Effect of the free healthcare policy on socioeconomic inequalities in care seeking for fever in children under five years in Burkina Faso: a population-based surveys analysis
Bibliographic record
Abstract
BACKGROUND: In 2016, Burkina Faso implemented a free healthcare policy as an initiative to remove user fees for women and under-5 children to improve access to healthcare. Socioeconomic inequalities create disparities in the use of health services which can be reduced by removing user fees. This study aimed to assess the effect of the free healthcare policy (FHCP) on the reduction of socioeconomic inequalities in the use of health services in Burkina Faso. METHODS: Data were obtained from three nationally representative population based surveys of 2958, 2617, and 1220 under-5 children with febrile illness in 2010, 2014, and 2017-18 respectively. Concentration curves were constructed for the periods before and after policy implementation to assess socioeconomic inequalities in healthcare seeking. In addition, Erreyger's corrected concentration indices were computed to determine the magnitude of these inequalities. RESULTS: Prior to the implementation of the FHCP, inequalities in healthcare seeking for febrile illnesses in under-5 children favoured wealthier households [Erreyger's concentration index = 0.196 (SE = 0.039, p = 0.039) and 0.178 (SE = 0.039, p < 0.001) in 2010 and 2014, respectively]. These inequalities decreased after policy implementation in 2017-18 [Concentration Index (CI) = 0.091, SE = 0.041; p = 0.026]. Furthermore, existing pro-rich disparities in healthcare seeking between regions before the implementation of the FHCP diminished after its implementation, with five regions having a high CI in 2010 (0.093-0.208), four regions in 2014, and no region in 2017 with such high CI. In 2017-18, pro-rich inequalities were observed in ten regions (CI:0.007-0.091),whereas in three regions (Plateau Central, Centre, and Cascades), the CI was negative indicating that healthcare seeking was in favour of poorest households. CONCLUSION: This study demonstrated that socioeconomic inequalities for under-5 children with febrile illness seeking healthcare in Burkina Faso reduced considerably following the implementation of the free healthcare policy. To reinforce the reduction of these disparities, policymakers should maintain the policy and focus on tackling geographical, cultural, and social barriers, especially in regions where healthcare seeking still favours rich households.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".