Last week I posted on COVID Case Mortality and County Health Ranking (CHR) measures. I also posted that there was a stronger correlation between the health behaviors and Trump's % of the vote than with COVID case mortality. This week I will look at which individual county statistics used by CHR are the best predictors of Trump's % of the vote.
Thirty-one out of the 48 county health rankings statistics were significantly correlated with Trump's % of the vote in the 67 Pennsylvania Counties. The variable with the strongest univariate correlation was the % of the population who were smokers in each county. This relationship accounted for 54% of the variability in Trump's vote in 2020. This relationship can be seen in the graph above. Philadelphia is an outlier in this model.
Trump % of the vote = 38.29 + 2.03*(% Smokers) - 0.24*(% access to exercise) + 0.55*(Social Assn) - 0.03*(Chlamydia) - 2.16*(Housing problems)
For the other county level statistics, I entered the variables with the strongest correlations into a multiple regression model. Variables that were significant were kept into the model. Five variables were settled on accounting for 91.9% of the variability in Trump's vote. The model is summarized in the equation above in italics.
The 38.29 value is the predicted value for Trump's vote % in PA if all of the predictor variables have a value of zero. Percent smokers has a regression coefficient of 2.03. This means that for every 1% increase in the percent of smokers, there is a predicted 2.03% increase in Trump's vote. The other predictor variables will be summarized below.
The univariate relationship between the % of the county with easy access to exercise opportunities and Trump's vote is presented in the above graph. In the multiple model there is a predicted decrease of 0.24% in Trump's vote for every 1% increase in access to exercise opportunities. Philadelphia county is less of an outlier in this model. Univariately, this relationship accounts for 45.1% of the variability in Trump's vote.
The rate of the number of social membership organizations per 100,000 for each PA county is compared to Trump's % of the vote in the graph above. In the multiple regression model, there is a predicted 0.55% increase in Trump's vote for each unit increase in the social association rate. Univariately, this relationship accounts for 42.6% of the variability in Trump's vote. Dauphin County is an outlier for this relationship with a relatively high social association rate 18.5/100,000 but only 45% of the vote for Trump.