self-ratings of health: do they also predict change in functional ability?

10
Journal of Gerontology: SOCIAL SCIENCES 1995, Vol. 50B, No. 6, S344-S353 Copyright 1995 by The Gerontological Society of America Self-Ratings of Health: Do They Also Predict Change in Functional Ability? Ellen L. Idler 1 and Stanislav V. KasP 'Institute for Health, Health Care Policy, and Aging Research, Rutgers University, department of Epidemiology and Public Health, Yale School of Medicine. Self-ratings of health by individuals responding to surveys have shown themselves to be potent predictors of mortality in a growing number of studies; they appear to contribute significant additional independent information to health status indicators gathered through self-reported health histories or medical examinations. A key question raised by these studies is: What are the mediating processes involved in the association? Specifically, do poor self-ratings increase the risk of disability and morbidity, and are these outcomes intervening steps in the link to mortality? In this report we address the first question, of self-ratings predicting future levels offunctional disability, our choice of an index of overall impact of morbidity. Data come from the New Haven Established Populations for Epidemiologic Studies of the Elderly (EPESE) site (N = 2,812). Results show that self-ratings of health in 1982, net of baseline functional ability, health and sociodemographic status, are associated with changes in functional ability over periods of one through six years. These findings extend our understanding of the meaning of excellent, good, fair, and poor ratings of health, and that they have implications not just for survival but for the loss or maintenance of functional ability in daily life. T HE series of studies linking self-assessments of health to mortality continues to expand. Since the early analyses of data from Canada (Mossey and Shapiro, 1982) and California (Kaplan and Camacho, 1983), prospective analy- ses from Israel (Kaplan, Barell, and Lusky, 1988), Great Britain (Jagger and Clarke, 1988), Wales (Shahtamasebi, Davies, and Wenger, 1992), Denmark (S0rensen, 1988), the Netherlands (Pijls et al., 1993), Connecticut (Idler and Kasl, 1991), Iowa (Idler, Kasl, and Lemke, 1990), Hong Kong (Ho, i991), Japan (Tsuji et al., 1994), Australia (McCal- lum, Shadbolt, and Wang, 1994), and U.S. national samples (Idler and Angel, 1990; Rakowski, Mor, and Hiris, 1991; Schoenfeld et al., 1994; Wolinsky and Johnson, 1992) have shown repeatedly that poor self-ratings of health are signifi- cantly associated with a greater risk of mortality. This association remains strong even after statistical adjustments for the influence of many other variables, including socio- demographics, multiple indicators of health status, health and life-style habits, and selected social-psychological mea- sures, such as social networks and depression. The finding is exciting because it indicates that these survey responses, often considered vague or too prone to measurement error or response bias to be useful, have had the most powerful kind of criterion validity extended to them. The finding confers a retroactive significance on the large body of social scientific work done since the 1950s; this work showed that discrepan- cies between subjective reports of health and more objective measures of health were often associated with social and demographic factors, and implied that perceptions of health status were frequently influenced in some way by the respon- dent's social position. Collectively, this evidence becomes one more indication of the important role psychosocial factors play in influencing health status. Because of the importance of the outcome (mortality), the possible significance of the association with self-assessment of health is quite high. However, the interpretation of the association remains unclear. In our earlier work (Idler and Kasl, 1991), we discussed two classes of possibilities. First, the association may be noncausal and potentially trivial or spurious. This could be for two somewhat similar reasons: (a) Important influences on both mortality and self- assessed health have not been measured and their influence has not been partialled out; examples of such influences might include parental longevity and elevated biological risk factors, (b) Baseline health status assessments are insensi- tive to subtle signs of poor health and early beginnings of morbid processes, but their influence is already incorporated in the self-assessments, because they do affect the respon- dent's physical well-being (prodromal stage). Second, the association can be interpreted in a causal framework, or at least in terms of risk factor formulations. Here again, two possibilities can be identified: (a) There is a direct effect of self-ratings on survival: self-ratings may reflect short-term and/or long-term influences on longevity, through unspecified and as yet unknown mechanisms, but not mediated by new experiences of morbidity or disability, (b) Self-assessed health may represent an influence on new, later episodes of morbidity or disability, and these then increase the risk for mortality. The purpose of this report is to examine the question: Do self-ratings of health predict future levels of functional disability, net of potential confounders and health status controls, including baseline levels of disability? Functional ability, as indicated by the capacity to perform activities of daily living, is a natural choice for an initial evaluation of the possible effects of self-ratings of health on changes in health status. The assessment of functional ability indexes the severity of disease and the impact of multiple conditions; it is in effect a measure of the consequences of morbidity. Devel- opment of self-report instruments for measuring disability in S344 at University of Sussex on October 28, 2014 http://psychsocgerontology.oxfordjournals.org/ Downloaded from

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Page 1: Self-Ratings of Health: Do they also Predict change in Functional Ability?

Journal of Gerontology: SOCIAL SCIENCES1995, Vol. 50B, No. 6, S344-S353

Copyright 1995 by The Gerontological Society of America

Self-Ratings of Health:Do They Also Predict Change in Functional Ability?

Ellen L. Idler1 and Stanislav V. KasP

'Institute for Health, Health Care Policy, and Aging Research, Rutgers University,department of Epidemiology and Public Health, Yale School of Medicine.

Self-ratings of health by individuals responding to surveys have shown themselves to be potent predictors of mortalityin a growing number of studies; they appear to contribute significant additional independent information to healthstatus indicators gathered through self-reported health histories or medical examinations. A key question raised bythese studies is: What are the mediating processes involved in the association? Specifically, do poor self-ratingsincrease the risk of disability and morbidity, and are these outcomes intervening steps in the link to mortality? In thisreport we address the first question, of self-ratings predicting future levels of functional disability, our choice of anindex of overall impact of morbidity. Data come from the New Haven Established Populations for EpidemiologicStudies of the Elderly (EPESE) site (N = 2,812). Results show that self-ratings of health in 1982, net of baselinefunctional ability, health and sociodemographic status, are associated with changes in functional ability over periodsof one through six years. These findings extend our understanding of the meaning of excellent, good, fair, and poorratings of health, and that they have implications not just for survival but for the loss or maintenance of functionalability in daily life.

