Old Age Mortality in Japan: Does the Socioeconomic Gradient Interact with Gender and Age?

Old Age Mortality in Japan: Does the Socioeconomic Gradient Interact With Gender and Age?

Jersey Liang,1,2 Joan Bennett,1 Neal Krause,1,2 Erika Kobayashi,3 Hyekyung Kim,3 J. Winchester Brown,1 Hiroko Akiyama,4,5 Hidehiro Sugisawa,3 and Arvind Jain1 Downloaded from https://academic.oup.com/psychsocgerontology/article/57/5/S294/609421 by guest on 28 September 2021

1School of Public Health, 2Institute of Gerontology, and 4Institute for Social Research, University of Michigan, Ann Arbor. 3Tokyo Metropolitan Institute of Gerontology, Japan. 5University of Tokyo, Japan.

Objectives. There is limited knowledge concerning how the effects of socioeconomic status (SES) on mortality interact with gender and age. In addition, current studies are largely based on data from the Western nations. The validity of prior observations needs to be further evaluated. This research examines socioeconomic inequalities in old age mortality in Japan, with a special emphasis on how inequalities interact with gender and age.

Methods. Data came from a 5-wave panel study of a national probability sample of 2,200 elderly Japanese conducted between 1987 and 1999. Hazard rate models involving time-varying covariates were used to ascertain the direct and indirect effects of SES. In addition, interaction effects involving SES variables with age and gender were evaluated.

Results. In contrast to prior ﬁndings from the Western developed nations, there is an educational crossover effect on mortality among older men, in that, at advanced age, those with less education live longer than those with higher education. On the other hand, there is some evidence that educational differences in the risk of dying tend to converge in the 70–79 age group. More interestingly, there is a crossover in the effect of education among the 80 and older age group.

Discussion. The observation that educational crossover exists only among elderly men may be because of gender and SES differences in causes of death, morbidity, and health behavior. On the other hand, possible explanations for age differences in the educational crossover include selective survival and cohort effects.

HERE is a well-established inverse gradient between trolling for important confounding variables, the linkage be- Tsocioeconomic status (SES) and mortality (Robert & tween gender differences in SES effects on mortality would House, 2000; Rogers, Hummer, & Nam, 2000). At the same be difficult to evaluate and interpret. Second, it has long time, it is now common to conceptualize SES, age, and gen- been recognized that gender difference in life expectancy der as distinct dimensions of social stratification (Lenger- varies across age spectrum, time period, and place mann & Niebrugge, 1996; Riley, 1987). Consequently, there (Nathanson, 1990; Östlin, George, & Sen, 2001). For in- is a growing interest in the interaction between the effects of stance, gender differences in mortality tend to be high in SES on mortality and those of other dimensions of social early adult years, but much lower at either end of the age status. This research examines gender and age differences in spectrum. At the same time, there is a steady widening of the effects of SES on mortality in a national sample of older sex mortality differentials in the developed nations over the adults over a 12-year period in Japan. course of the 20th century (Lopez, 1983; Nathanson, 1990). In addition, in countries where social discrimination against SES and Gender Interaction Effects women is less pervasive, women tend to outlive men. In so- Socioeconomic differentials in health and mortality are cieties where great female deprivation exists, women’s mor- generally more pronounced among men than among women tality rate is higher or equal to that of men (Hemström, (Koskinen & Martelin, 1994; Robert & House, 2000). This 1998; Lopez, 1983). conclusion has been supported by data from the United States, The variations across age, time, and place suggest that so- Canada, France, Hungary, England and Wales, and the Nor- cial factors have a significant influence on gender differ- dic nations (Elo & Preston, 1996; Nathanson, 1990; Valkonen, ences in survival (Nathanson, 1990; Östlin et al., 2001). Ex- 1989). However, it is not supported by several other studies isting studies of gender and SES interaction effects on (Christenson & Johnson, 1995; Kitagawa & Hauser, 1973; health are based on data from adult populations in general Liang et al., 2000). (e.g., Koskinen & Martelin, 1994). There is very limited un- These rather diverse findings are because of at least two derstanding concerning these interaction effects within the reasons. The first is the uncontrolled heterogeneity. In many elderly population. Furthermore, the vast majority of studies studies, important intervening variables, such as baseline are based on data from Western nations. Gender disparities health status, are often not taken into account (Christenson in socioeconomic well-being in Western countries may dif- & Johnson, 1995; Elo & Preston, 1996; Martelin, 1994; Mc- fer substantially from those in non-Western societies be- Donough, Williams, House, & Duncan, 1999). Without con- cause gender role is often reinforced by the educational

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OLD AGE MORTALITY IN JAPAN S295 system, employment policy, and family policy in determin- falls as economic development has continued. Despite in- ing the allocation of resources (Brinton, 1989). For instance, dustrialization and supposed Westernization, Japan has es- among developed countries, Japan is a persistent outlier in caped the CHD epidemic and has had among the lowest terms of women’s status (Brinton, 1989). In Japan, the male- rates of heart disease of any developed nation (Marmot & female wage gap is greater in that salaries among full-time Davey Smith, 1989; Marmot & Mustard, 1994). Finally, female employees are 63% of their male counterparts. At breast cancer mortality used to be higher among higher SES the same time, women are much more likely to be unpaid women because of higher incidence in this group. However, family workers in small family-run businesses or farms. now among White American women, this association has

