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Survey Measures of Health: How Well Do Self-Reported and Observed Indicators Measure Health and Predict Mortality?

Randall Kuhn, Omar Rahman, and Jane Menken

INTRODUCTION

The costs and difficulties associated with assessing the health of a population have led to an ongoing search for indicators of health status that can be readily collected from large numbers of individuals with minimal expenditure of resources, including time, money, training, and logistics. Measuring health can be demanding in terms of interviewer time and skill, respondent comprehension, and logistic and analytic complexity. Set against the potentially higher costs and returns of physical health testing or the collection of biomarkers, there are simple self-reported and objective indicators that are relatively easy and inexpensive to collect.

If these low-cost measures are valid, they could prove beneficial in sub-Saharan Africa for assessing the overall burden of disease and the effectiveness of health systems. They could help address emerging concerns relating to adult health as processes of population aging gather pace and as HIV/ AIDS continues to affect health and mortality at all ages, both directly and through socioeconomic pathways.

Yet data to compare health indicators are rare. Rarer still is follow-up information to assess how well these measures predict subsequent mortality—the ultimate measure of poor health—in developing countries. In fact, the only developing country data set containing both multiple measures of health and prospective mortality follow-up comes from the Matlab study site in rural Bangladesh. Analysis of this data set is included in this volume for two reasons. First, measures that prove useful in one developing country context merit consideration for application in other regions. Second, Matlab



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10 Survey Measures of Health: How Well Do Self-Reported and Observed Indicators Measure Health and Predict Mortality? Randall Kuhn, Omar Rahman, and Jane Menken INTRODUCTION The costs and difficulties associated with assessing the health of a popu- lation have led to an ongoing search for indicators of health status that can be readily collected from large numbers of individuals with minimal expen- diture of resources, including time, money, training, and logistics. Measur- ing health can be demanding in terms of interviewer time and skill, respon- dent comprehension, and logistic and analytic complexity. Set against the potentially higher costs and returns of physical health testing or the collec- tion of biomarkers, there are simple self-reported and objective indicators that are relatively easy and inexpensive to collect. If these low-cost measures are valid, they could prove beneficial in sub- Saharan Africa for assessing the overall burden of disease and the effective- ness of health systems. They could help address emerging concerns relating to adult health as processes of population aging gather pace and as HIV/ AIDS continues to affect health and mortality at all ages, both directly and through socioeconomic pathways. Yet data to compare health indicators are rare. Rarer still is follow-up information to assess how well these measures predict subsequent mortal- ity—the ultimate measure of poor health—in developing countries. In fact, the only developing country data set containing both multiple measures of health and prospective mortality follow-up comes from the Matlab study site in rural Bangladesh. Analysis of this data set is included in this volume for two reasons. First, measures that prove useful in one developing country context merit consideration for application in other regions. Second, Matlab 314

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315 SURVEY MEASURES OF HEALTH partners with community studies in sub-Saharan Africa that are part of the INDEPTH network (the International Network of field sites with continu- ous Demographic Evaluation of Populations and Their Health in develop- ing countries); they provide the opportunity to replicate this study inexpen- sively and reasonably quickly in African contexts and in a comparative framework. In this paper we consider five measures of health—three self-reported and two objectively observed—and address three questions: • How interrelated are these indicators? Can the same information on individual health be obtained with a smaller set of questions or observa- tions, thereby reducing costs? • How well does each serve as a predictor of mortality? Here we as- sume that the poorer the health of an individual, the greater the risk of dying in a defined period (in this case, 5 years) after health is measured. The greater the predictive power, under this assumption, the better the measure is as a gauge of individual health. • Does the predictive power of the indices vary by age or gender? Here we ask whether, for example, self-reported poor health is related to subse- quent mortality similarly across age and gender. The study is based on health measures collected in the 1996 Matlab Health and Socioeconomic Survey (MHSS). The MHSS was carried out with funding from the U.S. National Institute on Aging as a collaboration among researchers based at the International Centre for Health and Popu- lation Research in Dhaka, Bangladesh (ICDDR,B), and at several institu- tions in the united States. ICDDR,B began its Health and Demographic Surveillance System (HDSS) in Matlab in the early 1960s and, for over 40 years, has visited every household at least monthly to collect accurate infor- mation on vital events. The HDSS served as the sampling frame for the MHSS (Rahman et al., 1999). Information on MHSS respondents was there- fore automatically collected in the HDSS each month of the 5 years subse- quent to the survey. We first consider, using MHSS data alone, the extent to which each of the health measures provides information independent of the others. Next, the combined MHSS/HDSS data are used to test how well each health indicator serves as a predictor of mortality in the follow-up period. We then rank these measures in terms of mortality predictive power and discuss the prevalence of the poor health by age and gender they reveal. Finally, we discuss two issues regarding the usefulness of these simple and inexpensive to collect measures: international comparability and compara- bility of the indices to more specific information on health obtainable through other means, such as biomarkers. As part of this discussion, we propose a research agenda for measuring health and evaluating health mea-