THE series of studies linking self-assessments of health tomortality continues to expand. Since the early analyses

of data from Canada (Mossey and Shapiro, 1982) andCalifornia (Kaplan and Camacho, 1983), prospective analy-ses from Israel (Kaplan, Barell, and Lusky, 1988), GreatBritain (Jagger and Clarke, 1988), Wales (Shahtamasebi,Davies, and Wenger, 1992), Denmark (S0rensen, 1988), theNetherlands (Pijls et al., 1993), Connecticut (Idler and Kasl,1991), Iowa (Idler, Kasl, and Lemke, 1990), Hong Kong(Ho, i991), Japan (Tsuji et al., 1994), Australia (McCal-lum, Shadbolt, and Wang, 1994), and U.S. national samples(Idler and Angel, 1990; Rakowski, Mor, and Hiris, 1991;Schoenfeld et al., 1994; Wolinsky and Johnson, 1992) haveshown repeatedly that poor self-ratings of health are signifi-cantly associated with a greater risk of mortality. Thisassociation remains strong even after statistical adjustmentsfor the influence of many other variables, including socio-demographics, multiple indicators of health status, healthand life-style habits, and selected social-psychological mea-sures, such as social networks and depression. The finding isexciting because it indicates that these survey responses,often considered vague or too prone to measurement error orresponse bias to be useful, have had the most powerful kindof criterion validity extended to them. The finding confers aretroactive significance on the large body of social scientificwork done since the 1950s; this work showed that discrepan-cies between subjective reports of health and more objectivemeasures of health were often associated with social anddemographic factors, and implied that perceptions of healthstatus were frequently influenced in some way by the respon-dent's social position. Collectively, this evidence becomesone more indication of the important role psychosocialfactors play in influencing health status.

Because of the importance of the outcome (mortality), thepossible significance of the association with self-assessment

of health is quite high. However, the interpretation of theassociation remains unclear. In our earlier work (Idler andKasl, 1991), we discussed two classes of possibilities.

First, the association may be noncausal and potentiallytrivial or spurious. This could be for two somewhat similarreasons: (a) Important influences on both mortality and self-assessed health have not been measured and their influencehas not been partialled out; examples of such influencesmight include parental longevity and elevated biological riskfactors, (b) Baseline health status assessments are insensi-tive to subtle signs of poor health and early beginnings ofmorbid processes, but their influence is already incorporatedin the self-assessments, because they do affect the respon-dent's physical well-being (prodromal stage).

Second, the association can be interpreted in a causalframework, or at least in terms of risk factor formulations.Here again, two possibilities can be identified: (a) There is adirect effect of self-ratings on survival: self-ratings mayreflect short-term and/or long-term influences on longevity,through unspecified and as yet unknown mechanisms, butnot mediated by new experiences of morbidity or disability,(b) Self-assessed health may represent an influence on new,later episodes of morbidity or disability, and these thenincrease the risk for mortality.

The purpose of this report is to examine the question: Doself-ratings of health predict future levels of functionaldisability, net of potential confounders and health statuscontrols, including baseline levels of disability? Functionalability, as indicated by the capacity to perform activities ofdaily living, is a natural choice for an initial evaluation of thepossible effects of self-ratings of health on changes in healthstatus. The assessment of functional ability indexes theseverity of disease and the impact of multiple conditions; it isin effect a measure of the consequences of morbidity. Devel-opment of self-report instruments for measuring disability in

S344

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SELF-RATED HEALTH AND DISABILITY S345

performing activities of daily living began in the 1960s(Katz, Downs, Cash, and Grotz, 1970; Lawton and Brody,1969; Nagi, 1969; Rosow and Breslau, 1966). These instru-ments had the genius of focusing on activities which wereboth commonly performed (thereby enhancing the validityof responses) and also necessary for independent living.Poor functional ability is also a major risk factor for mortal-ity in this sample and in others (Chirikos and Nestel, 1985;Ferrucci et al., 1991; Guralnik et al., 1991; Idler, Kasl, andLemke, 1990; Reuben, Siu, and Kimpau, 1992).

The subsequent development of these measures has led totheir use in a number of fields. Functional assessment ofelderly patients is now commonly viewed as essential forgood clinical care, as the maintenance of the elderly patient'squality of life is increasingly seen as a major goal ofmedicine (Applegate, Blass, and Williams, 1990; Ruben-stein et al., 1988). Also, epidemiologists debating issuessuch as the implications of the declining mortality rates ofrecent decades, the compression of morbidity hypothesis(the idea that the onset of poor health in future decades willbe delayed to a brief period immediately preceding death[Fries, 1980; Guralnik, 1991]), and forecasts for active lifeexpectancy (Katz et al., 1983; Rogers, Rogers, and Be-langer, 1990) have all relied on functional ability as thecentral indicator of morbidity. A key finding of longitudinalstudies has been that states of disability are not necessarilypermanent, even among the elderly (Crimmins and Saito,1993; Manton, 1989; Verbrugge, 1991); functioning in ac-tivities of daily life in the elderly shows not only stability orsteady decline, but also fluctuating states of recovery anddecline, suggesting dynamic states rather than steady ones.Recent findings (Maddox and Clark, 1992) also show thatthese trajectories vary systematically by social location,particularly by sex, income, and education.

