Evolution of COVID-19 Health Disparities in Arizona
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Abstract
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SciScore for 10.1101/2021.12.27.21268462: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources In 20.8 % of the zip code areas we examined, Hispanic/Latino is the dominant ethnicity, surpassing non-Hispanic/Latino White (Fig. 2A). non-Hispanic/Latino Whitesuggested: NoneFor the other ethnicities including non-Hispanic or Latino White and Asian that respectively represents 54.1% and 3.7% of Arizona populations, we did not detect significant associations with the COVID-19 prevalence or the growth rates at most of the time intervals. Latino Whitesuggested: NoneSoftware and Algorithms Sentences Resources We performed these analyses in R and Python. Pythonsuggested: (IPython, …SciScore for 10.1101/2021.12.27.21268462: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources In 20.8 % of the zip code areas we examined, Hispanic/Latino is the dominant ethnicity, surpassing non-Hispanic/Latino White (Fig. 2A). non-Hispanic/Latino Whitesuggested: NoneFor the other ethnicities including non-Hispanic or Latino White and Asian that respectively represents 54.1% and 3.7% of Arizona populations, we did not detect significant associations with the COVID-19 prevalence or the growth rates at most of the time intervals. Latino Whitesuggested: NoneSoftware and Algorithms Sentences Resources We performed these analyses in R and Python. Pythonsuggested: (IPython, RRID:SCR_001658)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 several limitations. First, because individual-level data are not readily available, we used zip code level data. Thus, the identified vulnerable groups and their evolving patterns had restricted precision. However, the publicly available zip code level data allows us to track COVID-19 disparities easily and continuously throughout the present and into the future. Second, we did not include COVID-19 vaccination data in our analysis, although vaccination rates may have significant associations with COVID-19 prevalence and growth rates. This was again due to data availability. Third, the pandemic has disruptive impact on the financial status of many families [20, 21]. Such changes were not incorporated in our analyses because the population composition data were based on the 2019 US Census. Although our study is restricted to Arizona data, we expect that COVID-19 disparities in other regions may have also evolved. To better support disadvantaged populations, we need to monitor the progression and adjust resource allocations accordingly.
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.
Results from scite Reference Check: We found no unreliable references.
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