Copyright 1992 by The Geronwlogical Society of America

Journal of Gerontology: SOCIAL SCIENCES

1992, Vol.47. No. 6."S304-S3I2

Perceived Health Status and Mortality Among Older Men and Women Fredric D. Wolinsky12 and Robert J. Johnson3 'Department of Medicine, Indiana University School of Medicine. 2 Regenstrief Institute for Health Care, department of Sociology, Kent State University.

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ITH the advent of panel studies of older adults (such as the Longitudinal Study on Aging [LSOA]) and the potential to link respondents' survey data with archival information on decedent status (such as matching to the National Death Index [NDI]), interest in modeling mortality has increased substantially. Perhaps the most intriguing relationship that has been observed to date involves perceived health status. At least six major epidemiological investigations have shown perceived health status (usually measured by asking respondents whether they would rate their health as excellent, very good, good, fair, or poor) to be a significant predictor of mortality, even after controlling for a variety of physical health status indices (see Idler and Angel, 1990; Idler and Kasl, 1991; Idler, Kasl, and Lemke, 1990; Kaplan, Barell, and Lusky, 1988; Kaplan and Camacho, 1983; Mossey and Shapiro, 1982). What is not clear from these studies, however, is what the relationship between perceived health and mortality really means. In an excellent review of the literature, Idler and Kasl (1991) argued that there were three plausible explanations. One was that the methodological limitations of prior epidemiological studies resulted in a spurious relationship between perceived health and mortality. Another was that other psychosocial factors are really involved and, if included in the models, these other psychosocial factors would account for the relationship between perceived health and mortality. The third explanation was that perceived health does, indeed, have an independent, direct effect on mortality. Using data from the Yale Health and Aging Project, Idler and Kasl concluded that the last explanation seemed most plausible, assuming that the covariates they included in their logistic regression models represented a comprehensive set of statistical controls. The mechanism of that direct effect, however, has yet to be identified. Recently, Rakowski, Mor, and Hiris (1991) reviewed three possibilities that have emerged from prior epidemiological investigations. One involves psychoneuroimmunology and takes a traditional biopsychosocial apS304

proach (see Eisdorfer and Wilkie, 1977). In this scenario, negative self-perceptions of health stimulate the neurological system which calls for the release of various chemicals that compromise the immune system and leave the individual more susceptible to opportunistic disease. A second possibility is that poor self-perceptions of health reflect the self-detection of preclinical changes in bodily function. This scenario is consistent with Angel and Gronfein's (1988) suggestion, which builds on Mechanic's (1968, 1978) longstanding view that much of the difference between objective and subjective evaluations of health may reflect physicians' lack of access to patients' perceived but unreported symptoms. A third possibility is that the direct effect reflects the delay in taking health protective and health maintaining actions by those who perceive their health to be poor (see Dean, 1989; Haug, Wykle, and Namazi, 1989). Such delays in help-seeking reduce the efficacy of subsequent treatment. Although all three of these ad hoc explanations of the direct effect of perceived health on mortality are plausible, none of them has been subjected to empirical testing. There are two reasons for this. First, the epidemiological data used to identify the effect of perceived health on mortality were collected for other purposes. They are simply not up to the task of explaining the observed relationship. Second, it is not clear that the covariates used in the previous epidemiological investigations have sufficiently exhausted the possibility that the observed direct relationship between perceived health and mortality would be substantially diminished by introducing more and better measures of objective health status and other psychosocial factors (see Idler and Kasl, 1991; Rakowski, Mor, and Hiris, 1991). In this article we address the latter possibility by presenting separate hierarchical models of mortality for the 1,599 men and 2,904 women self-respondents in the LSOA. Baseline (1984) characteristics are used to predict death at any time prior to the second follow-up (1988). Covariates include demographic, socioeconomic, health status, and psychosocial factors. Our approach differs from prior studies in

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This article separately examines the relationship of perceived health status and mortality for the 1,599 men and 2,904 women self-respondents in the Longitudinal Study on Aging. Using hierarchical logistic regression, the zeroorder relationships are decomposed by the serial introduction of demographic, socioeconomic, health status, and psychosocial factors. For men, only those in poor health are significantly more likely to die than those in excellent health (adjusted odds ratio = 1.754), all other things being equal. For women, those in fair or poor health are more likely to die than those in excellent health (adjusted odds ratios = 1.870 and 2.181, respectively), all other things being equal.

