Assessing the Potential Impact of Disruptions Due to COVID-19 on HIV Among Key and Lower-Risk Populations in the Largest Cities of Cameroon and Benin
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic indirectly impacts HIV epidemiology in Central/West Africa. We estimated the potential impact of COVID-19-related disruptions to HIV prevention/treatment services and sexual partnerships on HIV incidence and HIV-related deaths among key populations including female sex workers (FSW), their clients, men who have sex with men, and overall. SETTING: Yaoundé (Cameroon) and Cotonou (Benin). METHODS: We used mathematical models of HIV calibrated to city population-specific and risk population-specific demographic/behavioral/epidemic data. We estimated the relative change in 1-year HIV incidence and HIV-related deaths for various disruption scenarios of HIV prevention/treatment services and decreased casual/commercial partnerships, compared with a scenario without COVID-19. RESULTS: A 50% reduction in condom use in all partnerships over 6 months would increase 1-year HIV incidence by 39%, 42%, 31%, and 23% among men who have sex with men, FSW, clients, and overall in Yaoundé, respectively, and 69%, 49%, and 23% among FSW, clients, and overall, respectively, in Cotonou. Combining a 6-month interruption of ART initiation and 50% reduction in HIV prevention/treatment use would increase HIV incidence by 50% and HIV-related deaths by 20%. This increase in HIV infections would be halved by a simultaneous 50% reduction in casual and commercial partnerships. CONCLUSIONS: Reductions in condom use after COVID-19 would increase infections among key populations disproportionately, particularly FSW in Cotonou, who need uninterrupted condom provision. Disruptions in HIV prevention/treatment services have the biggest impacts on HIV infections and deaths overall, only partially mitigated by equal reductions in casual/commercial sexual partnerships. Maintaining ART provision must be prioritized to minimize short-term excess HIV-related deaths.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".