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Aging in Sub-Saharan Africa: Recommendations for Furthering Research 4 The HIV/AIDS Epidemic, Kin Relations, Living Arrangements, and the African Elderly in South Africa M. Giovanna Merli and Alberto Palloni INTRODUCTION Although the effects of HIV/AIDS on individuals who contract it have been relatively well known for sometime (Quinn, Mann, Curran, and Piot, 1986), the understanding of the plethora of indirect effects and their pervasiveness in many realms of individual and social life is much less complete. Age selectivity, together with the disease’s relatively long periods of incubation and the associated morbidity and lethality, may affect a number of social relations and social organizations that are either unique or distinctly more powerful than those observed for other diseases in Africa or anywhere else. In particular, the levels and age patterns of the incidence of HIV and future increases in prevalence are likely to have a large impact on kin relations, residential patterns, household organization, and the well-being of family members. Faced with the escalating burden of excess morbidity leading to the disruption of normal activities and functions, families and households are likely to adopt coping strategies to contain the damaging effects of the epidemics. An interesting issue is the magnitude and nature of the costs borne by individuals and families as a consequence of the adoption of these strategies and whether or not they will be put into place without threatening the very fabric of family relations as they are known today. Ten years ago, Palloni and Lee (1992) reviewed the potential effects of the HIV/AIDS epidemic on mortality levels at various ages that would affect household and family organization. The main idea is that when levels of widowhood and orphanhood rise as much as they could due to the HIV/ AIDS effects on mortality alone (excluding effects on fertility and migration), the material basis of traditional kin relations (kin availability) and of
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research household organization (residential patterns) will weaken or cease to operate. In their place, one could expect to see the emergence of new forms of social relations. In addition to projecting high levels of widowhood and orphanhood, the authors anticipated the collapse of traditional family organization, kin networks, and the erosion of the foundations of typical household arrangements. They also predicted increasing prevalence of households in which children live with grandparents in the absence of their parents. In this paper, we update the work of Palloni and Lee and use a modified version of their model to calculate the demographic impact of HIV/AIDS on the elderly. Our evaluation rests on newly available data for South Africa. The AIDS epidemic is far worse in Southern Africa than it is in Central and Eastern Africa, where it first began. With its 5.3 million cases (Department of Health, 2003), South Africa is the country with the largest number of people infected with HIV. The rapidity with which HIV has spread is exceptional. In less than a decade, adult HIV prevalence from antenatal surveys increased from 1 percent in 1990, to 7.6 percent in 1994, to 27.9 in 2003 (Department of Health, 2003). On the basis of a combination of vital registration data and estimates derived from AIDS modeling, Dorrington, Bourne, Bradshaw, Laubscher, and Timaeus (2001) attributed to AIDS a significant increase in mortality at young and middle adult ages since the late 1980s, estimating that 40 percent of the deaths of adults ages 15-49 in 2000 were from AIDS. A more recent study estimates the increases in mortality related to HIV between 1996 and 2000-2001 at 7 per 1,000 among children, 2.7 per 1,000 among women ages 30-34, and 2.6 per 1,000 among men ages 35-39 (Groenewald, Nannan, Bourne, Laubscher, and Bradshaw, 2005). This age-specific increase is consistent with an AIDS-related acceleration of mortality in the latter part of the 1990s, estimated from demographic surveillance system data in KwaZulu-Natal (Hosegood, Vanneste, and Timaeus, 2004). Without treatment to prevent the progression from HIV to AIDS, Dorrington et al. (2001) estimate that the cumulative number of AIDS deaths is expected to reach between 5 and 7 million by 2010. Furthermore, in South Africa, as in much of the rest of Africa, the African elderly have been until very recently primarily supported by intra- and intergenerational familial networks. In particular, coresidence with an adult child is a common form of living arrangement and a form of exchange (Møller and Devey, 1995). Thus what we observe among the African population of South Africa may be replicated in other countries with similar patterns of intergenerational relations.1 1 The advent of a pension system in South Africa may be changing some of these patterns of generational transfers and may be consequential for the coresidence of the elderly and some or all of their adult children.