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316 AGING IN SUB-SAHARAN AFRICA sures in sub-Saharan Africa that takes advantage of INDEPTH. INDEPTH sites, including Matlab, routinely record mortality in their Demographic Surveillance System records, and improved measurement of adult health is one of its current goals. SIMPLE-TO-OBSERVE HEALTH INDICATORS The indicators examined in this study are • self-reported health (SRH), whereby respondents are asked to clas- sify their current health status as good, fair, or poor; • an index based on self-reported activities of daily living; • an index of self-reported major acute and chronic morbidity conditions; • an index of physical disability based on observed activities of daily living; and • current nutritional status as measured by body mass index. Each of these indicators has been collected and studied in health surveys. While these measures are significantly related to one another, each may capture unique dimensions of ill health. They vary widely in terms of data collection time and field costs, yet none requires extensive field training or expensive or delicate equipment. Because SRH is so easy and inexpensive to collect, there is great interest in determining its validity. Studies, primarily in more developed countries with literate populations and advanced medical systems, have demonstrated that it is a good predictor of mortality and functional ability, even after controlling for other objective health measurements (Appels, Bosma, Grabauskas, Gostautas, and Sturman, 1996; Borawski, Kinney, and Kahana, 1996; Idler and Benyamini, 1997; Idler and Kasl, 1991, 1995; Kaplan and Camacho, 1983; Mossey and Shapiro, 1982; Schoenfeld, Malmrose, Blazer, Gold, and Seeman, 1994; Sugisawa, Liang, and Liu, 1994; Wolinsky and Johnson, 1992). This predictive power may be related to its multifaceted nature, whereby SRH incorporates multiple dimensions of health (physical disability, functional or activity limitations, chronic and acute morbidity), self-assessment of severity, awareness of comorbidity, and past health trajectory (Idler and Benyamini, 1997). SRH may not be comparable across populations (Angel and Guarnaccia, 1989; Ferraro and Kelley-Moore, 2001; Jylhä, Guralnik, Ferrucci, Jokela, and Heikkinen, 1998; Rahman, Strauss, Gertler, Ashley, and Fox, 1994; Zimmer, Natividad, Lin, and Chayovan, 2000), especially across developed and developing countries. Specifically, due to lower levels of education and formal contact with the health care system, individuals in

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317 SURVEY MEASURES OF HEALTH the developing world may have less knowledge about their own acute and chronic morbidity conditions, which may affect their subjective reports of their health. High levels of family support and lower expectations of inde- pendence of movement in developing countries may lead to a weaker rela- tionship between physical disability or functional limitations and SRH. Ef- forts are under way to develop means of calibrating responses to SRH questions by giving respondents anchoring health vignettes (Salomon, Tandon, and Murray, 2004). Inconsistent gender differences in SRH have been reported: some stud- ies show a female disadvantage (Gijsbers van Wijk, van Vliet, Kolk, and Everaerd, 1991; Rahman et al., 1994; Zimmer et al., 2000) while others find no disadvantage (Jylhä et al., 1998; Leinonen, Heikkinen, and Jylhä, 1998; McDonough and Walters, 2001; Zimmer et al., 2000). More impor- tantly, several studies in the developed world show gender differences in the association between SRH and mortality, with a stronger association for men (Bath, 2003; Benyamini, Blumstein, Lusky, and Modan, 2003; Deeg and Bath, 2003; Deeg and Kriegsman, 2003; Idler, 2003; Spiers, Jagger, Clarke, and Arthur, 2003). Rather than reflecting a health disadvantage for women, these studies suggest, women’s higher reports may reflect their “greater sensitivity” to health conditions. There are relatively few studies for developing countries (Frankenberg and Jones, 2003; Rahman et al., 1994; Yu et al., 1998; Zimmer et al., 2000). In several nations in which allocation of resources is perceived to be biased in favor of men, men are less likely to report poor SRH; however, women have lower adult mortality rates. Rather than evidence of a sex difference in reporting, most studies have found that women’s higher rates of poor SRH actually reflect measurable sex differences in morbidity and disability. In Bangladesh, women’s life expectancy only recently reached equality with men’s. Female respondents to the MHSS were 50 percent more likely to report poor SRH than men. Women also reported greater disability and morbidity on more detailed self-reports, and they were observed to have higher levels of physical dysfunction than men (Rahman and Barsky, 2003; Rahman and Liu, 2000). Rahman and Barsky found, when controls for observed disability, self-reports of disability and morbidity, and their inter- actions were introduced, that no sex difference in SRH remained, so that the SRH differences were not due to women’s complaining more for a given level of health as measured in other ways. Body mass index (BMI) was found to be a strong predictor of survival over a 20-year follow-up period in a study that included only women. Their BMI was measured in an ICDDR,B study in the late 1970s, and they were followed subsequently in the HDSS until they died or migrated away (Duffy and Menken, 1998; Menken, Duffy, and Kuhn, 2003). In this group, average BMI was only

