Evidence map›Paper›PMID 38359056›Full record

ArticlePLOS global public health2024

Clustering of home delivery in Bangladesh and its predictors: Evidence from the linked household and health facility level survey data.

Kaniz Fatima, Shimlin Jahan Khanam, Md Mostafizur Rahman, Md Iqbal Kabir, Md Nuruzzaman Khan

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Article in PLOS global public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Kaniz FatimaDepartment of Population Science and Human Resource Development, University of Rajshahi, Rajshahi, Bangladesh.
Shimlin Jahan KhanamDepartment of Population Science, Jatiya Kabi Kazi Nazrul Islam University, Mymensingh, Bangladesh.ORCID https://orcid.org/0000-0001-6500-4537
Md Mostafizur RahmanDepartment of Population Science and Human Resource Development, University of Rajshahi, Rajshahi, Bangladesh.
Md Iqbal KabirClimate Change and Health Promotion Unit, Ministry of Health and Family Welfare, Dhaka, Bangladesh.
Md Nuruzzaman KhanDepartment of Population Science, Jatiya Kabi Kazi Nazrul Islam University, Mymensingh, Bangladesh.ORCID https://orcid.org/0000-0002-4550-4363

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Around half of births in Bangladesh occur at home without skilled birth personnel. This study aims to identify the geographical hot spots and cold spots of home delivery in Bangladesh and associated factors. We analyzed data from the 2017/2018 Bangladesh Demographic and Health Survey and the 2017 Bangladesh Health Facility Survey. The outcome variable was home delivery without skilled personnel supervision (yes, no). Explanatory variables included individual, household, community, and healthcare facility level factors. Moran's I was used to determine hot spots (geographic locations with notably high rates of home delivery) and cold spots (geographic areas exhibiting significantly low rates of home delivery) of home delivery. Geographically weighted regression models were used to identify cluster-specific predictors of home delivery. The prevalence of without skilled personnel supervised home delivery was 53.18%. Hot spots of non-supervised and unskilled supervised home delivery were primarily located in Dhaka, Khulna, Rajshahi, and Rangpur divisions. Cold spots of home delivery were mainly located in Mymensingh and Sylhet divisions. Predictors of higher home births in hot spot areas included women's illiteracy, lack of formal job engagement, higher number of children ever born, partner's agriculture occupation, higher community-level illiteracy, and larger distance to the nearest healthcare facility from women's homes. The study findings suggest home delivery is prevalent in Bangladesh. Awareness-building programs should emphasize the importance of skilled and supervised institutional deliveries, particularly among the poor and disadvantaged groups.

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PMID38359056
PMCPMC10868736

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