Above are the maps showing the rankings for Pennsylvania.  On the left are the rankings for health outcomes which are a composite of length of life and quality of life data.  Darker green means lower ranked.  


On the right are the rankings for health factors which contribute to the health outcomes. Likewise this ranking is a composite of health behaviors, clinical care, social and economic, and physical environment factors.  Darker blue counties are lower ranked. 





Philadelphia County was ranked last in both measures.  Union county was ranked first on health outcomes and Montgomery County was first on health factors. Ironically some of the highest ranked counties (except for Philadelphia) are the ones that have the fewest Corona Virus cases. I thought I would take a look at how the rankings correlate with the number of cases so far.

The correlation values with the ranking and the overall number of cases in each county are provided above.  With Philadelphia County included there is negligible correlation because it has a low ranking and a high number of cases.  The z scores are used to determine the ranking.  A high positive z score gives a low ranking.  


With Philadelphia County excluded, there are fairly strong negative correlations with the number of COVID-19 cases.  The strongest negative correlation is with the health factor z score (-0.464 or 21.5% of the variability).  This one I will look into further with a poisson regression analysis which is used to model count data.


The regression equation for health factors is 2.984 -2.001(zscore) and was statistically significant.  This means that for counties with a z score of zero they would be expected to have around 20 cases.  For every unit increase in the score, the number of cases is expected to decrease by 2.718 raised to the -2 power.    

The graph above is different from my usual regression plots because the y axis is on a logarithmic scale.  It indicates a good fit with the outliers of Montgomery, Fayette, and Montour counties.  More research will be needed to see if this pattern holds up elsewhere.


As previously mentioned, health factors is a composite of different sub measures.  These sub measures are determined by a dozens of county level statistics. Next I will look at which of these sub measures are most closely associated with Corona Virus cases.  Later I will look at the Southern Poverty Law Center’s new hate group numbers.