Heterogeneity in the validity of administrative-based estimates of immunization coverage across health districts in Burkina Faso: implications for measurement, monitoring and planning
Bibliographic record
Abstract
BACKGROUND: Data aggregation in national information systems begins at the district level. Decentralization has given districts a lead role in health planning and management, therefore validity of administrative-based estimates at that level is important to improve the performance of immunization information systems. OBJECTIVE: To assess the validity of administrative-based immunization estimates and their usability for planning and monitoring activities at district level. METHODS: DTP3 and measles coverage rates from administrative sources were compared with estimates from the EPI cluster survey (ECS) and Demographic and Health Survey (DHS) carried out in 2003 at national and regional levels. ECS estimates were compared with administrative rates across the 52 districts, which were classified into three groups: those where administrative rates were underestimating, overestimating or concordant with ECS estimates (differences within 95% CI of ECS rate). RESULTS: National rates provided by administrative data and ECS are similar (74% and 71% for DTP3 and 68% and 66% for measles, respectively); DHS estimates are much lower. Regional administrative data show large discrepancies when compared against ECS and DHS data (differences sometimes reaching 30 percentage points). At district level, geographical area is correlated with over- or underestimation by administrative sources, which overestimate DTP3 and measles coverage in remote areas. Underestimation is observed in districts near urban and highly populated centres. Over- and underestimation are independent of the antigen under consideration. CONCLUSIONS: Variability in immunization coverage across districts highlights the limitations of using nationally aggregated indicators. If district data are to be used in monitoring and planning immunization programmes as intended by decentralization, heterogeneity in their validity must be reduced. The authors recommend: (1) strengthening administrative data systems; (2) implementing indicators that are insensitive to population mobility; (3) integrating surveys into monitoring processes at the subnational level; (4) actively promoting the use of coverage information by local personnel and district-level staff.
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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 | 0.002 | 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 | 0.001 | 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".