Do self-ratings of health predict change in physical func-tioning? Are poor self-ratings of health a risk factor formorbidity as well as mortality? These are, with very fewexceptions, almost completely unexplored research ques-tions; Grand et al. (1988), in a sample of rural French elderly(n = 645), found that those with poor self-rated health had arelative risk of 2.2 for functional ability deterioration, evenwhen age and self-reported morbidity at the beginning of thefour-year follow-up period were adjusted. Similarly, Kaplanet al. (1993) report, with six-year follow-up data fromelderly Alameda County study respondents (n = 356), thatself-perceived health predicts change in physical function-ing, even with adjustments for COPD, hip fracture, seriousfalls, stroke, and heart attack. Jylha, Heikkinen, and Jokela(1993), with data from the Finnish contribution to the Euro-pean Longitudinal Study on Aging (ELSA), also show thatthose who rated their health as the same as or worse thantheir peers in 1979 were more than three times as likely toexperience functional decline than those who rated theirhealth as better. Finally, findings from a substudy of respon-dents to the New Haven EPESE project who had beenhospitalized for a myocardial infarction, hip fracture, orstroke, showed that self-ratings shortly (6 weeks) after theevent were significant predictors of recovery of functioningby the critical period of 6 months afterward, even when theseverity of the respondent's medical conditions and level of

functioning at 6 weeks were taken into account (Wilcox,Kasl, and Idler, in press).

Many questions are raised by these initial studies. Are theeffects equally evident for males and females? Generally,effects of self-rated health on mortality reported in theliterature have been equal for men and women althoughsome inconsistent effects have arisen where gender differ-ences played a role (Idler and Angel, 1990; McCallum,Shadbolt, and Wang, 1994). Given the predominant pattern,however, we hypothesize (a) that the effect of self-ratings ofhealth on subsequent functional ability will not differ formales and females. Are they equal for younger elderly aswell as older elderly persons? We know that, in this sampleof elderly people, the oldest respondents give more positiveself-ratings of health, relative to their health status, than theyounger respondents (Idler, 1993). We also know that theearliest cohorts have experienced selective survivorship,some of it on the basis of self-rated health (Idler, Kasl, andLemke, 1990). Thus, we hypothesize (b) that the effects ofself-rated health will be stronger for the younger respondentswhere there is more variation in self-rated health and lessselection out of the sample. What about those who havealready lost some functioning compared with those in goodhealth? This speaks directly to the issue of causality. Unlikethe mortality analyses, where all respondents occupy asingle status at the start of the study (alive), an analysis ofdisability data allows us to see if self-rated health is associ-ated with improvements in functional ability, or decline, orboth. With no previous studies available to cite, we hypothe-size (c) that effects will be found in both directions.

METHODS

Data. — The data come from a representative sample ofelderly New Haven, Connecticut, residents (n = 2,812)who were interviewed annually from 1982 to 1988. The datawere collected as part of the collaborative Established Popu-lations for the Epidemiologic Study of the Elderly (EPESE)project of the National Institute on Aging. The stratifiedprobability sample was drawn from three predominant typesof housing for elderly people: public housing, privatelyfinanced housing for the elderly, and the remaining commu-nity neighborhoods. Males and those living in public andprivate elderly housing were oversampled. The overall re-sponse rate for the three strata for the initial 1982 interviewwas 82 percent; response rates for the annual follow-upinterviews averaged about 96 percent for surviving membersof the sample.

Measures. — The independent variable of interest, self-rated health, is a one-item indicator based on the response tothe question, "How would you rate your health at thepresent time? Excellent, good, fair, poor, or bad?" Becauseof the long follow-up period and relatively high mortalitywithin this elderly sample, the number of respondents re-porting "bad" or "poor" health (only 8% in 1982) is quitesmall by 1988; these respondents were combined with thosereporting "fair" health. In 1982, almost 60 percent of thesample said their health was good or excellent.

The outcome variable is a composite scale of functional

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S346 IDLER AND KASL

disability which incorporates information from three scalesdeveloped by Katz et al. (1970), Rosow and Breslau (1966),and Nagi (1976) and modified for common use by all theEPESE sites (Cornoni-Huntley et al., 1986). The Katz scaleis composed of seven items measuring needing help or beingunable to perform activities of daily living, including walk-ing across a small room, bathing, grooming, dressing, eat-ing, getting from bed to a chair, and using the toilet. In 1982only, the Katz items are also asked with difficulty ratings.The Rosow and Breslau scale (all years) contains three itemsabout the ability to do heavy housework, climb one flight ofstairs, and walk half a mile. The Nagi scale (all years) ismade up of five items asking about the degree of difficultythe respondent has with moving large objects, stooping,carrying 10-pound weights, reaching above shoulder level,and writing or handling small objects. Respondents werescored 1 for a scale if they reported needing help or havingany difficulty in any item. The three scales form a hierarchy,in that very few respondents reporting a Katz disability didnot also report a disability on at least one of the other scales.The percentage of respondents missing because they failedto show this pattern is less than 1 percent in every year except1985, when it was 1.3 percent. For the outcome measureswe created a summary scale ranging from 1 to 5, in which 1= no difficulty or need for help in any of the 15 items, 2 =any difficulty in one or more of the Nagi items only, 3 =unable to do one or more Rosow/Breslau items only, 4 =unable to do one or more Rosow-Breslau items and difficultyin one or more Nagi items but not in any Katz item, and 5 =disability in all three scales (see also Berkman et al., 1986).The scale exhibits temporal stability, with annual test-retestcoefficients which range from .64 to .70 for each of theannual 6 years of follow-up. Because interviews were done ayear apart, however, it is difficult to know how much of theuncorrelated variance is measurement error and how much istrue change.

It is crucial that the analysis adjust for health status at thestart of the follow-up period, in addition to adjustments forbaseline functioning status. The present analysis attempted afull utilization of all the health status data available, includingindicators of recency, severity, and site (for cancer) notpreviously included in published analyses. The use of theseadditional data permits a more rigorous test of the associationof self-rated health with subsequent physical functioning.

