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National Research Council (US) Committee on Population; Martin LG, Soldo BJ, editors. Racial and Ethnic Differences in the Health of Older Americans. Washington (DC): National Academies Press (US); 1997.

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Racial and Ethnic Differences in the Health of Older Americans.

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3Health and Disability Differences Among Racial and Ethnic Groups

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INTRODUCTION

Racial and ethnic differences in the age patterns of incidence and prevalence of chronic morbidity and disability are important because they provide data on (1) biological mechanisms, endogenous and exogenous, of age-related morbidity, (2) how the quality of life for groups changes over age and time, and (3) lead indicators of future mortality and disability trends that aid in the development of health policy by improving mortality, health service, and population forecasts.

We have previously examined epidemiological, demographic, and other data on racial and ethnic health differences (Manton et al., 1987). Here we examine more recent biomedical and epidemiological research on African-American, white, and Hispanic differences in health and disability. Though data on racial and ethnic health differences have improved and have generated many new hypotheses, there are many areas in which no definitive studies have been done for different racial and ethnic groups. Consequently, there is often little consensus on the magnitude or age dependence of racial and ethnic differences in health or on the biological mechanisms underlying many differences.

Since the scientific record on racial and ethnic health differences, especially at late ages, is incomplete, we must assemble available data into a coherent description to compare age-related health differences across racial and ethnic groups. For this purpose, a life-table model of the age relation of three health processes—disability, morbidity, and mortality—is useful. Figure 3-1 portrays changes in two hypothetical cohorts, one with high mortality and one with low mortality, as linked life-table functions.

FIGURE 3-1. Interrelations of morbidity, disability, and mortality in two hypothetical populations.

FIGURE 3-1

Interrelations of morbidity, disability, and mortality in two hypothetical populations. SOURCE: Duke University Center for Demographic Studies.

The area between curves for each cohort represents the person-years lived free of morbidity and disability, with morbidity but free of disability, or with both morbidity and disability. The high-mortality cohort reflects a population with more time spent in morbid states but less time spent with disability (e.g., in developing countries, where acute and long-term medical care for disabled elderly persons is scarce, or in socioeconomically disadvantaged groups). The low-mortality cohort spends less time in morbid states, but more time with disability.

The first scenario suggests that in populations with low life expectancy, people spend time in clinically latent morbid states and that once disability occurs, the lack of social and long-term care infrastructure causes rapid terminal declines due to a loss of physical homeostasis (e.g., Colantonio et al., 1992). It is uncertain whether this scenario holds for U.S. African Americans and Hispanics who, often socioeconomically disadvantaged over much of the life course, may have greater social resources and relatively equitable access to medical and postacute services at age 65 through Medicare—and to long-term care services through Medicaid. In populations with high life expectancy, health care may defer chronic morbidity to later ages, with disabled people able to live a long time with adequate long-term care. This scenario, however, may not occur even in economically advanced countries. In Japan, one of the world's richest economies, only 6 percent of gross national product was spent on health services in 1990 even though, because of high life expectancy and declining fertility, the population is aging. Consequently, the health services provided to the elderly are limited, with many elderly enduring stays of 6 months or more in acute care hospitals because of a lack of long-term care and rehabilitation facilities (Okamoto, 1992).

The two hypothetical models represent scenarios against which the health experience of U.S. racial and ethnic groups can be compared. Race-related physiological differences (e.g., hypertension; Cooper and Rotimi, 1994) may produce more complex scenarios than the ones shown in Figure 3-1, as may differences in access to health care.

Ideally, longitudinal data on levels and types of disability, on flows into and out of morbidity and disability states, and on ageand gender-specific effects of diseases on mortality are used to construct such models. For example, a disabling event prevalent in U.S. postmenopausal women is hip fracture. The natural history of hip fractures, like that of other events and conditions, has evolved. The mean age for hip fractures in Britain increased from 67 years in 1944 to 79 years in 1990 (Keene, 1993). This 12-year shift marked a change in hip fracture's natural history. At age 67, hip fractures often do not involve the hip socket and are due to exogenous forces. Hip fractures at later ages often involve the hip socket, are due to osteoporosis, and require more health care. Such age changes in disease presentation could affect U.S. racial and ethnic differences in disability and mortality because African-American and Hispanic females have lower risks of hip fracture than white females.

Unfortunately, longitudinal data are not equally available for the groups reviewed. Consequently, we need to assess the quality and scope of the literature on racial and ethnic differences on specific diseases and conditions and then integrate these various findings. This is a multistep process.

First, we review data on the racial and ethnic differences in the risks of specific diseases and in the rates at which the diseases progress. When possible, we make gender-specific comparisons because racial and ethnic health differences often vary in degree, and sometimes in direction, by gender. Since many studies of racial and ethnic groups were done for individual diseases, studies of racial and ethnic differences have to be reviewed for a number of areas. Thus, the first stage of the review provides insight into racial and ethnic differences in disease-specific mechanisms and their age and gender dependence.

Since most disease-specific studies are of select populations, as a second stage we review the age relation of racial and ethnic differences in health, disability, and mortality in two sets of national data. First, we examine age patterns of total and cause-specific mortality by race to determine if they are consistent with the epidemiological data on racial and ethnic differences in the age dependence of disease processes. This is logical since racial and ethnic differences in disease in large part cause racial and ethnic differences in mortality age patterns. Second, we examine the National Long Term Care Surveys, where race-specific disability and mortality trajectories can be linked.

Thus, an assemblage of data is used to identify (1) the age dependence of disease mechanisms, (2) mortality and morbidity linkages, and (3) disability and mortality linkages. These relations, from different data sets, provide information to construct models like Figure 3-1.

MAJOR MORBIDITY PROCESSES AND THEIR AGE DEPENDENCE ACROSS RACIAL AND ETHNIC GROUPS

There are few studies that describe a number of diseases in a longitudinally followed elderly population. One such study (Guccione et al., 1994), though representing only whites, provides a context in which to discuss racial and ethnic differences in disease. In the study, 2,731 people were followed for 38 years to determine which of 10 conditions caused one or more of seven disabilities in people aged 64 to 98. The proportion of disability due to each condition, adjusted for age, sex, and comorbidity, is presented in Table 3-1. Stroke and heart disease caused disability on all seven functions. Diabetes, osteoarthritis, and hip fracture were also responsible for significant disability.

TABLE 3-1. Percentage of Disability Attributable to Specific Conditions After Adjustment for Age, Sex, and Comorbidity, by Condition and Activity for 2,731 Whites Followed for 38 Years in the Framingham Heart Study.

TABLE 3-1

Percentage of Disability Attributable to Specific Conditions After Adjustment for Age, Sex, and Comorbidity, by Condition and Activity for 2,731 Whites Followed for 38 Years in the Framingham Heart Study.

These results suggest that as a minimum, we should examine studies on hip fractures (osteoporosis), stroke, and heart disease in African Americans and Hispanics, and that it would be good to examine atherosclerosis, hypertension, diabetes, and anemia as well. In the review we look for differences between racial and ethnic groups (e.g., hip fractures are more prevalent among white females).

We examine age differences in disease as well as risk factors and disease mechanisms. We examine other prevalent diseases (notably cancer) that are not listed in Table 3-1. Also, to describe possible physiological mechanisms, we occasionally refer to cross-national data to distinguish social from physiological factors in racial and ethnic differences in disease. A complication in reviewing disease mechanisms is that there are racial and ethnic differences in disease interactions (e.g., atherosclerosis and osteoporosis, diabetes and stroke), especially at later ages.

Osteoporosis and Hip Fracture

Osteoporosis is usually viewed as a disease of postmenopausal women that accelerates with age. Two separate mechanisms have been proposed for osteoporosis. One, prevalent from age 55 to 74, is related to bone density and the rapidity of postmenopausal declines in estrogens. A second, dominating after age 75, is due to age differences in vitamin D metabolism (e.g., in the kidney and liver) and intestinal absorption of calcium (Eastell et al., 1991).

The morbidity of both types of osteoporosis is manifest in two ways. The first is increased risk of hip, spinal, and wrist fractures. Hip fracture is often studied because its consequences are severe—hip fracture mortality is estimated to be between 5 percent and 20 percent with long-term nursing home care necessary for 15 percent to 33 percent of patients (Boult et al., 1991). Spinal fractures have a slower course. A second effect of osteoporosis is its interaction with atherosclerosis. Here we discuss racial and ethnic differences in the effect of osteoporosis on hip fracture. Interaction with atherosclerosis is deferred to the discussion of heart disease and stroke.

Early onset osteoporosis is a disease of postmenopausal women that accelerates with age and is often associated with hip fracture. Hip fracture is more prevalent in white than in African-American or Hispanic women. Hip fracture rates begin to increase exponentially at age 70 for white women. They start to increase exponentially for white men and black women at age 75 and for black men past age 85. Since the incidence rates of hip fracture double every 5 years, this means that white men and black women have half the incidence of white women. Hispanic female rates are similar to black female rates (Kellie and Brody, 1990; Riggs and Melton, 1992). Possible explanations for racial differences in the incidence of hip fracture (and the rate of osteoporotic changes) are differences in bone density at menopause (possibly due to early differences in nutrition, physical activity, and body mass) and in the postmenopausal production of sex and parathyroid hormones.

One way to understand physiological differences in osteoporosis in racial and ethnic groups is to examine differences between males and females. The risk of hip fracture is lower, and occurs at later ages, in males than in females. Edelstein and Barrett-Connor (1993) found that body mass relative to height was associated with bone mineral density for both genders. However, whereas lean body mass predicted bone density in both genders, fat mass predicted a larger proportion of bone mineral density in women than men, possibly because of the conversion of androgens to estrogens in adipose tissue. Thus, both mechanical (i.e., weight-bearing physical activity) and hormonal factors likely affect bone density.

To analyze the effect of hormones on bone mineral density in elderly African-American, Hispanic, and white females, researchers often study serum estrone, the dominant type of estrogen produced postmenopausally. In postmenopausal women it is produced by aromatization of androstenedione in adipose (fat) tissue. Cauley et al. (1994) examined serum estrone levels to see if they explained differences in bone mass in black and white females. Serum estrone levels were higher in African-American than in white women, and bone mass was 23 percent to 27 percent greater in black women. Levels of serum estrone were related to bone mass differences among white, postmenopausal women. Bone mass decreased linearly with age for white, but not black, females. This could be due to several factors. Black-white differences in body mass explained most differences in serum estrone. However, nonobese black females had greater bone mass than nonobese white females. Race still significantly predicted bone mass differences after serum estrone was controlled for. Edelstein and Barrett-Connor's (1993) study of gender differences in bone density suggests that female racial differences in bone mass that are not explained by serum estrone might be due to the greater lean body mass of African-American females in comparison with white females and to the mechanical effects of greater muscle mass on physical activity and bone metabolism. The greater bone density of African-American females may be due to higher body mass and to greater postmenopausal production of estrone due to a higher proportion of body fat, factors that could increase the risk of hypertension and adult onset diabetes. If, instead, greater bone density is due to greater lean body mass and physical activity in African-American females, then the metabolic and circulatory effects of a higher body mass would be less likely to cause stroke and heart disease. Other risk factors for African-American women, including thinness, prior stroke, using walking aids, and alcohol consumption, were also risk factors for white women. Lower hip fracture risks for Hispanic females are also associated with greater body mass. Racial and ethnic differences in the risk of osteoporosis and hip fracture among females may involve several additional factors.