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318 AGING IN SUB-SAHARAN AFRICA 18.5, and very few had a BMI greater than 25; the log-odds of survival increased linearly with BMI. No comparable information is available for men. This analysis seeks to investigate relationships among these measures of health further, with particular emphasis on age and gender differences and to examine how well they predict mortality. DATA AND METHODS Setting Matlab is a rural area about 55 km from the Dhaka, the capital of Bangladesh. ICDDR,B has long maintained a research station that sponsors the HDSS and extensive research on the health of a population of over 200,000 people. It also provides health services that supplement the gov- ernment system of health care. The overwhelming majority of older indi- viduals live with adult children (mostly sons), and alternative sources of support—financial and otherwise—outside the family are scarce. Bangladesh per capita income in 2000 was US$370/year. The predominant occupation for rural men is agriculture, with labor force participation rates remaining very high even for older men. Women are largely restricted by convention to activities in the home, with relatively little opportunity to venture outside the homestead, although these restrictions have decreased in recent years. Given the high level of poverty and the scarcity of health providers (4,671 persons/physician, 3,312 persons/hospital bed), contact with the formal health care system is thought to be relatively infrequent. National life expectancy in 2000 was 58 for both men and women; at age 50 it was approximately 30 years, again, with no significant gender differ- ences. Approximately 10 percent of the population was over age 50 (Bangladesh Bureau of Statistics, 2002). The 1996 Matlab Health and Socioeconomic Survey The MHSS is a multistage, multisample household survey that collected information from 11,150 individuals age 15 and over in 4,538 households. While designed for comparability to similar nationally representative fam- ily life surveys, such as the Indonesian and Malaysian Family Life Surveys, MHSS eschewed a nationally representative sample in favor of a sample based entirely in Matlab because of the availability of the HDSS (Rahman et al., 1999). Matlab HDSS data have been used extensively in the demo- graphic literature, and the HDSS is considered to be one of the few high- quality (i.e., complete, accurate, and up-to-date) demographic data sources in the developing world (Fauveau, 1994). In particular, age reporting is

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319 SURVEY MEASURES OF HEALTH considered to be highly accurate, a feature not found in other South Asian data sources (Menken and Phillips, 1990). As part of a collaborative study funded by the U.S. National Institute on Aging, investigators at the Harvard School of Public Health, the Univer- sity of Colorado at Boulder, ICDDR,B, and Independent University, Bangladesh, MHSS respondents were tracked through the HDSS from 1996 through 2000. The matching resulted in an event history database that iden- tifies whether a respondent died in a particular calendar year and whether he or she was censored from the HDSS population through migration. The technique of matching survey data to subsequent surveillance data, often employed using the Current Population Survey and death registration data in the United States, offers some advantages over a panel survey. Fore- most among these is cost, since longitudinal analysis requires only matching the survey to subsequent data collected as part of an ongoing system rather than fielding a follow-up study. Furthermore, a surveillance system may date death or censoring more accurately and may be more accurate in dis- tinguishing between these two types of exits from the sample than a peri- odic panel survey (which collects no information except at the survey points). Panel surveys in other developing countries typically have had attri- tion rates of 10 percent or more, leading to uncertainty about the cause of attrition and potential bias in assessing mortality. In addition, survey costs can rise considerably as follow-up efforts intensify (Frankenberg and Thomas, 2000). Panel surveys have the advantage of offering far more de- tailed data on longitudinal changes in health status, nutrition, and morbid- ity. However, the survey plus surveillance approach offers a crucial oppor- tunity to study the effects of health on subsequent survival. The complementarity of these approaches is obvious. We plan a 10-year follow- up of the MHSS households. Sampling The HDSS provided the sampling frame for the MHSS (Rahman et al., 1999). The Matlab surveillance area consists of 8,640 baris or residential compounds, of which roughly one-third (31.1 percent) or 2,687 baris were randomly selected. The bari is the basic unit of social organization in rural Bangladesh and in Matlab in particular (Aziz, 1979; Rahman, 1986). Baris usually consist of a cluster of households linked in many instances in a kin network (however, about 16 percent of baris consist of only a single house- hold and, even in multihousehold baris, kin networks may exist only for subclusters of households). Sampling baris rather than households provides a better representation of family networks, a major focus of the MHSS survey. In single-household baris, that household was selected for detailed interviews. In all other baris, two households were selected. All individuals

