converging health inequalities in later life-an artifact of mortality selection?

Converging Health Inequalities in Later Life-An Artifact of Mortality Selection?Author(s): Megan BeckettSource: Journal of Health and Social Behavior, Vol. 41, No. 1 (Mar., 2000), pp. 106-119Published by: American Sociological AssociationStable URL: http://www.jstor.org/stable/2676363 .

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Converging Health Inequalities in Later Life- an Artifact of Mortality Selection?*

MEGAN BECKETT RAND

Journal of Health and Social Behavior 2000, Vol 41 (March): 106-119

An emergent issue in the health inequalities debate is how socioeconomic status (SES) and physical health relate over the life course. Many studies indicate that the SES-health relationship diminishes in later life. The present research tests the hypothesis that this convergence in health inequalities is an artifact of mortality selection, which biases downwards the "true" association between SES and health in later life. By including respondents who had subsequently died or were loss-to-followup into the analysis, I assess the sensitivity of the age-specific association between education and health to sample selection processes. I study US. adults followed for approximately ten years using the NHANES I Epidemiologic Followup Study. Results based on the surviving sample are robust to the inclusion ofpeople selected out of the sample due to mortality or attrition. Sample selection biases do not appear to explain the convergence in health inequalities in late life.

A recent strand of research on health inequalities addresses how and why age modifies the association between socioeconomic status (SES) and health or, conversely, how SES modifies the association between age and health (Williams and Collins 1995). With few exceptions, cross-sectional studies show that socioeconomic inequalities in health are largest in early adulthood (Kunst and Mackenbach 1994; Mustard et al. 1997) or middle ages (Antonovsky 1967; Kitigawa and Hauser 1973) and smaller again at older ages. In order to understand the complexities of SES inequalities in health more generally, it is nec- essary to gain an understanding about why and

* Earlier versions of this paper were presented at the 1996 Population Association of America in New Orleans, LA and the 1998 American Sociological Association in San Francisco, CA. Support for this project comes from NIA through grants 5T32AG000221 to Population Studies Center, The University of Michigan and 5T32AG00244 to RAND. I am indebted to James House, John Mirowsky, Melissa Farmer, and two anonymous reviewers who offered helpful comments on earlier versions of this paper and to Marc Elliott for his statistical guidance. Address all correspondence to: Megan Beckett, RAND, PO. Box 2138, Santa Monica, CA 90407-2138.

how the size of health inequalities varies over the life-cycle.

A major hypothesis about what causes the apparent convergence in health differentials in later life is selective survivorship. The selective survivorship thesis is rooted in the racial mortality cross-over debate where research finds that the survival advantage enjoyed by whites becomes a mortality disadvantage at the oldest ages. Markides and Machalek (1984) propose that racial mortality crossover results from early high mortality, which removes from the population less hardy blacks in early and mid-life. In turn, this leaves in late life a disadvantaged (i.e., black) group of robust survivors relative to the advantaged (i.e., white) subpopulation. Subsequently, selective survivorship has been applied to account for the narrowing of health and mortality differentials by SES (Robert and House 1994).

A second source of sample selection may be due to the exclusion of people who are institutionalized or otherwise unavailable for an interview for health reasons (Ross and Wu 1996). Previous research has not adequately addressed either potential source of sample selection bias, so it has been impossible to determine whether the convergence observed

106



CONVERGING HEALTH INEQUALITIES 107

in previous research is real or an artifact of mortality or sample selection. The only way to address the issue of mortality selection is through the use of a panel study so that differential rates of mortality can be identified and adjusted for. While it is very difficult to iden- tify differential rates of selection into a sample initially, panel data can provide insights into differential rates of attrition (which is a form of sample selection).

In an effort to fill this gap in the health inequalities literature, the present paper uti- lizes a major national data set to address the following research issues: first, do education differences in self-reported chronic and serious conditions converge in later life? And second, does sample selection due to mortality and/or loss to followup (LTF) explain any convergence in education differentials in chronic or serious conditions in later life?

REVIEW OF THE LITERATURE

Early and more recent studies in Europe and the U.S. of the relationship between SES and mortality that are broken down by age categories and based predominantly on vital statistics data show a similar pattern-socioeconomic differentials in health are largest in mid- life but relatively smaller in later life (Antonovsky 1967; Kitigawa and Hauser 1973; Rogot 1992; Xie 1994). Contemporary research, also based on vital statistics as well as survey data, is largely consistent with earlier research, indicating that SES differences in health are smaller in later life than in mid-life (e.g., Elo and Preston 1996; Feldman et al. 1989; House et al. 1990, 1994; Kaplan et al. 1987; Newacheck et al. 1980; Santariano 1986; Preston and Elo 1995). To my knowledge, only two published studies- Aneshensel, Frerichs, and Huba (1984) and Ross and Wu (1996)-report that the relationship between SES and health is larger in late life than in early or mid-life. The former study, which is not explicitly concerned with identi- fying the age-pattern of the relationship between SES and health, aggregates people 45-92 years-too crude an age group to iden- tify a narrowing of education differentials in later life. Using two nationally representative telephone samples, the second analysis (Ross and Wu 1996) finds that educational differences in three health measures (physical func-

tioning, self-reported health, and physical well-being) are larger with increasing age. There is some issue, however, as to whether these telephone samples suffer from health measurement or population coverage issues (Williams and Collins 1995).

