Saturday, April 25, 2020

Inconsistencies in Reporting COVID-19 in Pennsylvania



This week the PA Health department has changed how it reports the cases and deaths.  Before they reported the cases and deaths.  Later,they reported the number of negative tests in addition to the positive tests.  They then added the demographics of the cases and deaths.  Along the way they created the chart above showing the density of cases in the state (on the right) and a histogram with the number of new cases per day by region in the state (the grey area on the chart shows where there is a lag in reporting).  Later still they added the number of cases and deaths in nursing homes.  Finally they added probable cases and deaths but dropped probable deaths the next day.  These concerns were addressed by PA secretary of health in the video below.

 

Sex

Positive Cases 

Percent of Cases**

Deaths 

Female

21,584

54%

713

Male

17,902

45%

818

Neither

2

0%

0

Not reported

561

1%

6

Race

Positive Cases

Percent of Cases**

Deaths 

African American

3,874

10%

167

Asian 

401

1%

21

White 

7,949

20%

566

Other 

169

0%

3

Not reported*

27,656

69%

780

* 69% of race is not reported. Little data is available on ethnicity.
* 69% of race is not reported. Little data is available on ethnicity. 

** Percentages may not total 100% due to rounding


The demographics of cases and deaths has been reported in the table above.  Most of the cases have been female but most of the deaths have been male.  It has been reported that African Americans are disproportionately affected by the virus but data on race is underreported here in Pennsylvania.  Of the cases where race is reported, one out of three is African American when they are 11% of the state's population.  Also for those whose race is reported, 22% is African American.  This raises questions about what the racial/ethnic background of cases/deaths are for this unreported.

 


For Cambria County, the it is unknown what the demographic breakdown of cases is.  The sole death in the county was a white elderly male.  A map showing the distribution of cases by zip code can be seen here.  The map shows that the cases are evenly distributed across the county. 

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Sunday, April 19, 2020

COVID-19 and County Health Rankings in PA: Which Variables Predict Cases and Deaths

The trend in Covid-19 Cases in Cambria County

Last week I posted on how county health rankings overall measures predicted the number of cases and deaths in each county in Pennsylvania.  These measures were composites of dozens of more specific county level measures.  There are too many univariate correlations to summarize here.  The cases used in the analysis are from April 18. The variables were added one at a time and stayed in the Poisson regression model if they were significant.

The final Poisson model for the number of cases is:

ln(cases) = -1.033 + 0.000002*(population) + 0.045*(% with access to exercise opportunities) - 0.070*(Social Association Rate) + 0.13*(% who Drive Alone to Work) + 
0.41*(% not proficient in English) + 0.14*(% with severe housing problems)

  • Access to exercise opportunities, is a component of health behaviors and is positively associated with the number of cases in each county.  
  • The social association rate is the number of membership organizations per 100,000 people and is negatively associated with the number of cases.  This measure is a component of the social and economic z-score.
  • The percent who drive alone to work is also a component of social and economic z-score and is positively associated with the number of cases.  
  • The percent with severe housing problems is a component of the physical environment z-score and is positively associated with the number of cases.  It is correlated with poor length of life outcomes.
  • The percent not proficient in English is a demographic variable that is not a component of the rankings.  It has the strongest association with the number of cases.
The model for deaths last week was:

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)

The final model with the submeasures that was settled on was:

ln(number of deaths) = -4.94 + 0.000001*(population) + 0.07*(% with access to exercise opportunities) + 0.78*(%Unemployed) - 0.16*(% of Children in poverty) + 0.46*(% not proficient in English)
  • Access to exercise opportunities, is a component of health behaviors and is positively associated with the number of deaths in each county.  
  • The percent unemployed is a component of the social and economic factors z-score and is positively associated with the number of deaths.
  • The percent of children in poverty is also a component of the social and economic factors z-score and is negatively associated with the number of deaths.  This seems counter intuitive but counties with higher rates of child poverty may have less social interaction with more susceptible populations such as the elderly.
  • Like the number of cases, The percent not proficient in English was positively associated with the number of deaths.  This variable and the % unemployed could be positively correlated with poor quality of life.
These variables would be better to study at the individual level than the county level.  I looked at these variables as they were readily available.  The graph above shows the trend in cases in my home county, Cambria County.  It does provide some clues as to what factors may be exacerbating this pandemic.


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