Faculty Bibliography

BACKGROUND:Characterising disparities in HIV infection across populations by gender, age, and HIV risk is key information to guide intervention priorities. We aimed to assess how indicators measuring HIV acquisitions, transmissions, or potential long-term infections influence estimates of the contribution of different populations to new infections, including key populations (including female sex workers, their clients, men who have sex with men). METHODS:) measured the proportion of new infections averted if transmission involving a specific population was blocked over a specific time period. We compared estimates of the four indicators across seven populations and 15 settings and assessed if the contribution of specific populations ranked differently across indicators for ten settings. FINDINGS/RESULTS:), whereas more infections were transmitted than acquired in non-key population men aged 25 years and older (median 1·4 times more) and clients of female sex workers (1·6 times more) in all but one model. Estimates of the 10-year tPAFs accounting for transmission in the long-term were substantially larger than the direct transmission indicator for all populations, especially for female sex workers (2·0 times higher). INTERPRETATION/CONCLUSIONS:Indicators that reflect HIV acquisitions and transmissions in the short and long term can be used to capture the complexity of HIV epidemics across different populations and timeframes. The added nuance would improve the effectiveness of the HIV prevention response across all populations at risk. FUNDING/BACKGROUND:US National Institutes of Health and UK Medical Research Council. TRANSLATION/UNASSIGNED:For the French translation of the abstract see Supplementary Materials section.

Extreme heat events are a growing health threat, but their impact is heterogeneous because different settings have different levels of heat adaptation. Previous reviews have assessed morbidity and mortality as a function of meteorological conditions such as air temperature. We aimed to conduct the first systematic review and meta-analysis of the association between meeting local definitions of extreme heat events and risks of hospitalization and mortality overall and by population segment. We searched PubMed, Web of Science, Scopus, ScienceDirect, and Google Scholar on February 05, 2025, with no restrictions on language or publication date. Data extraction and risk of bias assessment were conducted by multiple reviewers. We estimated the risk ratio (RR) for hospitalization and mortality when meeting local definitions of extreme heat events using a random effects model to account for heterogeneity. In subgroup analysis, we examined variations by date, global region, country income group, extreme heat event definition type (absolute thresholds, percentile-based, composite indices), and population characteristics including age, socioeconomic context, and urban versus rural settings. In sensitivity analysis, we assessed the robustness of results by excluding lower-quality studies and applying alternative regression models. From 6,015 initial records, 21 studies (n = 126,930,288 individuals) met inclusion criteria. The pooled RR for mortality associated with extreme heat events was 1.24 (95% CI: 1.06-1.46) with substantial heterogeneity across studies (I² = 99.8%), which was explored through subgroup, meta-regression, and sensitivity analysis. The meta-regression showed a significant increase in RR over time (p < 0.05). No significant publication bias was detected (Begg's test, p = 0.458). In subgroup analysis, higher RRs were observed in studies from Europe (RR range: 1.16-4.24) and low- and middle-income countries, in older adults (RR range: 1.16-2.24), in urban populations, in older populations, and in studies using absolute temperature thresholds or composite indices. Findings were similar when excluding lower-quality studies and applying alternative regression models. Extreme heat events were significantly associated with mortality, with risks increasing over time and elevated among older adults and urban populations. Municipal and health authorities should prioritize extreme heat adaptation, as mortality is likely to increase as the climate warms.

