Catastrophic Health Expenditure and Disease Burden among Rural Households in Bangladesh: A Cross-Sectional Study from Mirsharai, Chittagong
Bibliographic record
Abstract
Households in rural Bangladesh face severe financial hardship due to rising healthcare costs, particularly for chronic and non-communicable diseases. This study examined the patterns of illness, treatment practices, and catastrophic health expenditure among residents of Masjidia village, Mirsharai, Chittagong. A community-based cross-sectional survey was conducted in 2016 among 152 households to retrospectively assess healthcare utilization and expenditure patterns for 2015-2016. Convenience sampling was used due to geographical constraints. Catastrophic health expenditure was defined as spending exceeding 10% of total household income. Data were collected using structured questionnaires and analyzed descriptively. Illness prevalence was 84.2% among respondents. The average annual household income was BDT 35,352, while total healthcare expenditure accounted for 52.5% of this income, exceeding the catastrophic threshold by fivefold. Medication costs were the major expense, comprising 49–82% of total healthcare spending, followed by consultation, investigation, and transport costs. Most treatments were sought from private clinics and specialists, indicating high out-of-pocket dependency. The reliance on allopathic medicine remained dominant (above 94%), reflecting both accessibility and perceived efficacy. Health spending in rural Bangladesh imposes a catastrophic financial burden on households, driven primarily by medication costs and private-sector dependence. Targeted interventions—including subsidized essential medicines, expansion of community-based insurance, and improved public primary healthcare—are essential to reduce financial vulnerability and promote equitable access. Bangladesh Med J. 2024 Sept; 53(3): 7-15
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".