Author: Parzuchowski, A S; Peters, A T; Johnson-Sasso, C P; Rydland, K J; Feinglass, J M
Title: County-level association of COVID-19 mortality with 2020 United States presidential voting. Cord-id: su5vams1 Document date: 2021_6_23
ID: su5vams1
Snippet: OBJECTIVE The objective of this study was to assess the association between United States county-level COVID-19 mortality and changes in presidential voting between 2016 and 2020. STUDY DESIGN The study design is a county-level ecological study. METHODS We analysed county-level population-weighted differences in partisan vote change, voter turnout and sociodemographic and health status characteristics across pre-election COVID-19 mortality quartiles. We estimated a population-weighted linear reg
Document: OBJECTIVE The objective of this study was to assess the association between United States county-level COVID-19 mortality and changes in presidential voting between 2016 and 2020. STUDY DESIGN The study design is a county-level ecological study. METHODS We analysed county-level population-weighted differences in partisan vote change, voter turnout and sociodemographic and health status characteristics across pre-election COVID-19 mortality quartiles. We estimated a population-weighted linear regression of the 2020-2016 Democratic vote change testing the significance of differences between quartiles of COVID-19 mortality, controlling for other county characteristics. RESULTS The overall change in the 2020-2016 Democratic vote was +2.9% but ranged from a +4.3% increase in the lowest mortality quartile counties to +0.9% in the highest mortality quartile counties. Change in turnout ranged from +9.1% in the lowest mortality counties to only +6.2% in highest mortality counties. In regression estimates, the highest mortality quartile was associated with a -1.26% change in the Democratic 2020-2016 vote compared with the lowest quartile (P < 0.001). CONCLUSIONS Higher county-level COVID-19 mortality was associated with smaller increases in Democratic vote share in 2020 compared with 2016. Possible explanations to be explored in future research could include fear of in-person voting in heavily Democratic, high-mortality counties, fear of the economic effects of perceived Democratic support for tighter lockdowns and stay-at-home orders and general exhaustion that lowered political participation in hard-hit counties.
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