Modeling the Population-Level Effects of Male Circumcision as an HIV-Preventive Measure: A Gendered Perspective
Bibliographic record
Abstract
BACKGROUND: Evidence from biological, epidemiological, and controlled intervention studies has demonstrated that male circumcision (MC) protects males from HIV infection, and MC is now advocated as a public-health intervention against HIV. MC provides direct protection only to men, but is expected to provide indirect protection to women at risk of acquiring HIV from heterosexual transmission. How such indirect protection interacts with the possibility that MC campaigns will lead to behavior changes, however, is not yet well understood. Our objective here is to investigate the link between individual-level effects of MC campaigns and long-term population-level outcomes resulting from disease dynamics, looking at both genders separately, over a broad range of parameters. METHODS AND FINDINGS: We use simple mathematical models of heterosexual transmission to investigate the potential effects of a circumcision scale-up, combined with possible associated behavioral disinhibition. We examine patterns in expected long-term prevalence using a simple equilibrium model based on transmission factors, and validate our results with ODE-based simulations, focusing on the link between effects on females and those on males.We find that the long-term population-level effects on females and males are not strongly linked: there are many possible ways in which an intervention which reduces prevalence in males might nonetheless increase prevalence in females. CONCLUSIONS: Since an intervention that reduces long-term male prevalence could nonetheless increase long-term female prevalence, MC campaigns should explicitly consider both the short-term and long-term effects of MC interventions on females. Our findings strongly underline the importance of pairing MC programs with education, support programs and HIV testing and counseling, together with other prevention measures.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".