The majority of the research to date is based predominantly on cross-sectional studies of health and mortality, making identification of sample selection processes difficult. Longitu- dinal data are needed for this purpose. To my knowledge, only two longitudinal studies exist that use longitudinal data to replicate cross- sectional analysis within the same study: House et al. (1994) and Ross and Wu (1996). Both of these studies establish a strong prospective relationship between baseline SES and change in health, but they yield inconsistent patterns of age differences in the relative strength of this relationship. Using 2.5-year longitudinal panel data to replicate their earlier cross-sectional analysis, House et al. (1990, 1994) conclude that age differences in change in health by SES are neither consistent nor inconsistent with the cross-sectional age differences. For one health outcome (i.e., index of functional impairment), they find smaller socioeconomic differentials in health change at the oldest ages in the longitudinal data, but for a second health outcome (i.e., chronic conditions) they find no consistent pattern of age differences in inequalities in health change. In contrast, Ross and Wu (1996) report increasing education and income inequalities with age over a one-year interval with a telephone panel study, a report similar to their cross-sectional results. Both studies look at self-reported changes in health over a short period of time; neither empirically considers the impact of sample selection. A better test of these issues requires studying the health of a panel followed over a longer period of time.

Although no researchers have empirically tested the selective survivorship thesis within the specific context of socioeconomic differentials in health, there have been several attempts to test and adjust for survival selection with respect to gender and other social differentials. Markides, Timbers, and Osberg (1984) utilized a "pseudovariables" approach that included respondents who died between baseline and followup. Deceased subjects were assigned a self-reported health value lower than the worst possible health score reported by surviving subjects. Inclusion of decedents



108 JOURNAL OF HEALTH AND SOCIAL BEHAVIOR

in the surviving sample "explains" the sex differential in self-rated health but does not explain differences by other "social factors," including marital status and education. Strauss et al. (1993) explain about half of the sex differentials in self-rated health at older ages in the U.S., Bangladesh, Jamaica, and Malaysia using an adaptation of the method employed by Markides and his colleagues. Ferraro and Farmer (1996) conclude that mortality selection biases (downwards) racial and sex differentials in later life for two out of the four health measures they examined using the NHANES I Epidemiologic Followup Study.

The pseudovariables method for estimating mortality selection bias-i.e., comparing social differentials in health first without and then with decedents included in the sample- has two drawbacks: (1) the types of health outcomes that can be considered are limited, and (2) it is not possible to assess bias due to nonresponse. The first limitation is readily apparent when one considers that two of the three studies cited above (Markides et al. 1984; Strauss et al. 1993) assess mortality selection bias on a single health outcome: self-rated health. As Strauss et al. (1993:799) note, while it is defensible to assume that decedents would have reported their health as poor or worse, "it would be heroic to assume that the dead would have reported (specific) problems with physical functioning" or with specific health conditions. The second limitation is that there is no ready mechanism for assessing bias due to loss to followup or nonresponse, although sample attrition can be substantial in panel studies, and the characteristics of non-respondents may be related to poor health, especially at older ages (Goldman, Korenman, and Weinstein 1995). In other words, the impact of attrition bias may be in the same direction and strongest at the same ages as hypothesized for mortality selection. Efforts to estimate the impact of sample selection bias need to assess such bias due to early mortality as well as nonresponse.

The present research is designed to describe age differences in the relationship between SES and health using cross-sectional and longitudinal data within the same study as well as to consider whether the pattern of age differences is biased by sample selection. I modify the pseudovariables approach to permit predic- tion of what the health status would be for all respondents out of the sample at followup.

Thus, I am able to consider sample selection due to both mortality and nonresponse.

DATA AND METHODS

Data

The data for this analysis are drawn from the NHANES I Epidemiologic Followup Study (NHEFS) 1982-84 through 1991. The NHEFS is a multi-year followup study of participants in the First National Health and Nutrition Examination Survey (NHANES I). Fielded from 1971 to 1975, NHANES I collected data from a national probability sample of the U.S. noninstitutionalized population. From the original NHANES I sample, 14,407 people aged 25-74 years who completed a medical examination in addition to the face-to-face household survey form the NHEFS analytic cohort (Cohen et al. 1987). The sample used in the present study is based on the 10,517 respondents' who were alive and reinterviewed (themselves or via a proxy) in the 1982-84 NHEFS. NHEFS respondents may be institutionalized. Proxy interviews were used if the respondent was unable to participate in the interview.

