爱滋病的流行对坦桑尼亚老年人健康的影响

WPs S61f POLICY RESEARCH WORKING PAPER 2649 The Impact of the AIDS The elderly in Tanzania suffer a temporary decline in Epidemic on the Health physical well-being (body of the Elderly in Tanzania mass index) irmediately after a prime-age adult death. Among factors that could Martha Ainsworth improve the physical well- Julia Dayton being of the elderly: raising their incomes and assets, improving road infrastructure, and immunization and oter campaigns to control epidemics of communicable diseases. The World Bank Development Research Group Public Services Delivery July 2001   The impact of the AIDS epidemic on the health of the elderly in Tanzania Martha Ainsworth* and Julia Dayton** *Development Research Group, World Bank **Department of Epidemiology and Public Health, Yale University July 2001 This paper is a product of the research project on "The economic impact of fatal adult illness due to AIDS and other causes in Sub-Saharan Africa," sponsored by the World Bank, USAID, and Danida. We are grateful to UNAIDS, particularly Anita Alban, for the financial support for this paper, to Paurvi Bhatt, David Bishai, Deon Filmer, Robert Hecht, John Knodel, Sukhontha Kongsin, Robert Ssengonzi, John Stover, and John Strauss for comments on an earlier draft, and to Anna Marie Marafion for expert assistance in producing the paper. A preliminary draft was presented at the March 2001 annual meetings of the Population Association of America, in Washington, D.C. Our characterization of adults over 50 as "elderly" is purely for convenience; we wish to affirm that none of our friends, colleagues or co- investigators over 50 could in any way be described as elderly.   "Bena Nakayima has buriedfour of her children, deadfrom AIDS, beneath the bananas next to her packed, mud homestead. ... Two more of her children are buried elsewhere... In her 70s and a widow, [she] has come to be the caretaker for 35 grandchildren orphaned by her children 's deaths. At an age when she expected to be "laughing with my children ", she is instead searching for ways to feed her grandchildren. "' I. Introduction High HIV infection rates in Sub-Saharan Africa are producing dramatic increases in the mortality of prime-aged adults. As of the end of 1999, an estimated 24.5 million Africans were living with HIV/AIDS, accounting for more than 70% of all global infections (UNAIDS 2000). In Tanzania an estimated 1.3 million people out of a total population of nearly 33 million were believed to be infected with HIV and 140,000 had already died of AIDS. The estimated infection rate among prime-aged adults (aged 15-49) was 8.1%, or about one in every 12 adults. In neighboring Uganda, a study of Masaka District found that when HIV prevalence reached this level, two-thirds of all adult mortality was due to AIDS (Nunn et al 1997). The probability of death for a 15 year old in Masaka before reaching 60 had already reached 61%, while in the absence of HIV/AIDS it would have been only 24% (Boerma et al 1998). The high mortality of prime-aged adults due to AIDS has left in its wake orphaned children and elderly in households with fewer breadwinners to support them (Bamett and Blaikie 1992; Hunter and Williamson 1998; National Research Council 1996; World Bank 2000). African couples have large families in part because of the role of adult children in supporting their parents in old age. However, as a result of the AIDS epidemic, the elderly are often the caregivers for their adult children stricken with HIlV/AIDS, the guardians of orphaned grandchildren, and substitute workers for ill or deceased adults in the home and on the farm. In Uganda, nearly a third of AIDS patients were cared for by their parents in 1995 (Ntozi and Nakayiwa 1995). In Northwestern Tanzania, more than a quarter of sick household members are cared for by those 50-65 (Beegle 1998). In Thailand, elderly parents were the main source of finance for medical care for 60% of the AIDS patients who lived with their parents while ill, for 40% of the AIDS patients who lived adjacent to their parents and for about 25% of the AIDS patients whose parents lived elsewhere (Knodel et al 2000).2 Up to now, however, most of what is known about the impact of AIDS on the elderly is anecdotal, based on case studies of those who are highly affected and without comparison to a control group. Are these impacts typical? Which elderly are most seriously affected? What policies can be most cost-effective in helping them? ' Drusilla Menaker, "Elderly are left to raise orphans: Disease's toll stretches their limited means", Dallas Morning News, December 28, 1999. 