Matching comprehensive health insurance reimbursements to their real costs: the case of antenatal care visits in a region of Peru
Bibliographic record
Abstract
BACKGROUND: Prepaid contributory systems are increasingly being recognized as key mechanisms in achieving universal health coverage in low and middle-income countries. Peru created the Seguro Integral de Salud (SIS) to increase health service use amongst the poor by removing financial barriers. The SIS transfers funds on a fee-for-service basis to the regional health offices to cover recurrent cost (excluding salaries) of pre-specified packages of interventions. We aim to estimate the full cost of antenatal care (ANC) provision in the Ventanilla District (Callao-Peru) and to compare the actual cost to the reimbursement rates provided by SIS. METHODS: The economic costs of ANC provision in 2011 in 8 of the 15 health centres in Ventanilla District were estimated from a provider perspective and the actual costs of those services covered by the SIS fee of $3.8 for each ANC visit were calculated. A combination of step-down and bottom-up costing methodologies was used. Sensitivity analysis was conducted to test the uncertainty around estimated parameters and model assumptions. Results are reported in 2011 US$. RESULTS: The total economic cost of ANC provision in all 8 health centres was $569,933 with an average cost per ANC visit of $31.3 (95 % CI $29.7-$33.5). Salaries comprised 74.4 % of the total cost. The average cost of the services covered by the SIS fee was $3.4 (95 % CI $3.0-$3.8) per ANC visit. Sensitivity analysis showed that the probability of the cost of an ANC visit being above the SIS reimbursed fee is 1.4 %. CONCLUSION: Our analysis suggests that the fee reimbursed by the SIS will cover the cost that it supposed to cover. However, there are significant threats to medium and longer term sustainability of this system as fee transfers represent a small fraction of the total cost of providing ANC. Increasing ANC coverage requires the other funding sources of the Regional Health Office (DIRESA) to adapt to increasing demand.
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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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".