Furthermore, women’s representation in managerial rank is largely disappeared (Heck, Wagner, Schatzkins, Devesa, & Downloaded from https://academic.oup.com/psychsocgerontology/article/57/5/S294/609421 by guest on 28 September 2021 lower with less than 10% in 1997 (Ministry of Foreign Breen, 1997). Affairs of Japan, 2001). Although high school seems to have become the minimum acceptable level of education for both Old Age Mortality in Japan sexes in Japan, men are far more likely to go on to 4-year To address the previous issues, the present research ex- universities than are women. The gap has narrowed consid- amines socioeconomic differentials in old age mortality in erably, but even in 1997, 26% of girls and 43% of boys went Japan. Japan provides an ideal context for further research to universities (Ministry of Foreign Affairs of Japan, 2001). on old age mortality, particularly in contrast with the United States. Both nations are similar in economic development SES and Age Interaction Effects and are experiencing rapid population aging. In 2000, 12.5% In addition to gender, socioeconomic inequalities in health of the U.S. population was 65 years of age and over. This may interact with age. In particular, SES differences in health proportion is projected to reach 17.5% in 2020. In Japan, the and mortality are small in early adulthood, greatest in mid- proportion of the aged population was 16.5% in 2000 and is dle and early old age, and relatively small again in late old projected to be 25.6% in 2020 (OECD, 1998). age (Robert & House, 2000). This is because of the greater The Japanese have the highest life expectancy at birth (77 exposure of lower SES persons to a wide range of psychoso- years for males and 83.6 years for females in 1995) in the cial risk factors. Health eventually converges across differ- world, and can live 3 to 6 years longer than Americans (72.7 ent socioeconomic strata because people inevitably weaken years for males and 79.4 years for females in 1995; OECD, and die in old age regardless of social class (Elo & Preston, 1998). In terms of the leading causes of death, Japan has 1996; House, Kessler, & Herzog, 1990). In contrast, Ross significantly lower rates of death from cancer and circula- and Wu (1996) advocate a hypothesis of cumulative advan- tory system disease than the United States. What is so re- tage, because they find no evidence of convergence in health markable is that the high life expectancy in Japan was only among different education and income levels in old age. accomplished during the last 4 decades (Evans, Barer, & Whereas prior studies contribute significantly to our un- Marmor, 1994). derstanding of health changes across the adult life course, In terms of social stratification, Japan has three distinct two important issues remain unresolved. First, current studies characteristics. First, there is less income inequality in Ja- in general treat the elderly population as a whole without pan than in other market economies. According to the stan- differentiating young-old from older-old and the oldest-old. dard deviation calculated from percentage share of income In particular, does SES exert the same effect at 60 as well as distribution by quintile, Japan ranks 14th (11.14), whereas 80? Does the SES effect on health continue to increase in the United States ranks 40th (14.37) in equality in income old age or converge? In addition, the number of elderly sub- distribution among 85 nations (World Bank, 2001). Second, jects included in some studies is quite modest (Ross & Wu, there is a great emphasis on educational credentials and in- 1996). Second, theories of convergence and cumulative ad- tense competition for higher education. However, family vantage imply changing interindividual differences over the background (i.e., parental income, education, and occupa- adult life span. This calls for repeated observations of the tion) exerts a greater effect on educational attainment in Ja- same individuals over a very extended period of time, if not pan than in the United States and Britain (Ishida, 1993). Third, an entire life span. However, current studies are based on Japan has a large middle class whose members are highly either cross-sectional data or 2-wave short-term panel homogeneous in their attitudes and lifestyles (Kosaka, 1994). studies of the adult population (House et al., 1994; Ross & The traditional support of elderly people by their children Wu, 1996). These hypotheses have not been evaluated by is still very strong in Japan today. In 1996, 52% of elderly prospective studies over an extended period of time. Japanese coresided with their adult children (Brown et al., Third, observations concerning age and SES interaction 2002), compared with only 12% in the United States (U.S. effects on health are almost exclusively derived from West- Bureau of the Census, 1999). In addition, Japanese elders ern developed nations, particularly the United States. Their are much more likely to receive financial support from their external validity needs to be carefully evaluated, because children than older adults in the United States (Maeda & the linkages between a given disease and SES may vary Shimizu, 1992). However, the popularity of joint households across different societal and cultural contexts. For example, in Japan has declined significantly, and there has been a shift Western Europe, North America, and Japan differ signifi- toward other sources of support, including public pensions cantly with reference to the incidence of coronary heart dis- (Maeda & Shimizu, 1992; Schulz, Borowski, & Crown, ease (CHD). In Western countries, typically there has been a 1991). On the other hand, far more elderly people work in sharp increase in CHD mortality in parallel with the rapid Japan than in other industrialized nations. In 1988, 36% of increase in prosperity after World War II, followed by sharp older Japanese men and 16% of older Japanese women

S296 LIANG ET AL. worked, whereas in the United States, the corresponding ﬁg- human capital one has, whereas income and home owner- ures were 16% and 8% (Bass, 1996). However, in both na- ship are indicators of the ﬂow and stock of one’s material re- tions, the labor-force participation rates of the elderly popu- sources. High SES individuals have the knowledge, resources, lation have been declining for the past several decades and social connections to avoid risk or to minimize the effects (Noguchi & Wise, 1994; Ogawa, 1998). of disease, impairment, and disability (Link & Phelan, 2000). With regard to the socioeconomic inequalities in health, In accordance with the current literature, an inverse relation- Japan also exhibits a distinct pattern. Whereas a strong so- ship between SES and mortality is hypothesized (Elo & Pres- cioeconomic gradient has been observed in both infant and ton, 1996; Kaplan, 1997).

adult mortality, mortality rates among female professional On the other hand, social relationships have been recog- Downloaded from https://academic.oup.com/psychsocgerontology/article/57/5/S294/609421 by guest on 28 September 2021 workers are as high as among service workers and higher nized as risk factors of mortality and morbidity since the than among clerical workers (Hasegawa, 2001). Moreover, early 1970s. Prospective studies have consistently shown in- Cockerham, Hattori, and Yamori (2000) recently reported creased risk of death among persons with a low quantity, that Okinawans traditionally rank at the top in health and and sometimes low quality, of social relationships (House, life expectancy and at the bottom in socioeconomic indica- Landis, & Umberson, 1988). Social relationships can affect tors. They suggested that the social gradient thesis does not individuals by providing them with a more positive view of apply in Japan, and lifestyles such as diet and social support themselves and their abilities, such as mastery, control, and are more important factors. Nevertheless, the aforemen- social competence that, in turn, may protect or better pre- tioned studies have been entirely based on macro data and pare individuals in coping with a health event (Antonucci, have not focused on old age mortality. Although there have 1990). Finally, health status at the baseline, including dis- been numerous studies of old age mortality in Japan, very eases, functional limitations, and self-rated poor health, is little attention is directed to the impact of SES and how it postulated to be associated with a greater risk of dying dur- interacts with age and gender (Honma, Kagamimori, & ing the subsequent 3 years (Idler & Benyamini, 1997; Rogers Nruse, 1998; Iwamoto et al., 1994; Nakanishi & Tatara, et al., 2000). 2000; Nakanishi et al., 1997). The predictors of mortality are interrelated among themselves. Specifically, SES varies by age, gender, and place of Model Specifications residence (Hayward et al., 1997; O’Rand, 1996). In addi- To analyze how socioeconomic differences in old age tion, social relations are correlated with demographic vari- mortality interact with gender and age, a conceptual frame- ables and SES. At the same time, demographic variables, work is proposed. Factors associated with mortality are dif- SES, and social relations influence baseline health status ferentiated into four domains, including demographic variables (House et al., 1994). Accordingly, baseline health condi- (i.e., age, gender, and community size), SES (i.e., education, tions are conceptualized as intervening variables, in that income, and home ownership), social relationships (i.e., SES may influence the risk of dying directly and/or indi- marital status, employment, household size, and emotional rectly. The theoretical rationale underlying the proposed and instrumental support), and baseline health characteris- framework stems from a sociomedical perspective of health tics (disease, functional status, self-rated health, depression, (House et al., 1994; Kaplan, 1989; Rogers et al., 2000). and cognitive impairment). In the following, a brief rationale for the inclusion of these variables is presented. METHODS The importance of age and gender in affecting mortality is well substantiated in the literature. In general, death rates Sample and Data decline from infancy to early teen ages and increase thereaf- Data for this research are from a 5-wave panel study of ter, growing more rapidly as old age approaches (Rogers et health and well-being of older adults in Japan. The baseline al., 2000). At older ages, the rate of mortality increase slows survey was conducted in November 1987, which involved a with age (Horiuchi & Wilmoth, 1998). On the other hand, two-stage stratified national probability sample of 2,200 health status and mortality vary significantly between the Japanese aged 60 and over. The respondents were reinter- sexes. In the developed nations, men have higher mortality viewed every 3 years (i.e., in 1990, 1993, 1996, and 1999). rates than women, but women have higher rates of morbidity Proxy interviews were obtained, if possible, for those indi- and health care utilization. According to Verbrugge (1989), viduals unable to complete the survey themselves. A nonre- this paradox can be explained by the fact that women have sponse questionnaire was used to provide limited informa- high rates of acute illnesses and of most nonfatal chronic tion in the case of death or other reasons for nonparticipation. conditions, whereas men experience higher prevalence rates With a response rate of 69%, the sample derived from the of the leading fatal conditions. In addition to age and gen- baseline survey was found representative of the total elderly der, residential setting can influence mortality. As an indica- population (Jay, Liang, Liu, & Sugisawa, 1993). tor of urbanization, size of community may reflect dispari- A number of procedures were taken to minimize the rate ties in health care services and social structural conditions of attrition over time. In particular, all respondents were pre- pertinent to individual health (e.g., ambient hazards and so- sented a small gift at the conclusion of the interview. A cial disorders; Hayward, Pienta, & McLaughlin, 1997). newsletter describing the findings was mailed to all respon- SES status may involve knowledge, resources, commu- dents before the next wave of survey took place. In addition nity standing, and power. In this model, SES is represented to the proxy interviews, a significant effort was made to by multiple measures, including education, household income, convert each individual who had completed the baseline in- and home ownership. Education represents the amount of terview, but refused to participate in the subsequent follow-up.

