Out-of-pocket payments in the context of a free maternal health care policy in Burkina Faso: a national cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: In April 2016, Burkina Faso introduced a free health care policy for women. Instead of reimbursing health facilities, as many sub-Saharan countries do, the government paid them prospectively for covered services to avoid reimbursement delays, which are cited as a reason for the persistence of out-of-pocket (OOP) payments. This study aimed to (i) estimate the direct expenditures of deliveries and covered obstetric care, (ii) determine the OOP payments, and (iii) identify the patient and health facility characteristics associated with OOP payments. METHODS: A national cross-sectional study was conducted in September and October 2016 in 395 randomly selected health facilities. A structured questionnaire was administered to women (n = 593) who had delivered or received obstetric care on the day of the survey. The direct health expenditures included fees for consultations, prescriptions, paraclinical examinations, hospitalization and ambulance transport. A two-part model with robust variances was performed to identify the factors associated with OOP payments. RESULTS: A total of 587 women were included in the analysis. The median direct health expenses were US$5.38 [interquartile range (IQR):4.35-6.65], US$24.72 [IQR:16.57-46.09] and US$136.39 [IQR: 108.36-161.42] for normal delivery, dystocia and cesarean section, respectively. Nearly one-third (29.6%, n = 174) of the women reported having paid for their care. OOP payments ranged from US$0.08 to US$98.67, with a median of US$1.77 [IQR:0.83-7.08]). Overall, 17.5% (n = 103) of the women had purchased drugs at private pharmacies, and 11.4% (n = 67) had purchased cleaning products for a room or equipment. OOP payments were more frequent with age, for emergency obstetric care and among women who work. The women's health region of origin was also significantly associated with OOP payments. For those who made OOP payments, the amounts paid decreased with age but were higher in urban areas, in hospitals, and among the most educated women. The amounts paid were lower among students and were associated with health region. CONCLUSION: The policy is effective for financial protection. However, improvements in the management and supply system of health facilities' pharmacies could further reduce OOP payments in the context of the free health care policy in Burkina Faso.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".