A central thrust of the Health and Retirement Study (HRS) is to examine the impact of health status on the decision to stop working. A related goal is to understand the longer-term health consequences of the retirement process. The HRS conceptualizes "health" as a multidimensional construct. By combining measures of respondent health, functional status, and health care usage with economic and family variables, the HRS helps us to understand how health influences-and is influenced by-socioeconomic status through the course of life. As the HRS data grow richer over time and as analytic methodologies improve, researchers increasingly will use the data to answer questions of causation that thus far have eluded social scientists and epidemiologists.
This chapter offers insight into the physical and mental health status, health insurance coverage, and health care utilization of community-dwelling older adults. It also provides a snapshot of the effects of health and unexpected health events on employment, as well as a look at disability and physical functioning among HRS participants.
There are wide variations in the health of Americans age 50 and older, with differences that vary by age, race/ethnicity, and lifestyle. According to HRS data:
The HRS data on health are based largely on what respondents report about themselves. While self-reported evaluations are inherently subjective-and related to individual personality, outlook, and context-research in a wide variety of cultures and contexts suggests that self-reported health status is a very good predictor of more objective health measures such as chronic illness, hospitalization, and longevity. Individuals' beliefs about their own health status also have been found to influence their expectations of retirement and the retirement process itself.
Figure 1-1 suggests that HRS participants who live in the community consider themselves to be in reasonably good health and that self-reported health status decreases with age. Almost half of HRS participants ages 55 to 64, compared with 42 percent of participants ages 65 to 74, 32 percent ages 75 to 84, and 25 percent age 85 and older, say they are in very good or excellent health. Conversely, the proportion reporting that they are in fair or poor health increases steadily from 21 percent among people ages 55 to 64 to 43 percent among those age 85 and older.
Gender differences in self-reported health status are small, while differences by race/ethnicity are large. Men are slightly more likely than women to report excellent or very good health (43 percent compared with 41 percent). Only about 25 percent of Black and Hispanic respondents, compared with 45 percent of White respondents, report being in excellent or very good health (Figure 1-2). Additionally, about 42 percent of Black and Hispanic participants, compared with 24 percent of White respondents, report their health to be fair or poor.
Most studies find that some, but not all of the racial and ethnic disparities in health can be attributed to differences in socioeconomic factors such as education, income, and wealth that are related to health and differ by race and ethnicity. One study found that socioeconomic factors explained only a relatively small part of the racial difference in the prevalence of chronic conditions, but that the racial disparity in physical functioning could be almost completely explained by a combination of socioeconomic status differences and the racial differences in chronic conditions (Kington and Smith 1997).
Advancing age is associated with an increasing prevalence of a number of diseases and other health problems. The HRS is uniquely poised to describe these problems in terms of their effects on the everyday function of older people. Figure 1-3 presents the prevalence of selected health problems reported within different age groups. Arthritis and hypertension are the most common conditions, at all ages, followed by heart problems. The likelihood of having (or having had) most problems increases steadily with age, although diabetes, hypertension, and chronic lung disease appear to be somewhat less common above age 85.
Gender differences with regard to health conditions are generally small. The most notable difference pertains to arthritis. Nearly two-thirds of all female respondents but only one-half of male respondents report having this potentially disabling condition.
Several race/ethnicity differences in the prevalence of some conditions are notable. As has been found in other data sources, Blacks have higher rates of hypertension than those of other population subgroups. More than two-thirds of Black HRS participants report having hypertension, compared with one-half of the White and Hispanic participants. Blacks and Hispanics have significantly higher levels of diabetes than do Whites. Whites are most likely and Hispanics least likely to report cancer, lung disease, and heart problems. Hispanics' reported rates of arthritis and stroke also are lower than those of Blacks and Whites.
Co-morbidity, or the combination of multiple chronic problems, is an especially challenging situation for health management. The HRS examines older adults' risk of having multiple chronic health problems. Table 1-1 summarizes the combined prevalence of six major health problems reported by the 2002 HRS sample: diabetes, hypertension, cancer, bronchitis/emphysema, a heart condition, and stroke. (Arthritis, which is common among all age groups, is not included.) The percentage of people free of chronic problems falls with age, and the percentages with multiple problems increase. Roughly half of the people over age 75 report two or more chronic conditions. However, the burden of co-morbidity appears to stabilize at the oldest ages; the distribution of chronic problems among people 85 and older is very similar to that of those 75 to 84, at least in the community-dwelling population.
With recent and projected increases in national health care expenditures, public attention has focused on preventing unhealthful behaviors and controlling behavioral and lifestyle factors that contribute to disease, disability, and death. The HRS examines several of these health behaviors and risk factors, including smoking, alcohol consumption, and obesity, and helps frame questions designed to inform public health policy in these areas. One book, based on the first four waves of HRS data, is devoted to exploring risk perceptions and choices made by smokers and addressing policy questions such as the efficacy of different educational strategies, class-action suits, and regulation/prohibition (Sloan et al. 2003).