The health status data are divided into two sets. The firstset contains chronic conditions about which the respondentcan report and which in most cases have been diagnosed by aphysician, and the reported medications for them. To arriveat the list of conditions shown in Table 1, we tested a muchlarger set of measures of hospitalizations, medications, andonset of the chronic conditions, retaining only those whichwere associated with change in functioning over one year.The group includes self-reports of heart attack(s); stroke andremaining difficulties from stroke; any cancer during the pastfive years, or lung, breast, or colon cancer at any time in thepast; diabetes and oral or injected medication for diabetes;liver disease; hip fracture; other broken bones; arthritis;Parkinson's disease; and any amputation, plus an additionalindicator if the amputation was of the leg or if it was disease-related. In addition, because interviews took place in the

participant's home, interviewers requested that they beshown all current prescription medications; from these werecoded cardiovascular medications, and the total number ofcurrent prescription medications. This is an objective mea-sure, then, which captures important clinical data and doesnot rely on respondent self-report.

The second set of health status data is based on symptomsreported in the interviews. Such symptoms may signal undi-agnosed or prodromal conditions which the respondentmight have experienced, and which might be influencingtheir self-ratings of health, but which they might not be ableto identify with a diagnostic label. The data contain sets ofsymptom-items used in survey screening instruments forchronic obstructive pulmonary disease (Higgins and Keller,1973), angina (Rose, 1962), claudication (Rose, 1962), andcognitive impairment (Pfeiffer, 1975). Finally, there is themean of two resting blood pressures, measured by theinterviewer. These data are also objective, not relying onself-report, and carry the potential of disclosing previouslyunknown or uncontrolled hypertension.

Additional sociodemographic data include sex, age, race,perceived income adequacy, marital status, and education.

Scoring, descriptive statistics, and bivariate correlationsof all of these variables with 1982 self-rated health andfunctional status are shown in Table 1.

Analysis. — Self-rated health, the independent variable,is measured in 1982, at the beginning of the study. Thedependent variable, functional disability, is measured atone-year intervals from 1982 to 1988. The single scalewhich incorporates information from all three subscales isthe dependent variable for each year of follow-up; baselinefunctioning levels are included in all models so that initialfunctioning levels are partialled out of the model and thedependent variable represents change in functioning over thefollow-up period. Preliminary analyses comparing modelsusing the single scale versus the separate Katz, Rosow/Breslau, Nagi scales as the 1982 covariate(s) showed that thethree subscales (continuous variables, see Table 1) producedmodels with higher R2, explaining more of the variance inlater functional ability. Because of our desire to obtain amore rigorous test of the effect of self-rated health, weemployed the three subscales, with their additional sensitiv-ity and detail, as covariates.

Statistical tests were provided by multivariate regressionof functional disability on the previously described sets ofcovariates. A hierarchical approach was taken, with concep-tually similar variables introduced in sets. Models wereestimated for each of the six years of follow-up, but for thesake of parsimony, results are shown for 1983, 1984, 1986,and 1988 only. Covariates with significant effects in morethan one of the six follow-up periods were retained as thenext set of covariates was entered.

Because of the complex, stratified design of the sample,the assumption of simple random sampling necessary forordinary least squares regression is violated. Taking thesample design into account requires incorporating a weight-ing factor which corrects for the different sampling fractionsfor the three strata's males and females. This makes thesample approximate the 1980 census data for New Haven,

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SELF-RATED HEALTH AND DISABILITY S347

Table 1. Descriptive Statistics for Variables Used in Analysis; New Haven EPESE Data, 1982

Variable

Functional disabilitySelf-rated healthADL score — 7 itemsRosow score — 3 itemsNagi score — 5 itemsHeart attackStrokeCancer

DiabetesLiver diseaseHip fractureOther broken bonesArthritisParkinson's diseaseAmputation

SexAgeCardiovascular medications

Number of prescription drugsCongestive heart

failure symptomsChronic obstructive

pulmonary disease symptomsAngina symptomsClaudication symptomsCognitive functionMeasured blood pressureRaceIncome inadequateMarital statusEducation

n

27392775281127902696280128052806

2807280528052803279928062806

281228112686

26862802

2800

28042801276426712803270127882725

Minimum

1 = None !1 = Bad/poor/fair I

Maximum

= Severe= Excellent

0 = No difficulty with any 28 = Unable to do any0 = Can do all 2 = Unable to do any0 = No difficulty with any 20 = Unable to do any0 = None0 = None 10 = Never or more

than 5 years ago0 = No 20 = No0 = No0 = Not since age 500 = No0 = No0 = No :

1 = Male :

= One or more. = Yes + has difficulties

= Recent, or lung,breast, colon (ever)

. = Yes + takes medication= Yes- Yes= One or more= Yes= Yes

1 = Yes + of leg ordisease-related

I = Female65 990 = None

0 = None0 = No

0 = No

0 = No0 = No1 = Severe impairment '.0 = Under 140/900 = Non-White0 = No0 = Not married0 = None

= One or morerecorded prescription

8 = Recorded prescriptions= Yes

I = Yes

I = Yes[ = Yes5 = Little or no impairment1 = Equal or over 140/901 = WhiteI = YesI = Married17 = 17 years or more

Mean

2.621.701.06.78

3.63.13.13.08

.28

.02

.04

.18

.43

.01

.04

1.5874.53

.52

2.74.25

.17

.06

.022.46

.56

.79

.09

.379.00

Pearson rSelf-rated health

-.341.00-.22-.32-.33-.12-.13-.03

-.09-.04-.04-.03-.16-.04-.03

-.02-.04-.19

-.29-.20

-.17

-.11-.06

.08-.00

.06-.09

.04

.12

Pearson rFUNC82

1.00-.34

.44

.86

.74

.13

.21

.04

.14

.04

.16

.08

.23

.09

.13

.16

.25

.21

.38

.27

.12

.12

.06-.21

.05

-.08.07

-.10-.11

and also corrects for differential nonresponse. Coefficientsresulting from such weighted regressions must then be testedfor significance with standard errors that have been adjusted(usually upward) by an application of the Taylor series forestimating variances of linear functions. The Taylor seriesprocedure used for the proposed analyses is SUDAAN (Shahet al., 1992), which in most cases increases standard errorsand results in more conservative tests of significance.

