Covid-19 and Inequity: A comparative spatial analysis of New York City and Chicago hot spots

Abstract

There have been numerous reports that the impact of the ongoing COVID-19 epidemic has disproportionately impacted traditionally vulnerable communities, including well-researched social determinants of health, such as racial and ethnic minorities, migrants, and the economically challenged. The goal of this ecological cross-sectional study is to examine the demographic and economic nature of spatial hot and cold spots of SARS-CoV-2 rates in New York City and Chicago as of April 13, 2020.In both cities, cold spots (clusters of low SARS-CoV-2 rate ZIP code tabulation areas) demonstrated typical protective factors associated with the social determinants of health and the ability to social distance. These neighborhoods tended to be wealthier, have higher educational attainment, higher proportions of non-Hispanic white residents, and more workers in managerial occupations. Hot spots (clusters of high SARS-CoV-2 rate ZIP code tabulation areas) also had similarities, such as lower rates of college graduates and higher proportions of people of color. It also appears to be larger households (more people per household), rather than overall population density, that may to be a more strongly associated with hot spots.Findings suggest important differences between the cities' hot spots as well. They can be generalized by describing the NYC hot spots as working-class and middle-income communities, perhaps indicative of service workers and other occupations (including those classified as "essential services" during the pandemic) that may not require a college degree but pay wages above poverty levels. Chicago's hot spot neighborhoods, on the other hand, are among the city's most vulnerable, low-income neighborhoods with extremely high rates of poverty, unemployment, and non-Hispanic Black residents.

COVID-19 incidence and mortality in the Metropolitan Region, Chile: time, space, and structural factors

“…Second, as long as different models are required to tell different stories (34 estimations in this case), a significant part of the municipal variation in infections and deaths can be explained by a rather small number of variables (as inTable 5). In any case, all the results highlight the role of social determinants of health in explaining the dissimilar impact of COVID-19 in the MR as well as other findings in other countries about unequal distribution of COVID-19(83)(84)(85)(86).…”

“…There is also evidence of regional inequities in access to healthcare (24)(25)(26)(27)(28)(29)(30). Space and place have increasingly been used to analyze and understand health decisions and outcomes (31,32); this relationship has already proven to be important for the new COVID-19 disease (33)(34)(35)(36)(37)(38). Understanding some of these features behind the infection and death patterns-in the context of an unequal and segregated city-is important for people, health-workers and policymakers, to address better prevention policies and community tailored intervention care programs.…”

Black/African American Communities are at Highest Risk of COVID-19: Spatial Modeling of New York City ZIP Code-Level Testing Results

“…Despite the recent onset of the current COVID-19 pandemic, there is already growing evidence about both individual risk factors and population-level drivers of disease and mortality. This study adds to the number of very recent similar spatial analyses of ZCTA-level testing data released by the New York City Department of Health and Mental Hygeiene, [22][23][24] and illustrates the importance of sharing these kinds of data, as well as the informative nature of spatial epidemiology as the pandemic evolves across the nation and the world. Consistent with prior reports, we find that the clustering of positive COVID-19 testing results in NYC are unlikely to be due to chance, 9,23 and is driven in large measure by socioeconomics, age distribution, 24 and race.…”

County-level factors influence the trajectory of Covid-19 incidence

“…CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) currently inconsistent [8][9][10][11][12]. Notably, findings vary substantially by the modeling technique used, inclusion/exclusion of independent variables, granularity (e.g., county-level vs. state-level), and domain of outcome being measured (e.g., concentrated disadvantage vs. social vulnerability index).…”