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Typically, those considered by the interviewers and their su- in the study in terms of all covariates included in our pervisors as likely to reverse their refusal were identiﬁed models. A further comparison of the surviving respondents and reassigned to another interviewer, often more experi- (self-respondents and those with proxy interviews) with the enced. Moreover, all those who were unable to participate in nonrespondents revealed no systematic differences either. one survey (i.e., subjects with proxy interviews and refusals) These results suggest that our estimates of the effects of were contacted again at the subsequent follow-up. As a result, SES and other covariates on the risk of dying are unlikely to many of them were recovered and retained in later surveys. be biased. The results are not included here, but will be As an example, in the 1993 survey, 210 individuals who did made available on request.

not participate in 1990 were followed-up, and 135 of them Downloaded from https://academic.oup.com/psychsocgerontology/article/57/5/S294/609421 by guest on 28 September 2021 were recovered as respondents in our study. According to Measures Table 1, 7,143 three-year episodes between 1987 and 1999 Deaths among the 2,200 respondents included in the have been included in our analysis, whereas 530 episodes baseline survey were verified periodically against official (or 6.9% of the total 7,704 observations) were excluded. records. Between 1987 and 1999, 724 members (33%) of this Because we were able to check periodically against the cohort died. In particular, 3 months before each survey, resi- Japanese resident registration system about the vital status dential cards were sent to various local bureaus of resident of all members of the cohort, death rates for self-respondents, registration across Japan to ascertain whether the respon- those with proxy interviews, and nonrespondents could be dents were still living at the last known addresses. If a re- ascertained. Although the death rates among the nonrespon- spondent had moved, he or she would be interviewed at the dents during the four 3-year intervals were higher than those new location. In addition, we were able to learn whether a of self-respondents, their risk of dying was substantially less given respondent was alive or not. In Japan, by law, a death than that of the respondents with proxy interviews. In gen- must be reported to the Resident Registration Bureau with a eral, death rates tend to be slightly underestimated because death certificate within 7 days. Should a respondent be found of the exclusion of nonresponses, particularly for the inter- deceased, the date of death was recorded. In addition, intervals of 1990–1993 and 1996–1999. viewers who reported deaths that had occurred during the The differences between those who dropped out (alive or last 3 months further updated this information. These field dead) and those who remained in the study (including self- reports were subsequently checked against the official records. respondents, those with proxy interviews, and those de- Through this procedure, we were able to ascertain the sur- ceased) were also examined. In particular, a multivariate vival status of all 2,200 members of the cohort regardless of logistic regression analysis of the probability of dropping whether they chose to remain in the study or not. Given that out was done for each 3-year interval, by using the baseline the Japanese system of resident and vital registration is inter- characteristics to predict the response status at the follow- nationally known for its high quality, we are quite confident up. According to our multivariate analysis, the nonrespon- of the validity and reliability of our data on survival status. dents did not differ significantly from those who remained SES was assessed by home ownership, education, and in-

Table 1. Survival Status at the Follow-up by Response Status at the Baseline, 1987–1999

Survival Status at Follow-up

Response Status at Baseline, n Alive Dead Death Rate Response status in 1987 Alive in 1990 Dead in 1990 Death Rate 1987–1990 Self-respondents 2,200 2,033 167 7.6% Subtotal 2,200 2,033 167 7.6% Response status in 1990 Alive in 1993 Dead in 1993 Death Rate 1990–1993 Self-respondents 1,671 1,566 105 6.3% Proxy interviews 152 92 60 39.5% Nonresponsesa 210* 187 23 11.0% Subtotal 2,033 1,845 188 9.3% Response status in 1993 Alive in 1996 Dead in 1996 Death Rate 1993–1996 Self-respondents 1,532 1,400 132 8.6% Proxy interviews 173 102 71 41.0% Nonresponsesa 140a 124 16 11.4% Subtotal 1,845 1,626 219 11.9% Response status in 1996 Alive in 1999 Dead in 1999 Death Rate 1996–1999 Self-respondents 1,247 1,129 118 9.5% Proxy interviews 199 119 80 40.2% Nonresponsesa 180a 150 30 16.7% Subtotal 1,626 1,398 228 14.6%

Notes: From 1987 to 1999, 7,174 three-year episodes have been included in our analysis. This can be derived by adding up the subtotals of the four intervals (i.e., 1987/1990, 1990/1993, 1993/1996, and 1996/1999) and subtracting from them the 530 nonresponses (i.e., 210 in 1990, 140 in 1993, and 180 in 1996). Of a total of 7,704 (i.e., 530 7,174) observations, 6.9% (or 530) were excluded because of nonresponse. aExcluded from our analysis because of nonresponse at the baseline.