Examining the relationship between health beliefs and health behavior, Schoenbaum (1997) investigated whether HRS participants understand the mortality effects of smoking, i.e., do they realize that smoking can shorten one's life? In one survey year, participants were asked how long they expected to live. For "never," "former," and "current" light smokers, survival expectations were quite close to actuarial predictions of life expectancy for their ages. Among current heavy smokers, however, the expectation of reaching age 75 was nearly twice that of actuarial predictions. In other words, heavy smokers significantly underestimated their risk of premature mortality linked with smoking.
|Number of Problems||55-64||65-74||75-84||85+|
|4 or more||2||4||5||6|
Other research has examined whether the perceptions of smokers reflect a true lack of understanding of health risks or a form of indifference or denial. Smith et al. (2001) investigated how subjective beliefs change in response to new information. This study found that when HRS smokers experience smoking-related health shocks, such as a heart attack or cancer diagnosis, they are likely to reduce their expectations of longevity significantly, more so than when they experience general (non-smoking-related) health shocks.
A more traditional analysis of health outcomes addressed the effects of smoking on disability, impaired mobility, health care utilization, and self-reported health (Ostbye et al. 2002). As expected, smoking was strongly related to mortality and self-reported ill health. Researchers were also able to characterize the benefits of quitting smoking. People who had quit smoking in the 15 years preceding the survey were as likely as those who had never smoked to report good health. Further analysis indicated that males ages 50 to 54 years who are heavy smokers lose approximately 2 years of healthy life, and females in the same age group who are heavy smokers lose about 1.5 years of healthy life, relative to former smokers.
In another study of smoking cessation, Wray and colleagues (1998) analyzed data for smokers who had had heart attacks. Controlling for a variety of health factors, level of education emerged as the major positive influence on the decision by middle-aged HRS participants to quit smoking after the cardiac event.
Recent reports have suggested that moderate alcohol consumption has potentially healthful effects, but HRS data clearly show that heavy drinking takes its toll. Perreira and Sloan (2002) analyzed 6 years of HRS data to examine links between excessive alcohol consumption and health outcomes for men. Men who were heavy drinkers (five or more drinks per day) but not functionally impaired in the initial survey year had a four-fold risk of developing at least one functional impairment (including memory problems) during the 6-year follow-up period. This finding held true even when controlling for the effects of smoking and other factors.
Perreira and Sloan (2001) also used multiple waves of HRS data to explore changes in drinking behavior that occurred with and after major health, family, and employment stresses. Two-thirds of the sample did not change their use of alcohol in the 1990s. However, when changes did occur, they were related to several life events: Retirement was associated with increased drinking; hospitalization and the onset of a chronic condition were associated with decreased drinking; and widowhood was associated with increased drinking, but only for a short time.
A COMMUNITY-DWELLING SAMPLE
The original HRS (1992) and AHEAD (1993) samples were drawn from community-dwelling individuals and did not include people living in institutions such as nursing homes. This sampling procedure also applies to cohorts added to the study after 1993. Unless otherwise noted, data in the tables and graphs in this report refer only to community-dwelling people and do not include people who have moved into nursing homes after they were initially selected for the study.
The HRS does, however, follow individuals as they move into and out of institutional settings. As the number of study participants in institutions increases, the HRS is becoming an important source of information about this segment of the U.S. population. In certain parts of this report, such as the description of living arrangements in Chapter 4, the HRS nursing home component is included.
Ostermann and Sloan (2001) analyzed 8 years of HRS data to examine the effects of alcohol use on disability and income support for people with disabilities. Their analysis demonstrated that a history of problem drinking, especially when combined with recent heavy drinking, was associated with a greater prevalence and incidence of limitations in home and work activities. However, despite increased disability, problem drinkers' higher rates of activity limitations were not associated with a greater likelihood of receiving income support from the Federal Government's Social Security Disability Insurance (SSDI) or Supplemental Security Income (SSI) programs.
HRS data have been used to document an association between obesity and impairments in physical function that will translate into rising disability rates in the future if obesity trends continue (Sturm et al. 2004). A causal analysis of HRS respondents over age 70 suggested that being overweight or obese (using conventional body mass index measures) makes an older person more likely to become functionally impaired in the future. While this relationship is often complex, obesity appears to have an independent effect on the onset of impairment in strength, lower body mobility, and activities of daily living (Jenkins 2004).