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research Strategies adopted by households and families to cope with the depletion of human and material resources induced by HIV/AIDS may range from changes in household structure, to reorganization of the division of labor in the domestic domain, to shifts in norms regarding female, child, and elderly labor force participation, and depletion of assets and cash reserves. The particular menu of strategies chosen will depend on the social group, and some, though not all, of the changes in household structure introduced by HIV/AIDS will be reflected in observable shifts in the living arrangements of the elderly. Increases in AIDS morbidity and mortality will reduce the availability of members of the young adult generation. Adult children will be sick or disabled for long periods of time and later die. They may lose the capacity to earn the income that would have been otherwise transferred to their aging parents. They may also require additional resources for their own support and medical care. Thus the elderly suffer a double burden with likely implications for their own health status and wellbeing: they become caregivers of the younger generations, first of their adult children and then of the AIDS orphans, and they may find themselves without the income transfers from the middle generations, so that net resource flows may be from rather than to aging parents. Moreover, the physical and psychological well-being of older persons will be affected not only by the death of adult children and foregone transfers of income, goods, and services, but also by the need to raise additional cash by diluting assets or deploying more hours of work to satisfy the increased burden entailed by the protracted nature of the illness. With its implied long-lasting health impairments on adult individuals, the disease jeopardizes households’ ability to generate resources for the care of their most vulnerable members, namely, children and the elderly, and thus aggravates the social and psychic costs of the illness (Ainsworth and Dayton, 2001). This damaging consequence of the disease will start long before the time of death of those already infected. This phenomenon is what was referred to early on as the “bottom of the iceberg” (Palloni and Lee, 1992:82). The effects of deterioration of the health status of adults on the well-being of children and the elderly is in all likelihood much larger than those implied by the direct effect via excess mortality. Our central objective in this paper is rather modest, since we only estimate the effects of HIV/AIDS on residential patterns of the South African elderly and evaluate observable changes in their living arrangements over a decade. We eschew assessment of other effects of the epidemic on the elderly but argue that these may be reflected, at least in part, in changes in residential arrangements. We use information from three data sources collected before and after the onset of the HIV/AIDS epidemic in South Africa: the 1991 census of the Republic of South Africa, the 10 percent sample of
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research South Africa’s first postapartheid census conducted in 1996, and the 10 percent sample of the 2001 census. We proceed in two steps. First, we evaluate macromodels of the epidemic through backward and forward projections of HIV incidence and related mortality. The models are based on a backward projection of a current population of elderly people who are then projected forward, subjecting their children to an estimated HIV/AIDS regime. These models yield estimates of the expected availability of adult children for the elderly, lower bounds for the prevalence of sickness among the children born to elderly people, and 10- to 15-year projections of changes in the availability of adult children and the prevalence of sickness. Second, we use the macromodel to contrast some of the epidemic’s expected outcomes derived from the model with observed changes in living arrangements of the elderly over time and across provinces. These contrasts depend on descriptions of observable patterns. We focus on the impact of AIDS mortality as well as the burden of illness associated with the presence of sick adult children. A significant difficulty made evident by these comparisons is that of identifying the direction and magnitude of changes in the living arrangements of the elderly that can be unequivocally associated with the impact of HIV/AIDS. PREVIOUS RESEARCH ON THE IMPACT OF EPIDEMICS ON FAMILIES AND HOUSEHOLDS Demographically speaking, the HIV/AIDS epidemic is not far removed from the large shocks suffered by preindustrial populations. In fact, all evidence available to us seems to point to a catastrophe of much larger proportions. Although the parallel suggests that we could learn from the past by examining studies of the population impact of epidemics, famines, and wars, this literature is in general devoid of systematic analysis of the complex effects on family and household organization. Attempts to assess the relation between past crisis mortality and the day-to-day operation of households, families, and social relations are scarce. The effects most successfully examined are those directly related to global excess mortality, deficits in fertility, and increased regional displacement of individuals.2 Only rarely have extant studies of past population crises attempted to identify mechanisms translating raised levels of individual mortality or morbidity into shifts of the size distribution of households and the likelihood of fusion, fission, or outright disappearance of family units. An important exception is Livi-Bacci’s (1978) assessment of the demographic effects of epi- 2 For a summary of this literature, see Palloni (1990).
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research demics suffered by preindustrial populations on the distribution of families by size.3 We attempt to follow this lead to understand the effects of HIV/ AIDS on the living arrangements of the African elderly in South Africa, but we adopt completely different assumptions to reflect the operation of a unique epidemic, with a distinct age pattern of incidence, a protracted period of incubation and infectiousness, and singular lethality. With the exception of a few studies on the direct economic costs for individuals and households (Ainsworth and Over, 1999), most research on the effects of the African epidemic focus on particular members of families, such as mothers or children, and on the impact of adult male deaths that raise widowhood and orphanhood. Studies of the impact of HIV/AIDS on widowhood have focused on traditional behaviors, such as the role of widow inheritance, where a widow is inherited by one of her husband’s brothers or other male relatives, in exposing women to HIV infection, and the changes in such traditional arrangements due to the epidemics in Uganda (Mukiza-Gapere and Ntozi, 1995). Ntozi, Ahimbisibwe, Ayiga, Odwee, and Okurut (1999a) found that the stigmatization of AIDS widows upon the loss of their spouse in Uganda influenced their movements. Less healthy widows were more likely to leave their late husbands’ homes and seek care in their natal villages, while healthier AIDS widows were more likely to remarry or form new sexual partnerships. A review of a series of case studies on the impact of HIV/AIDS on orphanhood by Zaba and Gregson (1998) reveals that in areas with high HIV/AIDS prevalence, the prevalence of paternal orphanhood was higher than that of maternal orphanhood. It was attributed to polygynous unions whereby, at the father’s death, all children born to his widows become orphans. In Tanzania, Urassa and colleagues (1997) found that 8 percent of children under age 15 and 9 percent of children under age 18 had lost one or both parents. In the region of Manicaland in Zimbabwe, the rapid increase in the number of parental deaths posed demands that exceed the capacity of relatives to fulfill their traditional role of caring for orphans and triggered the emergence of child-headed households (Foster, Makufa, Drew, and Kralovec, 1997). In the Kagera region of Tanzania, excess adult deaths not only implied higher levels of orphanhood but severely affected the nutritional status of orphaned children (Ainsworth and Semali, 1998). Community studies provide evidence for the effects of the epidemic on household organization (Barnett and Blaikie, 1992; Boerma, Urassa, 3 Livi-Bacci’s original idea (1978), in his work on demographic crises, was to use this technique to retrieve a measure of the intensity of the mortality crisis from observed statistics on the size distribution of families during the period following the crisis.