First, late onset osteoporosis (age 75 and older) is linked to age changes in vitamin D metabolism (in the kidney and liver) and intestinal absorption of calcium (Eastell et al., 1991). Vitamin D increases osteoclastic activity (i.e., a breaking down of the bone matrix by cells in the constant remodeling of bone tissue) and bone resorption (decreases in bone mass). Thus, both too little and too much vitamin D causes osteoporosis (Moon et al., 1992). This effect, however, differs by race. Blacks are less sensitive than whites to both vitamin D toxicity and deficiency (Taussig, 1966; Seelig, 1969). Studies of blacks aged 68 to 93 and whites 70 to 89 showed that hypovitaminosis D with secondary hyperparathyroidism occurred often in blacks (Perry et al., 1993). However, even with lower vitamin D levels, there was less bone resorption (and less turnover of skeletal mass) in U.S. blacks. In native Africans the incidence of hip fracture is even lower—only half that of U.S. blacks. A lower rate of bone formation was also indicated by lower serum osteocalcin in U.S. blacks. Blacks had higher ionized calcium levels than whites with similar parathyroid hormone levels; this suggests that bone metabolism is less sensitive to parathyroid hormone in blacks. The reduced sensitivity may be an adaptation in blacks to (1) a higher melanin content of the skin, which therefore produces less vitamin D, and (2) a lower intake of dairy products because of a high prevalence of intestinal lactase deficiency (Pollitzer and Anderson, 1989). A decreased sensitivity to parathyroid hormone and greater stability of skeletal mass in African-American females thus appear as important in their lower risk of osteoporosis as body mass differences and their effects on the postmenopausal production of sex hormones. Consistent with findings on the two types of osteoporosis, vitamin D and calcium supplementation, at least in white women, decreased hip fracture to advanced ages (e.g., in a group with a mean age of 84; Chapuy et al., 1994). Supplemental estrogens reduced the risk of fracture in black women, as it did for white women, up to age 75 (Grisso et al., 1994).

Heart Diseases and Stroke

U.S. heart disease and stroke mortality has changed markedly (DeStefano et al., 1993; Ghali et al., 1990; Pathological Determinants of Atherosclerosis in Youth Research Group, 1993). Although U.S. mortality for most types of heart disease and stroke has declined, racial differences in mortality are still marked: the age-adjusted mortality rates from major cardiovascular diseases for blacks is 1.5 times the rate for whites (Singh et al., 1996:Table 12).

Congestive heart failure is an exception to the general trend toward lower mortality from cardiovascular disease. Congestive heart failure occurs at late ages and reflects the cumulative effect of prior is chemic heart disease, hypertension, and atherosclerosis. Mortality from congestive heart failure increased until at least 1988 (Centers for Disease Control and Prevention, 1994). Mortality increases were paralleled by increased morbidity and service use and affected all race and gender groups. Age-standardized congestive heart failure hospitalization rates increased 60 percent for both whites and blacks from 1973 to 1986 (Ghali et al., 1990). This is consistent with declines from 1980 to 1989 in nonfatal coronary heart disease at ages 45 to 54—with increases at ages 75 to 84 (DeStefano et al., 1993).

Changes in other circulatory diseases differ by race and gender. To better differentiate African-American, Hispanic, and white trends in circulatory disease (Sempos et al., 1988), we identified factors affecting race and age trends of several processes related to circulatory events: atherosclerosis, diabetes, body iron stores, and hypertension.

Atherosclerosis

Atherosclerosis is a degenerative process that increases the risk of many circulatory diseases. It affects coronary heart disease, stroke, and, at late ages, peripheral vascular disease. Thus, in some senses the relation of atherosclerosis (the age-dependent process) to heart disease and stroke (the morbid events) parallels the relation of osteoporosis (the age-dependent process) to hip and other fractures (the morbid events). An evaluation of African-American and Hispanic differences in the progression of atherosclerosis is handicapped by a lack of longitudinal studies; the Framingham Heart Study, for example, studied only whites. The two best known longitudinal studies of circulatory disease containing significant numbers of African Americans and whites are the Charleston Heart Study and the Evans County study. There are, however, cross-sectional studies of risk factors for circulatory disease that represent African Americans and whites; a few represent Hispanics. Other studies examine differences in pathologies of the circulatory system in African Americans and whites at death.

One widely studied risk factor for atherosclerosis is cholesterol. Some data suggest that atherosclerosis in whites is more susceptible to cholesterol than in blacks (Eggen et al., 1965; Strong, 1972). In the 28-year follow-up of the Charleston Heart Study, the dependence of coronary heart disease mortality on cholesterol differed in African Americans and whites. White female cholesterol values had a J-shaped relation to mortality (i.e., mortality was higher at both very low and very high values of cholesterol). At intermediate values, mortality risk was lowest. The cholesterol-mortality relation was linear for African-American females. The increase in the risk of coronary heart disease with an increase of 1 standard deviation in cholesterol was 60 percent for white, compared with 40 percent for black, females (Knapp et al., 1992). For males, both the Charleston Heart Study and the Evans County Study suggest higher risk of coronary heart disease for whites than for blacks. In Charleston, black men and women had lower total cholesterol at baseline. Cholesterol was not a significant predictor of risk for black males (Keil et al., 1993).

To examine African-American, Hispanic, and white differences in the national distribution of, and changes in, total cholesterol, the National Health and Nutrition Examination Surveys for 1976 to 1980 (NHANES II) and for 1988 to 1991 (NHANES III; Sempos et al., 1993) can be used. Declines in cholesterol were similar for whites, African Americans, and Hispanics—though the proportion in a high-risk group was 8 percentage points lower for both African American and Hispanics than for whites. Lower cholesterol levels in African Americans and Hispanics may be due to differences in activity and nutrition. Studies showed that black Seventh Day Adventists with vegetarian (or partly vegetarian) diets had lower cholesterol than blacks eating an omnivorous diet; that is, a dose-response effect between cholesterol and nutritional factors (e.g., fats) was evident for African Americans as it is for whites (Melby et al., 1994).

Total cholesterol is a general indicator of the risk of coronary heart disease. There is growing interest in cholesterol subtypes and other lipoproteins. High-density lipoprotein (HDL) cholesterol protects against atherosclerosis by transporting lipids away from atheromas (irregularly distributed lipid deposits in the large- and medium-sized arteries). Low-density lipoprotein (LDL) cholesterol accelerates atherosclerosis, as may very low density lipoproteins (VLDL) or triglycerides, because LDL is trapped in atheromas after being oxidized. Other lipoproteins—for example, lipoprotein (a) [Lp(a)] or apolipoprotein E (Apo E)—also predict racial differences in atherosclerosis.

Lp(a) is associated with heart disease, stroke, and peripheral vascular disease. It appears to prevent blood clots from being dissolved by blocking plasminogen—an enzyme responsible for thrombolysis. It may also increase the uptake of LDLs by atheromas (Valentine et al., 1994).

The Atherosclerosis Risk in Communities (ARIC) study (1986 to 1989) of 14,254 persons measured HDL, LDL, and Lp(a) for blacks and whites of both genders. Lp(a) strongly discriminated black and white risks of coronary heart disease. Lp(a) levels were twice as high among blacks of both genders as among whites. Few environmental factors affected Lp(a). Selby et al. (1994) showed that Lp(a) levels had genetic determinants and that levels were higher in African-American than in white women. A genetic determination of Lp(a) levels for U.S. blacks is consistent with findings that blacks from the Sudan (Sandholzer et al., 1991), Ghana (Helmhold et al., 1991), and the People's Republic of the Congo (Parra et al., 1987) had Lp(a) levels similar to U.S. blacks. Also consistent with a genetic determination is the fact that racial differences in Lp(a) levels exist at early ages. The Coronary Artery Risk Development in Young Adults (CARDIA) study of 5,115 persons aged 18 to 30 found mean Lp(a) values twice as high for blacks as for whites—with black medians three times as high—results consistent with the Houston, Texas (Gayton et al., 1985), Bogalusa Heart (Srinivasan et al., 1991), and ARIC studies.

HDL and LDL in ARIC, in contrast, were similar for women of both races. Black men had more HDL than white men. White men had 16 percent to 26 percent higher triglyceride levels and the lowest HDL levels. Thus, HDL and triglyceride levels suggest lower risks of coronary heart disease for black men. The per-unit risk of elevated Lp(a) is less in blacks than whites, suggesting that Lp(a) interacts with other metabolic factors in blacks (Marcovina et al., 1993). If Lp(a) inhibits plasminogen, it might elevate black risks of stroke (Shintani et al., 1993). If Lp(a) increases cholesterol uptake in atheromas, this suggests interactions with cholesterol—an effect that could be dampened because black males have lower LDL and higher HDL levels.

Thus, Lp(a) and hypertension may explain the higher mortality from strokes for blacks than whites in middle and late-middle ages. Since Lp(a) has genetic determinants, and mortality selection has been shown in a number of twin studies to eliminate elevated genetic risks of mortality from coronary heart disease by age 85 (e.g., Marenberg et al., 1994; Carmelli et al., 1994; Reed et al., 1991; Heller et al., 1993), these age patterns of disease risk (and mortality selection) could contribute to a convergence of mortality patterns in blacks and whites owing to a different age dependence of circulatory diseases related to atherosclerosis at late ages.

Apo E is important in circulatory disease and dementia. One study suggested that the effects of Apo E differ in men and women (Ferrières et al., 1994). In some studies, Apo E2 and E4 subtypes lowered Lp(a), a fact significant for black circulatory disease risks. E2 was associated with reduced, and E4 with elevated, risks of heart disease (Tiret et al., 1994). E2 was associated with higher stroke risks—possibly interacting with diabetes (Couderc et al., 1993). The effects of E2 were stronger in postmenopausal females (Schaefer et al., 1994), lowering LDL twice as much as for premenopausal females.

Racial differences in atherosclerosis are also evaluated by studying circulatory pathologies in African Americans and whites. The Pathobiological Determinants of Atherosclerosis in Youth (PDAY, 1993) study collected material on thoracic and abdominal aortas and right coronary arteries in 1,532 autopsied black and white, male and female, subjects aged 15 to 34 for 1987 to 1990. PDAY did not show more coronary artery raised lesions in white, than black, males. This differed from the International Atherosclerosis Project (Guzmán et al., 1968) and other earlier studies (Strong and McGill, 1963; Eggen and Solberg, 1968). In autopsies of 1,243 blacks and whites aged 30 to 69 in New Orleans (Eggen et al., 1965) and in the International Atherosclerosis Project, where 23,000 autopsies from 15 geographic locations and four race-sex groups were assessed (Strong, 1972), there was less calcification of atheromas in blacks than in whites. Strong concluded that race differences were due to such factors as diet, activity, smoking, and stress. One dietary factor could be vitamin D consumption and age differences in its metabolism and its effects on calcium. The New Orleans study of autopsies from the 1950s to the 1960s suggested that atherosclerosis was 10 years of age more advanced in whites than in blacks. Differences between the findings of PDAY and the International Atherosclerosis Project may be due to recent decreases in the prevalence of atheromas in white males and a stable prevalence in black males.

