growing older

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the health & RetiRement Study

GRowinG oldeR

national inStitute on aGinG national inStituteS of health

u.S. depaRtment of health and human SeRviceS

in ameRica


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Bug 31, R 5c27

Bths, md 20892

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NatioNal iNstitute oN agiNg NatioNal iNstitutes o HealtHu.s. DepartmeNt o HealtH aND HumaN services

tHe HealtH & retiremeNt stuDy

growiNg olDeriN america


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preface

preace 4

list o igures aND tables 7

iNtroDuctioN 9Objectives and Design o the HRS 10

How Can the HRS Data Be Used? 1

Unique Features o the HRS 13

Study Innovations 14

Protecting HRS ParticipantCondentiality 15

Linkages to Other Datasets 16

Background and Development o the HRS 16

The HRS: A Model or Other Countries 18

table o coNteNts

cHapter 1:HealtH

Chapter Highlights 0

Health Status and Specic Conditions 1

Health Behaviors and Outcomes 3

A Community-Dwelling Sample 4

Cognitive Function 5

Depressive Symptoms and Depression 6

The Aging, Demographics, andMemory Study 6

Health Care Coverage 8

Health Care Use 9

Use o Alternative Medicines andSupplements 31

Aging and Medical Expenditures 31

Eects o Unexpected Health Events 3

Disability and Physical Functioning 33

Health and Work 35

How Long Do People Think Theyll Live? 36

Health Status o U.S. versus EnglishOlder Adults 38

cHapter :worK & retiremeNt


Labor Force Participation 41

The Changing Nature o Work 43

Occupations Ater Age 70 45

Hours and Pay 45

Job Flexibility 46

Reasons People Retire 47Health versus Financial Factors 48

The Role o Medicare and PrivateHealth Insurance 48

Diseases and Retirement 48

Trends in Retirement Timing 49

Early Retirement Incentives 50

Gradual Retirement 51

Pension Plan Trends and Retirement 51

Knowledge About Pension Plans 5

The Impact o Stock Market Changeson Retirement 5

Retirement and Consumption 53

Enjoyment o Retirement 53

Helping Others 54


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preface

3

cHapter 3:iNcome & wealtH


Amount and Sources o Income 57

Pre-Retirement Saving Behavior 57

Health and Income 61

Unexpected Health Events and Income 61

Social Security Benet Acceptance 6

Conversion o Investments to Annuities 6Wealth and Its Distribution 63

Rening the Measurement o Wealth 66

Marriage and Wealth 67

Pension Wealth 68

Aging and Housing Equity 68

Wealth and Health 69

Unexpected Health Events and Wealth 70

Probabilistic Thinking andFinancial Behavior 7

cHapter 4:

amily cHaracteristics &iNtergeNeratioNal traNsers


Living Situations 75

Living Arrangements and Health 75

Family Status and PsychologicalWell-Being 76

Marital Status and Physical Well-Being 76

Marital Status and Wealth 77

Multiple Family Roles and Well-Being 77

Amount o Bequests 78

Patterns o Intergenerational Transers 79

Reciprocity and IntergenerationalTransers 8

Participants Transers to Parents 8

Trade-Os Between Employmentand Care 8

Caregiving Costs, Insurance 83

Grandparents Care o Grandchildren 84

tHe uture 85

reereNces 88

appeNDix aHrs experimeNtal moDules 94

appeNDix bHrs co-iNvestigators,

steeriNg committee, aNDData moNitoriNg committee 100


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This publication is about one major resourcethe Health and

Retirement Study (HRS)designed to inorm the national

retirement discussion as the population so dramatically ages.

Since its launch in 1992, the HRS has painted a detailed portrait

o Americas older adults, helping us learn about this growing

populations physical and mental health, insurance coverage,

nancial situations, amily support systems, work status, and

retirement planning. Through its unique and in-depth interviews

with a nationally representative sample o adults over the age

o 50, the HRS provides an invaluable, growing body o multidis-

ciplinary data to help address the challenges and opportunities

o aging.

The inspiration or the HRS emerged in the mid-1980s, when

scientists at the National Institute on Aging (NIA) and elsewhere

recognized the need or a new national survey o Americas

expanding older population. By that time, it had become clear

that the mainstay o retirement research, the Retirement History

Study, or RHS (conducted rom 1969 to 1979), was no longer

adequately addressing contemporary retirement issues. For

example, the RHS sample underrepresented women, Blacks,

and Hispanics who, by the mid-1980s, accounted or a larger

portion o the labor orce than in the past. The RHS also did

not ask about health or physical or mental unction, all o which

can impact the decision and ability to retire. Moreover, research

on the retirement process was ragmented, with economists,sociologists, psychologists, epidemiologists, demographers, and

biomedical researchers proposing and conducting studies within

their own silos, oten without regard to the relevant research

activities o other disciplines.

Determining that a new approach was needed, an Ad Hoc

Advisory Panel convened by the NIA, a component o the

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National Institutes o Health, recommended in early 1988 the

initiation o a new, long-term study to examine the ways in which

older adults changing health interacts with social, economic,

and psychological actors and retirement decisions. Government

experts and academic researchers rom diverse disciplines set

about to collaboratively create and design the study. Ultimately,

relevant executive agencies and then Congress recognized the

value o this major social science investment, and the HRS was

established. Today, the study is managed through a cooperative

agreement between the NIA, which provides primary unding, and

the Institute or Social Research at the University o Michigan,

which administers and conducts the survey.

Many individuals and institutions have contributed to the

scrupulous planning, design, development, and ongoing adminis-

tration o the study since its inception. We are especially grateul

or the studys leadership at the University o Michigans Insti-

tute or Social Research in Ann Arbor, specically HRS Director

Emeritus and Co-Principal Investigator . th J, who led

the eort to initiate the HRS and held the reins until 1995, and to

r J. w and Dd r. w, the study co-directors. We also

acknowledge the vital contributions o the HRS co-investigators,

a multidisciplinary group o leading academic researchers at the

University o Michigan and other institutions nationwide.

We thank the HRS Steering Committee and working groups,

which have provided critical advice about the studys design

and monitored its progress, and the NIA-HRS Data Monitoring

Committee, an advisory group comprised o independent mem-

bers o the academic research community and representatives

o agencies interested in the study. In particular, we extend our

appreciation to the late g m and to Dd w, the past

chairs o the monitoring committee, and toJ sh , the

current chair, who also served as chair o the Ad Hoc Advisory

Panel. An extraordinary number o researchers and others have

been involved in the review, conduct, and guidance o the HRS,

but special thanks are due to the co-investigators and members

o the Data Monitoring Committee (see Appendix B).

In addition, we thank the Social Security Administration, which

has provided technical advice and substantial support or the

study. Over the HRSs history, other important contributors have

included the U.S. Department o Labors Pension and Welare

Benets Administration, the U.S. Department o Health and

Human Services Oce o the Assistant Secretary or Planning

and Evaluation, and the State o Florida.

Many people have contributed to the development o thispublication. In particular, we thank Kn Kn o the

International Programs Center, Population Division, U.S. Census

Bureau, or his analytic expertise and inormation-gathering skills.

A special note o appreciation is due to c D. r, Institute on

Aging, University o Wisconsin; and rhd wd, National

Bureau o Economic Research, or providing text and analysis o

some o the secondary sources used in this report.

We also thank mh D. Hd, RAND Labor and Population;

lnd J. w, Center on Aging, National Opinion Research Center,

University o Chicago; andJ p. sh, RAND Labor and

Population, who contributed data and reerences. mhd u.K andJd sh, research associates at the University

o Michigan, were responsible or providing the data tabulations

that orm the basis o many o the report gures.

For their careul review o and suggestions regarding various

chapters, we are grateul to lnd p. d, Center on Aging

and Health, Johns Hopkins Bloomberg School o Public Health;

5


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rhd J. Hd, m.D.

DirectorNational Institute on AgingNational Institutes o Health

an l. gn, Department o Economics, Dartmouth College;

Jhn H, NIA Behavioral and Social Research Program;

Jhn c. Hn, Department o Sociology, University o Florida;

. th J, Survey Research Center, University o Michigan

and Director Emeritus o the HRS; Dd ln, Department o

Economics, Harvard University; Knnh m. ln, Department

o Internal Medicine, University o Michigan; r m. l, Rose

Li & Associates, Inc.; o s. mh, The Wharton School,

University o Pennsylvania; bh J. sd, Population Studies

Center, University o Pennsylvania; r b. w, Department

o Epidemiology, University o Iowa; and Dd r. w and

r J. w o the Institute or Social Research, University

o Michigan.

We also thank sn r. , JBS International, Inc., or her

overall editing o this report. vk chn, director o the NIA

Oce o Communications and Public Liaison, also contributed

her editing skills, and she and dd K, NIAs publications

director, were instrumental in the publication process. ch

l, HRS project associate at the University o Michigan,

and r m. l, Rose Li & Associates, Inc., rendered

invaluable contracting and inormation management services.

Jnn J, K mchn, andJhn vn, Levine &

Associates, Inc., developed the graphics and layout.