RESULTS

Cross-sectionally, self-ratings of health have a moderatelystrong correlation with functional ability. As shown in Table1, the Pearson r for the two variables is -.34 in 1982. Asidefrom comparable correlations with two of the components ofdisability, self-rated health is also moderately correlatedwith the number of prescription medications (r = -.29).

Preliminary models (not shown), estimating change infunctional ability with just baseline functioning, age, andsex as covariates, show that respondents with bad/poor, fair,and good self-rated health all have significantly poorerfunctioning in every year of the six years of follow-up thanthose with excellent self-rated health. The relationships arelinear, with the biggest effects for those with poor health,

and the contrasts of each with excellent health are significantin every case but one (good vs excellent health in 1983).Moreover, the effects do not diminish over time, suggestingthat the effects are not limited to a prodromal period.

Do pre-existing conditions (known to and reported by therespondent) explain the effect? — The measures of chronicconditions and currently prescribed medications are impor-tant because they indicate the presence of disease which mayhave a progressive course and which could be expected toworsen in the period between baseline and follow-up(s).These health status measures are not necessarily made su-perfluous by the baseline disability measure, with which theyare contemporaneous, because they may provide valuableinformation about what could happen during the follow-up.

Table 2 shows the association of reported conditions andmedications with change in functioning over one, two, four,and six years of follow-up. Covariates include sex and age,as well as baseline functioning in the form of the threesubscales. Self-reports of stroke with remaining difficulties,diabetes with prescribed medication, arthritis, Parkinson'sdisease, leg or disease-related amputation, and the numberof current prescription medications are associated with

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S348 IDLER AND KASL

Table 2. Predictors of Change in Physical Functioningfor 1 to 6 Years Follow-up; New Haven EPESE Data, 1982,

Self-Reported Chronic Conditions and Medications8

Table 3. Predictors of Change in Physical Functioningfor 1 to 6 Years Follow-up; New Haven EPESE Data, 1982,

Signs and Symptoms"

1982 Status

Self-rated healthBad/poor/fait*Goodb

Baseline functioningADL scoreRosow scoreNagi score

Reported conditionsHeart attackStroke + remaining difficulties0

Stroke onlyc

CancerDiabetes + medicationd

Diabetes onlyd

Liver diseaseHip fractureOther broken bonesArthritis

Parkinson's diseaseAmputation of leg or

disease-related'Other amputations'Cardiovascular medicationsNumber of prescription meds

DemographicsSexAge

R2

N

1983

.39**

.05

.0045**.06**

.08

.29*-.09

.26**

.16 +-.04

.12-.01-.02

.23**

.03

.91**

.13-.06

.05**

-.09.03**

.402283

1984

.53**

.18

.00

.48**

.05**

.11

.18-.03

.13

.19 +

.02

.54**-.12-.07

.21**

.14

1.01*.27.06.04**

-.20**.05**

.432149

1986

.47**

.22

.01

.41**

.03*

-.09.60**.27.06

.13

.14

.11

.17-.16*

.16*

.49*

1.22*.06.14.06**

-.29**.07**

.391859

1988

.44**

.15

.01

.37**

.03**

-.12.51**.40.06

.35*-.22-.03

.51*

.02

.13

.76**

.90 +-.19

.13

.06**

-.30**.07**

.331477

"Table shows unstandardized coefficients with significance tests based onstandard errors adjusted by Taylor Series method foir complex sample

1982 Status

Self-rated healthBad/poor/faii*Good6

Baseline functioningRosow scoreNagi score

Reported conditionsStroke + remaining difficulties'Stroke onlyDiabetes + medications'1

Diabetes onlyd

ArthritisParkinson's diseaseAmputation of leg or

disease-related'Other amputation'Number of prescription meds

Signs and symptomsClaudicationCongestive heart failure

symptomsCOPD symptomsHypertensionAnginaCognitive function poor1

Moderately impaired'

DemographicsSexAge

R2

N

1983

.35**

.04

.43**

.06**

.30*-.04

.16-.12

.21**-.23

.96**

.14

.05**

-.11

.10

.02

.00

.02

.18 +

.21**

-.09.03**

.39

2206

1984

49**

.15

.46**

.05**

.15-.03

.19 +

.13

.19**-.11

1.06*.00.05**

.54*

.04

.13-.06

.10

.33*

.01

-.19**.05**

.43

2078

1986

.46**

.21 +

.38**

.03**

.57**

.32

.11

.07

.16*

.46*

1.22**.02.06**

.83**

.23*

.10

.10-.02

.15-.05

- .31**.07**

.40

1800

1988

.39**

.11

.36**

.03*

49**.44.36**

-.39.10

.70**

.89*-.39

.07**

.01

.33**-.00

.06-.25*

.17-.00

-.35**.07**

.33

1432design.

•"Compared to reference category of Excellent health.'Compared to reference category of No stroke.dCompared to reference category of No diabetes.'Compared to reference category of No amputation.+p< A0;*p< .05;**p< .01.

change in disability in more than one follow-up year (includ-ing 1985 and 1987), so these variables are retained in thenext analyses.

The introduction of these variables, however, does noteliminate the effect of self-rated health on functioning.Respondents reporting bad, poor, or fair health in 1982 hadsignificantly poorer functional ability at each follow-upperiod than did those who reported excellent health. The betacoefficients for the bad/poor/fair category are smaller thanthe "bad" category alone (in the preliminary, unshownresults) by about 50 percent in each of the six follow-upyears. The signs for the coefficients for good health (com-pared with excellent health) are consistent with increasedrisk for this group as well, but they are also smaller by about50 percent, and just miss being marginally significant. Theanswer to the question appears to be that pre-existing condi-tions diminish but do not explain the effect.

