Assessment of a national voucher scheme to deliver insecticide-treated mosquito nets to pregnant women
Bibliographic record
Abstract
Background: The benefits of a health-related intervention may be compromised by the challenges of delivering the intervention on a large scale. We analyzed the process involved in the Tanzania National Voucher Scheme, a system for delivering insecticide-treated mosquito nets to pregnant women. We aimed to identify potential ways to equitably improve overall coverage of the intervention. Methods: We defined five steps in the process. We collected data from a multistage cluster survey of nationally representative households conducted in 2007 across 21 districts in Tanzania. Using these data, we multiplied the rate of success of each step cumulatively to estimate the overall success of the system. Results: The rate of coverage for use of insecticide-treated nets among pregnant women was 23% (95% confidence interval [CI] 19%–27%). We observed large differences in coverage by socio-economic status, from 7% (95% CI 4%–13%) among participants in the poorest households to 48% (95% CI 38%–59%) among those in the richest households. The rate of success of each step in the process was high (60%–98%). However, the cumulative rate of success for the process as a whole was low (30%). The largest and most inequitable reduction in coverage occurred in the step involving treatment of nets with insecticide. Interpretation: The cumulative effect of modest attrition at several steps in the process substantially diminished the overall rate of coverage for all women, but most markedly among the poorest participants. Analysis of the process suggests that delivery of nets treated with long-lasting insecticide rather than untreated nets packaged with an insecticide-treatment kit could result in an improvement in coverage of 22 percentage points, from 30% to 52%.
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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 | 0.059 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".