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AbstractIntroductionAs the HIV field evolves to better serve populations which are diverse in risk and access to services, it is crucial to understand and adapt the conceptual tools used to make sense of the HIV pandemic. In this commentary, we discuss the concept of general population. Using a synthetic and historical review, we reflect on the genesis and usage of the general population in HIV research and programme literature, pointing to its moral connotations and its impact on epidemiologic reasoning.DiscussionFrom the early days of the HIV pandemic, the category of general population has carried implicit normative meanings. General population represented those people considered to be undeserving of HIV acquisition, and therefore deserving of a response. Framing the HIV epidemic in sub‐Saharan Africa as a generalized epidemic primarily affecting the general population has contributed to the exclusion of men who have sex with men from epidemic responses. The usage of this category has also masked heterogeneity among those it includes; the increasing focus on the use of interventions such as circumcision and HIV treatment as general population HIV prevention approaches has been marked by a lack of attention to heterogeneity among beneficiaries.ConclusionsWe recommend that the term general population be retired from the field's lexicon. HIV programmes should strengthen their capacity to describe the heterogeneity of those they serve and plan their interventions accordingly. To increase the efficiency and impact of the HIV response, it is urgent to stratify the category of general population by risk. Sexual networks are a promising basis for this stratification.

AbstractIntroductionScreening for acute and early HIV infections (AEHI) among men who have sex with men (MSM) remains uncommon in sub‐Saharan Africa (SSA). Yet, undiagnosed AEHI among MSM and subsequent failure to link to care are important drivers of the HIV epidemic. We conducted a systematic review and meta‐analysis of AEHI yield among MSM mobilized for AEHI testing; and assessed which risk factors and/or symptoms could increase AEHI yield in MSM.MethodsWe systematically searched four databases from their inception through May 2020 for studies reporting strategies of mobilizing MSM for testing and their AEHI yield, or risk and/or symptom scores targeting AEHI screening. AEHI yield was defined as the proportion of AEHI cases among the total number of visits. Study estimates for AEHI yield were pooled using random effects models. Predictive ability of risk and/or symptom scores was expressed as the area under the receiver operator curve (AUC).ResultsTwenty‐two studies were identified and included a variety of mobilization strategies (eight studies) and risk and/or symptom scores (fourteen studies). The overall pooled AEHI yield was 6.3% (95% CI, 2.1 to 12.4; I2 = 94.9%; five studies); yield varied between studies using targeted strategies (11.1%; 95% CI, 5.9 to 17.6; I2 = 83.8%; three studies) versus universal testing (1.6%; 95% CI, 0.8 to 2.4; two studies). The AUC of risk and/or symptom scores ranged from 0.69 to 0.89 in development study samples, and from 0.51 to 0.88 in validation study samples. AUC was the highest for scores including symptoms, such as diarrhoea, fever and fatigue. Key risk score variables were age, number of sexual partners, condomless receptive anal intercourse, sexual intercourse with a person living with HIV, a sexually transmitted infection, and illicit drug use. No studies were identified that assessed AEHI yield among MSM in SSA and risk and/or symptom scores developed among MSM in SSA lacked validation.ConclusionsStrategies mobilizing MSM for targeted AEHI testing resulted in substantially higher AEHI yields than universal AEHI testing. Targeted AEHI testing may be optimized using risk and/or symptom scores, especially if scores include symptoms. Studies assessing AEHI yield and validation of risk and/or symptom scores among MSM in SSA are urgently needed.

AbstractIntroductionIn Kenya, men who have sex with men (MSM) are increasingly using virtual sites, including web‐based apps, to meet sex partners. We examined HIV testing, HIV prevalence, awareness of HIV‐positive status and linkage to antiretroviral therapy (ART), for HIV‐positive MSM who solely met partners via physical sites (PMSM), compared with those who did so in virtual sites (either solely via virtual sites (VMSM), or via both virtual and physical sites (DMSM)).MethodsWe conducted a cross‐sectional bio‐behavioural survey of 1200 MSM, 15 years and above, in three counties in Kenya between May and July 2019, using random sampling of physical and virtual sites. We classified participants as PMSM, DMSM and VMSM, based on where they met sex partners, and compared the following between groups using chi‐square tests: (i) proportion tested; (ii) HIV prevalence and (iii) HIV care continuum among MSM living with HIV. We then performed multivariable logistic regression to measure independent associations between network engagement and HIV status.Results177 (14.7%), 768 (64.0%) and 255 (21.2%), of participants were classified as PMSM, DMSM and VMSM respectively. 68.4%, 70.4% and 78.5% of PMSM, DMSM and VMSM, respectively, reported an HIV test in the previous six months. HIV prevalence was 8.5% (PMSM), 15.4% (DMSM) and 26.7% (VMSM), p < 0.001. Among those living with HIV, 46.7% (PMSM), 41.5% (DMSM) and 29.4% (VMSM) were diagnosed and aware of their status; and 40.0%, 35.6% and 26.5% were on antiretroviral treatment. After adjustment for other predictors, MSM engaged in virtual networks remained at a two to threefold higher risk of prevalent HIV: VMSM versus PMSM (adjusted odds ratio 3.88 (95% confidence interval (CI) 1.84 to 8.17) p < 0.001); DMSM versus PMSM (2.00 (95% CI 1.03 to 3.87), p = 0.040).ConclusionsEngagement in virtual networks is associated with elevated HIV risk, irrespective of individual‐level risk factors. Understanding the difference in characteristics among MSM‐seeking partners in different sites will help HIV programmes to develop subpopulation‐specific interventions.