Most importantly, we thank the HRSs most valuable assetthe

thousands o HRS participants who, or more than a decade,

have graciously given their time and have sustained their interest

in this study. We salute their contributions, which are, indeed,

without measure.

rhd sn, ph.D.

Director, Behavioral andSocial Research Program,

and HRS Program Ofcer

National Institute on AgingNational Institutes o Health

6

What all o the people involved in the HRS have created is one

o the largest and most ambitious national surveys ever under-

taken. The studys combination o data on health, retirement,

disability, wealth, and amily circumstances oers unprecedented

opportunities to analyze and gain insight into our aging selves.

This publication is designed to introduce these opportunities to

a wider audience o researchers, policymakers, and the public

to help maximize the use o this incredible research resource.

We invite you to explore in these pages just a sample o what the

HRS has already told us and to examine its potential to teach us

even more.


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igures

a-1 Growth in Number o HRS Publicationsa- The Allocation o HRS Interview Time by Broad Topic

a-3 The HRS Longitudinal Sample Design

1-1 Health Status, by Age: 2002

1- Health Status, by Race/Ethnicity: 2002

1-3 Selected Health Problems, by Age: 2002

1-4 Severe Cognitive Limitation, by Age and Gender: 1998

1-5 Severe Depressive Symptoms, by Age: 2002

1-6 Insurance Coverage or Persons Ages 55-64, byRace/Ethnicity: 2002

1-7 Service Use in the Past Two Years, by Age: 2002

1-8 Health Service Use, by Race/Ethnicity: 2002

1-9 Average Out-o-Pocket Medical Expenditure, by Age:2000-2002

1-10 Components o Medical Out-o-Pocket Spending, byAge: 2000-2002

1-11 Limitation in Instrumental Activities o Daily Living, byAge: 2002

1-1 Limitation in Activities o Daily Living, by Age: 20021-13 Health Limitations and Work Status, Ages 55-64: 2002

1-14 Percent Dying between 1992 and 2002 Among theOriginal HRS Cohort, by Subjective Survival Outlookin 1992

1-15 Percent o Respondents Age 70 and Older Dying Between1993 and 2002, by Subjective Survival Outlook in 1993

1-16 Health Conditions Among Workers Age 55 and Over: 2002

-1 Full-Time and Part-Time Work, Ages 62-85: 2002- Retirement Pattern or Career Workers in the First HRS

Cohort: 1992-2002

-3 Absolute Dierence in Percent o Career Workers WhoAre Retired, by Age and Race/Ethnicity: 1992-2002

-4 Stress on the Job, by Age: 2002

-5 Occupation o Workers Age 70 and Older: 2002

-6 Sel-Employment Among Workers, by Age: 2002

-7 Willingness to Consider Changing Jobs, by Age : 2000

-8 Motivations to Stop Working Between 2000 and 2002,

by Age-9 Expectation o Working Full-Time Ater Age 65, by

Education: Respondents Ages 51-56 in 1992, 1998, and 2004

-10 Change in Educational Attainment o Successive Cohorts inthe HRS

-11 Level o Satisaction with Retirement: 2000

-1 Volunteer Work or Charitable Organizations, by Age:1996-1998

3-1 Components o Household Income or Married

Respondents, by Age and Income Quintile: 20023- Components o Household Income or Unmarried

Respondents, by Age and Income Quintile: 2002

3-3 Mean Income or Married-Person Households, bySel-Reported Health Status: 2002

3-4 Mean Income or Unmarried-Person Households, bySel-Reported Health Status: 2002

3-5 Cumulative Income Eects o New Health Shocks:1992-2000

7

list o igures aND tables


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3-6 Components o Net Household Worth or MarriedRespondents, by Age and Wealth Quintile: 2002

3-7 Components o Net Household Worth or UnmarriedRespondents, by Age and Wealth Quintile: 2002

3-8 Changes in Womens Household Net Worth, byMarital Status: 1992-1998

3-9 Poverty Rate or Widows, by Duration o Widowhood: 1998

3-10 Health and Net Worth: 2002

3-11 Impact o New Health Problem in 1992 on Total Wealth andOut-o-Pocket Medical Expenses: 1992-1996

4-1 Living Situation, by Age: 2002

4- Living Close Relatives, by Age o Respondent: 2002

4-3 Transers to/rom Parents and Their Children, by Age

and Marital Status o Parent: 2002

4-4 Receipt o Money, Time, and Co-Residence, orRespondents with and without ADL Limitation: 2002

4-5 Households That Gave at Least $500 to Their Child(ren)Between 2000 and 2002, by Age o Respondent

4-6 Proximity to Children, by Age o Respondent: 2002

4-7 National Annual Cost o Inormal Caregiving or FiveChronic Conditions: Circa 1998

4-8 Grandparent Health, by Level o Care Provision toGrandchildren: 1998-2002

tables1-1 Health Problems, by Age: 2002

1- Insurance Coverage, by Marital Status and WorkStatus: 2002

1-3 Prescription Drug Coverage and Likelihood o FillingPrescriptions, by Age: 1998

1-4 Supplement Use: 2000

-1 Labor Force Status o Not-Married and Married HRSRespondents: 2002

- Job Requirements o Employed Respondents, by

Age: 2002

-3 Job Characteristics o Employed Respondents, byAge: 2002

-4 Expected Retirement Ages, by Pension CoverageCharacteristics

-5 Retirement Satisaction, by Dened-Benet PensionReceipt and Retirement Duration: 2000

-6 Expected and Actual Changes in RetirementSpending: 2000-2001

3-1 Social Security Benet Acceptance, by Age andRetirement Status: Data rom the 1990s

3- Average and Median Household Wealth, by WealthComponent: 2000

3-3 Mean Household Net Worth, by Health o Husbandand Wie: 1992

3-4 Health Status and Household Portolio Distributions:Data rom the 1990s

4-1 Distribution o Expected Bequests, by Parent Cohortand Selected Wealth Percentile

4- Type o Respondent Transers to Parents, by Age oRespondent: 2002

Note: th nd n h d n Hrs00 d n h ndd.

8

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figures

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tables


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iNtroDuctioN


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10

During each 2-year cycle o interviews, the HRSteam surveys more than 20,000 people whorepresent the Nations diversity o economic

conditions, racial and ethnic backgrounds, health,marital histories and amily compositions,occupations and employment histories, livingarrangements, and other aspects o lie. Since1992, more than 27,000 people have given200,000 hours o interviews.

The HRS is managed jointly through a coopera-tive agreement between the National Institute onAging (NIA) and the Institute or Social Research(ISR) at the University o Michigan. The studyis designed, administered, and conducted by theISR, and decisions about the study content are

made by the investigators. The principal investiga-tors at the University o Michigan are joinedby a cadre o co-investigators and working groupmembers who are leading academic researchersrom across the United States in a variety odisciplines, including economics, medicine,demography, psychology, public health, and surveymethodology. In addition, the NIA is advised by aData Monitoring Committee charged with maintain-ing HRS quality, keeping the survey relevant andattuned to the technical needs o researchers whouse the data, and ensuring that it addresses the

inormation needs o policymakers and the public.

Since the study began, 7,000 people haveregistered to use the data, and nearly 1,000researchers have employed the data to publish

more than 1,000 reports, including more than600 peer-reviewed journal articles and bookchapters, and 70 doctoral dissertations. Figure A-1shows that the number o studies using HRS datahas grown rapidly as the scientic communitybecomes more aware o the richness and availabilityo the HRS data.

In the coming years, the NIA seeks to expandeven urther the use o the HRS database,viewed by the Institute and experts worldwideas a valuable national research resource in aging.This publication seeks to engage new audiences

o scientists, policymakers, media, and othercommunities with an interest in aging to usethis treasure trove o data, by showcasing howthe HRS can help examine the complex interplayo health, economic, and social actors aectingthe lives o older people and their amilies.

The chapters are organized into several broadthemes. This introduction presents an overviewo the HRS objectives, design, content, anduses. Subsequent chapters present contenton health, work and retirement, income

and wealth, and amily characteristics and

e , hnd d an h . Q, n, h n qn h h

h n, h h n nn, h h nn h nd h. th d h n n h u.s. Hh nd

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intergenerational transers. Data highlights arepresented throughout.

obJectives aND DesigN o tHe HrsThe HRS collects data to help:

Explain the antecedents and consequences oretirement

Examine the relationships among health,income, and wealth over time

Examine lie cycle patterns o wealth accumula-tion and consumption

Monitor work disability

Examine how the mix and distribution o eco-nomic, amily, and program resources aect keyoutcomes, including retirement, dissaving,health declines, and institutionalization

Designed over 18 months by a team o leadingeconomists, demographers, psychologists,health researchers, survey methodologists, andpolicymakers, the study set out to provide eacho these sciences with ongoing data collected ina methodologically sound and sophisticated way.Figure A-2indicates the share o time during thehour-plus HRS interview that is devoted to three

iNtroDuctioN


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11

FIG. A-1

GROWTH IN NUMBER OF HRS PUBLICATIONS

broad areas of inquiryeconomics, health,

and family. Within these categories, the HRS

specifically focuses on:

Economic Circumstances

The HRS collects detailed information about older

Americans economic circumstancessourcesand amounts of income; the composition and

amounts of assets; and entitlements to current and

future benefits such as those provided through

Social Security, Medicare, Medicaid, employer

pension plans, and employer-sponsored health

insurance. Data describing the movement of

assets, including gifts and bequests, time (e.g.,

to provide daily living assistance), and housing

within families, are also collected, as are data

about earnings, savings, and spending of individu-

als and families as they approach retirement and

over the course of their retirement until death.

