Disparities in case frequency and mortality of coronavirus disease 2019 (COVID-19) among various states in the United States

Version published to 10.1080/07853890.2020.1840620

Nov 5, 2020

SciScore for 10.1101/2020.07.28.20163931: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Institutional Review Board Statement	IRB: This study utilized publicly available, deidentified, state-level data and so no institutional review board approval was required or sought.
Randomization	not detected.
Blinding	not detected.
Power Analysis	not detected.
Sex as a biological variable	not detected.

Table 2: Resources

Software and Algorithms
Sentences	Resources
All statistical analyses were done using the user-coded, syntax-based interface of SPSS Version 23.0.	SPSS suggested: (SPSS, RRID:SCR_002865)

Results from OddPub: Thank you for sharing your data.

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

However, our current analysis showed a lack of …

SciScore for 10.1101/2020.07.28.20163931: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Institutional Review Board Statement	IRB: This study utilized publicly available, deidentified, state-level data and so no institutional review board approval was required or sought.
Randomization	not detected.
Blinding	not detected.
Power Analysis	not detected.
Sex as a biological variable	not detected.

Table 2: Resources

Software and Algorithms
Sentences	Resources
All statistical analyses were done using the user-coded, syntax-based interface of SPSS Version 23.0.	SPSS suggested: (SPSS, RRID:SCR_002865)

Results from OddPub: Thank you for sharing your data.

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

However, our current analysis showed a lack of association between social distancing and COVID-19 case frequency, which may also represent a limitation of the process of measuring the social distancing score. This is particularly important as some countries like Sweden have encountered higher COVID-19 case frequencies after adopting more lenient social distancing measures29. This analysis also showed a lack of impact of climate-related factors on case and testing frequency. These findings require further validation as conflicting reports have been published5,30,31. Surprisingly, in our analysis states with higher number of uninsured patients were found to have lower mortality which could possibly be related to underreporting in such populations as both case numbers and testing numbers were also lower in such states. Finally, most of the comorbidities analyzed were not found to be independently associated with the case mortality in this analysis. This may suggest the complex interplay between demographics, environmental factors and diseases processes32,33. The findings from this analysis also highlight some of the potential limitations of state-level data rather than patient-level data, as previous studies have found a few comorbidities to be associated with increases in case frequency and percent mortality. These analyses offer some early assessment of the factors that may be mediating COVID-19 case frequency, testing frequency, and percent mortality. These analyses present a...

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.

Read the original source

SciScore for 10.1101/2020.07.28.20163931: (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

Software and Algorithms
Sentences	Resources
All statistical analyses were done using the user-coded, syntax-based interface of SPSS Version 23.0.	SPSS suggested: (SPSS, RRID:SCR_002865)

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

However, our current analysis showed a lack of association between social distancing and COVID-19 case frequency, which may also represent a limitation of the process of measuring the social distancing score. This is particularly important as some countries like Sweden have encountered higher COVID-19 case frequencies after adopting more lenient social distancing measures29. This analysis also showed a lack of impact of climate-related factors on case and testing frequency. These findings require further validation as conflicting reports have been published5,30,31. Surprisingly, in our analysis states with higher number of uninsured patients were found to have lower mortality which could possibly be related to underreporting in such populations as both case numbers and testing numbers were also lower in such states. Finally, most of the comorbidities analyzed were not found to be independently associated with the case mortality in this analysis. This may suggest the complex interplay between demographics, environmental factors and diseases processes32,33. The findings from this analysis also highlight some of the potential limitations of state-level data rather than patient-level data, as previous studies have found a few comorbidities to be associated with increases in case frequency and percent mortality. These analyses offer some early assessment of the factors that may be mediating COVID-19 cas frequency, testing frequency, and percent mortality. These analyses present associations using state-level data and not patient-level data. While these analyses offer novel data regarding case frequency, testing frequency, and percent mortality in the US, these analyses are not without their limitations. First, all the study data was captured from publicly available sources which only had data until 2018. The use of state-level data reduced the power of analyses, as we used for the multivariate regression models the number of states as the subjects. Although the data collection carries a risk of bias, this was minimized by utilizing multiple investigators for accuracy of data captured. Due to data unavailability other important outcomes such as case positivity rate could not be assessed. The ecologic design of the paper and use of various data sources with varying methods are other limitations of our study. As the pandemic continues some of the variables selected for this study may be subject to change, particularly as state resources may become overwhelmed. Finally, lack of granularity to county or city level data further limits our interpretations. With these limitations in mind, it is important to frame the intentions of this study appropriately. These analyses are by no means intended to be definitive data but are intended to be exploratory data to help identify variables that should be accounted for in larger, multicenter studies that utilize patient-level data. Factors such as the environmental and local infrastructural characteristics appear to modulate the case frequency and percent mortality and thus could be beneficial to capture in future studies. The data from those variables may assist with the understanding of viral spreading and the pandemic evolution. For instance, the identification of the association between higher tourism volume with higher case frequency and percent mortality may help implement faster travel restrictions for future pandemics. Similarly, the association between public transportation volume and its association with increased case frequency and percent mortality may assist in developing future public response. Conclusion This observational analysis of publicly reported state-level data identified factors associated with increased case frequency, testing frequency, and percent mortality for COVID-19. These data can guide future study design and develop risk prediction models. Acknowledgement: None Disclosure: None In Memoriam: We dedicate this paper to the memory of Luis Carlos Gamboa Chavez (19742020), devoted father, son, and friend.

