Childhood diarrhoea management practices in Bangladesh: private sector dominance and continued inequities in care
Bibliographic record
Abstract
BACKGROUND: Monitoring for disparities in health and services received based upon gender, income, and geography should continue as renewed efforts to reduce under-five mortality are made in response to millennium development goal #4. The purpose of this survey was to provide a nationally representative description of current childhood diarrhoea management practices and disparities in Bangladesh. METHODS: A nationally representative, cross-sectional, cluster-sample survey was carried out in randomly selected rural and urban populations across Bangladesh. The survey was completed over an 8 month period between November 2003 and June 2004. RESULTS: A total of 7308 children with a prevalent diarrhoeal illness episode within 560 clusters were identified and enrolled in the survey. In 61% of the cases help was sought from a health care provider, with over 90% practicing in the private sector. Caretaker practice disparities favouring males and higher income households were identified. Significant trends (P < 0.001) favouring higher income households were found for having sought help from any provider or a licensed doctor and for treating their child with oral rehydration solution or an antibiotic. Female children in urban households were less likely to be seen by a licensed allopath, adj OR 0.73 (95% CI 0.57, 0.94). Among rural households gender disparities were limited to females being less likely to receive an antibiotic, adj OR 0.74 (95% CI 0.65, 0.86). CONCLUSION: Households seeking help from a health provider overwhelmingly utilize the private sector in Bangladesh. Gender inequities in the utilization of licensed providers and purchase of antibiotics, favouring males were identified. Findings suggest that higher income, urban households tend to practice greater gender discrimination. In order to better understand health dynamics in urban populations, in particular slum-dwellers, there is a need to disaggregate survey data by household location.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".