FIG. A-2

THE ALLOCATION OF HRS INTERVIEW

TIME BY BROAD TOPIC


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iNtroDuctioN

1

on nd enOccupation and employment inormation col-lected by the HRS covers job characteristics, jobmobility, work hours, attitudes toward retirement,employer-provided benets (including healthinsurance, pensions, 401(k) plans, and otheremployer-sponsored saving programs), retirementbenets, and early retirement incentive oers.

Hh nd Hh c

The HRS collects inormation about chronicillness, unctional ability, depression, and sel-assessed health status, and examines health-related behaviors such as smoking, alcoholuse, and exercise. Health care utilization datagathered through the study describe physicianvisits, hospitalizations, nursing home stays,surgeries, dental care, prescription drug use,use o assistive devices (e.g., eyeglasses andwalkers), and receipt o caregiving services, aswell as health and long-term care insurancecoverage, out-o-pocket medical costs, andreceipt o assistance with medical expenses.

In the 2006 data collection, the HRS expandedto include biological inormation about theparticipants in an updated eort to matchbiological actors with health and social data.This new eort records participants height and

weight, measurements o lung unction, bloodpressure, grip strength, and walking speed. Italso collects small samples o blood to measurecholesterol and glycosylated hemoglobin (anindicator o blood sugar control) levels, and DNArom salivary samples or uture genetic analyses.

cnn

The HRS is unique among large surveys in itsuse o direct measures o cognition, drawn romestablished clinical instruments. These measuresprovide invaluable data on cognitive change with

aging and the impact o dementia on amilies.

They have also ound new application in studies oeconomic behavior and survey response patterns.

ln nd Hn ann

The survey explores the relationships between

peoples living arrangements and the availabilityor use o long-term care services such as nursinghome residence, services oered to residentsliving in other housing arrangements, and specialhousing eatures or people who are physicallyimpaired. It also gathers data about the type ohousing structure in which HRS participants live,housing ownership or nancial arrangements,entry ees or association payments, and thesharing o housing with children or others.

Dh nd rnh

The HRS gathers standard demographic actssuch as age, racial/ethnic background, education,marital status and history, and amily composi-tion. Among married participants, detailed healthand economic inormation is collected rom bothspouses. General demographic inormation aboutHRS participants parents, children, and siblingsis also gathered. In addition, survey interviewsdocument the relationships among amily mem-bers and the nature o intergenerational amilysupports, including nancial transers, caregiving,joint housing arrangements, and time spent with

amily members.

How caN tHe Hrs Data be useD?

The research team that designed the HRSmade a number o dicult decisions about howmany people to include in the survey, whether tosurvey the same people over time or to surveynew participants, how oten to conduct interviews,and what questions to include in the interviews.The outcome o these decisions is a steadystate model that:

Is nationally representative o the populationover age 50

Follows individuals and their spouses rom thetime o their entry into the survey until death

Introduces a new 6-year cohort o participantsevery 6 years (as detailed elsewhere in thischapter)

This design allows researchers to use the data ina number o important ways:

ann indd anRegular re-interviews with HRS participants arean essential eature o the survey design. Analystscan ollow individuals evolving circumstances andanswer general questions about what happens

in amilies as their members age. For example,analyses o the data can reveal the extent towhich people spend down their assets as theyage, nd out whether people hold steady employ-ment or move into and out o the labor orce, andassess the dynamics o health deterioration andimprovement with age. Further important questionsto be explored ask: What are the circumstancesleading up to major lie transitions such as retire-ment or health events? How do people respond tothose transitions? What are the consequences othose transitions?

ann tndThe HRS is a rich resource or exploring nationaltrends in health and economic status over time.It allows or examination o cohort dierences,or example, by comparing the characteristics andbehavior o 61-year-olds in 1992 with the char-acteristics and behavior o 61-year-olds in 2002.The data can show whether people have more orewer nancial assets now than in previous years,are more or less likely to work, and are more orless likely to be caring or an aging parent or

providing childcare or a grandchild. Analysts


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14

can also track trends in age-adjusted health andunction, and they can investigate whether or notsmoking, alcohol use, and tness behaviors arechanging. Use o the survey to study trends overtime depends less on ollowing individuals as they

age and more on comparisons o similarly situatedindividuals at dierent points in time.

undndn g DnBy representing the U.S. population as a whole,the HRS provides researchers a way to exam-ine and compare circumstances across income,racial/ethnic, gender, and other subgroups. Forexample, the nancial resources o people withthe least income and those at the median and inthe highest income bracket can be compared. Thedata can be used to contrast outcomes or people

who have suered heart attacks with those opeople who develop diabetes, dementia, arthritis,or cancer. They also permit targeted analyses othe characteristics o people whose health statusor poverty may make them particularly vulnerable,including the study o how well government saetynets protect vulnerable individuals. The dataurther can look at dierences among marriedand unmarried people; those with and withoutchildren; and those who retire young, who retireat typical ages, and who continue working paststandard retirement ages.

en cThe HRS survey design supports analyses owhat causes things to happen. Collection osuch a wide range o inormation about amiliesover time enables analyses o how older adultscircumstances change and how one dimensiono their lives relates to other dimensions. Forinstance, it is interesting that many Americanschoose to retire at relatively young ages, but criti-cal questions or policymakers are why peopleretire young and whether they can support them-

selves over the course o long retirement spans.

As HRS data accumulate over time, scientistshope to understand better a broad array ocausal issues. For example, the HRS data mightbe used to determine specically why someolder Americans all into poverty, the propensity

or certain smokers to quit while others continuesmoking, actors that lead some people toleave large bequests and others none, theeect o employer-provided health insurance orMedigap insurance on retirement decisionsor the use o medical services, and why peoplewith similar unctional ability choose dier-ent living arrangements and dierent orms ocare. The data can also be used to explore thereasons why some people save ar more thanothers, even i they have equivalent salaries andlie circumstances. Additionally, HRS analyses

can identiy obstacles that delay retirement inorder to pay or the extra years o lie, given therise in lie expectancy and improved health.

sn p o

Armed with some knowledge o causality,researchers can use the HRS data to simulatewhat might happen under dierent policyscenarios and the likely implications o aging-related policy reorms. For example, they canask: What will happen to decisions about workat older ages as the earnings test on Social

Security benets is eliminated? What wouldhappen to retirement decisions i the age oeligibility or early Social Security benets wereincreased rom 62 to 65? To what extent wouldthe economic circumstances o widows beaected i Social Security survivorship benetswere increased? What is the impact o thenew Medicare Part D prescription drug benet?What would happen to saving rates i thecontribution limits on individual retirementaccounts were raised?

stuDy iNNovatioNs

The HRS is unique because o several surveyinnovations. These include:

mn in nd aSurveys asking about income and assets histori-cally have been troubled by participants reusalto answer nancial questions or inability to answerthem knowledgeably. Further, many surveys alsohave not accounted or major components oassets or income and/or have used measures thatdo not truly refect assets and income. The HRShas made major advances in both o these areas.The study developed a technique known asrandom-entry bracketing, which reduces thenumber o nonresponses by eliciting ranges o

values rom respondents who would otherwisegive no inormation at all. To improve the mea-surement o income rom assets, the surveybrought together questions about the ownershipo certain assets (e.g., stocks and bonds) andthe income obtained rom those assets. In addi-tion, traditional measures o income and wealthhave been integrated with detailed data aboutSocial Security, pensions, and other utureentitlementsa signicant accomplishment othe HRS, particularly because uture entitlementsrepresent a major component o the nancial

status o older Americans. These new methodshave been widely adopted by many other surveys.

enn pn en

The decisions people make as they age areinfuenced not only by past and current circum-stances, but also by what they expect to happenin the uture. Most surveys ocus on measuringcurrent circumstances and, to some extent, whatpeople can remember about the past. An excitinginnovation in the HRS is the exploration o partici-pants uture expectations. This novel approach

yields valuable inormation about how long people


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16

expect to work in the uture, their estimates ohow long they will live, the likelihood o givingmajor nancial assistance to amily members inthe uture, whether or not they expect to leavea bequest and the amount o that bequest, and

whether they think they will enter a nursing homeor move to a new home or other living arrange-ment in the uture. Initial analysis o these datasuggests that expectations have an importantinfuence on the decisions that people make.

