A comparison of maternal and newborn health services costs in Sindh Pakistan
Bibliographic record
Abstract
Pakistani women suffer the highest rate of maternal mortality in South Asia. A lack of comprehensive knowledge about maternal and newborn health (MNH) services costs impedes policy decisions to maximize the benefit from existing, as well as emerging, MNH interventions in Pakistan. We compared MNH service costs at different levels of care. A cross-sectional survey was conducted during January to March 2016 as part of a large economic evaluation in Sindh, Pakistan. Health providers and facilities were selected from a sampling frame, inclusive of public and private sectors. This study utilized a broad perspective (i.e. costs to the health system and patients/families). The unit costs of MNH services were determined through a simultaneous allocation method in the public facilities; and patient billing department in the private facilities. Descriptive analysis was performed, and an analysis of variance (ANOVA) test was applied to compare overall mean costs both within and between health facilities. A total of 31 eligible health providers and facilities (n = 25 in private; n = 7 in public) were included in the final analysis. An ambulatory visit (AV) for routine antenatal care (ANC) costs $3.6 and $0.9 at secondary- and tertiary-level public facilities, respectively. In the private sector, the costs of an AV for ANC were slightly less ($2.8) at secondary-level and much higher ($6) at tertiary-level facilities compared to the public sector. Diagnostic test costs were much higher in private facilities. The average costs of inpatient admissions were $30.5 at general ward (GW), and $151 at the intensive care unit (ICU) in public facilities. In-patient admissions costs were lower such as $9.3 at GW and $36.5 at ICU in private facilities. Understanding cost is critical to guide decisions of resource allocation within the public sector; and risk mitigation for excessive OOP costs through third party payer for services in the private sector.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".