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320 AGING IN SUB-SAHARAN AFRICA TABLE 10-1 Sample Exclusions Missing All Indicators Present Individual Respondent No sampling weight 54 11,096 No HDSS match 165 10,931 Self-Reported Health Indicators SRH—general health 0 10,931 ADL—activities of daily living 13 10,918 Major disease 0 10,918 Observed Health Indicators ADL—activities of daily living 1,001 9,950 BMI—body mass index 1,304 9,536 SOURCES: 1996 Matlab Health and Socioeconomic Survey and 1996 Health and Demo- graphic Surveillance System. in these households who were age 50 and over were interviewed. For those below age 50, certain criteria were followed to reduce the interviewing load vis-à-vis large households. Probability weights reflecting this multistage sam- pling scheme were assigned to each individual and are included in the MHSS public-use data set (Rahman et al., 1999; Rahman, Menken, Foster, and Gertler, 2001). There were 11,150 individuals age 15 and over in the bari sample. People were eliminated from this analysis (see Table 10-1) if the individual probability weight could not be calculated (54) or the MHSS identification number was erroneous and could not be matched in the HDSS (165). Thus we begin with 10,931 respondents who could be followed in the HDSS. Individuals were dropped if information was missing on self-reported ac- tivities of daily living, the major disease index, or observed activities of daily living, or if BMI was either missing or out of range. Most nonresponses stem from the fact that there were special teams for collection of observed health indicators and for anthropometric measurement. They visited only subsets of all MHSS households. Very few respondents refused to partici- pate. Thus for the purposes of this study, we focus on 9,536 respondents age 15 and older (4,399 men, and 5,137 women) for whom we have com- plete information. Rahman and Barsky (2003) found little difference be- tween individuals who had complete information on health indicators and all respondents. Mortality Analytic Plan Longitudinal models of mortality subsequent to MHSS fieldwork were estimated to investigate the predictive power of the health measures. Each respondent contributed an observation for each person-year from 1996 to

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321 SURVEY MEASURES OF HEALTH 2000 unless censored by death or out-migration. The logistic hazards model of mortality tested has the following form: J M µ  Log  ik  = β0 + β1Femalei + β2 Ageik + ∑ β2+ j SRij + ... + m = 1∑ β2+ j + mOBSim + ... + εik  1 − µik  j =1 m=1 where µik is the risk that person i who survived to the start of follow-up year k dies in that year, Femalei is 1 if person i is female and 0 if male, Ageik is person i’s age at the start of follow-up year k, SRij is the jth of J self- reported health indicators, and OBSim is the mth of M observed health indicators for person i. The health indicators were recorded once—in the MHSS. Therefore, Age is the only time-varying variable. Estimation of models incorporates a Huber-White correction for intracluster correlation in the distribution of the probability of mortality across observation years for a given respondent. Results presented in the tables display the relative odds associated with a variable, or the relative change in µ 1– µ resulting from a one-unit change in a variable. Table 10-2 presents the number of MHSS respondents by sex and broad age group and the number of deaths recorded for each sex/age group in the TABLE 10-2 Respondents, Deaths, Person-Years, and Death Rates Percentage with Percentage Recorded Recorded Person- Dying Per Respondents Deaths Death Years Person-Year Men <50 2,861 26 0.9 12,697 0.2 50+ 1,539 197 12.8 7,912 2.5 Total 4,399 223 5.1 20,609 1.1 Women <50 3,765 18 0.5 17,136 0.1 50+ 1,372 115 8.4 7,387 1.6 Total 5,137 133 2.6 24,523 0.5 Total <50 6,626 44 0.7 29,833 0.1 50+ 2,910 312 10.7 15,299 2.0 Total 9,536 356 3.7 45,132 0.8 SOURCES: 1996 Matlab Health and Socioeconomic Survey; 1996-2000 Health and Demo- graphic Surveillance System Mortality Files.