inn en md

There are limits to the number o questions thatcan be asked and answered in a population sur-vey, and there is great value in maintaining thatsame core o questions in a longitudinal study.Alternative vehicles may be needed, however,

to allow researchers to explore narrowly ocusedtopics or test new survey ideas. The HRS usesexperimental modulesshort sequences oquestions administered to randomly selectedsubgroups o participants at the end o the survey.To date, more than 70 experimental moduleshave asked about physiological capacity, earlychildhood experiences, personality, quality o lie,employment opportunities, use o complementaryand alternative medicines, parental wealth, activi-ties and time use, nutrition, medical directives,living wills, retirement expectations and planning,

sleep, and unctional ability. Appendix A providesmore inormation about these modules.

liNKages to otHer Datasets

Despite the comprehensive nature o the HRS,limitations exist in terms o what can be learnedrom population interviews. To provide moredetailed and elaborate inormation in particularareas, the HRS team asks participants or permis-sion to link their interview responses to other dataresources, as described below. Linked administrative

records are available only as restricted data underspecial agreements with a highly restricted groupo individual researchers that guarantee securityand condentiality.

s s rdThe Social Security Administration keeps detailedrecords on the past employment and earningso most Americans. For those who have appliedor Social Security payments, records o benetdecisions and benets paid, including those paidthrough the Social Security Disability Insurance(SSDI) or Supplemental Security Income (SSI)programs, are available to researchers. By link-ing these records to HRS participants interviewresponses, a signicantly richer body o data canbe analyzed to better understand the relationships

between health and economic circumstances,public and private retirement policies, and thework and retirement decisions that people makeas they age.

md rdThrough the administration o the Medicareprogram, the Centers or Medicare & MedicaidServices (ormerly the Health Care FinancingAdministration) maintain claims records orthe medical services received by essentially allAmericans age 65 and older and those less than

65 years who receive Medicare benets. Theserecords include comprehensive inormation abouthospital stays, outpatient services, physicianservices, home health care, and hospice care.When linked to the HRS interview data, thissupplementary inormation provides ar moredetail on the health circumstances and medicaltreatments received by HRS participants thanwould otherwise be available. For instance, theseMedicare records will enhance research on theimplications o health changes, the infuence ohealth-related behaviors on health, the relationships

between health and economic circumstances asthey evolve jointly over the course o later lie,and the impact o supplementary insurance onmedical care decisions.

e s nd rd DData rom HRS interviews have been supple-mented with inormation obtained rom or aboutparticipants employers, without revealing theidentities o HRS participants to employers. Oneimportant area o ocus is pension plans. Whilemost pension-eligible workers have some ideao the benets available through their pensionplans, they generally are not knowledgeableabout detailed provisions o the plans. By linkingHRS interview data with specic inormation onpension-plan provisions, researchers can better

understand the contribution o the pension to eco-nomic circumstances and the eects o the pen-sion structure on work and retirement decisions.

bacKgrouND aND DevelopmeNto tHe Hrs

The HRS began as two distinct though closelyrelated surveys that were merged in 1998 and areadministered under the cooperative agreementbetween the NIA and the University o MichigansInstitute or Social Research. The rst study,

reerred to as the original HRS, was initiallyadministered in 1992 to a nationally representa-tive sample o Americans between the ages o 51and 61 (strictly speaking, born in the years 1931through 1941). In the case o married couples,both spouses (including spouses who were youngerthan 51 or older than 61) were also interviewed.These participants continue to be contacted every2 years as part o the ongoing HRS.

The second survey, originally reerred to as theStudy o Assets and Health Dynamics Among the


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17

FIG. A-3

tHe Hrs loNgituDiNal sample DesigN

Oldest Old, or AHEAD, was rst administered in1993 to a nationally representative sample oAmericans age 70 and older (strictly speaking,born in 1923 or earlier). Again, in the case omarried couples, interviews were conductedwith both spouses. About 8,000 people wereinterviewed as part o the 1993 AHEAD survey.These individuals were re-interviewed in 1995and 1998, and they, too, continue to be inter-viewed on the 2-year cycle o the study.

The original HRS and AHEAD surveys wereintegrated in 1998, and the consolidated projectis now reerred to as the Health and RetirementStudy. Two new groups o survey participants

(including spouses) were added in 1998. The rstgroup consists o people in the age group that allsbetween the original HRS and AHEAD samples.Born between 1924 and 1930 and raised duringthe Great Depression, these participants are calledthe Children o the Depression Age, or CODA,cohort. The second group added in 1998 was therst reresher cohort brought in to replenish thesample o people in their early 50s as the originalHRS cohort aged. It is known as the War Babycohort, consisting o people born between 1942and 1947 and their same-age or younger spouses.

Figure A-3shows the past and projected evolutiono the HRS sample, including survey years or the

dierent participant cohorts. In the uture, theresearch team plans to supplement the samplewith groups o younger people as they reach their50s. For example, participants born between1948 and 1953the early years o the post-World War II baby boomwere added to the HRSsample in 2004. By continuing to reresh thesample, the HRS will provide a long-term sourceo data on the transition rom middle age to theinitial stages o retirement and beyond. (Fora more complete overview o and background tothe development o the HRS, see Juster andSuzman 1995.)


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Many nations, particularly in Europe, are further along than the United

States in population aging, and they have found the multidisciplinary,

longitudinal nature of the HRS appealing as a way to obtain a holistic

picture of health and retirement trends in their graying populations.

One of the first nations to put such a study in place was Great Britain,

where a team of researchers in the late 1990s began planning the

English Longitudinal Study of Ageing (ELSA), a survey that is directly

comparable to the HRS. ELSA is supported by grants from several

departments of the British Government, as well as by the U.S. National

Institute on Aging (NIA). The British Government supports ELSA

because of its ability to inform both short- and long-term policy options

for an aging population. The NIA supports ELSA because of the benefit

from comparative analyses of data obtained from people living under

very different health and social services arrangements and economic

policies. The first rounds of ELSA data were collected in 2002 and

2004, and subsequent waves began in 2006.

The success of the HRS and ELSA has spawned a major international

study that now tracks health and retirement trends in Europe. SHARE

the Survey of Health, Ageing and Retirement in Europeinvolves

Sweden, Denmark, France, Belgium, The Netherlands, Germany,

Switzerland, Austria, Spain, Italy, and Greece. Approximately 130

researchers from the participating nations have been organizedinto multidisciplinary country teams and cross-national work-

ing groups, assisted by a number of expert support and

advisory teams.

The European study also features many technical

innovations designed to maximize cross-national

comparability. For example, it employs a

single, centrally programmed survey

instrument that uses an underlying

language database to create country-

and language-specific instruments.

The initial success of SHARE generated extraordinary interest and led to

extending this project to Israel, Ireland, the Czech Republic, and Poland.

Population aging is also becoming a major policy concern in developingcountries. The HRS concept is being applied in the Mexican Health and

Aging Study (MHAS), the first such effort in a developing country. The

MHAS is a prospective panel study of Mexicans born prior to 1951. Its

2001 baseline survey was nationally representative of the older Mexican

population and similar in design and content to the HRS. A second round

of data collection was undertaken in 2003. In addition to the range

of issues that can be considered using HRS data, the MHAS offers an

opportunity to explore aging and health dynamics in the context of

international migration.

The HRS and SHARE concepts have also been emulated in Eastern

Asia. South Korea is already planning the second wave of the Korean

Longitudinal Study on Aging, while planning for initial waves is well

advanced in China, Thailand, and Japan, and initial planning for an

Indian HRS has begun.

THE HRS: A MODEL FOR OTHER COUNTRIES

IN

TRODU

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18


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0

cHapter 1: HealtHa n h h Hh nd rn sd (Hrs) n h hh n h dn kn. a d

ndnd h n- hh nqn h n . th Hrs n hh dnn n. b nn

ndn hh, nn , nd hh h n nd , h Hrs h ndnd h hh

nfnnd nfnd n hh h . a h Hrs d h nd n hd, h nn h d n qn n h h h dd n nd d.

th h nh n h h nd n hh , hh nn , nd hh n n-dn d

d. i d nh h hh nd nd hh n n n, k d nd h

nnn n Hrs n.

people who had never smoked, had quit, orwere light smokers at the time they weresurveyed have a realistic sense o their mortal-ity, their expectations coinciding with actuarialprojections. Heavy smokers, however, signi-cantly underestimate their premature mortality,in denial o the potential eects o their smok-ing habit. Another study ound level o educa-tion to be the major positive infuence on thedecision to quit among heart attack survivors.

cn hh dn h .A preliminarystudy based on HRS data indicates that some

10 percent o people age 70 and older havemoderate to severe cognitive impairment, andprevalence rises sharply with age. In the com-munity, an estimated 6 percent o people over70 have moderate to severe impairment, whilesome 50 percent o those institutionalized do.The HRS data on cognition are among the rstto measure cognitive health at the populationlevel, and these preliminary analyses are beingexamined urther to see how they compare witha number o other estimates, primarily derivedrom studies in specic communities.

