Predicting the Risk of Human Immunodeficiency Virus Type 1 (HIV-1) Acquisition in Rural South Africa Using Geospatial Data
Year: 2022
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Accurate human immunodeficiency virus (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 in 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). RESULTS: Our analysis included 19 556 individuals contributing 44 871 person-years and 1308 seroconversions. Incidence among the highest predicted risk quintile using the full model was 6.6/100 person-years (women) and 2.8/100 person-years (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; 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.
Source: Clinical Infectious Diseases
DOI: 10.1093/cid/ciac069
Institutions: University of Washington, University of Cincinnati, Heidelberg University, Heidelberg Institute of Global Health, Africa Health Research Institute, South African Population Research Infrastructure Network, Centre for the AIDS Programme of Research in South Africa, University of Lincoln, University of KwaZulu-Natal, Gates Foundation, Institute for Disease Modeling
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,320
Funders: Bill and Melinda Gates Foundation, Fred Hutchinson Cancer Research Center, University of Washington, London School of Hygiene and Tropical Medicine, Regeneron Pharmaceuticals, Wellcome Trust, Academy of Medical Sciences, South African Medical Research Council, National Institutes of Health, Center for AIDS Research, University of Washington, University of California, San Francisco, Medical Research Council, National Institute on Aging, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Institute on Drug Abuse, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Child Health and Human Development
Awards: Estimating the cost-effectiveness of HIV pre-exposure prophylaxis delivery through maternal and child health clinics in Kenya, G1323885547, G1453795850, G2219438539, Africa Health Research Institute (GBP record) 6 month partial award, G3407947818, G3985073149, G4160532866, G4393346530, G4847806209, G6106727576, Africa Health Research Institute (GBP record), G726642961, G7791962275, G7889379128, G8475355304, G872593607, G9000186388, University of Washington/Fred Hutch Center for AIDS Research
OpenAlex ID: W4210503295