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322 AGING IN SUB-SAHARAN AFRICA HDSS over the subsequent 5 years. Deaths of 10.7 percent of those over age 50 in the MHSS were recorded in the HDSS within the follow-up period, compared with only 0.7 percent of those under age 50 and 3.7 percent overall. These figures, however, do not adjust for out-migration (censor- ing). Among those over 50 in the MHSS, only 3.2 percent out-migrated during the follow-up period, compared with 15.1 percent of those under age 50 and 11.5 percent of all respondents (not shown). Out-migrants were censored at the end of the last full year of their observation. Those who died contributed a person-year for each year in which they were alive at the start. Those for whom neither death nor out-migration was recorded in the HDSS in the follow-up period each contributed 5 person-years of observa- tion. The number of person-years of observation in which age was under 50, over 50, and all ages at the start of the year is given in Table 10-2 along with the yearly death rates (deaths/person-years). This death rate was 0.8 percent for all ages. While there was a relatively high rate in the older group (2.0 percent), death was a quite rare event in the younger subsample (0.1 percent). For this reason, all mortality analyses were replicated using the Rare Events Logistic Regression package for STATA (King, 2004). In all cases, rare event models resulted in higher coefficient estimates and lower standard errors, so we choose to report the more conservative logistic re- gression results in our later tables. Table 10-2 also shows lower mortality rates for women than for men; however, as is shown later, with more pre- cise controls for age, sex differences are not significant. Health Indicators The five health indicators are defined below. The self-reported and ob- served activities of daily living (ADL) scores and self-reported indicator of major disease were originally developed by Rahman and Barsky (2003). SRH was assessed with the simple question “What is your current health status?” Less than 30 seconds were required to administer this question. Responses were healthy, fairly healthy, or in poor health. SRH was coded as 1 if the respondent reported he or she was in poor health, and as 0 for those who reported they were healthy or fairly healthy The latter responses were combined because we were most concerned with poor health. It is important to note, however, that a different coding scheme (i.e., poor or fair versus good health) could affect the results. Following Merrill, Seeman, Kasl, and Berkman (1997), Rahman and Liu (2000), and Rahman and Barsky (2003), we constructed a series of measures for functional limitations in self-reported ADLs. We used self- report information on 10 ADL items, which were divided into two clusters: (I) limitations in personal care—four items—ability to (a) bathe, (b) dress, (c) get up and out of bed, and (d) use the toilet, and (II) limitations in range

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323 SURVEY MEASURES OF HEALTH of motion—six items—ability to (a) carry a 10 kg weight for 20 yards, (b) use a hand-pump to draw water, (c) stand up from a squatting position on the floor, (d) sit in a squatting position on the floor, (e) get up from a sitting position on a chair or stool without help, and (f) crouch or stoop. Collect- ing the entire battery of questions required about 5 minutes of interview time. Because men and women appear to have different norms regarding per- sonal care, in this paper we consider only the second cluster of six range-of- motion questions. Each of the six components of range of motion was scored as 0 (can easily do all the activities in the cluster) or 1 (have trouble with one or more activities in the cluster, resulting in an self-reported ADL score ranging from 0 (no limitation) to 6. Self-reported chronic morbidity (Rahman et al., 1999), to which we refer as major disease, was assessed with a checklist of 14 sentinel condi- tions (anemia, arthritis, broken bones, cataracts, vision problems, asthma, other breathing difficulty, diabetes, pain or burning on urination, paralysis, tuberculosis, gastric/ulcer problems, edema, and a residual category called other conditions). For each condition, respondents were asked to report whether they had experienced it in the three months prior to the survey, and if so whether it had caused them no difficulty, some difficulty, a great deal of difficulty, or inability to carry out their day-to-day activities. Collection of the full battery of morbidity questions required about 6 minutes of inter- view time. Those who had experienced one or more of the sentinel condi- tions that had caused a great deal of difficulty/inability to carry out their day-to-day activities were scored as 1, while those who had none of the sentinel conditions or who experienced only minor chronic disease were scored as 0. Physical disability was assessed objectively as in prior studies (Merrill et al., 1997; Rahman and Liu, 2000; Rahman and Barsky, 2003) by asking respondents to perform four timed physical tasks: maintaining side by side, semi-tandem and tandem positions (balance); walking 8 feet twice (gait); chair-rises (lower extremity movement); and shoulder rotation (upper ex- tremity movement). Collection of these measures required about 10 min- utes of data collection team time, as well as moderately higher equipment and training costs. Each task had a three-level score: 2 (unable to do the activity), 1 (had some difficulty doing the activity), and 0 (could do the activity easily), assigned by an independent observer. The four individual subscales were added to form a scale ranging in value from 0 to 8, with lower scores indicating better performance and 0 indicating the person could perform all activities easily. When this measure was dichotomized, those with scores of 3 or above were considered in poor health. Height and weight were measured and BMI calculated as (weight in kg)/(height in meters)2. Collection of these measures required 5 minutes of

