 0.0 0.0 0.2 0.9 13.1 77.7 206.3 320.9 340.2 234.563.7 79.2 (5.4) 0.0 (100.0) 55.0 (37.8) (29.2) 17.2 65.7 114.7 161.2 137.1

7.1 12.7 (50.0) (100.0) 0.0 0.0 (30.8) (20.6) (13.4) 4.0 21.6 39.8 35.6

1.16 1.07 1.20 0.00 0.58 0.73 1.41 1.82 1.61 1.17 0.89 0.81 0.841.31 1.22 4.00 0.0 0.0 2.00 1.86 1.83 1.67 1.41 1.09 0.96 0.931.26 1.21 2.00 0.0 0.0 2.00 1.29 1.87 1.63 1.29 1.15 1.04 1.03

9.4 12.9 66.5 0.0 0.0 173.1 (8.6) 3.1 1.5 10.4 29.2 29.4 21.9

(3.1) (1.2) (50.0) 0.0 0.0 0.0 (30.8) 2.1 (2.5) (8.7) 5.5 8.6 10.3

47.9 64.1 0.2 0.0 0.1 0.2 1.6 16.1 69.6 192.1 324.4 313.7 217.059.7 83.8 0.3 0.0 0.0 0.2 1.0 12.6 79.8 223.8 422.0 511.5 386.359.7 86.6 0.1 0.0 0.0 0.2 0.9 9.6 65.7 229.5 425.4 579.8 492.824.6 35.1 (42.7) (100.0) (100.0) (16.0) (42.7) (40.3) (5.6) 19.4 31.2 84.9 127.0

0.0 3.4 (66.7) 0.0 0.0 0.0 (10.0) (23.8) (17.7) 2.5 0.8 13.4 27.6

8.9 11.4 0.2 0.1 0.1 0.1 0.6 6.1 18.8 31.6 41.9 53.9 61.8

18.3 23.5 0.1 0.1 0.1 0.0 0.6 6.8 34.8 74.5 106.1 98.0 96.324.4 33.0 0.1 0.0 0.0 0.1 0.5 5.6 35.0 102.2 164.1 169.9 125.7

175.7 190.7 (45.1) (100.0) (100.0) (6.3) (17.0) (8.5) 86.6 223.8 291.8 215.0 103.6

33.9 40.6 0.0 (100.0) (100.0) 0.0 (16.7) (17.6) 0.6 37.2 54.7 73.4 30.55.40 5.64 0.96 0.16 0.82 2.23 2.61 2.63 3.71 6.09 7.74 5.82 3.513.27 3.56 3.00 0.00 0.00 0.0 1.67 1.85 2.29 3.00 3.98 5.22 4.012.44 2.62 1.00 0.0 0.0 2.00 1.80 1.71 1.88 2.25 2.59 3.41 3.92

(54.8) (53.5) 4.2 0.0 0.0 (10.3) (30.9) (34.8) (49.4) (63.1) (66.5) (41.3) 11.5(25.3) (26.5) (66.7) 0.0 0.0 0.0 8.0 (7.5) (18.1) (25.2) (34.8) (34.6) (2.3)
ates based on the 1940 and 1990 standards and death rates by 10-year age groups for Cerebrovascular diseases, by selected characteristics:

d 1988

Age-adjusted death rate

1940

standard

1990

standard

Under

1 year

1–4

years

5–14

years

15–24

years

25–34

years

35–44

years

45–54

years

55–64

years

65–74

years

75–84

years

85 years

and over

66.2 134.9 4.5 0.8 0.7 1.5 3.6 12.0 35.1 101.3 375.0 1,312.5 3,698.638.0 79.5 3.6 0.4 0.2 0.8 2.0 6.6 20.2 56.0 202.1 776.2 2,328.427.5 57.5 3.0 0.3 0.2 0.7 1.7 5.0 14.9 43.3 142.3 542.0 1,739.4

(58.4) (57.4) (33.3) (62.5) (71.4) (53.3) (52.8) (58.3) (57.5) (57.3) (62.1) (58.7) (53.0)

(27.7) (27.6) (16.7) (25.0) 0.0 (12.5) (15.0) (24.2) (26.2) (22.7) (29.6) (30.2) (25.3)

125.8 198.0 9.3 1.3 0.8 3.6 14.7 56.4 139.4 339.5 795.9 1,442.4 2,676.1

68.5 114.8 8.8 0.6 0.4 1.9 7.3 25.0 71.0 161.5 408.9 976.4 1,888.651.5 86.3 8.8 0.6 0.3 1.0 6.0 21.9 53.6 124.2 290.6 736.5 1,445.4

(59.0) (56.4) (5.4) (53.8) (62.5) (72.2) (59.2) (61.2) (61.5) (63.4) (63.5) (48.9) (46.0)(24.8) (24.9) 0.0 0.0 (25.0) (47.4) (17.8) (12.4) (24.5) (23.1) (28.9) (24.6) (23.5)

1.90 1.47 2.07 1.63 1.14 2.40 4.08 4.70 3.97 3.35 2.12 1.10 0.721.80 1.44 2.44 1.50 2.00 2.37 3.65 3.79 3.51 2.88 2.02 1.26 0.811.87 1.50 2.93 2.00 1.50 1.43 3.53 4.38 3.60 2.87 2.04 1.36 0.83

(1.6) 2.3 41.9 23.1 31.2 (40.5) (13.6) (6.8) (9.4) (14.4) (3.8) 23.6 14.8

4.0 3.9 20.0 33.3 (25.0) (39.8) (3.3) 15.6 2.3 (0.5) 0.9 8.0 2.4

78.7 151.4 5.6 0.8 0.7 1.8 4.8 16.4 47.9 144.2 483.1 1,418.2 3,591.644.9 88.4 5.0 0.4 0.3 1.1 2.6 8.7 27.3 74.7 259.2 868.3 2,199.232.4 63.5 4.2 0.4 0.2 0.8 2.4 7.5 21.0 59.5 176.5 603.2 1,625.6

(58.8) (58.1) (25.0) (50.0) (71.4) (55.6) (50.0) (54.3) (56.2) (58.7) (63.5) (57.5) (54.7)(27.7) (28.2) (16.0) 0.0 (33.3) (27.3) (7.7) (13.8) (23.1) (20.3) (31.9) (30.5) (26.1)

65.3 131.4 4.9 0.9 0.6 1.7 4.9 16.6 41.5 101.4 351.6 1,248.8 3,618.337.6 78.0 3.8 0.5 0.3 0.8 2.6 8.4 23.3 56.9 189.0 741.6 2,328.227.6 57.0 3.7 0.4 0.2 0.7 2.1 6.2 17.4 44.0 137.3 523.7 1,738.4

(57.8) (56.6) (24.5) (55.6) (66.7) (58.8) (57.1) (62.7) (58.1) (56.6) (60.9) (58.1) (52.0)

(26.8) (26.9) (2.6) (20.0) (33.3) (12.5) (19.2) (26.2) (25.3) (22.7) (27.4) (29.4) (25.3)

1.21 1.15 1.14 0.89 1.17 1.06 0.98 0.99 1.15 1.42 1.37 1.14 0.991.19 1.13 1.32 0.80 1.00 1.38 1.00 1.04 1.17 1.31 1.37 1.17 0.941.18 1.11 1.14 1.00 1.00 1.14 1.14 1.21 1.21 1.35 1.29 1.15 0.94

(2.3) (3.3) (0.7) 12.5 (14.3) 7.9 16.7 22.4 4.6 (4.9) (6.4) 1.4 (5.8)(1.3) (1.7) (13.7) 25.0 0.0 (16.9) 14.3 16.8 3.0 3.0 (6.3) (1.6) (1.0)
ates based on the 1940 and 1990 standards and death rates by 10-year age groups for Homicide, by selected characteristics: United States, 1968,

Age-adjusted death rate

1940

standard

1990

standard

Under

1 year

1–4

years

5–14

years

15–24

years

25–34

years

35–44

years

45–54

years

55–64

years

65–74

years

75–84

years

85 years

and over

4.2 4.2 4.0 1.1 0.5 4.8 6.9 6.3 5.0 4.0 3.3 2.9 3.36.9 6.9 4.3 1.7 0.9 10.1 11.6 9.7 7.3 4.8 4.4 4.4 4.8

5.3 5.2 5.8 1.9 0.9 7.8 8.9 6.8 5.0 3.9 3.1 3.4 3.5

25.6 23.8 45.0 72.7 80.0 62.5 29.0 7.9 0.0 (2.5) (6.1) 17.2 6.1

(23.8) (23.8) 34.9 11.8 0.0 (22.8) (23.3) (29.9) (31.5) (18.8) (29.5) (22.7) (27.1)

42.3 41.8 9.5 3.8 2.2 49.8 83.7 70.2 49.1 30.2 16.0 10.1 8.740.6 40.3 15.7 6.8 2.6 50.6 81.2 59.7 44.5 29.7 19.3 14.8 11.334.1 33.7 21.4 6.9 3.6 59.1 64.8 44.0 24.5 16.9 16.4 17.6 18.3

(19.3) (19.4) 125.3 81.6 63.6 18.7 (22.6) (37.3) (50.1) (44.0) 2.5 74.3 110.3(16.0) (16.4) 36.3 1.5 38.5 16.8 (20.2) (26.3) (44.9) (43.1) (15.0) 18.9 61.9

10.05 9.85 2.38 3.45 4.40 10.38 12.13 11.14 9.82 7.55 4.85 3.48 2.64

5.85 5.85 3.65 4.00 2.89 5.01 7.00 6.15 6.10 6.19 4.39 3.36 2.356.45 6.42 3.69 3.63 4.00 7.58 7.28 6.47 4.90 4.33 5.29 5.18 5.23

(35.8) (34.9) 55.4 5.1 (9.1) (27.0) (40.0) (41.9) (50.1) (42.6) 9.1 48.6 98.3

10.2 9.7 1.1 (9.2) 38.5 51.2 4.0 5.1 (19.6) (30.0) 20.6 53.9 122.113.4 13.3 4.7 1.5 0.9 16.4 25.3 20.8 15.1 10.5 7.6 5.4 5.9

6.9 6.9 4.3 1.7 0.9 10.1 11.6 9.7 7.3 4.8 4.4 4.4 4.8

5.3 5.2 5.8 1.9 0.9 7.8 8.9 6.8 5.0 3.9 3.1 3.4 3.5

(60.5) (60.6) 23.4 26.7 0.0 (52.4) (64.8) (67.3) (66.9) (62.9) (59.2) (37.0) (40.7)(23.8) (23.8) 34.9 11.8 0.0 (22.8) (23.3) (29.9) (31.5) (18.8) (29.5) (22.7) (27.1)

4.1 4.0 4.9 2.3 1.1 5.8 6.2 5.4 3.7 2.6 2.7 3.4 3.74.5 4.4 5.6 2.2 1.1 6.6 7.0 5.7 4.1 2.8 3.0 3.5 4.34.2 4.2 8.7 2.3 1.1 6.0 7.3 4.6 3.1 2.5 2.9 3.5 3.72.3 3.1 77.6 0.0 0.0 3.4 17.7 (14.8) (16.2) (3.8) 7.4 2.9 0.0

(6.9) (6.1) 55.4 4.5 0.0 (9.1) 4.3 (19.3) (24.4) (10.7) (3.3) 0.0 (14.0)

3.29 3.30 0.96 0.65 0.82 2.83 4.08 3.85 4.08 4.04 2.81 1.59 1.591.55 1.55 0.77 0.77 0.82 1.53 1.66 1.70 1.78 1.71 1.47 1.26 1.121.27 1.26 0.67 0.83 0.82 1.30 1.22 1.48 1.61 1.56 1.07 0.97 0.95

(61.4) (61.8) (30.5) 26.7 0.0 (54.0) (70.1) (61.6) (60.5) (61.4) (62.0) (38.8) (40.7)(18.1) (18.8) (13.2) 6.9 0.0 (15.0) (26.4) (13.1) (9.4) (9.0) (27.1) (22.7) (15.3)
Table 6. Deaths, age-adjusted death rates based on the 1940 and 1990 standards, standard errors, and relative standard errors in

percent for All causes, by selected characteristics: United States, 1988

Characteristic Deaths

Age-adjusted death rates Standard errors

Relative standard errors

(percent)

1940

standard

1990

standard

1940

standard

1990

standard

1940

standard

1990

standard

White

Observed number . . . . . . . . . .D 1,876,969 509.8 841.6 0.4 0.6 0.1 0.1

Ten-percent sample ...... .0.1D 187,697 509.8 841.6 1.3 1.9 0.3 0.2

One-percent sample ..... .0.01D 18,770 509.8 841.6 4.1 6.0 0.8 0.7

Black

Observed number . . . . . . . . . .D 264,026 788.8 1,142.6 1.6 2.2 0.2 0.2

Ten-percent sample ...... .0.1D 26,403 788.8 1,142.6 5.0 7.0 0.6 0.6

One-percent sample ..... .0.01D 2,640 788.8 1,142.6 15.8 22.0 2.0 1.9

Male

Observed number . . . . . . . . . .D 1,125,537 696.7 1,110.7 0.7 1.0 0.1 0.1

Ten-percent sample ...... .0.1D 112,554 696.7 1,110.7 2.1 3.2 0.3 0.3

One-percent sample ..... .0.01D 11,255 696.7 1,110.7 6.8 10.2 1.0 0.9

Female

Observed number . . . . . . . . . .D 1,042,465 404.4 687.2 0.5 0.7 0.1 0.1

Ten-percent sample ...... .0.1D 104,246 404.4 687.2 1.5 2.1 0.4 0.3

One-percent sample ..... .0.01D 10,425 404.4 687.2 4.7 6.6 1.2 1.0

Table 7. Deaths, age-adjusted death rates based on the 1940 and 1990 standards, standard errors, and relative standard errors in

percent for Cancer, by selected characteristics: United States, 1988

Characteristic Deaths

Age-adjusted death rates Standard errors

Relative standard errors

(percent)