Diabetes Mellitus (Adult Onset)

There are two types of diabetes mellitus. One, juvenile onset, is due to the autoimmunological destruction of pancreatic cells that produce insulin. Here we examine adult onset diabetes mellitus, which is not insulin dependent but is associated with degenerative changes in glucose and fat metabolism. Further references to diabetes are to the adult type.

Diabetes is more prevalent in U.S. blacks than whites (Cowie et al., 1993). In the 16-year follow-up of NHANES I the age-adjusted incidence was 15 percent for black women and 10.9 percent for black men. White women had an incidence of 7.0 percent; white men, 6.9 percent. Risk factors for diabetes include body mass index (body weight in kilograms divided by height in meters squared, i.e., kg/m2), although age-adjusted diabetes risks were higher for both lean (body mass index < 20) and overweight (body mass index > 26) blacks. Low education and low activity were also associated with the incidence of diabetes (Lipton et al., 1993). However, known risk factors do not fully explain the higher prevalence of diabetes among blacks. Thus, genetic differences in metabolic adaptation to obesity by race may be involved (Cowie et al., 1993). The relative risk of diabetes for those with one diabetic parent was 2.3; with two diabetic parents, 3.9. Efforts to find a genetic factor have focused on maternal mitochondrial DNA (Lin et al., 1994). Changes in mitochondrial DNA have been suggested as a general marker of aging and degenerative disease (Wallace, 1992).

In NHANES, diabetes was found to be higher in most Hispanic groups (Flegal et al., 1991). Though body mass index and socioeconomic status (which affect early nutrition) are important determinants of diabetes in Hispanics, so may be genetic determinants of body fat distribution and tissue resistance to insulin. As was true for blacks, adjustment for standard risk factors left a diabetes risk of 1.9 for Hispanics relative to whites (Marshall et al., 1993).

The excess risk of diabetes in blacks may be recent (in the last 30 years). Among male draftees aged 18 to 45 in 1924, black diabetes rates were one-third those of whites. They were two-thirds of white rates by 1944. In the 1960s in Chicago, blacks had low diabetes rates despite high rates of obesity. Though U.S. data show that black women had relatively high, but stable, obesity over the past 30 years, black females did not have higher diabetes rates in the 1960s. Diabetes rates for blacks and whites apparently crossed over in the 1970s. Because these increases (e.g., a 105% increase for black men from 1973 to 1983) were inconsistent with moderate increases in black obesity, other risk factors are likely to be involved (Lipton et al., 1993).

One hypothesis is that the incidence of diabetes reflects poor nutrition in mothers of low socioeconomic status. Poor maternal nutrition may affect the fetal development of such organs as the pancreas and liver, and this may show up later in life as chronic diseases (e.g., Barker and Meade et al., 1992; Barker and Godfrey et al., 1992). This hypothesis suggests that pancreatic beta-cell failure and the lessened ability to produce insulin are due to poor fetal and early postnatal nutrition. Evidence of this is also suggested by analyses of Civil War veterans by Fogel (1994). In groups with genetically high risks of diabetes, such as the Pima Indians, the problem may relate more strongly to insulin resistance (the “thrifty phenotype” hypothesis) than to insulin production (McCance et al., 1994). In Pima Indians, selective mortality of infants of low birth weight may play a significant role in the incidence of diabetes at later ages. Other models of diabetes incidence emphasize the role of insulin resistance or the genetics of the production of glucokinase, which is linked to diabetes in U.S. blacks—but not whites (Yki-Järvinen, 1994).

So far we have examined the incidence of diabetes. We are also interested in the racial and ethnic differences in age progression of diabetes—as we were for atherosclerosis. Important in diabetes progression is the degree of glycemic (blood sugar) control as measured by glycosylated hemoglobin (GHb). Small differences in GHb are related to the risk of diabetic complications (e.g., retinal degeneration). In one study there was a significant difference in GHb means (10.5% vs. 8.4%) for blacks and whites reporting diabetes. Race and GHb were also associated in those not reporting diabetes. Insulin use significantly reduced this association. Some consequences of GHb elevation occurred 4 to 7 years before diabetes was diagnosed (Harris et al., 1992). Thus, one factor affecting diabetes progression, and its morbid consequence, may be differences between blacks and whites in access to health care and the identification and control of elevated GHb. In Hispanics, no relation of socioeconomic status to blood glucose was found (Haffner et al., 1989).

Although the effect of diabetes on stroke and other circulatory disease risks is well known, the process may differ in whites, blacks and Hispanics (Sempos et al., 1988). Comparisons of racial and ethnic groups in similar environments have elucidated some of these differences. Diabetes prevalence among South Asians in England (19.6%) was 4.3 times that among British white males (4.8%), as indicated by mean serum insulin fasting and 2-hour glucose loading levels. Among African-Caribbean men in England, diabetes prevalence (14.6%) was almost as high as among South Asians, but insulin and triglycerides were lower and HDL was higher (McKeigue et al., 1991). For South Asians, high resistance to insulin produced increased central obesity and elevated triglyceride levels (Sheu et al., 1993). The natural history of diabetes in South African blacks is characterized by an accelerated (relative to whites) decline in beta-cell function, which produces insulinopenia (i.e., low levels of insulin). Insulinopenia may be the reason diabetes has fewer macrovascular complications for blacks than for whites (Joffe et al., 1992). The risks of coronary heart disease in diabetics could be due to central obesity associated with the failure of insulin to suppress release of nonesterified fatty acids from intra-abdominal fat cells. This increases triglycerides, reduces production of HDL cholesterol, and increases atherogenesis (the formation of lipid deposits in the arteries). Thus, elevated triglyceride levels, with insulin resistance, glucose intolerance, and hyperinsulinemia (high levels of insulin), can occur independently of high total cholesterol levels. Hyperinsulinemia in central obesity (e.g., high levels of IGF-1, insulin growth factor) may stimulate smooth muscle growth, adversely affecting vasculature and microvas culature in white males (McKeigue et al., 1991, 1993). In white males, higher insulin levels were associated with circulatory disease. Indeed, all excess circulatory disease risk in white males could be related to hyperinsulinemia. White female risks were elevated in those whose fat distribution resembles that of typical males (Modan et al., 1991; Fontbonne, 1991). Plasma insulin was a better marker than hypertension of abnormal glucose tolerance in obese males in the Paris Prospective Study (Fontbonne et al., 1988). The different metabolic mechanisms of diabetes in black males, and their higher levels of HDL, may explain the lower risk of coronary heart disease in blacks (as in Hispanics).

As for coronary heart disease, there was evidence of selective survival, with people age 80 and older having less variability in insulin and blood pressure levels, body mass index, and waist-hip ratios. Thus, people with better physiological control of insulin, blood glucose, and body mass index have better survival to late ages (Campbell et al., 1993; Bild et al., 1993).

Body Iron Stores

The role of physiological iron in disease is complex. Both elevated and depressed levels produce morbidity. For blacks, the risk is often due to nutritional deficiencies that produce anemia. Salive et al. (1992) found elderly black males and females (36.4% and 30.3%, respectively) over twice as likely to be anemic as white males and females (14.3% and 11.6%, respectively). Hemoglobin means converged for males and females above age 90 (i.e., 136 g/L vs. 132 g/L), though twice as many males (40.0% <130 g/L) had anemia as females (20.7% < 120 g/L). Male iron levels can be reduced by physical activity (Lakka et al., 1994). Thus, physical activity could affect the risk of cardiovascular disease by reducing iron levels—along with having other positive metabolic effects.

The higher prevalence of anemia among blacks may lower the late-age risk of circulatory disease by slowing the oxidation of LDL in atheromas. Among white males, elevated iron levels may accelerate atherosclerosis because free iron oxidizes LDL cholesterol; that is, hypercholesterolemia (high total cholesterol levels) and body iron levels interact (Kiechl et al., 1994). In 847 men and women aged 40 to 79, serum ferritin (a measure of iron in the body) predicted carotid artery disease in both sexes and was synergistic with hypercholesterolemia (Salonen et al., 1992). In men, ferritin levels varied moderately with age. For women, ferritin increases were marked for those age 50 to 59 (i.e., postmenopausally). Hematocrit, another measure of iron in the blood, was related to high blood pressure (Smith et al., 1994) possibly owing to higher blood viscosity (Löwick et al., 1992). The ability of ferritin levels to predict coronary heart disease declined for the elderly owing to nutritional deficits, differential survival, and the lower variance of ferritin. Differences in gender risk also moderated with age (dropping from 2.4 to 1 to 1.4 to 1 by age 80; Matthews et al., 1994). Thus, excess risk of coronary heart disease in white males may be partly explainable by higher iron stores in males than in females up to, and past, the age of menopause. After the age of menopause, white female iron stores are stable while iron stores of white males start to decrease. Atherosclerosis risks increase with age more rapidly for postmenopausal white females than for white males of similar ages (Matthews et al., 1994; Sullivan, 1989); this may be due to the interaction of iron stores with the release of calcium into the blood (and its uptake by smooth muscle cells) because of menopausal changes in bone metabolism. This explanation is suggested by the association of increased stroke risks in women with osteoporosis. Low bone density was one of the strongest risk factors for stroke in women; that is, there is a 74 percent increase in mortality per 1 standard deviation decrease in the bone density of the heel (Browner et al., 1991). One hypothesis is that in addition to calcium release, production of parathyroid hormone is increased, which causes both smooth muscle cell absorption of calcium and hypertension (Browner et al., 1993). Thus, the age acceleration of the risk of circulatory disease should be greater for white than for black or Hispanic females because of the more rapid progression of osteoporosis (and skeletal calcium release) in white females (Moon et al., 1992).

Hypertension

Hypertension is an important cause of certain types of heart disease and stroke in blacks, Hispanics, and whites. The prevalence of hypertension peaks at younger ages for blacks than for whites (Svetkey et al., 1993). In whites, older age was associated with hypertension. This pattern appears due to mortality selection; that is, the early elevation of blood pressure in blacks caused early mortality from stroke, coronary heart disease, and renal disease to be higher than for whites. In elderly whites, diabetes was the best predictor of hypertension. It was not significant for blacks.

Hypertension has many causes. Recently, interest has focused on the genetic determinants of renin (an enzyme affecting kidney function) and angiotensinogen (a protein affecting vasodilation, dilation of the blood vessels) as factors in hypertension (Dzau and Re, 1994). A single gene codes for angiotensinogen. Its mutation may predispose a person to hypertension (Jeunemaitre et al., 1992; Griendling et al., 1993). This mutation was found in 36 percent of whites with normal blood pressure (normotensives) and 47 percent of white hypertensives. The frequency of this mutation among blacks is 80 percent to 90 percent (Rotimi et al., 1994). Though predisposing to hypertension, the mutation is not sufficient in that West Africans (as opposed to U.S. blacks) have little hypertension (Cooper and Rotimi, 1994). Psychosocial stress and behavioral or environmental factors (e.g., sodium intake) also play important roles.

