County Health Rankings and COVID Case Mortality

I finished being at the Juneteenth festivities in Johnstown and am ready to get back to county health rankings (CHR) and local COVID numbers. Case mortality (the number of deceased divided by the number of cases) will be the focus of this post. Two weeks ago, I focused on vaccination rates in the 10 county region. Case mortality and vaccination rates were negatively correlated (meaning that as one variable increases the other decreases) accounting for 54% of the variability. There were eight other CHR statistics correlated with vaccination rates. Eighteen other CHR statistics were correlated with case mortality. 

The correlation with the average number of mentally unhealthy days in the last month is summarized in the graph above. The graph shows a strong positive relationship with case mortality accounting for 70.1% of the variability. The regression equation states that for every one day increase in the average number of mentally unhealthy days there is a predicted 1.5% increase in the case mortality rate. There is also a significant but weaker negative correlation that I summarized two weeks ago between mentally unhealthy days and COVID vaccination rates accounting for 42.2% of the variability.  The % of the variability accounted for is simply the correlation coefficient squared.

The strongest negative correlation for case mortality is with the % in the county who are vaccinated for the flu. This correlation accounts for 79.9% of the variability in COVID case mortality. The regression equation says that for every 1% increase there is a predicted 0.06% decrease in case mortality rate. If 100% of the variability were accounted for, all of the counties would fall on the regression lines. Surprisingly this relationship is even stronger than the one with case mortality and COVID vaccination rates which were also negative and only accounted for 54% of the variability.

The 18 significant correlations are summarized in the table below. The positive correlations were with years of potential life lost, both the average number of physically and mentally unhealthy days, % smokers, % physically inactive, the teen birth rate, % unemployed, the social association rate, the injury death rate, and the % who drive alone to work. The negative correlations are as follows: the % with access to exercise opportunities, the % with an annual mammogram, % with flu vaccinations, % completed high school, % with at least some college, the higher the income level in the 80th percentile in the county, the % with severe housing problems and those with a high housing cost burden.

Variable Correlated with Case Mortality	Correlation Coefficient	% Variability Explained
Years of Potential Life Lost Rate (YPLL)	0.741	54.9%
Average Number of Physically Unhealthy Days	0.690	47.6%
Average Number of Mentally Unhealthy Days	0.837	70.1%
% Smokers	0.840	70.5%
% Physically Inactive	0.771	59.5%
% With Access to Exercise Opportunities	-0.662	43.9%
Teen Birth Rate	0.705	49.8%
% With Annual Mammogram	-0.625	39.0%
% Vaccinated for the flu	-0.894	79.9%
% Completed High School	-0.673	45.2%
% Some College	-0.721	51.9%
% Unemployed	0.734	53.9%
80th Percentile Income	-0.796	63.4%
Social Association Rate	0.662	43.8%
Injury Death Rate	0.664	44.1%
% Severe Housing Problems	-0.634	40.2%
Severe Housing Cost Burden	-0.698	48.7%
% Drive Alone to Work	0.732	53.6%

One should always be careful about inferring cause and effect relationships between  correlated variables. Variable A could cause variable B or vice versa. There is always a potential 3rd variable that could explain the correlation such as poverty. Many of these variables are also correlated with each other. This method does allow one to see how they could be interrelated. Next I will look at how different ethnicities correlate with case mortality.

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County Health Rankings Statistics and COVID Vaccination Rates

I have talked in the last few posts about COVID rates and County Health Rankings (CHR). I flagged significant correlations between the rates and the rankings (greater than 0.632 or less than -0.632 for 10 counties). While the rankings are easy to explain and are often quoted in the media, the individual statistics (more than 60 or them) used to create the rankings provides more specific information about the variables that predict local COVID rates.

This post focuses on COVID vaccination rates and individual CHR statistics.  The graph above shows the correlation between Vaccination rates (for those who have received the first 2 shots) and COVID case mortality rates. It shows a negative relationship between the two where every 1 % increase in the vaccination rate yields a predicted 0.046% decrease in the case mortality rate. This relationship accounts for 54% of the variability in the Case mortality rate. Next I will summarize the CHR statistics that are significant with the vaccination rates.