NHANES I collected information on basic social and demographic information; extensive self-reported health and risk factor information; as well as a physical examination. The response rate of the NHEFS analytic cohort at the time of the NHANES I interview was 69.4 percent. Extensive efforts have been made to trace and reinterview the analytic cohort peri- odically. The current analysis examines change in health status over an average nine-year interval between 1982-84 NHEFS and 1992 NHEFS. I restrict the analysis to these two waves because the collected health information, question wording, and instrument design were highly comparable (Cox et al. 1997). In contrast, despite some efforts to retain item comparability between NHANES-I and 1982-84 NHEFS, questionnaire items were modified, added, and deleted to take advantage of improvements in questionnaire methodology during the late 1970's (Cohen et al. 1987). Perhaps the largest impact of these modifica- tions is that functional impairment items (one of the two health outcomes I examine in this analysis) were asked of the minority of NHANES-I respondents who reported arthritis




or joint problems, yet, beginning with the 1982-84 NHEFS, these items were asked of everyone on subsequent reinterviews. The response rate for the 1982-84 reinterview was 91.3 percent among people not known to be deceased (Cox et al. 1997).

Mortality information has been obtained using tracing methods and through continuous linkage with the National Death Index. By 1992, 2,221 (21.1 percent) members of the 1982-84 NHEFS analytic cohort had died, thus leading to potentially very important mortality selection biases.

Analysis

I organized the analysis in three stages. First, using cross-sectional (NHANES I) data, I regressed the effects of the number of health conditions and an index of functional impairment on age, education, and education conditional on age using ordinal logistic regression models. Ordinal logistic regression is appro- priate when the outcome variables are ordinal and skewed (with the majority of cases at or near the lowest level). With an outcome of j categories, the number of intercept parameters estimated is j-1. For example, the index of functional impairment is measured in four categories; thus, I estimated three intercept parameters. I estimated a single parameter for each of the predictor variables since the model posits that an independent variable's effect is the same at all levels of the outcome variable (Agresti 1990). Once estimated, I obtained coefficients; estimation of predicted logits and transformation to predicted cumulative proba- bilities is straightforward and used to represent graphically a few of the models.

I follow an approach similar to that used by Ross and Wu (1996) to test the functional forms of age and education on health and of education conditional on age. I first estimate the following equation to test these effects on health:

Health = b0 + b ~age - b2age2 - b3ed- b4(ed x age) - b5(ed x age2)

Next, I restrict the equations to the significant effects. I test three patterns with these restricted equations: divergence, convergence, and divergence then convergence. Support for

these predictions is provided by the following restricted equations:

Divergence: Health = bo + boage - b3ed - b4(ed x age)

Convergence: Health = bo + boage - b3ed + b4(ed x age)

Divergence then convergence: Health = bo + boage - b3ed - b4(ed x age) + b5(ed x age2)

Second, I use hierarchical logit models (Agresti 1990) to estimate the probability of death or loss to followup using longitudinal data. These models provide insight as to the potential size and direction of selection due to mortality and loss to followup between baseline and followup interviews. I use a logit model to describe factors related to survival; next, among survivors, I use a logit model to describe factors related to participation in reinterview.

Finally, using longitudinal data, I describe the age-pattern of educational differences in health in 1992 among people who are in the sample at followup and assess the sensitivity of these results to inclusion of people out of the sample due to death or loss to followup. I use a modified pseudovariables approach to impute the health status at followup of respondents out of the sample. In the first step, I estimate ordinal logit models in which the number of chronic health conditions and an index of functional impairment in 1992 are each predicted by sex, race, age, age squared (age2), education, any significant age by education or age2 by education interactions, and 1982-84 health status. Second, I apply the estimated coefficients obtained from the observed 1992 sample to the out-of-sample subjects to obtain their predicted health scores in 1992 had they been in the sample. Third, I assess the sensitivity of my results to sample selection by show- ing how the results differ for the following contrasts: (1) respondents in the 1992 sample, (2) in-sample respondents plus decedents, and (3) in-sample respondents, decedents, and LTF respondents.

