Evidence map›Paper›PMID 40165129›Full record

ArticleBMC pregnancy and childbirth2025

Unraveling the complexity of selected adverse neonatal outcomes in India: a multilevel analysis using data from a nationally representative sample survey.

Anuj Kumar Pandey, Benson M Thomas, Diksha Gautam, Arun Balachandran, Dyah Anantalia Widyastari, Shyamkumar Sriram, Sutapa Bandyopadhyay Neogi

Abstract read
In one paragraph

Article in BMC pregnancy and childbirth, 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

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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.

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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

7 authors.

Anuj Kumar PandeyDepartment of Health Systems and Implementation Research, International Institute of Health Management Research New Delhi, Dwarka, India.
Benson M ThomasSchool of Public Health, SRM Institute of Science and Technology, Chennai, India.
Diksha GautamDepartment of Health Systems and Implementation Research, International Institute of Health Management Research New Delhi, Dwarka, India.
Arun BalachandranMailman School of Public Health, Columbia University, New York City, NY, USA.
Dyah Anantalia WidyastariInstitute for Population and Social Research, Mahidol University, Nakhon Pathom, Thailand.
Shyamkumar SriramDepartment of Rehabilitation and Health Services, College of Health and Public Service, University of North Texas, Denton, TX, USA.
Sutapa Bandyopadhyay NeogiDepartment of Health Systems and Implementation Research, International Institute of Health Management Research New Delhi, Dwarka, India. sutapa@iihmrdelhi.edu.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe burden of adverse neonatal outcomes (ANOs), encompassing preterm birth(PTB), low birth weight(LBW), and early neonatal deaths, remain significant public health challenge globally, particularly in developing countries. The study aims to provide estimates of adverse birth outcomes and examine their correlates by using a multi-level model analysis at individual/household/community level. METHODOLOGY: The study has chosen three ANOs such as preterm birth(PTB), low birth weight(LBW), and early neonatal deaths (based on available data) for constructing a combined indicator which is calculated by the presence of any one of these variables. We used National-Family-Health-Survey India data(2019-21). Multilevel(three-level) logistic regression model was used to find the probability of binary adverse neonatal outcomes with the effects of individual/household/community level variables among the recently delivered women.

resultBetween 2019-21, a total of 26.5% ANOs were reported from 1.7 million pregnant women surveyed, a rate that has increased since 2005-06 (20%). Final multilevel model asserts that women having higher education [OR 0.92, 95%CI 0.88, 0.96), and those registered for antenatal checkups (OR 0.95, 95%CI OR 0.9, 0.99) and know all components of birth-preparedness-and-complication-readiness (OR 0.88, 95%CI 0.84, 0.92) have a higher protective odd of having adverse outcomes. Difficulty in seeking medical help (OR 1.2, 95%CI 1.15, 1.25) and belonging to poor wealth status and no intention to become pregnant (OR 1.11 95% CI 1.05, 1.18) acts as a risk factor. Multilevel model with household, community and district level variables added to the null model showed a decline in the ICC values to 4.7%, 18.8% and 30.9% respectively across district, community, and household levels.

conclusionThe study underscores that specific ANOs in India has shown an increase, prompting significant concern. There is need to institute a mechanism for generating knowledge amongst women to protect them from unwanted pregnancies and later adverse outcomes.

Indexed as

Infant, Low Birth WeightPerinatal DeathPregnancy OutcomePremature BirthAdultFemaleHealth SurveysHumansIndiaInfant MortalityInfant, NewbornLogistic ModelsMultilevel AnalysisPregnancyRisk FactorsSocioeconomic FactorsAdverse neonatal outcomeANOsEarly neonatal deathLBWPreterm birthSDG

Identifiers

PMID40165129
PMCPMC11956479

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LicenceCC BY-NC-ND
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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.