There were 8 CHR variables that were significantly associated with the vaccination rate. The first that was significant was the average number of mentally unhealthy days (a quality of life statistic). This model states that for every one day increase in mentally unhealthy days, there is a predicted 19.3% decrease in the vaccination rate. This relationship accounts for 42.18% of the variability in the vaccination rate. 

To save space, I will summarize verbally the remaining significant correlations. The next significant correlation is the % of the county who are physically inactive which is also negative. This relationship accounts for 42.3% of the variability. Paradoxically, the % with access to exercise opportunities was positively correlated with the vaccination rate accounting for 49% of the variability.

The next significant correlation with the vaccination rate was with the primary care physician rate in the county. These rates were positively associated accounting for 42.3% of the variability. Likewise the flu vaccination rate was positively associated accounting for 49% of the variability. The high school completion rate and the % completing at least some college were both positively associated with the vaccination rate accounting for 44.9% and 39.7% of the variability respectively. Finally the income amount in the 80th percentile in the county was positively associated accounting for 39.7% of the variability. 

Next week I will summarize case mortality correlations.

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County Health Ranking Factors that Predict COVID Case Mortality in PA

County Health Rankings predict COVID Mortality in the 10 County Area

 

Last year I looked at COVID mortality and County Health Rankings (CHR) numbers for the whole state of PA. This year, the state does not make cumulative mortality numbers readily available. This makes updating the numbers subject to copying error. For this year, I thought I would focus on ten county region surrounding Cambria County.

The 10 county area is listed in the table at the top. The univariate correlation coefficients are presented in the table below for the COVID measures above and the CHR rankings for health outcomes and health factors. Health outcomes is a composite of length of life and quality of life measures. Health factors is a composite of health behaviors, clinical care, social and economic, and physical environment factors. The correlation matrix is presented below. Because of the small sample size, correlation coefficients of 0.632 or higher or -0.632 or lower were flagged as significant and presented in bold below.

	% COVID Fully Vaccinated	Case Mortality %	COVID Case Rate /100,000	COVID Mortality /100,000	Hosp  /100,000	County Health Outcomes Rank	Health Factors Rank
% COVID Fully Vaccinated	1
Case Mortality	-0.738	1.000
COVID Case Rate	0.158	0.311	1.000
COVID Mortality /100,000	-0.579	0.953	0.583	1.000
Hosp /100,000	0.360	-0.440	0.134	-0.339	1.000
County Health Outcomes Rank	-0.289	0.632	0.344	0.642	-0.254	1.000
Health Factors Rank	-0.462	0.730	0.398	0.747	-0.409	0.850	1.000
Length of life rank	-0.297	0.696	0.232	0.659	-0.242	0.896	0.683
Quality of Life Rank	-0.160	0.428	0.368	0.477	-0.254	0.925	0.826
Health Behaviors rank	-0.363	0.678	0.477	0.726	-0.227	0.676	0.913
Clinical Care rank	-0.548	0.803	0.232	0.764	-0.420	0.828	0.785
Soc & Econ Factors rank	-0.357	0.598	0.318	0.608	-0.414	0.843	0.950
Phys env rank	0.031	0.135	-0.381	0.003	-0.242	0.385	0.116

The COVID case and population adjusted mortality rates were most strongly associated with the CHR rankings of length of life, health behaviors, and clinical care. A positive correlation with the rankings suggest that the lower the ranking is, the higher the COVID mortality. Case mortality is simply the number of COVID deaths divided by the number of COVID cases.

The above graph shows the scatter plot for the length of life rank showing a linear association with COVID case mortality. The R squared statistic of 0.485 means that 48.5% of the variability in case mortality is accounted for by the length of life ranking. The regression equation says that for every unit a county is ranked lower, there is a predicted increase of 0.02% in the case mortality. There were no strong outliers in in this plot. 

The strongest correlation for the health factors rankings was clinical care at 0.808. The scatter plot above shows a stronger correlation between that and case mortality accounting for 64.5% of its variability. If it were 100% of the variability, all of the counties would fall on the regression line. Like length of life, the regression equation predicts that for every unit lower that a county is ranked, a 0.02% increase in case mortality should happen.

These sub rankings are themselves composites of dozens of statistics. The individual statistics should shed more light on what variables may be driving COVID vaccination, case, mortality, and hospitalization rates. The devil is always in the details. This will be the next step.

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