PERCEIVED HEALTH AND MORTALITY

that we include a broader array of physical and functional health status measures, as well as several other psychosocial factors. Accordingly, our analysis provides for a somewhat more comprehensive look at the net relationship between perceived health status and mortality in a large, nationally representative sample of noninstitutionalized older adults. METHODS

Data The data are taken from the LSOA, which is a collaborative effort of the National Center for Health Statistics (NCHS) and the National Institute on Aging. The LSOA involves a special six-year follow-up to the 1984 Health Interview Survey (HIS). The HIS is an ongoing, annual survey that first began in 1956. To this day, the HIS remains as the principal source of all "official" statistics on the health and health services utilization of the United States. Because it is such a well-known data source, it will not be discussed here. For the interested reader, a detailed description of the history, design, and logistics of the HIS can be found in several readily available publications (NCHS, 1975, 1985). In 1984 the HIS was augmented by two special supplements. One involved the collection of detailed information on health insurance coverage among all respondents. The

other (referred to as the Supplement on Aging [SOA]) involved the collection of detailed information by way of a 30-minute add-on interview concerning the health, social functioning, and living arrangements of 16,148 individuals aged 55 and older. A sample of 5,151 SO A individuals aged 70 years or more in 1984 were selected for follow-up interviews (by telephone if they had one, or by mail if they did not) in 1986, 1988, and 1990. In addition, abstracts from the Medicare Part A and B tapes, as well as the results of matching searches of the NDI to document decedent status, are linked to the respondents' records on an annual basis. It is this sample of 5,151 individuals, known as the LSOA, that is the source of data for our research. Greater detail on the design and execution of the LSOA can be found in Fitti and Kovar(1987). With the exception of mortality, all of the data used in the present analysis are based on self-reports given in the baseline interview by the older persons themselves (selfrespondents) or by their proxies (proxy-respondents). Because this article focuses on the effects of respondents' perceptions of their own health on their subsequent mortality, we excluded the 573 proxy-respondents. Other analyses (not shown) that included the proxy-respondents produced results essentially identical to those reported here, demonstrating that the theoretically justifiable exclusion of the proxyrespondents does not introduce bias. Similarly, we excluded the 75 self-respondents who did not answer either the perceived health or other psychosocial items. These exclusions resulted in a usable sample of 1,599 men and 2,904 women self-respondents. We used the unweighted data for these 4,503 men and women self-respondents because it has been empirically shown that the complex schemes necessary to take the disproportionately stratified multi-stage cluster sampling design of the LSOA into account have little impact on variance estimation (Fitti and Kovar, 1987). Moreover, that impact is sufficiently attenuated by the inclusion of age and race as covariates in multivariate models to warrant analyses of the unweighted data. Measurement The measures of the demographic and socioeconomic characteristics are rather typical of the literature, and have been described in detail elsewhere (Wolinsky et al., 1992; Wolinsky and Johnson, 1991, 1992; Wolinsky, Johnson, and Fitzgerald, 1992). Accordingly, we shall only elaborate here on the measures of health status, which are the principal covariates, and the psychosocial factors, which are of particular interest to Idler and Kasl's (1991) second plausible explanation. There are four measures of the demographic characteristics. These are age, sex (by means of the separate analyses), race (Black vs White), and living alone. There are two measures of socioeconomic status: educational attainment and financial dependence on Social Security. Health status. — There are 12 measures of health status. Five are multiple-item scales that emerged from theoretically directed factor and principal components analyses of 21 questions routinely taken from or modeled after various measures of activities of daily living. These included the Activities of Daily Living (ADL) scale developed by Katz et al. (1963), the Instrumental Activities of Daily Living

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Model In the six previous studies that examined the relationship between perceived health status and mortality, a variety of covariates were introduced to decompose the zero-order association (Idler and Angel, 1990; Idler and Kasl, 1991; Idler, Kasl, and Lemke, 1990; Kaplan, Barell, and Lusky, 1988; Kaplan and Camacho, 1983; Mossey and Shapiro, 1982). We classify these characteristics into four broad categories: demographic, socioeconomic, health status, and psychosocial factors. Controlling for demographic factors is necessary given the longstanding relationship between age, sex, race, and life expectancy (see Rice and Feldman, 1983). Similarly, it is necessary to include socioeconomic factors, given their classic relationship with mortality, although that relationship appears to be more significant at younger ages (see Antonovsky, 1967; Kaplan et al., 1987; Kitagawa and Hauser, 1973). Functional health status and disease history must also be controlled, because they are central in the formulation of perceived health status (see Fillenbaum, 1979; Liang, 1986; Stoller, 1984; Wan, 1976; Whitelaw and Liang, 1991), and because sicker individuals are simply more likely to die (see Soldo and Manton, 1985). Finally, to assess whether Idler and Kasl's (1991) second plausible explanation is correct, other psychosocial factors that might be collinear with perceived health status must be included. We use hierarchical models to serially decompose the relationship between perceived health status and mortality, introducing these four categories of covariates in the order given above. This sequence is generally consistent with the most widely used causal modeling approaches for health status, the use of health services, and mortality (Andersen, 1968; Greene and Ondrich, 1990; Idler and Kasl, 1991; Wolinsky, 1990).