1940

standard

1990

standard

1940

standard

1990

standard

1940

standard

1990

standard

White

Observed number . . . . . . . . . .D 425,123 130.0 191.4 0.2 0.3 0.2 0.2

Ten-percent sample ...... .0.1D 42,512 130.0 191.4 0.7 0.9 0.5 0.5

One-percent sample ..... .0.01D 4,251 130.0 191.4 2.2 2.9 1.7 1.5

Black

Observed number . . . . . . . . . .D 53,971 171.3 242.2 0.8 1.0 0.4 0.4

Ten-percent sample ...... .0.1D 5,397 171.3 242.2 2.4 3.3 1.4 1.4

One-percent sample ..... .0.01D 540 171.3 242.2 7.6 10.5 4.4 4.3

Male

Observed number . . . . . . . . . .D 258,035 162.4 249.2 0.3 0.5 0.2 0.2

Ten-percent sample ...... .0.1D 25,804 162.4 249.2 1.0 1.6 0.6 0.6

One-percent sample ..... .0.01D 2,580 162.4 249.2 3.3 4.9 2.0 2.0

Female

Observed number . . . . . . . . . .D 226,924 111.2 158.2 0.3 0.3 0.2 0.2

Ten-percent sample ...... .0.1D 22,692 111.2 158.2 0.8 1.1 0.7 0.7

One-percent sample ..... .0.01D 2,269 111.2 158.2 2.6 3.3 2.3 2.1

43
Table 8. Deaths, age-adjusted death rates based on the 1940 and 1990 standards, standard errors, and relative standard errors in

percent for Cancer of the respiratory system, by selected characteristics: United States, 1988

Characteristic Deaths

Age-adjusted death rates Standard errors

Relative standard errors

(percent)

1940

standard

1990

standard

1940

standard

1990

standard

1940

standard

1990

standard

White

Observed number . . . . . . . . . .D 121,938 39.4 55.0 0.1 0.2 0.3 0.3

Ten-percent sample ...... .0.1D 12,194 39.4 55.0 0.4 0.5 1.0 0.9

One-percent sample ..... .0.01D 1,219 39.4 55.0 1.2 1.6 3.0 2.9

Black

Observed number . . . . . . . . . .D 14,950 49.8 66.5 0.4 0.5 0.8 0.8

Ten-percent sample ...... .0.1D 1,495 49.8 66.5 1.3 1.7 2.7 2.6

One-percent sample ..... .0.01D 149 49.8 66.5 4.2 5.5 8.4 8.2

Male

Observed number . . . . . . . . . .D 91,839 59.7 86.6 0.2 0.3 0.3 0.3

Ten-percent sample ...... .0.1D 9,184 59.7 86.6 0.6 0.9 1.1 1.1

One-percent sample ..... .0.01D 918 59.7 86.6 2.0 2.9 3.4 3.3

Female

Observed number . . . . . . . . . .D 46,393 24.4 33.0 0.1 0.2 0.5 0.5

Ten-percent sample ...... .0.1D 4,639 24.4 33.0 0.4 0.5 1.6 1.5

One-percent sample ..... .0.01D 464 24.4 33.0 1.2 1.5 5.0 4.7

Table 9. Deaths, age-adjusted death rates based on the 1940 and 1990 standards, standard errors, and relative standard errors in

percent for Cerebrovascular diseases, by selected characteristics: United States, 1988

Characteristic Deaths

Age-adjusted death rates Standard errors

Relative standard errors

(percent)

1940

standard

1990

standard

1940

standard

1990

standard

1940

standard

1990

standard

White

Observed number . . . . . . . . . .D 130,025 27.5 57.5 0.1 0.2 0.3 0.3

Ten-percent sample ...... .0.1D 13,003 27.5 57.5 0.3 0.5 1.0 0.9

One-percent sample ..... .0.01D 1,300 27.5 57.5 0.9 1.6 3.1 2.8

Black

Observed number . . . . . . . . . .D 18,484 51.5 86.3 0.4 0.6 0.8 0.7

Ten-percent sample ...... .0.1D 1,848 51.5 86.3 1.3 2.0 2.5 2.3

One-percent sample ..... .0.01D 185 51.5 86.3 4.0 6.4 7.8 7.4

Male

Observed number . . . . . . . . . .D 59,749 32.4 63.5 0.1 0.3 0.4 0.4

Ten-percent sample ...... .0.1D 5,975 32.4 63.5 0.4 0.8 1.4 1.3

One-percent sample ..... .0.01D 597 32.4 63.5 1.4 2.6 4.3 4.1

Female

Observed number . . . . . . . . . .D 90,770 27.6 57.0 0.1 0.2 0.4 0.3

Ten-percent sample ...... .0.1D 9,077 27.6 57.0 0.3 0.6 1.3 1.1

One-percent sample ..... .0.01D 908 27.6 57.0 1.1 1.9 4.0 3.3

44
Table 10. Deaths, age-adjusted death rates based on the 1940 and 1990 standards, standard errors, and relative standard errors in

percent for Homicide, by selected characteristics: United States, 1988

Characteristic Deaths

Age-adjusted death rates Standard errors

Relative standard errors

(percent)

1940

standard

1990

standard

1940

standard

1990

standard

1940

standard

1990

standard

White

Observed number . . . . . . . . . .D 10,991 5.3 5.2 0.1 0.1 1.0 1.0

Ten-percent sample ...... .0.1D 1,099 5.3 5.2 0.2 0.2 3.1 3.0

One-percent sample ..... .0.01D 110 5.3 5.2 0.5 0.5 9.7 9.5

Black

Observed number . . . . . . . . . .D 10,389 34.1 33.7 0.3 0.3 1.0 1.0

Ten-percent sample ...... .0.1D 1,039 34.1 33.7 1.1 1.1 3.1 3.1

One-percent sample ..... .0.01D 104 34.1 33.7 3.4 3.3 9.9 9.9

Male

Observed number . . . . . . . . . .D 16,763 5.3 5.2 0.1 0.1 1.3 1.3

Ten-percent sample ...... .0.1D 1,676 5.3 5.2 0.2 0.2 4.0 4.0

One-percent sample ..... .0.01D 168 5.3 5.2 0.7 0.7 12.6 12.5

Female

Observed number . . . . . . . . . .D 5,295 4.2 4.2 0.1 0.1 1.4 1.4

Ten-percent sample ...... .0.1D 529 4.2 4.2 0.2 0.2 4.5 4.4

One-percent sample ..... .0.01D 53 4.2 4.2 0.6 0.6 14.1 13.8

45
Part IV

Areas of application
Chapter 6

The choice of the standard

for age adjustment

by Richard Rothenberg, M.D., Center for

Chronic Disease Prevention and Health

Promotion, Centers for Disease Control, and

Robert Hahn, Ph.D., Epidemiology Program

Office, Centers for Disease Control

For five decades, the National Center for Health

Statistics (NCHS) has used the population of the United

States in 1940 as the standard for age adjustment of

mortality statistics. Any choice of standard is arbitrary, a

convenient construct for making comparisons. However,

as 1940 recedes, its relevance has been questioned, because the current population structure differs markedly

from that of 1940. It is tempting casually to dismiss the

1940 standard as outmoded, but the issue is complex and

its resolution difficult for several reasons. Theoretically, is

there a best or most appropriate standard? Empirically,

does a change in standard make a difference in the

perception of disease burden or the ranking of public

health problems? Economically, what effort and cost are

engendered by a change? Socially, what is the impact of

discontinuity on public perception?

These issues have been revisited on several occasions

during the last 30 years. In 1966, Spiegelman and Marks

compared mortality data using the populations of 1940,

1950, and 1960 as standards (1). In 1981, Curtin, Maurer,

and Rosenberg compared standardization using the 1940

and 1970 populations (2). Although their methods differed

in detail (table 1), their results were similar. Spiegelman

and Marks noted a major change in the absolute level of

the age-adjusted rate (AAR) and that the amount of

change varied by disease (major change for stroke, moderate change for diabetes, insignificant change for motor

vehicle accidents and homicide). The choice of standard

had little influence on the AAR for the population 65

years of age and over. The standard exerted no influence

on the male-female or the black-white ratios. There was

little difference in observed trends using any of the three

standards.

Curtin, Maurer, and Rosenberg demonstrated a high

correlation between the AAR (1940 standard) and life

expectancy at birth (using 1969–72 life tables), between

the AAR (1970 standard) and life expectancy at birth, and

between the two standards. They noted that the AAR is

higher when using the 1970 standard for total mortality

and for causes for which the average age at death is higher

than that for total mortality. The AAR (1970 standard) is

lower, however, for causes for which the average age at

death is lower than the overall average age at death. Using

the percent change as a measurement of trend, they

observed a small effect of a change in standard. Using

annual average percent change, however, they noted an

important effect of the change in standard for suicide and

for cancer. By comparing two time periods for cancer,

whose mortality increases were accelerating, they were

able to demonstrate that the 1970 standard revealed a

greater increase for cancer than the 1940 standard. In this

subanalysis, they concluded that a change in standard may

affect the perception of trend because of variable changes

in age-specific trends—a situation for which age standardization is not optimal. Finally, they demonstrated that the

change of standard had little effect on the male-female

ratio and that the relative standard error of the AAR and

the coefficient of variation were unaffected. Both groups

of researchers concluded that a shift from the 1940 standard was not warranted at the time.

Methods

In the current analysis (table 2), we compared the

effects of eight standard populations: the populations

determined at each census from 1940 to 1980; an estimate

of the 1990 population (3); the standard population used

by the World Health Organization, referred to here as

WORLD (mentioned in (4)); and a uniform standard,

referred to here as UNIFORM, for which each of n age

groups receives 1/nth of the weight. (A uniform standard

is equivalent to the mean age-specific rate.)

Numerator data for total mortality were derived from

the underlying-cause-of-death (UCD) tapes for 1968–88

supplied by NCHS (5). Numerator data for the 15 leading

causes of death were taken from the UCD data for 1987.

In a separate analysis, four causes of death—ischemic

heart disease, cancer, chronic obstructive pulmonary disease (COPD), and Alzheimer’s disease—were examined

in greater detail. For them, data were extracted from the

multiple-cause-of-death tapes for 1968–87 supplied by

NCHS (6). A death was included if there was mention of

these four conditions anywhere on the original death

certificate (the ‘‘record axis’’ in the electronic format).

Intercensal population estimates for 1968–79 were provided by the U.S. Bureau of the Census (3), and estimates

for 1980–88 were provided by a private contractor (7).

These denominators differ slightly from those currently

used by NCHS, which incorporate the periodic updates of

intercensal estimates supplied by the Bureau of the Census and do not use the private contractor. Nonetheless,

the rate estimates generated in this analysis differ only in a

minor way from those published by NCHS.

49
In addition to a comparison of U.S. decennial population, WORLD, and UNIFORM standards, a set of

artificial weights was constructed to examine the particular effect of different types of population structures on the

AAR. Four different population pyramids were examined:

predominance of the young, predominance of the elderly,

predominance of the middle-aged, and a population with

predominance of both young and old with deemphasis of

the middle-aged. In each subset, the degree of predominance was varied over a range of values.

The major factors examined were the relative change

in the AAR, trends, sex and race ratios, rankings of States

for ischemic heart disease, rankings of major causes of

death, effect of alternative age groupings, and difference

between the crude rate and the AAR. Four methods,

similar in their nomenclature but somewhat different in

their statistical approach, were used to examine trends:

the overall percent change, which uses the first and last

values to compute the change ((Pn−Po)/Po) (8); the

average annual percent change, which also uses the first

and last values and assumes uniform change in each

interval (((Pn/Po)**1/n)−1) (8); the slope of a line fitted

by linear regression through all the data points; and the

corrected slope (slope/intercept), which is equivalent to

the average annual percent change for the fitted line. For

simple and multiple linear regression, year was converted

so that the 0 point was at 1977.5, thereby centering the

intercept at the midpoint of the data, which equates the

intercept with the mean. For each State, a separate

multiple regression analysis was performed using rank as

the dependent variable and year and standard as independent variables. In these multiple regressions, the 1940–90

standards were coded 0–5. The average change in rank for

each State was calculated by substituting in the regression

equation.

Results

Relative effect on AAR

The choice of standard has a dramatic effect on the

size of the AAR, as expected (table 3). Using the 1950–70

standards, the AAR drifts slowly upward, but there is a

sharp increase with 1980 and a subsequent small fall with

1990. The rate ratios for each of the standards compared

with the 1940 standard increase slightly over time; this

effect is most marked for the UNIFORM to 1940 ratio.

The WORLD standard, although quite different in

structure from the 1940 standard (table 4), produces a

similar AAR. For instance, for 1988 the rate ratio for

AAR (WORLD) to AAR (1940) was 1.045. The 1990

standard, as expected, produces a value for 1988 that is

closest to the crude rate. (Obviously, if a 1990 standard

were used for 1990 data, the crude rate and AAR would

be identical.)

The UNIFORM standard, which gives greater total

emphasis to older age groups than does any of the others,

produces a very large AAR. As noted, it is equivalent to

the mean age-specific rate, and in fact it corresponds most

closely to the age-specific rate for persons aged 65–69

years for the period 1968–88. Thus, it may be viewed as

providing information usually not incorporated in the

AAR.

Trends

For total mortality, the overall percent change and the

average annual percent change do not vary appreciably

with the standard chosen (table 5). The slope of mortality

decline, however, is accentuated by the more recent standards. Although the slope is a larger negative number

using the 1990 standard than using the 1940 standard, it is

actually a smaller percent of the intercept (mean AAR).

The WORLD standard produces results similar to those

for the 1940 standard. The UNIFORM standard is considerably different. The two measures of percent change are

smaller, the slope is steeper, and the slope is a smaller

proportion of the mean AAR.

The specific causes of mortality exhibit different trend

patterns with different standards. For ischemic heart disease, which has undergone dramatic declines, the trends

are similar to those for overall mortality. There is less

evidence of decline with more recent standards using

either percent measure. The slope is increasingly negative

for the later standards, but the slope is also a smaller

percent of the mean AAR.

In the case of cancer, for which there have been small

increases in overall mortality, the opposite occurs. The

percent measures are magnified by using more recent

standards; using 1990, the overall percent increase is

5.5 percent, compared with 2.4 percent for 1940. The

slope also increases, but more importantly, the slope is a

greater proportion of the mean AAR with more recent

standards (1980 and 1990) than with the 1940 standard.

Again, the WORLD standard produces results similar to

those for 1940. The UNIFORM standard accentuates

these increases to an even greater extent than do the 1980

and 1990 standards.