Variables

The cross-sectional and longitudinal depen-




dent variables are two indicators of health status: (1) the number of major chronic conditions, and (2) an index of functional impairment. Respondents were asked, "Did a doctor ever tell you that you had:" (a) cancer, (b) health trouble (including heart attack or heart failure), (c) hypertension, (d) stroke, (e) diabetes, (f) kidney disease, (g) urinary or kidney infection, and (h) cataracts. I sum up the number of reported conditions and construct four categories: zero or one condition, two conditions, three conditions, and four to eight conditions.2

Respondents were asked to report if: "you have no difficulty, some difficulty, much difficulty, or are unable to do these activities at all when you are by yourself and without the use of aids."3 Among the activities respondents were asked about were activities of daily living (ADLs) (i.e., dressing, washing body, walking from room to room, lifting cup to mouth, cut- ting meat) and instrumental activities of daily living (IADLs) (i.e., heavy housework, light housework, using a pen to write, running errands/shopping, preparing meals, walking several blocks). The index of functional impairment is coded: (1) no or some difficulty with all ADLs and IADLs, (2) much difficulty or is unable to do any IADLs (but no or some difficulty with all of the ADLs), (3) much difficulty or unable to do at least one ADL, and (4) confined to bed. These categories are mutually exclusive.

I use prevalent measures of health as the dependent variables in the cross-sectional and longitudinal analysis because prevalent measures are a function of differential odds of health decline and health improvement with age. Ability to allow for health improvement is particularly important in functional impairment where there may be important SES differences in how people adapt to and cope with a disease. For example, Wray et al. (1998) doc- ument strong education differences in smoking cessation among smokers who had had a heart attack (but not among people with no history of a heart attack).

Because of its various qualities, I use education as a measure for SES. Education directly and indirectly influences all other indicators of SES in adulthood, including occupational status, income, and wealth (Liberatos et al. 1988). Education is more strongly related with many diseases compared to other SES indicators, in part, presumably, because of education's

strong association with lifestyle factors (Liberatos et al. 1988). Finally, education is generally fixed some time in late adolescence or early adulthood, thus making it among the least susceptible of all measures to reverse causality. I treat education as a continuous variable, ranging from zero to 17 years (corre- sponding to more than four years college). I center education at 12 years.

Other covariates include indicators for race (i.e., black vs. nonblack), age, and sex. Blacks generally report poor health status, although at least some of this association is mediated by SES (Williams and Collins 1995). The omitted race category includes all non-blacks. Sex is a strong correlate of health and mortality with women generally reporting worse health status but lower mortality rates (Verbrugge 1989). I code age at time of the NHEFS interview as a continuous variable. Age in 1982-84 (range: 32-86) is centered at 55 years; age in 1992 (range: 41-96) is centered at 65 years. To adjust for the variable period between the NHANES I interview (fielded from 1971-75) and the 1987 NHEFS, I include a continuous variable indicating year of NHANES I interview as a control variable in the longitudinal component of the present analysis. I impute age in 1992 for deceased and LTF respondents by adding the number of years between 1992 and the 1982-84 NHEFS interview to age in 1982-84.

Table 1 summarizes the dependent and independent variables included in my analysis.

RESULTS

Cross-sectional SES, Health, andAge Relationship

Using the 1982-84 NHEFS data only, I first examined whether the initial cross-sectional relation of education and of the interaction of education with age is consistent with previous research. Table 2 presents the coefficients of the ordinal logistic regressions in which number of chronic illnesses is predicted by sex, race, age, age2, education, age by education, age2 by education, and a control for missing education. The first column, "Number of health conditions" shows that, as expected, the linear effect of age is positively (and linearly) related to the probability of reporting more health conditions (b = .059, p < .00 1). Also, as




TABLE 1. Characteristics of the NHANES I Epidemiologic Followup Study Sample in 1982-84 and 1992

Characteristics Mean or % Socio-demographics

Female (%) 62.7 Age in 1982-84 (range: 32-86) 57.1 Black (%)' 13.4 Education (range: 0-17 years) 11.0 Missing education (%) 0.6 Number years between waves (range: 8-11) 9.4

1982-84 Health Status Number of health conditions (%)bc

Zero or one 71.9 Two 16.6 Three 7.5 Four to seven 4.1

Functional impairment index (%)4e No limitations 83.8 IADL limitation 10.9 ADL limitation 4.8 Confined to bed 0.5

1992 Health Status Died (%) 21.1 Loss-to-followup (%) 6.9 Number health conditions (%)bsf

Zero or one 55.4 Two 21.8 Three 12.8 Four to eight 10.0

Functional impairment index (%)dg No limitations 79.8 IADL limitation 12.3 ADL limitation 6.2 Confined to bed 1.7

