There are large and persistent racial and ethnic disparities in preterm birth and low birth weight. Individual-level risk factors do not fully explain the observed disparities. There is increasing evidence for the role of area-level racial bias in explaining these disparities, but we currently lack both the measures, methods, and findings to empirically evaluate its influence. The proposed research will advance the research in all 3 areas. We will be using online and social media data and machine learning models to create two measures of area-level racial bias and implement a robust research design to determine whether area-level racial bias impacts birth outcomes. Our investigative team?comprised of experts in the field of epidemiology, health disparities, machine learning, social media data, biostatistics, and community engaged research?is uniquely suited to implement the study aims.
Our Specific Aims are to 1) track and detect changes in area-level racial bias and identify local and national race-related events during these time points, 2) determine the impact of changes in area-level racial bias on changes in adverse birth outcomes, and 3) identify protective factors for adverse birth outcomes. Because our data is collected repeatedly and finely across the United States, we can explicitly account for temporal trends and place effects. The proposed study uses new data to capture trends in racial bias with sophisticated machine learning models, and represents a critical advancement in the investigation of racial disparities in birth outcomes.
Individual-level risk factors are not fully explaining the large and persistent racial disparities in birth outcomes, limiting policy and intervention strategies to reduce these disparities. This project will advance our understanding of the potential effects of racial bias at a population level on the risk of preterm birth and low birth weight and identify protective factors to buffer its impact.