A similar phenomenon occurs for Alzheimer’s disease, whose increases have been among the most dramatic

for any cause of death, including AIDS. A change of

standard from 1940 to 1990 more than doubles the percent

change and produces large increases for all the other

parameters. If the UNIFORM distribution is used, the

overall percent increase is elevated almost fourfold.

Chronic obstructive pulmonary disease is a condition

for which there has been no significant mortality trend

during the period of observation. It is of interest that the

choice of standard can actually alter the perceived direction of change. The overall percent change is -1.1 percent

using 1940 and 8.3 percent using 1990, although it is

important to remember that none of the slopes is significant.

Thus, standards that reflect more recent population

structures have a small but consistent influence on the

perception of trends. It is reasonable to generalize that,

for diseases with decreasing mortality, the decline will be

50
less pronounced using a standard that reflects the current

population composition than using the 1940 standard. For

diseases with increasing mortality, the increase will be

more pronounced. In the examples cited (with the exception of Alzheimer’s disease), these differences are small to

moderate. For Alzheimer’s disease, the change in standard produces a marked alteration in the perception of

trend.

Sex and race ratios

As with the total population, more recent standards

alter the actual level of the AAR for each sex, race, and

sex-race group. This alteration is not uniform across the

groups or across years. The ratio of the AAR using the

1990 standard to the AAR using the 1940 standard increases from 1968 to 1988 for each of the sex, race, and

sex-race groups (table 6). It is greatest for black females

(9.5 percent) and smallest for white females (2.2 percent). In general, the change is greater for black than

white persons and greater for men than women.

The male-female ratio, the black-white ratio, and the

sex-specific black-white ratios all are smaller when more

recent standards are used (table 7). The amount of change

differs with each comparison and over the years. For

example, the 1990 standard produces a ratio of black to

white mortality among women in 1968 that is 20.1 percent

less than that produced by the 1940 standard. In 1988, the

ratio using the 1990 standard was only 12.9 percent lower

than that using the 1940 standard.

State rankings

Each standard alters the relative rankings of States for

ischemic heart disease mortality. The degree of alteration

is greater with more recent standards than with the 1940

standard (table 8) and is greatest with the 1990 standard.

Comparing 1990 with 1940, there is a mean movement of

2.4 ranks, with a range of 0–23 ranks. For 1987, only three

States had no change in rank over all the standards: Utah

(48th), Hawaii (49th), and New York (1st).

Change in rank is, of course, a function not only of

standardization but of varying secular trends within States.

A shift in population structure or a shift in true risk can

make a State more susceptible to the effect of a change in

standard. Using multiple regression analyses for each

State to compare the 1990 standard with the 1940 standard, the largest negative change occurred for Georgia

(-8.6) and the largest positive change for Connecticut (7.5)

(table 9). The average change over all the States was 2.4

ranks. These values represent the effect of the change in

the standard while controlling for secular trends, and they

suggest that secular trend did not confound the change in

ranks produced by different standards.

Disease rankings

The leading causes of death have different ranking

patterns depending on the standard used (table 10). These

changes do not affect the two major causes of death (heart

disease and malignant neoplasms), but they do create

generally small perturbations in the ordering of the remaining diseases. The rank order correlation coefficient for the

ranks produced by the 1990 standard compared with those

for the 1940 standard was 0.93 (p<0.01), indicating a high

degree of correlation. The change in ranks was not statistically significant, but it does alter to some extent the

perception of disease importance.

Relationship of crude rate and AAR

The percent difference between the crude rate and

the AAR is a rough measure of the difference in structure

between the population under consideration and the standard (table 11). Averaged over the years, the 1970 standard keeps the AAR closest to the crude rate. The 1940

standard provides the greatest disparity between the two

measures. The 1990 standard is midway between but, as

noted, provides the closest approximation of the current

crude rate.

Effect of age intervals

Mortality has been significantly postponed over the

period of observation. As a result, if 10-year age groups

are used, each 10-year age group has a larger proportion

of individuals in its upper portion than was the case 20

years ago. Use of 5-year age groups for standardization,

instead of the 10-year age groups employed by NCHS, has

little or no effect on the age-adjusted rate with any

standard, however.

Constructed standards

A series of 16 constructed standards, each emphasizing different portions of the population, confirmed most

intuitive notions of how the standards affect the relative

perception of rate. A weighting system that stresses younger

groups tends to magnify downward trends; conversely, a

system that stresses older persons diminishes downward

trends. Intermediate weighting systems have less effect in

either direction. A system that stresses the middle years

shows the greatest consistency over time but, under the

conditions examined, elevates the AAR by a factor of 3.5

to 4.0. (The data generated by this analysis are extensive

and are not presented in detail here.)

Discussion

The purpose of age standardization is to permit comparison of populations that differ in their age structure. In

usual epidemiologic circumstances, several populations

are compared using some intuitively acceptable standard

(for instance, counties are compared using the State

population as a standard, or a study and control group are

compared by standardizing each to their combined population). The choice of standard is, as noted, arbitrary.

However, once a standard is chosen, there is obvious value

in using it consistently, particularly in the context of

51
long-term trend data. A change in standard is not undertaken lightly. Theoretical considerations aside, an important part of the justification of standard change rests on

the empirical demonstration of important (and desirable)

differences in the results generated by the new standard.

Previous analyses (1,2) that compared the 1940 standard with a 1950, 1960, or 1970 standard demonstrated

some differences, and these were deemed relatively unimportant. The current study confirms that the perception of

disease burden is altered relatively little by use of the

1950, 1960, or 1970 standard. The data suggest, however,

that a larger change occurs with 1980; this change, although slightly attenuated, continues with the 1990 standard. (Final judgment on the 1990 standard cannot be

made until the official population counts are available.

These analyses were based on estimates from the U.S.

Bureau of the Census (3).)

Compared with the 1940 standard, the 1990 standard

places the AAR in close proximity to the current crude

rate. It causes attenuation of the rate of decline of total

mortality and of diseases whose mortality is declining;

conversely, it accentuates the rate of increase for diseases

that are increasing. In general, the 1990 standard diminishes sex, race, and sex-race ratios but does so to differing

degrees, depending on the groups being compared. The

1990 standard alters the relative ranking of States for the

leading cause of death (ischemic heart disease). For most

States, this effect is small to moderate; for several States,

the effect is large; and for three States at either end of the

ranking, there is no effect. It is unclear whether States in

the middle of the distribution would be concerned about

the alterations. There is a change in the rank ordering for

the leading causes of death, but it is not statistically

significant.

Given that these changes are real, the subjective

question as to whether they are important remains. Several theoretical issues may be raised in this regard. First,

the magnitude of the difference between the crude rate

and the AAR is related to the perceived difference in

trend. An adjusted trend eliminates the effect of change in

population structure and provides a better perception of

the ‘‘true’’ rate of change than does the crude rate. It may

be argued that standards can be constructed to manipulate this rate of change at will, and the best standard is one

that reports a minimum difference between the crude rate

and the adjusted rate over the whole range of the data.

This is likely to be a standard that emphasizes a population from the center of the chronologic distribution rather

than one at either extreme. Empirically, the actual changes

in the perception of trend with any standard are small

(table 5), so this ‘‘theoretical’’ advantage may be helpful

in decisionmaking. If the notion of a central population is

pursued, its centrality should be related to the overall

timespan contemplated; 1990, for example, is the central

point in the 100 years of data that begin in 1940.

Dr. Kleinman has argued (9) that the use of a standard such as the 1990 population introduces an element of

‘‘bias’’ by weighting the standard toward an older population. The 1940 standard counterbalances this effect. It is

unlikely that Dr. Kleinman was using the term ‘‘bias’’ in its

statistical sense but rather in its more generic sense: the

introduction of a preference. Again, we are dealing with

subjective choice. It may well be worthwhile in some

instances to blunt the effect of aging in the population by

using a younger population as the standard. On the other

hand, judgment could dictate that a population that reflects the current one provides the best public health

perspective. An AAR that is close to the crude rate has an

air of verisimilitude of which the 1940 standard AAR, by

its distance from the crude rate, is deprived. If Dr.

Kleinman’s argument were to be pursued, however, it

would appear that an ‘‘unbiased’’ standard would weight

all age groups equally. The UNIFORM standard employed here has, as noted, the advantage of providing a

piece of information not present with any other standard—the mean age-specific rate, which, in these data, is

closest to the rate for persons aged 65–69 years. This may

well be an argument for using the UNIFORM standard in

some capacity.

These considerations do not appear to provide an

incontrovertible argument for or against changing the

current 1940 standard. One point that does emerge, however, is that there is no ‘‘standard’’ standard. It is likely

that a standard can outlive its usefulness and that the

same data should be approached with different standards

for different purposes. Perhaps the notion under severest

scrutiny is not whether 1940 is appropriate but whether a

single standard is the optimum policy.

The data that NCHS acquires and subsequently publishes are really of three logical types: observations, estimates, and constructs. The observations (counts of the

number of deaths) may be viewed as incontrovertible, or

at least difficult to challenge, because they represent

reports from the only collection mechanism available.

Although perhaps estimates in the statistical sense, the

numbers are usually accepted as the truth. The estimates

(crude rates and age-specific rates) require the addition of

denominators. Such population estimates are altered periodically and may be challenged. (The Bureau of the

Census is in court at this very moment.) Estimates command a somewhat different type of belief from that

required for observations. Third, the constructs (ageadjusted rates) depend on several subjective choices (standard, age interval). Constructs have considerable

epidemiologic and statistical utility, but they are not to be

believed in the same sense as observations or estimates.

This line of reasoning suggests that alternative standards

may be useful.

Finally, in addition to empirical and theoretical issues,

a number of economic and social factors influence the

choice of standard. Following is a summary of some

theoretical, empirical, economic, and sociopolitical

arguments that have been raised for and against change of

the current standard.

52
Arguments for change

1. Even though the AAR is a construct, it should be of

the same order of magnitude as the crude rate in

order to minimize the difference between the crude

and adjusted rates. The most appropriate compromise

is a population that is central to the populations being

compared.

2. Changing the standard produces small but consistent

changes in the empirical results. The most appropriate

measure of trend (the slope/intercept) changes with

more recent standards, as do sex and race ratios,

rankings of States, and rankings of diseases.

3. Because it is generally agreed that the choice of

standard is to a large extent arbitrary and subjective, it

makes little sense to have a ‘‘standard’’ standard. The

AAR is not the same ‘‘logical type’’ as a crude rate,

which in turn is different from the reported number of

deaths. Taken in reverse order, these measures represent a progression from counting to construction, and

the degree of tenacity in maintaining their inviolability

should decrease accordingly.

4. The cost of conversion is considerable only if the

decision is made to retrofit the data. Concurrent new

standards, which would run parallel with and eventually replace 1940, would require minimal expense. The

ultimate goal would be to provide electronic access to

the data so that users could employ a standard that

they deem appropriate to the work that they are

doing.

5. As noted, the 1940 standard is viewed by many as

antiquated (even if for the wrong reasons). The standard may be perceived as inappropriate, and NCHS

must deal with the perception. A change of standard

would align most diseases with their current crude

rate and perhaps imbue the actual number with a bit

more relevance.

6. If the 1940 standard were not abandoned but used in

parallel with another standard and then replaced after

a suitable period of time, social and political reaction

to the discontinuity could be minimized.

Arguments against change

1. There is no theoretically correct standard. The chosen

standard should provide a balance that minimizes the

effect of population trends, thereby avoiding ‘‘bias’’ in

estimation of disease impact. The 1940 standard minimizes the bias imposed by the current aging population.

2. Observed differences produced by using different standards are small, with minor exceptions. The changes

usually affect the magnitude but not the direction of

change. Many of the differences that result from

changing standards occur in situations where age

adjustment may not be an appropriate tool. For example, in cancer mortality, divergent age-specific rates

make age adjustment inappropriate, and this is one of

the conditions for which changing the standard makes

a real difference.

3. The costs of changeover are considerable, both in

personnel and other resources. In addition, the added

burden on State data centers would be poorly tolerated.

4. On the social and political levels, a change in standard

would produce a dramatic discontinuity, and the abrupt

change would generate considerable confusion. For

researchers, the loss of continuity would have a substantial effect on prior and contemplated work.

5. Although there is no ‘‘right’’ standard, NCHS has the

responsibility to choose a de facto standard. Despite

possible limitations, codification of the AAR with a

single standard serves a vital social and political function.

6. The introduction of other standards contemporaneously with the 1940 standard would create disruption

and cumbersome presentations in NCHS publications,

whose year-to-year continuity is one of their great

strengths. It might be simple enough to present alternate AAR’s, but a table of the top 15 causes of death

would be confusing.

Summary and recommendations

There is reasonable argument on both sides of this

issue. We suggest that the main issue does not hinge on

the use of 1940 as a standard but on whether it is

appropriate to have an absolute standard. The standardized rate that NCHS produces is an important epidemiologic measure, but not an incontrovertible number. We

recommend that NCHS attach two new standards to a

portion of its data (the 1990 population, when it becomes

available, and the WORLD standard); that it publish the

UNIFORM standard (mean age-specific rate); that it not

attempt to retrofit data; and that it establish a new

continuum. For several years, there should be considerable overlap—perhaps even dual publication of the major

tables—with eventual changeover to another standard that

more closely approximates the population structure of the

past 20 and the coming 50 years.

References

1. Spiegelman M, Marks HH. Empirical testing of standards

for age adjustment of death rates by the direct method.

Hum Biol 38:280–92. 1966.

2. Curtin LR, Maurer JD, Rosenberg HM. Comparison of

alternative standards in computing age-adjusted death rates.

Paper presented at the annual meeting of the Population

Association of America, Mar, Washington, D.C. 1981.

3. U.S. Bureau of the Census. Projections of the population of

the United States, by age, sex, and race: 1983 to 2080.

Current population reports; series P-25, no 952. Washington: U.S. Department of Commerce. 1984.

4. Connecticut State Cancer Registry. Forty-five years of cancer incidence in Connecticut. NCI Monograph 70. Washington: National Cancer Institute. 1986.

5. National Center for Health Statistics. Underlying cause of

death public use data tapes, 1968–88. Hyattsville, Maryland:

Public Health Service. 1990.

53
6. Israel RA, Rosenberg HM, Curtin LR. Analytical potential

for multiple cause-of-death data. Am J Epidemiol 124:161–79.

1986.

7. Irwin R. Data tapes. Demo-detail. Alexandria, Virginia.

1988.