Number of respondents 10,517 a Based on a revised race variable included in the 1992 vital status file that adjudicated between interview observed race in NHANES I and respondent reported race from the 1982-84 NHEFS. bBased on total number of following conditions fespon- dent reports ever having been diagnosed with: cancer, heart trouble (including heart attack or heart failure), hypertension, stroke, diabetes, kidney disease, urinary tract or kidney infection, and cataracts. c Excludes 14 respondents with missing health condition information. dRespondents with no limitations are people who are not confined to bed, and report no or some difficulty with all of the IADLs and ADLs (see text for definitions); IADL limitation refers to 'much difficulty' or 'unable to do' any of the IADLs but some or no difficulty with all of the ADLs; ADL limitation refers to 'much difficulty' or 'unable to do' any of the ADLs; confined to bed refers to someone who has been confined to bed for most of the day. Categories are mutually exclusive. eExcludes 75 respondents with missing functional impairment score. f Based on 7,555 respondents who were alive and reinterviewed in 1992. Excludes 22 respondents with missing 1992 health condition information. 9 Based on 7,513 respondents who were alive and reinterviewed in 1992. Excludes 64 respondents with missing 1992 functional impairment information.

expected, years of education is negatively related to number of chronic conditions (b = -.073, p < .001). The positive and significant coefficient associated with age x education (b = .002, p < .001) indicates that the protective effect of education on the odds of reporting more health conditions declines with age. In other words, I see a pattern of converging education differences with age in the odds of reporting more health conditions in these 1982-84 cross-sectional data.

The second column in Table 2 presents the model associated with the index of functional impairment. Age is linearly and quadratically related to the odds of functional impairment in such a way that the effect of an additional year of age increases with age. Education is strongly and negatively related to odds of functional impairment (b = -.157,p < .001). The positive and significant coefficient associated with the education by age interaction term is consistent with the convergence pattern. These cross-sectional results are consistent with previous research documenting a convergence in health inequalities in later life in cross-sectional data. I find no evidence of diverging health inequalities in education differences in odds of poor health later life in the cross-sectional data.

Figure 1 plots the predicted probability of reporting zero or one health condition by age and education. One thing of note in Figure 1 is that the negative linear effect of education on the odds of reporting each level of health conditions with age does not necessarily translate into a linear decline in education differences in the absolute probability of reporting zero or one health condition. Figure 1 shows a widening of education differences in the absolute probability of reporting zero or one condition through age 61 followed by a convergence. Figure 2 plots the predicted probability of reporting no functional impairment. In absolute probability space, this model trans- lates into a pattern of widening education differences in probability of no functional impairment through the mid-70's, followed by a convergence.

Longitudinal Hierarchical Logit Models of Sample Selection due to Mortality and Loss- to-followup

In order to assess the potential effects of mortality and attrition bias, I estimate hierar-




TABLE 2. Ordered Logistic Regressions of Number of Health Conditions and Functional Impairment in 1982-84 NHANES I Epidemiologic Followup Study on Age, Education, and their Interactions, Controlling for Sex and Race

Number of Health Functional Conditionsa Impairmentb

Female .369*** .615*** Black .060 .299*** Age in 1982-84 .059*** .058*** Age2 .001 *** Education -.073*** -.157*** Age x education .002*** .004*** Age2 x education Missing education -.353 -1.198*** Cut point 1 1.510 2.890 Cut point 2 2.743 4.294 Cut point 3 3.934 6.722 -LL -8,207.0 -5,046.3 Adjusted R2 .091 .141 Sample size 10,503 10,442 Note: Age is modeled as age - 55 and education is modeled as education - 12. a See footnote b in Table 1. bSee footnote d in Table 1. *p < .05; **p < .01; ***p < .001 (two-tailed tests)

chical logits of mortality and attrition at the 1992 NHEFS reinterview. This model includes controls for initial health status (as measured

by number of health conditions and index of functional impairment) and for years elapsed between 1982-84 interview and 1992. Age still refers to age at 1982-84 NHEFS.

In Table 3, the first column (labeled "Death") is a model that predicts the probability that a respondent died by 1992. Gender, race, age, number of health conditions, and functional impairment at baseline are each associated with death in the anticipated direction. In particular, male, black, older, less educated, and less healthy respondents were sig- nificantly more likely to die between 1982-84 and 1992. There is a significant interaction between age and education in the direction anticipated by the mortality selection hypothesis: the older the respondent, the weaker the negative association between education and the odds of dying. In other words, the effects of mortality selection are strongest at younger ages.

The second column in Table 3 predicts the odds of LTF in 1992. Among respondents not known to be deceased by 1992, male, black, and less-educated people were more likely to be LTE These patterns are consistent with previous work indicating that sample attrition is largely due to migration, particularly among younger, socioeconomically disadvantaged, and minority men. Functionally impaired peo-

FIGURE 1. Age by Predicted Probability of Having Zero or One Health Condition by Education, Controlling for Sex and Race, 1982-84 NHANES I Epidemiologic Followup Study

1.0 -

0

-: 0.8 Education

o \-i_-- 16 0 1 2 0.7-

- - 12 N8

M 0.6-

. 0.5 -

Age in 1982-84 NHEFS

Note: Figure 1 graphs regression model from the first equation in Table 2.