"Table shows unstandardized coefficients with significance tests based onstandard errors adjusted by Taylor Series method for complex sampledesign.

•"Compared to reference category of Excellent health.'Compared to reference category of No stroke.••Compared to reference category of No diabetes.'Compared to reference category of No amputation.'Compared to reference category of Minor or no cognitive impairment.

AO;*p< .05;**p< .01.

Do signs and symptoms of (perhaps undiagnosed or pro-dromal) conditions explain the effect? — The second set ofhealth status measures is composed of indicators of whatmay be underlying disease, that is potentially undiagnosedand unavailable for self-report as a diagnosed condition bylay respondents. Among the measures, only claudication,congestive heart failure symptoms, and cognitive impair-ment explain any of the remaining unexplained variance infunctional disability in more than one of the years of follow-up. Table 3 shows that the addition of these measuresslightly diminishes the size of some of the coefficients forself-rated health, but the effect of bad/poor/fair self-ratingson future functioning remains strongly significant in allfollow-up periods.

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SELF-RATED HEALTH AND DISABILITY S349

Does sociodemographic status explain the effect? — Themodels so far have been adjusted for age and sex. Theaddition of race, income inadequacy, marital status, andeducation adds little to the explained variance; nor are thecoefficients for self-rated health reduced (see Table 4).Individuals with the poorest self-ratings of health in 1982 aresignificantly more likely than those reporting excellenthealth to be functionally disabled up to six years later. Thisassociation is not explained by the chronic disease status,medications, signs and symptoms, or sociodemographicstatus of the respondents.

Does the effect vary by subgroup? — Models were esti-mated separately for males and females in each of the years

Table 4. Predictors of Change in Physical Functioningfor 1 to 6 Years Follow-up; New Haven EPESE Data, 1982,

Sociodemographic Status"

1982 Status

Self-rated healthBad/poor/fair*Good"

Baseline functioningRosow scoreNagi score

Reported conditionsStroke + remaining difficulties0

Stroke onlyc

Diabetes + medications'1

Diabetes onlyd

ArthritisParkinson's diseaseAmputation of leg or

disease-relatede

Other amputationNumber of prescription meds

Signs and symptomsClaudicationCongestive heart failure

symptomsCognitive function poorf

Moderately impaired'

DemographicsSexAgeWhitePerceives income inadequateMarriedEducation

/?2

N

1983

.41**

.08

.43**

.06**

.30*-.05

.19*-.10

.21**-.24

.92**

.15

.04**

.06

.14

.28*

.21*

-.09.03**.04

-.05.01.00

.402183

1984

.55**

.19 +

.45**

.05**

.15-.00

.21*

.05

.20**-.13

1.04*.30.06**

.50*

.09

.28 +-.01

-.20**.05**

-.03-.04

.06-.00

.422057

1986

.48**

.23 +

.39**

.03**

.60**

.30

.16

.17

.13 +

.45 +

1.22**-.06

.07**

.84**

.24**

.19-.02

-.30**.07**.00

-.14-.03

.00

.391784

1988

.39**

.11

.38**

.02 +

.57**

.60

.37**-.19

.09

.69**

-.92*-.26

.06**

.08

.29**

.09-.08

-.32**.07**.08

-.17-.07-.02

.341424

"Table shows unstandardized coefficients with significance tests based onstandard errors adjusted by Taylor Series method for complex sampledesign.

••Compared to reference category of Excellent health.cCompared to reference category of No stroke.dCompared to reference category of No diabetes.'Compared to reference category of No amputation.'Compared to reference category of Minor or no cognitive impairment.+p< A0;*p< .05;**p< .01.

of follow-up (not shown). Because of the smaller samplesizes, particularly by the end of the follow-up period,slightly reduced sets of the most important covariates wereincluded: Rosow score, Nagi score, stroke, arthritis, Parkin-son's disease, amputation, amputation of leg, age, andprescription medications. Results were very similar to thefull sample; poor self-rated health was significantly associ-ated with increasing disability in every year of follow-up, formales and females alike.

We also looked at the sample stratified by age. The effectsare stronger for the younger elderly respondents, those aged65-74; significance levels are/? < .001 for the estimates forpoor self-rated health in each year of follow-up for thisgroup. For those aged 75 and over, we see only a short-termeffect: significance levels are p < .001 for the first threeyears of follow-up, then/? < .05 for the fourth, p = .06 forthe fifth, and there is no effect at all in the sixth year offollow-up. Respondents began the study in 1982 at varyinglevels of functional ability. Models were estimated sepa-rately for those with and without any initial disability (scoresof 0 on each of the three subscales). For those with somedisability, effects of self-rated health were significant foryears one through three, but nonsignificant for years fourthrough six. So while there did appear to be short-termeffects of self-rated health for those who were alreadydisabled, they did not persist throughout the follow-upperiod. For the nondisabled, however, the effects are muchstronger and more persistent: p < .0001 for poor self-ratedhealth in every year of follow-up. This finding based onstratifying the sample at the start of the follow-up periodsuggested an additional way of analyzing the data.

Dichotomizing the outcome variable at the end of eachfollow-up period allows us to ask, do (good) self-ratings ofhealth predict recovery of functioning, or do (poor) self-ratings presage decline, or are both effects present? Themultivariate regression analyses to this point have takenfunctional disability as a continuous variable and the signifi-cant effects we have obtained so far could be interpreted aseither improvement or decline or both. When we ask thequestion more specifically about recovery or decline, alogistic regression analysis for dichotomous outcomes isrequired. This approach also permits the calculation of oddsratios and associated confidence intervals.