8. National Center for Health Statistics. Health, United States,

1989. Hyattsville, Maryland: Public Health Service. 1990.

9. Kleinman JC. Personal communication. Hyattsville, Maryland: National Center for Health Statistics. 1991.

Table 1. Summary of methods from prior studies of standards for age adjustment of mortality statistics

Comparison Spiegelman and Marks Curtin, Maurer, and Rosenberg

Standards 1940, 1950, 1960 1940, 1970

Conditions Total mortality Total mortality

Tuberculosis Diseases of the heart

Diabetes Malignant neoplasms

Vascular central nervous system lesions Respiratory cancer

Congenital malformation Cerebrovascular disease

Motor vehicle accidents Suicides

Homicides Homicides

Factors examined Relative change in AAR Correlation of AAR and life expectancy

AAR for persons 65 years and over Rank order correlation

Sex ratio Relative change in AAR

Race ratio Trends (percent change, average annual percent change)

Trends (percent change) Ratio of change over 2 time periods

Sex ratio

Relative standard error of covariance

Time periods studied 1956, 1940, 1920 1940, 1970, 1977

NOTE: AAR is age-adjusted rate.

SOURCES: Spiegelman and Marks, 1966 (1); Curtin, Maurer, and Rosenberg, 1981 (2).

Table 2. Summary of methods from current study of standards for age adjustment of mortality statistics

Comparison Items studied

Standards 1940, 1950, 1960, 1970, 1980, 1990, WORLD, UNIFORM, and a series of artificially constructed standards

Conditions Total mortality (18 5-year age groups)

Total mortality (11 10-year age groups)

Ischemic heart disease

Cancer (malignant neoplasms)

Chronic obstructive pulmonary disease

Alzheimer’s disease

Factors examined Relative change in AAR

Trends (percent change, average annual percent change, slope)

Relative difference between crude rate and AAR

Sex ratio

Race ratio

Sex-race ratio

State rankings (effect of standard on Ischemic heart disease ranking)

Disease rankings (for 1988)

Effect of alternative age groupings

Time periods studied 1962–88 (total mortality)

1968–87 (cause-specific mortality)

1988 (disease rankings)

NOTE: AAR is age-adjusted rate.

54
Table 3. Age-adjusted rate for total mortality, by standard used or age adjustment: United States, 1968–88

[Rate per 100,000 population]

Year 1940 1950 1960 1970 1980 1990 WORLD UNIFORM

1968 .............. 743.1 849.8 926.3 995.8 1,290.1 1,232.9 793.2 3,524.1

1969 .............. 705.9 807.0 879.3 944.6 1,221.6 1,169.0 753.4 3,327.0

1970 .............. 714.0 814.1 884.9 948.4 1,211.8 1,166.9 757.4 3,227.3

1971 .............. 679.9 775.5 844.4 909.2 1,174.7 1,125.9 721.0 3,196.0

1972 .............. 698.2 795.6 865.6 931.4 1,198.3 1,150.0 738.4 3,226.9

1973 .............. 687.6 783.7 853.0 919.0 1,186.9 1,136.4 727.8 3,217.9

1974 .............. 659.8 752.0 818.6 881.6 1,137.8 1,089.7 698.5 3,080.5

1975 .............. 630.8 718.2 780.8 840.3 1,079.2 1,036.2 665.9 2,894.7

1976 .............. 618.9 705.6 768.5 828.8 1,071.9 1,025.3 654.8 2,913.5

1977 .............. 600.9 684.0 744.3 803.1 1,036.4 992.4 634.0 2,805.1

1978 .............. 592.7 675.0 735.0 794.2 1,028.7 983.2 625.6 2,802.1

1979 .............. 577.6 657.5 715.7 773.1 1,000.5 956.9 609.2 2,716.4

1980 .............. 583.7 665.1 725.1 785.4 1,023.8 976.2 616.0 2,814.7

1981 .............. 566.5 645.3 703.2 761.5 993.4 946.2 597.6 2,729.3

1982 .............. 554.0 632.0 689.5 747.1 973.3 929.0 583.9 2,674.0

1983 .............. 551.0 629.4 687.6 746.5 976.5 930.9 580.8 2,704.6

1984 .............. 546.1 623.9 681.7 740.9 971.7 925.4 575.5 2,701.5

1985 .............. 546.7 624.9 683.2 743.2 978.3 930.3 576.6 2,736.4

1986 .............. 542.1 618.8 675.9 735.3 968.4 920.9 570.9 2,705.2

1987 .............. 536.0 611.9 668.3 727.3 959.2 911.8 564.5 2,685.9

1988 .............. 536.2 612.3 669.0 728.6 964.0 915.2 565.2 2,712.4

Table 4. Weights derived from alternative standards for age adjustment of mortality statistcs, by age: United States, 1988

Age 1940 1950 1960 1970 1980 1990 WORLD UNIFORM

Under 5 years......... 0.0801 0.1073 0.1133 0.0844 0.0717 0.0769 0.120 0.0556

5–9 years ........... 0.0811 0.0876 0.1042 0.0982 0.0724 0.0745 0.100 0.0556

10–14 years .......... 0.0892 0.0738 0.0935 0.1023 0.0795 0.0673 0.090 0.0556

15–19 years .......... 0.0937 0.0705 0.0737 0.0938 0.0920 0.0680 0.090 0.0556

20–24 years .......... 0.0880 0.0763 0.0602 0.0806 0.0932 0.0744 0.080 0.0556

25–29 years .......... 0.0843 0.0813 0.0606 0.0663 0.0858 0.0862 0.080 0.0556

30–34 years .......... 0.0778 0.0765 0.0666 0.0562 0.0774 0.0881 0.060 0.0556

35–39 years .......... 0.0725 0.0746 0.0696 0.0547 0.0613 0.0801 0.060 0.0556

40–44 years .......... 0.0667 0.0677 0.0647 0.0590 0.0511 0.0715 0.060 0.0556

45–49 years .......... 0.0627 0.0601 0.0607 0.0596 0.0481 0.0560 0.060 0.0556

50–54 years .......... 0.0551 0.0548 0.0536 0.0546 0.0510 0.0457 0.050 0.0556

55–59 years .......... 0.0444 0.0479 0.0470 0.0491 0.0506 0.0418 0.040 0.0556

60–64 years .......... 0.0359 0.0401 0.0398 0.0424 0.0442 0.0425 0.040 0.0556

65–69 years .......... 0.0289 0.0331 0.0349 0.0344 0.0384 0.0400 0.030 0.0556

70–74 years .......... 0.0195 0.0226 0.0264 0.0268 0.0298 0.0322 0.020 0.0556

75–79 years .......... 0.0114 0.0143 0.0170 0.0189 0.0210 0.0251 0.010 0.0556

80–84 years .......... 0.0059 0.0074 0.0088 0.0112 0.0128 0.0164 0.005 0.0556

85 years and over ...... 0.0028 0.0038 0.0052 0.0074 0.0198 0.0133 0.005 0.0556

55
Table 5. Measures of trend for total mortality and mortality from selected conditions, by standards used for age adjustment: United

States, 1968–88

Condition and measure 1940 1970 1980 1990 WORLD UNIFORM

Total mortality

Percent change............... −27.9 −27.0 −25.6 −26.0 −28.2 −24.3

Annual percent change1 ......... −1.6 −1.6 −1.5 −1.5 −1.6 −1.4

Slope2 .................... −10.6 −13.5 −16.1 −16.0 −11.1 −32.8

Slope/intercept (percent) . . . . . . . . . 1.7 1.6 1.5 1.5 1.7 1.4

Ischemic heart disease

Percent change............... −51.7 −50.5 −49.1 −49.7 −51.1 −48.0

Annual percent change1 ......... −3.6 −3.5 −3.3 −3.4 −3.5 −3.2

Slope2 .................... −8.3 −12.0 −15.9 −15.4 −8.6 −38.1

Slope/intercept (percent) . . . . . . . . . 4.1 4.0 3.8 3.9 4.0 3.7

Cancer

Percent change. . . . . . . . . . . . . . . 2.4 4.6 5.6 5.5 2.9 8.3

Annual percent change1 . . . . . . . . . 0.1 0.2 0.3 0.3 0.1 0.4

Slope2 . . . . . . . . . . . . . . . . . . . . 0.2 0.5 0.7 0.7 0.3 2.2

Slope/intercept (percent) . . . . . . . . . 0.1 0.3 0.3 0.3 0.2 0.5

Alzheimer’s disease

Percent change............... 2,212.5 3,347.4 4,716.6 4,523.3 2,405.2 8,360.3

Annual percent1 .............. 17.0 19.4 21.4 21.1 17.5 24.8

Slope2 . . . . . . . . . . . . . . . . . . . . 0.2 0.4 0.5 0.5 0.2 1.6

Slope/intercept (percent) ......... 16.9 18.3 19.4 19.2 17.2 20.6

Chronic obstructive pulmonary disease

Percent change............... −1.1 4.9 7.8 8.3 −1.0 16.0

Annual percent change1 ......... −0.1 0.2 0.4 0.4 −0.1 0.7

Slope3 .................... −0.2 −0.1 0.0 0.0 −0.2 0.8

Slope/intercept (percent) . . . . . . . . . 0.5 0.1 0.0 0.0 0.5 0.5

1Assuming uniform change over years.

2All slopes were statistically significant, p < 0.01.

3No slopes were statistically significant, p > 0.05.

Table 6. Ratio of age-adjustment rate using 1990 standard to

age-adjustment rate using 1940 standard for total mortality, by

sex, race, and sex-race group: United States, 1968 and 1988

Characteristic

Ratio Percent difference

between 1940

1968 1988 and 1990 standards

Female .................. 1.78 1.82 2.3

Male ................... 1.62 1.70 4.9

White ................... 1.72 1.76 2.3

Black ................... 1.42 1.52 7.0

White female .............. 1.84 1.86 2.2

White male ............... 1.66 1.74 4.8

Black female .............. 1.47 1.61 9.5

Black male................ 1.40 1.49 6.4

56
Table 7. Sex, race, and sex-race ratios produced by 1990 and

1940 standards for age-adjustment of mortality statistics: United

States, 1968–88

Ratio and year

Standard

Difference

between

1940 and

1990

1940 1990 standards

Ratio Percent

Black-white

1968 .............. 1.55 1.29 17.1

1988 .............. 1.56 1.35 13.4

Male-female

1968 .............. 1.74 1.59 8.7

1988 .............. 1.74 1.62 6.8

Black-white, male

1968 .............. 1.48 1.23 15.8

1988 .............. 1.57 1.35 14.1

Black-white, female

1968 .............. 1.67 1.34 20.1

1988 .............. 1.57 1.35 12.9

Table 8. Mean and maximum change in ranking of States for Ischemic heart disease mortality for each standard used for age adjustment

compared with 1940 standard: United States, 1968–87

Year

1950 standard 1960 standard 1970 standard 1980 standard 1990 standard

Mean Maximum Mean Maximum Mean Maximum Mean Maximum Mean Maximum

1968 . . . . . . . . . . . . . . 0.6 2 1.1 5 1.6 6 3.0 10 2.6 8

1969 . . . . . . . . . . . . . . 0.6 4 1.0 5 1.5 6 2.6 13 2.3 13

1970 . . . . . . . . . . . . . . 0.4 1 0.7 3 1.3 8 2.7 13 2.1 10

1971 . . . . . . . . . . . . . . 0.8 3 1.3 5 1.9 5 3.2 10 2.5 9

1972 . . . . . . . . . . . . . . 0.5 3 1.0 4 1.5 7 2.7 11 2.2 11

1973 . . . . . . . . . . . . . . 0.6 3 1.2 4 1.6 7 3.0 12 2.5 8

1974 . . . . . . . . . . . . . . 0.7 5 1.1 5 1.6 6 2.8 12 2.2 8

1975 . . . . . . . . . . . . . . 0.8 3 1.4 6 1.8 7 2.9 12 2.6 10

1976 . . . . . . . . . . . . . . 0.7 2 1.0 3 1.5 6 2.9 12 2.2 10

1977 . . . . . . . . . . . . . . 0.4 2 0.7 4 0.9 6 2.2 8 1.6 7

1978 . . . . . . . . . . . . . . 0.3 2 0.7 3 1.1 3 2.4 8 1.9 8

1979 . . . . . . . . . . . . . . 0.5 3 1.1 6 1.6 6 3.4 11 2.9 11

1980 . . . . . . . . . . . . . . 0.5 3 1.1 4 1.7 5 3.0 12 2.6 8

1981 . . . . . . . . . . . . . . 0.7 3 1.4 4 2.1 6 4.0 11 3.2 9

1982 . . . . . . . . . . . . . . 0.5 2 1.1 5 1.9 7 3.6 11 2.7 8

1983 . . . . . . . . . . . . . . 0.5 3 1.0 5 1.5 7 3.1 11 2.4 9

1984 . . . . . . . . . . . . . . 0.5 3 1.0 4 1.5 8 3.4 13 2.5 10

1985 . . . . . . . . . . . . . . 0.6 3 1.1 4 1.6 5 3.5 12 2.7 8

1986 . . . . . . . . . . . . . . 0.5 3 1.0 4 1.4 6 3.1 18 2.5 15

1987 . . . . . . . . . . . . . . 0.5 3 1.2 7 2.2 10 4.2 15 3.5 12

57
Table 9. Multiple regression results on effect of standard used for age adjustment and

year on ranking of States for Ischemic heart disease mortality, by State: United States,

