08. Characterizing racial and economic disparities and predictors of Gestational Diabetes Mellitus in AAPI Populations: Secondary Analysis of PRAMS data, 2016-2022

GDM affects between 2 and 10% of pregnancies in the United States with trends of increasing prevalence with a significant amount of variability due to racial/ethnic factors, maternal age, insurance at the individual level, and state-level factors. Asian American Pacific Islander (AAPI) have been documented to have a higher prevalence and risk of developing GDM compared to non-Hispanic White populations. We aim to explore racial and economic disparities in GDM and conduct within group analysis focusing on AAPI populations to identify risk factors and predictors of GDM within this vulnerable racial group. This is a secondary analysis of the Pregnancy Risk Assessment Monitoring System (PRAMS) 2016-2022 dataset. PRAMS consists of state-specific and national data on current and emerging issues in reproductive and maternal child health. Subset analyses were based on aggregated race groups: AAPI ethnic subgroups and non-AAPI populations. Bivariate analysis was performed to explore the relationship between potential risk factors for GDM among the subsets and multivariable logistic regression was used to investigate potential predictors of GDM.
In both the overall dataset and AAPI subset, the odds of GDM diagnosis consistently increased with maternal age and pre-pregnancy BMI. However, while significant risk factors of GDM in the overall population were a combination of demographic, BMI, psychosocial, and structural/socioeconomic factors, only demographic factors (ethnicity, maternal age, pre-pregnancy BMI) in the AAPI population were significant predictors of GDM diagnosis. This study sought to make a significant impact on policy and clinical practices on obstetric care concerning racial/ethnic minorities, low-income women, and at-risk AAPI individuals. The findings presented in this study contribute to new insights on potential predictors of GDM diagnosis and may inform targeted or earlier GDM screening for at-risk individuals.

Mid-Level

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Women in Statistics and Data Science 2024