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324 AGING IN SUB-SAHARAN AFRICA data collection team time, as well as extra costs for measurement tools and training of specific enumerators to operate scales and measuring sticks. Those with a BMI less than 16 were considered to be severely malnourished and scored as 1, while those with a BMI of 16 or above were scored as 0. It is worth noting that for self-reported major disease, the summary measure is comprised of heterogeneous categories of symptoms and disease labels that reflect the prevailing morbid conditions in rural Bangladesh. As such they are locally specific, and cross-country comparisons using these summary measures would be difficult to interpret. RESULTS Figures 10-1 and 10-2 present, by age group and sex, the percentage estimated by self-reported and observed indicators respectively to be in poor health. Poor health increases with age and the rise begins early in adult- hood. In this society, the slowest rise with age is in observed severe malnu- trition. There are striking sex differences in poor health according to nearly 100 Poor SRH male Poor SRH female ADL limitation male ADL limitation female 80 Major disease male Major disease female 60 Percentage 40 20 0 15 25 35 45 55 65 75 Age Group FIGURE 10-1 Self-reported health indicators, by age and sex. SRH = self-reported health; ADL = activities of daily living.

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332 TABLE 10-6 Stepwise Logistic Regression Models of Mortality Adding Health Measures to Age and Sex All Age <50 Age 50+ Pseudo-R2 Odds Pseudo-R2 Odds Pseudo-R2 Odds By Entry Order Gain Ratio Gain Ratio Gain Ratio Observed ADL Score 0.014 1.28*** — — 0.020 1.29*** Reported ADL Score 0.017 1.26*** 0.048 1.94*** 0.015 1.21*** BMI < 16 0.006 1.91*** — — 0.006 1.72** SRH 0.003 1.51* — — 0.002 1.42* Major Disease — — — — — — Final Pseudo-R2 0.220 0.050 0.135 Observations 45,098 29,807 15,291 NOTE: The gain and odds ratio come from the model in which the indicator first entered (see note to Table 10-4). SRH = self-reported health; ADL = activities of daily living; BMI = body mass index. * p<.05; ** p<.01; *** p<.001.

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333 SURVEY MEASURES OF HEALTH TABLE 10-7 Logistic Regression Model of Mortality with Age, Sex, and Health Measures as Predictors All Age <50 Age 50+ Odds Odds Odds Ratio S.E. Ratio S.E. Ratio S.E. Age 1.06*** 0.01 0.97 0.04 1.07*** 0.01 Female 0.44*** 0.07 0.62 0.34 0.46*** 0.08 Self-Reported SRH 1.51* 0.28 3.00* 1.64 1.42* 0.25 ADL score 1.21*** 0.04 1.83*** 0.08 1.17*** 0.04 Observed ADL score 1.18*** 0.04 1.47 0.60 1.21*** 0.03 BMI < 16 1.81** 0.33 2.46 1.39 1.65** 0.28 Observations 45,098 29,807 15,291 Pseudo-R2 0.220 0.084 0.135 NOTE: Major disease is excluded because it was not significant in any model. SRH = self- reported health; ADL = activities of daily living; BMI = body mass index. * p<.05; ** p<.01; *** p<.001. that the same poor health multiplier effect is applied to a lower baseline mortality effect. This result holds in rare events logistic regression models and probit regression models, both of which also apply the very realistic assumption that mortality risks are multiplicative in nature, not additive. These models were also run using a linear probability model in order to estimate the size of interaction effects if mortality risks were indeed addi- tive. Interactions between sex and SRH were found to be highly nonsignifi- cant in these models as well. Discussion This analysis demonstrates both the multidimensional nature of the rela- tively simple and low-cost health measures in the MHSS and their substan- tial value and validity in a low-income setting such as Bangladesh. We be- lieve this is the only study to examine subsequent mortality in relation to these multiple measures of health. Each measure is a significant predictor of subsequent mortality, particularly for elders. With the exception of major disease, each appears to capture, at least in part, a different dimension of health from the others. These findings support the notion that individuals can effectively assess their own health status even in settings of poor educa- tion and low levels of interaction with modern health systems. Furthermore, the measures seem equally appropriate for men and women. The higher age-specific prevalence of poor health, whether reported

