Risk scores for predicting HIV incidence among the general population in sub-Saharan Africa: a systematic review and meta-analysis

Introduction: Several HIV 9risk scores9 have been developed to identify individuals for prioritised HIV prevention in sub-Saharan Africa. We systematically reviewed HIV risk scores to: (i) identify factors that consistently predicted incident HIV infection, (ii) review inclusion of community-level HIV risk in predictive models, and (iii) examine predictive performance. Methods: We systematically searched nine databases for studies developing and/or validating HIV risk scores among the general population in sub-Saharan Africa from database inception until February 15, 2021. Studies not prospectively observing seroconversion or recruiting only key populations were excluded. Record screening, data extraction, and critical appraisal were conducted in duplicate. We used random-effect meta-analysis to summarise hazard ratios and the area under the receiver-operating characteristic curve (AUC-ROC). Results: From 1563 initial search records, we identified 14 risk scores in 13 studies. Seven studies were among sexually active women using contraception enrolled in randomised-controlled trials, three among adolescent girls and young women (AGYW), and three among cohorts enrolling both men and women. Consistently identified HIV prognostic factors among women were younger age (pooled adjusted hazard ratio: 1.62 [95% Confidence Interval: 1.17, 2.23], compared to above-25), single/not cohabiting with primary partners (2.33 [1.73, 3.13]) and having sexually transmitted infections (STIs) at baseline (HSV-2: 1.67 [1.34, 2.09]; curable STIs: 1.45 [1.17; 1.79]). Among AGYW only STIs were consistently associated with higher incidence, but studies were limited (n=3). Community-level HIV prevalence or unsuppressed viral load strongly predicted incidence but were only considered in three of 11 multi-site studies. The AUC-ROC ranged from 0.56 to 0.79 on the model development sets. Only the VOICE score was externally validated by multiple studies, with pooled AUC-ROC 0.626 [0.588, 0.663] (I2: 64.02%). Conclusions: Younger age, non-cohabiting, and recent STIs were consistently identified as predicting future HIV infection. Both community HIV burden and individual factors should be considered to quantify HIV risk. However, HIV risk scores had only low-to-moderate discriminatory ability and uncertain generalizability outside of the study populations. Further evidence on the relative value of specific factors and data outside high-risk populations will help inform optimal implementation of risk scoring algorithms in HIV programmes. PROSPERO Number: CRD42021236367