Evaluating the biomedical and behavioural drivers of HIV-1 incidence decline in adolescent girls and young women in Uganda: A mathematical modelling study
Bibliographic record
Abstract
Background: Recent declines in HIV incidence among adolescent girls and young women (AGYW) in Africa are often attributed to the expansion of biomedical interventions such as antiretroviral therapy and voluntary medical male circumcision. However, changes in sexual behaviour may also play a critical role. Understanding the relative contributions of these factors is essential for developing strategies to sustain and further reduce HIV transmission. Methods: We conducted a mathematical modelling study of data from the Rakai Community Cohort Study (RCCS), an open, population-based cohort of 15- to 49-year-olds in 30 communities in Rakai, Uganda, to investigate the biomedical and behavioural drivers of HIV incidence decline in AGYW (15-24 years of age). We estimated changes in the HIV incidence rate between 2000-2019 using retrospective cohort data to validate our modelled incidence estimates. We ran modelled counterfactual scenarios to quantify the independent and combined effects (cumulative infections averted and difference in incidence rates) of antiretroviral therapy (ART), voluntary medical male circumcision (VMMC), and delays in age of first sex (AFS) over historical (between 2000-2020) and projected (between 2000-2050) time horizons. Findings: Incidence in women 15-24 years of age declined by 83% between 2000-2019 (from 1.72 per 100 person-years in 2000 to 0.30 per 100 person-years in 2019), the largest reduction in incidence of all age groups of women. Increasing AFS over the last two decades (by 3 years in women and 2 years in men) was the largest contributor to incidence decline in women 15-19 years of age, averting 17% of cumulative infections between 2000-2020 and 37% between 2000-2050. Incidence in 15-19-year-old women was 69% lower in 2020 and 75% lower in 2050 compared to counterfactual scenarios without changes in AFS. ART scale-up contributed the most to incidence declines among women 20-24 years of age, averting 13% of infections between 2000-2020 and 43% of infections between 2000-2050. VMMC averted < 5% of infections in 15-24-year-olds to-date, with larger reductions in incidence between 2000-2050 in both 15-19 year-olds (13% reduction in cumulative infections) and 20-24 year-olds (22% of cumulative infections). ART, VMMC, and increasing AFS acted additively to reduce HIV incidence in AGYW, with little redundancy when combined. Interpretation: Our results provide strong support for maintaining both the protective changes in sexual behaviours and effective biomedical interventions to sustain continued reductions in HIV incidence among AGYW.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".