S298 LIANG ET AL. come. As an indicator of asset, home ownership was defined on morbidity was derived from a checklist of conditions. An as a dummy variable, with “owning your own home” coded index of serious conditions (i.e., those thought to be fatal) as 1. Education was indexed to differentiate among those was generated by including diabetes, heart disease, hyper- who completed primary school (6 years), junior high (9 tension, and stroke, whereas the remaining conditions (e.g., years), and senior high or more (12 years or more). Specifi- arthritis/rheumatism, respiratory diseases, chronic back pain, cally, with a reference category of 0–5 years of education etc.) were grouped to be chronic conditions (Ferraro & (4.3% of the total sample), three dummy variables were Farmer, 1999). Next, an index of functional status was cre- constructed to reflect 6–8 years of education (56%), 9–11 ated using (a) four 4-point items of functional limitations years of education (28%), and 12 years or more (13%). With (i.e., crouching, grasping, lifting, and reaching; alpha Downloaded from https://academic.oup.com/psychsocgerontology/article/57/5/S294/609421 by guest on 28 September 2021 a reference category of fewer than 1.65 million yen (i.e., ap- .839) and (b) six 5-point items of activities of daily living proximately US$15,000 assuming an average exchange rate (i.e., bathing, climbing stairs, walking a half mile, using the of 110 yen per US dollar; 11% of total sample), four dummy phone, shopping, and traveling by bus or boat; alpha variables were created to represent various levels of annual .935). Z-scores for these two measures (r .831) were household income, including (a) 1.65 through 3.95 mil- computed and then summed. Finally, self-rated health was lion yen ($15,000–$35,909; 27%), (b) 3.95 through 5.75 assessed via three indicators: (a) a rating of physical health million yen ($35,909–$52,727; 24%), (c) 5.75 through [coded: excellent (1), fairly good (2), average (3), not very 9.75 million yen ($52,727–$88,636; 30%), and (d) 9.75 good (4), and poor (5)], (b) health comparisons with other million yen or greater ($88,636 or more; 9%). We chose people one’s age [better (1), about the same (2), and worse these specific cut points in representing education and in- (3)], and (c) a report of overall satisfaction with one’s health come for two reasons. First, they should represent the entire [coded: very satisfied (1), relatively well satisfied (2), can’t distribution well. Second, to the extent possible, cut points say (3), not very satisfied (4), and not at all satisfied (5)]. should represent meaningful social categories as in the case Health compared with others was scaled to reflect a 5-point of education. Finally, those with the least education and scale, and then the three items were summed (alpha those with the lowest income were used, respectively, as ref- .796). All physical health measures were coded to reflect erence categories in our multivariate Cox regression analy- greater morbidity, impairment, or poor health. ses. One may be concerned with the fact that they repre- Regarding mental health, depressive symptoms were rep- sented rather small proportions of the total sample, thus resented by seven items drawn from the Center for Epidemi- resulting in greater errors in estimation. However, we have ological Studies–Depression scale (CES-D; Radloff, 1977). experimented with several different choices of the reference These items included: (a) appetite was poor, (b) sleep was group, and the results have remained invariant. restless, (c) could not get going, (d) everything I did was an Three demographic measures (i.e., gender, age, and urba- effort, (e) felt depressed, (f) felt lonely, and (g) felt sad nicity) were included to control for population heterogene- [coded: most of the time (3), sometimes (2), and rarely (1)]. ity. Gender was defined as a dummy variable, with “female” All items were scored such that a higher score reflects coded as 1. Age was measured in terms of actual years of higher levels of depression (alpha .807). In addition, cog- age. Urbanicity was measured by a single 5-interval scale nitive impairment at each survey wave was assessed using item indicating a range of the population size of the area in Pfeiffer’s (1975) Short Portable Mental Status Question- which the respondent resided. The intervals were recoded to naire. Specifically, a count of the number of incorrect re- reflect a midpoint representative population size for each of sponses across nine questions covering short- and long-term the five categories (.25 25,000; .75 75,000; 1.5 memory, orientation to surroundings, knowledge of current 150,000; 6 600,000; and 20 2,000,000). events, and the ability to perform mathematical tasks was Several social relations measures were used, including obtained. Unanswered questions were counted as incorrect marital status, work status, size of household, and emotional (Fillenbaum, 1980). With a range of 0–9, a higher score re- and instrumental support. Both marital and work status flects greater cognitive impairment. Table 2 includes the de- were defined as dummy variables with “currently married” scriptive statistics for selected variables in each episode and and “currently working full or part time” coded as 1. Size of in the pooled sample. household reflected the actual number of individuals, including the respondent, who resided within that household. Data Analysis Emotional support received was assessed as a composite of Hazard rate models involving time-varying covariates two items concerning how often the respondent felt that were used to ascertain the direct and indirect effects of SES someone “listened to them” and “made them feel cared for” on mortality, whereas ordinary least squares regression was (r .747). Instrumental support received also comprised a used to assess the interrelationships among the covariates. linear composite of two items asking how often someone With the exception of gender and education, the values of all provided “help to you when you were sick” and “help to covariates might change once every 3 years. For example, a you when you needed financial assistance” (r .412). For respondent, who was married during the interval of 1987– all support items, a 4-point scale was used with the follow- 1990, might become widowed during the interval of 1990– ing coding scheme (scoring in parentheses): very often (4), 1993. The same would apply to income, social support, and fairly often (3), once in a while (2), never (1). Higher scores health status. To capture the time-varying nature of these co- reflect greater support received. variates, episodes instead of individual respondents were Physical health status was assessed by measures of mor- used as the unit of analysis (Allison, 1995, pp. 219–223). bidity, functional status, and self-rated health. Information Each episode involved linking the independent variables

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Table 2. Descriptive Statistics for Selected Variables in Each Episode and in the Pooled Sample

1987–1990 1990–1993 1993–1996 1996–1999 Pooled (n 2,200) (n 1,823) (n 1,705) (n 1,446) (n 7,174) Death % deceased 7.59 9.05 11.38 13.69 10.09 Gender % female 54.77 55.68 57.71 59.34 56.62 Marital status % married 62.85 61.02 56.91 49.15 58.21 Downloaded from https://academic.oup.com/psychsocgerontology/article/57/5/S294/609421 by guest on 28 September 2021 Work status % working 25.83 25.92 23.58 17.79 23.70 Age Mean 69.16 71.62 74.08 76.57 72.45 SD 6.75 6.41 6.08 5.79 6.89 Education Mean 8.62 8.71 8.69 8.78 8.69 SD 2.82 2.74 2.70 2.69 2.75 Serious illness Mean .43 .52 .58 .56 .51 SD .63 .73 .73 .71 .70 Chronic illness Mean .66 .92 .91 .81 .82 SD .95 1.22 1.13 1.04 1.09 Self-rated ill health Mean 7.80 7.60 7.81 7.96 7.78 SD 2.93 2.72 2.90 2.89 2.86 Cognitive impairment Mean .68 1.04 1.20 1.49 1.06 SD 1.16 1.36 1.43 1.55 1.39

(IVs) for each respondent at a given wave with his or her tively easy to implement and computationally more efficient. survival status during the subsequent 3 years [i.e., wave 1 For illustrations of this approach, please refer to Crimmins, (1987) IVs with 1987–1990 survival information; wave 2 Hayward, and Saito (1996), Hayward, Friedman, and Chen (1990) IVs with 1990–1993 survival information; wave 3 IVs (1998), and Miller, Longino, Anderson, James, and Worley (1993) with 1993–1996 survival information; and wave 4 (1999). IVs (1996) with 1996–1999 survival information]. Accord- As longitudinal data were available over a 12-year period, ingly a surviving respondent in 1999 could conceivably measures of the period of observation were derived in addi- have contributed up to four episodes. The pooled data began tion to age. In particular, with the period of 1987–1990 as with the 2,200 episodes from the 1987 baseline survey. Ex- the reference, three dummy variables were created to repre- cluding deaths and nonresponse, these 2,200 episodes were sent the 1990–1993, 1993–1996, and 1996–1999 periods. pooled with 1,823 episodes from 1990–1993, 1,705 epi- These measures may be viewed as representing changes sodes from 1993–1996, and 1,446 episodes from 1996– over time that were not fully captured by measures of other 1999 resulting in 7,174 episodes available for analysis. covariates, particularly those at the individual level. To capture the time-varying nature of the covariates, their values are updated during each interval. One may be con- Missing Data cerned with our approach of using episodes as the unit of Given the large number of variables involved at each analysis. However, Allison (1982, 1995, pp. 219–224) has wave of the survey, a suitable strategy for handling missing shown that the creation of multiple observations is not an ad data needed to be derived. Only three variables contained hoc method, and it follows directly from factoring the likeli- missing data in more than 20% of the episodes, whereas hood function for the data. In particular, the probability of the remaining variables had missing values in less than 10% of dying over the 12-year period for a given individual can be the episodes. To minimize the loss of subjects from missing expressed as a product of a series of probabilities during the data, multiple imputation (MI) was undertaken with soft- four 3-year intervals. Each of these terms may be treated as ware developed by Schafer (1997). In MI, each missing though it came from a distinct and independent observation. value is represented by a set of m 1 plausible values Furthermore, Allison has provided several empirical illus- drawn from their predictive distribution. The variations trations in which the data were analyzed by using persons as among the m imputations reflect the uncertainty with which well as episodes as the units of analysis. The results are vir- the missing values can be predicted from the observed ones tually identical. When there are many ties and many time- (Rubin, 1987). MI has been shown to be an efficient imputa- dependent covariates, the episode-based approach is relation procedure with a sound statistical basis. In the present