INTRODUCTION/BACKGROUND:Depression is the most common mental disorder in sub-Saharan Africa (SSA). Group interpersonal therapy (IPT-G) is a recommended first-line treatment for depression, shown to be safe and effective in clinical trials. However, less is known about its real-world retention and effectiveness when delivered at scale in SSA. We describe retention patterns and associated factors in a large IPT-G programme in Uganda and Zambia. METHODS:test. RESULTS:Among 45 349 clients, overall average attendance was 82%. Four classes emerged from attendance patterns: high attendance (63%), moderate attendance (27%), early dropout (6%) and late dropout (4%). Relative to the high attendance class, the early drop-out class had higher odds of being age <25 (adjusted OR (aOR) 1.19, 95% CI 0.99 to 1.44), in teletherapy (aOR 3.46, 95% CI 2.89 to 4.13) and presenting with moderate than moderately severe to severe depression (aOR 1.27, 95% CI 1.17 to 1.40). The overall mean reduction in PHQ-9 scores was 13.0 (SD 4.2), but the early drop-out group showed smaller improvements (10.32, SD 8.78) compared with the high attendance group (13.3, SD 6.15, p=0.001). CONCLUSION/CONCLUSIONS:Retention in a scaled IPT-G programme in Uganda and Zambia was high. Still, early dropout-linked to younger age, teletherapy and moderate depression-was associated with less reduction in depressive symptoms. These findings highlight areas for innovation in IPT-G implementation to improve engagement and outcomes.

BACKGROUND:The USA has traditionally been the largest donor to health programmes in low-income and middle-income countries (LMICs). In January 2025, almost all such funding was stopped and prospects for its resumption are uncertain. The suddenness of the funding cuts makes it difficult for national health programmes in LMICs to adapt. We aimed to estimate the impact of these cuts on deaths and other outcomes (new infections, number of family planning users, and unplanned pregnancies) for four health areas that have been a focus of a substantial amount of US foreign assistance: HIV, tuberculosis, family planning, and maternal and child health. METHODS:We applied established mathematical models to the countries receiving US foreign assistance in each domain to estimate health impacts over the period 2025 to 2030. We used six models of HIV, three different approaches to estimate family planning impact, and one model each for tuberculosis and maternal and child health, applying these models to as many as 80 countries. We compared model projections assuming constant funding (status quo) with projections assuming complete elimination of US funding in each country. Some models also considered partial cuts or restoration of funding over time. FINDINGS/RESULTS:A complete cessation of US funding without replacement by other sources would lead to drastic increases in deaths from 2025 to 2030: 4·1 million (range 1·6-6·6) additional AIDS-related deaths across 55 countries, 606 900 (95% uncertainty interval [UI] 466 000-768 800) additional tuberculosis deaths across 79 countries, 40-55 million additional unplanned pregnancies and 12-16 million unsafe abortions across 51 countries, and 2·5 million (1·3-4·5) additional child deaths from causes other than HIV and tuberculosis across 24 countries. Restoration of funding for HIV treatment but not prevention would avoid most of the increase in deaths but still result in nearly 1 million more new HIV infections from 2025 to 2030. INTERPRETATION/CONCLUSIONS:Substantial progress has been made in improving global health in the past few decades. This progress has strengthened hope in reaching global development goals. However, the recent funding cuts threaten to change these trajectories and could lead to sharp increases in avoidable mortality for the poorest countries. Even a partial restoration of US funding would combat the most severe effects and provide time for countries that have received substantial US foreign assistance to adjust to the new funding landscape. FUNDING/BACKGROUND:Economic and Social Research Council; Engineering and Physical Sciences Research Council; European and Developing Countries Clinical Trials Partnership; Gates Foundation; Global Fund to Fight AIDS, Tuberculosis, and Malaria; Open Philanthropy; UK Foreign, Commonwealth & Development Office; UK Medical Research Council; UN Population Fund; UNAIDS; US National Institute of Allergy and Infectious Diseases; University of Edinburgh; US National Institutes of Health; US President's Emergency Plan for AIDS Relief; Wellcome Trust; World Bank; WHO.