Disability does change over time in both directions in thissample. For example, only 50 percent of the sample re-mained stable in functional ability from 1982 to 1983.Twenty-seven percent showed some loss of functioning (bymoving up one or more levels in the index), and the remain-ing 23 percent improved in functioning.

Table 5 shows odds ratios from models containing the fullset of covariates derived from the previous steps. In the firstpanel of Table 5, the dependent variable is defined as 1 ifsubsequent functional ability in the follow-up year is betterby one or more levels than 1982 functional ability, 0 if it isthe same or worse. If (good) self-rated health has the effectof promoting recovery, we would expect to see odds ratiosand confidence intervals for bad/poor/fair which are belowone. However, self-rated health shows no significant effectshere at all; odds ratios are near one and confidence intervalsspan one in every category. Furthermore, the direction ofeffects is unstable over time.

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Table 5. Odds Ratios and 95% Confidence Intervals for Self-Rated Health and Binary PhysicalFunctioning Status at 1 to 6 Years Follow-Up; New Haven EPESE Data"

Bad/poor/fairGoodExcellent

N

Bad/poor/fairGoodExcellent

N

OR

.891.231.00

1.551.021.00

1983

2237

1

2237

95% CI

.57, 1.41

.81, 1.86

.07, 2.25

.79, 1.32

OR

.821.261.00

2.111.381.00

1984

95% CI

Recovery1"

.51, 1.35

.82, 1.94

2111

Decline0

1.32,3.39.89,2.12

2111

OR

1.091.121.00

2.391.861.00

1986

95% CI

.59, 1.99

.63, 1.89

1834

1.64,3.461.21,2.86

1834

OR

1.461.391.00

2.411.651.00

1988

1455

11

1455

95% CI

.74, 2.90

.82, 2.36

.51,3.86

.16,2.35

"From maximum likelihood estimates for multivarate models including, in addition to self-rated health, all variables significant in models in Table 4:Rosow score, Nagi score, stroke, diabetes, arthritis, Parkinson's disease, amputation, prescription medications, claudication, congestive heart failuresymptoms, cognitive function, sex, and age.

•"Dependent variable defined as:1983:1984:1986:1988:

1983:1984:1986:1988:

= (FUNC83 < FUNC82), 0 = (FUNC83 > = FUNC82)= (FUNC84 < FUNC82), 0 = (FUNC84 > = FUNC82)= (FUNC86 < FUNC82), 0 = (FUNC86 > = FUNC82)= (FUNC88 < FUNC82), 0 = (FUNC88 > = FUNC82)

cDependent variable defined as:= (FUNC83 > FUNC82), 0 = (FUNC83 < = FUNC82)= (FUNC84 > FUNC82), 0 = (FUNC84 < = FUNC82)= (FUNC86 > FUNC82), 0 = (FUNC86 < = FUNC82)= (FUNC88 > FUNC82), 0 = (FUNC88 < = FUNC82)

The second panel is quite different. Here the dependentvariable is defined as 1 if later functional ability is poorerthan at baseline, 0 if it is the same or better. In these analyseswe would expect to see odds ratios and confidence intervalswhich are greater than one for the bad/poor/fair category,and that is in fact what we see. The size of the odds ratios issubstantial and does not diminish over time; in 1988 respon-dents who reported bad/poor/fair health in 1982 were 2.4times more likely to have experienced functional deteriora-tion by 1988 than respondents who reported excellent healthin 1982. Those who reported (only) good health were alsosignificantly more likely to have declined by 1988. To-gether, these tables tell us that the recovery group is not sig-nificantly different from the combined stability and declinegroup with respect to initial self-ratings of health; it is thedecline group which is different from the other two, with sig-nificantly poorer 1982 self-ratings of health.

DISCUSSION

Self-ratings of health appear to be strongly associated withchange in functioning; elderly persons who reported poorhealth in 1982 were almost two-and-a-half times as likely asthose with excellent health to have experienced a decline infunctional ability as many as six years later. The associationpersists in models containing not only baseline functioning,but also chronic conditions incorporating measures of sever-ity, signs and symptoms of prodromal or diagnosed orundiagnosed conditions, and demographic characteristics ofthe respondent. The finding holds equally for males andfemales, but is somewhat stronger for younger than older

elderly persons. Moreover, there are only short-term effectsfor the presently disabled; the effects are much stronger forthose with no current disability. Finally, we saw that themechanism at work is not one of improving functional abilitylevels for those with better self-ratings of health, but rather agreater risk of decline for those with poorer self-ratings.

This study of the impact of self-ratings of health onmorbidity was undertaken explicitly as a next step in under-standing the meaning of this commonly available but stillpuzzling variable. The mortality studies cited in the intro-duction have been tremendously consistent in finding self-ratings of health associated with survival. The variation insample sizes, lengths of follow-up periods, sources of otherhealth status information, the wording of the question, andeven the language of the question as there are a number ofinternational studies now represented, all speak to the ro-bustness of the association. What these studies have notdone, however, is move us much beyond a general apprecia-tion of the value of subjective knowledge of respondents inpredicting mortality. How do the present results advance ourunderstanding of the underlying dynamics?

First, they address the issue raised in a previous study: doself-ratings of health influence longevity directly, or do theyalso predict changes in morbidity or disability in the remain-ing years of life? Since self-rated health discriminates notonly between survivors and deceased, but also among levelsof functioning within the surviving group, these results showa much more penetrating effect of self-rated health than dothe mortality studies alone. Bear in mind that the peoplemost at risk from poor self-ratings of health have already

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been removed from the sample at every year of follow-up,and yet the predictive power of the self-ratings has not beenspent, even by the end of the six-year period.

Second, there are some very suggestive findings about thedirection of the relationship. The picture is one of increasedrisk of functional decline, especially among the youngerelderly and those who are not manifesting any presentdisability but who nevertheless rate themselves as being inpoor health. It is apparently not a picture of "positivethinking" (optimistic self-ratings) promoting recoveryamong those who are initially more disabled.