Extra pounds may also be expensive, at least for middle-aged women. Looking at the relationship between weight and financial net worth, Fonda et al. (2004) found that in 1992 the individual net worth of moderately to severely obese women ages 51 to 61 was 40 percent lower than that of normal-weight peers, controlling statistically for health status, education, marital status, and other demographic factors. These individuals' situation also appears to worsen over time. In 1998, the self-reported individual net worth of moderately to severely obese women in the same cohort (then ages 57 to 67) was 60 percent less than that of their counterparts (an average difference of about $135,000 in 1998). No such pattern could be found for men. While HRS data allow relationships among obesity, gender, and financial status to be measured in new and important ways, researchers caution that the causal mechanisms underlying these findings are still poorly understood.
Family characteristics may also play a role in obesity risk and how we might intervene to prevent obesity. After adjusting for age, race, income, and several behavioral factors, researchers analyzing HRS data found a positive correlation between number of children and obesity for both women and men (Weng et al. 2004). The association between obesity and family size is an intriguing finding and suggests the need for further exploration of the idea that parents of larger families might be an important target population for obesity prevention.
The decline of cognitive function with age is an often-unspoken fear that many people have as they grow older, and the burden of cognitive impairment on individuals, families, caregivers, and society at large is enormous. Severe cognitive impairment is a leading cause of institutionalization of older people. Before 2003, estimates of the prevalence of cognitive impairment had to be derived from local clinic-based studies, typically in urban areas, and extrapolated to the larger population. With the advent of the HRS, and more specifically the AHEAD portion of the study, researchers could attempt for the first time to tap nationally representative data to assess cognitive function in older people.
The HRS is one of the first national health surveys to measure cognitive health at the population level and to examine on a large scale the biological and environmental factors associated with cognition. The HRS measurement of cognition employs two well-tested cognition assessments: the Telephone Interview for Cognitive Status (TICS), a brief, standardized test of cognitive functioning that was developed for use in situations where in-person cognitive screening is impractical or inefficient, and the Mini-Mental State Examination (MMSE), a widely used tool for assessing cognitive mental status. In addition, a special assessment tool for third-party observations, the Jorm IQCODE, is used when a proxy reporter provides an interview on behalf of a respondent. This is an essential tool when cognitive impairment makes an interview otherwise unobtainable.
Initial estimates, while preliminary, indicate that in 1998, approximately 10 percent of the U.S. population age 70 and older had moderate to severe cognitive impairment (Suthers et al. 2003). The prevalence of moderate to severe cognitive impairment among non-institutionalized people was 6 percent, while the level among the institutionalized exceeded 50 percent. On average, the data suggest, a person reaching age 70 with a life expectancy of 14 remaining years will spend 1.5 of those years with moderate or severe cognitive impairment. As the original HRS sample and its additional cohorts age, researchers will be able to update and refine these important data. The analysis also indicated that the prevalence of cognitive impairment increases steeply with advanced age. Among people ages 75 to 79 who participated in the 1998 HRS, fewer than 5 percent had severe limitation (Figure 1-4). After age 80, however, the prevalence rate rises steeply, approaching 20 percent for people age 85 and older.
The HRS also provides valuable information about the need for and provision of caregiving for older people with cognitive impairment. Estimates from the baseline AHEAD survey in 1993 indicated that people with mild impairment received 8.5 more hours of care per week, while those with severe impairment received 41.5 more hours of care per week than their peers with normal cognitive function (Langa et al. 2001). The same study found that valuing this family-provided care at the average hourly wage of a paid home aide, this informal care amounts to $17,700 per year for an individual with severe impairment, and a total national cost of $18 billion per year for informal care for all forms of cognitive impairment.
Recent studies suggesting a decline in overall rates of disability among the older U.S. population have prompted researchers to consider the utility of the HRS in measuring trends in cognitive impairment over time. Analyses of HRS data from the 1990s showed a significant decline in the prevalence of severe cognitive impairment among people age 70 and older, from about 6 percent in 1993 to less than 4 percent in 1998 (Freedman et al. 2001, 2002). In contrast, another analysis of the same cohort using additional controls found very little change from 1993 to 2000 in cognitive impairment rates, after adjustment for demographic composition (gender, race, and ethnicity) (Rodgers et al. 2003). Scientists concentrating on the cognitive health aspects of participants in the HRS will continue to examine these contradictory findings in an effort to sort out the national trends.
Mental health, while critically important to the health of the population, is extremely difficult to assess in population surveys. The HRS developers decided at the outset to focus on depression, the most prevalent mental health condition in the older population and a leading cause of disability. At baseline, respondents are given a series of questions to identify major depressive episodes in the prior year. In each wave of the study, respondents are asked about eight common symptoms of depression, taken from the CES-D instrument. In validation studies against the full CES-D battery, the presence of four out of the eight symptoms is associated with clinically significant depression.
