Total AIDS Orphans
Because maternal and paternal AIDS orphan numbers include children whose parents have both died of AIDS, estimates of the total number of orphans as a result of AIDS need to account for this overlap (see Fig. 2). Our approach does not distinguish those dual AIDS orphans whose parents have both died of AIDS from the smaller number whose parents have both died, but only 1 as a result of AIDS. It seems reasonable to assume that 1 parent's probability of dying of AIDS is independent of the other parent's probability of dying of some unrelated disease or condition. On this basis, the total number of AIDS orphans aged a at time t can be estimated as follows:
Projections of the Number of Orphans
To illustrate the methods described, we project orphan numbers for Tanzania and compare these with estimates from the 1992 and 1999 DHSs. Population projections were obtained using the EasyProj function of the Spectrum System of Policy Models (Spectrum; Futures Group International) software package.32 This package uses United Nations Population Division demographic data33 and estimates the impact of HIV/AIDS on mortality and fertility using UNAIDS' estimates of HIV prevalence and the recommended methods of the UNAIDS Reference Group on Estimates, Modeling, and Projections.11 We reduced male and female non-AIDS mortality rates to reflect a more realistic level of adult mortality for Tanzania than the default Coale-Demeny “West” model life table used in the EasyProj function of Spectrum. Further details of the parameter estimates are provided in Table A1.
Concordance of HIV Status of Partners
The regression of prevalence of HIV among the female partners of HIV-positive men against prevalence in the general population as measured at ANCs is highly significant (α = −0.84, β = 9.1; P < 0.001; Fig. 3a). In the absence of HIV in the general population, 30% of women with a positive partner are predicted to be positive themselves as a result of transmission within that partnership. As prevalence in the general population rises, a greater fraction of these women are predicted to be positive, mainly because of the increased risk of preexisting infection. Significant scatter exists about the regression line; in particular, a relatively large number of sub-Saharan African populations seem to have low female HIV prevalence (early in the epidemic) but high rates of concordance. This is confirmed by the logistic regression of just the 8 sub-Saharan African populations (α = −0.22, β = 4.2; P = 0.005; see Fig. 3a). This may reflect a higher prevalence of other sexually transmitted infections (STIs) that enhance the transmission of HIV. This is not seen for the sub-Saharan African countries with high HIV prevalence, however, particularly Zambia and Uganda (Rakai). Because data on STI prevalence are only available for some countries, we model concordance as a function of female HIV prevalence alone.
The prevalence of HIV infection among women with uninfected partners is lower than in the general female population (see Fig. 3b). Estimates based on Equation 10 show good agreement with the data when regression coefficients are used from the analysis of all data and just the sub-Saharan African populations. As HIV prevalence for all women rises, so does the prevalence of HIV infection among women with uninfected partners. The rate of increase in discordance declines as prevalence increases; in fact, the regression coefficients from the analysis of all data suggest that concordance actually declines for extremely high antenatal HIV prevalence (>20%). A decline in discordance is not likely to be a result of only high HIV prevalence per se. High prevalence implies a relatively low proportion of recent infections and thus discordance. It is also a proxy for high STI prevalence and other risk factors that facilitate transmission of HIV and thereby reduce partner discordance.
In the analyses that follow, we use the estimated concordance relation based on all the available data rather than that on the 8 sub-Saharan African countries alone.
Prevalence and Determinants of Dual Orphanhood
Before the onset of the HIV epidemic, approximately 5% of the fathers of children aged less than 15 years had died and approximately 2.25% of their mothers had died in most African countries. The difference reflects the higher mortality of men and the fact that they tend to be older than their wives. The prevalence of orphanhood rose to approximately twice this level in Uganda and Zambia by the mid-1990s and in Zimbabwe by 1999 (see Table 1). Orphanhood is also common in countries that have recently experienced wars or civil wars, such as Eritrea and Mozambique. In general, the countries in which orphanhood is most common are those that have high levels of HIV infection. The probability that a child is an orphan is low just after birth and rises rapidly with age. For example, by the end of the 1990s, 18% of children aged 10 to 14 years in Uganda and Zimbabwe had dead fathers.
If the risk of death of the 2 parents were independent, only approximately 1 in 500 African children aged 0 to 14 years would have been a dual orphan before the rise in mortality from AIDS. Even in Zimbabwe in 1999, one would only expect 1 in 180 children to have lost both parents. In fact, in Zimbabwe, 1 in 47 children was a dual orphan, whereas across the 34 surveys, this is true of 1 in 138 children (see Table 1). The actual risk of being a dual orphan for children aged 0 to 14 years varies from twice the independent risk in Eritrea (1995) to 5.7 times the independent risk in Burkina Faso (1992).