1968–87

State Intercept

Slope for

standard

Slope for

year

Calculated

change

in rank

Alabama ..................... 36.419 0.836 0.473 −4.2

Alaska ...................... 45.214 0.824 0.594 −4.1

Arizona ...................... 44.179 −0.331 0.510 1.7

Arkansas ..................... 37.795 0.599 0.339 −3.0

California..................... 28.074 −1.123 −0.187 5.6

Colorado ..................... 43.681 −0.816 0.103 4.1

Connecticut ................... 25.633 −1.490 0.287 7.5

Delaware..................... 17.831 0.384 −2.822 −1.9

Washington, D.C. ............... 34.576 0.923 −2.209 −4.6

Florida ...................... 34.074 −0.253 0.562 1.3

Georgia ..................... 19.457 1.727 0.057 −8.6

Hawaii ...................... 44.550 −0.230 −0.709 1.2

Idaho ....................... 45.433 −0.390 0.174 2.0

Illinois....................... 7.726 −0.417 −0.143 2.1

Indiana ...................... 14.710 0.223 0.883 −1.1

Iowa........................ 29.219 −0.254 0.043 1.3

Kansas ...................... 29.836 −0.434 0.171 2.2

Kentucky ..................... 13.764 0.144 0.357 −0.7

Louisiana..................... 12.848 1.054 −0.332 −5.3

Maine....................... 9.636 0.006 0.074 0.0

Maryland ..................... 25.931 −0.046 −2.912 0.2

Massachusetts ................. 24.160 −0.887 1.072 4.4

Michigan ..................... 8.381 −0.236 0.461 1.2

Minnesota .................... 33.783 −0.080 0.032 0.4

Mississippi.................... 29.910 0.853 −0.053 −4.3

Missouri ..................... 21.333 −0.580 0.555 2.9

Montana ..................... 45.598 0.044 0.447 −0.2

Nebraska .................... 36.941 −0.433 0.754 2.2

Nevada ...................... 32.302 0.286 −0.398 −1.4

New Hampshire ................ 19.948 −0.076 0.240 0.4

New Jersey................... 2.821 −0.099 −0.002 0.5

New Mexico ................... 50.714 −0.076 −0.021 0.4

New York .................... 5.069 −0.474 0.378 2.4

North Carolina ................. 11.545 0.889 0.203 −4.4

North Dakota .................. 35.050 −0.370 0.308 1.9

Ohio ....................... 7.279 0.059 0.416 −0.3

Oklahoma .................... 23.824 0.207 1.255 −1.0

Oregon ...................... 37.438 −0.229 0.668 1.1

Pennsylvania .................. 7.357 −0.053 −0.398 0.3

Rhode Island .................. 4.531 −0.336 0.062 1.7

South Carolina ................. 12.524 1.477 −0.546 −7.4

South Dakota .................. 29.943 −0.857 0.695 4.3

Tennessee.................... 19.002 0.286 0.096 −1.4

Texas ....................... 45.121 0.061 0.109 −0.3

Utah........................ 48.445 −0.311 0.002 1.6

Vermont ..................... 20.071 0.021 −0.597 −0.1

Virginia ...................... 17.850 0.710 −0.770 −3.6

Washington ................... 36.645 −0.451 −0.729 2.3

West Virginia .................. 4.812 0.389 0.318 −1.9

Wisconsin .................... 19.748 −0.986 0.519 4.9

Wyoming..................... 33.269 0.316 −0.386 −1.6

NOTE: Fitted values are shown for average change in rank using 1990 standard compared with 1940 standard, controlling for

year.

58
Table 10. Ranking of leading causes of death, by standard used for age adjustment: United States, 1988

Cause 1990 1980 1970 1960 1950 1940 WORLD UNIFORM

Diseases of the heart .............. 1 11111 1 1

Malignant neoplasm ............... 2 22222 2 2

Cerebrovascular disease ............ 3 33434 4 3

Accidents ..................... 4 44343 3 6

Chronic obstructive pulmonary disease . . . 5 65555 5 5

Pneumonia and influenza ........... 6 56666 6 4

Diabetes ...................... 7 77788 8 8

Suicide ....................... 8 8 8 8 7 7 7 12

Chronic liver disease .............. 9 10 9 9 9 9 9 11

Atherosclerosis . . . . . . . . . . . . . . . . . . 10 9 12 13 14 14 14 7

Nephritis . . . . . . . . . . . . . . . . . . . . . . 11 11 11 11 11 12 11 9

Homicide. . . . . . . . . . . . . . . . . . . . . . 12 13 10 10 10 10 10 13

Septicemia. . . . . . . . . . . . . . . . . . . . . 13 12 13 12 13 13 13 10

Human immunodeficiency virus. . . . . . . . 14 14 14 14 12 11 12 14

Table 11. Percent difference between crude rate and age-adjusted rate, for total mortality by standard used for age adjustment: United

States, 1968–88

Year 1940 1950 1960 1970 1980 1990 WORLD UNIFORM

Percent difference

1968 ............................ 23.2 12.2 4.2 −2.9 −33.4 −27.5 18.0 −264.3

1969 ............................ 23.0 12.0 4.1 −3.0 −33.3 −27.5 17.8 −263.0

1970 ............................ 24.3 13.7 6.2 −0.5 −28.4 −23.7 19.7 −242.1

1971 ............................ 26.4 16.1 8.6 1.6 −27.2 −21.9 22.0 −246.0

1972 ............................ 25.7 15.3 7.9 0.8 −27.6 −22.4 21.4 −243.5

1973 ............................ 26.4 16.1 8.7 1.7 −27.0 −21.6 22.1 −244.3

1974 ............................ 27.3 17.2 9.8 2.9 −25.3 −20.0 23.1 −239.4

1975 ............................ 28.3 18.3 11.2 4.5 −22.7 −17.8 24.3 −229.1

1976 ............................ 29.6 19.7 12.6 5.7 −22.0 −16.7 25.5 −231.5

1977 ............................ 30.6 21.0 14.1 7.3 −19.7 −14.6 26.8 −223.9

1978 ............................ 31.7 22.2 15.3 8.5 −18.5 −13.3 27.9 −222.8

1979 ............................ 32.3 23.0 16.2 9.4 −17.2 −12.1 28.6 −218.3

1980 ............................ 33.5 24.2 17.3 10.5 −16.7 −11.3 29.8 −220.9

1981 ............................ 34.3 25.2 18.5 11.7 −15.2 −9.7 30.7 −216.4

1982 ............................ 35.1 25.9 19.2 12.4 −14.1 −8.9 31.6 −213.4

1983 ............................ 36.2 27.1 20.3 13.5 −13.1 −7.8 32.7 −213.3

1984 ............................ 36.7 27.7 20.9 14.1 −12.7 −7.3 33.3 −213.3

1985 ............................ 37.4 28.5 21.8 14.9 −12.0 −6.5 34.0 −213.2

1986 ............................ 37.9 29.1 22.5 15.7 −11.0 −5.5 34.6 −210.0

1987 ............................ 38.5 29.8 23.3 16.5 −10.1 −4.6 35.2 −208.2

1988 ............................ 39.2 30.5 24.1 17.3 −9.4 −3.8 35.9 −207.8

59
Chapter 7

Use of age-adjusted disease

rates for cancer

by Edward J. Sondik, Ph.D., Division of

Cancer Prevention and Control, National

Cancer Institute

In a recent report commissioned by the Senate Appropriations Committee, the adequacy of measures of progress

against cancer was assessed (1,2). The report, written by

an extramural committee and facilitated by the National

Cancer Institute (NCI), divided cancer measures into two

types, direct and indirect. Cancer incidence, mortality, and

survival were considered direct measures because they

measure different dimensions of the extent of the disease

in the population. Indirect measures include a variety of

other measures such as smoking and other risk factor

prevalence, screening rates, and dietary patterns. The

Committee felt that mortality is the most important of

these measures; in essence, it is the bottom line of the

fight against the disease. In turn, it is the trend in

mortality—and in incidence and survival—that chronicles

the impact of the National Cancer Program.

Influence of age on direct measures

Age is closely tied to cancer incidence and mortality,

and as the higher ages predominate in the population, the

crude rate of cancer incidence and mortality will rise, all

other factors remaining constant. Because cancer incidence and mortality are so strongly linked to age (table 1),

as we analyze trends in the direct measures of cancer, it is

essential to ‘‘decouple’’ the effect of age from all other

effects, particularly the effects of cancer prevention and

control programs and the effects of known (and unknown)

etiologic agents. As noted in the report on measures of

cancer (1,2), there are several issues with respect to the

use of age adjustment of cancer data, including:

• The choice of base year, the topic of this workshop.

• Whether the adjustment procedure should include

period and cohort effects, some procedure is essential

to account for the influence of population size and the

aging of the population. To not account for these

factors may lead to very different conclusions. Table 2

shows the quantitative effect on percent change in

mortality of using the number of deaths, the crude

rate, and the age-adjusted (to 1970) rate to assess

change over time. Clearly all three are valid measures,

but each measures something different.

NCI uses of age-adjusted cancer rates

NCI uses age-adjusted cancer data for a number of

different purposes. For example:

• Each year NCI publishes an extensive statistical review

that examines trends in the incidence, mortality, and

survival of cancer (3). The incidence and mortality

data are all age adjusted to 1970.

• We have stated our goals for the year 2000 (developed

in 1985) in terms of age-adjusted rates, in this case age

adjusted to the 1980 U.S. population.

• NCI makes a number of comparisons among States,

regions, socioeconomic groups, etc., almost always

doing so using age-adjusted rates. For the recently

published atlases of U.S. cancer mortality among white

persons and persons of other races for the period

1950–80, 1960 was used as the base year (4,5).

In summary, age-adjusted incidence and mortality

rates coupled with relative survival rates (not age adjusted) are the primary measures NCI uses to report

trends in cancer and thereby assess changes in the patterns and burden of cancer.

Desirable characteristics of measures of

cancer

In general, the desirable characteristics of cancer

measures are as follows:

• We seek measures that will remove, to the greatest

extent possible, the influence of a changing age distribution.

• Secondly, measures of cancer should reflect the current magnitude of the problem as much as possible.

We would like to be able to compare age-adjusted

rates across cancer sites and obtain an assessment of

the relative impact of different cancers. The same

holds true for comparing cancer rates with the rates of

other diseases. Therefore, one would hope that if age

adjustment is used, the base year would be as close to

the current year as practical.

61
Clearly, the absolute numbers of cases and deaths

provide some of the best measures of the direct impact of

the disease, but trends in cases and deaths are confounded

by the changing age distribution.

Rationale for using 1970 as the base year

As noted, NCI uses 1970 as the standard for age

adjustment of incidence and mortality trends. Our rationale is that 1970 was the most recent census year prior to

the start of the SEER (Surveillance, Epidemiology, and

End Results) Program. The SEER Program is a set of

population-based cancer registries collecting information

on cancer incidence and survival from a selected set of

regions comprising about 10 percent of the U.S. population. NCI uses the series of data collected from SEER,

coupled with national mortality data, as the data base for

trends on incidence and mortality.

Age adjustment of cancer-related data

Choice of base year

The following figures show the impact of the choice of

a base year on cancer mortality rates per 100,000 persons:

1986 mortality rate:

Crude 194.7

Adjusted to 1970 171.3

Adjusted to 1940 133.2

The deemphasis on the older populations in the 1940

base year is particularly troublesome to NCI. Because

persons 65 and over account for more than 50 percent of

cancer cases and deaths from cancer, changes in the

age-specific rates for those 65 and over (either increases

or decreases) are not fairly reflected through the 1940

base year measure. One also could argue that those 65 and

over are not fully accounted for through the 1970 base

year measure as well. I would not debate that point. In

fact, a more recent year, 1980, is a more appropriate base

year at this time.

Relative survival

In the past NCI has not adjusted relative survival data

for age. The definition of relative survival and the methods

used by NCI to calculate the measure estimate an essentially age-independent measure of the force of mortality

resulting solely from a particular form of cancer. Yet we

know from analyzing data by specific age groups that

relative survival may differ significantly by age for some

cancers. For example, recent 5-year relative and observed

survival rates for kidney and renal pelvis cancer are shown

in table 3. As NCI extends its series of survival data, the

question of whether to adjust for age effects may become

an issue.

Rates of change

Over the last few years NCI has made considerable

use of the percent change in incidence and mortality

calculated from the age-adjusted rates. The percent change

is usually calculated over the full range of the period for

which the SEER data are available, 1973–88 in the latest

report. We have also used the equivalent measure of the

estimated annual percent change. The choice of base year

for age adjustment will influence the magnitude of change

of these measures.

Use of age-adjusted figures in establishing disease

prevention and control objectives

In 1985 NCI set cancer prevention and control objectives for the year 2000 for smoking prevalence, dietary

change, screening rates, and the adoption of state-of-theart treatment regimens. Our analysis of the impact on

cancer incidence and mortality rates of achieving these

objectives was based on rates age adjusted to the base year

1980. The reason for selecting 1980 was not only to

remove the effects of the changing age distribution of the

population, but at the same time, to keep the rates related

as much as possible to the current magnitude of the

problem.

Use of age-adjusted figures in reporting smoking

data

Smoking is the cause of some 30 percent of all cancer

deaths. Therefore, a key measure of the Nation’s progress

toward reducing the burden of cancer is the smoking

prevalence rate. Several years ago I was surprised to see

age-adjusted figures reported as the primary measure of

trends in smoking. The question I would raise is whether

these figures should be age adjusted at all, at least as a

primary measure. We age adjust cancer incidence and

mortality rates to eliminate an uncontrollable factor driving the trends in the cancer rates. It is not in our power to

change the age distribution of the population and thereby

bring cancer incidence rates down. On the other hand, it is

within the realm of possibility to drastically change the

smoking rates in the United States. A smoke-free or

nearly smoke-free society is a distinct possibility. Because

we can affect smoking behavior, or at least individuals can

affect their own smoking behavior, regardless of age, the

use of age-adjusted figures masks an important aspect of

smoking data. When we at NCI report smoking data, we

generally do not age adjust the rates. I would make the

same conclusion for other behaviors that, although tied to

age, are not dictated by age.

Summary

The use of 1940 as the base year for age adjustment in

these NCI applications seems problematic. It is more than

a half century away from today and 60 years from the year

2000. The age distribution today is very different from the

age distribution of the 1940’s in terms of implications for

62
the burden of cancer. If cancer control or disease prevention objectives are to stimulate actions toward prevention,

the associated measurements must reflect the magnitude

of the problem and changes in this magnitude as accurately as possible. Age adjustment to the year 1940 will not

reflect changes in incidence or mortality rates as accurately as an adjustment to a more recent base year.

In some discussions prior to the publication of Healthy

People 2000 (6), it was suggested that the crude rate would

be the most appropriate measure in which to couch the

objectives. Although this may seem an anathema to some,

it may have been the most appropriate solution. A quick

study would be sufficient to assess the impact of expected

changes in the age distribution of the United States from

1990 to 2000 on measures of the various objectives outlined in Healthy People 2000. It may turn out that any

change in the age distribution has minimal effect on the

measures of the objectives compared with the effect of

changes projected from the various prevention regimens.

