By lf johnson and re dorrington



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THE DEMOGRAPHIC AND EPIDEMIOLOGICAL IMPACT OF HIV/AIDS TREATMENT AND PREVENTION PROGRAMMES: AN EVALUATION BASED ON THE ASSA2000 MODEL
By LF Johnson and RE Dorrington
Presented at the 2002 Demographic Association of Southern Africa Conference
Do not quote without authors’ consent.

ABSTRACT
The purpose of this paper is to present a model for assessing the demographic and epidemiological impacts of a range of HIV/AIDS treatment and prevention programmes in South Africa. The model on which this analysis is based is the ‘lite’ version of the ASSA2000 AIDS and Demographic model. A number of adaptations were made to the model prior to the development of intervention-specific components. Following this, four prevention and treatment programmes were modelled: improvement to treatment for sexually transmitted diseases, voluntary counselling and testing, mother-to-child transmission prevention and antiretroviral treatment. Results suggest that all four programmes can significantly reduce the level of new HIV infections. However, prevention programmes alone will not address the short-term consequences of the epidemic in terms of mortality, morbidity and orphanhood.

KEYWORDS
HIV; AIDS; intervention; prevention; treatment; model

CONTACT DETAILS


Leigh Johnson, Centre for Actuarial Research, Department of Actuarial Science, Leslie Commerce Building, University of Cape Town, Private Bag, Rondebosch 7701. Tel: (021) 650 5761. Fax: (021) 689 7580. E-mail:

ljohnson@commerce.uct.ac.za.

1. INTRODUCTION
1.1 In the early years of South Africa’s AIDS epidemic, AIDS models were used primarily as a means of alerting policy-makers to the threats facing the country. Projections were commonly made on the assumption that there would be no change in sexual behaviour and no significant health interventions in response to the epidemic. Little emphasis was placed on the use of AIDS models in the development of solutions to the problems presented by the AIDS epidemic.
1.2 Of the HIV/AIDS intervention models that have been developed to date, many suffer from a variety of limitations. Many are capable only of static, short-term analyses. Others do not allow for investigation into the interactions that may be expected between intervention programmes. Almost all are based on hypothetical parameter changes, for which there is little empirical evidence. Our objective has therefore been to develop a model that overcomes these limitations, and provides a holistic perspective on the treatment and prevention of AIDS.
1.3 The purpose of this paper is to present a model for assessing the demographic and epidemiological impacts of a range of HIV/AIDS treatment and prevention programmes in South Africa. Thus far, four prevention and treatment programmes have been modelled: improvement to treatment for sexually transmitted diseases (STDs), voluntary counselling and testing (VCT), mother-to-child transmission prevention (MTCTP) and antiretroviral treatment (ART).
1.4 Although the results are provisional, it is anticipated that they will be useful in formulating health policy. This paper summarizes the findings of a more detailed report (Johnson and Dorrington, 2002). The interested reader is referred to this report for more detailed discussion of the modelling issues and the key components of intervention programmes.

2. PREREQUISITES FOR AN INTERVENTIONS MODEL
The model on which this study is based is the ASSA2000 AIDS and Demographic model. The modelling of interventions necessitated four major adaptations to this model prior to the development of the intervention-specific components. Before describing these four adaptations, we provide a background description of the ASSA2000 model.
2.1 BACKGROUND TO THE ASSA2000 MODEL
2.1.1 The ASSA2000 AIDS and Demographic model is a spreadsheet model that is used to project the future demographic impact of a heterosexual HIV/AIDS epidemic on a population, and to calculate various HIV/AIDS statistics. The fundamental assumption of the ASSA2000 model is that the adult population can be divided into four ‘risk groups’ that represent different levels of sexual activity and different risks of HIV transmission. In descending order of level of sex activity, these four groups are referred to as PRO, STD, RSK and NOT.
2.1.2 The parameters in the model are set in such a way as to enable the model to reproduce observed levels of mortality and HIV prevalence. The major source of information on HIV prevalence is the annual antenatal clinic surveys conducted by the Department of Health. Figure 1 shows the match between the levels of prevalence estimated in the surveys of pregnant women attending public antenatal clinics, and the levels of prevalence estimated by the adapted ASSA2000 model for pregnant women attending public sector clinics. Although the current model estimates of prevalence are close to the current levels of prevalence being observed in the antenatal surveys, there has been a deliberate attempt to produce estimates of prevalence that are lower than those observed in the antenatal surveys in the earlier years of the epidemic, to allow for a bias towards urban areas in the early antenatal clinic survey results (Webb, 1994).