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discriminate more on the advanced ADL than they do with either the basic or household ADLs. Thus, there appears to be no evidence that the underlying commonality of the advanced ADL is fine motor skill difficulties. The two remaining scales are drawn from Nagi's (1976) disability items. One taps lower body limitations (5 items; minimum factor loading = .744; alpha = .863). It includes difficulties in walking a quarter of a mile, walking up 10 steps without rest, standing or being on your feet for two hours, stooping, crouching, or kneeling, and lifting or carrying 25 pounds. The other taps upper body limitations (4 items; minimum factor loading = .463; alpha = .588). It includes difficulties in sitting for two hours, reaching up over your head, reaching out as if to shake hands, and using fingers to grasp objects. The remaining measures of health status are derived from the results of extensive factor and principal component analyses of a list of 13 medical conditions. Seven related dimensions were identified, and appropriate dichotomous measures were constructed to indicate whether or not the respondent reported ever having had atherosclerotic heart disease, valvular heart disease, osteoporosis, a fractured hip, cerebrovascular disease (mostly hypertension), cancer of any kind, or Alzheimer's disease. Concurrent face validity was established by having a board-certified internist independently sort the 13 medical conditions into the smallest number of clinically relevant categories. The resulting set of categories was identical to the dimensions derived from the factor analyses. We use these seven measures as added controls for the effects of underlying medical problems that might not be sufficiently tapped by the functional health status measures. Psychosocial factors. — There are four measures of the psychosocial characteristics. Two of these involve what may be called internal resources (House, Landis, and Umberson, 1988). First, respondents were asked whether their overall health status for the past 12 months had caused them a great deal of worry, some worry, hardly any worry, or no worry at all. To maximize the contrasts involved, we dichotomized the respondents' answers into expressing worries vs not expressing worries. Second, respondents were asked how much control they think they had over their future health: would they say a great deal, some, very little, or none. Here again, we dichotomized the responses into expressions of control vs no control in order to maximize the contrasts. Based on prior studies (e.g., Newman, 1975; Rodin, 1986; Rodin, Timko, and Harris, 1985), we expect that those who are more worried about their health and those who feel that they are less in control of their health are more likely to die. The two other indicators of the psychosocial characteristics involve what may be called external resources (House, Landis, and Umberson, 1988). These were constructed as follows. In the LSOA there are seven items that tap the structural or network aspects of social supports (Cohen and Syme, 1985). These include whether the respondent had done volunteer work in the past 12 months, and whether in the past two weeks the respondent had socialized with friends and neighbors (or relatives, not in the household), talked with friends and neighbors (or relatives, not in the

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(IADL) scale developed at the Duke University Center for the Study of Aging and Human Development (1978), and the Nagi (1976) disability scale. We call the first the basic activities of daily living scale (basic ADL; minimum factor loading = .718; alpha = .827). It consists of five items from the traditional ADL, including the need for help with such personal activities as bathing, dressing, getting out of bed, walking, and toileting. We call the second scale household activities of daily living (household ADL; minimum factor loading = .727; alpha = .828). It consists of four items taken from the IADL, including the need for help with such household chores as meal preparation, shopping, and light and heavy housework. The third scale is called advanced activities of daily living (advanced ADL; minimum factor loading = .680; alpha = .641) and is composed of the three items from the original ADL and IADL scales that do not load with the other items. These are the questions about the need for help with managing money, in using the telephone, and in eating. We believe that the advanced ADL taps a set of activities of daily living that focuses more precisely on cognitive capacity. This belief is derived from two propositions. First, none of these activities requires much physical activity. Indeed, the underlying construct is more likely to be cognitive. Cognitive impairments common to this factor include an inability to communicate (on the telephone), to remember (both telephone numbers and the necessity of eating), and to plan for the future (as in scheduling meals or managing money). Empirical support for this interpretation is available. Using the LSOA, we have shown that the advanced ADL is significantly more correlated with memory and confusional state problems than are the basic and household ADLs (Wolinsky and Johnson, 1991). Using data from a Veterans Administration hospital readmission study, we have further shown that among the three ADLs, only the advanced ADL significantly (and, we might add, rather substantially) predicts scores on the Short Portable Mental Status Questionnaire (Pfeiffer, 1975), which is a wellestablished standard for the assessment of cognitive impairment (Fitzgerald et al., in press). Second, and equally noteworthy, the advanced ADLs are independent of upper body limitations (which are separately measured; see below). This mitigates against any interpretation of ah underlying physical problem. Nonetheless, one competing alternative explanation warrants further attention. It is possible that the common factor underlying the advanced ADL is the fine motor skill difficulties resulting from osteoarthritis of the hand and its digits, a condition that afflicts more than 75 percent of older adults (Lawrence et al., 1989). To explore this possibility, we looked at the ratios of the means on each of the ADLs for those who reported having osteoarthritis vs those who did not. The smallest ratio was observed for the advanced ADL, providing further support for the cognitive interpretation. Because the osteoarthritis information is not specific to the hand and its digits, however, some masking may have occurred. To correct for this, we calculated a second set of ratios of the means on each of the ADLs for those who reported having any difficulties in grasping objects with their hands vs those who did not. The resulting ratios indicate that grasp problems do not

