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Updated: Sep 19, 2025

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Leveraging neighborhood-level Information to Improve Model Fairness in Predicting Prenatal Depression.

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    Integrating neighborhood data into perinatal depression (PND) prediction models improves fairness and identifies key factors influencing bias across racial groups. This approach helps reduce disparities in maternal mental healthcare.

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    Area of Science:

    • Public Health
    • Machine Learning in Healthcare
    • Health Disparities Research

    Background:

    • Perinatal depression (PND) affects 10-20% of pregnant women, exhibiting significant racial disparities in prevalence, screening, and treatment.
    • Neighborhood-level factors are known to influence PND risk, especially among women of color, but are often omitted from machine learning models utilizing electronic medical records (EMRs).

    Purpose of the Study:

    • To evaluate if integrating neighborhood-level data with EMRs enhances fairness in PND prediction models.
    • To identify specific neighborhood factors that influence model bias across different racial and ethnic groups.

    Main Methods:

    • A study of 6,137 pregnant women (58% Non-Hispanic Black, 10% Non-Hispanic White, 28% Hispanic) from 2010-2019.
    • Merged 125 neighborhood factors from the Chicago Health Atlas with 61 EMR features based on residential location.
    • Assessed model performance (ROCAUC, PRAUC) and fairness (disparate impact, equal opportunity difference, equalized odds), using Shapley values for feature importance.

    Main Results:

    • Models incorporating neighborhood data demonstrated moderate predictive performance (ROCAUC: NHB 55%, NHW 57%, H 58%) and significantly improved fairness metrics (p<0.05) compared to EMR-only models.
    • Neighborhood factors like suicide mortality and safety rates helped reduce prediction bias.
    • Non-Hispanic Black women showed stronger correlations between PND risk and neighborhood variables; factors differentially impacted bias across groups, reducing it for Hispanic women while increasing it for NHB women.

    Conclusions:

    • Integrating neighborhood information into PND prediction models improves fairness without compromising predictive ability.
    • The differential impact of neighborhood factors underscores the necessity of considering socio-environmental context in clinical risk assessments to mitigate disparities in prenatal depression care.