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334 AGING IN SUB-SAHARAN AFRICA or observed, among women does not appear to result from their overreporting poor health compared with men. The proportional increase of mortality for those whose health was poor versus not poor, whichever the health measure used, did not differ for women and men. At the same level of health, women had lower mortality risks than men; however, these lower risks were combined with much higher rates of poor health. These differences countered one another; as a result, we found no sex differences in overall age-adjusted mortality. Objective health measures are frequently assumed to be superior to self-reports because they minimize concerns over reporting bias and inter- national comparability. We find, however, that they are not necessarily bet- ter predictors of mortality. The self-reported and observed measures were similar in predicting mortality at older ages, while self-reports were the only significant predictors of mortality at younger ages. The marginal time and financial costs of collecting each measure vary substantially. Cost estimates based on the MHSS are given in Table 10-8, along with the improvement in mortality prediction found when each health measure was added to the model based on age and sex (repeated from Table 10-5). For comparison, we consider the high expected costs of drawing and analyzing a blood sample. For the second round of the MHSS, we have proposed a battery of seven biomarker analyses.2 The estimated costs are included in Table 10-8. Self-reported measures take less survey time than observational tests or drawing a blood sample and incur no additional capi- tal costs; in addition, they can be collected by less skilled interviewers. The observed indicators cost at least 2.5 times more to collect than the self- reports. The current cost of collecting biomarkers is extremely high, but is likely to decrease in the future for two reasons. First, as collection is stan- dardized, less expensive technology may be developed. Second, with con- tinuing research, a small set of essential indicators is likely to be agreed upon. These results raise questions regarding a minimal set of survey-based health measures that ensures precision and accordance with international standards. The least expensive measure, a single SRH question requiring $0.01 per respondent, adds significantly in predicting subsequent mortality for respondents of all ages. In fact, it predicts mortality better than a num- ber of more complex health indicators. 2The battery includes blood cholesterol (total cholesterol, high density lipoprotein, low density lipoprotein, triglycerides); glycosolated hemoglobin; complete blood count (white blood cell count and morphology); homocysteine and related cofactors (e.g., folate); c-reactive pro- tein; serum albumin; and serum creatinine. Because HIV prevalence is so low in Bangladesh, testing is not planned for MHSS2.

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TABLE 10-8 Cost of Health Measures and Increase in Pseudo-R2 Marginal Cost ($) Increase in Pseudo-R2 (%) Time Labor Capital Total All Under 50 Age 50+ Self-Reported SRH 0:20 0.01 0 0.01 1.2 2.8 1.2 ADLs 4:40 0.12 0 0.12 2.4 4.8 2.3 Major disease 6:00 0.16 0 0.16 0.6 2.8 0.6 Observed ADLs 10:00 0.50 0.05 0.55 1.4 0.1 2.0 Body mass index 5:00 0.30 0.10 0.40 1.0 1.7 1.0 Blood-drawn biomarkers 20:00 22.00 38.00 60.00 – – – NOTE: Taken from Table 10-5; change when each health measure is added to model with age and sex. SRH = self-reported health; ADL = activities of daily living; BMI = body mass index. 335

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336 AGING IN SUB-SAHARAN AFRICA The self-reported ADL index, the next least expensive indicator to col- lect, adds even more to the predictive power of our models. This indicator seems to work at least as well, and possibly better, than observed ADLs and anthropometric measures that are far more demanding of resources. How- ever, as for SRH, it is important to ask whether self-reported ADLs produce results that are truly comparable across different populations and subgroups within populations. The results here indicate they are promising enough to be included in studies in other countries in order to assess their usefulness. The detailed questions on specific conditions that went into the major disease index were far more complicated to implement than the ADL ques- tions and the index added no explanatory power to the analysis of mortal- ity. It may be that the questions on specific conditions are better predictors than the overall index; we plan to address this issue in further analyses of the MHSS. The observed ADL index and BMI add significantly in predicting mor- tality for elders. They are not significant for those under 50. It may be that the activities that go into the MHSS ADL index are not sufficiently difficult to detect disabilities that contribute to higher short-term mortality risk in younger adults. Neither the observed ADL index nor BMI picks up the increase in poor health with age for those under 50, especially for females, that is self-reported. In contrast, SRH and the self-reported ADL index in- crease with age and are significant mortality predictors, perhaps because younger adults recognize change in their own abilities or differences in com- parison with peers even when their disabilities do not put them in a range considered unusual by the observer. We suspect that, unless the set of tasks is changed to detect more subtle disability, observed ADL measures for younger adults should be included in survey data collection only as a baseline for longitudinal follow-up rather than as valid health indicators. The four measures discussed here—SRH, self-reported and observed ADLs, and BMI—are strong predictors of survival over the next 5 years in the rural Bangladesh context, although the observed measures work best for elders. They are therefore good markers of general health and the effects of the health system and can be used to examine basic gender and socioeco- nomic differentials in that society. The high prevalence of poor health based on the self-reports, even at low ages, in Bangladesh makes these measures perhaps even more valuable than they would be in more advanced societies. More importantly, compared with complex measures such as biomarkers or disability adjusted life years, these measures can be obtained more fre- quently and in much larger samples for the same cost. If resources are espe- cially constrained, it may be possible to restrict the set of measures collected further and still be able to capture levels, differentials, and trends in overall health over time. Given the urgent need to assess health and track its trends over time in