References

1. Extramural Committee to Assess Measures of Progress

Against Cancer. Measurement of progress against cancer.

Report to the U.S. Senate Appropriations Committee. Washington. 1988.

2. Extramural Committee to Assess Measures of Progress

Against Cancer. Measurement of progress against cancer. J

Natl Cancer Inst 82(10), special report. 1990.

3. Ries LAG, Hankey BF, Edwards BK, eds. Cancer statistics

review: 1973–87. Bethesda: National Cancer Institute. 1990.

4. Pickle LW, Mason TJ, Howard N, Hoover R, Fraumeni JF

Jr. Atlas of U.S. cancer mortality among whites: 1950–80.

Washington: National Institutes of Health. 1987.

5. Pickle LW, Mason TJ, Howard N, Hoover R, Fraumeni JF

Jr. Atlas of U.S. cancer mortality among nonwhites: 1950–80.

Washington: National Institutes of Health. 1990.

6. Office of the Assistant Secretary for Health. Healthy people

2000: National objectives for health promotion and disease

prevention. Washington: Public Health Service. 1990.

Table 1. Age-specific incidence and mortality cancer rates, by

age: United States, 1983–87

[All rates per 100,000 persons]

Age Incidence Mortality

Under 5 years.............. 18.8 3.9

5–9 years ................ 11.1 3.9

10–14 years ............... 11.4 3.4

15–19 years ............... 20.0 4.9

20–24 years ............... 30.3 5.9

25–29 years ............... 47.7 9.1

30–34 years ............... 79.1 16.1

35–39 years ............... 129.1 29.9

40–44 years ............... 211.0 58.8

45–49 years ............... 335.0 114.9

50–54 years ............... 514.3 210.0

55–59 years ............... 795.3 346.0

60–64 years ............... 1,148.9 514.3

65–69 years ............... 1,574.2 735.9

70–74 years ............... 1,967.1 966.9

75–79 years ............... 2,286.6 1,187.2

80–84 years ............... 2,500.5 1,432.0

85 years and over ........... 2,383.3 1,618.0

SOURCE: National Center for Health Statistics, National Cancer Institute.

Table 2. Number of deaths, crude death rate, and age-adjusted

death rate, by age: United States, 1970–87

Year

Number of

deaths1

Crude

death rate

Age-adjusted

death rate2

1970 ................... 331,000 162.8 162.8

1980 ................... 417,000 183.9 168.1

1987 ................... 477,000 195.9 171.5

Percent change

1970–87 ................. +44.1 +20.3 +5.4

SOURCE: National Center for Health Statistics, National Cancer Institute.

Table 3. Five-year relative and observed survival rates for kidney

and renal pelvis cancer, by age: United States, 1982–87

[All rates per 100,000 persons]

Age Relative Observed

All ages ................. 53.2 44.3

Under 45 years ............. 71.9 71.2

45–54 years ............... 57.9 55.7

55–64 years ............... 56.0 51.2

65–74 years ............... 48.1 39.6

75 years and over ........... 39.7 24.0

SOURCE: National Center for Health Statistics, National Cancer Institute.

63
Chapter 8

Age adjustment for the year

2000 health objectives

by Michael A. Stoto, Ph.D., Institute of

Medicine

Introduction

Choosing the proper procedure for age adjustment of

vital rates is complex. The choice involves both scientific

and practical issues. Moreover, vital rates constitute an

important aspect of communication with the public and

decisionmakers about the health status of the American

population.

In Healthy People 2000 (1), for instance, the vast but

often hidden potential of health promotion and disease

prevention is recognized by presenting in concrete statistical terms the health status gains that can be expected

from health promotion and disease prevention activities.

Stating objectives for the future in quantitative terms

portrays the benefits of prevention in a way that can lead

to action now. If this process is to be successful, statistical

presentations of progress toward the objectives must be

meaningful and accurate. In considering whether and how

to standardize, therefore, it is important to have in mind a

few points about the purpose of age adjustment, especially

as it might be used in assessing progress toward the Year

2000 Health Objectives.

Reasons for adjustment

Overall population death rates are essentially weighted

averages of a series of death rates for different age and sex

groups. With ‘‘crude,’’ or unstandardized, death rates, the

weights change as the population ages, so it is impossible

to disentangle changes in age structure from changes in

underlying death rates. For instance, even if there were no

changes in the age-specific cancer rates from 1987 to 2000,

we would expect the death rate to rise from 195.9 to 217.1

per 100,000, an increase of 10.8 percent (assuming the

Census Bureau’s median population projection for the

United States). Such a change does not indicate progress

toward the objectives, and no one would want to represent

it as such. The problem is not so clear cut when the

age-specific rates change, however. We will likely see a

decrease in the overall cancer mortality rate that reflects a

combination of decreases in age-specific rates plus an

increase corresponding to the aging of the population in

the 1990’s. Because the objectives are aimed at preventing

death and disability, not the aging of the population, we

would like to somehow ‘‘subtract out’’ the changes associated with population aging. This is one of the main

purposes of age adjustment, or direct standardization.

Standardization serves this purpose well. Applying the

age-specific rates from various years to the 1940 U.S.

population, for instance, shows that while the cancer

death rate increased by 20 percent in crude terms from

1970 to 1987, it increased by only 2 percent when standardized to the 1940 population. This is illustrated in

figure 1. During the same period, ischemic heart disease

fell by 50 percent in standardized terms but only 36 percent with crude rates.

Another way of looking at this is to see how standardization affects the year 2000 targets. One early draft of the

target for coronary heart disease mortality called for a

33-percent reduction in the rate—that is, a decrease in the

age-adjusted rate from 135.2 to 90.0 per 100,000. Suppose

that this same percentage applied to the ischemic heart

disease death rate, which was 113.9 per 100,000 in 1987

(when adjusted to the 1940 population). If this 33-percent

decrease applied at every age, the standardized target

would be 75.8 per 1,000, and the crude target using the

projected year 2000 population would be 165.6 per 1,000.

The targeted 33-percent decrease at each age is masked by

population aging and looks like only a 21-percent decrease

overall. The adjusted figures thus seem to represent the

anticipated mortality gains more ‘‘accurately’’ and certainly represent them in a way that is more compelling to

policymakers.

Standardization, however, serves a second and very

different purpose at the same time. Because States and

other geographic areas differ in the age, race, and sex

composition of their population, States with the same

age-, race-, and sex-specific death rates will have different

crude death rates (both overall and cause specific). Similarly, some of the differences that we find among States

and other areas reflect differences in population composition rather than differences in underlying rates. In setting

their own targets for the year 2000, States will want to look

at the national target as well as current rates of other

States. This comparison makes sense only if differences in

the composition of the national and State populations are

‘‘removed.’’ If all States are adjusted to the same standard

NOTE: The views expressed in this paper are those of the author, not

necessarily those of the Institute of Medicine or the National Academy

of Sciences.

65
population, standardization provides a bridge from the

national targets to State and local targets.

Finally, we should note that, for some purposes,

standardization might lead to difficulties. Some States and

other areas will want to set priorities among the objectives. Many factors go into such choices, but the current

level of mortality associated with a disease or other health

problem is a major one. Standardized rates can and do

lead to different priorities than the crude rates do. For

example, accidents and adverse effects have a somewhat

higher mortality rate than cerebrovascular diseases when

adjusted to the 1940 population (35.0 versus 29.7 per

100,000), but in absolute terms the cerebrovascular mortality rate is more than 50 percent higher than the accident mortality rate (61.2 versus 39.5) One might argue

that crude rates accurately reflect the burden of illness in

a public health sense; they are proportional to the number

of deaths per cause. There are, however, other indexes of

the burden associated with each cause, such as years of

potential life lost.

Choice of a standard population

If death rates are to be standardized, the choice of a

standard can make a substantial difference. Figure 1, for

example, shows the overall cancer death rate in crude

terms and standardized to the 1940 and the estimated

1990 populations. The largest difference is in the level of

the rates; the 1987 rate is fully 50 percent higher (199.9

compared with 132.9 per 100,000) when the 1990 population rather than the 1940 population is chosen as the

standard. The choice of standard affects trends as well.

Using the 1990 standard, the cancer death rate increased

by 6.2 percent from 1970 to 1987, but using the 1940

standard, it increased by only 2.3 percent. We cannot say

that either of these standards is ‘‘correct’’ in some absolute sense, but it is important to note that they are

different. We can say that the 1990 standard more closely

reflects the current burden of mortality by cause.

Whatever decision is made about adjustment and

choice of standard, it is important that the decision be

applied consistently to all of the mortality objectives.

Without a common standard, it is impossible to compare

death rates for different causes to get some sense of

priority. For instance, at one point during the drafting of

the Year 2000 Health Objectives, the cancer rates were

adjusted to 1970 and heart disease rates to 1940. The

result was that the overall cancer death rate was higher

than that of coronary heart disease, although the relationship is reversed when using any reasonable standard.

Figure 1. Crude and adjusted cancer mortality rates for 1970–87 and projected to 2000: United States

66
Furthermore, the statisticians who will have to monitor

progress on the objectives at the national, State, and other

levels will have much more difficulty in doing so if different methods of standardization are used in different

priority areas.

A group of statisticians from different Public Health

Service agencies who met last year seemed to approach

some agreement that the death rates should be presented

age adjusted and that the 1940 standard should be used.

Two arguments were put forward. First, the 1940 adjustment would be consistent with the long-term practice of

the National Center for Health Statistics and widespread

use by other groups in reporting death rates. Using this

standard would facilitate the efforts of States trying to

monitor their own progress on the objectives. Second,

using the 1940 population, with its younger age structure,

helps emphasize the levels of preventable mortality that

exist at younger ages. Personally, I am concerned about

this second argument. Although I too would like to see

more attention paid to children’s health problems, I would

like to get there through the front door and not introduce

value arguments into the technical choice of a statistical

standard.

Others argued against adjusting, especially to the 1940

population, because it masks the public health impact of

the levels seen in crude death rates. Note in figure 1 that

the standardized and crude rates agree when a standard

close to the year of the crude rate is chosen. Using the

1940 standard, the relationship among adjusted causespecific death rates is different from that among the crude

rates.

Conclusions

One compromise is to adjust the rates using a more

recent standard population, such as 1990. This would give

a better picture of the current public health impact of the

various diseases (as measured by the relative numbers of

deaths) and would provide the analytical benefits of age

adjustment. The difficulty with using a new standard is

that special calculations would be needed each year to

monitor changes in these rates. Calculating targets and

monitoring changes for States and other subpopulations

probably would be made more complicated by using a new

reference population. However, if the Centers for Disease

Control and National Center for Health Statistics are

planning to set up a system to report on progress on the

Year 2000 Health Objectives on a regular basis at the

State (and perhaps lower) level, the introduction of this

system by itself is a major change and a major effort. Now

might be just the time to switch all mortality reporting to a

more current standard.

The Healthy People 2000 process will be successful if

local newspapers occasionally run on their front pages a

small number of statistical charts illustrating progress in

their areas toward meeting the Year 2000 Health Objectives. Such charts, often in the form of time series of

mortality rates, perhaps for a small number of causes, will

provide a rare opportunity to present public health information to the public. Thus, it is important that they convey

as much information as accurately as possible. The trend

information will be the same regardless of the standard

population chosen, but the relative importance of the

causes of death will be reasonably accurate only if a

near-current standard population is used as the basis of

the adjustment.

Reference

1. Office of the Assistant Secretary for Health. Healthy people

2000: National objectives for health promotion and disease

prevention. Washington: Public Health Service. 1990.

67
Part V

Considerations for the future
Chapter 9

Age adjustment in National

Center for Health Statistics

mortality data: Implications

of a change in procedures

by John E. Patterson, Division of Vital

Statistics, National Center for Health

Statistics

I have been asked to examine some of the practical

implications of changing National Center for Health Statistics (NCHS) procedures for producing age-adjusted

mortality data. I am very pleased to have this opportunity,

because our division will have the responsibility for implementing any such changes. We also will be responsible for

releasing the data through our annual volumes of Vital

Statistics of the United States and such widely disseminated

publications as the ‘‘Advance Report of Final Mortality

Statistics.’’ In addition, we respond to a large number of

inquiries regarding our mortality data from Congress,

other components of the Public Health Service, the academic community, the media, advocacy groups, and the lay

public. Finally, we work closely with our colleagues in the

State health departments in developing standards and

uniform procedures for vital statistics. Whatever the outcome of our deliberations, the Division of Vital Statistics

will have an important role in implementing, justifying,

and explaining the results.

Three practical considerations are involved in any

possible change in NCHS procedures for producing ageadjusted mortality data: the level of effort required to

make the change, the price we may pay in terms of

confusing our data users, and the need to prepare a sound

rationale for any change in order to dispel any possible

suspicion that it was motivated by other than scientific

considerations.

Level of effort

If the l990 census population or a projected population were substituted for the 1940 standard, NCHS would

need to prepare a Vital and Health Statistics series report

and a Monthly Vital Statistics Report (MVSR) supplement

to explain the rationale for the change and its effect on the

data. This would require a total of about 2.5 person-years

of effort if a new annual series of age-adjusted rates were

prepared using the standard NCHS summary cause-ofdeath categories going back to 1960. The level of effort

would be substantially higher if the new rates were prepared for years prior to 1960, because the required data

for the earlier years are not available in machine-readable

form. If NCHS were to implement a new series of rates

based upon a new population standard while retaining and

continuing to publish the series based upon the 1940

standard, it also would be necessary to prepare a series

report and periodic MVSR supplements to provide our

users with ongoing guidance and information relating to

the two series of rates. This would require a total of

approximately 2.75 person-years of effort, assuming the

same level of detail going back to 1960.

Confusion

Modification of current NCHS practices in ageadjusted mortality data can be expected to result in

considerable confusion among our data users. If the traditional time series of age-adjusted death rates based on the

1940 standard population is discontinued and a new standard is instituted, many data users are likely to compare

current rates based on the new standard with earlier rates

based on the old standard. The practice of mixing rates

based on the two different standard populations may be

widespread and persistent, even with strong cautionary

notes in NCHS publications. Our data users will need to

be careful to carry out trend analyses within the time

period for which a single standard population is used but

not across time periods that result in the use of two

different standard populations. Our users also will find

that the results of studies based on the new standards may

not be comparable with the results of earlier studies based

on the old standards.

