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Record W4407082912 · doi:10.1136/ip-2024-045434

Socioeconomic, demographic and environmental factors of child drownings in Northern Bangladesh

2025· article· en· W4407082912 on OpenAlexaff
Edris Alam, Khawla Saeed Al Hattawi, Habiba Akter, Jahangir Alam, Elizabeth Álvarez, Md Kamrul Islam, Abu Reza Md. Towfiqul Islam

Bibliographic record

VenueInjury Prevention · 2025
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsSocioeconomic statusEnvironmental healthInjury preventionPoison controlPovertyGeographySocioeconomicsOccupational safety and healthSuicide preventionHuman factors and ergonomicsMedicineDemographyPopulationEconomic growth

Abstract

fetched live from OpenAlex

BACKGROUND: Drowning is the leading cause of death among children aged 0-17 years in rural Bangladesh, resulting in over 14 438 deaths annually-an average of 43 deaths per day. This study aims to identify socioeconomic, demographic and environmental factors linked to child drowning deaths in Northern Bangladesh-a region of high poverty, which is behind in overall socioeconomic indicators compared with other regions in the country. METHODS: We conducted a cross-sectional survey through purposive sampling to identify child fatal and non-fatal drownings among a total of 18 004 households, comprising 71 185 people, in 2 unions in Northern Bangladesh. Interviews were conducted between January and March 2024 with the households that experienced child drownings in the region. We employed a mixed-methods approach to data collection, using quantitative analysis to examine socioeconomic, demographic and environmental factors, alongside qualitative analysis to explore situational factors associated with drownings in the region. RESULTS: Through household visits, a total of 117 households were identified that faced child drowning incidents, comprising 84 fatal (71.8 %) and 33 non-fatal (28.2 %) drownings between 2018 and 2023. The households that faced drownings were comparatively of lower income groups, had lower rates of education and were mostly engaged in agriculture and other domestic work. In 2023, the number of drowning incidents was 34. Out of 117 drownings, 95% occurred between 9:00 and 15:00 hours, and more than 82% occurred between June and October. Out of 117 drowning incidents, approximately 97% of children did not know how to swim prior to the incident. Out of 117 respondents, 73.5% stated that they did not teach their child how to swim. Of those who taught their child to swim, the average age for learning to swim was 8.33 years. Out of 84 child drowning deaths, 75% were male and 25% were female, and the average age was 3.9 years. Out of the 84 fatal drowning deaths, 72.6% occurred in ponds. CONCLUSION: Identification of socioeconomic, demographic and environmental factors associated with child drownings will help to develop feasible prevention strategies and interventions in the region.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.267
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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