OBJECTIVES/OBJECTIVE:To compare the impact of different HIV self-testing (HIVST) distribution modalities on population-level HIV outcomes. DESIGN/METHODS:Mathematical modelling study. SETTING/METHODS:Six counties in western Kenya. METHODS:We projected population-level HIV outcomes among individuals aged 15+over 30 years (2022-2052) using EMOD-HIV, an agent-based network transmission model calibrated to the HIV epidemic in western Kenya. We simulated the impact of three HIVST distribution modalities: (1) secondary distribution to male partners via women who attend antenatal care visits ('ANC'); (2) secondary distribution to partners of individuals newly diagnosed with HIV at health facilities ('partner services'); and (3) distribution to any individuals attending outpatient clinics ('outpatient'). We informed our model assumptions on HIV testing uptake for each HIVST distribution modality using the estimates from a meta-analysis of randomised clinical trials published between 2006 and 2019 and compared the outcomes for each HIVST distribution modality to standard testing without HIVST. OUTCOMES MEASURES/METHODS:The number of HIV tests performed (HIVST and non-HIVST), HIV diagnoses, HIV infections, and HIV-related deaths. RESULTS:With standard testing alone, the average number of HIV tests was 4.69 million per year, amounting to 81.0 tests per 100 adults. The average number of tests per year increased by 2.9% with ANC, 0.6% with partner services, and 23.7% with outpatient distribution of HIVST. Compared with standard testing alone, partner services with HIVST will avert the largest number of new HIV infections (10.2%, 95% CI 9.9% to 10.5%), followed by outpatient distribution (8.5%, 95% CI 8.2% to 8.7%) and ANC (6.1%, 95% CI 5.8% to 6.3%). Compared with standard testing, the number of HIVST needed per one additional HIV infection averted was 216 with ANC, 17 with partner services and 2009 with outpatient, while the number of HIVST per one additional HIV-related death averted was 364 with ANC, 17 with partner services and 3851 with outpatient. CONCLUSIONS:Secondary distribution of HIVST to partners of individuals newly diagnosed with HIV could prevent the most HIV infections and HIV-related deaths. HIVST can be an important strategy to improve uptake of testing and long-term population-level health effects.

BACKGROUND:Communities hardest-hit by early SARS-CoV-2 outbreaks accrued more immunity, but prioritizing these communities for vaccination could reduce health disparities. Optimal vaccine allocation depends on inequality aversion, i.e., willingness to trade off aggregate health benefits to increase distributional equity. We evaluated the impact of vaccine prioritization strategies on COVID-19 infections and mortality in New York City (NYC). METHODS:We used a susceptible-exposed-infected-recovered COVID-19 transmission model calibrated to NYC neighborhood-level data to compare three vaccine distribution strategies: 1) uniform across neighborhoods (no prioritization); 2) prioritizing hardest-hit neighborhoods (exposure-based prioritization); and 3) prioritizing hardest-hit neighborhoods while maintaining mitigation measures in other neighborhoods (exposure-based prioritization plus mitigation). The model accounted for vaccine efficacy, rollout pace, pre-vaccine immunity, and heterogeneous neighborhood exposure risk. We categorized 42 NYC neighborhoods into quintiles of cumulative COVID-19 mortality rates from March 1, 2020, until first vaccine availability (December 14, 2020). We modeled total deaths and equally-distributed-equivalent (EDE) deaths (i.e., the equally preferred number of deaths, considering equity and efficiency) across a range of inequality aversion (Atkinson's index, ε=0-20). RESULTS:Exposure-based prioritization plus mitigation was estimated to avert the most citywide COVID-19 deaths (32.5 %) relative to no vaccination, regardless of adjustment for inequality aversion. Relative to no prioritization, exposure-based prioritization was estimated to avert 45 % fewer citywide deaths but generated 2.5 % more EDE-adjusted deaths at an Atkinson index of 10. Exposure-based prioritization outperformed no prioritization at an Atkinson index of ≥ 6. CONCLUSIONS:Prioritizing vaccination within the hardest-hit communities, paired with sustained mitigation efforts in communities with the greatest advantage, resulted in the greatest overall reduction in mortality and inequities. Emergency response teams should consider a community's ability to continue non-pharmaceutical mitigation efforts when allocating limited pharmaceutical supplies.

