How much does community-based targeting of the ultra-poor in the health sector cost? Novel evidence from Burkina Faso
Bibliographic record
Abstract
BACKGROUND: Targeting efforts aimed at increasing access to care for the poorest by reducing to a minimum or completely eliminating payments at point of use are increasingly being adopted across low and middle income countries, within the framework of Universal Health Coverage policies. No evidence, however, is available on the real cost of designing and implementing these efforts. Our study aimed to fill this gap in knowledge through the systematic assessment of both the financial and economic costs associated with designing and implementing a pro-poor community-based targeting intervention across eight districts in rural Burkina Faso. METHODS: We conducted a partial retrospective economic evaluation (i.e. estimating costs, but not benefits) associated with the abovementioned targeting intervention. We adopted a health system perspective, including all costs incurred by the government and its development partners as well as costs incurred by the community when working as volunteers on behalf of government structures. To trace both financial and economic costs, we combined Activity-Based Costing with Resource Consumption Accounting. To this purpose, we consulted and extracted information from all relevant design/implementation documents and conducted additional key informant structured interviews to assess the resource consumption that was not valued in the documents. RESULTS: For the entire community-based targeting intervention, we estimated a financial cost of USD 587,510 and an economic cost of USD 1,213,447. The difference was driven primarily by the value of the time contributed by the community. Communities carried the main economic burden. With a total of 102,609 ultra-poor identified, the financial cost and the economic cost per ultra-poor person were respectively USD 5,73 and USD 11,83. CONCLUSION: The study is first of its kind to accurately trace the financial and economic costs of a community-based targeting intervention aiming to identify the ultra-poor. The financial costs amounted to USD 5,73 and the economic costs to USD 11,83 per ultra-poor person identified. The financial costs of almost USD 6 represents 21% of the per capita government expenditure on health.
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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 | 0.019 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".