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Using Big Data to measure discrimination impacts on birth outcomes

New National Institute on Minority Health and Health Disparities grant

The unfiltered public forum of Twitter may present a new tool to help understand the relationship between racial hostility and health in a geographic area.

Supported by a new $3.3 million grant from NIMHD, Faculty Associate Quynh Nguyen is part of a research team that will analyze the content of tweets, and other factors, to characterize the racial climate in regions across the United States. Those results will illuminate the team’s study of disparities in birth outcomes, including preterm births and low birth weight, both of which their earlier research indicated can be affected by area-level racial bias.

“Racial discrimination is often studied as an individual-level experience, but we know that it is pervasive and has social-environmental components,” said Dr. Nguyen, a co-investigator on the study. “In the past, we have linked living in communities with higher racial hostility to a higher risk of adverse birth outcomes and cardiovascular disease.”

The project, led by Dr. Thu Nguyen at the University of California San Francisco, brings together experts in epidemiology, health disparities, machine learning, social media data, biostatistics and community-engaged research fields. They will use a range of online and social media data coupled with machine learning models to create two measures of area-level racial bias, and then employ analytics to see if the bias correlates to birth outcomes.

Specifically, Dr. Nguyen writes, 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 project will advance the research in all these areas. Nguyen and her team will use 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. The investigative team includes experts in the fields of epidemiology, health disparities, machine learning, social media data, biostatistics, and community engaged research. 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 the data is collected repeatedly and finely across the United States, the group can explicitly account for temporal trends and place effects. The project will use 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.

 

"Risk and strength: determining the impact of area-level racial bias and protective factors on birth outcomes", NIMHD, 1R01MD015716

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