Evidence map›Paper›PMID 40638680›Full record

ArticlePloS one2025

Generalized linear mixed model approach for analyzing water, sanitation, and hygiene facilities in Bangladesh: Insights from BDHS 2022 data.

Mahmila Sanjana Mim, Md Lutfor Rahaman, Anamul Haque Sajib

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Mahmila Sanjana MimDepartment of Statistics, University of Dhaka, Dhaka, Bangladesh.ORCID https://orcid.org/0009-0002-4419-1542
Md Lutfor RahamanDepartment of Statistics, University of Dhaka, Dhaka, Bangladesh.
Anamul Haque SajibDepartment of Statistics, University of Dhaka, Dhaka, Bangladesh.ORCID https://orcid.org/0009-0000-0583-4045

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study explores the key determinants and barriers to effective WASH facilities in Bangladesh, which are crucial for mitigating health issues and ensuring equitable access. By analyzing the 2022 Bangladesh Demographic and Health Survey (BDHS) data and accounting for design clustering using a Generalized Linear Mixed Model (GLMM), this study's methodology demonstrates superior performance compared to conventional logistic regression, as supported by Akaike Information Criterion (AIC) and likelihood ratio test (LRT). The study found that basic handwashing facility was significantly linked to the household head's age, partner's education, media exposure, women's empowerment, wealth index, and maternal factors such as - age and education of mothers of under 5 children. Basic sanitation was associated with regional factors, the household head's sex and age, household size, partner's education, working status, wealth index, and maternal factors. Access to basic drinking water was largely driven by the wealth index, while combined WASH facilities were influenced by household head's sex and age, household size, partner's education, working status, media exposure, wealth index, and maternal characteristics. The findings indicate that addressing WASH challenges in Bangladesh requires an integrated, multi-dimensional policy approach that considers key socio-demographic and economic factors-a strategy essential for achieving 6th Sustainable Development Goal (SDG).

Indexed as

Drinking WaterHygieneSanitationAdolescentAdultBangladeshFamily CharacteristicsFemaleHand DisinfectionHealth SurveysHumansLinear ModelsMaleMiddle AgedSocioeconomic FactorsYoung AdultDrinking Water

Identifiers

PMID40638680
PMCPMC12244552

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.