Predicting the Risk of Human Immunodeficiency Virus Type 1 (HIV-1) Acquisition in Rural South Africa Using Geospatial Data

Abstract

Background Accurate HIV risk assessment can guide optimal HIV prevention. We evaluated the performance of risk prediction models incorporating geospatial measures. Methods We developed and validated HIV risk prediction models in a population-based cohort in South Africa. Individual-level covariates included demographic and sexual behavior measures, and geospatial covariates included community HIV prevalence and viral load estimates. We trained models on 2012-2015 data using LASSO Cox models and validated predictions on 2016-2019 data. We compared full models to simpler models restricted to only individual-level covariates or only age and geospatial covariates. We compared the spatial distribution of predicted risk to that of high incidence areas (≥ 3/100 person-years (PY)). Results Our analysis included 19,556 individuals contributing 44,871 PY and 1,308 seroconversions. Incidence among the highest predicted risk quintile using the full model was 6.6/100 PY (women) and 2.8/100 PY (men). Models using only age group and geospatial covariates had similar performance (women: AUROC = 0.65, men: AUROC = 0.71) to the full models (women: AUROC = 0.68, men: AUROC = 0.72). Geospatial models more accurately identified high incidence regions than individual-level models; the 20% of the study area with the highest predicted risk accounted for 60% of the high incidence areas when using geospatial models but only 13% using models with only individual-level covariates. Conclusions Geospatial models with no individual measures other than age group predicted HIV risk nearly as well as models that included detailed behavioral data. Geospatial models may help guide HIV prevention efforts to individuals and geographic areas at highest risk.

Risk scores for predicting HIV incidence among adult heterosexual populations in sub‐Saharan Africa: a systematic review and meta‐analysis

“…The majority (10 of 13) were RCTs or quasi‐experimental studies that restricted recruitment and/or eligibility to specific at‐risk population groups: (1) sexually active, contraception‐seeking women who attended the family‐planning, STIs or research clinics (7 of 10, all RCTs) [8,9,24–28], or (2) school‐attending AGYW (3 of 10) [29–31]. The remaining three were large‐scale cohort studies or community trials that recruited all consenting members within the communities [32–34]. Geographic locations, study periods, age groups and settings are given in Table 1.…”

“…Final regression results were also not available for Roberts et al. (abstract only) [34]. For the remaining studies, Table 2 reports the predictors considered for inclusion and retained in the final model for each independently developed score.…”

“…Only 3 of 11 multi‐site studies considered community‐level HIV prevalence as a covariate, and in all the three studies it was selected into one or more of the final models [9,32,34]. In Peebles et al.…”

“…Roberts et al. [34] also found community HIV prevalence and unsuppressed viral load to be highly predictive, but aHRs were not available.…”

“… b AUC‐ROC was based on a “testing” set different from the “training” set for which the score was derived. Wand (2012) [24] and Wand (2018) [25] did a random split of data sets, while Roberts [34] split the data by time periods (2012–2015 vs. 2016–2019).…”