Effect of cost-reduction interventions on facility-based deliveries in Burkina Faso: a controlled interrupted time-series study with multiple non-equivalent dependent variables
Bibliographic record
Abstract
BACKGROUND: Evaluating health intervention effectiveness in low-income countries involves many methodological challenges to be addressed. The objective of this study was to estimate the sustained effects of two interventions to improve financial access to facility-based deliveries. METHODS: In an innovative controlled interrupted time-series study with primary data, we used four non-equivalent dependent variables (antenatal care) as control outcomes to estimate the effects of a national subsidy for deliveries (January 2007-December 2013) and a local 'free delivery' intervention (June 2007-December 2010) on facility-based deliveries. The statistical analysis used spline linear regressions with random intercepts and slopes. RESULTS: The analysis involved 20 877 observations for the national subsidy and 8842 for the 'free delivery' intervention. The two interventions did not have immediate effects. However, both were associated with positive trend changes varying from 0.21 to 0.52 deliveries per month during the first 12 months and from 0.78 to 2.39 deliveries per month during the first 6 months. The absolute effects, evaluated 84 and 42 months after introduction, ranged from 2.64 (95% CI 0.51 to 4.77) to 10.78 (95% CI 8.52 to 13.03) and from 9.57 (95% CI 5.97 to 13.18) to 14.47 (95% CI 10.47 to 18.47) deliveries per month for the national subsidy and the 'free delivery' intervention, respectively, depending on the type of antenatal care used as a control outcome. CONCLUSION: The results suggest that both interventions were associated with sustained non-linear increases in facility-based deliveries. The use of multiple control groups strengthens the credibility of the results, making them useful for policy makers seeking solutions for universal health coverage.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".