ArticleProceedings of the National Academy of Sciences of the United States of America2021
Using machine learning to estimate the effect of racial segregation on COVID-19 mortality in the United States.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Can past variants of SARS-CoV-2 predict the impact of future variants? Machine learning for early warning of US counties at risk.Health care management science · 2025Article
- Urban and Rural Patterns of Health Care Utilization Among People With Rheumatoid Arthritis and Osteoarthritis in a Large US Patient Registry.Arthritis care & research · 2025Article
- Bias in mobility datasets drives divergence in modeled outbreak dynamics.Communications medicine · 2025Article
- The causal impact of segregation on a disparity: A gap-closing approach.Sociological science · 2025Article
- Still Separate, Still Not Equal: An Ecological Examination of Redlining and Racial Segregation with COVID-19 Vaccination Administration in Washington D.C.Journal of urban health : bulletin of the New York Academy of Medicine · 2024Article
- 7-day patterns in Black-White segregation in 49 metropolitan areas.Scientific reports · 2024Article
- Association of race/ethnicity and severe housing problems with COVID-19 deaths in the United States: Analysis of the first three waves.PloS one · 2024Article
- Racial Disparities in Occupational Distribution Among Black and White Adults with Similar Educational Levels: Analysis of Middle-Aged and Older Individuals in the Health and Retirement Study.Journal of rehabilitation therapy · 2024Article
- The Structure of Pandemic Vulnerability: Housing Wealth, Residential Segregation, and COVID‑19 Mortality.Population research and policy review · 2023Article
- Spatial Variation in COVID-19 Mortality in New York City and Its Association with Neighborhood Race, Ethnicity, and Nativity Status.International journal of environmental research and public health · 2023Article
- Associations between institutional-social-ecological factors and COVID -19 case-fatality: Evidence from 134 countries using multiscale geographically weighted regression (MGWR).One health (Amsterdam, Netherlands) · 2023Article
- The COVID-19, racism, and violence syndemic: Evidence from a qualitative study with Black residents of Chicago.SSM. Qualitative research in health · 2023Article
- Understanding mobility change in response to COVID-19: A Los Angeles case study.Travel behaviour & society · 2023Article
- Psychology Meets Biology in COVID-19: What We Know and Why It Matters for Public Health.Policy insights from the behavioral and brain sciences · 2023Article
- Race, employment, and the pandemic: An exploration of covariate explanations of COVID-19 case fatality rate variance.PloS one · 2023Article
- A comparison of COVID-19 outbreaks across US Combined Statistical Areas using new methods for estimating REpidemics · 2022Article
- Unequal airborne exposure to toxic metals associated with race, ethnicity, and segregation in the USA.Nature communications · 2022Article
- Moderating Effects of Racial Segregation on the Associations of Cardiovascular Outcomes with Walkability in Chicago Metropolitan Area.International journal of environmental research and public health · 2022Article
- A novel method to monitor COVID-19 fatality rate in real-time, a key metric to guide public health policy.Scientific reports · 2022Article
- Inequities in spatial accessibility to COVID-19 testing in 30 large US cities.Social science & medicine (1982) · 2022Article
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Abstract
This study examines the role that racial residential segregation has played in shaping the spread of COVID-19 in the United States as of September 30, 2020. The analysis focuses on the effects of racial residential segregation on mortality and infection rates for the overall population and on racial and ethnic mortality gaps. To account for potential confounding, I assemble a dataset that includes 50 county-level factors that are potentially related to residential segregation and COVID-19 infection and mortality rates. These factors are grouped into eight categories: demographics, density and potential for public interaction, social capital, health risk factors, capacity of the health care system, air pollution, employment in essential businesses, and political views. I use double-lasso regression, a machine learning method for model selection and inference, to select the most important controls in a statistically principled manner. Counties that are 1 SD above the racial segregation mean have experienced mortality and infection rates that are 8% and 5% higher than the mean. These differences represent an average of four additional deaths and 105 additional infections for each 100,000 residents in the county. The analysis of mortality gaps shows that, in counties that are 1 SD above the Black-White segregation mean, the Black mortality rate is 8% higher than the White mortality rate. Sensitivity analyses show that an unmeasured confounder that would overturn these findings is outside the range of plausible covariates.
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