2 This is not the pattem observed in the US, however, where only 6% of the caregivers for AIDS patients in the US were 60 or older, according to one study (Turner et al 1994).  This paper uses longitudinal household data from the Kagera Region of Tanzania collected in 1991-94 to measure the impact of mortality of prime-aged adult household members on the level and changes in physical well-being of the elderly. Our measure of physical well- being is the body mass index (BMI), defined as the respondent's weight in kilograms divided by height in meters squared. BMI is considered a strong measure of overall health status for adults as it is predictive of morbidity and mortality and responds to shorter-term changes in energy inputs and outputs. In industrialized countries, extreme low and high values of BMI have been associated with higher risk of morbidity and adult mortality (Duerksen et al 2000, Engelman et al 1999, Expert Subcommittee 1998, Kushner 1993, Stefanovic et al 2000, Waaler 1984). Adults with a BMI greater than 35 double their mortality risk and run even higher risks of morbidity from cardiovascular disease, diabetes and certain cancers (Kushner 1993). The mortality risk of low BMI in industrialized countries is confounded by smoking behavior, as there tends to be a positive correlation between leanness and smoking and those who smoke also suffer from smoking-related causes of morbidity and mortality. Mortality associated with low BMI is due to tuberculosis, lung disease, and lung and stomach cancer (Expert Subcommittee 1998). There are very few studies of health risks for the BMI of adults or the elderly in developing countries, where the highest risks of morbidity and mortality in adults are associated with extremely low values of BMI, indicative of acute under-nutrition (WHO Expert Committee 1995). In a longitudinal study of Chinese 70 and older, those with lower BMI had higher mortality, after controlling for age, sex and physical activity (Woo et al 1998). In a study of poor men in Calcutta, those with BMI less than 16 were more likely to have respiratory tract infections and tuberculosis (Campbell and Ulijaszek 1994). The relation between BMI and infection runs in both directions: morbidity contributes to changes in BMI and low or high BMI raises the susceptibility to ill health (Expert Subcommittee 1998). Aging affects both stature and weight, which can confound the interpretation of BMI levels and health risk at older ages (Expert Subcommittee 1998). Height declines by 1-2 cm/decade due to compression and other changes in the vertebra, reduced muscle tone and worse posture. In western countries, weight plateaus after age 65 in men and 75 in women due to reduced cell mass and loss of body water. However, in the short run, changes in weight and BMI are predictive of greater health risk. Thus, the recommended "healthy" range for BMI among adults varies according to the source and sometimes by age and gender, but in general the range for the elderly is higher than for middle-aged adults (Kushner 1993). For example, the U.S. Department of Health and Human Services recommends a BMI range of 19-25 for those aged 19-34 but a range of 21-27 for those 35 and older. The U.S. National Academy of Sciences suggests a range of 23-28 for those 55-64 and 24-29 for those 65 and older, compared to a range of 20-25 for those aged 25-34 (Kushner 1993). In this paper we analyze the impact of prime-aged adult mortality and other determinants of nutritional status on the level and changes in BMI of the elderly in Northwestern Tanzania. The elderly are defined as everyone over 50, to distinguish between those in the prime-aged 2  group (15-50) subject to mortality (and potentially low BMI) due to AIDS and those who are older and presumably suffer the adverse impacts of prime-age adult mortality. The next section describes the analytical framework that justifies the choice of explanatory variables in the model of the determinants of BMI, the channels through which an adult death in the household can affect the physical well-being of the elderly, and the evidence from the literature on key determinants. The third section presents the setting, the dataset, econometric models and descriptive statistics. The fourth section reports the results of multivariate analysis of the determinants of the level and changes in physical well-being of the elderly following adult deaths. The final section summarizes the results and conclusions for policy. We find that the elderly in non-poor households suffer a decline in BMI prior to the death of a prime-aged adult, while this is not true for the elderly in the poorest households. The impact on BMIfollowing an adult death in the household is confounded by the fact that many households receive