Prevalence and determinants of menstrual regulation among ever-married women in Bangladesh: evidence from a national survey
Bibliographic record
Abstract
BACKGROUND: Despite the remarkable reduction of maternal mortality, unsafe and untimely menstrual regulation (MR) remains a major maternal health problem in Bangladesh. This study aimed to determine the prevalence and identify determinants of MR among ever-married women in Bangladesh. METHODS: Data for this study have been extracted from Bangladesh Demographic and Health Survey (BDHS) 2014. The survey followed a two-stage stratified sampling procedure and the study used a sub-sample of 8084 ever-married women aged 15 to 49 years extracted from survey sample of 17,863. Univariate and multivariate mixed-effect logistic regression analyses were used to identify risk factors for MR accounting for potential between-clusters variations. RESULTS: The weighted prevalence of MR was 12.3% (95% CI: 11.1-13.4%) among (991/8084) ever-married women. Women were less likely to have MR if they were from Chittagong (AOR 0.74, 95% CI: 0.57-0.96; p = 0.026) and Sylhet (AOR 0.53, 95% CI: 0.36-0.77; p = 0.001) divisions. Women were more likely to have MR if they were from high (AOR 1.47, 95% CI: 1.18-1.83; p = 0.001) and the highest (AOR 1.62, 95% CI: 1.27-2.05; p < 0.001) socioeconomic status (SES) group; being employed (AOR 1.35, 95% CI: 1.16-1.56; p < 0.001), having one or two children (AOR 1.73, 95% CI: 1.24-2.40: p = 0.001) and ≥ 3 children (AOR 2.56, 95% CI: 1.82-3.58; p < 0.001), and having membership of non-government organization (NGO) (AOR 1.18, 95% CI: 1.02-1.38; p = 0.030). CONCLUSION: MR is prevalent among Bangladeshi women and independently associated with geographic location, SES, parity, employment and NGO membership status. Health policy should prioritize in reducing spatial and socioeconomic inequalities in relation to MR services by ensuring accessibility and availability of MR services, especially in suburban divisions. Furthermore, abortion should be legalized in Bangladesh that will ultimately reduce the morbidity and mortality associated with unsafe abortion.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".