The final models of the excess risk of dual orphanhood are shown in Table 2. The first includes those characteristics of a population that were found to affect dual orphanhood; the other predicts the risk based on the age of the child alone. We investigated whether the age of a child is a random effect, that is, whether the relation between age and the risk of orphanhood varies between African populations, but we found no evidence of this. Thus, age is modeled as a straightforward fixed effect. Several other variables were included in the regression analysis initially but were found to have no net effect on the excess risk of dual orphanhood. They include the mortality rate for children less than 5 years of age, the date when the survey was conducted, and the contraceptive prevalence rate, which we hypothesized might affect age patterns of fertility and thus the ages of the parents. In addition, we tested for other relations between HIV prevalence and the risk of dual orphanhood and for all the possible 2-way interactions between the explanatory variables before settling on the simpler models shown in Table 2. The overdispersion of the residual individual-level errors is unsurprising, because we have made no attempt to model individual heterogeneity in risk except that arising from age.
Together, the 2 age coefficients imply that the excess risk of dual orphanhood is greatest soon after birth, when relatively few children are orphaned. Because the parents of young children tend to be young themselves, it is likely that a relatively high proportion of these deaths involve a catastrophic event that affects both parents, such an accident, act of violence, or fatal infection. As the children get older and more of them become orphaned, their excess risk of dual orphanhood falls rapidly before leveling off at approximately 8 to 9 years.
The excess risk of dual orphanhood varies significantly with the severity of the HIV epidemic. Although the proportion of children who are dual orphans rises rapidly with HIV prevalence, as maternal and paternal orphanhood become more common, the excess risk of dual orphanhood rises only moderately relative to the independent risk. After some experimentation to find the best specification of the model, we lagged adult HIV prevalence by 5 years to reflect the fact that changes in orphanhood lag behind those in HIV prevalence just as rises in AIDS deaths lag behind rises in HIV incidence. The natural log of HIV prevalence was selected as the predictor, because untransformed prevalence tends to overestimate the excess risk in countries with severe epidemics.
Our regression model is intended for use in countries with generalized epidemics and is based on data from such countries. Although log adult HIV prevalence is a good predictor of excess risk in African populations, this specification of the regression model implies that the risk of dual orphanhood tends to 0 as HIV prevalence drops to extremely low levels. In practice, the HIV epidemic has a negligible impact on the excess risk of dual orphanhood until adult HIV prevalence rises above 1%, and the predicted excess risk at this prevalence can be taken as applying to populations in which HIV prevalence is less than 1%.
Both indicators of marriage patterns affect the prevalence of dual orphanhood. The excess risk of dual orphanhood is high in populations in which women marry late, because mothers tend to be older than if women marry early, and in populations where polygyny is common, because fathers' ages tend to exceed those of their wives by more than in a monogamously marrying population.
The random intercept coefficient measures the variation between African populations in the excess risk of dual orphanhood. In both models, the coefficient is statistically significant; the unexplained variation between populations in excess risk is unlikely to be a chance result of sampling the populations. The substantive significance of the coefficient is easier to gauge from Table 3. This refers to the model that includes population-level covariates. It shows the expected excess risk of dual orphanhood for 4 age groups and 2 levels of HIV infection and the range within which the actual excess risk is predicted to fall for 95% of populations. Caution is required in the interpretation of these ranges because they have been calculated not from a random sample of African populations but from those that conducted DHSs, a disproportionate number of which occurred in either 1992 or 1998. Bearing this in mind, one can tentatively conclude that the model usually predicts the excess risk of dual orphanhood to within ±10%. Errors in the estimated excess risk map directly into errors in estimates of the prevalence of dual orphanhood and the number of dual orphans. Thus, if the numbers of children and maternal and paternal orphans are known accurately, one can usually produce an estimate of how many of these children are dual orphans to within ±10% of the actual number. With the same caveats, the random intercept of the second model suggests that even if one knows nothing about the population in question except the numbers of maternal and paternal orphans, one can estimate how many of them are dual orphans to within ±15%.
Projections of the Number of Orphans
Comparison of orphan projections for Tanzania with the 1992 and 1999 DHSs reveals good agreement, suggesting that the methods described here are appropriate (Fig. 4). The absolute levels of all types of orphanhood projected by the model are slightly higher than those indicated in the surveys. This may be a result of inaccuracies in the underlying demographic projections. For instance, AIDS and other-cause mortality or fertility may be overestimated. Alternatively, the DHS may fail to enumerate some orphans, such as street children or children living in institutions, or may misclassify foster children who are orphans as living with their biologic parent. The patterns of orphanhood are in close agreement. Paternal orphans are most common, numbering approximately twice the maternal orphans. The fraction of children who are orphans increases with age, because the older they are, the more time has elapsed since their birth during which a parent could have died.
The model projections of orphan numbers can be broken down by cause, indicating the significance of the HIV epidemic in the orphaning of children (see Figs. 4 a, b). By 1999, half of all maternal and paternal orphans in Tanzania are estimated to have been orphaned by AIDS. For dual orphans, this fraction increases to three quarters, resulting from the transmission of HIV between parents.