PERCEIVED HEALTH AND MORTALITY

household) on the telephone, gone to church or temple, or gone to a group event (e.g., movies, sports events, classes). Extensive factor and principal components analyses of the data revealed that these seven items formed two meaningful scales, one for the five items tapping nonkin social supports (alpha = .600), and one for the two items tapping kin social supports (alpha = .531). Both scales are unidimensional and produce factor loadings greater than .53. Based on prior studies (e.g., Berkman and Breslow, 1983) we expect those with more social supports to be less likely to die.

Mortality. — Mortality status is the outcome measure for this study. It indicates whether or not the respondent was known to be dead at the time of the 1988 reinterview. For this article, that classification was based on the reports of collaterals originally identified at baseline (1984). Of the 4,578 LSOA self-respondents, 939, or 20.5 percent, were reported by their collaterals to have died by the 1988 reinterview. As would be expected (see Rice and Feldman, 1983), men were significantly more likely to die than women (26.9 vs 17.0 percent, p< .0001). Alternative classification schemes incorporating information obtained from the NDI as of December 31, 1988 were also considered. Basically, these involved reliance on a computerized algorithm for matching LSOA respondents with death certificates based on Social Security number, date of birth, sex, race, marital status, states of residence, birth, and death, first and last name, middle initial, and, for women, father's surname (see Fitti and Kovar, 1987, for greater detail). The maximum attainable score was 37, and the minimal attainable score was 4. Scores of 28 or more are considered "good" matches and are "presumed deceased" (22.1%), scores of 22 and 24 through 27 are considered "fair" matches and are "probably deceased" (3.8%), and scores of 23 (indicating a match on the Social Security number, but little else) or less than 22 are considered "poor" matches and are "probably not deceased" (23.3%). Two additional categories involve those for whom no NDI input record was available (1.3%), and those for whom no match occurred (49.5%). The latter are "presumed not deceased." To assess the comparability of such alternatives, we first correlated the collateral reports with two dichotomous measures based on NDI status. One measure reflected "good" matches vs other possibilities, and the other reflected

