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Modeling the Ranked Antenatal Care Visits Using Optimized Partial Least Square Regression.

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Reduced antenatal care visits are linked to maternal and infant health risks. This study identified key factors like education and socioeconomic status influencing care access, using advanced statistical modeling for better insights.

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Area of Science:

  • Public Health
  • Biostatistics
  • Demography

Background:

  • Antenatal care (ANC) frequency and timing are critical determinants of maternal and infant morbidity and mortality.
  • Understanding risk factors for reduced ANC visits is essential for improving maternal and child health outcomes.

Purpose of the Study:

  • To identify significant risk factors associated with reduced antenatal care visits.
  • To apply an optimized partial least square regression model for analyzing ranked data on ANC utilization.

Main Methods:

  • Utilized data from the Pakistan Demographic and Health Surveys (2017-2018).
  • Employed partial least square regression (PLS) models combined with rank correlation measures (e.g., PLSρ).
  • Applied filter-based factor selection, leave-one-out cross-validation with linear discriminant analysis, and Monte Carlo simulations for model optimization.

Main Results:

  • The PLSρ model demonstrated superior performance in modeling ranked ANC visit data.
  • Identified 29 influential factors contributing to inadequate antenatal care utilization.
  • Key factors included women's education, wealth index, parity, husband's education, domestic violence, and prior C-section history.

Conclusions:

  • Optimized partial least square regression algorithms with rank correlation coefficients offer efficient estimation for ranked data, even with multicollinearity.
  • Addressing identified socioeconomic and demographic factors is crucial for enhancing antenatal care access and improving maternal and infant health.