Modeling Impact and Cost-Effectiveness of Increased Efforts to Attract Voluntary Medical Male Circumcision Clients Ages 20–29 in Zimbabwe
Bibliographic record
Abstract
BACKGROUND: Zimbabwe aims to increase circumcision coverage to 80% among 13- to 29-year-olds. However, implementation data suggest that high coverage among men ages 20 and older may not be achievable without efforts specifically targeted to these men, incurring additional costs per circumcision. Scale-up scenarios were created based on trends in implementation data in Zimbabwe, and the cost-effectiveness of increasing efforts to recruit clients ages 20-29 was examined. METHODS: Zimbabwe voluntary medical male circumcision (VMMC) program data were used to project trends in male circumcision coverage by age into the future. The projection informed a base scenario in which, by 2018, the country achieves 80% circumcision coverage among males ages 10-19 and lower levels of coverage among men above age 20. The Zimbabwe DMPPT 2.0 model was used to project costs and impacts, assuming a US$109 VMMC unit cost in the base scenario and a 3% discount rate. Two other scenarios assumed that the program could increase coverage among clients ages 20-29 with a corresponding increase in unit cost for these age groups. RESULTS: When circumcision coverage among men ages 20-29 is increased compared with a base scenario reflecting current implementation trends, fewer VMMCs are required to avert one infection. If more than 50% additional effort (reflected as multiplying the unit cost by >1.5) is required to double the increase in coverage among this age group compared with the base scenario, the cost per HIV infection averted is higher than in the base scenario. CONCLUSIONS: Although increased investment in recruiting VMMC clients ages 20-29 may lead to greater overall impact if recruitment efforts are successful, it may also lead to lower cost-effectiveness, depending on the cost of increasing recruitment. Programs should measure the relationship between increased effort and increased ability to attract this age group.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".