Evidence map›Paper›PMID 39616348›Full record

ArticleBMC medical informatics and decision making2024

Exploring parental factors influencing low birth weight on the 2022 CDC natality dataset.

Sumaiya Sultana Dola, Camilo E Valderrama

Abstract read
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Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

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0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Sumaiya Sultana Dola *Applied Computer Science Department, University of Winnipeg, 515 Portage Avenue, Winnipeg, R3B 2E9, MB, Canada.
Camilo E Valderrama *Applied Computer Science Department, University of Winnipeg, 515 Portage Avenue, Winnipeg, R3B 2E9, MB, Canada. c.valderrama@uwinnipeg.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimsLow birth weight (LBW), known as the condition of a newborn weighing less than 2500 g, is a growing concern in the United States (US). Previous studies have identified several contributing factors, but many have analyzed these variables in isolation, limiting their ability to capture the combined influence of multiple factors. Moreover, past research has predominantly focused on maternal health, demographics, and socioeconomic conditions, often neglecting paternal factors such as age, educational level, and ethnicity. Additionally, most studies have utilized localized datasets, which may not reflect the diversity of the US population. To address these gaps, this study leverages machine learning to analyze the 2022 Centers for Disease Control and Prevention's National Natality Dataset, identifying the most significant factors contributing to LBW across the US.

methodsWe combined anthropometric, socioeconomic, maternal, and paternal factors to train logistic regression, random forest, XGBoost, conditional inference tree, and attention mechanism models to predict LBW and normal birth weight (NBW) outcomes. These models were interpreted using odds ratio analysis, feature importance, partial dependence plots (PDP), and Shapley Additive Explanations (SHAP) to identify the factors most strongly associated with LBW.

resultsAcross all five models, the most consistently associated factors with birth weight were maternal height, pre-pregnancy weight, weight gain during pregnancy, and parental ethnicity. Other pregnancy-related factors, such as prenatal visits and avoiding smoking, also significantly influenced birth weight.

conclusionThe relevance of maternal anthropometric factors, pregnancy weight gain, and parental ethnicity can help explain the current differences in LBW and NBW rates among various ethnic groups in the US. Ethnicities with shorter average statures, such as Asians and Hispanics, are more likely to have newborns below the World Health Organization's 2500-gram threshold. Additionally, ethnic groups with historical challenges in accessing nutrition and perinatal care face a higher risk of delivering LBW infants.

Indexed as

Infant, Low Birth WeightAdultCenters for Disease Control and Prevention, U.S.Datasets as TopicFemaleHumansInfant, NewbornMachine LearningMaleParentsPregnancyRisk FactorsSocioeconomic FactorsUnited StatesInterpretable predictive modelsLow birth weightMachine learningMaternal healthParental factorsStatistical analysis

Identifiers

PMID39616348
PMCPMC11608488

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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.