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. 2017 Jul 28;18(1):695.
doi: 10.4102/sajhivmed.v18i1.695. eCollection 2017.

HIV epidemic drivers in South Africa: A model-based evaluation of factors accounting for inter-provincial differences in HIV prevalence and incidence trends

Leigh F Johnson  1 , Rob E Dorrington  2 , Haroon Moolla  1
Affiliations

HIV epidemic drivers in South Africa: A model-based evaluation of factors accounting for inter-provincial differences in HIV prevalence and incidence trends

Leigh F Johnson et al. South Afr J HIV Med. .

Abstract

Background: HIV prevalence differs substantially between South Africa's provinces, but the factors accounting for this difference are poorly understood.

Objectives: To estimate HIV prevalence and incidence trends by province, and to identify the epidemiological factors that account for most of the variation between provinces.

Methods: A mathematical model of the South African HIV epidemic was applied to each of the nine provinces, allowing for provincial differences in demography, sexual behaviour, male circumcision, interventions and epidemic timing. The model was calibrated to HIV prevalence data from antenatal and household surveys using a Bayesian approach. Parameters estimated for each province were substituted into the national model to assess sensitivity to provincial variations.

Results: HIV incidence in 15-49-year-olds peaked between 1997 and 2003 and has since declined steadily. By mid-2013, HIV prevalence in 15-49-year-olds varied between 9.4% (95% CI: 8.5%-10.2%) in Western Cape and 26.8% (95% CI: 25.8%-27.6%) in KwaZulu-Natal. When standardising parameters across provinces, this prevalence was sensitive to provincial differences in the prevalence of male circumcision (range 12.3%-21.4%) and the level of non-marital sexual activity (range 9.5%-24.1%), but not to provincial differences in condom use (range 17.7%-21.2%), sexual mixing (range 15.9%-19.2%), marriage (range 18.2%-19.4%) or assumed HIV prevalence in 1985 (range 17.0%-19.1%).

Conclusion: The provinces of South Africa differ in the timing and magnitude of their HIV epidemics. Most of the heterogeneity in HIV prevalence between South Africa's provinces is attributable to differences in the prevalence of male circumcision and the frequency of non-marital sexual activity.

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Conflict of interest statement

The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.

Figures

FIGURE 1
FIGURE 1
Male circumcision rates and posterior estimates of sexual behaviour parameters, initial HIV prevalence and antenatal bias. Panel (a) shows the modelled prevalence of male circumcision in men aged 15–49 years in 2000 (prior to MMC promotion campaigns); (b) Multiplicative adjustment to high risk proportion; (c) Sexual mixing parameter; (d) Multiplicative adjustment to condom usage; (e) Initial HIV prevalence in women aged 15–49 years (initial HIV prevalence in high-risk women multiplied by the fraction of women in the high-risk group); (f) Antenatal bias (on logit scale). Panels (b)–(f) show posterior means of the input parameters for which prior distributions have been specified (Table 1), and error bars represent the 95% confidence intervals from the posterior distributions. In all panels, the dashed line represents the national average.
FIGURE 2
FIGURE 2
HIV prevalence levels in pregnant women attending public antenatal clinics: (a) Eastern Cape; (b) Free State; (c) Gauteng; (d) KwaZulu-Natal; (e) Limpopo; (f) Mpumalanga; (g) Northern Cape; (h) North West; (i) Western Cape. Dark blue lines represent posterior means and shaded light blue areas represent posterior 95% confidence intervals (model estimates have been adjusted to reflect the modelled antenatal bias). Dots represent antenatal survey estimates (95% confidence intervals for survey estimates prior to 1998 are not shown, as the reported confidence intervals did not account for survey design effects).
FIGURE 3
FIGURE 3
HIV incidence: (a) and prevalence (b) trends in 15–49-year-olds. Lines represent posterior means (95% confidence intervals not shown).
FIGURE 4
FIGURE 4
Effect on adult HIV prevalence (15–49 years) in the national HIV model of substituting province-specific parameter values: (a) Substituting provincial marriage rates; (b) Substituting provincial sexual mixing parameters; (c) Substituting provincial high risk proportions; (d) Substituting provincial initial HIV prevalence levels; (e) Substituting provincial rates of condom use; (f) Substituting provincial male circumcision rates. For panels (b)–(e), province-specific parameters substituted into the national model are the posterior means shown in Figure 1.
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References

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