S300 LIANG ET AL. research, a multivariate normal distribution involving all cova- indirect effects of SES. Finally, SES effects in interaction riates was assumed to generate imputations for the missing with those of gender and age are examined. values. This model creates a system of simultaneous regression equations in which each variable potentially depends on all Gross Effects other variables. Although such a model is at best approximately The gross effects of all covariates are listed in Table 3 true, experience has repeatedly shown that MI tends to be quite (under the column of bivariate analyses). The gross effect is forgiving for such departure. Three complete data sets were im- measured by using a given covariate as the only predictor of puted and analyses were run on each of these three data sets. the risk of dying without controlling for any other variable.

Estimates were averaged across the three imputations to gener- Two of the three SES variables show a significant gross ef- Downloaded from https://academic.oup.com/psychsocgerontology/article/57/5/S294/609421 by guest on 28 September 2021 ate a single point estimate. Standard errors were then calculated fect on mortality. In comparison with those with less than 6 using a formula that combines the average of the squared errors years of education, the mortality risk for those with higher of the estimates and the variance of the parameter estimates education is only 43%–54% as large. Similarly, in contrast across the three samples (Schafer & Olsen, 1998). with those with the lowest household income (less than 1.65 million yen or roughly US$15,000 a year), the risk of RESULTS dying is only 60%–78% as large for those with higher In the following, the gross effects of SES on old age mor- household incomes. Home ownership is associated with tality are presented first. Findings from multivariate analysis slightly higher mortality (eb 1.012), but is not statisti- are described subsequently with emphases on the direct and cally significant.

Table 3. Risk Ratios (eb) From the Cox Regression Analyses With Income and Education as Dummy Variables (N 7,174)

Model 3 SES, Model 2 Demographic Model 5 Model 1 SES & & Social Model 4 Complete SES Demographic Relations Complete With Time Covariate Bivariate (df 8) (df 11) (df 16) (df 22) (df 25) Age 1.119*** 1.118*** 1.101*** 1.083*** 1.085*** Gender (1 female) .520*** .472*** .381*** .405*** .406*** Population size 1.011* 1.004 1.004 .999 .999 Education 0–5 years (reference group) 1.000 1.000 1.000 1.000 1.000 1.000 6–8 years .533*** .552*** .693** .649** .883 .893 9–11 years .429*** .452*** .667** .873 .897 .912 12 years or more .534*** .570** .720 .892 1.059 1.079 Own home (1 yes) 1.012 1.066 .942 .969 .992 .991 Household income (million yen) 0–1.65 (reference group) 1.000 1.000 1.000 1.000 1.000 1.000 1.65–3.95 .596*** .612*** .731* .741* .905 .918 3.95–5.75 .777* .785* .838 .837 .986 1.006 5.75–9.75 .615*** .627** .641** .614** .790 .817 9.75 .669* .686* .645** .661* .902 .956 Marital status .711*** .779** .883 .880 Work status .368*** .480*** .691** .690** Household size 1.024 1.039 1.009 1.001 Emotional support .894*** .959 .994 .980 Instrumental support .934** .982 .996 .991 Serious illness 1.261*** .999 1.000 Chronic illness 1.086** .947 .944 Functional status 1.348*** 1.123*** 1.126*** Self-rated ill health 1.222*** 1.122*** 1.121*** CES-D 1.133*** .984 .985 Cognitive impairment 1.424*** 1.133*** 1.136*** Time 1987–1990 (reference group) 1.000 1.000 1990–1993 1.205 .943 1993–1996 1.535*** .919 1996–1999 1.888*** .812 2 LL without covariates 12,782.23 12,782.23 12,782.23 12,782.23 12,782.23 2 LL with covariates 12,733.55 12,201.80 12,141.45 11,822.08 11,818.83 Model 2 48.69 580.43 640.78 960.33 963.41

Note: SES socioeconomic status; CES-D Center for Epidemiological Studies–Depression scale; LL log likelihood. *p .05; **p .01; ***p .001. All measures of ﬁt are signiﬁcant at p .001. OLD AGE MORTALITY IN JAPAN S301

