Evidence map›Paper›PMID 40176929›Full record

ArticleSafety and health at work2025

Health Challenges Among Waste Collectors in Bangladesh: Exploring Risk Factors Using Multi-level Modeling.

Safayet Hossain, Md Farhad Hossain, Bowen Liu, Anjuman Ara, Haneen Alsaoud, Md Abdul Majed Patwary

Abstract read
In one paragraph

Article in Safety and health at work, 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
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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

6 authors.

Safayet HossainDepartment of Statistics, Comilla University, Cumilla 3506, Bangladesh.
Md Farhad HossainDepartment of Statistics, Comilla University, Cumilla 3506, Bangladesh.
Bowen LiuDepartment of Mathematics and Statistics, Division of Computing, Analytics and Mathematics, Science and Engineering, University of Missouri, Kansas-City, 64110, USA.
Anjuman AraDepartment of Statistics, Comilla University, Cumilla 3506, Bangladesh.
Haneen AlsaoudDepartment of Mathematics and Statistics, Division of Computing, Analytics and Mathematics, Science and Engineering, University of Missouri, Kansas-City, 64110, USA.
Md Abdul Majed PatwaryDepartment of Chemistry, Comilla University, Cumilla 3506, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Waste collectors face multi-faceted health risks including exposure to musculoskeletal disorders, respiratory diseases, and workplace injuries because of their hazardous work environment. The purpose of this study was to determine the risk factors that affect health of the waste collectors across Bangladesh. Methods: Data on a cross-sectional survey obtained information from 481 waste collectors about their demographics, housing conditions, hygiene practices, security measures, and disease prevalence. Descriptive analyses and multi-level models are used. Results: As per univariate analysis, 81.3% did not have access to clean water, 58.4% did not have access to sanitation, and 65.9% of people lived in unhealthy housing. According to bivariate analysis, there is an association between unhealthy working conditions i.e., respiratory illnesses (19.1% asthma, 29.7% cough), gastrointestinal issues (59.3% discomfort, 24.1% diarrhea), musculoskeletal disorders, and dermatological diseases, and unsafe working conditions (i.e., 60% no masks, 71% no gloves, and 75% no boots). Considering geographic clustering, multi-level modeling examined how different factors affected particular illnesses. The following were significant protective factors: better housing (80% lower odds of acute irritation), security measures (50% lower odds of gastrointestinal diseases), good hygiene (62% lower odds of dermatological diseases), and younger age (2% higher odds of respiratory diseases per year). Conclusion: In summary, unsafe living and working conditions greatly push the risks of illness for waste collectors. These risks to occupational health can be reduced with targeted interventions that enhance housing, hygiene, security protocols, and working conditions.

Indexed as

DiseasesMachine learningMulti-level modelingOccupational healthWaste collectors

Identifiers

PMID40176929
PMCPMC11959559

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LicenceCC BY
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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.