Improving equity by removing healthcare fees for children in Burkina Faso
Bibliographic record
Abstract
BACKGROUND: This study evaluated the effects on healthcare access inequities of an intervention exempting children under 5 years from user fees in Burkina Faso. METHODS: The design consisted of two complementary studies. The first was an interrupted time series (56 months before and 12 months after) study of daily curative consultations according to distance (<5, 5-9 and ≥10 km) in a stratified random sample of 18 health centres: 12 with the intervention and 6 without. The second was a household panel survey (n=1214) assessing the evolution of health-seeking behaviours. Multilevel regression was used throughout. RESULTS: Attendance doubled under the intervention, after adjusting for Centres de Santé et de Promotion Sociale size, districts, secular trend and seasonal variation. Utilisation increased for all distance ranges and in all of the 12 health centres of the intervention area. The exemption benefited all children (rate ratios (RR)=1.52 (1.23 to 1.88)), whether their health needs were high (RR=1.69 (1.22 to 2.32)) or not (RR=1.46 (1.10 to 1.93)) and whether the children lived near (RR=1.42 (1.09 to 1.85)) or far from a health centre (RR=1.79 (1.31 to 2.43)). The exemption benefited the children of poor families when health need was high and services near (RR=5.23; (1.30 to 20.99)). The amount saved for a child's treatment by the exemption was on average and median 2500 F CFA (≈US$5). CONCLUSIONS: Exempting children under five from user fees is effective and helps reduce inequities of access. It benefits vulnerable populations, although their service utilisation remains constrained by limitations in geographic accessibility of services.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".