As expected, mortality increases with age (eb 1.119), and sex composition, as well as variations in social net- and there is a lower risk of dying among women (eb works and social support. Educational differences in mortal- .520). In addition, increased urbanization is associated with ity are no longer significant when baseline health status and a higher mortality (eb 1.011). On the other hand, being time of observation are included in the equation, indicating married (eb .711) and currently employed (eb .368) that education influences mortality risk indirectly through tend to lower the risk of subsequent mortality. Emotional baseline health conditions (Models 4 and 5 in Table 3). The and instrumental supports also diminish the risk of dying same pattern of changes may be applied to the effects of significantly (eb .894 and eb .934). With reference to household income. In contrast, home ownership does not b the effects of health status, serious health conditions (e show any significant effect on the risk of dying throughout Downloaded from https://academic.oup.com/psychsocgerontology/article/57/5/S294/609421 by guest on 28 September 2021 1.261), chronic conditions (eb 1.086), functional limita- the hierarchical regression analyses. tions (eb 1.348), and self-rated poor health (eb 1.222) Social relations, including marital status and work status, are all predictors of higher mortality. Two mental health are important predictors of old age mortality among the Jap- measures, depressive symptoms (eb 1.133) and cogni- anese. In particular, marital status exerts an independent ef- tive impairment (eb 1.424) also show significant associ- fect (eb .779) on mortality in addition to demographic and ations with mortality. Finally, compared with the mortal- other measures of social relations (Model 3, Table 3). How- ity between 1987 and 1990, the risk of dying increased by ever, its effect fails to maintain statistical significance when 54% and 89%, respectively, for the periods from 1993 to baseline health conditions are included in the equation, sug- 1996 and from 1996 to 1999. gesting that the effect of marital status is mediated through health variables (Model 4). In contrast, the effects of em- Direct and Indirect Effects ployment status on mortality persist even when baseline health Next, the effects of SES variables are evaluated by con- conditions are taken into account (Models 4 and 5). This re- trolling various covariates hierarchically (Table 3). In partic- flects that the effect of work status on mortality cannot be ular, SES effects are examined by themselves first (Model completely explained by variation in baseline health. 1) and then by controlling for age, gender, and community Baseline physical and mental health variables are potent size (Model 2). Following this, they are evaluated by adding predictors of the risk of dying during the following 3 years variables of social relations (Model 3), and then health sta- (Model 4 in Table 3). In particular, poor functional status tus measures to the equation (Model 4). Finally, period dif- (eb 1.123), self-rated poor health (eb 1.122), and cogni- ferences are included (Model 5). By analyzing the stability tive impairment (eb 1.133) are associated with higher and change in the relative risk ratios across the hierarchical mortality. The fact that the net effects of serious conditions, regressions, one may gain some insights concerning the di- chronic conditions, and CES-D are not significant may be rect as well as indirect effects of the predictors of mortality. from the substantial correlations among the health measures. As various covariates are brought into the equations, the In particular, the correlations between CES-D and other relative risk ratios associated with age diminish somewhat physical and mental conditions range from .145 to .407. but remain quite robust. All else being equal, an increase of A relatively unique aspect of this research is that data were 1 year in age leads to a marginal increase of 9% in mortality collected over a period of 12 years (i.e., 1987–1990, 1990– (Model 5). Because age is treated as a time-varying covari- 1993, 1993–1996, and 1996–1999). Measures of these four ate, this effect measures a mixture of cross-sectional and lon- intervals may be viewed as representing changes over time gitudinal differences. In contrast, when all covariates are that are not fully accounted for by various covariates in- controlled, gender differences in mortality become even more cluded in the equation (Model 5). According to Table 3, the accentuated in that female mortality decreases from 52% to initially significant variation in mortality across time peri- only 41% of that of the male. This suggests that the age and ods ceases to be statistically significant, when all other co- gender differences in mortality are substantial, and they can- variates were included. This indicates that the observed in- not be explained by intervening variables, including SES, crease in mortality over time can be explained by the changes social relations, and baseline health conditions. More im- in population heterogeneity as measured by various other portantly, the observed gender differences are often masked covariates in the model. According to a further analysis, this by population heterogeneity. is largely because age is treated as a time-varying covariate Given the multidimensional nature of SES, the effects of in Model 5, and it is significantly correlated with measures education, household income and home ownership differ of time periods. somewhat from one another. In particular, higher levels of To obtain further insights concerning the indirect influ- education and income are associated with lower mortality ences of SES on mortality, linear regressions were used to than the respective reference categories (i.e., 0–5 years of predict physical and mental health status at the follow-up by schooling and an annual income of 1.65 million yen or less), using baseline demographics, SES, and social relationship but there are no systematic differences among themselves variables (Table 4). As suggested by Table 4, higher educa- (Models 1 and 2). This is consistent with the hypothesis of tion predicts better functional status and less cognitive im- the health ceiling effects among those of higher SES (Rob- pairment. Household income is significantly associated with ert & House, 2000). When demographic characteristics and all three health status predictors of mortality. In particular, social relationships are controlled, the net effects of educa- higher household income at the baseline is correlated with tion diminish somewhat but remain statistically significant less functional impairment, less self-rated poor health, and (Model 3 in Table 3). This suggests that educational differ- less cognitive impairment at the follow-up. Home owner- ences in mortality are partially from heterogeneity in age ship is associated with less self-rated ill health, suggesting S302 LIANG ET AL.

Table 4. Standardized Regression Coefficients for Direct those who are employed are likely to have better functional Relationships Among the Significant Health Covariates status, better self-rated health, and less cognitive impair- Dependent Variables at Follow-up ment. Accordingly, being employed may also reduce the risk of dying through baseline health. Functional Self-rated Cognitive Independent Variables Status Ill Health Impairment Interaction Effects at Baseline (n 5,691) (n 5,707) (n 6,226) According to Table 5, there is a statistically significant in- Age .307*** .015 .204*** teraction between the effect of education and gender. That Gender (1 female) .022 .025 .028*

is, higher education reduces the risk of dying significantly Downloaded from https://academic.oup.com/psychsocgerontology/article/57/5/S294/609421 by guest on 28 September 2021 Population size .009 .031* .006 Education more so among Japanese elderly women than their male b 0–5 years (reference group) 1.000 1.000 1.000 counterparts (e .944). Another way to examine these in- 6–8 years .144*** .008 .212*** teraction effects is to compare the survival curves for men 9–11 years .131*** .029 .277*** and women at different levels of education. On the basis of 12.096*** .044 .232*** Model 5 (Table 3), Figure 1 presents the proportions of sur- Own home (1 yes) .015 .029* .003 vivors during the 12-year period at three levels of education Household income (million yen) (i.e., 6, 9, and 12 years) for both men and women. Because 0–1.65 (reference group) 1.000 1.000 1.000 1.65–3.95 million yen .032 .034 .012 of the inclusion of time-varying covariates, survival curves 3.95–5.75 million yen .042 .058* .023 were plotted for each of the four time periods and then com- 5.75–9.75 million yen .080*** .107*** .048* bined. Gender differences in the educational effect on sur- 9.75 million yen .061*** .091*** .067*** vival are fairly evident. Among women, educational differ- Marital status (1 married) .022 .033* .013 entials are maintained throughout the 12-year period. On the Work status (1 working) .098*** .149*** .044*** other hand, among men, there appears to be a crossover in Household size .056*** .039 .070*** Emotional support .004 .066*** .007 the effect of education on mortality. In particular, educa- Instrumental support .015 .046** .022 tional differences in survival begin to converge in 4 years. R2 .136 .052 .093 Between 6 and 9 years into follow-up, there are no educational differences. After following up for 9 years, those with *p .05; **p .01; ***p .001 12 years or more education have a lower probability of survival than those with 6 or fewer years of education. There is also evidence that the educational effect on mor- the effect of home ownership has an indirect effect on mortality differs significantly across age groups. Relative to tality. In addition to SES variables, there are statistically sig- those between 60 and 69, more education is associated with nificant associations between health conditions at follow-up higher mortality among elderly Japanese who are 80 years and other covariates, including demographic characteristics of age or older (eb 1.086). Survival curves are presented and social relationships at baseline (Table 4). For instance, for three age groups (i.e., 60–69, 70–79, and 80) at 6, 9,

Table 5. Risk Ratios (eb) From the Cox Regression With Age by SES and Gender by SES Interactions (N 7,174)

Demographics, SES, Demographics, SES, Demographics & & Social Relations Social Relations, Health, SES Controlled Controlled & Time Controlled Covariate (df 16) (df 21) (df 30) Education by Age 60–69 years (reference group) 1.000 1.000 1.000 70–79 years 1.043 1.039 1.044 80 years 1.072 1.065† 1.086* Income by Age 60–69 years (reference group) 1.000 1.000 1.000 70–79 years .956 .942 .955 80 years 1.011 .989 1.006 Own Home by Age 60–69 years (reference group) 1.000 1.000 1.000 70–79 years 1.003 1.003 .964 80 years .737 .670 .689 Education by Gender .938* .938* .944* Income by Gender .992 .980 .950 Own Home by Gender .818 .787 .887 2 LL without covariates 12,782.230*** 12,782.230*** 12,782.230*** 2 LL with covariates 12,252.493*** 12,179.188*** 11,824.004*** Model 2 529.740*** 603.045*** 958.229***

Notes: For the above interactions, education and income are continuous variables. SES socioeconomic status; LL log likelihood. †Signiﬁcance level is marginal. *p .05; ***p .001. OLD AGE MORTALITY IN JAPAN S303 Downloaded from https://academic.oup.com/psychsocgerontology/article/57/5/S294/609421 by guest on 28 September 2021

Figure 1. Old age survival by gender and 6, 9, and 12 years of education.

and 12 years of education (Figure 2). In comparison with the DISCUSSION young old (i.e., 60–69), educational differences in survival This research contributes to current knowledge concern- are maintained during the entire 12-year period. For the 70– ing the effects of SES on old age mortality in at least two 79 age group, educational differentials begin to converge ways. First, it yields new information by examining SES and after 10 years into the follow-up, there is a crossover. differences in old age mortality in Japan. Findings from this Among the oldest-old (80 years of age or over), those with research are useful in assessing the external validity of pre- higher education have a lower probability of survival. Fur- vious observations, which were largely derived from Western thermore, such differences appear to be widening over time. developed nations. Second, with prospective data over a

Figure 2. Old age survival by age group and 6, 9, and 12 years of education. S304 LIANG ET AL.

