Identifying 'corridors of HIV transmission' in a severely affected rural South African population: a case for a shift toward targeted prevention strategies

Abstract

Background: In the context of a severe generalized African HIV epidemic, the value of geographically targeted prevention interventions has only recently been given serious consideration. However, to date no study has performed a population-based analysis of the micro-geographical clustering of HIV incident infections, limiting the evidential support for such a strategy.

Methods: We followed 17 984 HIV-uninfected individuals aged 15-54 in a population-based cohort in rural KwaZulu-Natal, South Africa, and observed individual HIV sero-conversions between 2004 and 2014. We geo-located all individuals to an exact homestead of residence (accuracy <2 m). We then employed a two-dimensional Gaussian kernel of radius 3 km to produce robust estimates of HIV incidence which vary across continuous geographical space. We also applied Tango's flexibly shaped spatial scan statistic to identify irregularly shaped clusters of high HIV incidence.

Results: Between 2004 and 2014, we observed a total of 2 311 HIV sero-conversions over 70 534 person-years of observation, at an overall incidence of 3.3 [95% confidence interval (CI), 3.1-3.4] per 100 person-years. Three large irregularly-shaped clusters of new HIV infections (relative risk = 1.6, 1.7 and 2.3) were identified in two adjacent peri-urban communities near the National Road (P = 0.001, 0.015) as well as in a rural node bordering a recent coal mine development (P = 0.020), respectively. Together the clusters had a significantly higher age-sex standardized incidence of 5.1 (95% CI, 4.7-5.6) per 100 person-years compared with a standardized incidence of 3.0 per 100 person-years (95% CI, 2.9-3.2) in the remainder of the study area. Though these clusters comprise just 6.8% of the study area, they account for one out of every four sero-conversions observed over the study period.

Conclusions: Our study has revealed clear 'corridors of transmission' in this typical rural, hyper-endemic population. Even in a severely affected rural African population, an approach that seeks to provide preventive interventions to the most vulnerable geographies could be more effective and cost-effective in reducing the overall rate of new HIV infections. There is an urgent need to develop and test such interventions as part of an overall combination prevention approach.

Figure 1

Age-sex standardized HIV incidence by year for individuals aged 15–54 [70 534 person-years (PY), 2311 sero-conversions at crude incidence of 3.3 per 100 PY].

Figure 2

Female and male age variations in HIV incidence (95% CI) for entire sample of repeat-testers aged 15–54. Superimposed on the graphs are log-normal functions (obtained by maximum likelihood) fitted to 1-year incidence estimates.

Figure 3

Geographical variations in population-level HIV incidence (ages 15–54) as measured by a standard Gaussian kernel (3.0 km radius). Superimposed on the map are the high-risk clusters identified by the Tango's flexibly shaped spatial scan statistic: Cluster 1 [322 sero-conversions, 6233 person-years of observation (PYO), RR = 1.59, P = 0.001, area = 8.3 km²]; Cluster 2 (185 sero-conversions, 3630 PYO, RR = 1.57, P = 0.015, area = 12.7 km²); and Cluster 3 (64 sero-conversions, 891 PYO, RR = 2.27, P = 0.020, area = 9.4 km²). Phase 1 of a recent opencast coal mining development is shown immediately north of cluster 3.

Figure 4

Geographical variations in population-level HIV incidence (ages 15–54) in females (a) and males (b), as measured by a standard Gaussian kernel (3.0 km radius). Superimposed on the map are the high-risk clusters identified by the Tango's flexibly shaped spatial scan statistic: (a) Cluster 1 = 296 sero-conversions, RR = 1.52, P = 0.002; Cluster 2 = 91 sero-conversions, RR = 1.57, P = 0.081; and (b) Cluster = 131 sero-conversions, RR = 1.92, P = 0.001.

Figure 5

Estimated HIV sero-conversions per km² per year for population aged 15–54, obtained using the Gaussian kernel (radius = 3.0 km). The Z axis is proportional to the total HIV sero-conversions per km² per annum for any geographical location.