Neighborhood-level Racial/Ethnic and Economic Inequities in COVID-19 Burden Within Urban Areas in the US and Canada
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Abstract
The COVID-19 pandemic exhibits stark social inequities in infection and mortality outcomes. We investigated neighborhood-level inequities across cities in the US and Canada for COVID-19 cumulative case rates (46 cities), death rates (12 cities), testing rates and test positivity (12 cities), using measures that characterize social gradients by race/ethnicity, socioeconomic composition, or both jointly. We found consistent evidence of social gradients for case, death and positivity rates, with the most privileged neighborhoods having the lowest rates, but no meaningful variation in the magnitude of inequities between cities. Gradients were not apparent in testing rates, suggesting inadequate testing in the most deprived neighborhoods. Health agencies should monitor and compare inequities as part of their COVID-19 reporting practices and to guide pandemic response efforts.
HIGHLIGHTS
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Within urban regions with available data in the US and Canada, there were strong social gradients for case, death and positivity rates
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The most racially and/or economically privileged neighborhoods had the lowest rates
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Social gradients were similar for neighborhood-level measures of racial/ethnic composition, income, racialized economic segregation, and racialized occupational segregation
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Testing rates did not show consistent social gradients, which suggests that the most deprived neighborhoods have inadequate access to testing relative to their higher disease burden
Article activity feed
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SciScore for 10.1101/2020.12.07.20241018: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization We included random intercepts for each city, and observation (neighborhood)-level random intercepts to account for overdispersion. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has a number of limitations, largely due to data quality issues affecting COVID-19 data generally. For case data, only a subset of all infections in the population are detected, subject to variations …
SciScore for 10.1101/2020.12.07.20241018: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization We included random intercepts for each city, and observation (neighborhood)-level random intercepts to account for overdispersion. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has a number of limitations, largely due to data quality issues affecting COVID-19 data generally. For case data, only a subset of all infections in the population are detected, subject to variations in testing criteria and access over time and between jurisdictions. This may affect comparison of case burden across cities, but it is unlikely to lead to overestimation of inequities within cities (Pitzer et al., 2020). In fact, within the subset of regions where we had test data, because test positivity was higher in deprived neighborhoods, and because a higher fraction of tests being positive suggests under-ascertainment of infections, our study likely underestimates inequities in infection risk. Deaths attributed to COVID-19 are also generally less than the surge in excess deaths that are observed, which may partly be due to under-ascertainment of COVID-19 as a cause of death (Chen et al., 2020). In presenting cumulative incidence proportions, we bias results such that regions with earlier onset of community transmission (e.g. New York City) have higher burdens compared to those with later onset. Ideally, the denominator would be person-time exposed from the time that some threshold of community transmission was passed. However, given the complex transmission dynamics, multiple introductions of COVID-19 into communities, and changing non-pharmaceutical interventions, it is difficult to do so consistently. Thus, while between-city differences should be interpreted wi...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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