12-year period from a national sample of Japanese elderly study differs significantly from the present one in several re- people, this study affords an opportunity for an in-depth spects. First, their sample consisted of over 18,000 male analysis of the effects of SES on mortality in old age. Such British civil servants. Second, SES is measured in terms of an extended follow-up of a sizable sample of older persons employment grade and car ownership. Third, in their analy- is necessary to strengthen our causal understanding and to sis, there is no control for psychosocial and health risk fac- adequately evaluate how SES interacts with gender and age tors at the baseline. These differences make a direct compar- in affecting mortality. ison and the interpretation difficult. Further replications based Old age mortality in Japan is directly influenced by age, on comparable longitudinal data from other populations are

gender, employment, functional status, self-rated ill health, clearly required to further evaluate the validity of our findings. Downloaded from https://academic.oup.com/psychsocgerontology/article/57/5/S294/609421 by guest on 28 September 2021 and cognitive impairment. In addition, SES, along with de- Why is an educational crossover observed only among mographic variables and social relations, exerts indirect ef- Japanese elderly men? Although we are not sure what the fects on the risk of dying. Despite our use of longitudinal underlying causes are, a plausible explanation may lie in data gathered over a 12-year period in Japan, these findings gender and SES differences in major causes of death, mor- are consistent with those from previous studies in the West bidity, and health behavior. According to Nathanson (1990), (see, e.g., Kaplan, 1997; Martelin, 1994). Thus, there is fur- the gender-social class-mortality interaction may be because ther evidence in support of the causal effects of SES on old the improvement of survival is more likely to be experi- age mortality. In addition, although there is a substantial ac- enced by members of higher SES, and this increasing SES celeration in mortality between 1987 and 1999 for the co- effect is more pronounced for males than females. Parallel hort of 2,200 respondents, such a trend is no longer signifi- trends in heart disease have been observed in Great Britain. cant when individual attributes are controlled. This suggests Marmot and McDowall (1986) reported a reversal of the so- that the initially observed long-term increase in mortality is cial class gradient for heart disease among men but not largely due to the changes in individual characteristics over women between 1931 and 1971. On the other hand, the inci- the 12-year period. This may offer a partial explanation why dence of myocardial infarction appeared to decline more our findings are similar to those based on short-term follow-up. rapidly in white-collar male workers than their blue-collar These results are consistent with prior findings suggesting counterparts, whereas no consistent trends were observed the diminishing SES effects on health. For instance, there are among female employees (Pell & Fayerweather, 1985). In substantially diminishing returns of higher income to health, addition, men of lower levels of education are substantially with decreasing and even nonexisting relationships between more likely to smoke than either better-educated men or income and mortality (Backlund, Sorlie, & Johnson, 1996; women (Nathanson, 1990). McDonough, Duncan, Williams, & House, 1997) and mor- This study is among the first to evaluate the interactive bidity (House et al., 1994; Mirowsky & Hu, 1996) at higher effects of education and age on health among the elderly levels of income. This partially reflects a health “ceiling ef- population by using repeated observations over an extended fect” in that people in the upper SES strata maintain overall period of time. According to the thesis of social stratifica- good health until late in life, leaving little opportunity for tion of aging and health, SES differences in health are small improvement in average health among these groups through- in early adulthood, greatest in middle and early old age, and out much of adulthood (House et al., 1994; Robert & House, relatively small again in late old age (House et al., 1994). 2000). This is consistent with cross-national data in that This study significantly augments this thesis by showing there is a clear diminishing return of health (i.e., life expec- that educational differentials in old age mortality not only tancy) with increasing income per capita (World Bank, 1993, will converge, but also eventually cross over. Among the p. 34). On the other hand, because we have observed signif- young-old (i.e., 60–69), the educational gradient is main- icant gender and age differences in the educational effects tained during the entire 12-year period. For the older-old (i.e., on mortality (Table 5), we have to interpret the main effects 70–79), educational differentials begin to converge, and after of education in conjunction with the interaction effects. This 10 years into the follow-up, there is a crossover. Among the is because the main effects alone no longer adequately rep- oldest-old, those with higher education have an increasingly resent the educational effects on mortality. Instead, the fo- lower probability of survival over time (Figure 2). On the cus has to be placed on the simple effects of education with other hand, our research provides no support for the hypothe- respect to specific gender or age groups. Given the evidence sis of cumulative advantage of education (Ross & Wu, 1996). that the effects of education on mortality depend on gender We are not quite sure why there is an educational cross- and age, the hypothesis of the SES gradient effects on health over among those 70 or over in Japan. Nonetheless, this ob- may be an oversimplification. servation is parallel to the racial crossover in mortality Among the most significant contributions of this research observed in prior studies. In the United States, whereas Black are its findings concerning the interaction between educa- persons generally have higher mortality than White per- tion and other stratification variables, such as gender and sons, a mortality crossover occurs around age 80. After that age. For women, educational differentials are maintained age, Black older persons survive longer than White older throughout the 12-year period. On the other hand, for men, persons (Corti et al., 1999). The existence of a racial cross- there appears to be a crossover in the effect of education on over makes the possibility of an educational crossover the risk of dying. Based on data from the first Whitehall somewhat plausible. study, Marmot and Shipley (1996) suggest that SES differ- In this regard, at least two hypotheses concerning educa- ences in mortality persist beyond retirement age and in tional crossover need to be entertained. The first line of rea- magnitude, increasing up to 89 years of age. However, their soning involves the selective mortality among those with OLD AGE MORTALITY IN JAPAN S305 less education. That is, high-risk individuals in this group tials. For instance, in the United States, a racial crossover is are much more likely to die young, leaving behind a rather observed for coronary heart disease deaths only (Corti et al., hardy group of survivors. Those better educated are more 1999). On the other hand, Koskinen and Martelin (1994) likely to postpone the onset of diseases and disability until suggest that sex differences in the effects of SES on mortal- late in old age. In comparison with the less educated, a large ity may be explained by the differences in the structure of proportion of those better educated remaining alive at later the major causes of death. In particular, mortality with large ages may have a higher burden of disease and unfavorable SES inequalities (e.g., cardiovascular disease and lung can- risk factor profiles. Alternatively, for the well educated, cer) is common among men, whereas causes of death for