FIGURE 2. Age by Predicted Probability of Having No Functional Impairment by Education, Controlling for Sex and Race, 1982-84 NHANES I Epidemiologic Followup Study

1.0-

E 0.9-

E

0; .8 Ed uction 0

"'0 -- - 11\ z~~~~~~~~~~~~~~~~~~~~ 0.7-

a.

DC 0.6-

0.5

Age in 1982-84 NHEFS

Note: Figure 2 graphs regression model from the second equation in Table 2.

ple have higher odds of LTE However controlling for functional impairment, the number of chronic conditions is unrelated to sample attrition. The effect of age on the probability of attrition diminishes with age at an accelerating rate, as indicated by a negative (marginally significant - p < .10) coefficient of age and a positive age2 term. Consistent with the general sample selection thesis, the positive effect of education on the odds of LTF is strongest in early adulthood and diminishes in a linear fashion with age (bage x education = .004, P < .0001).

My longitudinal hierarchical models of sample selection due to mortality and loss-to- followup each indicate that the association between education and processes of sample selection varies with age in such a way that less educated people are disproportionately removed from the NHEFS sample, particularly at younger ages.

Longitudinal Health Models Unadjusted and Adjustedfor Sample Selection

Hierarchical models of mortality and loss- to-followup by 1992 indicate potential biases related to differential sample selection. In particular, younger, less educated, and less

healthy respondents were particularly likely to be selected out of the sample due to mortality and attrition. Table 4 shows how results differ for the following samples: (1) people alive and reinterviewed in 1992, (2) persons in the followup sample or known to be deceased, and (3) persons in the followup sample, known to be deceased, or LTF. As described in the methods section, I use initial socio-demographic characteristics-including any significant interaction between age (in 1992), age2, and education, and 1982-84 health status-to impute number of health conditions and level of functional impairment in 1992 for people not in the sample. If sample selection is bias- ing estimates of predictors of poor health, particularly with respect to the way that age modifies the health impact of education, I would expect these contrasts to yield different signs and significance levels associated with the age by education and age2 by education interaction terms across these samples. The nature of these changes, if any, will indicate whether sample selection magnifies the size and extent of convergence in health differences in later life.

The coefficients in Table 4 depict a similar pattern of how age affects education differences associated with odds of more health conditions and functional impairment to the pat-




TABLE 3. Ordered Logistic Regressions of Death and Loss to Followup in 1992 NHANES I Epidemiologic Followup Study on Age, Education, and their Interactions, Controlling for Sex, Race and 1982-84 Health Status

Death LTF Female -.902*** -.193* Black .193* .986*** Age in 1982-84 .092*** -.007 Age2 .001*** .001*** Education -.077*** -.164*** Age x education .003** .004*** Age2 x education # health conditions (1982-84)

Zero or one Two .370*** .008 Three .584*** .107 Four to seven 1.074*** -.085

Functional impairment index (1982-84) No limitations IADL limitation .727*** .441*** ADL limitation 1.420*** .586* Confined to bed 3.972*** a

Years since 1982-84 interview -.096 -1.075***

Missing education -.757* -1.184** Missing health conditions 1.212 .859 Missing functional

limitations .157 -.695 Constant -1.287* 7.097*** -LL -3,459.8 -2,205.5 Adjusted R2 .361 .102 Sample Size 10,517 8,298 Note: Age is modeled as age - 65 and education is modeled as education - 12. Italics indicate reference groups. aThis coefficient could not be estimated because of empty cell. *p < .05; **p < .01; ***p < .001 (two-tailed tests)

tern I observed in the 1982-84 cross-sectional analysis. The columns labeled "Alive" present the coefficients obtained for the sample who were alive and reinterviewed in 1992. As with the 1982-84 pattern, the size of education differences in the odds of reporting more health conditions or functional impairment declines with increasing age, as indicated by significant and negative coefficients associated with education as well as positive and highly significant coefficients associated with the interaction between age and education. As in the 1982-84 health models, the coefficient associated with the age2 and education interaction term is not significant. The "Alive and deceased" columns present the coefficients based on the alive sample plus people who had died by 1992 with imputed 1992 health outcomes. The coefficients associated with education and its inter-

action with age and age2 are in the same direction and of same level of significance as observed in the "Surviving" sample as well as in the "Alive, Deceased, or LTF" sample. The unchanged direction and significance levels of the age, education, and age by education coefficients, along with the failure of the age2 by education term to attain statistical significance in any of these models, indicates that sample selection processes do not bias the nature of the association among health, education, and age.