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337 SURVEY MEASURES OF HEALTH sub-Saharan Africa, we strongly recommend including a range of health indicators in data collection—in a way that permits research on indicators in this region. Studies are needed that collect, in selected settings, simple and low-cost health indicators like those examined in this paper; expanded versions of these indicators (e.g., SRH with more categories); other objec- tive measures (e.g., lung capacity, grip strength) that are more costly in terms of survey-team time, equipment, and training; and selected biomarkers that are far more expensive to collect and more difficult to analyze. It would also be appropriate to address issues of comparability of measures across population through techniques such as calibrating vignettes. Simultaneous collection—in a few selected studies—of all of these indicators in compa- rable manner will permit the kind of “head-to-head” analysis performed here for one country and for the measures included in the MHSS. This research would be most informative if carried out where it is possible to follow respondents for some period of time to observe survival outcomes. The expected outcomes of this research include • Identification of measures that provide valid information on preva- lence of specific conditions or diseases and those which provide informa- tion on overall health. • Estimates of costs and difficulties of collecting each indicator. • Cost-benefit analyses, which would help investigators decide on the package of indicators to be included in future data collection. One obvious locus for this type of research is the INDEPTH network. Its Demographic Surveillance System sites offer crucial advantages. Since the sites carry out continuous longitudinal follow-up of large populations, they provide data on mortality trends prior to collection of the health mea- sures. While few sites have been operating as long as the Matlab HDSS (which is a member of INDEPTH), a number, such as Agincourt in South Africa and Navrongo in Ghana, have been operating for at least 10 years. A set of health indicators measured in a one-time survey would contribute to the assessment of measures described in this paper. INDEPTH sites rou- tinely collect the follow-up data needed to study relationships of health measures to subsequent mortality. In addition, if these measures were in- cluded periodically in subsequent DSS rounds, their sensitivity to trends and emerging differentials in health could be assessed. The issue of comparability between populations could be addressed in two ways—for which DSS sites are again especially appropriate. Following the work on vignettes (see Salomon et al., 2004), calibrating vignettes could be included in at least one DSS round that includes health indicators. These vignettes could be extended to include ADLs as well as other aspects of health for which self-reports are collected. In addition, mortality prediction

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338 AGING IN SUB-SAHARAN AFRICA across sites could be analyzed to determine if, as for men and women in Bangladesh, the increase in the odds of mortality for those in poor health as measured by a specific indicator is the same across countries. Development of low-cost and informative health status modules could be guided by this research. They could be regularly included in DSS data collection rounds, providing valuable comparable information across sites on health levels and trends over time. These modules could also be included in standalone surveys covering larger geographic areas. Nationally repre- sentative surveys could then provide the basis for comparative studies of countries, with data from INDEPTH sites providing greater detail and lon- gitudinal validation. In addition to tracking changes in health over time in defined popula- tions, DSS sites have demonstrated they are ideal settings for conducting and evaluating health and social interventions intended to improve health either directly (e.g., family planning programs, oral rehydration programs) or indirectly (education of women, micro-credit programs). They offer well- established sampling frames with regular follow-up so that program and control groups can be established and compared over time. Increasingly, programs include direct adult health interventions, such as Agincourt’s South African Stroke Prevention Initiative. Regular observation of simple health measures can provide a baseline for assessing differences between experimental and control groups, and follow-up information for assessing the long-term impact of interventions on comparable health status mea- sures. At the same time, repeated observation of health status allows re- searchers in DSS sites to measure the health impact of programs not aimed directly at health, including education, family planning, and women’s sta- tus interventions. Finally, such measures facilitate analysis of the health effects of unexpected economic or political crises and other changing social or health patterns. As societies in sub-Saharan Africa grow older and continue to absorb the consequences of the HIV/AIDS epidemic, it will grow increasingly im- portant to assess their health and health needs on a regular basis and to evaluate the impact of intervention programs. For these reasons, further research on the promising set of simple indicators evaluated here for one developing country setting and on the more detailed and expensive mea- sures discussed here is an essential part of a research agenda on aging in Africa. ACKNOWLEDGMENTS This work was supported by NIA grants R01 AG16308, R03 AG19294-01, P30 AG17248, and P01 AG11952-05 to Harvard University and the University of Colorado. The research is based on collaboration

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