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Am J Public Health ; 112(1): 144-153, 2022 01.
Artículo en Inglés | MEDLINE | ID: covidwho-1841232

RESUMEN

Objectives. To describe associations between neighborhood racial and economic segregation and violence during the COVID-19 pandemic. Methods. For 13 US cities, we obtained zip code-level data on 5 violence outcomes from March through July 2018 through 2020. Using negative binomial regressions and marginal contrasts, we estimated differences between quintiles of racial, economic, and racialized economic segregation using the Index of Concentration at the Extremes as a measure of neighborhood privilege (1) in 2020 and (2) relative to 2018 through 2019 (difference-in-differences). Results. In 2020, violence was higher in less-privileged neighborhoods than in the most privileged. For example, if all zip codes were in the least privileged versus most privileged quintile of racialized economic segregation, we estimated 146.2 additional aggravated assaults (95% confidence interval = 112.4, 205.8) per zip code on average across cities. Differences over time in less-privileged zip codes were greater than differences over time in the most privileged for firearm violence, aggravated assault, and homicide. Conclusions. Marginalized communities endure endemically high levels of violence. The events of 2020 exacerbated disparities in several forms of violence. Public Health Implications. To reduce violence and related disparities, immediate and long-term investments in low-income neighborhoods of color are warranted. (Am J Public Health. 2022;112(1):144-153. https://doi.org/10.2105/AJPH.2021.306540).


Asunto(s)
COVID-19/epidemiología , Violencia con Armas/estadística & datos numéricos , Factores Raciales , Características de la Residencia/clasificación , Segregación Social , Factores Socioeconómicos , Violencia/estadística & datos numéricos , Ciudades/estadística & datos numéricos , Homicidio/estadística & datos numéricos , Humanos , Violación/estadística & datos numéricos , Características de la Residencia/estadística & datos numéricos , Robo/estadística & datos numéricos , Estados Unidos/epidemiología
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