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.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

Read the original source

SciScore for 10.1101/2020.07.28.20163931: (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

Software and Algorithms
Sentences	Resources
All statistical analyses were done using the user-coded, syntax-based interface of SPSS Version 23.0.	SPSS suggested: (SPSS, RRID:SCR_002865)

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

However, our current analysis showed a lack of association between social distancing and COVID-19 case frequency, which may also represent a limitation of the process of measuring the social distancing score. This is particularly important as some countries like Sweden have encountered higher COVID-19 case frequencies after adopting more lenient social distancing measures29. This analysis also showed a lack of impact of climate-related factors on case and testing frequency. These findings require further validation as conflicting reports have been published5,30,31. Surprisingly, in our analysis states with higher number of uninsured patients were found to have lower mortality which could possibly be related to underreporting in such populations as both case numbers and testing numbers were also lower in such states. Finally, most of the comorbidities analyzed were not found to be independently associated with the case mortality in this analysis. This may suggest the complex interplay between demographics, environmental factors and diseases processes32,33. The findings from this analysis also highlight some of the potential limitations of state-level data rather than patient-level data, as previous studies have found a few comorbidities to be associated with increases in case frequency and percent mortality. These analyses offer some early assessment of the factors that may be mediating COVID-19 cas frequency, testing frequency, and percent mortality. These analyses present associations using state-level data and not patient-level data. While these analyses offer novel data regarding case frequency, testing frequency, and percent mortality in the US, these analyses are not without their limitations. First, all the study data was captured from publicly available sources which only had data until 2018. The use of state-level data reduced the power of analyses, as we used for the multivariate regression models the number of states as the subjects. Although the data collection carries a risk of bias, this was minimized by utilizing multiple investigators for accuracy of data captured. Due to data unavailability other important outcomes such as case positivity rate could not be assessed. The ecologic design of the paper and use of various data sources with varying methods are other limitations of our study. As the pandemic continues some of the variables selected for this study may be subject to change, particularly as state resources may become overwhelmed. Finally, lack of granularity to county or city level data further limits our interpretations. With these limitations in mind, it is important to frame the intentions of this study appropriately. These analyses are by no means intended to be definitive data but are intended to be exploratory data to help identify variables that should be accounted for in larger, multicenter studies that utilize patient-level data. Factors such as the environmental and local infrastructural characteristics appear to modulate the case frequency and percent mortality and thus could be beneficial to capture in future studies. The data from those variables may assist with the understanding of viral spreading and the pandemic evolution. For instance, the identification of the association between higher tourism volume with higher case frequency and percent mortality may help implement faster travel restrictions for future pandemics. Similarly, the association between public transportation volume and its association with increased case frequency and percent mortality may assist in developing future public response. Conclusion This observational analysis of publicly reported state-level data identified factors associated with increased case frequency, testing frequency, and percent mortality for COVID-19. These data can guide future study design and develop risk prediction models. Acknowledgement: None Disclosure: None In Memoriam: We dedicate this paper to the memory of Luis Carlos Gamboa Chavez (19742020), devoted father, son, and friend.

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.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

Read the original source

SciScore for 10.1101/2020.07.28.20163931: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Institutional Review Board Statement

Materials and Methods This study utilized publicly available, deidentified, state-level data and so no institutional review board approval was required or sought.

Randomization

not detected.

Blinding

not detected.

Power Analysis

not detected.

Sex as a biological variable

We found direct relationships between case frequency in a state and female gender, underinsured status, average household income and per capita healthcare spending and inverse relationship with obesity, smoking and UV light exposure.

Table 2: Resources

Software and Algorithms
Sentences	Resources
All statistical analyses were done using the user-coded, syntax-based interface of SPSS Version 23.0.	SPSS suggested: (SPSS, SCR_002865)

Data from additional tools added to each annotation on a weekly basis.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

Read the original source

Disparities in case frequency and mortality of coronavirus disease 2019 (COVID-19) among various states in the United States

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