Friday, April 10, 2020

The Number of Corona Virus Cases in Cambria County has Grown Exponentially While Health Behaviors Predict Cases in PA

 

The number of corona virus cases has grown exponentially in Cambria County.  I have been keeping track of the number of cases in a google sheet as can be seen above.  The cumulative case line has been following a cubic trend with the polynomial, y = 0.0347x2 - 3051.6x + 7E+07.  This equation accounts for 98.5% of the variability in the solid trend line.  

Two weeks ago I correlated the number of COVID-19 cases at the county level in Pennsylvania with the county health ranking for that county using Poisson regression.  This week I thought I would take a look at the submeasures for the rankings with the case and death numbers from April 8.  Population numbers for each county were added so that Philadelphia county could be added.

Number of Corona Cases

Corona Deaths 

Length of Life   Z-Score

0.046

0.067

Quality of Life Z-Score

0.286

0.284

Health Behavior Z-Score

-0.038

0.065

Clinical Care   Z-Score

-0.059

0.114

Social Economic   Z-Score

0.301

0.412

Physical Environment Z-Score

0.062

-0.449

Number of Corona Cases

1.000

0.957

Corona Deaths 

0.957

1.000

population

0.841

0.792


The table above shows the univariate correlations of the submeasures with Philadelphia included.  For the number of cases, the quality of life z score (part of the health outcomes ranking) and the social economic z score (with the health factor ranking) were correlated.  For the number of deaths, quality of life, social economic, and physical environment (part of health factors) were correlated. Z scores are numbers scaled so that the mean is zero and 

For the case numbers, three of the county health ranking submeasures were significantly associated with the outcome along with population.  The poisson regression equation is given by:

ln(number of cases) = 4.15 -5.91*(health behavior z-score)  + 4.31*(social economic z score) - 0.74*(length of life z score) + 0.000002*(population)

This means that the number of cases increases as the health behavior and length of life z scores improve and (a negative score is better).  The number of cases decrease as the social economic z score improves.  Ln is the natural logarithm of the number of cases.

For the number of deaths in each county as of April 8, three submeasures were significantly associated with the number of cases.  The poisson regression equation is given by:

ln(number of deaths) = -0.14 - 7.97*(health behavior z-score) + 2.83*(social economic z score) + 1.62*(quality of life z score) + 0.000003*(population)

Like the number of cases, the natural logarithm of the predicted number of deaths at the county level increase as the health behavior z score decreases.  The predicted number of deaths decrease as the social economic, quality of life z scores, and population decrease.  




Adding multiple predictors often leads to variables that were not significant univariately to being significant in a multiple regression model, especially after population is adjusted for.  In the graphs above we see that Philadelphia county is an extreme outlier.  This is mostly due to its population.  Adding population to the model helps to negate its outlier effect.

These submeasures are themselves composites of dozens of county level statistics.  The next step is to look at these individual measures and the up to date counts of COVID-19 cases and deaths.

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