SUMMARY AND DISCUSSION

These findings indicate that education differentials in the odds of cross-sectional and longitudinal health outcomes (i.e., number of health conditions and functional impairment) are largest in early and mid-life and smaller in later life, consistent with previous cross-sectional studies. Moreover, the way that age modifies the relationship between education and health is not strongly influenced by sample selection processes. The age differences in the odds of poor health in 1992 for the different education levels when people out of the sample due to death or mortality are excluded is the same as when the 1992 health status of these people is imputed and they are included in the sample. In other words, I find no evidence that sample selection contributes to a convergence in education differentials in odds of more health conditions or functional impairment.

If convergence in health inequalities in later life cannot be explained by mortality and selection due to attrition, what does explain the convergence? One interpretation is that the declining risks associated with lower levels of education on health with age reflects postponement of morbidity in higher socioeconomic groups, as House and his colleagues have described (House et al. 1990). Robert and House (1994) identified several mechanisms through which such postponement can occur, some of which they or others are currently test- ing, including life cycle variation in the differential distribution and impact of psychosocial, environmental, and behavioral risk factors such as smoking, drinking, and exercise. Existing studies indicate that while large socioeconomic differences in the distribution of health-related risk factors certainly exist (House et al. 1992; Winkleby, Fortmann, and



0

TABLE 4. Ordered Logistic Regressions of Number of Hlealth Conditions and Functional Impairment in NHANES I Epidemiologic Followup Study on Age, Education, and their Interactions, Controlling for Sex and Race

- ~~z

Number of Health Conditions Functional Impairment i

Alive, Alive Alive, Alive or Deceased, or or Deceased, or

Alive Deceased LTF Alive Deceased LTF Female .295*** .299*** .317*** .454*** .685*** .687*** Black .179* .080 .064 .545*** .649*** .625*** Age in 1992 .071 *** .075*** .076*** .062*** .065*** .068*** Age2 -.001*** -.001*** -.001* .002*** .002*** .002*** Education -.060*** -.067*** -.062*** -.149*** -.169*** -.159*** Age x education .002*** .002*** .002*** .005*** .004*** .003*** Age2 x education Years since 1982-84 interview .066 -.023 .006 .264*** .128** .169*** Missing education -.704* -.760** -.798** -1.010** -1.409*** -1.320*** Cut point 1 .802 -.142 .154 4.659 3.560 4.110 Cut point 2 2.003 1.097 1.397 5.977 4.956 5.541 Cut point 3 3.109 2.199 2.498 7.711 8.679 7.864 -LL -7,874.7 -10,873.3 -11,589.4 -4,360.2 -6,463.1 -6,798.3 Adjusted R2 .096 .115 .116 .146 .223 .225 Sample size 7,555 9,772 10,495 7,513 9,730 10,453 Note: Age in 1992 is modeled as age - 55 and education is modeled as education - 12. *p < .05; **p < .01; ***p < .001 (two-tailed tests)




Barrett 1990), the ability of the more tradition- al health-related risk factors (e.g., smoking, drinking, and exercise) to account for health and mortality inequalities is limited (Haan, Kaplan, and Camacho 1987; Lantz et al. 1998). However, a broader set of risk factors including psychosocial factors like social support, chronic and acute stressors, and self-efficacy- do seem to account for more of the social differentials in health (House et al. 1994).

Medical care provides another potential explanation for why health inequalities narrow at older ages, particularly in countries like the U.S. where Medicare reduces differentials in access to and quality of health care of older people. Although contribution of medical care to improvements in health status from the eighteenth through early twentieth centuries has been significant, it has probably been less than the contribution of improvements in the environment, rise in the standard of living, better diet, and improvements in the immune sys- tem (Fogel 1994; McKeown 1980; McKinlay and McKinlay 1990). Moreover, in countries that have instituted universal health care, health inequalities persist (Pamuk 1985; Wilkinson 1986; Wolfson et al. 1993). Despite the compelling evidence that medical care his- torically contributed only modestly to population health, recent and provocative findings on health trends in industrialized countries suggest that medical care may be increasingly important in the health of aging population and thus may be reducing health inequalities at these ages. Kannisto (1994) reports substantial and seemingly unaccountable declines in 16 industrialized countries in oldest-old age mortality rates that occurred simultaneously with a period of significant technological and phar- maceutical advances, including angioplasty and angiography and the widespread availabil- ity and use of antihypertensives. Future research should explore the contribution of medical care advances to recent declines in mortality and morbidity as well as investigate the contribution of leveling of medical care access to the narrowing of health inequalities in later life.