"good" or "fair" matches vs other possibilities. The respective coefficients obtained were .81 and .86. Because these were less robust than we had hoped, we next conducted a one-way analysis of variance using NDI status as the grouping variable and collateral reports as the outcome. This indicated that only 88.1 percent of the "good" matches and 72.3 percent of the "fair" matches were consistent with collateral reports. Moreover, of the 3,840 respondents not classified by the NDI as "good" or "fair" matches, nearly 2 percent had collateral reports of their death. Thus, our confidence in relying solely on NDI status for classification purposes was somewhat diminished. Nonetheless, we did conduct parallel analyses using a classification scheme that considered respondents to be dead if they had either a collateral report of death, or an NDI status of a "good" or "fair" match. Those results (not shown) were consistent with the analyses reported below. Indeed, for men the variables found to have significant effects were the same, and for women the only difference was the substitution of one marginally significant covariate for another. Thus, we are confident that our results are not an artifact of the classification scheme. Estimation Procedures It is well recognized that logistic regression is preferable for estimating binary outcomes (Cox and Snell, 1989). The distributional split on mortality in these data, however, would arguably be appropriate for ordinary least squares (OLS) regression as well (Cleary and Angel, 1984), despite the artificial upper limits that would be imposed on the obtained R2 levels (Cox and Wermuth, 1990). Also appropriate for these data, inasmuch as the date of death is known for most decedents, would be proportional hazard models (Cox and Snell, 1989). We actually used all three methods, and obtained virtually identical models both in terms of the patterns of significant effects and the magnitudes of the coefficients themselves. Indeed, the only difference was that in the proportional hazard model for men, the effect of education became significant (although it deteriorates over time). Thus, we are confident that the results presented below are not artifacts of the estimation procedure. Here, we simply report the results of the logistic regressions. Consistent with previous studies, the analyses presented here are sex-specific (Idler and Angel, 1990; Idler and Kasl, 1991; Idler, Kasl, and Lemke, 1990; Kaplan, Barell, and Lusky, 1988; Kaplan and Camacho, 1983). That is, the models described above were estimated separately for men and women. The main effect of sex was determined by pooling the men and women in the LSOA, and adding sex as another variable to the final model to be estimated (i.e., Model 5; see Tables 1 and 2 below). The adjusted odds ratio for the main effect of sex on mortality obtained in that analysis was 0.4482 (p < .0001). This indicates that, as expected, even older women are substantially less likely to die than older men (Rice and Feldman, 1983). RESULTS

Men. — Table 1 contains the odds ratios obtained from using logistic regression to hierarchically model the relation-

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Perceived health. — Consistent with the six prior studies (Idler and Angel, 1990; Idler and Kasl, 1991; Idler, Kasl, and Lemke, 1990; Kaplan, Barell, and Lusky, 1988; Kaplan and Camacho, 1983; Mossey and Shapiro, 1982), the excellent response to the perceived health status question is used as the referent in constructing the set of dummy variables for the logistic regression analyses (see Pollisar and Diehr, 1982). A chi-squared test demonstrates that the distribution across the response categories does not significantly differ for men and women. Indeed, the markedly similar respective percentages for men vs women are: (a) for excellent health, 15.5 vs 16.4 percent; (b) for very good health, 20.2 vs 22.3 percent; (c) for good health, 32.1 vs 30.4 percent; (d) for fair health, 21.0 vs 20.9 percent; and, (e) for poor health, 11.2 vs 10.0 percent.

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Table 1. Odds Ratios Obtained From the Hierarchical Logistic Regression Modeling of the Effect of Perceived Health Status on Mortality Among the 1,599 Older Men Who Were Self-Respondents in the LSOA Independent Variables Perceived Health Very good (vs Excellent) Good (vs Excellent) Fair (vs Excellent) Poor (vs Excellent)

Model 1

1.776* 2.642***

Model 3

Model 4

Model 5

1.895* 2.869***

1.9266* 2.945***

1.619 1.970*

1.754

1.083*** .561*

1.083*** .589*

1.073*** .584

1.072*** .625

1.162* .793

1.144* .787

1.388

1.428

Socioeconomics Education Social Security dependence Health Status Basic ADL Household ADL Advanced ADL Lower body limits Upper body limits Atherosclerosis Valvular heart disease Osteoporosis Hip fracture Cerebrovascular disease Cancer Alzheimer's disease Psychosocial Factors Health worries Health control Kin social supports Nonkin social supports Intercept Model G2, df

.709 .869* -1.396***

-7.575***

26, 4***

98, 7***

-7.650*** 94 9***

-6.940***

-6.425***

119,21***

136,25***

Note. Odds ratios not significantly different from one at the p < .05 level are omitted for clarity. The one exception is the adjusted odds ratio for cancer in Model 4, where/? = .0522.

*p< .0\;**p< .001 ;***p< .0001.

ship between perceived health and mortality among the 1,599 men who were self-respondents in the LSOA. Serially comparing the results obtained from Model 1 with those obtained from Models 2, 3, 4, and 5 indicates the decomposition of the zero-order effect of perceived health status on mortality associated with the cumulative introduction of the demographic, socioeconomic, health status, and psychosocial factors. Because our focus is on the direct effect of perceived health on mortality, discussion of the findings reported in Table 1 (and subsequently in Table 2) is limited to three issues: (1) the zero-order effects of perceived health (shown in Model 1); (2) the serial decomposition of the effects of perceived health (shown in Models 2 through 5); and, (3) the net contribution of perceived health to the overall fit of the final model. A more complete discussion of the intermediary and final effects of the demographic, socioeconomic, health status, and psychosocial factors can be obtained from the senior author on request. At the zero-order level, no significant differences are found for being in either very good or good (vs excellent)

