Socioeconomic inequalities and its spatial pattern in sanitary napkin use in Bangladesh: evidence from the 2019 multiple indicator cluster survey
Bibliographic record
Abstract
BACKGROUND: Utilization of unhygienic menstrual products has been associated with various adverse health consequences, particularly in many low- and middle-income countries (LMICs), including Bangladesh. In this context, this study attempted to measure socioeconomic inequalities in sanitary napkin use among women aged 15-49 and assess its spatial pattern at the disaggregated level (district). METHODS: We used the latest available nationally representative cross-sectional Bangladesh Multiple Indicator Cluster Survey (MICS) 2019 data. The analytical sample size was 54,702 reproductive-aged (15-49 years) women. The Erreygers Index (EI) and the Wagstaff Index (WI) were employed to measure and decompose the socioeconomic inequalities in sanitary napkin use. Natural Break (Jenks) classification method, Global Moran's I and the Cluster and Outlier Analysis were used to analyze the spatial pattern of socioeconomic inequalities at the district level. RESULTS: The findings reveal that approximately 26.22% of women used sanitary napkins at the national level. Both the EI (0.41208; p-value < 0.001) and the WI (0.53251; p-value < 0.001) indicate a pro-rich inequality in sanitary napkin use. Decomposition results indicated that wealth status, educational attainment, household characteristics (particularly educational attainment of household head) and exposure to media were the most important factors accounting for socioeconomic inequalities. From the spatial analyses, we found significant district-level variations in both sanitary napkin use and its socioeconomic inequalities. Global Moran's I value indicated positive spatial autocorrelation, meaning that similar values tend to cluster together. Notably, a northwestern and southeastern divide was found between High-High and Low-Low clusters of socioeconomic inequalities. CONCLUSIONS: Our study provides evidence for informed policymaking targeting women from the lower socioeconomic stratum, especially those living in the northwestern and southeastern regions to increase sanitary napkin use.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".