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research Senkoro, Klokke, and Ng’weshemi, 1999; Ntozi and Zirimenya, 1999; Urassa et al., 1997). In Uganda’s Rakai district, two or three generations with at least one orphan and individuals living alone were more common in AIDS-affected households than unaffected ones, and in a significant fraction of households containing AIDS victims, grandparents cared for orphans (Barnett and Blaikie, 1992). The burden of AIDS mortality and morbidity for households is shared by their members in a strict hierarchy. In Uganda, care of AIDS orphans was left to the surviving parent, then to grandparents, followed by older orphans, stepparents, and members of the extended family, such as uncles. Paternal orphans were more likely to be fostered by uncles than cared for by their mothers, because children belong to their father’s lineage (Ntozi, Ahimbisibwe, Odwee, Ayiga, and Okurut, 1999b). In a study in Zimbabwe, grandparents were the main care providers to AIDS orphans (Foster et al., 1995). Data from the Kisesa community study show that terminally ill people travel back to rural homes in search of care by the extended family (Urassa et al., 2001). Elderly parents are the most likely caregivers of their infected children, because parents are the most sympathetic and are likely to be informed of their children’s AIDS diagnosis first (Ntozi, 1997). Strikingly, similar patterns of caregiving were found in Thailand (Knodel, VanLandingham, Saengtienchai, and Im-em, 2001), where 27 percent of adults with “symptomatic” AIDS were cared for by a parent. Two-thirds of the adults who died of an AIDS-related disease had lived with or next to a parent by the terminal stage of illness, and a parent, usually the mother, had acted as a main caregiver for about half. For 70 percent, either a parent or other older-generation relative had provided at least some care. The vast majority of parents were age 50 or more and many were 60 or older. The foregoing summary identifies two important albeit weak regularities. First, most studies underscore transformations of living arrangements to accommodate AIDS orphans and widows, with an increased prevalence of households composed of the elderly with their widowed children and grandchildren, as well as households with grandparents and grandchildren but no member of the intermediate generation. Second, there is a rearrangement of the household to adjust to the needs of caring for sick adult children. These changes may lead to increases in headship among the elderly and to a more influential presence of households composed of elderly parents, their adult children, and grandchildren. Besides the somewhat elusive evidence connecting HIV/AIDS and concomitant changes in families and households, demographic models that attempt to identify the population-level effects of HIV/AIDS have not succeeded in providing a benchmark against which to evaluate empirical evidence (Zaba and Gregson, 1998). For example, although orphanhood is the most amenable outcome to modeling because it requires assumptions
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research only about mortality and fertility, modeling the impact of HIV/AIDS on orphanhood is complicated by time lags between the onset of HIV and orphanhood and the difficulties of quantifying pre-HIV/AIDS levels and patterns of orphanhood. Models predicting the impact of HIV on widowhood require additional conjectures about nuptiality and are more complicated to implement, especially in sub-Saharan Africa, where polygyny and remarriage are frequent.4 Changes in household organization in general and in the living arrangements of the elderly in particular have proven to be even less amenable to modeling than orphanhood or widowhood. This is because, in addition to information on demographic determinants, one needs to assess the influence of propensities to coreside and of internal migration flows, both of which may mimic the effects of HIV/AIDS on the availability of kin and confound the epidemic’s independent effect. Efforts to isolate the contribution of each of these factors are rare, as they generally require the combined use of simulation and empirical observations. One study uses microsimulation, in combination with aggregate demographic analysis, to estimate how patterns of coresidence of elderly parents in Thailand would adjust in response to the HIV/AIDS epidemic (Wachter, Knodel, and VanLandingham, 2002). The authors project that 11.9 percent of the present generation of Thai elderly (age 50 and over) will lose one or more children to AIDS, and 13 percent of those who lose at least one child will lose two or more before death. They also estimate that, of the cohort of Thai men and women age 55 in 1995, 1 in 9 could expect to experience the loss of at least one child to AIDS, while 1 in 14 could expect to have lived with a child during illness and have provided care. The most important lesson emerging from this brief review of previous studies is that even the most direct effects—those working through augmented levels of orphanhood and widowhood—present themselves in a veiled form or not at all in aggregate data. Problems with identification of the proper time lags, imperfect knowledge of relations prevailing in the period preceding the epidemic, and the widespread use of norms typical of most African societies, such as those regulating fosterage and remarriage, tend to mask or dampen the observed effects of the epidemic. 4 The practice of levirate, or remarriage to members of the widow’s former husband’s family, is common in parts of Africa (Potash, 1986). To the extent that it takes place rapidly after widowhood, levirate will conceal the impact of excess adult mortality. Similarly, child fosterage accompanied by a blurring of the distinction between biological and foster parents will lead to underestimates of the impact of adult mortality.
