Mapping and situation analysis of basic WASH facilities at households in Bangladesh: Evidence from a nationally representative survey
Bibliographic record
Abstract
Background Ensuring water, sanitation, and hygiene (WASH) facilities for households remains a major public health concern in low- and middle-income countries (LMICs). This study investigated the current situation of basic WASH facilities for households in Bangladesh and drew a national coverage map. Methods We analyzed the publicly available nationally representative 2019 Multiple Indicator Cluster Survey (MICS) dataset that was carried out by the Bangladesh Bureau of Statistics (BBS) with support from the United Nations Children's Emergency Fund (UNICEF). A total of 61,209 households (weighted) were included in the analysis. Both bivariate and multivariate analyses were employed to examine the relationships between independent variables (socio-demographic and economic status) and their distributions over outcome variables (basic water, sanitation, and hygiene). Further, the spatial distribution of WASH facilities at the household level was depicted. Results Coverage of access to basic water facilities at the household level was 99.5% (95% CI 99.4% to 99.6%), sanitation 60.7% (95% CI 60.0% to 61.5%), and hygiene 56.3% (95% CI 55.6% to 57.0%). However, coverage of combined access to all three components was 40.2% (95% CI 39.4% to 40.9%). Among all 64 administrative districts of Bangladesh, we found comparatively lower coverage of WASH facilities in the South and South-East regions and relatively higher in the households of the North and North-Western regions. An adjusted regression model revealed that richest households [AOR = 29.64, 95% CI 26.31 to 33.39], households in the rural areas [AOR = 1.64, 95% CI 1.50 to 1.79], household heads with higher educational attainment [AOR = 2.28, 95% CI 2.09 to 2.49], and households with 5+ family members [AOR = 1.64, 95% CI 1.56 to 1.71] had the higher likelihood to have basic WASH facilities. Conclusion Less than half of the Bangladeshi households had access to all three major WASH components (basic water, sanitation, and hygiene facilities); however, variation exists at the individual parameter of basic water, sanitation, and hygiene facilities. A comprehensive WASH approach may reduce the gap and improve the quality of WASH facilities in Bangladesh.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".