In interpreting these findings, it is important to keep themin the context of the mortality analyses which precededthem. We hypothesized that there would be smaller effectsamong the most elderly members of the sample as attritiondue to mortality accumulates and the explanatory power ofself-rated health is played out. The finding that the effect isconcentrated most strongly among the previously nondis-abled members of the sample, who then decline, accordswell with the recent findings from the MacArthur FieldStudy of Successful Aging showing very large effects ofpoor self-ratings of health on mortality among these other-wise optimally healthy elders (Schoenfeld et al., 1994).

But while we have to some extent filled out the picture ofself-ratings and health, we have, in a sense, only pushed thecentral question to the next level of discourse. Specifically,we are still facing two issues. The first concerns the ade-quacy of the health status covariates in the analysis. Despitethe additional efforts we have made in the present analysis,is the observed association with future disability levelsnoncausal and potentially trivial, because of failure to con-trol for important clinical variables which may have aninfluence on the course of disability and on self-ratings?Although we can point to the presence in the data of someinterviewer-observed data (prescription medications andmeasured blood pressure), the self-reported nature of thedata remains a potential limitation of this study, a limitationshared by most other work in the area. The issue of thevalidity and quality of self-reported data in general hastraditionally been a troublesome one for epidemiologicalresearch, which often depends on self-reported health statusinformation in community studies. Several recent studiescomparing self-reported data on conditions with medicalrecord data conclude that the accuracy of self-reports variesby condition, but that overall there is generally good agree-ment between the two sources when questionnaires are well-designed (Colditz et al., 1986; Harlow and Linet, 1989;Kehoe et al., 1994; Paganini-Hill and Chao, 1993). If someunderlying factor is influencing self-reports, such as opti-mism or a tendency to avoid illness behavior which mightproduce underreporting, or psychological distress or per-ceived lack of control which might produce overreporting,that factor would be likely to affect global self-ratings ofhealth in the same way that it affects specific self-reports ofconditions. This would amount to a conservative bias for thepresent study: for example, if a respondent reports excellenthealth and fails to report a previous condition such as cancer,their risk for subsequent disability or death would be higher,not lower. Only the accumulation of data from varioussources can resolve this question.

The second issue which still faces us concerns the pro-cesses which may be intervening between the initial self-ratings and the subsequent states of disability. If the ob-served association can be interpreted in a risk factorframework, then does it indicate a direct effect on disability,through unspecified and as yet unknown mechanisms, or is itmediated by measurable changes in health status, such asnew or repeat events of morbidity (e.g., stroke, or myocar-dial infarction)?

There is some irony in the fact that, armed with these newfindings, we are still facing the same issues with respect tocausality. We seem to be peeling away very similar layers inan onion. But these are very exciting, if incomplete, find-ings, even by comparison with the mortality studies. Thecontribution of this study is that we have identified anintermediate stage in the progression. If self-rated health hasan effect on, or even simply indicates, health status trajecto-ries that are potentially reversible as mortality is not, theclinical relevance of these findings could be substantial. Thepersistence of the size of the effect across the follow-upperiod underscores the substantive significance of the find-ings. A great deal more research is called for.

The range of possible processes or underlying mecha-nisms now available for study is fairly wide. For example,do self-ratings of health influence health status in the mannerof self-fulfilling prophecies? Do people with poor self-ratings of health, for instance, fatalistically refuse to makethe effort in physical therapy to recover functioning after astroke? Or do people with family history of heart diseaseneglect their exercise and diet because they feel their healthis not under their control? Poor self-ratings of health appar-ently do predict a trajectory of decline, but do they alsoaccelerate it? These are questions which can be answeredonly with frequent monitoring of cohorts. We are convincedthat the imperative for research in this area becomes evenmore urgent when self-ratings of health appear to be relatedto morbidity as well as mortality.

Functional disability is one way of measuring morbiditybut it is very general, and one would quickly like to knowabout the course of specific diseases. Studies might bedesigned with the specific natural history of a disease inmind. Findings from a related study mentioned above indi-cated that self-ratings of health shortly (6 weeks) after theevent were significant predictors of recovery of functioningby the critical period of 6 months afterward, even when therespondent's medical condition was taken into account(Wilcox, Kasl, and Idler, in press). These dynamics ofshort-term recovery (or deterioration) following acute eventscould not have been picked up by the annual follow-updesign of the study in this report.

Finally, one would like to know to what extent functionaldecline, predicted in part by these self-ratings of health, is anintermediate stage on the trajectory to mortality. If the effectof self-rated health on mortality is "explained" by thismediating variable, the focus of research interest mightprofitably be shifted from mortality studies to morbiditystudies.

Self-ratings of health in this sample are related, not only tolength of life in this sample, but also to states of health in theyears remaining. They appear to be particularly telling for

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younger elderly people in good health, and with the highestlevels of functioning. They are equally predictive for menand women, but they appear to tell us more about the risks ofdecline than about the course of recovery. On the whole,these findings extend our understanding of the meaning ofexcellent, good, fair, and poor ratings of health, and theirimplications, not only for survival but also for necessaryphysical functioning in daily life.

ACKNOWLEDGMENTS

This research was supported by the National Institute on Aging (contractN01-AG-0-2105) for the Established Populations for the EpidemiologicStudies of the Elderly (EPESE), by a National Institute on Aging grant R01AG11567-01A1 (Ellen L. Idler, principal investigator), and by the Institutefor Health, Health Care Policy, and Aging Research of Rutgers University.

Address correspondence to Ellen L. Idler, PhD., Institute for Health,Health Care Policy, and Aging Research, Rutgers University, 30 CollegeAvenue, New Brunswick, NJ 08903. Internet: [email protected]

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