64 reported a health problem that limited theirwork activity, but one-th o those repor ting ahealth limitation were working in some capacity.More than hal o men and one-third o womenwho let the labor orce beore the Social Securityearly-retirement age o 62 said that healthlimited their capacity to work. Longitudinaldata rom the HRS have shown that the onseto major health problems, such as a strokeor heart attack, requently leads directly towithdrawal rom the labor orce.

l nfn d d hh

nd h -n. One study ound thatmen who were heavy drinkers (ve or moredrinks per day) but not unctionally impairedwhen rst interviewed have a our-old risk odeveloping at least one unctional impairment(including memory problems) over a 6 -yearperiod o time. Among HRS respondents overage 70, overweight and obesity also are actorsin unctional impairment, having an independenteect on the onset o impairment in strength,lower body mobility, and activities o daily living.

H k nd h

kn. One analysis shows that

cHapter HigHligHtsThere are wide variations in the health o Americansage 50 and older, with dierences that vary by age,race/ethnicity, and liestyle. According to HRS data:

Hh n . Onestudy ound that the pattern o disease at age50 or people with less than a high schooleducation is similar to that at age 60 or peoplewith college degrees.

od an n n d hh, h kn dn

nd nd hn. Almost hal o HRSparticipants ages 55 to 64, but only about onequarter o those age 65 and older, say they arein very good or excellent health. White respon-dents report very good or excellent health at arate almost double that o Blacks and Hispanics.Studies using HRS data have ound that part butnot all o these racial disparities can be attrib-uted to dierences in socioeconomic status.

Hh h n n nfn n d k.In 2002, 20 percent

o men and 25 percent o women ages 55 to


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cn n h h d d hn n ndn n . Using HRS data, the totalnational cost has been estimated at $18 billion,and the annual cost o caring or a amily

member with dementia at about $18,000.

th dn h .Severe depression is evident in about 20 percento people age 85 and older, compared with15 percent among people age 84 or younger.

th nd dn n h hh , n hh nd,nd n h nn nd nd hn. For example, racial andethnic dierences in health insurance coveragepersist among older adults not yet eligible or

Medicare. One in 14 Whites and 1 in 8 Blackslack private health insurance, and about 1 in 4Hispanics do not have private coverage. Hispanics

have the highest probability o not visiting aphysician at least once in a given 2-year period.

od n dn ndn n d.AmongHRS respondents in the year 2000, more thanhal say they had used some kind o dietaryor herbal supplement. Nearly hal had seena chiropractor, and 20 percent had usedmassage therapy.

wh an 55 64

hh hn h bh n, dhh n nd hh hh ndn. A comparison o datarom the HRS and a parallel study, the EnglishLongitudinal Study o Ageing, showed thatthe healthiest middle-aged Americans inthe studythose in the highest income andeducation levelshad rates o diabetes andheart disease similar to the least healthy inEnglandthose in the lowest income andeducation levels.

FIG. 1-1

HealtH status, by age: 00(Percent in each health category)

55 -64 65 -74 75- 84 85+

Excellent Very Good Good Fair Poor

0%

20%

40%

60%

80%

100%

HealtH status aND speciiccoNDitioNs

The HRS data on health are based largely on whatrespondents report about themselves. While sel-reported evaluations are inherently subjectiveand related to individual personality, outlook, andcontextresearch in a wide variety o culturesand contexts suggests that sel-reported healthstatus is a very good predictor o more objectivehealth measures such as chronic illness, hospital-

ization, and longevity. Individuals belies abouttheir own health status also have been ound toinfuence their expectations o retirement and theretirement process itsel.

Figure 1-1 suggests that HRS participants wholive in the community consider themselves to bein reasonably good health and that sel-reportedhealth status decreases with age. Almost hal oHRS participants ages 55 to 64, compared with42 percent o participants ages 65 to 74, 32percent ages 75 to 84, and 25 percent age 85and older, say they are in very good or excellent


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He

altH

health. Conversely, the proportion reporting thatthey are in air or poor health increases steadilyrom 21 percent among people ages 55 to 64 to43 percent among those age 85 and older.

Gender dierences in sel-reported health statusare small, while dierences by race/ethnicity arelarge. Men are slightly more likely than womento report excellent or very good health (43percent compared with 41 percent). Only about25 percent o Black and Hispanic respondents,compared with 45 percent o White respondents,report being in excellent or very good health(Figure 1-2). Additionally, about 42 percent oBlack and Hispanic participants, compared with24 percent o White respondents, report theirhealth to be air or poor.

Most studies nd that some, but not all o theracial and ethnic disparities in health can be

attributed to dierences in socioeconomic actorssuch as education, income, and wealth that arerelated to health and dier by race and ethnicity.One study ound that socioeconomic actorsexplained only a relatively small part o the racial

dierence in the prevalence o chronic conditions,but that the racial disparity in physical unction-ing could be almost completely explained by acombination o socioeconomic status dierencesand the racial dierences in chronic conditions(Kington and Smith 1997).

Advancing age is associated with an increasingprevalence o a number o diseases and otherhealth problems. The HRS is uniquely poised todescribe these problems in terms o their eectson the everyday unction o older people. Figure 1-3presents the prevalence o selected health problemsreported within dierent age groups. Arthritis andhypertension are the most common conditions,

at all ages, ollowed by heart problems. Thelikelihood o having (or having had) most problemsincreases steadily with age, although diabetes,hypertension, and chronic lung disease appear tobe somewhat less common above age 85.

Gender dierences with regard to health condi-tions are generally small. The most notabledierence pertains to ar thritis. Nearly two-thirdso all emale respondents but only one-hal omale respondents report having this potentiallydisabling condition.

Several race/ethnicity dierences in the preva-lence o some conditions are notable. As hasbeen ound in other data sources, Blacks havehigher rates o hypertension than those o otherpopulation subgroups. More than two-thirds o

Black HRS participants report having hyperten-sion, compared with one-hal o the White andHispanic participants. Blacks and Hispanicshave signicantly higher levels o diabetes thando Whites. Whites are most likely and Hispanicsleast likely to report cancer, lung disease, andheart problems. Hispanics reported rates oarthritis and stroke also are lower than those oBlacks and Whites.

Co-morbidity, or the combination o multiplechronic problems, is an especially challenging

situation or health management. The HRSexamines older adults risk o having multiplechronic health problems. Table 1-1 summarizes thecombined prevalence o six major health problemsreported by the 2002 HRS sample: diabetes,hypertension, cancer, bronchitis/emphysema, aheart condition, and stroke. (Arthritis, which iscommon among all age groups, is not included.)The percentage o people ree o chronic problemsalls with age, and the percentages with multipleproblems increase. Roughly hal o the peopleover age 75 report two or more chronic conditions.

However, the burden o co-morbidity appears to

FIG. 1-2

HealtH status, by race/etHNicity: 00(Percent in each health category)

Black Hispanic White/Other

Excellent Very Good Good Fair Poor

0%

5%

10%

15%

20%

25%

30%

35%


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stabilize at the oldest ages; the distribution ochronic problems among people 85 and older isvery similar to that o those 75 to 84, at least inthe community-dwelling population.

HealtH beHaviors aNDoutcomes

With recent and projected increases in nationalhealth care expenditures, public attention hasocused on preventing unhealthul behaviorsand controlling behavioral and liestyle actorsthat contribute to disease, disability, and death.The HRS examines several o these health behav-iors and risk actors, including smoking, alcoholconsumption, and obesity, and helps ramequestions designed to inorm public health policy

in these areas. One book, based on the rstour waves o HRS data, is devoted to exploringrisk perceptions and choices made by smokersand addressing policy questions such as theecacy o dierent educational strategies, class-action suits, and regulation/prohibition (Sloan etal. 2003).

sknExamining the relationship between health beliesand health behavior, Schoenbaum (1997) inves-tigated whether HRS participants understand the

mortality eects o smoking, i.e., do they realizethat smoking can shorten ones lie? In onesurvey year, participants were asked how longthey expected to live. For never, ormer, andcurrent light smokers, survival expectationswere quite close to actuarial predictions o lieexpectancy or their ages. Among current heavysmokers, however, the expectation o reachingage 75 was nearly twice that o actuarial predic-tions. In other words, heavy smokers signicantlyunderestimated their risk o premature mortalitylinked with smoking.

TBL. 1-1

HealtH problems, by age: 00

Notes: Health problems include six major categories: hypertension, diabetes, cancer, bronchitis/emphysema, heart condi-

tion, and stroke. Columns may not sum to 100% due to rounding.

N d md

pn rndnN Hh p 55-64 65-74 75-84 85+

0 40% 26% 18% 17%

1 35 36 34 34

17 24 29 29

3 6 10 16 14

4 2 4 5 6

FIG. 1-3

selecteD HealtH problems, by age: 00(Percent ever having)

55-64 65-74 75-84 85+

Hypertension Diabetes Cancer ChronicLung

Disease

ArthritisHeart

Condition

Psychiatric/Emotional

Problem

Stroke

0%

10%

20%

30%

60%

40%

50%

70%


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H

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preceding the survey were as likely as those who

had never smoked to report good health. Further

analysis indicated that males ages 50 to 54 years

who are heavy smokers lose approximately 2 years

of healthy life, and females in the same age group

who are heavy smokers lose about 1.5 years ofhealthy life, relative to former smokers.

In another study of smoking cessation, Wray and

colleagues (1998) analyzed data for smokers who

had had heart attacks. Controlling for a variety

of health factors, level of education emerged as

the major positive influence on the decision by

middle-aged HRS participants to quit smoking

after the cardiac event.