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research THE IDENTIFICATION PROBLEM Minimum Identification Conditions Efforts to estimate empirically the impact of HIV/AIDS on the residential arrangements of the elderly can be successful only if a set of minimum identification conditions are satisfied at the outset. These conditions are associated with processes that either uniquely determine or loosely bound the observable patterns of the elderly’s living arrangements. Living arrangements of the elderly are determined by two factors. The first is a function of purely demographic forces and influences the availability of kin. Preexisting levels and patterns of mortality, fertility, and migration limit the supply of kin that could reside with older people and therefore affect the ability to observe certain types of living arrangements. The second factor is the set of individual propensities to live with blood kin and other relatives. Residential propensities are a function of culturally bounded patterns of preferences and are likely to vary greatly across social classes and ethnic groups in the same society. Thus the prevalence of living alone or with a spouse but no children among the elderly age x at time t, P(x,t), is simply the product of D(x,t), a measure of the supply of children available to the elderly age x—the proportion of elderly who have surviving children to live with—and p(x,t), a measure of the conditional probability of residing with one of the surviving children.5 Since excess mortality associated with HIV/AIDS affects D(x,t), one could argue that the difference between estimates of demographic availability in contexts with and without HIV/AIDS is sufficient to identify the effects of HIV/AIDS on living arrangements of the elderly. But this line of thought ignores a number of difficulties. First, when examining changes in the living arrangements of the elderly, both D(x,t) and p(x,t) need to be identified simultaneously. While changes in D(x,t) can be assessed through a variety of procedures, including micro- and macrosimulations, estimation of p(x,t) is almost always problematic. This difficulty has already confronted scholars who have worked on microsimulation of households and families (Ruggles, 1987; Wachter, Hammel, and Laslett, 1978), but it has been met with no straightforward solution. Furthermore, the relationship among the observed distribution of living arrangements of the elderly, the demographic availability of kin, and individual propensities involves sources of misidentification that, if not properly neutralized, will bias inferences regarding the effects 5 To simplify, we ignore the possibility of age variation (across children and elderly) in p(x,t).
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research of HIV/AIDS on the living arrangements of the elderly. While some of these sources of misidentification are unique to South Africa, others apply to broader contexts. In the foregoing formulation, we assumed that the estimation of past levels and patterns of vital events (including nuptiality and migration) is unproblematic. Although this may be so for fertility, mortality, and nuptiality, it is not so for migration. Migration exerts a severe drag on the supply of adult children. In the absence of proper controls for out-migration flows, one can mistake a decline in P(x,t) for changes in other demographic determinants, particularly mortality. In the absence of direct estimates of the tug of migration, a minimum first condition for the identification of the effects of HIV/AIDS is a comparison of measured effects across areas exposed to similar incidence of HIV/AIDS but experiencing different levels of migration. The second source of misidentification is peculiar to the nature of HIV/ AIDS. Because the median duration from infection to full-blown AIDS and mortality in sub-Saharan Africa is about 7.5 years (Boerma, Nunn, and Whitworth, 1998), one cannot expect to see large changes in patterns of living arrangements until some time after the onset of the epidemic. In South Africa, the first AIDS cases were reported in 1984-1985 (Sher, 1986), but the full force of the epidemics could not have been felt before 1995. Using a data source for a year before 1995 is tantamount to choosing a baseline against which changes induced by the epidemic can be measured. The selection of a target date is also problematic, for it should be sufficiently distanced from the benchmark to allow time for the effects to accumulate. Thus, a second minimum identifying condition is to examine information on residential arrangements after 1995 relative to those prevailing some time before this benchmark date. Third, the above formulation rests on a “whopper” assumption—to paraphrase Ruggles’s terminology (Ruggles, 1987)—namely, that changes in demographic forces do not significantly alter individual residential preferences. However, sudden changes in mortality levels could simultaneously shift preferences among kin by decreasing the propensity of the elderly to live with a surviving adult child. If one is unaware of this, one will attribute a larger fraction of changes in P(x,t) to observed changes in mortality levels than one ought to, with an ensuing exaggeration of the effects of exogenous changes in mortality due to HIV/AIDS. Conversely, a sudden rise in adult morbidity may increase the propensity to live with a surviving (and possibly ill) adult child. The resulting increase in p(x,t) will offset the mortality-induced decrease in D(x,t) and yield an error in the opposite direction, namely, an underestimation of the demographic effects of excess mortality. These examples ignore time lags and the precise mechanisms through which demographic availability influences residential preferences, but the main