morbidity is much more compressed (Fries, 1980). Both which SES differentiation is less or reversed (e.g., breast can- Downloaded from https://academic.oup.com/psychsocgerontology/article/57/5/S294/609421 by guest on 28 September 2021 scenarios lead to a convergence first and eventually a cross- cer), tend to be common among women. In 1990, malignant over of educational differentials in mortality. neoplasm, heart disease, and cerebrovascular disease were What are the methodological implications of selective the most important causes of mortality in Japan. It would be survival? According to a recent review, it may affect the es- interesting to find out whether the educational crossover ob- timation of age-related trends in risk factor-outcome associ- served in this research can be replicated with mortality from ations in three ways (Kaplan, Haan, & Wallace, 1999). First, these causes. if those with a given risk factor experience a higher and ear- Similar questions may be posed concerning the socioeco- lier mortality, then the distribution of this risk factor among nomic differentials in the incidence, duration, and trajectory the survivors would be altered relative to the “original” dis- of morbidity, impairment, functional limitation, and disabil- tribution. To the extent that the amount of variation in the risk ity. For instance, do the better-educated survivors have in- factor is reduced, this may decrease the strength of the asso- creased impairment and functional limitations? Or should ciation. Second, there may be unmeasured heterogeneity in we expect that morbidity in old age be increasingly com- the susceptibility to the risk factor. If over time the exposed pressed among those well educated? For instance, according group becomes increasingly composed of those less suscep- to a recent study of 10,932 decedents 50 years of age at the tible to the risk factor, then the association between the risk baseline interview in the United States, decedents with higher factor and the outcome will diminish. Within the context of SES experience lower morbidity and disability and better the present study, those with higher SES and those with lower quality of life even in their last years of life (Liao, McGee, SES, but less susceptible to the unmeasured ill effects, are Kaufman, Cao, & Cooper, 1999). In addition, Wingard and likely to be overrepresented. Finally, as illustrated earlier in Cohn (1990) show that sex differences in health vary remark- the case of racial crossover in old age mortality, a particular ably by age, cause, and outcome (i.e., morbidity vs. mortality). disease may be postponed to later ages rather than being In this regard, more attention needs to be devoted to the completely prevented. Given that our data are limited to description and explanation of the trajectories of changes in those aged 60 and over, the hypothesis of selective survival old age. In particular, what are the major patterns of changes before age 60 cannot be evaluated. Further research involv- in health and other domains, such as financial well-being ing longitudinal data on adults aged 60 or over, as well as and social relationships? How do the trajectories in financial those under 60, is required for this purpose. well-being, social relationships, and health relate to one an- In addition to selective survival, the second hypothesis other? However, current knowledge concerning these trajec- postulates a cohort effect. Those born before 1917 in Japan tories is extremely limited because of the lack of high-quality might have lived through periods when certain lifestyles and longitudinal data. According to the current literature, we risk factors led to the narrowing and crossover of educa- only know that there are a multitude of such trajectories, tional differences in mortality. However, this hypothesis can and some of them are likely to be nonlinear (Aldwin, Spiro, only be evaluated with the accumulation of data derived from Levenson, & Cupertino, 2001). Our approach of using time- long-term follow-up of multiple cohorts. Even when observ- varying covariates in predicting the risk of dying in old age able longitudinal, cross-sectional, and time-lag differences represents an incremental step in this direction. On the other can be measured, the inference and correct interpretation of hand, there have been some attempts of modeling changes the underlying age, period, and cohort effects are only pos- spanned beyond a single episode. For example, Anderson, sible with certain strong assumptions (Holford, 1991; Palmore, James, Miller, Worley, & Longino (1998) examined func- 1978). Moreover, when age, period, and cohort effects can be tional transitions by pooling up to three 2-year intervals separated, underlying causes need to be identified. These in- within each of the more than 5,000 respondents in the Lon- clude biological and psychosocial changes, environmental gitudinal Study of Aging. To explore whether the 2-year tran- and genetic shifts, and interaction of historical circumstances sitions might also be dependent on the respondent’s previous and specific cohorts. functional status, they made an effort to include functional To further our understanding of the linkages between SES status measured 4 years prior as a predictor in their analysis. and mortality, future research should focus on how and why This was a modest but important step in the right direction. SES is related to various risk factors, health conditions, and Finally, our research also demonstrates the importance of specific causes of death. As suggested by House and associ- examining the intersection between societal conditions and ates (1994), social stratification of aging and health is pri- the biological differences. At issue is whether the observed marily from differential exposure to, and perhaps impact of, age and gender differences in mortality stem chiefly from major psychosocial and biomedical risk factors across SES innate factors or reflect social divisions as mediated by SES. and age groups. This information is important for under- In this respect, a life course perspective (Hagestad, 1990) in standing the pathways responsible for mortality differen- conjunction with the framework of social stratification would S306 LIANG ET AL.

be most useful. Recent research suggests that exposure cause and coronary heart disease mortality in an older population: structured by socioeconomic circumstances may accumu- The North Carolina EPESE. American Journal of Public Health, 89(3), 308–314. late and ultimately increase the risk of adult disease (Lynch, Crimmins, E. M., Hayward, M. D., & Saito, Y. (1996). Differentials in ac- 2001). However, how such an observation can be extended tive life expectancy in the older population of the United States. Jour- to old age is still not well understood. At the same time, nal of Gerontology: Social Sciences, 51B, S111–S130. given the current state of knowledge, it is no longer sufﬁ- Elo, I. T., & Preston, S. H. (1996). Educational differentials in mortality: cient to assert merely that a socioeconomic gradient in old United States, 1979–85. Social Science & Medicine, 42, 47–57. Evans, R. G., Barer, M. L., & Marmor, T. R. (1994). Why are some people age mortality exists. Nor is it sufﬁcient to continue the de- healthy and others not? The determinants of health of populations.

bate between proponents of the “compression of morbidity” New York: Aldine De Gruyter. Downloaded from https://academic.oup.com/psychsocgerontology/article/57/5/S294/609421 by guest on 28 September 2021 (Fries, 1980) and those of the “failure of success” (Gruen- Ferraro, K. F., & Farmer, M. M. (1999). Utility of health data from social berg, 1977). We need to learn more about the underlying surveys: Is there a gold standard for measuring morbidity? American Sociological Review, 64(2), 303–315. mechanisms and to specify the circumstances under which Fillenbaum, G. G. (1980). Comparison of two brief tests of organic brain these results may take place. Our findings suggest that edu- impairment, the MSQ and the Short Portable MSQ. Journal of the cation interacts with other ascribed statuses, such as gender American Geriatrics Society, 28, 381–384. and age, in affecting old age mortality. Fries, J. F. (1980). Aging, natural death, and the compression of morbidity. 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New York: Oxford University Press. tremely useful advice concerning analytical strategies. Rod Little contrib- Hayward, M. D., Friedman, S., & Chen, H. (1998). Career trajectories and uted valuable insights regarding multiple imputation of missing data. We older men’s retirement. Journal of Gerontology: Social Sciences, 53B, have also benefited from the excellent comments and advice given by John S91–S103. Lynch, Stephanie Robert, and George Kaplan. Hayward, M. D., Pienta, A. M., & McLaughlin, D. K. (1997). Inequal- Address correspondence to Dr. Jersey Liang, Department of Health Man- ity in men’s mortality: The socioeconomic status gradient and geo- agement and Policy, The University of Michigan School of Public Health, graphic context. Journal of Health and Social Behavior, 38, 313– 109 S. Observatory, Ann Arbor, MI 48109-2029. E-mail: [email protected] 330. Heck, K., Wagner, D., Schatzkins, A., Devesa, S., & Breen, N. (1997). 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