THE AGING, DEMOGRAPHICS, AND MEMORY STUDY
The Aging, Demographics, and Memory Study (ADAMS), a supplement to the HRS, is the largest national study of the prevalence of dementia in the United States. This supplemental study has three goals: first, to establish national estimates of the prevalence of dementia and cognitive impairment without dementia; second, to increase understanding of the natural history of preclinical and clinical dementia, as well as the role of dementia in changing the health and social functioning of older Americans; and third, to use the data collected to assess the validity of HRS cognitive functioning measures as screening tools for cognitive impairment or dementia. The ADAMS also will provide an opportunity to conduct in-depth investigations related to the impact of dementia on formal health care utilization, informal caregiving, and total societal costs for dementia care.
The study is the first of its kind to conduct in-home assessments of dementia on a national scale that represents the U.S. elderly population. The assessments are being conducted through a collaboration with Duke University. From August 2001 through March 2005, selected HRS participants were visited by a clinical research nurse and psychometric technician, both of whom were specially trained in the evaluation of dementia. Conducted in the presence of a family member, friend, or paid helper, the assessments included obtaining clinical and medical histories, neuropsychological testing, and collecting DNA samples to determine the apolipoprotein E (APOE) genotype. Follow-up assessments have so far been conducted with approximately 30 percent of respondents to gather additional data to clarify trajectories. Additional follow-ups are planned for future years. Information about caregiving and its costs and health services utilization was also collected.
The primary ADAMS dataset consists of 850 respondents from the HRS for whom assessments are completed. The ADAMS data, with restrictions on accessibility and use to protect the confidentiality of participants, were made available for research purposes in early 2007.
Data for 2002 suggest that the prevalence of severe depression for men and women combined is approximately 15 percent within each 10-year age category between ages 55 and 84 (Figure 1-5) and approaches 20 percent for the 85 and older group. For all of the age groups, women are consistently more likely than men to report severe depressive symptoms.
HRS longitudinal data can help address an important question about the correlation between depression and physical health: Do disease and disability lead to depression, or does depression lead to disease and disability? Blaum (1999) found that depressive symptoms are precursors to the development of future disease. As expected, physical limitations (e.g., the inability to walk several blocks, climb stairs, or lift a 10-pound object) were the strongest predictors of developing a new disease 2 years later, increasing the odds of developing at least one new disease by nearly 50 percent. At the same time, participants age 70 and older who reported having several symptoms of depression were one-third more likely than others to develop a new disease within 2 years. The effect was seen with relatively mild depressive symptoms, such as decreased energy and restless sleep, as well as with more severe clinical depression.
Stopping driving is one activity of daily living that appears to be associated with increased depressive symptoms. An analysis of a 6-year period of early HRS data showed that older people who stopped driving were 1.4 times more likely to experience worsening depressive symptoms than those who continued to drive after the 6 years (Fonda et al. 2001). Longer-term restrictions on driving further increased the risk of depressive symptoms. Having a spouse who still drove did not significantly affect the respondents' depressive symptoms.
|Not married, not covered||Not married, covered||Married, neither spouse covered||Married, one spouse covered||Married, both spouses covered|
|Not married, not covered||Not married, covered||Married, neither spouse covered||Married, one spouse covered||Married, both spouses covered|
|Not married, not covered||Not married, covered||Married, neither spouse covered||Married, one spouse covered||Married, both spouses covered|
The HRS can be used to assess health care coverage among pre-retirees and retirees and to examine the ways in which changes in health insurance policy can affect retirement decisions and labor market participation as a whole. Of particular interest are people ages 55 to 64, most of whom are not yet eligible for Medicare. Figure 1-6 depicts racial/ethnic differences in types of health insurance coverage for this age group in 2002, indicating that Blacks and Hispanics are much less likely than Whites to have private health insurance, and hence are more likely to rely on public sources. About 1 in 4 pre-Medicare-age Hispanic respondents has no health insurance, compared with roughly 1 in 8 Blacks and 1 in 14 Whites.
A further breakout of these data illustrates differences between married and unmarried individuals (Table 1-2). Regardless of age and work status, unmarried respondents are more likely than their married counterparts to be without insurance. Among married Black and Hispanic couples, a significant proportion of households have coverage for only one member of the couple.
In addition to comparing people with differing health insurance status, the HRS data have been used to examine the implications of insurance status for health in later life. Baker et al. (2001) assessed the risks of a major decline in general health and the risks of developing new difficulties according to whether HRS respondents were continuously uninsured, intermittently uninsured, or continuously insured between 1992 and 1996. Continuously uninsured individuals were 63 percent more likely than privately insured people to experience a deterioration of overall health and 23 percent more likely to have new difficulties with an activity of daily living involving mobility. Sudano and Baker (2003) found that intermittent lack of insurance coverage, even across a relatively long period, was associated with lower usage of preventive services. Looking at the same HRS data from a different perspective, Dor et al. (2003) found that providing insurance to previously uninsured working-age adults resulted in a 7 percent improvement in overall self-reported health.