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research idea should be transparent: if our mission is to assess the impact of an external event on P(x,t) and to determine how much of this change occurs via changes in D(x,t) alone, identification will be problematic insofar as we do not account for the impact of changes in D(x,t) on p(x,t). It follows that a third minimum condition for the identification of HIV/AIDS effects is the assessment of changes during a period of time short enough to support the assumption that endogenous effects have not significantly altered residential preferences prevailing prior to the onset of HIV/AIDS. It should be noted, however, that if one is interested in the total effect of HIV/AIDS, the third identification condition is superfluous. Indeed, in this case all we need is a rough measure of change in P(x,t)—whether reflecting changes in D(x,t) or in p(x,t) induced by the epidemic itself. The only caveat is that inconsistent estimates of the effects of the epidemic will obtain if any of the changes in p(x,t) are exogenous to the event of interest. Furthermore, if changes in D(x,t) and p(x,t) offset each other perfectly, no changes in P(x,t) will be observed, leading one to conclude that the HIV/ AIDS epidemic is inconsequential for living arrangements. Identification Conditions in South Africa In South Africa, identification problems are exacerbated by the fact that the period of fastest growth of the incidence rates in HIV/AIDS coincided with a period of tumultuous social and demographic transformations that occurred just before and after the collapse of apartheid. Apartheid and its associated system of separate development imposed restrictions on spatial mobility, education, and employment of black South Africans, by forcibly resettling them to the homelands, four of which were made “independent states” in the 1960s and 1970s (Transkei, Bophuthatswana, Venda, and Ciskei, or the TBVC states). This regime supported a migrant labor system, of circular character, which affected almost every African household. Through the enforcement of influx control laws, African men working in the mining industries, on white farms, and in towns and cities were systematically denied the right to settle there with their families. Single sex hostels were built in all major cities to host rural African laborers. This system encouraged male out-migration but kept families divided by forcing heavy restrictions on residential changes by migrants’ wives, children, and elderly relatives (Murray, 1980, 1987; Russell, 1998). What were once undivided rural households became “stretched households,” that is, spatially divided units connected by kinship and remittances (Spiegel, Watson, and Wilkinson, 1996). After the collapse of apartheid, migration involved broader age groups as well as women (Collinson, Tollman, Kahn, and Clark, 2003; Posel and Casale, 2002). The intensi-
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research fication of migration resulted in the rapid periurbanization of formerly rural areas bordering large metropolitan areas and the swelling of the population of black townships living in backyard shacks (Kinsella and Ferreira, 1997; Percival and Homer-Dixon, 1995; Spiegel et al., 1996). If death were the only reason for children to be cared for by grandparents, we would expect a higher proportion of the elderly living in skipped-generation households—which are households composed by grandparents and grandchildren without members of the middle generation, in areas where the prevalence of HIV/AIDS is high. But in South Africa, children may lose a parent to migration as well as death (Bray, 2003), and migration provides a condition for grandparents to take in and support their grandchildren (Smit, 2001). Thus, an increase in the prevalence of grandparents living with their grandchildren without the presence of an adult child may not be due to HIV/AIDS but to high rates of population mobility. Apartheid and its forced labor migration system also changed the economic function of black South African households, from agricultural production to labor in South Africa’s gold mines and industrial development (Martin and Beitel, 1987; Marwick, 1978). Theories of modernization hold that economic transformations, brought about by industrialization and the establishment of wage labor, have decoupled production from the family division of labor characteristic of traditional agricultural societies. In line with these theories, the changes brought about by apartheid might have led to an erosion of social control over family members and a weakening of emotional ties that sustain traditional adherence to the family and its patriarch. Disintegration of the traditional intergenerational relationships has implications for the living arrangements of the elderly, for whom modernization assumes a shift from a preference for coresidence with adult children and grandchildren to a preference for solitary living. Thus, an observed increase in the prevalence of solitary living among the elderly may be due to forces of modernization rather than the outcome of demographic constraints imposed by HIV/AIDS. Moreover, in South Africa, there is a distinctive reason for alterations in the living arrangements of the elderly: a pension system that was extended to all South African elderly in 1993, mostly by increasing the benefits received by Africans. With rising rates of unemployment, pension sharing with an elderly relative has become a reason for adult children to join their elderly parents’ households (Burman, 1996; Møller and Sotshongaye, 1996). Nearly 80 percent of age-qualified Africans reported receiving a social pension in 1996 (Case and Deaton, 1998). Similar to the effects of HIV/AIDS, which may draw adult children back to their elderly parents’ homes, the elderly pension system may affect the propensities of adult children to coreside with the elderly, thus introducing another source of misidentification.
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research FIGURE 4-9 Proportion of African elderly living with dual orphan under age 15, noadult children, and HIV prevalence. SOURCES: 1996 and 2001 censuses, 10-percent samples.