health. This is understandable, inasmuch as these are the most minimal contrasts, with the very good contrast involving the only comparison of adjacent health perception categories. For the remaining categories of fair or poor (vs excellent) health, however, there is a significant, negative monotonic relationship between perceived health and mortality. That relationship remains fundamentally unaltered by the introduction of the demographic and socioeconomic factors (Models 2 and 3, respectively). The introduction of the health status measures, however, does substantially diminish the relationship between perceived health and mortality. In Model 4, the effects of being in fair or poor (vs excellent) health are substantially diminished, even though they retain statistical significance. In general, this is as expected, inasmuch as functional health status and disease history are central in the formulation of perceived health (see Fillenbaum, 1979; Liang, 1986; Stoller, 1984; Wan, 1976; Whitelaw and Liang, 1991). Thus, controlling for the antecedents of perceived health diminishes its consequences for mortality.

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Demographics Age Race(Black) Lives alone

Model 2

PERCEIVED HEALTH AND MORTALITY

Women. —Table 2 contains the odds ratios obtained from using logistic regression to hierarchically model mortality among the 2,904 women who were self-respondents in the LSOA. There are two differences worth noting in the zeroorder results (Model 1) for women as compared to men (see Table 1). First, three (vs two for men) of the comparisons to the reference group (i.e., excellent health) are significant for

women. Indeed, only the most minimal contrast, that involving the adjacent categories of very good and excellent, fails to achieve significance. Second, the magnitude of these effects is substantially greater for women, especially among the more extreme comparisons. This suggests that, for women, nonexcellent perceptions of health are more problematic than they are for men. Statistical comparison of the logistic regression coefficients (based on a two-sample test with a pooled estimate of the variance), however, indicates that significant gender differences exist only for those in fair (t = 2.45 , p < .02) or poor (/ = 2.57, p < .02) health. Other than that, Model 1 reveals the same monotonic pattern. The risk of dying is negatively associated with perceived health. The introduction of the demographic factors in Model 2 has no meaningful effect on any of the observed contrasts. The gender differences for those in fair (t = 2.28, p < .05) or poor {t = 2.40, p < .02) health remain significant. Similarly, and as expected, the introduction of the socioeconomic characteristics has no demonstrable effect on the relationship between perceived health and mortality among women (see Antonovsky, 1967; Kaplan et al., 1987; Kitagawa and Hauser, 1973). Indeed, the effects obtained from Model 3 are virtually identical to those obtained from Model 2, and the gender differences for those in fair (t = 2.20, p < .05) or poor (t = 2.21, p < .05) health remain significant. The introduction of the health status measures in Model 4, however, does substantially diminish the relationship between perceived health and mortality. Only the fair and poor (vs excellent) health contrasts remain significant, and they are markedly smaller than before. Moreover, the gender differences for those in fair or poor health are no longer significant. This suggests that women's health perceptions, especially their poorest health perceptions, may be more closely formulated by their functional health and disease histories than those of their male counterparts. The introduction of the psychosocial factors for women (Model 5) does not improve the overall model (the model G2 increment of 1, 4 df is not significant). At the same time, however, the effect of nonkin social supports is significant. This suggests that the effect of nonkin social support for women involves the reattribution of some of the existing fit of the model. The effects of the perceived health contrasts in Model 5 change only at the second decimal place from those in Model 4. Thus, the introduction of these psychosocial factors does not alter the effect of perceived health on mortality. To assess the overall, net contribution of perceived health to the prediction of mortality among women, a sixth model (not shown) was estimated. This model was identical to Model 5, except that we deleted the dummy variables for the categories of perceived health. Subtracting the G2 of the sixth model (model G2 = 259, 21 df;/? < .0001) from that of Model 5 provides a test of model improvement. The result (model G2 improvement = 15, 4 df;/? < .01) indicates that the net effect of perceived health on mortality does significantly improve the model among women. Moreover, the net contribution of perceived health to predicting mortality for these women is considerably stronger than that found by Idler and Kasl (1991).