Another study (McWilliams et al. 2003) analyzed differences in the receipt of basic clinical services among the continuously insured and the uninsured in 1996 and 2000-before and after respondents became eligible for Medicare at age 65. The analysis suggested that the acquisition of Medicare coverage significantly reduces the differences in the use of preventive services such as cholesterol testing, mammography, prostate examinations, and medical visits dealing with arthritis. Among adults with arthritis and/or hypertension, however, differences in the use of anti-arthritis/anti-hypertension medications between continuously insured and uninsured people were essentially unchanged after Medicare coverage began.
The HRS also can tell us who has prescription drug coverage and how they use it. The new Medicare Part D prescription drug coverage program was implemented in 2006, and the HRS will provide baseline estimates and then track changes in older adults' prescription drug coverage and use.
Other studies using HRS data also offer insights about prescription drug coverage. For instance, the survey showed that in 1998, HRS respondents under age 65 were more likely than those ages 65 to 79 and much more likely than those age 80 and older to have prescription-drug insurance coverage (80 percent, 71 percent, and 59 percent, respectively) (Table 1-3). Importantly, regardless of age, people who did not have prescription drug coverage were less likely to fill all of their prescriptions. Younger respondents were less likely than older respondents to fill prescriptions, regardless of drug insurance coverage. One study suggested that this cost-cutting by seniors may pose an increased risk for adverse health outcomes (Heisler et al. 2004).
As the U.S. population ages and Medicare expenditures continue to rise, the wealth of HRS data on use of health care services will become an increasingly important resource. Figure 1-7 illustrates HRS respondents' use of five major services during 2000 to 2002 and shows that more than 40 percent of people age 85 and older and 34 percent of those ages 75 to 84 made hospital visits. The use of hospitals and nursing homes rose with age, as did the consumption of home health services. More than 10 percent of HRS respondents ages 75 to 84 and 20 percent of respondents age 85 and older made some use of home health services during the 2-year period. In contrast, there was a marked decline in the use of dental care by age, probably driven at least in part by the fact that Medicare generally does not cover dental services.
|Percent with Prescription Drug Coverage||Percent Not Filling All Prescriptions With Insurance Coverage||Percent Not Filling All Prescriptions Without Insurance Coverage|
|80 and over||59||3||7|
Figure 1-8 contrasts health service use in 2002 for men and women of all ages, by race and ethnicity. Gender patterns did not differ greatly, although Black and Hispanic women were somewhat more likely than Hispanic men to make at least one hospital visit. Minority men and women were much less likely than Whites to visit a dentist or have outpatient surgery. Hispanic respondents were less likely than others to have visited a doctor at least once in a 2-year period; this difference corresponds to the lower level of health insurance coverage among Hispanics.
Health policy and cost-containment discussions are currently considering the efficacy of screening mammograms and Pap tests in older women. According to the HRS, usage rates for both of these tests increased for all age groups between 1995 and 2000. However, there are sharp differences in the rate of these tests taken with age. In 1992 through 2000, between 70 percent and 80 percent of women ages 50 to 64 reported receiving mammograms at least once every 2 years, with the proportion declining to about 40 percent among those ages 85 to 90. During the same time period, Pap test rates were about 75 percent for women ages 50 to 64 and about 25 percent for women ages 85 to 90, respectively.
Nonsmokers and women who perceived their health as good or excellent were the most likely to be screened, while smokers, sedentary individuals, and those who felt that their health was poor or fair were less likely to undergo screening.
|No doctor visits||6.9||7.9||12.7|
|No doctor visits||4.8||5.1||9.9|
Alternative medicine includes a broad range of healing philosophies, approaches, and therapies that conventional medicine does not commonly use or understand. These practices include, for example, the use of acupuncture, herbs, homeopathy, therapeutic massage, and traditional oriental medicine. Among HRS respondents to an experimental module in 2000, nearly half reported that they had been to a chiropractor, 20 percent had used massage therapy, and 7 percent had used acupuncture at least once in their lives (Ness et al. 2005).
In the same experimental module, more than half of respondents said they had used some kind of dietary or herbal supplement (Table 1-4). Nearly two-thirds of the respondents had used some kind of vitamin supplement in the month prior to the survey. On average, respondents spent $173 a year on those supplements. The most popular supplement, multivitamins, was taken by half the sample. About one in five people reported using some kind of herbal supplement during the previous month, and spent an average of $135 per year on herbals. Garlic, echinacea, gingko biloba, and ginseng were the most commonly used of these supplements.
