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

Abstract

Introduction: Several HIV 'risk scores' 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

Investigating inequalities in HIV testing in sub-Saharan Africa: spatial analysis of cross-sectional population-based surveys in 25 countries

“…Indeed, HIV prevalence drives, at least partly, the local risk of incident HIV infection as it reflects the probability for one's sexual partner to be infected by HIV. 23 We conducted a complete-case analysis. We excluded participants with missing values for the SEP indicator (ie, wealth index) and outcome variable (ie, recent HIV testing) at the national, province and PSU level analysis.…”

“…Community-level HIV prevalence has been found to be a strong predictor of HIV incidence in a recent meta-analysis. 23 The seemingly suboptimal efficiency of HIV testing programmes at subnational Open access levels suggest the failure of HIV programmes in some settings to reach those who are at higher risk of HIV. However, risk may have been affected when people with HIV (PWH) undergo ART which ensures viral suppression, thus preventing transmission.…”

“…Indeed, HIV prevalence drives, at least partly, the local risk of incident HIV infection as it reflects the probability for one’s sexual partner to be infected by HIV. 23 …”

The impact of prevention‐effective PrEP use on HIV incidence: a mathematical modelling study

“…In contrast, the Risk Group scenarios imply that a small sub‐population of individuals should be prioritized for PrEP use. Several risk‐scoring tools have been developed to identify high‐incidence subgroups based on individual‐level and geographic factors [53–55], which may allow for more targeted PrEP delivery programmes than the three risk groups included in our model and others. However, the predictive performance of these tools has been moderate, and even if such a subgroup can be identified and engaged in PrEP services, the population‐level impact is projected to be much smaller than if PrEP is used more generally due to the relatively smaller size of the high‐risk population.…”