Figure 1: Estimated and observed levels of prevalence among women attending public antenatal clinics


2.1.3 Attempts have also been made to ensure correspondence between the level of mortality reported in South Africa (Dorrington et al, 2001) and that estimated by the adapted ASSA2000 model. Figures 2(a) and (b) show that the model produces an acceptably close fit to the levels of mortality estimated by Dorrington et al.

Figure 2: Male and female mortality in 1999/2000


2.1.4 The model developments described below are based on the ‘lite’ version1 of the ASSA2000 model, which ignores racial and inter-regional differences in HIV prevalence. The adapted model is not yet publicly available. However, some of the adaptations presented in this paper will be incorporated in ASSA2001, the next version of the model due for release.
2.2 DIVISION OF THE HIV POSITIVE POPULATION BETWEEN THE PRIVATE AND PUBLIC HEALTH SECTORS
2.2.1 The costs of the interventions considered in this document will ultimately be divided between state and private providers of healthcare. In order to determine the extent to which these costs are borne by each, it is necessary to estimate the relative sizes of the insured (medical scheme) and uninsured (non-medical scheme) populations, and the relative levels of HIV prevalence in the two groups. The term ‘non-medical scheme population’ is used to refer to individuals who are not beneficiaries of medical schemes.
2.2.2 Rama and McLeod (2001) estimate that in 1991 roughly 17% of South Africa’s population were covered by medical schemes. This proportion fell to roughly 16% in 1999. In the ASSA2000 model it is assumed, for the sake of simplicity, that the initial (1985) proportion of the adult population in the medical scheme population is 17%, that 13% of all births2 are to women in medical schemes, and that 13% of individuals under the age of 14 are covered by medical schemes. This reflects the lower levels of fertility among women in medical schemes relative to women of lower socio-economic status. It is further assumed that on reaching age 14, 17% of individuals will either already be beneficiaries of medical schemes, or will join medical schemes in the near future.
2.2.3 As part of a separate exercise, the HIV prevalence in the medical scheme population was estimated using estimates of the demographic profile of the medical scheme population (based on data from the 1998 October Household Survey, the 1996 Census, and the September 2001 Labour Force Survey), and unpublished estimates of HIV prevalence by age, gender, race and skill level (currently being worked on by CARE). The method and results of this exercise are discussed in more detail in Appendix A.
2.2.4 The model parameters for the medical scheme population have been set to reproduce roughly the levels of HIV prevalence estimated in Appendix A. As shown in Appendix A, a range of future prevalence levels are possible in the medical scheme population, depending on legislative and socio-economic conditions. The objective has therefore been to set the parameters to obtain a prevalence curve lying roughly mid-way between the prevalence estimates for scenarios A and C, the two most extreme scenarios considered.
2.2.5 HIV prevalence levels in the non-medical scheme population are based on the prevalence of women attending public antenatal clinics (most of whom, it is assumed, are not medical scheme beneficiaries). Parameters for the non-medical scheme population are set so as to reproduce these levels of HIV prevalence for pregnant women.
2.3 DIVISION OF THE HIV POSITIVE POPULATION ACCORDING TO STAGE OF DISEASE
2.3.1 The modelling of the proportions of the HIV positive population in the various stages of HIV disease is significant for a number of reasons. Most importantly, it provides information on the burden of disease in the population, and the extent to which this burden of disease is reduced is a critical indicator of success for any HIV/AIDS intervention. In addition, the modelling of disease stage allows for improved accuracy, as levels of infectivity and sexual activity are known to vary by stage of disease and duration of infection.
2.3.2 In the original version of the ASSA2000 model, survival was modelled in aggregate using a Weibull distribution (Dorrington, 2000). In the model developed here, adults are assumed to progress through four stages of HIV infection before dying from AIDS. These four stages correspond to those defined in the WHO Clinical Staging System3, and the time spent in each stage is modelled using a Weibull distribution. The terms spent in each stage were determined from a review of studies conducted locally and internationally. This review, as well as the procedure used to set the median and shape parameters in each stage, is described in Appendix B. Table 1 shows the median and shape parameters assumed for each stage.

WHO

Age at infection


Clinical

14 - 24

25 - 34

> 34

Stage

Median

Shape

Median

Shape

Median

Shape

1

2.84

1.87

2.63

1.81

2.42

1.76

2

1.78

1.22

1.65

1.21

1.52

1.19

3

3.98

2.67

3.70

2.56

3.42

2.45

4

1.37

1.00

1.28

1.00

1.19

1.00

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