private transfers, in some cases to mitigate the impact of the loss. When we control for public transfers to the household and private transfers received by other household members, we find that a recent death (within the past 3 months) is associated with a sizeable drop in BMI among the elderly in households with no public or private transfers. However, BMI recovers over time. For comparison, we also exarnine the impact on BMI of losing a spouse within the past 6 months. The elderly in both poor and non-poor households suffer a decline in BMI following the death of a spouse, and the impact is greater for those in non-poor households. Again, however, BMI recovers and does not show long-term impacts. The elderly in wealthier households have higher BMI and those in communities with poor road infrastructure have substantially lower BMI. An increase in the number of teenagers in the household and an epidemic of communicable disease in the community in the past 6 months are associated with a short-run decrease in the physical well-being of the elderly. Availability of health infrastructure and the quality of health care have no relation with the physical well-being of the elderly in this dataset. II. Analytic Framework We analyze the determinants of the level of BMI and the short-run change in BMI as a function of exogenous individual characteristics, household characteristics, community characteristics, and recent adult deaths. The choice of explanatory variables is based on an underlying economic model in which the household maximizes the utility of its members in terms of health, leisure, and the consumption of other goods, subject to a budget constraint and a 'health production function' that expresses the relation between various household and individual inputs and health outcomes (e.g., Behrman and Deolalikar 1988). Consider a one- period model of the production of health of adult i in household j and community k: 3  (1) Hj=H(Zj;Ej,;, Cj,C, Ck), Zi={ Ni,Mi, TH"}, i=1,...n where Z7 represents a vector of endogenous health inputs (food intake, Ni, medical care, Mi, time of the individual in producing health, TH,), Es denotes the individual's education as an adult, which can enhance the availability of information and affect the efficiency of use of health inputs, i1i denotes the individual's genetic endowment, C1 denotes a vector of other exogenous endowments for the individual, Cj is a vector of the household's endowments, and Ck is a vector of the community or enviromnental endowments. The individual endowment includes the person's age, gender, and 'tastes' for health. Household endowments include productive assets and other indicators of wealth and the human capital, number, and demographic composition of the household. Exogenous community characteristics or endowments include the availability, price and quality of medical care, food prices, the presence of disease vectors, community wage and price levels, rainfall, and other infrastructure. The budget constraint equates the total value of the time of household members plus any net transfers or other non-wage income sources, Ij, with the value of consumption expenditure (including health) and the value of the leisure of household members. Maximizing utility subject to the health production function and budget constraint results in reduced-form demand equations in which the endogenous health (and health- input) demands are expressed as a function of all exogenous variables: (2) Hi = H (Ei, 1,, Ci, Ci, Ck, Pk, Wk, Ii) where Pk is a vector of food and non-food prices and wk is a vector of market wages at the community level. We will be estimating two variants of equation (2), to explain the current BMI level of the elderly and short-run (6-month) changes in BMI. The level of BMI observed at any point in time, Hit, will be a function not only of characteristics and inputs during the current period, but all of those in the past. In this instance, the vectors of individual, household, and community characteristics, and wages, prices, and household income include the values for time t, t-l, t-2, etc. A few time-invariant variables remain (in, and the gender component of C1 ). This can be expressed as: (2)' Hit = H ( E, lj Cb Cj, Cb pk wk, Ij) where italicized vectors Cj = {Cjt, Cjt l, Cjt 2, . .. }, and so forth. This is the model estimated for the level of BMI, although not all information on past variables is available. We estimate the determinants of changes in BMI by simply subtracting equation (2)' for time t-l from time t, such that the change in BMI is a function of the change in other right-hand side determinants in the same time increment: 4  (2)" Hit - Hit-, = H (Cit - CGtI , Cjt - Cjt l, Ckt - Cktl1, Pkt - Pkt-1, Wkt - Wkt-l, Ijt - Ijt-) Note that when we do this, all time-invariant variables (like Ei, ri and time-invariant variables in the vectors of household and community characteristics) disappear from the model. This model expresses changes in BMI as a function of all other variables that have changed between time t - 1 and time t .3 Our main interest is in the impact of the death of a prime-aged adult in the household. An adult death can shock the health of the elderly person through two channels - the budget constraint and the health production function. In this reduced form context, it appears as a component of Cj , and the sign of the coefficient on adult deaths cannot be predicted a priori. If the deceased adult produced more than he/she consumed, then holding other things constant the death could result in lower household income and human capital, reducing nutrients and health care of surviving household members. However, these reductions could be compensated for by increased transfers from other households, inheritance, and increased labor force participation/productive activities by the remaining household members. The fatal illness and death of an adult will also divert time and household resources away from the elderly to care for the terminally ill adult and to compensate for the patient's tasks in the household. To the extent that the adult death is due to an extended illness like AIDS, the major impact for other household members may occur prior to the death; following the death a reallocation of household resources to the health and nutrient intake of other household members may be possible, improving outcomes. Thus, while we cannot predict the direction of the impact of an adult death on the elderly, we expect that adverse impacts are more likely to be observed in the short run, because of lower disposable income, greater demand for medical expenditure for the ill adult at the expense of health inputs for other household members, possible diversion of the elderly person's time away from production of their own health, and the shorter time frame for mitigation measures.4 We do not dismiss the possibility, however, that the AIDS epidemic has hit some communities so hard and repeatedly that their coping mechanisms are permanently depleted and they are unable to recover. Thus, in addition to an indicator of recent and future adult deaths in the household, we will include in our model a measure of the community-level adult mortality rate the number of living children of each elderly person. The literature on the economic determinants of BMI of adults and especially the elderly is scarce in both industrialized and developing countries. From our priors based on economic 3 For example, since variables like the distance to a health center did not change in the course of the survey, we will not be able to analyze the impact of a reduction in the distance on a change in BMI. 4 There are also epidemiological and emotional reasons why the BMI of the elderly could decline with an adult AIDS patient in the household. Tuberculosis is the major AIDS opportunistic infection in Africa and it can be passed on to caregivers who are HIV-negative. Grief and depression could also produce a negative impact on BMI. 5  theory and the literature we can glean some expectations for the likely influence of the other exogenous variables. Education and individual characteristics. We expect the BMI of the elderly with more education to be higher because of their better access to information, possibly better health behaviors, and better ability to translate health inputs into better outcomes. Controlling for wealth, additional education was associated with higher BMI of women over 20 in Ghana (Alderman 1990), while in Cote d'Ivoire it was not among men and women 20-60 (Thomas, Lavy and Strauss 1996). BMI declined with age in Indonesia over the age range 20-69 (Frankenberg, Thomas and Beegle 1999), while in Sarawak, Malaysia, it peaked at age 50 for men and age 40 for woman and then declined linearly with age (Strickland and Ulijaszek 1993, 1994). Adult women 20-60 in C6te d'Ivoire had higher BMI than men, controlling for other factors (Thomas, Lavy and Strauss 1996). Household assets. We expect that household wealth and income will be associated with better health, as those with more resources can afford more and better-quality health inputs. The literature supports this. In C6te d'Ivoire and Ghana, higher per capita household expenditures were associated with higher BMI in adults 20-60 and adult women 20 and older, respectively (Thomas, Lavy and Strauss 1996; Alderman 1990). In Indonesia, an increase in per capita income between 1997 and 1998 