Improved equity in measles vaccination from integrating insecticide‐treated bednets in a vaccination campaign, Madagascar
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of integrating ITN distribution on measles vaccination campaign coverage in Madagascar. METHODS: Nationwide cross-sectional survey to estimate measles vaccination coverage, nationally, and in districts with and without ITN integration. To evaluate the effect of ITN integration, propensity score matching was used to create comparable samples in ITN and non-ITN districts. Relative risks (RR) and 95% confidence intervals (CI) were estimated via log-binomial models. Equity ratios, defined as the coverage ratio between the lowest and highest household wealth quintile (Q), were used to assess equity in measles vaccination coverage. RESULTS: National measles vaccination coverage during the campaign was 66.9% (95% CI 63.0-70.7). Among the propensity score subset, vaccination campaign coverage was higher in ITN districts (70.8%) than non-ITN districts (59.1%) (RR=1.3, 95% CI 1.1-1.6). Among children in the poorest wealth quintile, vaccination coverage was higher in ITN than in non-ITN districts (Q1; RR=2.4, 95% CI 1.2-4.8) and equity for measles vaccination was greater in ITN districts (equity ratio=1.0, 95% CI 0.8-1.3) than in non-ITN districts (equity ratio=0.4, 95% CI 0.2-0.8). CONCLUSION: Integration of ITN distribution with a vaccination campaign might improve measles vaccination coverage among the poor, thus providing protection for the most vulnerable and difficult to reach children.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".