Alcohol Consumption

Recent reports have suggested that moderatealcohol consumption has potentially healthful

effects, but HRS data clearly show that heavy

A COMMUNITY-DWELLING SAMPLE

The original HRS (1992) and AHEAD (1993) samples were drawn from community-dwelling individuals

and did not include people living in institutions such as nursing homes. This sampling procedure

also applies to cohorts added to the study after 1993. Unless otherwise noted, data in the tables

and graphs in this report refer only to community-dwelling people and do not include people

who have moved into nursing homes after they were initially selected for the study.

The HRS does, however, follow individuals as they move into and out of institutional

settings. As the number of study participants in institutions increases, the HRS

is becoming an important source of information about this segment of the

U.S. population. In certain parts of this report, such as the description

of living arrangements in Chapter 4, the HRS nursing home component

is included.

drinking takes its toll. Perreira and Sloan (2002)

analyzed 6 years of HRS data to examine links be-

tween excessive alcohol consumption and health

outcomes for men. Men who were heavy drinkers

(five or more drinks per day) but not functionally

impaired in the initial survey year had a four-foldrisk of developing at least one functional impair-

ment (including memory problems) during the

6-year follow-up period. This finding held true

even when controlling for the effects of smoking

and other factors.

Perreira and Sloan (2001) also used multiple

waves of HRS data to explore changes in drink-

ing behavior that occurred with and after major

health, family, and employment stresses. Two-

thirds of the sample did not change their use of

alcohol in the 1990s. However, when changes did

occur, they were related to several life events: Re-

tirement was associated with increased drinking;

Other research has examined whether the percep-

tions of smokers reflect a true lack of understand-

ing of health risks or a form of indifference or

denial. Smith et al. (2001) investigated how sub-

jective beliefs change in response to new informa-

tion. This study found that when HRS smokersexperience smoking-related health shocks, such

as a heart attack or cancer diagnosis, they are

likely to reduce their expectations of longevity

significantly, more so than when they experience

general (non-smoking-related) health shocks.

A more traditional analysis of health outcomes

addressed the effects of smoking on disability,

impaired mobility, health care utilization, and

self-reported health (Ostbye et al. 2002). As ex-

pected, smoking was strongly related to mortality

and self-reported ill health. Researchers were alsoable to characterize the benefits of quitting smok-

ing. People who had quit smoking in the 15 years


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hospitalization and the onset o a chronic condi-tion were associated with decreased drinking;and widowhood was associated with increaseddrinking, but only or a short time.

Ostermann and Sloan (2001) analyzed 8 yearso HRS data to examine the eects o alcoholuse on disability and income support or peoplewith disabilities. Their analysis demonstratedthat a history o problem drinking, especiallywhen combined with recent heavy drinking, wasassociated with a greater prevalence and inci-dence o limitations in home and work activities.However, despite increased disability, problemdrinkers higher rates o activity limitationswere not associated with a greater likelihoodo receiving income support rom the FederalGovernments Social Security Disability Insur-ance (SSDI) or Supplemental Security Income(SSI) programs.

oHRS data have been used to document anassociation between obesity and impairmentsin physical unction that will translate into risingdisability rates in the uture i obesity trendscontinue (Sturm et al. 2004). A causal analysiso HRS respondents over age 70 suggested thatbeing overweight or obese (using conventionalbody mass index measures) makes an older per-son more likely to become unctionally impairedin the uture. While this relationship is otencomplex, obesity appears to have an independenteect on the onset o impairment in strength,lower body mobility, and activities o daily living(Jenkins 2004).

Extra pounds may also be expensive, at least ormiddle-aged women. Looking at the relationshipbetween weight and nancial net worth, Fondaet al. (2004) ound that in 1992 the individualnet worth o moderately to severely obese women

ages 51 to 61 was 40 percent lower than that

o normal-weight peers, controlling statisticallyor health status, education, marital status, andother demographic actors. These individualssituation also appears to worsen over time. In1998, the sel-reported individual net worth o

moderately to severely obese women in the samecohort (then ages 57 to 67) was 60 percent lessthan that o their counterparts (an average di-erence o about $135,000 in 1998). No suchpattern could be ound or men. While HRS dataallow relationships among obesity, gender, andnancial status to be measured in new and im-portant ways, researchers caution that the causalmechanisms underlying these ndings are stillpoorly understood.

Family characteristics may also play a role inobesity risk and how we might intervene toprevent obesity. Ater adjusting or age, race,income, and several behavioral actors, research-ers analyzing HRS data ound a positive correla-tion between number o children and obesity orboth women and men (Weng et al. 2004). Theassociation between obesity and amily size is anintriguing nding and suggests the need or ur-ther exploration o the idea that parents o largeramilies might be an important target populationor obesity prevention.

cogNitive uNctioNThe decline o cognitive unction with age is anoten-unspoken ear that many people have asthey grow older, and the burden o cognitive im-pairment on individuals, amilies, caregivers, andsociety at large is enormous. Severecognitive impairment is a leading cause o insti-tutionalization o older people. Beore 2003, es-timates o the prevalence o cognitive impairmenthad to be derived rom local clinic-basedstudies, typically in urban areas, and extrapo-lated to the larger population. With the advent o

the HRS, and more specically the AHEAD por-

tion o the study, researchers could attempt orthe rst time to tap nationally representative datato assess cognitive unction in older people.

The HRS is one o the rst national healthsurveys to measure cognitive health at thepopulation level and to examine on a large scalethe biological and environmental actors as-sociated with cognition. The HRS measuremento cognition employs two well-tested cognitionassessments: the Telephone Interview or Cogni-tive Status (TICS), a brie, standardized test ocognitive unctioning that was developed or usein situations where in-person cognitive screeningis impractical or inecient, and the Mini-MentalState Examination (MMSE), a widely used toolor assessing cognitive mental status. In addi-tion, a special assessment tool or third-partyobservations, the Jorm IQCODE, is used when aproxy reporter provides an interview on behal oa respondent. This is an essential tool when cog-nitive impairment makes an interview otherwiseunobtainable.

Initial estimates, while preliminary, indicate thatin 1998, approximately 10 percent o the U.S.population age 70 and older had moderate tosevere cognitive impairment (Suthers et al.2003). The prevalence o moderate to severecognitive impairment among non-institutionalized

people was 6 percent, while the level amongthe institutionalized exceeded 50 percent. Onaverage, the data suggest, a person reaching age70 with a lie expectancy o 14 remaining yearswill spend 1.5 o those years with moderate orsevere cognitive impairment. As the originalHRS sample and its additional cohorts age,researchers will be able to update and renethese important data. The analysis also indicatedthat the prevalence o cognitive impairmentincreases steeply with advanced age. Amongpeople ages 75 to 79 who participated in the

1998 HRS, ewer than 5 percent had severe


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HealtH

6

th an, Dh, nd m sd (aDams), n

h Hrs, h nn d h n dn

n h und s. th n d h h : ,

h nn h n dn nd n-

n h dn; nd, n ndndn

h n h n nd n dn, h dn n hnn h hh nd nnn d

an; nd hd, h d d h d

Hrs n nnn nn n

n dn. th aDams d n n

nd n-dh nn d h dn

n hh n, n n, nd

dn .

th d h knd nd n-h n

dn n nn h n h u.s. d -

n. th n n ndd hh n h

Dk un. a 001 hh mh 005, d Hrs

n d n h n nd h

hnn, h h nd n h n

dn. cndd n h n , nd,

d h, h n ndd nn n nd d

h, nh n, nd n DNa d-n h n e (apoe) n. - n

h n ndd h 30 n n-

dn h ddn d j. addn -

nnd . inn n nd

nd hh n d.

th aDams d n 850 ndn h Hrs

h n d. th aDams d, h -

n n nd h ndn -

n, d h n 007.

tHe agiNg, DemograpHics, aND memory stuDy

limitation (Figure 1-4). Ater age 80, however,the prevalence rate rises steeply, approaching 20percent or people age 85 and older.

The HRS also provides valuable inormation aboutthe need or and provision o caregiving or olderpeople with cognitive impairment. Estimates romthe baseline AHEAD survey in 1993 indicatedthat people with mild impairment received 8.5more hours o care per week, while those withsevere impairment received 41.5 more hours ocare per week than their peers with normal cogni-tive unction (Langa et al. 2001). The same studyound that valuing this amily-provided care atthe average hourly wage o a paid home aide, thisinormal care amounts to $17,700 per year oran individual with severe impairment, and a totalnational cost o $18 billion per year or inormalcare or all orms o cognitive impairment.

Recent studies suggesting a decline in overall rateso disability among the older U.S. population haveprompted researchers to consider the utility o theHRS in measuring trends in cognitive impairmentover time. Analyses o HRS data rom the 1990s

showed a signicant decline in the prevalence osevere cognitive impairment among people age 70and older, rom about 6 percent in 1993 to lessthan 4 percent in 1998 (Freedman et al. 2001,2002). In contrast, another analysis o the samecohort using additional controls ound very littlechange rom 1993 to 2000 in cognitive impair-ment rates, ater adjustment or demographiccomposition (gender, race, and ethnicity) (Rodgerset al. 2003). Scientists concentrating on the cogni-tive health aspects o participants in the HRS willcontinue to examine these contradictory ndings

in an eort to sort out the national trends.