was associated with an increase in BMI for women, but not for men (Frankenberg, Thomas and Beegle 1999). In Bangladesh, higher household assets (number of rooms in the dwelling and ownership of a cow) was associated with a reduced mortality risk among elderly women (Rahman, Foster and Menken 1992). A second study in Bangladesh found that ownership of at least one household asset (cow, boat, watch, or quilt) at the beginning of the survey was associated with lower mortality both of elderly men and women, as compared with those having none of these assets (Rahman 1999). Household composition. We expect that the elderly who head their households will have better health outcomes because of their greater access to household resources and their likely role as the key decision-maker in resource allocation within the household. Using longitudinal data from Matlab, Bangladesh, Rahman (1999) found that the head of household or spouse of the head had lower mortality, even after controlling for disability status, the presence of spouses and sons and joint household resources. However, the effect of being the household head declined with age and became insignificant after age 75. Elderly Bangladeshi widows in rural households they do not head are at increased risk of mortality, compared with married women and widows who are heads of household (Rahman, Foster and Menken 1992). We also expect that the elderly in households with a larger number of prime-aged adults will benefit from higher income and a lower demand on their time for labor input on the farm or in home production. Holding other things constant, we expect that elderly in households with relatively more young children will have greater demand on their time and energy in caring for children, and thus have lower health status. The presence of children in the household may also divert household resources from the consumption of the elderly (including their health inputs) to 6  investments in children's health and education. In Bangladesh, the presence of one or more adult sons in the household was associated with reduced mortality for elderly women but not for elderly men (Rahman 1999). However, the (log of) household size had no relation with BMI for Indonesian men or women aged 20-69 (Frankenberg, Thomas and Beegle 1999). Community and policy variables. We expect higher food prices to reduce food intake or the quality of food purchased and a higher price of medical care or lower quality medical care to reduce its use, reducing BMI. Among adults aged 20-60 years in CMte d'Ivoire, higher food prices were associated with lower BMI, and these effects were greatest for those living in rural areas (Thomas, Lavy and Strauss 1996). The availability and quality of health services in the community had no effect (individually or jointly). In the sample of men and women in CMte d'Ivoire and of women in Ghana, those living in urban areas had significantly higher BMI than those living in rural areas (Alderman 1990). III. Setting, dataset, descriptive statistics and econometric model Our data come from the Kagera region of Tanzania, located to the west of Lake Victoria, adjacent to Uganda, Rwanda, and Burundi, in an area of high HIV prevalence and high adult mortality. The 1988 census reported more than 1.3 million people living in the region, with more than 80 percent residing in rural areas. Most of the population is engaged in agriculture - tree crops in the north and annual crops and livestock in the south. Household consumption expenditure in 1991 was US $217 per capita based on data used from this study, ranging US$118 to US$357 across Kagera's six districts. Kagera is at the epicenter of the African AIIDS epidemic. The first case of AIDS in the region was diagnosed in 1983, although HIV was most likely present at least a decade earlier. A population- based seroprevalence survey conducted in 1987 found that about one in four adults in the regional capital (Bukoba) were infected with HIV, and one in 10 adults in rural areas surrounding Bukoba (Killewo et al 1990). In the rural south and southwest of Kagera, in contrast, fewer than 1% of adults were infected; to the west the adult infection rate was 5%. These levels of infection are not unlike those projected nationally at present-about 8% of prime-aged adults. A follow-up study in 1993 found that HIV infection had declined from 24% to 18% in Bukoba town among those 15-54 years of age, and in the rural area surrounding Bukoba, from 10% to 6.8% (Kwesigabo et al 1998). However, in the latter case, the only population group that registered a significant decline was rural women aged 1 5-24.5 5 Since HIV infection is life-long, the only way that prevalence can decline over time in a cohort is for the mortality rate to exceed the new infection rate (incidence) or for HIV-positive people to migrate out of the study population. 