If a second population standard is adopted while

maintaining the time series based on the 1940 standard,

we will have an even greater potential for confusion. In

addition, our data users are likely to ask us to indicate

which is the ‘‘official’’ or ‘‘preferred’’ series of rates. In

presenting descriptive analyses in our summary publications such as the ‘‘Advance Report of Final Mortality

Statistics,’’ we will have to decide which standard will be

used for presenting trends and comparisons in order to

avoid having a lengthy, highly redundant text. The choice

of one over the other may suggest to many of our users

that one is better than the other.

We can anticipate that having two NCHS time series

would lead to the accidental mixing of the two series in the

same analysis, or to the selection of the standard that will

give greater emphasis to one set of patterns and trends

71
than the other. It is known, for example, that the 1940

standard population—which is younger than the current

population—results in greater emphasis on causes of death

associated with youth, specifically, deaths from homicide

and accidents rather than deaths from chronic diseases.

With a new standard, there probably would be a

transitional period during which some users would continue to use the older age standard while others would use

the new one. Some State vital statistics offices may be

eager to make the change; others may be reluctant to do

so.

To mitigate confusion, NCHS will have to devote

considerable resources and effort to clearly describe any

change in practices and its consequences for analysis and

interpretation of mortality data by a broad range of data

users, from those in the media to academicians with

training in biostatistics, demography, and epidemiology.

Motives and rationale

Questions may also be asked regarding our motives

for making a change in our age-adjustment practices.

Some users may be uncomfortable using a population

standard that is more than 50 years old, but others may be

bothered even more by a change that does not have

obvious scientific advantages. These users may wonder if

the data are being ‘‘manipulated’’ because of an ulterior

motive.

One effect of changing from the 1940 to a more

current standard such as 1990 is on comparisons of mortality by race. A new standard will tend to narrow the

differences in mortality between the white and black

populations for all causes of death combined and probably

for most causes of death. For example, for all causes of

death combined, the reduction will be from a 50 percent

differential using the 1940 standard to a 33 percent differential using the 1990 standard. A change in the presentation of NCHS mortality data could easily raise questions

as to motives for the change.

The public can interpret a change in longstanding

statistical practices as possibly manipulative, obfuscatory,

or politically inspired. For this reason, any change in

practice will require considerable effort on the part of

NCHS to provide a clear and compelling rationale for the

change in order to reassure the public that the reasons for

change are solely of a technical nature.

72
Chapter 10

Summary and

recommendations

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

Center for Health Statistics

The workshop on age adjustment proved to be a

stimulating presentation and review of the major problems

in age adjustment that have been discussed on numerous

occasions during the past 150 years.

We have been reminded that because unadjusted

rates can be seriously misleading, there is a clear need for

age adjustment.

It has been demonstrated that the choice of standard

for age adjustment inevitably affects the pattern of any

summary statistic. Nonetheless, while there may be great

differences in the magnitude of various rates, an overall

similarity is manifested in long-term trends regardless of

the standard employed.

We have reviewed statistical as well as nonstatistical

considerations in the selection of standard populations

and discussed the practical implications for implementing

new standards.

Examples were presented to show that for several

causes of death, somewhat different interpretations would

result from different standard populations but the use of

broad age groups might be used to show the differences in

trends that lead to these disparities.

Naturally, individual investigators are free to choose

whatever standard population they feel would most accurately reflect the import of their analyses. In the case of

official statistics, however, there is a need for a common

method and to minimize confusion on the part of data

users, and so we must return to the original question—

what standard population best serves the interest of the

majority of users.

Subsequent to the formal workshop session, a subgroup of participants (Joel Kleinman, John Patterson, and

Alvan Zarate) volunteered to review the discussions of the

workshop and to prepare a set of recommendations. These

recommendations were then circulated to all of the participants for comment. Although everyone felt that the

workshop had clarified the important issues, as might have

been anticipated, there was no unanimity on the recommended standard population. Going with the majority and

trying to balance the cogent minority arguments, the

following recommendations were adopted.

1. NCHS will continue to use the 1940 U.S. population

as the basis for calculating age-adjusted death rates.

This population will be converted into a relative

distribution totalling 1,000,000 and will be referred to

as the ‘‘U.S. Standard Million Population.’’

2. NCHS will study the following issues that may lead to

the introduction of a new or an additional standard by

the year 2000:

a. The effects on age-adjusted death rates of

using rates for ages 85–94 years and 95 years

and over, in place of 85 years and over in their

calculation.

b. The feasibility of producing tables of trends for

leading causes of death from 1960 to the present

for broad age groupings, and the desirability of

finer age-adjustment within the broad age

groups.

c. The utility and timeliness of producing ageadjusted rates based on the latest decennial

census.

3. NCHS shall develop suitable technical notes and expository material concerning the appropriate use of

age adjustment, differences in interpretation from

crude and other rates used in the scientific literature,

and clarification of issues expressed by data users.

4. Other official agencies should use the standard million

population when publishing age-adjusted mortality

rates. Researchers examining unique issues might consider the use of other standard populations when

appropriate and, to minimize confusion, point out this

departure from standard practice.

73
Part VI

Appendixes
Appendix I

Workshop participants

Robert J. Armstrong

Special Assistant to the Director

Division of Vital Statistics

National Center for Health Statistics

Lester Curtin, Ph.D.

Chief, Statistical Methods Staff

Office of Research and Methodology

National Center for Health Statistics

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

Director, National Center for Health Statistics

Ashley Files

Prevention Policy Advisor

Office of Disease Prevention and Health Promotion

Katherine Gray-Donald, Ph.D.

Assistant Professor, Department of Epidemiology and

Biostatistics

McGill University

Robert A. Israel

Deputy Director, National Center for Health Statistics

Joel Kleinman, Ph.D.

Director, Division of Analysis

Office of Analysis and Epidemiology

National Center for Health Statistics

Iwao Moriyama, Ph.D.

Executive Director, International Institute for Vital

Registration and Statistics

John Patterson

Director, Division of Vital Statistics

National Center for Health Statistics

Harry Rosenberg, Ph.D.

Chief, Mortality Statistics Branch

Division of Vital Statistics

National Center for Health Statistics

Richard B. Rothenberg, M.D.

Assistant Director for Science

Center for Chronic Disease Prevention and Health

Promotion

Edward Sondik, Ph.D.

Deputy Director, Division of Cancer Prevention and

Control

National Cancer Institute

Michael Stoto, Ph.D.

Senior Staff Officer, Institute of Medicine

National Academy of Sciences

Steven M. Teutsch, M.D.

Director, Division of Surveillance and Epidemiology

Epidemiology Program Office

Centers for Disease Control

George Van Amburg

State Registrar and Chief, Office of the State Registrar

Center for Health Statistics

Department of Public Health, State of Michigan

Ronald W. Wilson

Director, Division of Epidemiology and Health

Promotion

Office of Analysis and Epidemiology

National Center for Health Statistics

Theodore D. Woolsey

Former Director, National Center for Health Statistics

Alvan O. Zarate, Ph.D.

Special Assistant to the Director

Office of the Director, National Center for Health

Statistics

77
Appendix II

Age-specific weights of

age-specific percent

changes assumed in the

computation of percent

changes based on

age-adjusted death rates

by Jeffrey Maurer, M.S., Division of Vital

Statistics, National Center for Health

Statistics

Introduction

While not presented at the workshop, this paper was

developed in conjunction with that prepared by Rosenberg, Curtin, Maurer, and Offutt (part III, chapter 5) and

considers issues closely related to those discussed by

Gray-Donald (part II, chapter 3).

It is common practice in the National Center for

Health Statistics (NCHS) to compute percent changes

between two data years based on age-adjusted death rates

by cause of death, race, and sex. This paper demonstrates

that the percent changes based on age-adjusted death

rates are weighted averages of the age-specific percent

changes. The paper also shows that these weights are the

product of the age-specific weights of the standard population and age-specific death rates for the base year

divided by the sum of these products. The implication is

that the implicitly used weights for computing percent

changes based on age-adjusted death rates differ by race,

sex, cause of death, and the standard population. Some

comparisons using different weighting schemes of the

age-specific percent changes are made by race and cause

of death using the 1940 and 1990 standard populations.

The results show that percent changes based on ageadjusted death rates tend to minimize the white-to-black

ratios of the percent changes as compared with other

weighting schemes. There are, also, other differences

among the weighting schemes. Discussions of these implications may be warranted. Furthermore, and perhaps

more importantly, it is discovered that the weights of the

age-specific percent changes may be used to determine

which age groups contributed the most to the percent

change of two age-adjusted death rates.

Determination of the weights

Let C be the percent change for two age-adjusted

death rates as follows:

C=

Rn−R0

•100

R0

where Rn=age-adjusted rate for the nth year and R0= the

age-adjusted rate for the base year. Then

C=

∑wi

Rin−∑wiRi0 •100

∑wi

Ri0

where Rin=age-specific rate for nth year, Ri0=age-specific

rate for base year, and wi

=age-specific proportion for

standard population (say 1940), where ∑wi

=1.

Assume C* is a weighted average of ‘‘age-specific

percent changes’’ as follows:

C*=∑w*i

Rin−Ri0

•100

Ri0

As shown below, the following weights (wi*) make C*

equal to C:

w *i

= w i Ri0

∑wiRi0

by substitution,

C*=∑

wi

Ri0 Rin−Ri0

• •100

∑wi

Ri0 Ri0

C*=

∑wi

(Rin−Ri0) •100

∑wi

Ri0

C*=

∑wi

Rin−∑wiRi0

•100

∑wi

Ri0

C*=C

therefore, the w*i are the age-specific weights such that if

the age-specific percent changes were multiplied by these

weights and those results summed over all the age groups,

this would give the same results as computing a percent

change based on two age-adjusted death rates. The w*i are

the age-specific weights for the standard population

79
(wi

) times the corresponding age-specific death rate for

the base year (Rio) divided by the sum of those products.

The same formula is applicable to sex and race ratios

based on age-adjusted death rates, except that the weights

are applied to the age-specific sex and race ratios.

Implication of the weights

What is the implication of this finding? The implication is that for percent changes based on age-adjusted

death rates, the weights of the age-specific percent changes

will be different for every percent-change comparison

made between races, sexes, or causes of death. This is the

case since in the formula, w*i

=wiRio/∑wiRio, the baseyear age-specific death rates (Rio) are different for each

race, sex, and cause of death.

The next question is, ‘‘Are the weights so different as

to produce different results?’’ The mortality data presented in the paper by Rosenberg, Curtin, Maurer, and

Offutt (Part III, Chapter 5) are used for comparing four

different weighting schemes applied to age-specific percent changes (table 1). (The percent changes shown in

Rosenberg, et al may differ slightly from those in this

paper due to rounding.) These four schemes were used in

conjunction with the 1940 and the 1988 (hereinafter referred to as 1990) standard populations. The first weighting scheme is the ‘‘usual method,’’ which computes percent

changes based on age-adjusted death rates and, therefore,

implicitly uses different weights for the age-specific percent changes. The second and third schemes, ‘‘white

weights’’ and ‘‘black weights,’’ use the 1968 age-specific

death rates (Rio) for the white and black populations,

respectively, and the weights for the standard population.

The fourth weighting scheme uses the weights for the 1940

or 1990 standards only.

This paper examines the effects of these four schemes

on the percent changes for all causes, cerebrovascular

diseases, cancer, and homicide for the white and black

populations (table 1). It should be noted that for all

causes for the white population, the ‘‘usual method’’ and

the ‘‘white weights’’ produce the same percent changes

(−10.5), which empirically corroborates the formula for

the w*i

. Similarly, for the black population, the ‘‘usual

method’’ and the ‘‘black weights’’ have the same percent

change (−5.2). These same results occur for the other

causes of death for both the 1940 and 1990 standards.

In general, the ‘‘usual method’’ (based on different

weights) tends to minimize the white-to-black ratio of the

percent changes when compared with the other weighting

schemes (table 1). For example, for the 1940 standard for

all causes, the ‘‘usual method’’ produces a ratio of 2.02

followed by ‘‘black weights’’ (2.23), ‘‘1940 weights’’ (2.44),

and ‘‘white weights’’ (3.75).

It may be noted that for all causes and the 1990

standard, the percent change using ‘‘white weights’’ for

black persons is positive (1.1), while the corresponding

changes for the other three weighting schemes are negative. This positive percent change occurred because the

percent distribution of the ‘‘white weights’’ for the age

groups 75–84 years and 85 years and over (28 and 20 percent, respectively) were much higher than for the other

three weighting schemes and these weights were applied

to relatively high positive percent changes for those two

age groups. (Data are based on detailed tables not shown

in this paper.)

The white-to-black ratios differ comparatively little

among the four weighting schemes for cerebrovascular

diseases. This result occurs since the age-specific percent

changes are relatively uniform across all age groups for

white and black persons and, therefore, different weighting schemes have very little effect (tables 1,2).

For cancer, on the other hand, the weighting scheme

can have a major impact on the resulting summary percent

changes. For example, using the 1940 standard, the ‘‘1940

weights’’ produce negative percent changes for white persons (−29.2 percent) and black persons (−21.3 percent), while the other three weighting schemes produce

positive percent changes. These results occur mainly because the percent changes for cancer declined for every

age group under age 55 years and increased for every age

group 55 years and over, and because the age groups

under 55 years are weighted heavily by the ‘‘1940 weights’’

(accounting for 85 percent of the weights). (Data are

based on detailed tables not shown in this paper.) In

contrast, the ‘‘white weights’’ and ‘‘black weights’’ for the

age groups under 55 years account for only 27 and

32 percent, respectively, of the weights and, therefore,

heavier weights are applied to the increasing percent

changes of the age groups 55 years and over.

In addition, the ‘‘usual method’’ for cancer using the

1940 standard produces a percent change for black persons (9.8) that is about four times that for white persons

(2.4), while the ‘‘white weights’’ and ‘‘black weights’’

produce percent changes for black persons (13.4 and 9.8,

respectively) that are almost six and nine times those for

white persons (2.4 and 1.1, respectively). Comparing these

weighting schemes for the 1940 standard with the 1990

standard for cancer produces similar results, but at a

lower level. The percent changes for black persons are

three to four times the corresponding changes for white

persons.

Similar to cancer, the weighting scheme for homicide

can have a large impact on the summary percent changes.