Hypertension can affect health by causing left ventricular hypertrophy, a risk factor for congestive heart failure. Angiotensin-converting enzyme (ACE) inhibitors are beneficial, not only in reducing blood pressure but also in reversing left ventricular hypertrophy (Lindpaintner, 1994). The effects of ACE on growth factors for smooth muscle and left ventricular hypertrophy are significant findings about the mutation in the angiotensinogen gene (Caulfield et al., 1994; Schunkert et al., 1994). That is, renin (an enzyme) and angiotensinogen (a protein) can also affect hypertension by stimulating the proliferation of smooth muscle cells in blood vessels (Dzau and Re, 1994; Dzau, 1994).

At late ages, systolic hypertension elevates stroke, total mortality, and mortality from coronary heart disease (Rutan et al., 1988). Diastolic hypertension is less of a risk at late ages; its decline may indicate progression of aortic atherosclerosis (Witteman et al., 1994). Thus, interventions in hypertension in the elderly have to be carefully targeted. Antihypertensive drugs have different efficacy in different age and racial groups, which suggests different, age-evolving race-specific (and ethnic-specific) etiologies for hypertension. Calcium channel blockers were most effective in young (under 60 years) and old (over 60 years) black males. Captopril worked best in young white males. Beta blockers worked best for older white males. The lesser effect of captopril in black males is consistent with the lesser prognostic significance of left ventricular hypertrophy in black males (Sutherland et al., 1993). Calcium channel blockers and diuretics may be effective in black males because black males tend to have hypertension due to low renin levels—possibly associated with hyperaldosteronism (excessive production of a steroid hormone produced by the adrenal cortex) and electrolyte imbalances (Materson et al., 1993).

Complex hormonal feedback systems affect hypertension, with different systems having greater effects in some racial and ethnic groups. Both the kidneys and the adrenal glands help control blood pressure. Hyperaldosteronism is associated with higher levels of production of androgen (a male hormone) and with higher levels of kidney dysfunction (White, 1994). Kidney failure rates, and the need for dialysis, are six times higher for blacks than whites, partly because of hypertension (Mathiesen et al., 1991) though the black risks of kidney failure are not fully explained by differences in the prevalence or severity of hypertension (Perneger et al., 1993). Kidneys produce both renin and erythropoietin (a protein that enhances the formation of red blood cells). Thus, kidney damage may cause hypertension but, owing to declines in erythropoietin, may moderate atherosclerosis by reducing the availability of iron (i.e., an antagonism of two risk factors). The adrenals produce aldosterone, which affects electrolyte and mineral-corticoid equilibrium. Thus, dysfunction of aldosterone production affects both sodium and potassium retention and androgen production.

Cancers

Table 3-1 did not include cancers, possibly because disability is associated with cancer's terminal phases, which may be relatively short. Nonetheless, there are significant differences in the incidence, diagnosis, and treatment of cancer among whites, African Americans, and Hispanics, and these affect the age pattern of cancer morbidity and mortality. For example, the age-adjusted mortality rate from malignant neoplasms for blacks is 1.35 times the rate for whites (Singh et al., 1996:Table 12).

One difficulty in studying cancer is that it represents over 35 diseases. Some cancers are so lethal (e.g., pancreatic and liver) that there are no major racial or ethnic differences in survival. We selected to review cancers that reflect well-identified racial and ethnic differences. In some, early detection affects survival, with few racial and ethnic differences after adjustment has been made for stage at diagnosis. In others, after stage at diagnosis has been controlled for, survival differences existed (e.g., bladder, female rectal, breast, cervical, and uterine cancer; Ragland et al., 1991). We examined breast and cervical cancer because there are different risk factors and types of disease present in blacks, Hispanics, and whites. Indeed there may be a negative correlation between cervical and breast cancer risk owing to their joint dependence on reproductive behavior. We examined racial and ethnic differences in prostate cancer because of large racial and ethnic differences in incidence and because its risk rises at late ages. We examined multiple myeloma, a cancer prevalent at late ages, because there are large racial differences in its incidence.

Prostate Cancer

The incidence and mortality of prostate cancer from 1986 to 1990 were higher for black than for white males. For both there are increases to very old ages. Prostate cancer, diagnosed early, has a good prognosis. Consequently, prostate cancer often occurs on death certificates as a contributing cause of death at late ages (Manton et al., 1991). This is due to the relatively long survival of males diagnosed at earlier ages with prostate cancer. In addition, prostate cancer is frequently diagnosed at a nonlethal stage in males dying at late ages of other causes. However, even latent tumors were more prevalent in U.S. blacks and whites than in Japanese (Yantani et al., 1982).

There is some evidence that prostate cancer risk is related to low levels of the active form of vitamin D (Corder et al., 1993). Indeed, vitamin D isomers may be involved in regulating cell differentiation and growth in a number of tissues because vitamin D interacts with the receptor sites of other hormones. Another hypothesis suggests that higher prostate cancer risks for black males are due, in part, to 10 percent higher testosterone levels in black than in white males. Higher testosterone levels in black women (than in white women) in the first trimester of pregnancy may affect the male offspring's hypothalamic-pituitary-testicular feedback system (Ross and Henderson, 1994), raising the offspring's testosterone production. Production of the 5-alpha reductase enzyme, which converts testosterone into its active forms, may have genetic determinants. For example, the activity of 5-alpha reductase is lower in Japanese and Chinese males. Their prostate cancer risks are lower than for U.S. whites and only a 30th of risks for black males. Additionally, there is a twofold to threefold increase in the risk of prostate cancer from a polyunsaturated fatty acid that can only be obtained from dietary sources—alpha-linoleic acid. The level of alpha-linoleic acid is positively related to the consumption of red meat and butter. It is negatively related to the consumption of dark fish. Hence, alpha-linoleic acid may aid the progression of a tumor (rather than function as a tumor initiator) since the incidence of microscopic prostate cancer at autopsy is the same in the United States and Japan—despite much higher clinically significant prostate cancer rates in the United States (Yantani et al., 1982). Also implicated in the risk of prostate cancer is the lower intake of linoleic acid (which comes from vegetable sources such as corn oil). A low linoleic acid intake was found in African-American males (Kaul et al., 1987). Thus, the pattern of high red meat intake (increasing alpha-linoleic acid) and low vegetable intake (decreasing linoleic acid) could explain the high prostate cancer risks of U.S. blacks and whites, as opposed to Japanese and Chinese (who eat more fish and vegetables); such a pattern would operate by affecting synthesis of eicosanoids (which affect inflammatory response in tissue) or blocking the products of 5-alpha reductase activity (and altering testosterone metabolism; Gann et al., 1994).

Such factors may also affect the risk of prostate cancer in Hispanic males. White Hispanic and non-Hispanic males had incidence rates of prostate cancer (age standardized) nearly identical to black Hispanic males. Black non-Hispanic males had nearly double the prostate cancer risk of black Hispanic males. The lower risk of prostate cancer for black Hispanic males may be due to the heavy reliance on various vegetable staples (e.g., black beans) in the diet despite the use of considerable fat in cooking (Trapido et al., 1990, 1994a).

Breast Cancer

Breast cancer is prevalent among females but, if diagnosed early, has good cure rates. Four risk factors for breast cancer were the same for blacks and whites (age at first birth, parity, surgical menopause, benign breast disease), and two (family history and breast feeding) were different in magnitude. Early age at menarche, contraceptive use, and smoking had more complex black-white differences (Mayberry and Stoddard-Wright, 1992). This complexity may be because breast cancer, like many other diseases reviewed, changes in nature with age of onset. Early onset breast cancer is generally histologically more aggressive, grows rapidly, has genetically determined risks, and is not estrogen- receptor positive. This form of breast cancer is frequently manifested premenopausally. Late onset breast cancer is histologically less aggressive, slower growing, dependent on reproductive history, and often estrogen-receptor positive.

There are racial and ethnic differences in overall breast cancer behavior. Breast cancer has a high lifetime prevalence (about 12%) and is treatable in the early stages. Data from the National Cancer Institute's Surveillance of Epidemiology and End Results program show that 5-year relative survival rates increased from 63 percent to 76 percent in whites in the last 25 years. In black women, survival improved from 46 percent to 64 percent. Poorer survival for black women is, in part, due to their greater chance of receiving a diagnosis at a late stage. Limited access to health care, usual source of care (e.g., white women have more frequent access to private clinics or physicians as indicated by the higher availability of private health insurance for whites [92.7%] versus blacks [58.9%]), higher body mass index, and lower mammography rates were associated with a diagnosis at a late stage for blacks (Hunter et al., 1993). Three factors—histological tumor grade, patient delay, and a physician breast exam—explained half of the black excess mortality for stage III and IV (vs. stage I and II) disease.

Overall survival is significantly worse for both black and Hispanic women. However, adjustment for tumor stage eliminated white-Hispanic differences, but not white-black differences. The interval before diagnosis does not explain the differences because black women were diagnosed at younger ages than whites. There were some racial differences in treatment. Blacks and whites received similar systemic therapy for node-positive breast cancer. For node-negative disease, however, blacks received less systemic therapy. There were identifiable racial differences in tumor biology. Black women, relative to whites, (1) were younger at diagnosis, (2) were estrogen-receptor negative and progesterone-receptor negative, (3) had larger tumors (27.7% > 5 cm), and (4) had tumors with a higher proliferation factor, all suggestive of blacks' having a higher risk of the early onset, aggressive form of the disease (Elledge et al., 1994). Hispanics had biologically more aggressive tumors than whites. However, blacks had significantly more aggressive tumors than Hispanics.

Both blacks and Hispanics have a lower incidence of breast cancer (Trapido et al., 1994b). This may be because there are two types of breast cancer. The early, aggressive disease is not affected by age at pregnancy or by fertility behavior and is related to genetic factors. It is more prevalent in black females (and apparently Hispanics). However, although more aggressive, it is less prevalent than late onset breast cancer. Late onset breast cancer is less aggressive and less influenced by genetic factors and more dependent on reproductive history (Manton and Stallard, 1992). In populations with low fertility and later age at first pregnancy (MacMahon et al., 1973), postmenopausal disease is more prevalent. Thus, in whites there is a higher overall prevalence of breast cancer owing to the greater risk of postmenopausal disease. In blacks, overall prevalence is lower, but there is a greater proportion of the aggressive early onset form of the disease. The early form of the disease, with its genetic determinants, tends to be removed from the black population at early ages due to mortality selection.

Cervical Cancer

Cervical cancer is of interest because when detected early (by Pap smear), it is treatable. Cervical cancer risks, which are related to early onset of sexual activity (and infection with human papilloma virus; risk ratio = 23.5; Becker et al., 1994), are higher in both blacks and Hispanics than in whites. However, the incidence of cervical cancer is much lower for both white and nonwhite Hispanics than for non-Hispanic blacks. Cervical and breast cancer risks are negatively correlated because of their different relation to sexual and reproductive behavior. Black non-Hispanics have the lowest breast cancer risks, whites and nonwhite Hispanics are intermediate (and similar), and non-Hispanic whites have by far the highest risks. Although Hispanics and blacks are in high- risk groups for cervical cancer, they are significantly less likely to undergo a Pap test. However, if cancer screening is available in prepaid health plans, Hispanics use it as often as whites (Perez-Stable et al., 1994).