The production of reliable estimates of paternal AIDS and other-cause orphans has been obstructed by difficulties in estimating the ages of fathers and the survival of their children in populations where men and some of their spouses may be infected with HIV. This study proposes solutions to these problems based on an existing model of male fertility and regression analysis of concordance of heterosexual partners to estimate maternal HIV status. In this way, paternal AIDS orphans and non-AIDS orphans can be calculated using an extension of established methods for projection of maternal orphans. Dual orphans can then be estimated from the number of maternal and paternal orphans using a regression model fitted to DHS data. This predicts the excess risk of dual orphanhood over and above that expected if the death of parents were independent. Available DHS data suggest that the regression equation estimates the number of dual orphans to within ±10% if the prevalence of maternal and paternal orphanhood is known without error.
As an alternative to our approach, one could use a regression analysis of the DHS data to model paternal as well as dual orphan numbers. We attempted this, modeling paternal and dual orphanhood as a function of maternal orphanhood, the children's age, and characteristics of the population (eg, HIV prevalence, background mortality, levels of polygyny). Even with maternal orphanhood given directly by the surveys, our best-fitting model failed to predict the number of paternal orphans to within 20% of the correct value for 30% of the data points.
Comparison of orphan estimates based on our methodology with DHS data for Tanzania in 1992 and 1999 reveals good agreement (see Fig. 4). The relation between the fraction of children who are maternal orphans and the fraction who are paternal or dual orphans is equivalent, and both estimates show similar increases in the prevalence of orphanhood with age. Because model estimates give the prevalence of orphanhood by cause, they are able to indicate the extent to which HIV/AIDS has driven up orphan numbers in Tanzania over the last decade.
Of course, estimates of orphan numbers derived using the methods described in this study are only as accurate as the underlying demographic estimates and assumptions. The estimated numbers of maternal and paternal orphans are most sensitive to mortality and fertility estimates. Examining Equation 3 reveals a direct linear relation between changes in the numbers of adult deaths or births and the number of maternal orphans. Estimates of the number of paternal orphans show a similar relationship (Equation 8). Child survival is also directly related to the prevalence of orphanhood, but observed variation in child survival is small compared with that in the adult mortality and fertility estimates. Variation in other parameters, such as concordance of parental HIV status or the probability of mother-to-child transmission of HIV, has a much smaller impact on orphan numbers.
Estimates of dual orphanhood depend on the validity of the DHS data used to derive the regression coefficients in Table 2. One limitation of the DHS data is that household surveys do not provide information on children outside households, notably those living in institutions and street children. Such children seem more likely than children living in private households to be orphans, especially dual orphans. In most of Africa, however, relatively few children are institutionalized or homeless. The DHS may also be failing to enumerate orphans because of misclassification of foster children as living with their biologic parent or failure to interview child-headed households in which orphanhood is common. No “gold standard” exists against which DHS data on orphanhood can be evaluated. It is encouraging that both questions about the survival of parents were answered for more than 97% of children in the 34-survey database, with answers missing to either or both questions for fewer than 4% of children in every survey. In addition, the results pass crude tests of their plausibility. The proportion of children who are orphaned uniformly rises with age, paternal orphanhood is more common than maternal orphanhood, and the prevalence of orphanhood tends to be high in countries with severe AIDS epidemics or a recent history of warfare. Finally, the reported proportions of children in a survey with a dead mother or father are not associated with the proportions of living children who live with the parent in question. Thus, no evidence exists that absent parents tend to be reported as dead or vice versa.
A more extensive assessment of the validity of orphan estimates for sub-Saharan Africa produced by the UNAIDS and WHO using these methods is presented elsewhere.34 Comparison of model estimates of the prevalence of orphans using 19 DHSs and 24 UNICEF-sponsored multiple indicator cluster surveys (MICSs) in 39 countries found good agreement after adjusting earlier estimates of mortality as a result of causes other than AIDS. Significantly, comparisons in countries with extensive HIV epidemics were no more different than for countries with limited HIV prevalence, and in 80% of the comparisons, the model estimates of maternal and paternal orphans fell within ±40% of the survey estimates.
Without doubt, estimating and projecting the numbers of AIDS and other orphans in Africa involves numerous assumptions about demographic and epidemiologic processes and rates that remain poorly understood and inadequately quantified. Nevertheless, assumptions have to be made about most of these parameters to produce population projections for any African population experiencing an AIDS epidemic. Estimating orphanhood involves additional calculations, but the extra information required is rather limited and the assumptions involved no more heroic than those that have to be made for population projections. The methods we describe for the estimation of orphan numbers replace conjecture with explicit data-driven models that can support evidence-based policy and planning in response to the AIDS-related orphan crisis.
The authors thank Mohammed Ali and Christl Donnelly for their statistical advice and members of the UNAIDS Epidemiology Reference Group for their suggestions and assistance. In particular, Basia Zaba shared her estimates of excess mortality among orphans in Africa, Hania Zlotnik provided us with access to details of the United Nations Population Division's demographic estimates, and John Stover and Neff Walker supplied demographic projections from Spectrum that incorporate UNAIDS estimates and projections of HIV prevalence.
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The data sources and assumptions for parameter estimates are presented in Table A1.
Keywords:© 2005 Lippincott Williams & Wilkins, Inc.
orphanhood; AIDS orphans; AIDS impact; projections; sub-Saharan Africa