For example, using the 1940 standard, the ‘‘1940 weights’’

produced a positive percent change for black persons

while the other three weighting schemes produced negative percent changes. These results occurred mainly because the age groups 1–4 years and 5–14 years had very

large percent increases (81.6 and 63.6 percent, respectively), and these age groups are weighted more heavily by

the ‘‘1940 weights’’ than the other weighting schemes.

(Data are based on detailed tables not shown in this

paper.) The results using the ‘‘1990 weights’’ for white

persons are similar. The white-to-black ratio of the percent changes is somewhat higher using the ‘‘white weights’’

(−1.77) than the ‘‘usual method’’ (−1.33) or the ‘‘black

80
weights’’ (−1.33). Similar results occur using the 1990

standard.

Application of weights

Also, it is discovered that there is an important

application of the weights, wi

*. Using these weights, it is

possible to determine which age groups contributed the

most to a percent change based on age-adjusted death

rates. The basic principle is that the product of the ‘‘white

weights’’ or ‘‘black weights’’ and the age-specific percent

changes are additive. Since these products are additive,

the percent contribution for an age group can be determined.

An example that examines the percent change between 1968 and 1988 of age-adjusted death rates based on

the 1940 standard population for cerebrovascular diseases

by race is shown in table 2. Based on the age-adjusted

death rate, cerebrovascular diseases for white persons

declined 58.4 percent. Table 2 shows that the sum of the

products of the age-specific ‘‘white weights’’ and the

age-specific percent changes gives the same percent change

(−58.4). If each of the age-specific products is divided by

−58.4 and multiplied by 100, the results show the percent

contribution of each age group. Thus, for white persons

the age group 75–84 years contributed the most (35 percent, rounded to the nearest percent) to this decline,

followed by 65–74 years (29 percent), 85 years and over

(14 percent), 55–64 years (12 percent), 45–54 years (6 percent), and other ages (4 percent). For black persons, the

age group 65–74 years contributed the most (33 percent),

followed by 55–64 years (23 percent), 75–84 years (16 percent), 45–54 years (14 percent), 35–44 years (7 percent),

85 years and over (5 percent), and other ages (3 percent).

Discussion

This paper uncovers the age-specific weights implicitly

used when computing percent changes based on ageadjusted death rates. The weights are the age-specific

weights for the standard population times the age-specific

death rate for the base year divided by the sum of those

products. The implication is that for percent changes

based on age-adjusted death rates, the weights of the

age-specific percent changes will be different for every

percent-change comparison made between races, sexes, or

causes of death. In other words the percent changes based

on age-adjusted death rates are not standardized. The

weights are different because the age-specific death rates

for the base year are different for each race, sex, and

cause of death. Mortality sex and race ratios based on

age-adjusted death rates are affected in the identical way.

Using four different weighting schemes based on both the

1940 and 1990 standards, somewhat different results are

produced. The author recommends that there be some

discussion on whether or not the differing results are of

practical significance. If it is determined that the contrasting results are of practical significance, then a standard set

of weights would need to be agreed upon. The prospect of

coming to an agreement on such a standard would probably be as arduous and controversial as agreeing on a

standard for computing the age-adjusted death rate.

There is, however, a less controversial, and, perhaps

even more important, finding in this paper. By discovering

the weights associated with the age-specific percent changes,

it is possible to ascertain the percent contribution of every

age group to the percent change of two age-adjusted death

rates. Thus, for any percent change based on two ageadjusted death rates, one can determine those age groups

that contributed the most to that change.

81
Table 1. Summary of percent changes for selected causes of death using various

age-specific weighting schemes of the age-specific percent changes, by race: United

States, 1968–88

Weighting scheme and cause

Percent change Ratio of

percent

changes

White Black (white/black)

All causes

1940 standard:

Using usual method1....................... –10.5 –5.2 2.02

Using white weights2....................... –10.5 –2.8 3.75

Using black weights3....................... –11.6 –5.2 2.23

Using 1940 weights ....................... –13.9 –5.7 2.44

1990 standard:

Using usual method1....................... –7.9 –1.7 4.65

Using white weights2....................... –7.9 1.1 –7.18

Using black weights3....................... –9.4 –1.7 5.53

Using 1990 weights ....................... –12.7 –4.8 2.65

Cerebrovascular diseases

1940 standard:

Using usual method1....................... –58.4 –59.0 0.99

Using white weights2....................... –58.4 –55.5 1.05

Using black weights3....................... –58.8 –59.0 1.00

Using 1940 weights ....................... –58.6 –61.8 0.95

1990 standard:

Using usual method1....................... –57.4 –56.3 1.02

Using white weights2....................... –57.4 –52.4 1.10

Using black weights3....................... –58.3 –56.3 1.04

Using 1990 weights ....................... –58.3 –61.1 0.95

Cancer

1940 standard:

Using usual method1. . . . . . . . . . . . . . . . . . . . . . . 2.4 9.8 0.24

Using white weights2. . . . . . . . . . . . . . . . . . . . . . . 2.4 13.4 0.18

Using black weights3. . . . . . . . . . . . . . . . . . . . . . . 1.1 9.8 0.11

Using 1940 weights ....................... –29.2 –21.3 1.37

1990 standard:

Using usual method1. . . . . . . . . . . . . . . . . . . . . . . 5.9 17.9 0.33

Using white weights2. . . . . . . . . . . . . . . . . . . . . . . 5.9 22.7 0.26

Using black weights3. . . . . . . . . . . . . . . . . . . . . . . 4.5 17.9 0.25

Using 1990 weights ....................... –25.9 –17.2 1.51

Homicide

1940 standard:

Using usual method1....................... 25.6 –19.3 –1.33

Using white weights2....................... 25.6 –14.5 –1.77

Using black weights3....................... 25.6 –19.3 –1.33

Using 1940 weights ....................... 36.0 4.9 7.35

1990 standard:

Using usual method1....................... 23.6 –19.6 –1.20

Using white weights2....................... 23.6 –13.3 –1.77

Using black weights3....................... 24.2 –19.6 –1.23

Using 1990 weights ....................... 32.2 4.8 6.71

1Percent change is based on age–adjusted death rates. As a result, the age–specific weights of the age–specific percent

changes for the white and black populations are different.

2The white age–specific weights are wi40Ri68/∑wi40Ri68 and wi90Ri68/∑wi90Ri68 for the 1940 and 1990 standard populations,

respectively. The Ri68 are the death rates for the white population.

3The black age–specific weights are wi40Ri68/∑wi40Ri68 for the 1940 standard and wi90Ri68/∑wi90Ri68 for the 1990 standard. The

Ri68 are the death rates for the black population.

82
by age of percent changes based on age-adjusted death rates for cerebrovascular diseases, by race: United States, 1968–88

-adjusted death rate based on 1940 standard population]

d race

Ageadjusted

rate Total

Under

1 year

1–4

years

5–14

years

15–24

years

25–34

years

35–44

years

45–54

years

55–64

years

65–74

years

75–84

years

85

years

and

over

........ 66.2 . . . 4.5 0.8 0.7 1.5 3.6 12.0 35.1 101.3 375.0 1,312.5 3,698.6

........ 27.5 . . . 3.0 0.3 0.2 0.7 1.7 5.0 14.9 43.3 142.3 542.0 1,739.4

. . . . . . . . ... –58.4 –33.3 –62.5 –71.4 –53.3 –52.8 –58.3 –57.5 –57.3 –62.1 –58.7 –53.0

........ 125.8 . . . 9.3 1.3 0.8 3.6 14.7 56.4 139.4 339.5 795.9 1,442.4 2,676.1

........ 51.5 . . . 8.8 0.6 0.3 1.0 6.0 21.9 53.6 124.2 290.6 736.5 1,445.4

. . . . . . . . ... –59.0 –5.4 –53.8 –62.5 –72.2 –59.2 –61.2 –61.5 –63.4 –63.5 –48.9 –46.0

rd

. . . . . . . . ... 1.000000 0.015343 0.064718 0.170355 0.181677 0.162066 0.139237 0.117811 0.080294 0.048426 0.017303 0.002770

. . . . . . . . ... 100.00 1.53 6.47 17.04 18.17 16.21 13.92 11.78 8.03 4.84 1.73 0.28

. . . . . . . . 66.2 . . . 0.069044 0.051774 0.119249 0.272516 0.583438 1.670844 4.135166 8.133782 18.159750 22.710188 10.245122

. . . . . . . . ... 1.000000 0.001044 0.000783 0.001803 0.004120 0.008820 0.025258 0.062511 0.122958 0.274520 0.343309 0.154875

. . . . . . . . ... 100.00 0.10 0.08 0.18 0.41 0.88 2.53 6.25 12.30 27.45 34.33 15.49

. . . . . . . . 125.8 . . . 0.142690 0.084133 0.136284 0.654037 2.382370 7.8529671 6.4228532 7.2598133 8.5422532 4.957847 7.412797

. . . . . . . . ... 1.000000 0.001134 0.000669 0.001083 0.005197 0.018931 0.062400 0.130497 0.216609 0.306260 0.198317 0.058903

. . . . . . . . ... 100.00 0.11 0.07 0.11 0.52 1.89 6.241 3.052 1.66 30.63 19.83 5.89

. . . . . . . . ... –58.4 –0.03 –0.05 –0.13 –0.22 –0.47 –1.47 –3.60 –7.04 –17.03 –20.15 –8.20

. . . . . . . . ... 100.0 0.1 0.1 0.2 0.4 0.8 2.5 6.2 12.1 29.2 34.5 14.0

. . . . . . . . ... –59.0 –0.01 –0.04 –0.07 –0.38 –1.12 –3.82 –8.03 –13.74 –19.44 –9.71 –2.71

. . . . . . . . ... 100.0 0.0 0.1 0.1 0.6 1.9 6.5 13.6 23.3 32.9 16.4 4.6
Vital and Health Statistics

series descriptions

SERIES 1. Programs and Collection Procedures—These reports

describe the data collection programs of the National Center

for Health Statistics. They include descriptions of the

methods used to collect and process the data, definitions,

and other material necessary for understanding the data.

SERIES 2. Data Evaluation and Methods Research—These reports

are studies of new statistical methods and include analytical

techniques, objective evaluations of reliability of collected

data, and contributions to statistical theory. These studies

also include experimental tests of new survey methods and

comparisons of U.S. methodology with those of other

countries.

SERIES 3. Analytical and Epidemiological Studies—These reports

present analytical or interpretive studies based on vital and

health statistics. These reports carry the analyses further than

the expository types of reports in the other series.

SERIES 4. Documents and Committee Reports—These are final

reports of major committees concerned with vital and health

statistics and documents such as recommended model vital

registration laws and revised birth and death certificates.

SERIES 5. International Vital and Health Statistics Reports—These

reports are analytical or descriptive reports that compare U.S.

vital and health statistics with those of other countries or

present other international data of relevance to the health

statistics system of the United States.

SERIES 6. Cognition and Survey Measurement—These reports are

from the national Laboratory for Collaborative Research in

Cognition and Survey Measurement. They use methods of

cognitive science to design, evaluate, and test survey

instruments.

SERIES 10. Data From the National Health Interview Survey— These

reports contain statistics on illness; unintentional injuries;

disability; use of hospital, medical, and other health services;

and a wide range of special current health topics covering

many aspects of health behaviors, health status, and health

care utilization. They are based on data collected in a

continuing national household interview survey.

SERIES 11. Data From the National Health Examination Survey, the

National Health and Nutrition Examination Surveys, and

the Hispanic Health and Nutrition Examination

Survey—Data from direct examination, testing, and

measurement on representative samples of the civilian

noninstitutionalized population provide the basis for (1)

medically defined total prevalence of specific diseases or

conditions in the United States and the distributions of the

population with respect to physical, physiological, and

psychological characteristics, and (2) analyses of trends and

relationships among various measurements and between

survey periods.

SERIES 12. Data From the Institutionalized Population

Surveys—Discontinued in 1975. Reports from these surveys

are included in series 13.

SERIES 13. Data From the National Health Care Survey— These

reports contain statistics on health resources and the public’s

use of health care resources including ambulatory, hospital,

and long-term care services based on data collected directly

from health care providers and provider records.

SERIES 14. Data on Health Resources: Manpower and

Facilities—Discontinued in 1990. Reports on the numbers,

geographic distribution, and characteristics of health

resources are now included in series 13.

SERIES 15. Data From Special Surveys—These reports contain

statistics on health and health-related topics collected in

special surveys that are not part of the continuing data

systems of the National Center for Health Statistics.

SERIES 16. Compilations of Advance Data From Vital and Health

Statistics— Advance Data Reports provide early release of

information from the National Center for Health Statistics’

health and demographic surveys. They are compiled in the

order in which they are published. Some of these releases

may be followed by detailed reports in series 10–13.

SERIES 20. Data on Mortality—These reports contain statistics on

mortality that are not included in regular, annual, or monthly

reports. Special analyses by cause of death, age, other

demographic variables, and geographic and trend analyses

are included.

SERIES 21. Data on Natality, Marriage, and Divorce—These reports

contain statistics on natality, marriage, and divorce that are

not included in regular, annual, or monthly reports. Special

analyses by health and demographic variables and

geographic and trend analyses are included.

SERIES 22. Data From the National Mortality and Natality

Surveys—Discontinued in 1975. Reports from these sample

surveys, based on vital records, are now published in series

20 or 21.

SERIES 23. Data From the National Survey of Family Growth—These

reports contain statistics on factors that affect birth rates,

including contraception, infertility, cohabitation, marriage,

divorce, and remarriage; adoption; use of medical care for

family planning and infertility; and related maternal and infant

health topics. These statistics are based on national surveys

of childbearing age.

SERIES 24. Compilations of Data on Natality, Mortality, Marriage,

Divorce, and Induced Terminations of Pregnancy—These

include advance reports of births, deaths, marriages, and

divorces based on final data from the National Vital Statistics

System that were published as supplements to the Monthly

Vital Statistics Report (MVSR). These reports provide

highlights and summaries of detailed data subsequently

published in Vital Statistics of the United States. Other

supplements to the MVSR published here provide selected

findings based on final data from the National Vital Statistics

System and may be followed by detailed reports in series 20

or 21.

For answers to questions about this report or for a list of reports published

in these series, contact:

Scientific and Technical Information Branch

National Center for Health Statistics

Centers for Disease Control

Public Health Service

6525 Belcrest Road, Room 1064

Hyattsville, MD 20782

(301) 436–8500