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research availability of adult children for the elderly, prevalence of sickness among the children born to elderly people, and to project changes in availability and sickness over the next 10 to 15 years. We then used the macromodel as a heuristic tool and compared expected outcomes with observed changes in the living arrangements of the elderly over time and across provinces, using three consecutive South African censuses conducted before and after the onset of the HIV/AIDS epidemic, in 1991, 1996, and 2001. Because the progression of HIV/AIDS is expected to increase the proportion of children who are orphaned and the proportion of women who are widowed, we calibrated our ability to detect gross effects of HIV/AIDS by examining the association between patterns of orphanhood and widowhood among African children and women and HIV/AIDS in each data source. The examination of orphanhood and widowhood indeed revealed the signature of HIV/AIDS as the epidemic progresses over time. The results from the macromodel suggested that the fall in the number of healthy children and the growing loss of children to AIDS may leave the elderly with fewer or no surviving children to live with and may increase the propensities of grandparents to take in their grandchildren to ease the burden on their sick adult children or to care for their orphaned grandchildren. Our descriptive analysis of changes in the living arrangements of the elderly as they relate to the growth of HIV prevalence has revealed flickers of evidence suggesting the effect of HIV/AIDS. Some of the outcomes we analyzed have changed in the way expected by models of availability, whereas some others have done so in accordance with what one would expect given hypothesized changes in preferences. Most notably, in support of the expected decline in demographic availability of adult children due to HIV/AIDS, we have observed an increase in the proportion of the elderly who live with an orphaned grandchild in provinces that have experienced the fastest rise of HIV/AIDS prevalence. Where the record is inconclusive, it may be because the epidemic has not worked its way through with sufficient force, because individuals and groups react in ways that conceal the trail left by HIV/AIDS, or because we may be unable to distinguish the effects associated with HIV/AIDS from those triggered by migration, which mimics the effects of HIV/AIDS on the availability of kin, or those induced by modernization, which changes preferences for coresidence. As shown by the data, the observed growth of skipped-generation households is more likely to reflect levels and changes of provincial migration flows than the growth of HIV/AIDS. Similarly, our inability to detect the effects of HIV/AIDS on solitary living is overwhelmed by the tug of modernization, which may increase the elderly’s propensity for solitary living. Establishing benchmarks using model-based approaches as we have done here is useful but insufficient. Unless all the minimal identification
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research conditions are simultaneously met, conventional cross-sectional data sources, such as censuses, can sustain only weak inferences, because they reduce the analytical options to a handful of indicators, for example, household distributions and the living arrangements of the elderly, which do not reveal the processes that produced them. More promising ways to address the problem of identification of causal pathways include obtaining richer and better data, such as those provided by longitudinal studies or demographic surveillance systems performed at lower levels of aggregation. These data collection efforts can elicit direct information on residential preferences, changes in availability, and changes in actual living arrangements in subgroups affected and not affected by HIV/AIDS. Another promising approach to enhance knowledge of the effects of the HIV/AIDS epidemic on the elderly and to isolate the most important contributors to observed patterns in living arrangements is the implementation of microsimulations that combine the realistic modeling of the HIV/AIDS epidemic, of kin availability, and of coresidence. The realism of the simulations hinges on the availability of demographic information (such as marriage, fertility, and mortality), epidemiological information to estimate parameters governing the spread of the epidemic (such as transmission probabilities, rates of partner change, incubation times), and information that enables the estimation of explicit rules of coresidence, such as the timing of children’s leaving home and types of destination households. While censuses and cross-sectional surveys provide information on realized residential arrangements, longitudinal studies and demographic surveillance systems are the ideal sources of quantitative information on residential rules and preferences and would easily accommodate questions aimed at the explicit identification of residential rules and preferences under AIDS and non-AIDS regimes. APPENDIX TO CHAPTER 4 Assume we focus on an elderly woman age x who is alive in the census year 1995. This will be the target or target person and the year the target year. The main objective is to derive expressions for the following probabilities applicable to the female children born alive when the target was age x − y at time t − y: (a) that these female children have survived healthy (no HIV) to time t; (b) that they survived to t but contracted HIV along the way; and (c) that they have died due to HIV and other causes. In all these cases, the index variable y varies from a minimum equal to x − 50 to a maximum equal to x − 15, thus bounding the childbearing period of the target between ages 15 and 50. We assume that none of the targets contracted HIV prior to age x or that the fraction that does is trivial and can be