Health care expenditures can rise considerably with age, and the HRS provides detail on the amounts paid directly by respondents, sometimes called out-of-pocket expenditures. Data from 2002 show a steady increase with age in the dollar amount of out-of-pocket medical expenditures (Figure 1-9). Mean medical out-of-pocket expenditures during the 2-year period prior to the survey ranged from $2,900 for respondents ages 55 to 64 to $4,400 for people age 85 and older.
|St. John's Wort||4|
The major components of medical out-of-pocket spending vary by age as well. Data from the 2002 survey wave show that hospital and doctor visits were the largest component of out-of-pocket expenditures among younger respondents (ages 55 to 64), some of whom were not covered by health insurance, whereas most people over age 64 have Medicare coverage for hospital and physician visit costs. At the time of the survey, prescription drugs were not covered by Medicare, and an age-related rise in the proportion of medical out-of-pocket expenditures devoted to drugs was seen, at least until age 85 (Figure 1-10).
Medical spending by the elderly varies widely. One study using HRS data from 1998 found that in the 2 years prior to 1998, average out-of-pocket spending was about $2,022, but half the population spent less than $920, while 10 percent of the population spent more than $4,800 (Goldman and Zissimopoulos 2003).
Medical out-of-pocket expenditures tend to be greatest near death, and can be a financial challenge for a surviving spouse. A four-wave analysis of HRS data for non-institutionalized people who were age 70 and older in 1993 showed that medical out-of-pocket spending averaged approximately $6,000 in the last year of life-40 percent to 50 percent higher than at other points in old age (McGarry and Schoeni 2003). To put this into perspective, researchers compared out-of-pocket spending to annual income. The average couple's medical out-of-pocket expenditures were roughly 15 percent of annual income 5 to 7 years before the death of a spouse. The out-of-pocket expenditure share rose to about 25 percent 3 years before the death of a spouse and to 50 percent in the year before the spouse's death.
When calculation of poverty rates includes an adjustment for the high end-of-life medical out-of-pocket expenses, the rates rise steeply as a function of spousal death. This type of analysis helps demonstrate the potential effects of proposals to revise current health programs. For example, HRS data suggest that expanding Medicare coverage to include prescription drugs and long-term care (nursing home and hospital) would significantly lower medical out-of-pocket spending. Prescription drug coverage would lower out-of-pocket-adjusted poverty by between 21 percent and 33 percent for people who were many years removed from death, and between 10 percent and 18 percent for those in their final year of life. Nursing home and extended-hospital coverage would likely have little effect on poverty rates for those not near death, but could lower the medical out-of-pocket adjusted poverty rate by 17 percent for those in the last year of life.
Early HRS data indicated that over a 2-year period, respondents on the whole had a 5 percent chance of having a heart attack, stroke, or new cancer diagnosis; a 10 percent chance of having a new chronic illness diagnosis; and a 3 percent chance of having an accidental injury. A "health shock," or unexpected health event, may represent a turning point for an individual and her/his family, particularly if the individual is nearing retirement age.
To explore the implications of adverse health events on both short-term and longer-term labor force participation, one study followed the labor force behavior of HRS respondents through the first three interviews in 1992, 1994, and 1996 (McClellan 1998). Persons who had some form of health event (acute, chronic, or decline in functional ability) between 1992 and 1994 were about twice as likely to be out of the labor force in 1994 and 1996 compared with persons who did not experience a significant health event. The combination of an acute health event (such as a heart attack or stroke) and a decline in functional ability greatly elevated the likelihood of labor force withdrawal. Having both an acute event and a loss of functional ability between 1992 and 1994 reduced the chances of working in 1994 by 400 percent. Only a very small fraction of those who had both an acute event and a loss of functional ability between 1992 and 1994 had reentered the labor force by 1996 (see also Woodbury 1999).
In a separate study of HRS data from 1992 through 2000, Coile (2003a) examined the effect of the onset of a heart attack or stroke, accompanied by new difficulty in performing four activities of daily living, on remaining in the labor force. The analysis showed that men were 40 percent more likely and women 31 percent more likely to leave the labor force than they would have been without a health event.
An important dimension of household behavior following a health event is the response of the spouse, and previous research has been unable to account for this behavior. The HRS's collection of detailed data for both husbands and wives permits study of this area, such as the response involving the spouse's decision to work. Because a negative health event may diminish the family's income position, a spouse's decision to reduce employment could exacerbate the situation. However, analysis of data from 1992 through 2000 indicates that a major health event does not produce a major change in spouses' labor force participation. If a working person experiences a major health event, his or her spouse is not likely to begin or increase labor force participation to offset the income loss. This suggests that a health event causes real financial losses for the family, although these losses are offset to some extent by government disability insurance benefits. It also suggests that many people are underinsured against disability.
