P1-S6.51 Antiretroviral therapy, sexual behaviour, and their simulated impact on HIV epidemiologic trends in Uganda
Bibliographic record
Abstract
Background Debate exists concerning the potential impact of ART on the HIV epidemic in Africa. We combine empirical evidence for sexual behaviour change in response to ART in a Ugandan cohort, with mathematical modelling, to examine the likely impact of ART on the HIV epidemic, accounting for potential behaviour change. Methods Cohort participants are surveyed every 3 months on sexual behaviours. ART rollout began in 2004. Using regression, we examined potential associations between timing of ART initiation and sexual behaviour among HIV-infected, and timing of ART availability and sexual behaviour among HIV-uninfected. We then used a compartmental mathematical model to assess the impact of ART on HIV epidemiologic trends, under varying assumptions about rates of initiating ART and behaviour change. The model has been described previously in peer-reviewed literature. Results We found no evidence of increased risk behaviour after ART initiation to levels higher than 2 years before initiation. There is some evidence of rising risk behaviour among HIV-uninfected people in response to ART availability. Among HIV-uninfected, the mean number of casual partners in the past 3 months fell from 0.02 in 2002 to 0.01 by 2004 and then rose to 0.03 by late 2008 (p for change in trend from declining to rising numbers of casual partners over the period 2002–2008=0.030). The mean number of new partners in the past 3 months fell from 0.13 in early 2002 to 0.02 in the late 2004. By 4th quarter of 2008, the number of new partners in the past 3 months had risen to 0.20 (p=0.058). Regardless of changing sexual behaviour, the model suggests that ART will reduce HIV incidence, but increase prevalence. This occurs even when ART initiation begins in HIV stage 2 (∼3 months after infection) and 90% of HIV-infected are on ART and the probability of transmission while on ART declines greatly (right panel of Abstract P1-S6.51 Figure 1 baseline of no ART displayed in left panel). The conditions required for ART to reduce prevalence had to be more extreme than this (left panel). Abstract P1-S6.51 Figure 1 Sensitivity analyses of dual impact of ART and potential behaviour change on HIV prevalence. Conclusions Due to HIV+ people enjoying a longer life expectancy, and an insufficient drop in incidence, HIV prevalence will rise as a result of ART. Modelling suggests that even small increases in risky sexual behaviour will lead to further substantial increases in HIV prevalence. Policy makers are urged to continue promoting sex education, and be prepared for a higher than previously suggested number of HIV+ people in need of treatment.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".