Depressive symptoms aNDDepressioN

Mental health, while critically important to thehealth o the population, is extremely dicult to

assess in population surveys. The HRS develop-ers decided at the outset to ocus on depression,the most prevalent mental health condition in theolder population and a leading cause o disability.At baseline, respondents are given a series oquestions to identiy major depressive episodesin the prior year. In each wave o the study,respondents are asked about eight commonsymptoms o depression, taken rom the CES-Dinstrument. In validation studies against theull CES-D battery, the presence o our out othe eight symptoms is associated with clinically

signicant depression.


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Data or 2002 suggest that the prevalence osevere depression or men and women combinedis approximately 15 percent within each 10-yearage category between ages 55 and 84 (Figure1-5)and approaches 20 percent or the 85 and

older group. For all o the age groups, women areconsistently more likely than men to report severedepressive symptoms.

HRS longitudinal data can help address an im-portant question about the correlation betweendepression and physical health: Do disease anddisability lead to depression, or does depressionlead to disease and disability? Blaum (1999)ound that depressive symptoms are precur-sors to the development o uture disease. Asexpected, physical limitations (e.g., the inabilityto walk several blocks, climb stairs, or lit a10-pound object) were the strongest predic-tors o developing a new disease 2 years later,increasing the odds o developing at least onenew disease by nearly 50 percent. At the sametime, participants age 70 and older who report-ed having several symptoms o depression wereone-third more likely than others to develop anew disease within 2 years. The eect was seenwith relatively mild depressive symptoms, suchas decreased energy and restless sleep, as wellas with more severe clinical depression.

Stopping driving is one activity o daily living thatappears to be associated with increased depres-sive symptoms. An analysis o a 6-year periodo early HRS data showed that older people whostopped driving were 1.4 times more likely toexperience worsening depressive symptoms thanthose who continued to drive ater the 6 years(Fonda et al. 2001). Longer-term restrictions ondriving urther increased the risk o depressivesymptoms. Having a spouse who still drove didnot signicantly aect the respondents depres-sive symptoms.

FIG. 1-4

severe cogNitive limitatioN, by age aND geNDer: 1998

Note: Defnition o severe cognitive impairment: Errors on hal or more o 9 very easy items rom a standard geriatric screenor mental status or sel-respondents; IQCODE score o 3.9 or higher on Jorm proxy assessment.

Source: HRS 1998.

Men Women Total

70-7465-6960-6455-5951-54 75-79 80-84 85+

0%

5%

10%

15%

20%

FIG. 1-5

severe Depressive symptoms, by age: 00

Total Men Women

55-64 65-74 75-84 85+

0%

5%

10%

15%

20%


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TBL. 1-2

iNsuraNce coverage, by marital status aND worK status: 00

N md md

N cd cd Nh cd on cd bh cd

a 55-64, wkn p

wh/oh 10.4% 89.7% 2.0% 3.9% 94.2%

bk 17.3 82.7 1.3 11.1 87.6

Hn 35.6 64.4 15.3 12.5 72.2

a 55-64, N wkn p

wh/oh 12.7 87.3 2.8 6.5 90.8

bk 11.8 88.2 4.2 14.1 81.7

Hn 30.9 69.1 16.1 12.7 71.2

a 65 nd o

wh/oh 0.5 99.5 0.1 1.4 98.5

bk 1.1 98.9 0.6 6.9 92.6

Hn 2.7 97.3 1.3 9.0 89.7

Note: Coverage reers to public and/or private insurance.

FIG. 1-6

iNsuraNce coverage or persoNsages 55-64, by race/etHNicity: 00(Percent with each type)

White Black Hispanic

Public Private None

0%

10%

20%

30%

60%

40%

50%

70%

80%

used to examine the implications o insurancestatus or health in later lie. Baker et al. (2001)

assessed the risks o a major decline in generalhealth and the risks o developing new dicultiesaccording to whether HRS respondents werecontinuously uninsured, intermittently uninsured,or continuously insured between 1992 and1996. Continuously uninsured individuals were63 percent more likely than privately insuredpeople to experience a deterioration o overallhealth and 23 percent more likely to have newdiculties with an activity o daily living involvingmobility. Sudano and Baker (2003) ound thatintermittent lack o insurance coverage, even

across a relatively long period, was associated

4 pre-Medicare-age Hispanic respondents has nohealth insurance, compared with roughly 1 in 8

Blacks and 1 in 14 Whites.

A urther breakout o these data illustratesdierences between married and unmarriedindividuals (Table 1-2). Regardless o age andwork status, unmarried respondents are morelikely than their married counterparts to bewithout insurance. Among married Black andHispanic couples, a signicant proportion ohouseholds have coverage or only one membero the couple.

In addition to comparing people with diering

health insurance status, the HRS data have been

HealtH care coverage

The HRS can be used to assess health carecoverage among pre-retirees and retirees andto examine the ways in which changes in healthinsurance policy can aect retirement decisionsand labor market participation as a whole. Oparticular interest are people ages 55 to 64,most o whom are not yet eligible or Medicare.Figure 1-6depicts racial/ethnic dierencesin types o health insurance coverage or thisage group in 2002, indicating that Blacks andHispanics are much less likely than Whites tohave private health insurance, and hence are

more likely to rely on public sources. About 1 in


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with lower usage o preventive services. Lookingat the same HRS data rom a dierent perspec-tive, Dor et al. (2003) ound that providing insur-ance to previously uninsured working-age adultsresulted in a 7 percent improvement in overall

sel-reported health.

Another study (McWilliams et al. 2003) analyzeddierences in the receipt o basic clinical ser-vices among the continuously insured and theuninsured in 1996 and 2000beore and aterrespondents became eligible or Medicare at age65. The analysis suggested that the acquisitiono Medicare coverage signicantly reduces thedierences in the use o preventive services suchas cholesterol testing, mammography, prostateexaminations, and medical visits dealing witharthritis. Among adults with arthritis and/orhypertension, however, dierences in the useo anti-arthritis/anti-hypertension medicationsbetween continuously insured and uninsuredpeople were essentially unchanged ater Medicarecoverage began.

The HRS also can tell us who has prescriptiondrug coverage and how they use it. The newMedicare Part D prescription drug coverageprogram was implemented in 2006, and the HRSwill provide baseline estimates and then trackchanges in older adults prescription drug coverage

and use.

Other studies using HRS data also oer insightsabout prescription drug coverage. For instance,the survey showed that in 1998, HRS respondentsunder age 65 were more likely than those ages65 to 79 and much more likely than those age80 and older to have prescription-drug insurancecoverage (80 percent, 71 percent, and 59percent, respectively) (Table 1-3). Importantly,regardless o age, people who did not haveprescription drug coverage were less likely to llall o their prescriptions. Younger respondents

were less likely than older respondents to llprescriptions, regardless o drug insurancecoverage. One study suggested that this cost-cutting by seniors may pose an increased riskor adverse health outcomes (Heisler et al.2004).

HealtH care use

As the U.S. population ages and Medicareexpenditures continue to rise, the wealth o HRSdata on use o health care services will becomean increasingly important resource. Figure 1-7

N d md

pn N n a pn

pn h pn

D c

wh inn

c wh inn c

und 65 80% 6% 22%

65-79 71 4 11

80 nd o 59 3 7

TBL. 1-3

prescriptioN Drug coverage aND liKeliHooD o illiNg prescriptioNs, by age: 1998

Source: HRS 1998.

FIG. 1-7

service use iN tHe past two years, by age: 00(Percent using each type o service)

55-64 65-74 75-84 85+

Hospital Nursing Home Dental Care Home Health

0%

10%

20%

30%

60%

40%

50%

70%


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FIG. 1-8

HealtH service use, by race/etHNicity: 00(Percent using service between 2000 and 2002)

illustrates HRS respondents use o ve majorservices during 2000 to 2002 and shows thatmore than 40 percent o people age 85 and olderand 34 percent o those ages 75 to 84 made

Hospital NursingHome

No DoctorVisits

OutpatientSurgery

DentalCare

HomeHealth

MEN

0%

10%

20%

30%

40%

50%

60%

White/Other Black Hispanic

Hospital NursingHome

No DoctorVisits

OutpatientSurgery

DentalCare

HomeHealth

WOMEN

0%

10%

20%

30%

40%

50%

60%

hospital visits. The use o hospitals and nursinghomes rose with age, as did the consumption ohome health services. More than 10 percent oHRS respondents ages 75 to 84 and 20 percent

o respondents age 85 and older made some useo home health services during the 2-year period.In contrast, there was a marked decline in the useo dental care by age, probably driven at least inpart by the act that Medicare generally does not

cover dental services.