Ongoing interests in aging research include the trend in disability status among older individuals and people's transitions into and out of disability states. A number of studies in the United States have now documented a decline in disability rates among the population age 65 and older. Much research has focused on this age group, but relatively little research has focused on people in the late midlife, pre-retirement age group. It is interesting not only to observe the disability status and transitions among pre-retirement individuals, but also to investigate predisposing factors and behaviors that might influence the disability profile of tomorrow's elders.
|One or More IADL Limitations||7.8||9.7||18.1||33.5|
|Receive Help with IADL(s)||4.6||6.4||13.1||25.0|
|One or More IADL Limitations||9.6||10.6||21.1||47.0|
|Receive Help with IADL(s)||6.6||8.0||17.5||41.8|
Figures 1-11 and 1-12 present the percentages of male and female 2002 HRS participants with functional limitations or disabilities. Six indicators of functional disability are considered. The first and second indicators reflect instrumental activities of daily living (IADLs)-doing housework, doing laundry, preparing meals, grocery shopping and being mobile outside the home, managing money, and using a telephone-and the third indicator of functional disability is whether or not a person has stopped driving. The fourth and fifth indicators reflect limitation in conventionally defined activities of daily living (ADLs)-eating, dressing, bathing, toileting, getting in and out of bed, and being mobile in one's residence. The sixth indicator of functional disability is the use of an assistive device, such as a walker or cane.
Figure 1-11 depicts age and gender differences for IADL limitations, showing that limitations increased with age and were higher for women than men in 2002. The percentages of respondents with IADL limitations initially are lower than for ADL limitations, but increase more rapidly with age, such that by age 85 they are nearly the same. Although the age-specific IADL limitation percentages are lower than those for ADLs, HRS participants were more likely to receive help with IADLs than with ADLs.
The most dramatic gender difference in functional limitations was seen in the data for driving. At age 65 and older, men were twice as likely as women to still be driving. By age 85, 32 percent and 66 percent of non-institutionalized men and women, respectively, were no longer driving. In the sample as a whole, three-fourths of people ages 75 to 84 continued to drive, as did 45 percent of people age 85 and older.
|One or More ADL Limitations||9.9||12.0||19.3||31.6|
|Receive Help with ADL(s)||3.2||4.5||8.0||16.2|
|Use Assistive Device(s)||4.4||7.5||16.2||35.0|
|One or More ADL Limitations||13.1||15.7||25.6||49.4|
|Receive Help with ADL(s)||4.9||5.2||10.7||27.8|
|Use Assistive Device(s)||6.8||10.0||21.7||50.6|
The HRS data further show that for men ages 55 to 64, 13.2 percent report either an ADL or IADL limitation; of them, 4.2 percent work while 9 percent do not work. For women, 15.6 percent report a functional limitation; of them, 3.8 percent work while 11.8 percent do not work.
Figure 1-12 presents percentages of HRS respondents who reported in 2002 that they had one or more ADL limitations, received help with these activities, or used assistive devices. As with IADL limitations, for both men and women, the rates of having at least one ADL limitation, receiving help with ADLs, and using an assistive device rose with age. Without exception, the percentages were higher for women than for men.
As average life expectancy lengthens and our population ages, there is heightened debate about raising retirement ages and enabling individuals to work longer. To help inform this debate, it is useful to know the extent to which older individuals experience heath conditions that may affect their work activity. Figure 1-13 presents 2002 data for people ages 55 to 64, according to work status and health limitations. Twenty percent of men and 25 percent of women in this age group reported a health problem that limited their ability to work. Included in this group, about 5 percent of each sex worked despite a work-limiting health problem. Of those ages 55 to 64 who had work-limiting health problems, 48 percent of the men and 52 percent of the women reported at least one ADL or IADL limitation, while the others reported no such limitations.
|No Health Limitation, Working||62.7||50.3|
|No Health Limitation, Not Working||16.9||24.4|
|Health Limitation, Not Working||15.9||20.0|
|Health Limitation, Working||4.5||5.3|
HRS data from 1992 to 1996 revealed that more than one-half of men and one-third of women who leave the labor force before reaching the Social Security early retirement age of 62 reported that health limited their capacity to work. One study looked at three causes of workforce disability-cigarette smoking, a sedentary lifestyle, and obesity-between 1992 and 1998 (Richardson et al. 2003). Cigarette smoking and a sedentary lifestyle had a large impact on both the incidence of workforce disability and death.