Multiple Myeloma

Multiple myeloma is a cancer of plasma cells producing proteins for the immune system. It has a poor prognosis: a 5-year survival rate of 27 percent. Incidence and mortality for blacks (measured from 1986 to 1990) are twice the rates of whites. In contrast to many cancers, it continues to increase in incidence to advanced ages. Because myeloma is related to the aging of the immune system, it may be more an indicator of generalized aging processes than are many specific chronic diseases that have well-characterized risk factors. Thus, racial differences in multiple myeloma may reflect racial differences in generalized aging rates better than other diseases. This is suggested by data on a precursor condition to multiple myeloma called monoclonal gammopathy of unknown significance (MGUS). At current levels of life expectancy, multiple myeloma occurs in one-third of MGUS cases. As life expectancy increases and mortality from other diseases is deferred, higher proportions of the cases of MGUS may convert to multiple myeloma.

MGUS is defined by the detection of an abnormal immunological protein on electrophoresis. In whites it is 6 percent prevalent below age 80, 14 percent prevalent over age 90, and 19 percent over age 95 (Radl et al., 1975). MGUS has significant racial differences. It is rare (as is multiple myeloma) in Japanese, while blacks, consistent with their higher myeloma risk, have higher rates of MGUS than whites (Bowden et al., 1993; Singh et al., 1990). In contrast, Japanese have higher levels of normal immunoglobulins (i.e., IgG and IgA); this suggests that the Japanese have had long-term exposures to antigens that elevated these immunoglobulins but lack the genetic susceptibility that blacks have to produce monoclonal gammopathies. These data suggest racial differences both in the functioning of the immune system at late ages and in environmental exposures to immunological challenges.

White Hispanics and non-Hispanics have similar age-standardized incidence rates for multiple myeloma for both genders. In contrast, for black Hispanics and non-Hispanics (both genders), there is about a threefold elevation of multiple myeloma risks (Trapido et al., 1994a, 1994b). Thus, Hispanic status does not affect multiple myeloma risk but being black rather than white does.

Other data suggest that mortality selection will eventually operate on dysfunction of the immunological system—but at extreme ages. The risks of autoantibodies to the thyroid, pancreas, and other organs are not selected out of populations until almost age 100 (Takata et al., 1987; Mariotti et al., 1992).

Disease Interactions

To fully explain racial and ethnic differences in disease risks and progression, we must consider interactions of multiple diseases and multifactorial syndromes. For example, the “X syndrome” in circulatory disease—the clustering of insulin resistance, hyperinsulinemia, hyperglycemia, high triglycerides, and low HDL (Feskens and Kromhout, 1994)—involves aspects of both lipoprotein and glucose metabolism, which differ in blacks and whites. There appear to be metabolic differences in diabetes between black and white males (e.g., low insulin production in black male diabetes; insulin resistance in white male diabetics) that lower circulatory diseases for blacks.

Osteoporosis and atherosclerosis in postmenopausal females may be related to each other owing to changes in the production of estrogen and racial differences in the metabolism of vitamin D. Vitamin D is metabolized in the kidney and liver. As osteoporosis progresses and parathyroid hormone increases (especially in whites), the cells in artery walls absorb more calcium, and hypertension increases. White females are more susceptible to this process than black females. Another factor in this process may be body iron levels, which depend on kidney function (and the production of erythropoietin). A decline in kidney function may decrease body iron stores but increase hypertension. Blacks are more susceptible to kidney dysfunction than whites owing to the combined effects of hypertension and diabetes. Anemia may moderate atherosclerosis in blacks by reducing the potential for LDL to be oxidized and trapped in atheromas. The rise in iron levels in females postmenopausally, along with the progression of osteoporosis, may be responsible for the more rapid acceleration of atherosclerosis and mortality postmenopausally among white than among black women. If hemoglobin drops too low, the blood's oxygen-carrying capacity is impaired, and this aggravates cardiac ischemia. Differences in diabetes associated with hormonal factors and hyperinsulinemia may affect black-white differences in the age dependence of mortality risks at late ages, as may differences in Lp(a). If much of late onset dementia (85 and older) is due to circulatory disease (Skoog et al., 1993), there may be racial differences in dementia onset (Aronson et al., 1990), which is also suggested by the role of Apo E in atherosclerosis and the interactions of Apo E with Lp(a) that affect blood clotting. Racial and ethnic differences in dementia have been underresearched and require specialized longitudinal studies of risk factors in black, white, and Hispanic populations. Thus, there is a complex set of feedbacks that differ in Hispanics, blacks, and whites. We will need longitudinal data and specialized models to identify and describe these multiple disease interactions.

Health Care

There are differences in the type and quality of care delivered to racial and ethnic groups (Ayanian, 1994). Examples have been discussed for coronary heart disease, breast cancer, and diabetes. Medical innovations can have race-and ethnic-specific effects on both disease progression and mortality.

Two studies examined general differences in the quality of care by race and socioeconomic status. Kahn et al. (1994) examined the quality of care received by black or indigent Medicare beneficiaries. Similar results were found for both groups vis-à-vis whites and the affluent. In studying measures of quality of care (e.g., history taking, physical exam, chest X rays, common diagnostic tests, standard therapies) for 9,932 patients in hospitals in five states, they found that in urban teaching and nonteaching hospitals and in rural hospitals, blacks and the indigent received poorer care on average. An “apparent” advantage for blacks and the poor was that in aggregate they received more care in urban teaching hospitals. Since urban teaching hospitals had better care on average, estimates of racial quality of care differentials were downwardly biased. Peterson et al. (1994) examined racial differences in rates of coronary angiography, angioplasty, and bypass graft surgery in 33,641 men in Veterans Administration hospitals. Black veterans were less likely than white veterans to receive these procedures. Thus, there were racial differences in the quality of care in a common health-care system, although it was difficult to demonstrate mortality consequences.

In addition, medical innovations affect blacks, whites, and Hispanics differently owing to differences in disease risks and mechanisms. Initially, little could be done medically to control coronary heart disease, which was more prevalent in white males. Bypass surgery and thrombolytic agents can now reduce myocardial damage done by infarcts and allow heart attack survivors to live longer, even at ages 80 and older (Ko et al., 1992). In the second phase of the Thrombolysis in Myocardial Infarction (TIMI-II) trials, thrombolysis produced equally favorable outcomes (1-year mortality) in blacks and whites (Taylor et al., 1993). In Hispanics, outcomes were better than in the other two groups even though Hispanic (and black) profiles of risk factors appeared to be worse. Thus, while outcomes for all three groups were equally favorable, the higher incidence of coronary heart disease among whites means that successful treatment for coronary heart disease produces more total benefits for whites.

ACE-II inhibitors reduce the hospital use, disability, and mortality risks of congestive heart failure (SOLVD Investigators, 1991). Reductions in hospital use are considerable; about 350 stays were avoided per 1,000 persons in 3 years of follow-up. ACE-II inhibitors may also reverse left ventricular hypertrophy, a morbid condition related to hypertension that is prevalent among blacks. ACE-II inhibitors may also have beneficial effects in diabetes, again a condition more prevalent in blacks. By 1991, physicians increasingly prescribed ACE-II inhibitors and calcium channel blockers whereas newly treated hypertensives were half as likely to use diuretics, which have adverse effects in diabetics (Psaty et al., 1993). Thus, given the higher risks of hypertension, left ventricular hypertrophy, and diabetes for blacks, the use of ACE-II inhibitors may have greater per capita benefits for them.

Tighter control of insulin and blood glucose reduces the consequences of diabetes (Williams, 1994). This, however, requires intensive access to medical care and a high degree of patient involvement. With whites having greater access to health care, this may benefit white more than black diabetics. Strict dietary control and exercise can affect cholesterol (Ornish et al., 1990) and may be responsible for the secular changes in black-white differences in the rate of atherosclerotic progression noted between the PDAY and earlier studies. A recent anticholesterol drug trial shows significant reductions in total mortality as well as mortality from coronary heart disease. In this trial, 4,444 people with a prior heart attack had a total mortality rate 30 percent lower than that expected over 5 years. This drug could be of most benefit to white males, who have the lowest HDL levels and the greatest heart disease and atherosclerosis risks from hypercholesterolemia.

Effective treatment of hypertension has increased the age of chronic renal failure from the fourth to the fifth decade of life, a shift most evident for blacks (Qualheim et al., 1991). This shift may be extended because of the beneficial effects of ACE inhibitors on renal failure in diabetics (Chan et al., 1992). People aged 65 and older have become the fastest growing group in the end-stage renal disease program (59,000, or 40% of the end-stage renal disease population in 1990), and blacks are a large proportion (34.9%) of all persons on dialysis. Blacks, however, are a smaller proportion (17.9%) of those receiving kidney transplants. The growth of the dialysis population is highest at ages 75 and above, possibly owing to the use of erythropoietin (starting in 1988 in end-stage renal disease) to combat the anemia caused by dialysis. Thus, Medicare's end-stage renal disease program has benefits for both blacks and whites, but provides different services for blacks (more dialysis) than whites (more transplantation).

BLACK, HISPANIC, AND WHITE TOTAL AND CAUSE-SPECIFIC MORTALITY DIFFERENCES

There are two reasons for analyzing black, white, and Hispanic age differences in disease mechanisms. One is to study the age-specific effects of the risks, incidence, and management of diseases in different racial and ethnic groups. The second is to discover whether it is plausible that racial and ethnic differences in the age trajectory of mortality are due to the different age risks of specific diseases in the three groups and to the effects of mortality selection on the age prevalence of specific conditions. The morbidity data suggest that black, white, and Hispanic morbidity risks are multifactorial and complex and are unlikely to be proportional across age. For example, hypertension and diabetes, two risk factors for stroke, may raise the mortality of blacks relative to whites in middle age. At older ages, whites (especially males) are at greater risk for atherosclerosis than blacks; this suggests that whereas blacks susceptible to stroke die at earlier ages, white circulatory mortality risks may accelerate more rapidly at later ages. A similar pattern may occur for white females; their greater osteoporosis risk relative to black females may cause their risks of atherosclerotic heart disease to increase more rapidly postmenopausally.

The need to examine the mechanisms of specific diseases and subsequently their contribution to cause-specific (and total) mortality at specific ages is amplified by the greater uncertainty about the accuracy of age reporting at later ages in population data for U.S. blacks than for whites. Uncertainty about data quality also exists for Hispanics because their mortality is advantaged relative to whites and because Hispanics are a racially mixed group that is hard to define in population data. Thus, the morbidity review provides independent information that can be used to assess the plausibility of the race- or ethnic-specific age trajectory of mortality rates calculated from cause-specific and total mortality data. This is especially important for studies in the United States, which does not have long-standing population registries (like Sweden) or vital statistics programs (like Great Britain) to provide high-quality population and mortality data at late ages. In the United States the problem is compounded because the level and structure of age-reporting errors in census and mortality data may differ; error structures may also differ across censuses. To avoid the problem of using data sources with different error structures in life-table calculations, we calculated extinct cohort tables for U.S. whites and nonwhites using only the age at death reported on death certificates. To see if this is a reasonable strategy, we first examined the quality of age reporting on death certificates relative to other sources in matched record studies.

