Effects of a new health financing scheme on out-of-pocket health expenditure: findings from a longitudinal household study in Yangon, Myanmar
Bibliographic record
Abstract
Since 2017, Population Services International Myanmar (PSI/Myanmar) has been running Strategic Purchasing (SP) clinics in Hlegu and Shwepyithar townships in Yangon, Myanmar. In the project, Population Services International Myanmar simulated the role of a purchaser and contracted SP clinics through a capitation payment scheme. The project aimed to reduce the health-related financial burden of poor populations in the catchment area, by having them registered under respective SP clinics for access to a package of essential health services for a minimal fixed co-payment, as a replacement for usual fee-for-service payments. Four longitudinal surveys of households registered under SP clinics were conducted in 2017, 2018 and 2019. Among 2506 registered households, 867 households sought some health care in all surveys, resulting in 3468 observations. Multivariable linear mixed-effect regression model was used to analyse the changes in out-of-pocket expenditure for health care in relation to household capacity to pay (OOPCTP). The utilization of SP clinics increased over time, and the rates were much higher in Hlegu (20.5% in baseline to 61.9% in round three) compared with those in Shwepyithar (0.2 to 7.9%). Compared with the baseline assessment, household OOPCTP decreased significantly during and after the implementation (0.76 times in round one, 0.80 in round two and 0.82 in round three; P < 0.001). Households in Shwepyithar with less utilization of SP clinics had 1.8 times higher OOPCTP compared with those in Hlegu (1.82, 95% CI 1.58, 2.09; P < 0.001). Household direct expenditures on care-seeking and family planning were up to 50% lower among those who used SP clinics. Our study highlighted that capitation-based health financing schemes could successfully lower out-of-pocket health expenditures among the poor. Optimal utilization of services was paramount in the successful implementation of such programmes. Therefore, for the effective scale-up of new health financing schemes, service utilization rates should be carefully monitored as one of the critical indicators.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".