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

BMC Med Inform Decis Mak

Applied Computer Science Department, University of Winnipeg, 515 Portage Avenue, Winnipeg, R3B 2E9, MB, Canada.

Published: November 2024

AI Article Synopsis

  • Low birth weight (LBW) is a growing issue in the US, often linked to various factors, but previous studies usually examined these in isolation and overlooked paternal influences.
  • This study uses machine learning on the 2022 National Natality Dataset to analyze a wide range of factors affecting LBW, combining both maternal and paternal characteristics.
  • Key findings highlight that maternal height, pre-pregnancy weight, weight gain during pregnancy, and parental ethnicity significantly affect birth weight, indicating disparities among different ethnic groups in the US.

Article Abstract

Background And Aims: Low 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.

Methods: We 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.

Results: Across 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.

Conclusion: The 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11608488PMC
http://dx.doi.org/10.1186/s12911-024-02783-xDOI Listing

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