Table 3-2 compares ages at death reported on death certificates (grouped into 5-year age categories) for three racial groups and ethnic groups with ages at death reported on Medicare data from the Master Beneficiary Record (MBR) for 124,507 matched cases in 1987 for Massachusetts and Texas. This allows us to quantify the size and direction of errors in age reporting on death certificates for blacks, whites, and Hispanics for ages 65 to 100 and above (Kestenbaum, 1992). The rows in Table 3-2 refer to age groups in the MBR files. In column 1, the mean age within each MBR age group is assumed to be the midpoint of the 5-year age interval. Columns 2, 5, and 8 present the mean ages in the death certificate files for the same people. The death certificate files may classify some of these people in younger or older age groups, leading to discrepancies in the estimated mean ages. Columns 3, 6, and 9 display the discrepancies. For whites, the differences are small, except for the last two age groups (-0.18 and -0.51). For Hispanics, the differences are also small, with only the last two age groups exceeding ±0.2 year. For blacks, all but the first two of the differences exceed ±0.2 year with what appears to be a substantial downward bias at older ages.

TABLE 3-2. A Comparison of the Mean Age at Death and Ratio of the Number of Deaths Calculated from Death Certificates for Persons Assigned to a Given Medicare Master Beneficiary Record Age Group.

TABLE 3-2

A Comparison of the Mean Age at Death and Ratio of the Number of Deaths Calculated from Death Certificates for Persons Assigned to a Given Medicare Master Beneficiary Record Age Group.

Misclassification of age in the death certificate files may lead to underestimates and overestimates of the actual number of deaths in the various age groups. Columns 4, 7, and 10 display the ratios of (1) the number of deaths for matched cases in each age category for ages at death reported on the death certificate to (2) the number of deaths in the age categories generated from the MBR. For whites, all but two of the age groups are within 1 percent. For Hispanics, all but three are within 2 percent. For blacks, only one is within 1 percent, and two are within 2 percent. For blacks there were substantially more deaths reported on death certificates up to age 79 and fewer from ages 80 to 99. Deficits in the three older age categories from 85 to 99 were large for the death certificate data (i.e., -6.1%, -14.7%, -11.8%). These cases apparently were in the three lower age categories due to the underreporting of age at death on death certificates for blacks. The number of blacks (92) reported as age 100 and above on death certificates was similar to that in the MBR (91).

Also in Table 3-2 are two sets of ratios for blacks from Preston et al. (1996:Figure 1). Columns 11 and 12 refer to a sample of 2,657 deaths among African Americans in 1985 that were linked both to Social Security Administration (SSA) records and to the 1900-1920 U.S. censuses. The similarity of the census and SSA age distributions was used to argue for their accuracy relative to that of death certificate ages. SSA age was assumed to be most reliable because it was one of two sources where age agreed in 91 percent of 1,087 two-way agreements in the sample.

Preston's results differ from Kestenbaum's (1992). Both showed that the number of deaths reported at ages 65 to 69 was overstated on death certificates. For ages 70 to 84, the number of deaths recorded from death certificates may be overstated or understated. The number of deaths recorded from death certificates at ages 85-94 is understated, but the two studies disagreed on magnitude. For ages 95-99 the number of deaths recorded from death certificates may be overstated or understated. For ages 100 and above, the number of deaths from the death certificates is overstated, but the two studies disagreed on magnitude.

A difficulty in assessing mortality rates above age 100 is that the number of cases is small. Thus, it is important to assess the statistical precision of the results of match studies above age 100 to see if real differences are being identified. In the data in Preston et al. (1996), there appeared to be 26 death certificate, 20 SSA, and 16 census deaths above age 100 (calculated from the ratios in Table 3-2 and the 52 deaths reported at age 100 and older on death certificates, and with a three-way match rate of 50.5% assumed). If the number of deaths above 100 recorded by the SSA is assumed to be the most accurate and we assume that the 20 deaths are Poisson distributed, then the standard error of the estimate is 4.5, and a 2-standard error confidence interval ranges from 11 to 29 deaths. This confidence interval includes both the death certificate (26) and the census estimates (16) of deaths. Thus, the numbers of deaths reported above age 100 in the three sources are not significantly different.

In Kestenbaum (1992), 92 blacks had ages at death greater than 100 on death certificates, compared with 91 blacks in the MBR. The standard error for the 91 persons with ages at death over 100 in the MBR is 9.5, with a 2-standard error confidence interval of 72 to 110 deaths. This clearly includes the 92 deaths above age 100 recorded on death certificates. Again there is no statistically significant difference in the number of deaths above age 100 recorded in the death certificates and the MBR. The standard error for the 42 Hispanic deaths reported at age 100 and older from the MBR is 6.5, with a confidence interval of 29 to 55 deaths. This range includes the 49 deaths above age 100 recorded on the death certificates. Again, the difference is not statistically significant.

Thus, it can be concluded that the accuracy of death certificate ages differs over age and by race. For blacks, deaths at ages up to 84 were overreported and at ages 85 to 94 (and possibly later) underreported on death certificates. Given the differences between Kestenbaum (1992) and Preston et al. (1996), adjusting the data by using either ratio estimate is problematic. Neither match study showed that age reporting above age 100 on the death certificate is statistically different from reporting in the other data components.

The analysis of the two match studies suggests that it is not unreasonable to use death certificate data to calculate age-specific probabilities of death (qx's) for U.S. white and nonwhite males and females using extinct cohort methods. In this method, the numerator of each qx is the number of deaths recorded on death certificates for that age. The denominator is the sum of deaths reported on the death certificate at age x, plus deaths reported on death certificates for each subsequent year for each subsequent age. This specification of the denominator is not standard, but it can reduce the effects of certain types of errors in death certificate reports of age at death. For example, if there is a constant ratio of the number of deaths recorded on death certificates to the true number of deaths beyond age x, then the numerator and denominator used to calculate qx have the same relative error; errors will cancel out, and an accurate estimate of qx will be produced. This may be the case for nonwhite deaths for ages 85 to 99 recorded on death certificates. In Table 3-2 the downward bias in the denominator for ages 80 to 84 will produce overestimates of qx's even though the numerator is correct. The near constancy of ratios from ages 70 to 84 means that bias will decline over this range. Thus, using extinct cohort methods to calculate life expectancy should show a slight downward bias in life expectancy for nonwhites relative to whites (if white data are not subject to error, say, to age 95). The qx's for male cohorts aged 70, 75, and 80 in 1962, followed to 1990, are given in Figure 3-2. Female results are shown in Figure 3-3.

FIGURE 3-2. Mortality rates (qx) for three male cohorts.

FIGURE 3-2

Mortality rates (qx) for three male cohorts. SOURCE: Duke University Demographic Studies, analysis of 1960-1990 U.S. death certificate files.

FIGURE 3-3. Mortality rates (qx) for three female cohorts.

FIGURE 3-3

Mortality rates (qx) for three female cohorts. SOURCE: Duke University Demographic Studies, analysis of 1960-1990 U.S. death certificate files.

For both genders, nonwhite qx drops below that for whites at age 81 (i.e., about 5 years below the crossover age observed by Kestenbaum, 1992, in Medicare data for 1987; Figures 3-2 and 3-3 were not adjusted to reflect data in Table 3-2). At late ages, rates are variable owing to small numbers. Since we used death certificate data from 1960 to 1990, we had to estimate the number of deaths above ages 98, 103, and 108, the highest ages observed for the three cohorts. We did this by using the data from the cohort 5 years older to fill in the mortality experience at ages higher than we observed in the younger cohort. We excluded deaths above age 118 from all computations. This minimizes between-cohort differences for the younger cohorts, since the experience used to close out tables is identical to that of older cohorts, cohorts likely to be initially smaller and to have higher mortality to late ages (Manton and Stallard, 1996; Stallard and Manton, 1995).

Table 3-3 shows gender-specific ratios of white to nonwhite mortality at every 5th year of age for cohorts aged 70, 75, 80, 85, 90, and 95 in 1962. For both genders the gap for younger cohorts in the age range 80 to 90 narrows owing to declining white, and relatively stable nonwhite, mortality. For the cohort aged 70 in 1962, the white mortality excess at 85 is estimated to be 1.1 percent for females and 3.9 percent for males; at 95 the excess is 28 percent for both genders. The 28 percent white excess converts to a 22 percent nonwhite deficit, too large to be accounted for by any of the death certificate undercount ratios in Table 3-2. The ratios are sufficient to account for the 5-year difference in the crossover age in Kestenbaum (1992) and in the extinct cohort mortality rates in Figures 3-2 and 3-3.

TABLE 3-3. Mortality Ratios at Specific Ages for Each Cohort.

TABLE 3-3

Mortality Ratios at Specific Ages for Each Cohort.

Having examined racial differences in cohort mortality, we then examined cross-sectional (1992) data on several causes of death to see at what ages the peak differences in mortality relative risks occur. Figure 3-4 shows the black to white relative risks for males and females for total and cause-specific mortality. The peak black risk for cancer occurs at age 35 to 44 for females (1.5 to 1) and 45 to 54 for males (2 to 1). For heart disease the peak occurs at age 25 to 34 for males (2.8 to 1) and 35 to 44 for females (3.9 to 1). For both males and females the peak black stroke risk occurs at age 35 to 44 (about 4.5 to 1 and 4.0 to 1, respectively). For total mortality in 1992 the peak black risk occurs at age 35 to 44 (2.5 to 1 for males and 2.7 to 1 for females). For all causes (except cancer) and total mortality, there is a decline by age 85 to a mortality ratio less than 1.0. Thus, the mortality patterns show declines in black-white ratios beginning no later than age 45 to 54 (National Center for Health Statistics, 1994).

FIGURE 3-4. Ratios of black to white mortality rates, by age, cause of death, and gender.

FIGURE 3-4

Ratios of black to white mortality rates, by age, cause of death, and gender. SOURCE: Data from Kochanek and Hudson, 1994:Table 8.

Because of concerns about data quality, no U.S. analysis of racial survival based solely on population data is likely to resolve the issue of whether there is a black-white mortality crossover. However, there are several ways to increase the credibility of results. First, one can determine if population mortality analyses are consistent with differences in the age dependence of mechanisms causing morbidity (and subsequently, mortality) in Hispanics, blacks, and whites in epidemiological studies. Thus, the validity of the population results must be assessed in terms of the substance of the disease mechanisms whose mortality outcomes are observed in the population. As discussed in prior sections, there is ample evidence that the age trajectories of specific diseases for racial groups are not proportional and that certain mechanisms favor the largest mortality disadvantage for blacks in middle and late-middle ages, as found in Figure 3-4. Both mortality selection of frail persons and differences in the age dependence of the mechanisms of disease (which are documented in the section on morbidity) could cause the age trajectory of mortality for whites and nonwhites to cross over. A second way to validate population results is to compare them against national surveys where cause-specific and total mortality risks are assessed through independent data collection mechanisms.

