Comparison of administrative and survey data for estimating vitamin A supplementation and deworming coverage of children under five years of age in Sub‐Saharan Africa
Bibliographic record
Abstract
OBJECTIVE: To compare administrative coverage data with results from household coverage surveys for vitamin A supplementation (VAS) and deworming campaigns conducted during 2010-2015 in 12 African countries. METHODS: Paired t-tests examined differences between administrative and survey coverage for 52 VAS and 34 deworming dyads. Independent t-tests measured VAS and deworming coverage differences between data sources for door-to-door and fixed-site delivery strategies and VAS coverage differences between 6- to 11-month and 12- to 59-month age group. RESULTS: For VAS, administrative coverage was higher than survey estimates in 47 of 52 (90%) campaign rounds, with a mean difference of 16.1% (95% CI: 9.5-22.7; P < 0.001). For deworming, administrative coverage exceeded survey estimates in 31 of 34 (91%) comparisons, with a mean difference of 29.8% (95% CI: 16.9-42.6; P < 0.001). Mean ± SD differences in coverage between administrative and survey data were 12.2% ± 22.5% for the door-to-door delivery strategy and 25.9% ± 24.7% for the fixed-site model (P = 0.06). For deworming, mean ± SD differences in coverage between data sources were 28.1% ± 43.5% and 33.1% ± 17.9% for door-to-door and fixed-site distribution, respectively (P = 0.64). VAS administrative coverage was higher than survey estimates in 37 of 49 (76%) comparisons for the 6- to 11-month age group and 45 of 48 (94%) comparisons for the 12- to 59-month age group. CONCLUSION: Reliance on health facility data alone for calculating VAS and deworming coverage may mask low coverage and prevent measures to improve programmes. Countries should periodically validate administrative coverage estimates with population-based methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".