Effect of a free healthcare policy on health services utilisation for non‐malarial febrile illness by children under five years in Burkina Faso: an interrupted time series analysis
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of a free healthcare policy for children under five years old implemented in Burkina Faso since April 2016, on the use of health care of non-malarial febrile illnesses (NMFI). METHODS: To assess the immediate and long-term effect of the free healthcare policy in place, we conducted an interrupted time series analysis of routinely collected data on febrile illnesses from three urban primary health centres of Ouagadougou between 1 January 2015 and 31 December 2016. RESULTS: Of the 39 046 febrile cases reported in the study period, 17 017 NMFI were included in the study. Compared to the period before the intervention, we observed an immediate, non-statistically significant increase of 7% in the number of NMFI (IRR = 1.07; 95% CI = 0.75, 1.51). Compared to the trend that would have been expected in absence of the intervention, the results showed a small but sustained increase of 6% in the trend of monthly number of NMFI during the intervention period (IRR = 1.06; 95%CI = 1.01, 1.12). CONCLUSION: Our study highlighted an increase in the uptake of healthcare services, specifically for NMFI by children under five years of age, after the implementation of a free care policy. This analysis contributes to informing decision makers on the need to strengthen the capacities of healthcare centres and to anticipate the challenges of the sustainability of this policy.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".