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research dismissed. Because we use a minimum value for x of 60, this assumption is sensible but not entirely accurate, since some of these women could have been infected in the 10 years prior to 1995. However, since the HIV incidence rates between ages 49 (attained the year the epidemic started) and 60 (attained in the middle of the census year 1996) are very low, the assumption is highly realistic. Furthermore, to the extent the assumption departs from reality, our calculations will be in error only if the childbearing patterns to which targets not affected by HIV and the mortality and HIV incidence pattern of her children are different from those that apply to targets who were infected prior to the target year. To derive expressions for these probabilities, we rely on the following simplified scheme: let µ(z, t*) be the force of mortality (in the absence of HIV) at age z at year t*, the year when the target’s child attained age z, let ι(z, t*) be the rate of HIV infection at age z and time t*, and finally let γ(d) be the joint rate of incubation and mortality due to HIV/AIDS and other causes for individuals who have been infected d years. The set of all the target’s children born x − y years ago who could be age y at time t is the union of several disjoint subsets: one containing individuals who will not reach age y due to mortality in the absence of HIV, Q(y,t); another subset containing those who will attain age y at time t but are infected with HIV, QI(y,t); a third subset containing those who will die of HIV/AIDS-related causes, QID(y,t); and, finally, a subset including those who will attain age y at time t as healthy individuals (not infected), SI(y,t). The expression for the corresponding probabilities, referred to a target age x at time t, are as follows: If one knows the time distribution of children ever born reflected in the age pattern of fertility to which women age x at time t were exposed during childbearing, , it is possible to calculate the weighted probabilities of having a child in any of the four statuses defined above. This is achieved multiplying by each of the quantities defined above for every permissible value of y. These weighted values represent the average probabilities for an elderly woman age x at time t. In particular, is the average fraction of all children born to the target elderly women who are infected with HIV at age y at time t; is the average probability of having a child age y at time t who is healthy; and, finally, is the average prob-
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research ability of having lost a child to either HIV/AIDS or to mortality due to other causes. In addition to the assumptions discussed above, we rely on the simplification that one can follow the progression to mortality of infected individuals by combining the force of mortality due to other causes, the incubation function, and AIDS-related mortality in a single synthetic superfunction, γ(d), which depends only on duration since infection and not at all on age. Estimation of the Model Estimation of the model depends of five pieces of information. The first and most important are the yearly HIV incidence rates from the onset of the epidemic until time t. The second is the incubation function that determines the waiting time in the infected state. The third is the mortality of healthy individuals, of individuals who are HIV+, and of those with full-blown AIDS. The fourth piece of information is the time distribution of children ever born or, equivalently, the fertility function approximating the childbearing experience of the target population. Finally, we also need the distribution of targets by number of children ever born, w(j). Below we briefly define the nature of these inputs. Estimation of HIV Incidence A most difficult task is to derive estimates of HIV incidence for the period 1985-2010. We proceed in several stages. In a first stage, we obtain a time series of prevalence estimates for each province during the period 1990-1999. These estimates were obtained from calculations made at Statistics South Africa using the estimates prepared by the UNAIDS and independent estimates obtained from surveillance sites and gathered at the U.S. Census Bureau. The second stage consisted of fitting a curve to the estimated cumulative prevalence, Pre(t). We used a Gamma function of the form where d(t) is the duration of the epidemic at year t and k, α, and λ are parameters to be estimated.20 Our motivation in using this expression is more a matter of adherence to convention than of logical reasoning or empirical judgment; there is no compelling reason to prefer this function over 20 In all cases it was assumed that t(d) was equal to t − 1985, that is, that the epidemic started in earnest in 1985.
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research a virtually infinite set of equally plausible ones. Indeed, the utilization of this function leads to serious problems that require ad hoc solutions. The most important of these problems is that, by its own nature, the associated incidence curve—the density function associated with the cumulative Gamma function—will tend to peak very early and taper off and drift to zero very rapidly. For starters, this type of behavior is inconsistent with the possibility that HIV/AIDS becomes endemic with a constant level of incidence, a possibility that is not only mathematically feasible but also quite likely. Second, systematic comparisons with a number of simulated results using one detailed macromodel of HIV/AIDS (Palloni and Lamas, 1991) demonstrate that almost always, and regardless of the nature of input parameters, the Gamma incidence drops too fast and does not reflect at all the post-peak course of the epidemic. To resolve this problem, other researchers have adopted arbitrary solutions. They cannot be otherwise, since there are no observations beyond the peak of the epidemic. Estimates exceeding that point are anybody’s guess. In this paper we adopt a rather sui generis solution: we take a large set of simulated results using parameters that are deemed to represent well the situation in South Africa (with respect to mortality, fertility, number of partners per person per year, etc.) and then retrieve the estimated incidence curves after the onset of the simulated epidemics. We then fit a Gamma function to the simulated cumulated prevalence, derive estimates of incidence, and calculate the difference between the simulated and estimated incidence rates. These differences are tantamount to “Gamma-adjustment factors.” To modify the post-peak estimated incidence for South Africa, we search for the set of Gamma parameters estimated in the simulated model that most closely resemble those observed in South Africa and adopt the corresponding Gamma-adjustment factors. We do not adjust the pre-peak estimates, which are heavily determined by the observed prevalence, but only the post-peak profile of incidence rates. In all provinces and in South Africa nationwide, the adjustments apply to the years after 2003, not before. In this sense, the uncertainty surrounding the estimates of HIV incidence is larger in the post-2003 period than before, when the estimates are at least more closely anchored to the trajectory of observed prevalence.21 This fact makes the calculations of the key quantities via forward projections less sensitive to errors associated with the use of incorrect Gamma-adjustment factors because post-peak epidemic incidence rates will affect 21 This is a generous statement, for the “observed” prevalence is not so: it is estimated via procedures that are not always reproducible and rest on observed prevalence in small and selected samples of pregnant women.