The major health problems reported by HRS respondents age 55 and older who were working for pay in 2002 were arthritis and hypertension. Forty-seven percent of all workers reported having arthritis, and 44 percent reported having a hypertensive condition. Ten percent or more of the working respondents reported having heart conditions, diabetes, psychological problems, or cancer (Figure 1-16, page 38). By far, the largest reported causes of work limitation among people ages 55 to 64 who were not working were arthritis and other musculoskeletal conditions (47 percent), followed by cardiovascular conditions (16 percent), neurological problems (8 percent), and allergies and respiratory problems (7 percent).
As the HRS proceeds, it is likely that more sensitive analyses will be conducted on people's ability to continue working should they either need to or want to work longer.
HRS data show that job loss not only results in economic consequences, but also can impact
a person's health. Involuntary job loss was perceived to negatively affect both physical functioning and mental health. Likewise, becoming re-employed was found to be positively associated with improved physical functioning and mental health. Such results led researchers to argue for a causal relationship between job loss and morbidity among older workers, and to suggest that there is a significant health consequence to job loss in addition to the obvious economic consequences (Gallo et al. 2000). The links among health, work, and retirement offer a rich area of investigation, and are discussed more thoroughly in Chapter 2.
HOW LONG DO PEOPLE THINK THEY'LL LIVE?
From its first wave to the present, the HRS has asked respondents about their own assessments of their chances of survival to a "target" age (i.e., their subjective survival probability). The target age used in the survey varies with the age of the respondent. For example, respondents age 64 or less are asked about survival to age 75, while respondents ages 65 to 69 are asked about survival to age 80. The research objective of this question is to better understand intertemporal decision making. A good example is saving behavior. According to the leading economic model of saving, people who believe they will be especially long lived will save more to be able to finance more years of spending. In the past, researchers had to use life-table survival rates to estimate the subjective survival probabilities of individuals, but we know from actual mortality that survival rates of people grouped by observable characteristics such as education differ greatly. It is likely, therefore, that individuals have differing self-rated survival assessments, even within identifiable groups.
As with many innovations in the HRS, the actual use of survival assessments has expanded beyond what was initially foreseen. For example, they have been used to study the socioeconomic health gradient (How does subjective survival vary with income and wealth?), the "bereavement effect" (How does subjective survival change when a spouse dies?), and the effect of a health event (How does the onset of a cancer change subjective survival?).
Using the longitudinal data from the HRS, self-rated survival assessments can be related to actual mortality many years later. In 1992, an 11-point scale (from 0 to 10) was used to query HRS respondents about their outlook for survival. Figure 1-14 shows the percent of the original HRS sample who had died by 2002, as a function of their subjective survival outlook as of 1992. Mortality during the 10-year period was about 10 percent among those who stated that their subjective survival was 60 percent or greater, but more than 25 percent among those who reported very low subjective survival probabilities.
In 1993, a 101-point scale (from 0 to 100) was used for interviews of respondents age 70 or over in 1993. This scale change was made so the concept would fit more naturally with probabilistic information people normally hear in their everyday lives, such as "There is a 60 percent chance it will rain tomorrow." Figure 1-15 shows mortality by 2002 among those initially age 70 or over in 1993. Actual mortality among those with an initial subjective survival of zero was almost 60 percent-about twice the rate of those whose subjective survival was 51 to 75. As with the younger cohort of Figure 1-14, self-rated survival assessments are a powerful predictor of actual mortality.
|Subjective Survival Outlook in 1992||Percent|
|Subjective Survival Outlook in 1993||Percent|
The HRS has helped spawn the development of similar multidisciplinary, longitudinal studies of health and retirement in other countries. A comparison of HRS data with data from one of these studies, the English Longitudinal Study of Ageing (ELSA), has revealed important health-status differences-and important similarities-between White middle-aged Americans and their English counterparts (Banks et al. 2006). The research used the study participants' self-reports of health and biological measures to measure health status.
The study revealed that White Americans ages 55 to 64 are not as healthy as their English counterparts, and in both countries lower income and education levels were associated with poorer health. The healthiest Americans in the study-those in the highest income and education levels-had rates of diabetes and heart disease similar to the least healthy in England-those in the lowest income and education levels.
In addition, the lowest income and education group in each country reported the most cases of diabetes, hypertension, heart disease, heart attacks, stroke, and chronic lung disease, while the highest income and education groups reported the least. The only disease for which this inverse relationship was not true was cancer. Banks and colleagues also found that differences between the two countries in smoking, obesity, and alcohol use explained little of the difference. In a report published in the Journal of the American Medical Association (Banks et al. 2006), the researchers noted that the health-status differences they uncovered existed despite greater U.S. health care expenditures, similar patterns in life expectancy between the two countries, and the fact that smoking behavior in the two countries is similar.