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The extent to which the effects of perceived health are diminished, however, appears to exceed that reported in the previous studies (see Idler and Kasl, 1991). This may result from differences in the selection of the health status measures used as covariates. Here, we have included more functional health status and disease history measures than the extant studies. Moreover, we have also disaggregated several of the traditional measures into separate, conceptually distinct indices. Of these, it is the lower and upper body limitations scales, derived from Nagi's (1976) disability items, and the history of cancer (albeit with a p = .0522) that produce significant effects on mortality. Although previous studies have controlled for cancer in one way or another (either as a disease, with a proxy for smoking status, or both), they have generally relied on ADL rather than disability-based measures. These data suggest that disability-based measures may be more effective at decomposing the effect of perceived health, at least for men. The introduction of the psychosocial factors in Model 5 has only minimal impact on the measures already in the equation. The effect of being in fair (vs excellent) health marginally loses its significance (p = .0717), but its magnitude is relatively similar (adjusted odds ratio = 1.487). The effect of being in poor (vs excellent) health is not meaningfully altered. The adjusted odds ratios for the other factors in the model also experience only modest change. Thus, the psychosocial factors do not diminish the established pattern of effects. They do, however, add to it. Feeling in control of one's future health and having nonkin social supports both significantly reduce the likelihood of dying. Their effects add significantly to the logistic regression equation (model G2 improvement = 17, 4 df;/? < .01). To assess the overall, net contribution of perceived health to the prediction of mortality among men, a sixth model (not shown) was estimated. This model was identical to Model 5, except that we deleted the dummy variables for the categories of perceived health. Subtracting the model G2 of the sixth model (G2 = 130,21 d f ; p < .0001) from that of Model 5 provides a test of model improvement. The result (model G2 improvement = 6,4 df; p > .20) suggests that the effects of perceived health on mortality do not significantly improve the model for men, net of the other factors already in the equation, when those effects (of perceived health) are distributed over four degrees of freedom. When a single polytomous measure of perceived health is used (adjusted odds ratio = 1.144; p < .05), the result (model G2 improvement = 7, 1 df; p < .01) clearly indicates that the net effect of perceived health on mortality does improve the model for men. Regardless of how the net effect of perceived health on mortality is determined, however, that effect is considerably weaker than that found by Idler and Kasl (1991).

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Table 2. Odds Ratios Obtained From the Hierarchical Logistic Regression Modeling of the Effect of Perceived Health Status on Mortality Among the 2,904 Older Women Who Were Self-Respondents in the LSOA Independent Variables Perceived Health Very good (vs Excellent) Good (vs Excellent) Fair (vs Excellent) Poor (vs Excellent)

Model 1

1.725* 2.826*** 4.528***

Model 3

Model 4

Model 5

1.754* 2.954*** 4.812***

1.739* 2.973*** 4.809***

1.890* 2.254**

1.870* 2.181*

1.104***

1.103***

1.083***

1.081***

1.260**

1.217*

1.118*

1.117*

1.959***

1.979***

Socioeconomics Education Social Security dependence Health Status Basic ADL Household ADL Advanced ADL Lower body limits Upper body limits Atherosclerosis Valvular heart disease Osteoporosis Hip fracture Cerebrovascular disease Cancer Alzheimer's disease Psychosocial Factors Health worries Health control Kin social supports Nonkin social supports Intercept Model G2, df

.906 -2.267***

-9.988***

— 9 985***

-8.836***

-8.556***

84, 4***

210,7***

198, 9***

272,21***

273,25***

Note. Odds ratios not significantly different from one at the p < .05 level are omitted for clarity. *p< .0\;**p< .001; ***/?< .0001.

DISCUSSION

The principal finding to emerge from this study is that perceived health does have a significant direct effect on mortality for both men and women even after controlling for the demographic, socioeconomic, health status, and psychosocial factors. That net direct effect, however, is palpable only for the more distal contrasts (i.e., only the poor vs excellent comparison is significant for men, and only the fair or poor vs excellent comparisons are significant for women). In general, these results are consistent with those reported by prior epidemiological investigations of the relationship between perceived health and mortality (see Idler and Angel, 1990; Idler and Kasl, 1991; Idler, Kasl, and Lemke, 1990; Kaplan, Barell, and Lusky, 1988; Kaplan and Camacho, 1983; Mossey and Shapiro, 1982). To facilitate such comparisons, Table 3 contains the adjusted odds ratios for the most distal comparisons obtained in the six prior studies as well as those reported here. The adjusted odds ratios shown in the table consistently demonstrate that perceived health does have a net direct effect on mortality that the inclusion of increasingly comprehensive sets of theoretically appropriate

covariates fails to eliminate. Nonetheless, two patterns are worth noting. The first pattern is that both the present study and that of Kaplan and Camacho (1983) are the only ones to report larger adjusted odds ratios for women than for men. In the Alameda County Study, which involves a significantly younger sample than the LSOA used here, the reported adjusted odds ratio for women is much larger than that for men, both absolutely and relatively. This suggests that, among younger women, perceived health status is more important in predicting mortality than it is among older women. However, the nonsignificant adjusted odds ratio reported by Idler and Angel (1990) from the National Health and Nutrition Examination Study Follow-Up (which was restricted to those under age 74 at baseline) is inconsistent with such an interpretation. Thus, further exploration of the age- and sex-specific risk of mortality associated with perceived health is necessary to resolve this issue. The second pattern worth noting is that the largest adjusted odds ratios for men, and consistently large adjusted odds ratios for women, are obtained from the two projects