There are potential limitations with this analysis. Self-reported health conditions, in particular, are susceptible to charges of reporting bias whereby some groups (e.g., the less educated and older people) may be less likely to (a) see a doctor that could diagnose a health problem, (b) understand the definition of the

health condition, or (c) accurately recall health conditions (Edwards et al. 1994). Vargas and colleagues (1997) find that men (particularly nonwhites) and people who had not received medical care in the past year showed the least correspondence between self-reported and measured hypertension using the NHANES III. The present analysis may underestimate the association between education and self- reported health measures. To the extent that older people are more likely to have received medical care in the past year (due to Medicare) regardless of race or education, this underesti- mation of the education-health association is probably stronger at younger ages rather than older ages. A recent analysis using these data concludes that self-reported health conditions are valid health measures (Ferraro and Farmer 1999); in this study, self-reported conditions were better predictors of mortality than physi- cian examinations.

A related measurement issue is the reliance on education as the sole indicator of SES. Other work shows that income and wealth may be even more related to health than education (Lantz et al. 1998; McDonough et al. 1999). I opt to use education because a growing body of literature in economics suggests that a non- trivial proportion of the association between the two factors flows from health to income and wealth, particularly among middle- and older-aged adults (Smith 1998). In particular, an individual's health status over the life-cycle probably influences their schooling, marriage, childbearing, and lifetime earnings and household wealth (Smith and Kington 1997). Smith and Kington (1997) argue convincingly that disentangling the causal relationships between income/wealth and health in older adulthood requires looking at the subcomponents of total household income/wealth. Such detailed economic information is not available in most social or health surveys, including the data used here. Although reverse causality is less of an issue with education, education is not without its limitations (Krieger, Williams, and Moss 1997). First, the use of a stable SES measure such as education precludes capturing the impact of fluctuating socioeconomic resources on health. Second, the variation in education is more limited than for income/wealth, thus making it a potentially less sensitive measure of social inequalities. Third, the meaning of education varies across ethnic/racial, gender, and cohort groups.




Sample selection (nonresponse) prior to the 1982-84 NHEFS poses another potential limitation with this analysis. The initial NHANES I analytic sample was biased due to relatively high nonresponse rate (-32 percent in NHANES-I). Most of the NHANES-I nonresponse was due to nonresponse for the medical examination component of the NHANES I interview (approximately 30 percent), which meant that some information was available on most nonrespondents.4 Examination nonresponse varied by age and education, with older and less educated people most likely to be nonrespondents. Examination nonrespondents more closely resembled subjects who died before the 1992 NHEFS than subjects who were lost to followup in 1992 (which presumably included a mix of the highly mobile and people who were institutionalized or otherwise unavailable for an interview because of ill health). In this sense, the initial NHANES I sample excludes people who may have been too frail to travel to the mobile exam center. Additionally, there was a 7.1 percent nonresponse rate in the 1982-84 NHEFS reinterview. I doubt these LTF cases substantially altered my findings. The same characteristics that predict LTF in 1992 also predict LTF in 1982-84 (Cohen et al. 1987): black, male, and younger adults. Thus, any bias introduced by people lost to followup in 1982-84 should be in the same direction as I estimate the bias associated with being loss to followup in 1992.

A third limitation of my results is that I employ one method to approximate the likely bias caused by sample selection. In truth, no one can adequately estimate what the health status of people out-of-sample really would have been. The best I can do is to use alternative simulation methods to assess the sensitivity of these patterns to different assumptions. In that vein, this research provides one approx- imation for how I might estimate sample selection bias. Before ruling out the possibility that sample selection bias is operating in the age pattern of education differences in health, future research should replicate and extend this work using alternative health measures, methods of detecting sample selection, and other data sets.

My results suggest that the pattern of age differences in the relationship between education and health that I and others have observed is not an artifact of selection but rather seems to be the product of actual changes in how SES

and health relate over the life course. In particular, it may reflect the successful postponement of morbidity by higher socioeconomic groups until later in life. Future research should focus on identification of mediating factors that have a cumulative or contemporaneous impact on health and how the strength of the factors varies with age.

NOTES

1. Six cases are excluded because of missing health information.

2. I tested the robustness of the findings to alternative classification schemes, including using more categories and coding zero conditions and one condition as separate categories. My results are robust to these alternative specifications.

3. In 1992 NHEFS, the wording of this question was changed slightly. Respondents were asked if: "you have any difficulty doing these things when you are by yourself and not using special equipment." The response set was comparable to that used in 1982-84 NHEFS.

4. Nonresponse for the household interview component of the NHANES I was a remarkable 2 percent (NCHS et al. 1987).

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Megan Beckett is Associate Social Scientist at RAND. In addition to her work on socioeconomic status and health, she is completing a study of racial and ethnic differences in the relationship between marital status and health. Dr. Beckett is also interested in methodological issues related to health surveys, such as the consistency of self-reported health conditions in panel studies.



converging health inequalities in later life-an artifact of mortality selection?

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