The National Longitudinal Mortality Survey (NLMS) examined black-white differences in mortality. The NLMS used 10 samples (for 1978 to 1985) from the Census Bureau's Current Population Survey, which has a response rate of 96 percent. The NLMS samples covered 500,000 whites and 50,000 blacks aged 25 and above. Linkage to the National Death Index identified 32,508 deaths. The relative (black to white) risks for total mortality declined with age. For males the relative risks were 2.13, 1.67, and 1.04 for ages 25 to 44, 45 to 64, and 65 and above. For women the relative risks were 2.33, 1.82, and 1.16 for the same ages. Adjustments for income reduced male relative risks to 1.74, 1.30, and 0.98 and reduced female relative risks to 1.93, 1.48, and 1.16. For cause-specific mortality, cardiovascular disease showed the same age pattern of relative risk as total mortality (2.06, 1.59, and 0.93 for males; 4.07, 2.09, and 1.16 for females). For cancer there was no convergence for blacks up to age 65; there was a moderate convergence for black females after age 65. For all other causes of death there was a convergence for both genders. Income-adjusted relative risks for total mortality declined by 5-year categories from age 25 to age 85 and above, with convergence, then crossover, by age 60 for males and by 80 to 85 for females (Sorlie et al., 1992).

A similar study was done of Hispanic mortality using 12 Current Population Surveys. Of 700,000 persons aged 25 and above, 40,000 were Hispanic with 1,562 Hispanic deaths. The age patterns are different for Hispanics in that the age-adjusted standardized risk ratios for Hispanics to non-Hispanics were 0.74 for males and 0.82 for females. Age-specific risk ratios for total mortality were 0.97 (age 25 to 44), 0.73 (age 45 to 64), and 0.83 (age 65 and above). Risk ratios for cardiovascular disease declined with age for men (0.77 to 0.59) and increased moderately for females (0.72 to 0.84). Hispanic cancer ratios were lower at all ages (0.46, 0.52, and 0.76 for males; 0.46, 0.68, and 0.55 for females). This was confirmed in age-adjusted rates of cancer incidence, where the relative risk for white Hispanic males was 88 percent of that for white non-Hispanic males. The risk for black Hispanic males was only 71 percent of that for black non-Hispanic males. The cancer risk for black Hispanic males was only 85 percent of that for white Hispanic males (Trapido et al., 1994a). For females, both black and white Hispanic cancer risks were about 70 percent of that for black and white non-Hispanics, with black Hispanic risk about 78.5 percent that of white Hispanics (Trapido et al., 1994b). When the age-specific total mortality risks were adjusted for income, they dropped further: to 0.81 (age 25 to 44), 0.67 (age 45 to 64), and 0.66 (age 65 and above) for males; 0.64 (age 25 to 44), 0.74 (age 45 to 64), and 0.83 (age 65 and above) for females. One explanation of the Hispanic mortality advantage is their recent migrant status. Other explanations may involve differences in risk factors for specific diseases (e.g., while diabetes prevalence is high in Hispanics, so are HDL levels).

A final source of validating data is longitudinal studies of mortality in closed cohorts. Lew and Garfinkel (1990) assessed the mortality of 49,469 people passing age 75 from 1960 to 1987. There were 7,911 person-years of exposure for black males and 7,412 person-years of exposure for black females. Black male mortality rates dropped below white rates at about age 80 to 84. For females the crossover occurred between ages 85 to 89 and 90 to 94. In 4,107 people aged 65 and above in 1986 and followed for 5 years. Guralnik et al. (1993) found higher life and active life expectancies for black men and women above age 75.

WHITE-NONWHITE DIFFERENCES IN DISABILITY AND ACTIVE LIFE EXPECTANCY

Morbidity and disability are both powerful determinants of the age trajectory of mortality. As discussed above, hypertension, stroke, diabetes, renal disease, certain types of heart disease, and some cancers may create higher mortality for blacks in middle age. We compared the age dependence of race-specific disease mechanisms to total and cause-specific age patterns of mortality in several types of data in the preceding section. These represent two of three pairwise relations needed to evaluate the model in Figure 3-1. Here we evaluate the third linkage: the age relation of disability and mortality. We examined this using the 1982, 1984, and 1989 National Long Term Care Surveys (NLTCS) linked to Medicare mortality records for the period 1982 to 1991.

The NLTCS was used because large list samples of individuals were drawn from Medicare enrollment files and followed for up to 9.5 years. There are 30,308 distinct persons, not households, in the 1982, 1984, and 1989 longitudinal file; a total of 60,232 assessments were attempted, with 57,290 completed, an overall response rate of 95.1 percent. The Medicare list sample (which represents 98.4% or more of the U.S. elderly population) allows 100 percent of the sample to be followed for mortality. Both community and institutional persons are represented. In the three surveys, 16,485 detailed community interviews were completed. In 1984 and 1989, 3,100 detailed institutional interviews were done. About 11,000 deaths were recorded from 1982 to 1991 so there are a large number of deaths and person-years of exposure to assess—especially since people had to be aged 65 and older to be in the sample. Replenishment samples of about 4,900 people were drawn in 1984 and 1989 to replace the 65- to 66-year-olds (1984) or the 65- to 69-year-olds (1989) who would otherwise be missing owing to the aging of the original sample of those aged 65 and older. Age is recorded both from Medicare records and as reported by the sample person. If a sample person is too impaired to complete an in-person interview, the interview is conducted with a proxy. The date of birth is also recorded on the interview. Ages can be checked for temporal consistency for people who were interviewed on more than one date. A problem identified by Kestenbaum (1992), timely reporting of age at death, was handled by updating the Medicare mortality records 6 to 12 months after the time of the interview. Age reporting in NLTCS records should be of better quality than standard Medicare records because of validation by direct survey contact and the possibility of tracking people over time.

We analyzed these data in two steps. First, we examined the distribution of disability for whites and nonwhites, stratified by education to control for differences in socioeconomic status. These results are presented in Table 3-4; stratifications by age are found in Table 3-5. After examining white-nonwhite differences in the distribution of disability at specific ages, we modeled the dependence of mortality on disability.

TABLE 3-4. Disability Status of White and Nonwhite High School Graduates and Nongraduates Living in the Community, 1982-1989.

TABLE 3-4

Disability Status of White and Nonwhite High School Graduates and Nongraduates Living in the Community, 1982-1989.

TABLE 3-5. Disability Status of White and Nonwhite Community Residents, Stratified by Age and Education, for 1982 and 1989.

TABLE 3-5

Disability Status of White and Nonwhite Community Residents, Stratified by Age and Education, for 1982 and 1989.

As Table 3-4 shows, the proportion of nondisabled community residents declined 1.03 percent for white nongraduates, but increased 0.61 percent for nonwhite nongraduates from 1982 to 1989. For graduates the proportion of nondisabled whites increased 2.41 percent; for nonwhites it declined 0.41 percent. In general the proportion receiving help with three or more activities of daily living is higher for nongraduates. Stratified by education, race, and age, sample sizes for some estimates are small.

There were sizable changes from 1982 to 1989 in the age distribution of the population aged 65 and older. In Table 3-5, we present age-specific changes in disability for the education groups. There are large changes in the age distribution of disability across education. For whites aged 65 to 74 in 1982 there was little difference in disability between education groups. For nonwhites, the disabled group was 5.0 percent larger in 1982 for nongraduates. From 1982 to 1989, disability declined for white graduates. Whereas white nondisabled nongraduates increased 0.4 percent at age 65 to 74, white nondisabled graduates increased 2.9 percent from 1982 to 1989. Nonwhite, nondisabled graduates increased 1.3 percent.

For age 75 to 84, education effects were larger. For white graduates, the nondisabled proportion was 5.0 percent higher in 1982, 5.1 percent in 1989. For nonwhite graduates the nondisabled proportion was higher: 14.5 percent higher in 1982; 6.7 percent in 1989.

For whites aged 85 and older, disability increased 2.1 percent for graduates versus nongraduates in 1982—based on few (eight) cases. By 1989, with 30 persons in the disabled group, the effect reversed; disability declined 6.8 percent for white graduates, and there was a larger proportion (55.3%) nondisabled. For nonwhites, 85 and older who were graduates, sample sizes are small and estimates unstable.

To link disability to survival, we first identified dimensions of disability and then estimated individual scores on these dimensions. We identified the dimensions from multivariate analyses of 27 items in the 1982, 1984, and 1989 NLTCS (Manton et al., 1995). In the analysis, all years were pooled so that the dimensions identified describe the distribution of cases for any survey year. The analysis produced two types of coefficients. The coefficients describing the relation of the 27 variables to the six disability dimensions are listed in Table 3-6. In addition, we estimated scores for each person in the community sample each time he or she was interviewed. For persons with more than one interview, changes in the scores on the six dimensions can be assessed directly. Scores are estimated so that they sum to 1.0 across the six dimensions for each person at each time; that is, a person may be a partial member of multiple groups.

TABLE 3-6. Prevalence Rates for Select Functional Disabilities for Six Groups Identified in the 1982, 1984, and 1989 National Long Term Care Survey.

TABLE 3-6

Prevalence Rates for Select Functional Disabilities for Six Groups Identified in the 1982, 1984, and 1989 National Long Term Care Survey.

The six dimensions define six groups that can be characterized by examining which coefficients in Table 3-6 are large. The six groups are as follows:

  1. Nondisabled, with modest physical limitations, a group with no impairment in activities of daily living or instrumental activities of daily living (except heavy housework) and only moderate difficulty doing a few physical tasks.
  2. Active, a group with no impairments in activities of daily living or instrumental activities of daily living and few physical impairments.
  3. IADL impaired with performance limitation, a group with a number of impairments in instrumental activities of daily living (but none in the activities of daily living except bathing) and significant physical impairment.
  4. IADL impaired, a group with many impairments in the instrumental activities of daily living, including those related to cognitive performance (but none in the activities of daily living). Physical performance, however, is less impaired than group 3.
  5. ADL impaired, a group with impairments in four of the six activities of daily living, impairment in many of the instrumental activities of daily living, and moderate physical impairment.
  6. Frail, a group with impairments in almost all activities of daily living and instrumental activities of daily living.

This analysis produced six scores for each of the 16,485 community interviews conducted in 1982, 1984, and 1989. To generate scores for all 30,308 people in the longitudinal file, we made two additional calculations. First, there were about 35,700 people who screened out on one or more of the three surveys. These people had no chronic disabilities in activities of daily living or instrumental activities of daily living (the reason for screening out) for that time. Thus, they were assigned a score of 1.0 on the first dimension (i.e., were put into the nondisabled group). A plausible alternative would be to assign them a score of 1.0 on the second dimension (i.e., put them into the active group). We dealt with this uncertainty by combining the first two groups into a single “active/non-disabled” group, which appears later in Table 3-9 and Figure 3-7. Second, there were about 5,100 people in institutions in total in 1982, 1984, and 1989. Because the 1984 and 1989 detailed institutional surveys indicated that the average resident had 4.8 impairments in activities of daily living (no institutional interview was done for 1,992 institutionalized people in 1982), we created a seventh category for institutionalized people. Institutionalized persons received a 1.0 on this seventh category (and 0.0 on all other dimensions).