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research only the cohorts who reach age 15 some time after 2005. All our calculations refer to periods before 2010.22 Estimation of the Incubation Function We assumed that γ(d) follows a Weibull hazard function with parameters α = .08 and β = 3.2, dictating a survival distribution with a median survival time of approximately 10 years. Given the fact that mortality among HIV-infected individuals in Africa is likely to be much higher than normal even in the absence of full-blown AIDS, this assumption does not seem unrealistic. It is also consistent with reports suggesting that the median survival time of HIV-infected individuals in Africa is of the order of 7.5 years (Boerma et al., 1998).23 Estimation of Healthy Mortality Levels We use mortality levels estimated for South Africa by the United Nations (UN) for the period 1950-2000 and then the projected life expectancies through the year 2010 corresponding to the UN medium projections. For the years before 1950 we estimate life expectancy linearly, extrapolating backward from 1960. In all cases, we use the North female pattern of mortality from the Coale-Demeny model life tables.24 Estimation of Fertility and of and w Estimates of were obtained from the age pattern of fertility implicit in the Coale-Demeny stable models. We made no extra efforts to approximate 22 An important limitation of our estimates is that the estimated incidence curve depends heavily on the provincial antenatal clinic-based estimates of HIV prevalence that we found published. To the extent that these are in error (Bignami-Van Assche, Salomon, and Murray, 2005), our calculations will yield erroneous estimates of the key quantities describing the family burden of HIV/AIDS. 23 It is unlikely that either the median incubation time or the median survival time since from the onset of HIV were any longer than these figures in the pre-2000 period. Longer incubation and survival times can be attained but in disease environments quite different than those prevailing in Africa. In any case, our calculations are only mildly sensitive to errors of + or − 3 years on either side of the estimates we used. 24 The use of the North model to represent the HIV-free mortality patterns of sub-Saharan Africa has a long and distinguished tradition in demography. This tradition is critically based on the observed relations between adult and child mortality, not on the pattern of adult mortality alone. It is highly unlikely that our estimates would differ by much had we used a different pattern but holding constant the level of mortality, for in our calculations what matters most is the latter, not the former.
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Aging in Sub-Saharan Africa: Recommendations for Furthering Research closely a fertility scheduled for South Africa since what mattered in our calculation was the experience of women who are now 60 and above, that is, the childbearing experience pertaining to years as late as 1980 and as early as 1945. The fertility pattern between 1945 and 1975 at least is a matter of guesswork, and instead of deriving original estimates we chose to adhere to accepted age patterns that have been widely applied. Estimates of w(j) were obtained directly from the observed distribution of children ever born to mothers age 50 at the time of the 1996 census. Although this distribution may differ from the one that applies to mothers age 60 and above in 1995, it is unlikely that the difference will be major, since substantial fertility changes are unlikely to have been experienced by women younger than 30 or 35 in 1996. REFERENCES Ainsworth, M., and Dayton, J. (2001). The impact of the AIDS epidemic on the health of the elderly in Northwestern Tanzania. Presented at the 2001 meeting of the Population Association of America, Washington, DC. Ainsworth, M., and Over, M. (1999). Confronting AIDS: Public priorities in a global epidemic. New York: Oxford University Press. Ainsworth, M., and Semali, I. (1998). The impact of adult deaths on the nutritional status of children. Presented at the Workshop on the Consequences of Maternal Morbidity and Mortality, October 19-20, Committee on Population, National Research Council, Washington, DC. Barnett, T., and Blaikie, P. (1992). AIDS in Africa: Its present and future impact. New York: Guilford Press. Bignami-Van Assche, S., Salomon, J.A., and Murray, C.J.L. (2005). Evidence from national population-based surveys on bias in antenatal clinic-based estimates of HIV prevalence. Presented at the 2005 meeting of the Population Association of America, March 31-April 2, Philadelphia, PA. Boerma, T., Nunn, A.J., and Whitworth, A.G. (1998). Mortality impact of the AIDS epidemic: Evidence from community studies in less-developed countries. AIDS, 12(Suppl. 1), S3-S14. Boerma, T., Urassa, M., Senkoro, K., Klokke, A., and Ng’weshemi, J.Z.L. (1999). Spread of HIV infection in a rural area in Tanzania. AIDS, 13, 1233-1240. Bray, R. (2003). Predicting the social consequences of orphanhood in South Africa. African Journal of AIDS Research, 3, 39-55. Bureau of Market Research. (2000). The South African provinces: Population and economic welfare levels. (Report 276). Pretoria: BMR, University of South Africa. Available: http://www.unisa.ac.za/Default.asp?Cmd=ViewContent&ContentID=2456 [accessed May 5, 2004]. Burman, S. (1996). Intergenerational family care: Legacy of the past, implications for the future. Journal of Southern African Studies, 22, 585-598. Case, A., and Deaton, A. (1998). Large cash transfers to the elderly in South Africa. Economic Journal, 108, 1330-1361.
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Representative terms from entire chapter: