Inequity in Formal Health Care Use: Evidence from Rural Bangladesh
Bibliographic record
Abstract
This paper analyzes inequity in health care use in rural Bangladesh using data from a survey conducted by Microinsurance Research Unit (MRU) of the Institute of Microfinance (InM) of 4, 010 households drawn from 120 villages. The study focuses on formal health care use over the 12 months preceding the survey. We use both the ‘need standardized’ approach and ‘decomposition analysis’ for measuring inequity. The paper finds that the use of formal health care is incredibly low (40%); about two-thirds (65%) of which is private health care and only one-fourth utilizes public sector facilities. Inequity in formal health care use favors the better-off although the level of inequity is modest. Prevailing inequity resides mainly in the utilization of private health care while NCDs contribute significantly to this inequity. Thus, the main public health concern in rural areas of Bangladesh is the low utilization of formal health care (especially public health care), not inequity. From a policy perspective therefore, voluntary health insurance is not an answer so far as chronic NCDs are concerned; social insurance is not quite feasible either due to the large informal economy. Hope therefore lies in the public provision of health care although the latter is plagued by various supply side constraints including meager budgetary resources, daunting governance issues and hence the need for reforms to enhance efficiency.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".