Implications of red state/blue state differences in COVID-19 death rates
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Abstract
The study objective was to explore state death rates pre- and post- 4/19/2021 (date vaccines were assumed available) and the relative contributions of 3 factors to state death rates post- 4/19/2021: 1) vaccination rates, 2) prevalence of obesity, hypertension, diabetes, COPD, cardiovascular disease, and asthma and 3) red vs. blue states, to better understand options for reducing deaths. The ratio of red to blue state deaths/million was 1.6 pre-4/19/2021 and 2.3 between 4/19 and 2/28/2022 resulting in >222,000 extra deaths in red states or 305/ day. Adjusted betas from linear regression showed state vaccination rates had the strongest effect on death rates while red vs. blue states explained more of the difference in state death rates (60% vs. 46% for vaccination rates) with mean vaccination rates ~10% higher in blue states. Results suggest that increasing vaccination rates in red states could potentially save thousands of lives as the pandemic continues.
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SciScore for 10.1101/2022.04.08.22273628: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources Number of deaths per day and mean deaths/million population pre- and post- 4/19 were computed separately for red and blue states in Excel and compared. Excelsuggested: NoneResults 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:There are several limitations to this study. Data on COVID deaths …
SciScore for 10.1101/2022.04.08.22273628: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources Number of deaths per day and mean deaths/million population pre- and post- 4/19 were computed separately for red and blue states in Excel and compared. Excelsuggested: NoneResults 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:There are several limitations to this study. Data on COVID deaths include deaths in nursing homes while the BRFSS data only include non-institutionalized adults. The impact of this limitation can’t be measured but is likely to be greater in the period prior to April 19 when the pandemic hit nursing homes hard. The data for vaccinations do not distinguish booster shots or any differences in the effectiveness of different brands. It is also difficult to determine (beyond the standardized beta results) which differences between red and blue states have the most influence on death rates or how those factors might be modified to lower death rates. Perhaps putting these results in context with other studies might help. A study of 30 industrialized countries (9) found that obesity, population density, the age structure of the population, population health, GDP, ethnic diversity, and how the pandemic was handled explained 63% of the intercountry variation in COVID death rates. That regression R2 was very similar to the R2 of 0.60 for just red vs. blue states and the R2 of 0.62 for the study model with all 3 factors but the global study excluded vaccination rates. Population density, age and racial composition, pandemic response, etc. are likely among the differences between red and blue states that contribute to that coefficient of determination of 0.60. From this list, only the handling of the pandemic seems amenable to modification, along with vaccination rates noted from this stud...
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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