Evidence map›Paper›PMID 35688594›Full record

ArticleBMJ open2022

Examining infant and child death clustering among families in the cross-sectional and nationally representative Bangladesh Demographic and Health Survey 2017-2018.

Ronak Paul, Shobhit Srivastava, Rashmi Rashmi

Open access · goldAbstract read
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Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.1field-weighted citation impact, top 21% of its field
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 3 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Ronak PaulDepartment of Public Health & Mortality Studies, International Institute for Population Sciences, Mumbai, India.ORCID 0000-0001-6752-2549
Shobhit SrivastavaDepartment of Survey Research & Data Analytics, International Institute for Population Sciences, Mumbai, India.ORCID 0000-0002-7138-4916
Rashmi RashmiDepartment of Population & Development, International Institute for Population Sciences, Mumbai, India rashmir635@gmail.com.ORCID 0000-0003-4709-3569
International Institute for Population Sciences · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesWe aim to examine the phenomenon of infant and child death clustering while considering the unobserved heterogeneity (frailty) at the family level. DESIGN, SETTING, AND

participantsWe analysed Bangladesh Demographic and Health Survey 2017-2018 data, including the birth history information for 47 828 children born to 18 134 women. We used Gompertz shared frailty model to control the correlation between event times at the mother level and capture the unobserved risks in infant and child deaths. OUTCOME MEASURES: We estimated two sets of survival regression models where the failure event is the survival status of the index child during the infancy period, that is, from birth to 11 months, and childhood period, that is, between 12 and 59 months, respectively. All children who died during infancy and childhood were coded as 'yes'; otherwise, they were coded as 'no'.

resultsAbout 2% of mothers experienced two or more infant deaths, and cumulatively these mothers account for 20% of all infant deaths in the sample. Children whose previous sibling was not alive at the time of their conception had 1.86 times (95% CI 1.59 to 2.17) more risk of dying as an infant. However, we did not find a statistically significant effect of death scarring on the risk of child mortality among siblings. Statistically significant frailty effect with a variance of 0.33 (95% CI CI 0.17 to 0.65) and 0.54 (95% CI 0.14 to 2.03)] in infancy and childhood, respectively, indicates the clustering of survival risks within families due to unobserved family-level characteristics shared by the siblings.

conclusionThis study suggests that preceding birth interval, mother's age at first birth and mother's education are the most critical factors which can help in reducing scaring effect on infant mortality. Additionally, women from poor socioeconomic strata should be focused on as still an infant, and child mortality is concentrated among poor households.

Indexed as

FrailtyBangladeshBirth IntervalsChildCluster AnalysisCross-Sectional StudiesFemaleHumansInfantInfant DeathInfant MortalitySocioeconomic FactorsChild protectionCommunity child healthEpidemiologyHealth policyPUBLIC HEALTH

Identifiers

PMID35688594
PMCPMC9189828
OpenAlexW4281612574

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LicenceCC BY-NC
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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.