TABLE 3-9. Projected Survival Function (lt), Life Expectancy (et), Active Survival (lt[ḡ1[t]+ḡ2[t]]), and Group Prevalence Rates (ḡk[t]) by Age, Race, and Gender.

TABLE 3-9

Projected Survival Function (lt), Life Expectancy (et), Active Survival (lt[1[t]+2[t]]), and Group Prevalence Rates (k[t]) by Age, Race, and Gender.

FIGURE 3-7. Estimates of race-specific prevalence rates for active/nondisabled groups (groups 1 and 2 combined) at each age by gender.

FIGURE 3-7

Estimates of race-specific prevalence rates for active/nondisabled groups (groups 1 and 2 combined) at each age by gender. SOURCE: Duke University Demographic Studies, analysis of 1982, 1984, and 1989 National Long Term Care Surveys.

With these scores and mortality records for 1982 to 1991, we estimated two types of equations. First, we calculated the changes in the disability scores by (1) interpolating monthly values of the disability scores for people with interviews at the beginning and end of an interval, and (2) using a “nearest neighbor” match for people who died in an interval to determine the monthly rate of change for the person most like the decedent. In addition, we weighted cases by replicating each case by the ratio of its sample weight to the base weight. When this was done we calculated a monthly transition matrix,

gi(t+1)=Ctgi(t)+ei(t+1)
1
where gi(t) is the seven-element vector of disability scores for the ith individual at age t; gi(t + 1) is the corresponding vector at age t + 1; Ct is the 7 × 7 matrix of coefficients describing changes in the disability scores from age t to t + 1; and ei(t + 1) is the error in prediction of gi(t + 1).

A second equation describes mortality as a quadratic function of the current scores (gi(t)) and a term reflecting the dependence of mortality on age net of the changes in disability, that is,

μ(gi(t))=½[gi(t)]TB[gi(t)]·eθt
2

The 7 × 7 matrix B contains hazard coefficients describing how the pairwise interactions of the gik(t),k = 1, …, 7, affect mortality. Those effects are multiplied by the exponential term t, which represents the average effects of unobserved variables on mortality over age. As the gik(t) become more informative the effects of unobserved factors will decrease. This will decrease the value of θ. The matrix Bt is calculated for age t, as Bt = B · t, making the hazard function age dependent.

With the dynamic and hazard equations estimated, difference equations can use the coefficients from these equations to calculate life-table parameters, for each age t, by applying first the mortality equation to the distribution of the gik(t), and then the dynamic equations (Manton et al., 1994).

Mortality coefficients for disability scores estimated for white and nonwhite females are given in Table 3-7. Disability-specific mortality is roughly similar for white and nonwhite females (the confidence bounds of nonwhite coefficients were broader). There is a consistently higher level of mortality for nonwhite institutionalized females, and there are fairly large differences for terms involving group 4. The exponent θ, indicating the annual percentage increase in mortality, conditional on the time-variable disability profile, is higher (6.9%) for white females than for nonwhite females (4.3%). The higher age rate of mortality increase for white females will produce a convergence of white and nonwhite female mortality although white females start with lower mortality at age 65.

TABLE 3-7. White and Nonwhite Female Quadratic Mortality Coefficients.

TABLE 3-7

White and Nonwhite Female Quadratic Mortality Coefficients.

Table 3-8 shows mortality coefficients for white and nonwhite males. There are more differences in the coefficients for white and nonwhite males (e.g., 12.5 vs. 6.4 for the effect of group 3, with whites disadvantaged; 11.9 vs. 26.0 for group 4, with nonwhite males disadvantaged; 3.75 vs. 4.62 for active males) than for females. The θ for males differs for whites and nonwhites (i.e., conditional on disability, white male mortality increases 7.14% per year vs. 3.73% per year for nonwhites). The ratio of 1.91 for the male θ's (vs. 1.59 for females) suggests that the convergence of mortality with age is more rapid for males.

TABLE 3-8. White and Nonwhite Male Quadratic Mortality Coefficients.

TABLE 3-8

White and Nonwhite Male Quadratic Mortality Coefficients.

Figure 3-5 plots the white and nonwhite, male and female, age-specific probabilities of death calculated from equation (2). Female probabilities of death converge. A crossover is evident only at late ages. For males the crossover occurs about age 83—or roughly halfway between the extinct cohort estimate (age 81) and Kestenbaum's (1992) estimate (age 86).

FIGURE 3-5. Estimates of age-specific mortality rates for whites and nonwhites, by gender.

FIGURE 3-5

Estimates of age-specific mortality rates for whites and nonwhites, by gender. SOURCE: Duke University Demographic Studies, analysis of 1982, 1984, and 1989 National Long Term Care Surveys.

Figure 3-6 shows male and female survival probabilities (lt's) normed to 100,000 at age 65. For males, lt's converge at age 92-93. For females, although lt's converge, there is no crossover.

FIGURE 3-6. Race-specific numbers of survivors at estimates of each age by gender if we assume 100,000 males and 100,000 females alive at age 65.

FIGURE 3-6

Race-specific numbers of survivors at estimates of each age by gender if we assume 100,000 males and 100,000 females alive at age 65. SOURCE: Duke University Demographic Studies, analysis of 1982, 1984, and 1989 National Long Term Care Surveys.

The age trajectories of the mean scores (i.e.,k(t)) for the first two groups (active and nondisabled with modest physical limitations) for white and nonwhite males and females are shown in Figure 3-7. For both nonwhite males and females, disability (lower functioning) is greater. This is reasonable if (1) the survival of higher proportions of nonwhites to late ages implies their greater survival at higher disability levels, and (2) acute diseases cause mortality to increase more rapidly with age for whites. This may be explicated by examining Table 3-4, where the functioning of white high school graduates improved from 1982 to 1989. In contrast, the disability of nonwhite graduates increased while that of nonwhite nongraduates decreased. This could be due to differences in mortality; that is, nonwhite graduates could appear to be less functional if greater proportions of them survive to higher ages.

Such differences suggest the existence of acute disease mortality, not represented by disability dynamics, but by the age dependence coefficients (θ). The average score in the active/nondisabled group is higher for whites than for nonwhites and, after a long decline to age 95, begins to increase, owing to whites' higher θ's (6.9% and 7.3%), which may cause impaired whites to die off at late ages more rapidly than disability sets in. In nonwhites, θ is smaller (i.e., 4.3% and 3.7%) so there is a relatively slower mortality increase in impaired groups. Another possibly important factor in the higher disability of nonwhites is body mass index, which is higher in black women and is strongly related to mobility disability in the NHANES I follow-up study (Launer et al., 1994). High past and current body mass indexes (> 27) were significant risk factors for incident disability in the young old, possibly because of effects on osteoarthritis. Current weight loss and past high body mass index ( 28) (but not current high body mass index) were significant for the incidence of disability in the oldest old. Thus, different age changes in function for whites and nonwhites can result from mortality interactions with several age-dependent factors.

Table 3-9 presents life tables and average disability scores for white and nonwhite males aged 65, 75, 85, 95, and 105 calculated using the dynamic (1) and mortality (2) equations. Whites have higher life expectancies and higher proportions active/nondisabled at age 65 than nonwhites. Nonwhite survival rates eventually converge with those for whites. At late ages the average active/nondisabled score is lower for nonwhites than for whites. Mortality is higher for whites at late ages owing to their larger θ's so that mortality selection at late ages tends to preserve, or slightly increase, the aggregate level of functioning for whites. A larger θ generates higher mortality for nondisabled white persons producing life expectancies close to those in Social Security Administration (1992) cohort life tables. The column marked lt × [1(t) + 2(t)] gives survival-weighted activity levels for the gik(t) summed for the first two groups; that is, survival and functioning are summarized in a single trajectory. Higher white activity levels occur in disability changes observed over a total of seven years, (i.e., from 1982 to 1989). During that time, while the overall prevalence of chronic disability declined in the United States (Manton et al., 1993), the declines in disability were larger for whites than for blacks (Clark and Wolinsky, 1996; Clark et al., 1996). Since the disability declines over the seven years of follow-up occur for most age groups represented in 1982, the life-table calculations in Table 3-9 represent the splicing together of disability and survival experience of distinct cohorts. The trajectories of disability score changes are representative of the changes manifest in the distinct cohort trajectories—as well as the changes between the cross-sectional distributions of disability observed in 1982 and 1989.

SUMMARY

We examined race-specific and ethnic-specific age patterns of health and mortality changes in three ways. First, we examined epidemiological data on racial and ethnic differences in the age dependence of the risk of several diseases. The diseases examined were osteoporosis, heart disease and stroke, cancers, and diabetes; they represent 70 percent of all U.S. mortality above age 65. Not represented were chronic pulmonary conditions, pneumonia and influenza, externally caused deaths (except hip fracture), and liver and selected other conditions. Since a number of major risk factors (diabetes, hypertension, atherosclerosis, anemia, osteoporosis) were examined, the conditions examined probably affect more than 70 percent of deaths above age 65. For example, pneumonia and influenza often cause mortality in persons with other chronic diseases; cardiac problems may affect pulmonary function, and diabetes and hypertension can affect many organs such as the kidney, liver, and brain. There are over 35 types of cancer, so we selected several to represent racial and ethnic differences in risk. For example, prostate and breast cancer are the most frequently diagnosed U.S. cancers; black males have a 20 percent greater incidence of prostate cancer than whites, and white females have a 30 percent greater incidence of breast cancer (NCHS, 1994).

First, we reviewed disease mechanisms. Both the accelerating effect of hypertension and diabetes on black mortality and the stimulating effect of atherosclerosis and other factors on white mortality at late ages suggest the plausibility of a physiological basis for mortality convergences and crossover. We did not discuss specific nonlethal diseases (e.g., osteoarthritis), although their chronic effects on health at later ages are reflected in the disability and mortality linkages.

Second, we examined both the quality of data used to determine the age trajectory of total and cause-specific mortality and the ages of maximum black-white mortality differences. For cancer, heart disease, and stroke, the peak relative risk occurred in middle age, with declines in relative risk beginning before age 55. This was consistent with the morbidity review.

Third, we examined the linkage of disability with mortality at late ages. In a model describing the interaction of disability and mortality, there was a more rapid increase in mortality with age for whites than for blacks. This occurred for both males and females and was consistent with the higher disability found in blacks at later ages. The age at crossover in the NLTCS data was similar to ages found in extinct cohort analyses of death certificate data and Kestenbaum's (1992) matched record study.

Thus, we were able to examine three data elements (age trajectory of morbidity, mortality-morbidity linkages, and disability-mortality linkages) to develop a picture of U.S. racial differences in the age dependence of mortality, morbidity, and disability to extreme ages.

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Copyright © 1997, National Academy of Sciences.
Bookshelf ID: NBK109844

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