HIV and Population Dynamics: A General Model and Maximum-Likelihood Standards for East Africa

Abstract

In high-prevalence populations, the HIV epidemic undermines the validity of past empirical models and related demographic techniques. A parsimonious model of HIV and population dynamics is presented here and fit to 46,000 observations, gathered from 11 East African populations. The fitted model simulates HIV and population dynamics with standard demographic inputs and only two additional parameters for the onset and scale of the epidemic. The underestimation of the general prevalence of HIV in samples of pregnant women and the fertility impact of HIV are examples of the dynamic interactions that demographic models must reproduce and are shown here to increase over time even with constant prevalence levels. As a result, the impact of HIV on population growth appears to have been underestimated by current population projections that ignore this dynamic.National population forecasts are essential instruments of development planning. Since the mid-1950s, nearly all national and international agencies producing such forecasts have relied on a population-dynamics model known to demographers as the cohort-component model of population projection (hereafter, CCMPP). For a number of African countries, the main challenge to demographic forecasting has been to meet even the relatively modest data requirements of this model. Toward this end, international donors sponsored the first censuses and fertility surveys in a number of countries. Concurrently, demographers developed a series of ingenious "indirect" techniques (e.g., Brass 1975; United Nations (UN) 1967) and empirical models (e.g., model life tables; Coale and Demeny 1983; UN 1982) which allowed them to extract, correct, and supplement the required information from incomplete or inaccurate data.HIV has quickly rendered obsolete these contributions to demographic estimation and forecasting in the populations that have been the hardest hit by the epidemic. The new challenge to population forecasting for these countries is not merely the uncertainty about the future level of mortality. Because the age pattern of AIDS-related mortality is so different from the existing J-shaped model age patterns of mortality, no existing model accurately represents the age structure of mortality when AIDS accounts for a significant proportion of all deaths. The epidemic also invalidates many indirect techniques that rely on p-heuveline@uchicago.edu.

Adult mortality in sub-Saharan Africa: Evidence from demographic and health surveys

“…This will not be exactly true. Nevertheless, patterns of sexual behaviour and the epidemiology of AIDS share common features across sub-Saharan Africa, suggesting that age patterns of HIV infection and AIDS mortality are likely to be similar across the continent (Heuveline 2003). Thus, as DHS samples are too small to measure country-specific changes in the age pattern of mortality, our approach estimates instead the typical age pattern of mortality increase in African populations hit by AIDS.…”

“…A close parallel exists between this approach and that developed independently by Heuveline (2003) in order to model the typical age pattern of HIV incidence by sex in Eastern Africa.…”

Modelling the demographic impact of HIV/AIDS in South Africa and the likely impact of interventions

“…In some cases, the results of these simple deterministic models have been incorporated into cohort component projection models such as DemProj (Stover 2004), in an attempt to estimate the demographic impact of HIV/AIDS more accurately. However, Heuveline (2003) identifies a number of problems with this approach, including failure to allow for changes in the age profile of HIV cases over the course of the epidemic, difficulties in incorporating the effect of HIV on fertility, and difficulties in establishing parameters for a cohort component projection model in a hypothetical 'no AIDS' scenario.…”