Figure 1-8contrasts health service use in 2002or men and women o all ages, by race andethnicity. Gender patterns did not dier greatly,although Black and Hispanic women were some-what more likely than Hispanic men to make atleast one hospital visit. Minority men and womenwere much less likely than Whites to visit a dentistor have outpatient surgery. Hispanic respondentswere less likely than others to have visited adoctor at least once in a 2-year period; this di-erence corresponds to the lower level o healthinsurance coverage among Hispanics.

Health policy and cost-containment discussionsare currently considering the ecacy o screen-ing mammograms and Pap tests in older women.According to the HRS, usage rates or both othese tests increased or all age groups between1995 and 2000. However, there are sharp dier-ences in the rate o these tests taken with age. In1992 through 2000, between 70 percent and 80percent o women ages 50 to 64 reported receiv-ing mammograms at least once every 2 years,

with the proportion declining to about 40 percentamong those ages 85 to 90. During the sametime period, Pap test rates were about 75 percentor women ages 50 to 64 and about 25 percentor women ages 85 to 90, respectively.

Nonsmokers and women who perceived theirhealth as good or excellent were the most likelyto be screened, while smokers, sedentaryindividuals, and those who elt that theirhealth was poor or air were less likely toundergo screening.


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use o alterNative meDiciNesaND supplemeNts

Alternative medicine includes a broad range ohealing philosophies, approaches, and therapies

that conventional medicine does not commonlyuse or understand. These practices include, orexample, the use o acupuncture, herbs, homeopa-thy, therapeutic massage, and traditional orientalmedicine. Among HRS respondents to an experi-mental module in 2000, nearly hal reported thatthey had been to a chiropractor, 20 percent hadused massage therapy, and 7 percent had usedacupuncture at least once in their lives (Ness etal. 2005).

In the same experimental module, more than halo respondents said they had used some kind o

dietary or herbal supplement (Table 1-4). Nearlytwo-thirds o the respondents had used some kindo vitamin supplement in the month prior to thesurvey. On average, respondents spent $173 a yearon those supplements. The most popular supple-ment, multivitamins, was taken by hal the sample.About one in ve people reported using some kindo herbal supplement during the previous month,and spent an average o $135 per year on herbals.Garlic, echinacea, gingko biloba, and ginseng werethe most commonly used o these supplements.

agiNg aND meDical expeNDitures

Health care expenditures can rise considerablywith age, and the HRS provides detail on theamounts paid directly by respondents, sometimescalled out-o-pocket expenditures. Data rom2002 show a steady increase with age in the dol-lar amount o out-o-pocket medical expenditures(Figure 1-9). Mean medical out-o-pocket expendi-tures during the 2-year period prior to the surveyranged rom $2,900 or respondents ages 55 to64 to $4,400 or people age 85 and older.

visits were the largest component o out-o-pocket expenditures among younger respondents(ages 55 to 64), some o whom were not covered

TBL. 1-4

supplemeNt use: 000(Percent using each item in the month prior to the 2000 survey)

D sn H sn

mn 50% g 8%

vn e 38 ehn 8

c 34 gnk b 7

vn c 32 gnn 6

vn D 14 s p 4

mn 12 a 4

vn a 10 s. Jhn w 4

oh 24 oh 14

Source: HRS 2000.

FIG. 1-9

average out-o-pocKet meDical expeNDiture, by age: 000-00(For the 2-year period prior to the 2002 survey)

$0 $500

85+

75-84

65-74

55-64

$1,000 $1,500 $2,000 $2,500 $3,000 $3,500 $4,000 $4,500

The major components o medical out-o-pocketspending vary by age as well. Data rom the2002 survey wave show that hospital and doctor


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FIG. 1-10

compoNeNts o meDical out-o-pocKet speNDiNg, by age: 000-00

Hospital Visits Nursing Home Stays Doctor Visits Dental Care Prescription MedicineAges 55-64 Ages 75-84Ages 65-74 Age 85+

10.8%

15.1%

1.2%

51.9%20.9%

5.5%

14.7%

1.9%

63.4%

14.5%

7.7%

7.4%

17.0%

59.2%

8.8%

5.1%

12.4%

3.5%

69.1%

9.9%

by health insurance, whereas most people overage 64 have Medicare coverage or hospital andphysician visit costs. At the time o the survey,prescription drugs were not covered by Medicare,and an age-related rise in the propor tion o medi-cal out-o-pocket expenditures devoted to drugswas seen, at least until age 85 (Figure 1-10).

Medical spending by the elderly varies widely.One study using HRS data rom 1998 ound thatin the 2 years prior to 1998, average out-o-

pocket spending was about $2,022, but hal thepopulation spent less than $920, while 10 per-cent o the population spent more than $4,800(Goldman and Zissimopoulos 2003).

Medical out-o-pocket expenditures tend to begreatest near death, and can be a nancial chal-lenge or a surviving spouse. A our-wave analy-sis o HRS data or non-institutionalized peoplewho were age 70 and older in 1993 showedthat medical out-o-pocket spending averagedapproximately $6,000 in the last year o lie

40 percent to 50 percent higher than at other

and between 10 percent and 18 percent or those intheir nal year o lie. Nursing home and extended-hospital coverage would likely have little eect onpoverty rates or those not near death, but couldlower the medical out-o-pocket adjusted povertyrate by 17 percent or those in the last year o lie.

eects o uNexpecteDHealtH eveNts

Early HRS data indicated that over a 2-year period,respondents on the whole had a 5 percent chanceo having a heart attack, stroke, or new cancer diag-nosis; a 10 percent chance o having a new chronicillness diagnosis; and a 3 percent chance o havingan accidental injury. A health shock, or unexpect-ed health event, may represent a turning point oran individual and her/his amily, particularly i theindividual is nearing retirement age.

To explore the implications o adverse healthevents on both short-term and longer-term labororce participation, one study ollowed the labor

orce behavior o HRS respondents through the

points in old age (McGarry and Schoeni 2003). Toput this into perspective, researchers comparedout-o-pocket spending to annual income. The av-erage couples medical out-o-pocket expenditureswere roughly 15 percent o annual income 5 to 7years beore the death o a spouse. The out-o-pocket expenditure share rose to about 25 percent3 years beore the death o a spouse and to 50percent in the year beore the spouses death.

When calculation o poverty rates includes an

adjustment or the high end-o-lie medical out-o-pocket expenses, the rates rise steeply as a unc-tion o spousal death. This type o analysis helpsdemonstrate the potential eects o proposals torevise current health programs. For example, HRSdata suggest that expanding Medicare coverageto include prescription drugs and long-term care(nursing home and hospital) would signicantlylower medical out-o-pocket spending. Prescriptiondrug coverage would lower out-o-pocket-adjustedpoverty by between 21 percent and 33 percent orpeople who were many years removed rom death,

Note: Data may not sum to 100% due to rounding.


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33

rst three interviews in 1992, 1994, and 1996(McClellan 1998). Persons who had some ormo health event (acute, chronic, or decline inunctional ability) between 1992 and 1994 wereabout twice as likely to be out o the labor orce

in 1994 and 1996 compared with persons whodid not experience a signicant health event. Thecombination o an acute health event (such as aheart attack or stroke) and a decline in unctionalability greatly elevated the likelihood o labor orcewithdrawal. Having both an acute event and aloss o unctional ability between 1992 and 1994reduced the chances o working in 1994 by 400percent. Only a very small raction o those whohad both an acute event and a loss o unctionalability between 1992 and 1994 had reentered thelabor orce by 1996 (see also Woodbury 1999).

In a separate study o HRS data rom 1992through 2000, Coile (2003a) examined the eecto the onset o a heart attack or stroke, accompa-nied by new diculty in perorming our activitieso daily living, on remaining in the labor orce. Theanalysis showed that men were 40 percent morelikely and women 31 percent more likely to leavethe labor orce than they would have been withouta health event.

An important dimension o household behaviorollowing a health event is the response o the

spouse, and previous research has been unableto account or this behavior. The HRSs collec-tion o detailed data or both husbands and wivespermits study o this area, such as the responseinvolving the spouses decision to work. Becausea negative health event may diminish the amilysincome position, a spouses decision to reduceemployment could exacerbate the situation. How-ever, analysis o data rom 1992 through 2000indicates that a major health event does notproduce a major change in spouses labor orceparticipation. I a working person experiences a

major health event, his or her spouse is not likely

to begin or increase labor orce participationto oset the income loss. This suggests thata health event causes real nancial losses orthe amily, although these losses are oset tosome extent by government disability insurance

benets. It also suggests that many people areunderinsured against disability.

Disability aND pHysicaluNctioNiNg

Ongoing interests in aging research include thetrend in disability status among older individuals

and peoples transitions into and out o disabilitystates. A number o studies in the United States

FIG. 1-11

limitatioN iN iNstrumeNtal activites o Daily liviNg, by age: 00

One or More IADL Limitations Receive Help with IADL(s) Not Driving

55-64 65-74 75-84 85+

0%

10%

20%

30%

40%

50%

60%

70%

WOMEN

55-64 65-74 75-84 85+

0%

10%

20%

30%

40%

50%

60%

70%

MEN

Note: Percent not driving at ages 55-64 is zero.


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have now documented a decline in disab

growing older

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