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Demographics Age Race (Black) Lives alone

Model 2

PERCEIVED

HEALTH AND

Table 3. Adjusted Odds Ratios for the Most Distal Comparison of Perceived Health Status Categories in the Major Epidemiological Studies Epidemiological Studies, Samples (S), Response Categories (RC), and Time Periods (TP)

Odds Ratios Men

Women

Total

2. Kaplan and Camacho (1983) S: Alameda County Study (one county) RC: E, G,F, P TP: 9 years

2.33

5.10

3. Kaplan, Barell, and Lusky (1988) 2.4 S: Kiryat One, Israel (one city) RC: Healthy, Fairly Healthy, Sick, Very Sick TP: 5 years

1.8

4. Idler and Angel (1990) 1.5 S: National Health and Nutrition Examination Follow-Up (national sample) RC: E, Very Good (VG),G,F,P (poor and fair combined) TP: 12 years

1.0

5. Idler, Kasl, and Lemke (1990) S: Yale Health and Aging Project 5.33 (one city) S: Iowa and Washington Counties Project 4.84 (two counties) RC: E, G, F, P, Bad (B) (poor and bad combined) TP: 4 years



2.1



these two and the two remaining EPESE projects (East Boston and the Piedmont Catchment Study), is necessary by those with access to these currently proprietary studies. Finally, we would be remiss not to mention the limitations associated with directly comparing the adjusted odds ratios shown in Table 3. The seven studies are different in a number of important ways, including: (a) the sampling strategies employed; (b) the populations under investigation; (c) the ways that respondents are asked about their perceived health and the response categories into which they are forced; (d) the follow-up periods used; (e) the covariates specified for adjustment purposes; and, (f) the statistical modeling approaches selected. Accordingly, it is not possible to definitively identify and meaningfully interpret patterns-among the different results that are reported. At the same time, however, one must be impressed by the statistically significant adjusted odds ratios that are consistently observed for the most distal comparisons. Older men and women who respond so negatively have something important to say. And that statement goes well beyond the information retrieved with traditional approaches to health status measurement. Further research is needed, however, to determine exactly what the underlying mechanism is that so consistently relates perceived health status to mortality. ACKNOWLEDGMENTS

This study was supported by grant R37-AG09692 to Dr. Wolinsky from the National Institutes of Health. 2.99



3.16



6. Idler and Kasl (1991) 6.75 S: Yale Health and Aging Project (one city) RC: E, G, F, P, B (poor and bad combined) TP: 4 years

3.12



7. Wolinsky and Johnson (present study) S: Longitudinal Study of Aging (national sample) RC: E, VG,G, F, P TP: 4 years

2.181

1.990

1.754

S311

Note. The covariates used in the adjustment process vary across studies. Some studies used logistic regression while others used proportional hazard models. All odds ratios are statistically significant (p < .05), unless shown as 1.0.

associated with the Establishment of Populations for Epidemiologic Studies of the Elderly (EPESE). These adjusted odds ratios are quite consistent across the two EPESE sites, and, within the Yale Health and Aging Project, they are consistent across alternative model specifications. Because the EPESE projects are the only studies in which the most distal category is labeled "bad," this suggests that wording choice does indeed have an important effect on the observed relationship between perceived health status and mortality, especially for men. Further exploration of this issue, using

We thank John F. Fitzgerald, M.D., for his comments and suggestions on earlier drafts of this article, and for his invaluable assistance in assessing the clinical relevance of the medical conditions checklist. We also thank three anonymous reviewers whose comments and suggestions significantly clarified our conceptual framework and sharpened the focus of the paper. The opinions expressed here are those of the authors, and do not necessarily reflect those of the funding agencies or academic institutions involved. Address correspondence to Dr. Fredric D. Wolinsky, Department of Medicine, Indiana University School of Medicine, 1001 West Tenth Street, Regenstrief Health Center, Fifth Floor, Indianapolis, IN 46202-2859. REFERENCES

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Perceived health status and mortality among older men and women.

This article separately examines the relationship of perceived health status and mortality for the 1,599 men and 2,904 women self-respondents in the L...
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