Catastrophic healthcare expenditure and poverty related to out-of-pocket payments for healthcare in Bangladesh—an estimation of financial risk protection of universal health coverage
Bibliographic record
Abstract
The Sustainable Development Goals target to achieve Universal Health Coverage (UHC), including financial risk protection (FRP) among other dimensions. There are four indicators of FRP, namely incidence of catastrophic health expenditure (CHE), mean positive catastrophic overshoot, incidence of impoverishment and increase in the depth of poverty occur for high out-of-pocket (OOP) healthcare spending. OOP spending is the major payment strategy for healthcare in most low-and-middle-income countries, such as Bangladesh. Large and unpredictable health payments can expose households to substantial financial risk and, at their most extreme, can result in poverty. The aim of this study was to estimate the impact of OOP spending on CHE and poverty, i.e. status of FRP for UHC in Bangladesh. A nationally representative Household Income and Expenditure Survey 2010 was used to determine household consumption expenditure and health-related spending in the last 30 days. Mean CHE headcount and its concentration indices (CI) were calculated. The propensity of facing CHE for households was predicted by demographic and socioeconomic characteristics. The poverty headcount was estimated using 'total household consumption expenditure' and such expenditure without OOP payments for health in comparison with the poverty-line measured by cost of basic need. In absolute values, a pro-rich distribution of OOP payment for healthcare was found in urban and rural Bangladesh. At the 10%-threshold level, in total 14.2% of households faced CHE with 1.9% overshoot. 16.5% of the poorest and 9.2% of the richest households faced CHE. An overall pro-poor distribution was found for CHE (CI = -0.064) in both urban and rural households, while the former had higher CHE incidences. The poverty headcount increased by 3.5% (5.1 million individuals) due to OOP payments. Reliance on OOP payments for healthcare in Bangladesh should be reduced for poverty alleviation in urban and rural Bangladesh in order to secure FRP for UHC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".