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

Abstract

This paper describes an approach to incorporating the impact of HIV/AIDS and the effects of HIV/AIDS prevention and treatment programmes into a cohort component projection model of the South African population. The modelled HIV-positive population is divided into clinical and treatment stages, and it is demonstrated that the age profile and morbidity profile of the HIV-positive population is changing significantly over time. HIV/AIDS is projected to have a substantial demographic impact in South Africa. Prevention programmes -social marketing, voluntary counselling and testing, prevention of mother-to-child transmission and improved treatment for sexually transmitted diseases -are unlikely to reduce AIDS mortality significantly in the short term. However, more immediate reductions in mortality can be achieved when antiretroviral treatment is introduced. IntroductionSouth Africa is one of the few African countries with nationally representative HIV prevalence data and good vital registration data (Department of Health 2004;Bradshaw et al. 2004). However, these data cannot provide planners with a direct measure of the demographic impact of HIV/AIDS or an indication of the likely future evolution of the epidemic. For this, mathematical models, calibrated to these data, are necessary. These models, if appropriately constructed, can also be used to assess the likely effect of different prevention and treatment programmes, as well as likely needs for treatment and orphan care, and are therefore an important tool in policy formulation. A large number of mathematical models have been developed to simulate the impact of HIV/AIDS and the likely effect of prevention and treatment programmes. These models can be classified into two broad groups: individual-based stochastic simulation models, which randomly generate events such as infection and death for each individual in the population; and deterministic models, which typically divide the population into cohorts of individuals, and compute average numbers of events in each cohort on the assumption that all individuals in a cohort share the same characteristics.Of the stochastic models that have been developed, most have focussed on simulating the effects of HIV prevention rather than treatment (Korenromp et al. 2000;Van der Ploeg et al. 1998;Bracher, Santow and Cotts Watkins 2004;Robinson et al. 1995). Because of the heavy computational requirements associated with individualbased simulation, populations simulated are typically limited in size to 10 000 to 20 000 individuals. This results in a significant amount of stochastic variation, which makes it difficult to calibrate the model to HIV prevalence and mortality data (Korenromp et al. 2000). In addition, these models require an extensive range of assumptions as input.Deterministic models tend to be used for larger populations and in situations where data for setting assumptions are limited. Many of these models have been used to illustrate the differences between the effects of HIV prevention and treatment prog...

Critique of early models of the demographic impact of HIV/AIDS in sub-Saharan Africa based on contemporary empirical data from Zimbabwe

“…Over time, our understanding of the spread of HIV and its impact on fertility and mortality has improved and has been included in more recent models (5)(6)(7). Model predictions can be compared with empirical estimates of demographic impact obtained in surveys, census, and vital registration (8).…”

Parent absenteeism and adolescent work in South Africa: An analysis of the levels and determinants of adolescents who work 10 or more hours a week

“…Of all the population groups, Black/African adolescents are more affected by parent absenteeism than any other race in South Africa. Johnson and Dorrington (2001) found that the proportion of South African orphans who are Black/African is higher than the proportion of orphans in any other population group. The authors attribute this to the larger number of AIDS deaths among the Black/African population as well as higher levels of fertility among this group in the country (Johnson and Dorrington, 2001).…”

“…Johnson and Dorrington (2001) found that the proportion of South African orphans who are Black/African is higher than the proportion of orphans in any other population group. The authors attribute this to the larger number of AIDS deaths among the Black/African population as well as higher levels of fertility among this group in the country (Johnson and Dorrington, 2001). Employment inequalities and labour migration patterns of the Black/African population could also explain the large numbers of adolescents experiencing parent absenteeism.…”

Sexual behaviour patterns in South Africa and their association with the spread of HIV: insights from a mathematical model

“…The proportion of individuals who start HAART changes over time, and has been set at the same levels as estimated in the ASSA2003 AIDS and Demographic model, based on reported numbers of patients on HAART in the South African private and public health sectors . The average length of time spent in each stage is calculated by assuming an average total survival time of 11.5 years, in the absence of antiretroviral treatment, and multiplying this by estimated proportions of untreated HIV survival time spent in different WHO clinical stages (Johnson, Dorrington, and Matthews 2007;Johnson and Dorrington 2006). These are shown in Table 2.…”

“…First, there is often a lack of reliable sexual behaviour data for setting the sexual behaviour parameters in these models. In South Africa, for example, hardly any nationally representative sexual behaviour data were collected prior to 2000 (Eaton, Flisher, and Aarø 2003), and as a result, many early models of the HIV/AIDS epidemic in South Africa were forced to rely on fairly arbitrary assumptions about sexual behaviour (Doyle and Millar 1990;Schall 1990;Groeneveld and Padayachee 1992;Johnson and Dorrington 2006).…”