7  Dataset The Kagera Health and Development Survey (KHDS) is a longitudinal living standards survey of over 800 households, with 4 waves of data collected at 6 to 7 month intervals from 1991 to 1994 (Ainsworth et al 1992; Over and Ainsworth 1989). The objective of the data collection and research effort was to measure the impact of prime-aged adult deaths on the welfare of surviving household members. Even with the high HIV infection and mortality rates in Kagera, the sample of households had to be heavily stratified in order to observe a sufficient number of households likely to suffer an adult death during the short time frame of the panel (2.5 years). A total of 51 primary sampling units (PSU) were chosen from both high- and low- mortality communities in each of four geographical zones of the region.6 In each PSU, a sample of 16 households was randomly selected from one of two groups: 14 households were selected from among households reporting either an adult death from illness, an adult too sick to work, or both; and 2 households were selected from among those reporting neither event.7 A total of 816 households were selected for the original sample and 757 completed all four interviews.8 We will analyze the height and weight of a total of 695 persons over 50, observed at least once and as many as four times.9 The average BMI of the elderly in the KHDS was about 20% lower than that in the U.S. and the lowest among the four developing countries for which information is available (Table 1). In the developing countries, including in Kagera, the BMI of elderly women is generally greater than that for men, and BMI declines with age. In contrast, in the US, average male and female BMI are roughly the same. More than a third (36%) of the elderly 60 and older observed for the first time in the KHDS sample had a BMI less than 18.5 (not shown). This is a much larger share of low BMI than measured in Indonesia (14%) and Cote d'Ivoire (4%), although in these two countries the population included prime-aged adults (18 and older and 20-60, respectively), so the results are not strictly comparable (Frankenberg et al 1999, Thomas et al 1996). 6 An exhaustive listing was made of all households in these 51 PSUs, more than 29,000 households in total, each of which was asked about the recent mortality of prime-aged adults (in the 12 preceding months), the cause of death (illness, accident, childbirth, etc.) and whether there were any adults too sick to work. 7 The questionnaire and sampling are described in greater detail in Ainsworth et al (1992). 8 Since households that left the sample were replaced, a total of 915 households were interviewed at least once. 9 413 persons in our sample (59%) were interviewed 4 times, 107 (15%) 3 times, 87 (13%) twice, and 88 (13%/6) once. The most important reasons why some people were not interviewed four times were: they turned 51 during the survey; their household moved out of the sample during the survey; their household moved into the sample (replacing a household that dropped out) during the survey. Among the households interviewed four times, there was remarkable stability in the population of household members over 50 (see Ainsworth and Dayton 2000). 8  Table 1: Mean and standard deviation of Body Mass Index (BMI) by age group, KHDS and selected developing and industrialized countries (Standard deviations in parentheses) Age group Location 60-69 70-79 >=80 Men Kagera, Tanzania (1991-94) 20.3 (3.0) 19.3 (2.6) 18.1 (2.0) Brazil (1989) 23.7 (5.4) 22.9 (5.0) 22.4 (4.1) China 20.8 (3.0) 21.7 (3.9) 20.9 (2.6) Guatemala (rural) 21.3 (2.6) 20.2 (2.2) 19.6 (2.3) United States 26.4 (4.0) 25.6 (3.7) 24.6 (3.8) Women Kagera, Tanzania (1991-94) 21.3 (4.3) 19.7 (3.3) 19.7 (3.9) Brazil (1989) 25.8 (6.7) 25.0 (7.4) 23.9 (4.9) China 21.7 (3.9) 20.7 (3.6) 19.6 (3.1) Guatemala (rural) 22.4 (3.3) 21.4 (4.4) 20.7 (4.1) United States 26.5 (5.3) 25.7 (4.9) 24.5 (5.0) Source: Kagera: authors' calculations on observations of the elderly from wave I of the KHDS; Other countries: WHO Expert Comrmittee (1995). The KHDS results describe the sample and are not weighted to be representative of Kagera region. Independent variables The independent variables represent measures of adult deaths and arguments of the reduced form demand for health. The descriptive statistics in Table 2 are for two samples - the samnple of 586 adults over 50 the second tine they were observed and the 1512 "differenced" observations in adjacent periods, leaving from 1-3 observations on 695 adults over 50.10 '