Assessing differential impacts of COVID-19 on black communities
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Abstract
No abstract available
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SciScore for 10.1101/2020.05.04.20090274: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. 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:The results presented here should be interpreted in the context of several limitations. Given the aforementioned challenges in individual reporting of race in existing surveillance systems, the county-level data presented here represent an ecological analysis that could be subject to structural confounding where there are …
SciScore for 10.1101/2020.05.04.20090274: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. 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:The results presented here should be interpreted in the context of several limitations. Given the aforementioned challenges in individual reporting of race in existing surveillance systems, the county-level data presented here represent an ecological analysis that could be subject to structural confounding where there are more black people in urban centers and urban centers have been more likely to be affected to date in the first wave of COVID-19. This possibility can be addressed by the responses to several questions. Are disproportionately black counties the urban counties affected early by COVID-19? The theory of structural confounding suggests that no individuals or low numbers of individuals in extreme cells (in this case, the most rural areas) raise concerns about the possibility of structural confounding.45,46 Our data reveal a trend to higher proportions of black population in the most urban counties. However, all five strata for lower urbanization still have 35%-50% of the percent black residents observed in the most urban counties. Within disproportionately black counties, is the impact of COVID-19 homogenous, or do relatively more black and less black areas within a given county have differential impact? Even within heavily impacted urban areas, predominantly black neighborhoods have higher rates of COVID-19 disease and death. For example, in Prince George’s County, Maryland, white suburbs are relatively unimpacted, while predominantly black suburbs are heavily im...
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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