INTRODUCTION/BACKGROUND:Long-acting injectable cabotegravir (CAB-LA) demonstrated superiority to daily tenofovir disoproxil fumarate/emtricitabine (TDF/FTC) for human immunodeficiency virus (HIV) pre-exposure prophylaxis (PrEP) in two clinical trials. This analysis projects the impact of expanding PrEP coverage with CAB-LA in South Africa between 2022 and 2042. METHODS:Three independently calibrated models of HIV transmission in South Africa (Synthesis, EMOD-HIV, Thembisa) projected HIV acquisitions and effective coverage (average PrEP coverage across exposure groups, weighted by HIV incidence in the absence of PrEP in each group) over 20 years under multiple scenarios of PrEP expansion compared to no PrEP expansion. PrEP expansion scenarios differed in targeted overall coverage, speed of expansion, coverage of high-exposure groups, and relative coverage of women and men. RESULTS:Achieving 5% PrEP coverage with CAB-LA by 2032 prioritizing high-exposure groups resulted in 49% (Synthesis), 18% (EMOD-HIV), and 8% (Thembisa) effective coverage and averted a median of 43%, 29% and 10% of new HIV acquisitions, respectively. Similar expansion with TDF/FTC resulted in lower impact by 19 percentage points (pp), 18pp and 3pp, respectively. Increasing CAB-LA coverage to 15% led to an additional 7pp, 12pp and 16pp, respectively, of HIV acquisitions averted. Achieving 5% CAB-LA coverage expanding to women only resulted in a lower impact by 16pp (Synthesis) and 13pp (EMOD-HIV), and a higher impact by 2pp (Thembisa). Scenarios with similar effective coverage resulted in comparable impact estimates across models. CONCLUSIONS:Offering CAB-LA in South Africa may substantially impact the HIV epidemic based on these projections. Effective coverage proved to be a good predictor of intervention effectiveness.

BACKGROUND:HIV hotspots, regions with higher prevalence than surrounding areas, are observed across Africa, yet their formation and persistence mechanisms remain poorly understood. We hypothesized that random fluctuations during the early stages of the HIV epidemic (1978-1982), amplified by positive feedback between HIV incidence and prevalence, play a critical role in hotspot formation and persistence. To explore this, we applied a network-based HIV transmission model, focusing on randomness in the spatial structure of the epidemic. METHODS:We adapted a previously validated agent-based network HIV transmission model, EMOD-HIV, to simulate HIV spread in western Kenya communities. The model includes demographics, age-structured social networks, and HIV transmission, prevention, and treatment. We simulated 250 identical communities, introducing stochastic fluctuations in network structure and case importation. Outliers were defined as communities with prevalence > 1.5x the median, and persistence as meeting these criteria for >70% of 1980-2050. We systematically varied community size (1,000-10,000), importation timing (1978-1982), and importation patterns (spread over 1, 3, or 5 years), and calculated the proportion of outliers and persistent outliers. RESULTS:HIV prevalence outliers were more common in smaller communities: in 1990, 25.3% (uncertainty interval: 22.3%-28.2%) of 1,000-person communities vs. 9.1% (uncertainty interval: 6.9%-11.4%) of 10,000-person communities. By 2050, 21.6% of 1,000-person communities were persistent outliers, compared to none in larger communities. Autocorrelation of HIV prevalence was high (Pearson's correlation coefficient 0.801 [95% CI: 0.796-0.806] for 1,000-person communities), reflecting feedback that amplified early fluctuations. CONCLUSIONS:Early random fluctuations contribute to the emergence and persistence of prevalence outliers, especially in smaller communities. Recognizing the role of randomness in prevalence outlier formation in these settings is crucial for refining HIV control strategies, as traditional methods may overlook these areas. Adaptive surveillance systems can enhance detection and intervention efforts for HIV and future pandemics.