Saturday, December 25, 2021

Manchin's Political Decisions Have Real Impacts on Real People

Many progressives were disappointed in Sen. Joe Manchin's (D-WVa) no vote on the build back better act.  This is about more than merely scoring political points or infrastructure.  This is about programs like the expanded child care tax credit which gives assistance to low income parents like my friend Jennina Rose Gorman.  She was interviewed by MSNBC's Katy Tur which can be seen above.

A Facebook image from a MAGA friend

Does Senator Manchin realize the impact of his policies?  He drives a Maserati and has a yacht on the Potomac River.  His state (West Virginia) is one of the poorest in the union.  If any state needs built back better his does.  The same question should be asked of the state's other Senator Shelly Moore Capito (R-WVa) who is also a certain no vote on the bill.  

It's true that Trump won the state with 68.6% of the vote.  He carried every county in the state.  It used to be a reliable Democratic state but like Cambria County in PA has now flipped.  The challenge is now to get them to vote in favor of their self interest again.  This March, I will be presenting Appalachian Studies Association's annual conference in Morgantown on how to use County Health Rankings to gauge the health of your area.  Hope to see you there.  Buon Natale a` tutti.

**Related Posts**

African Americans Still Lag Behind in Life Expectancy in Cambria County


Income and Life Expectancy. What does it Tell Us About US?


Friday, December 10, 2021

PA Health Behavior Measures Correlate More with Trump % of the vote than with COVID Case Mortality

 

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.


The rate of chlamydia per 100,000 for each PA county is negatively associated with with Trump's % of the vote accounting for 47% of the variance.  In the multiple regression model, for every unit increase in the chlamydia rate, there is a predicted 0.03% decrease in Trump's vote.  Philadelphia county is an influential county for this relationship but not an outlier.
  


























The last variable in the model is the % of the county with housing problems for each county.  In the multiple regression model, for every 1% increase in this variable, there is a predicted 2.16% decrease in Trump's % of the vote.  Univariately, housing problems account for 48.2% of the variability in Trump's vote. Bedford County may be an outlier.  

These five variables account for 91.9% of the variance in Trump's vote when combined in a multiple regression model.  One should always be careful about assuming a cause and effect relationship between variables that are correlated.  There are always potential unknown variables which can explain this relationship.  These variables were the most robust when entered into the model and do not have a strong association with each other.  The five variables in this model can be ruled out as alternative explanations for each other.

**Update**

Some have asked me about the correlation coefficients for the univariate relationships in this model.  If one takes the square root of the r-squared statistic for % smokers (0.5452) we get a correlation coefficient 0.738  which is much stronger than any of the correlations for COVID case mortality.  For access to exercise opportunities the coefficient is -0.671.  For social association rates, it is 0.673.  For chlamydia rate, it is -0.685.  For housing problems, it is 0.694.

**Related Posts**



Thursday, December 2, 2021

Rates of Smoking and Social Associations Predict PA County COVID Case Mortality

Two weeks ago I looked at the correlation between COVID case mortality and Trump's % of the vote in 2020 for Pennsylvania counties and six sub measures used to determine County Health Rankings.  The measure that was most strongly associated with COVID case mortality was the health behavior z-score (a higher z score is worse) accounting for 6.9% of the variability.  Health behaviors was even more strongly associated with Trump's % of the vote as shown in the above graph accounting for 34.4% of the variability.  This association would be even stronger if Philadelphia were excluded.  This post will focus on the individual statistics used to determine the sub measures and their association with COVID Case Mortality.  The mortality rates occur on top of the the other health issues confronting each county.


The above graph shows the strongest univariate association for the number of social membership associations per 100,000 and COVID Case mortality.  This relationship accounts for 20.4% of the variability.  This measure is not a component of the health behaviors sub measure.  It is a component of the social and economic measure.  The number of social associations is weakly correlated with health behaviors, accounting for 4.9% of the variability.


The next strongest variable associated with COVID Case Mortality is the average number of mentally unhealthy days (as reported to the Behavioral Risk Factor Surveillance System).  This measure accounted for 18.6% of the variability.  This measure is a component of the quality of life sub measure.


 
The percentage of smokers in the county is positively associated with the COVID case mortality rate accounting for 11.9% of the variability.  This measure is part of the health behaviors sub measure. 


Access to exercise opportunities is negatively correlated with COVID case mortality with counties having higher mortality rates generally having lower access to exercise opportunities.  This and the other graphs have outliers.  If a correlation were perfect positive or negative, all of the counties would form a perfect straight line sloping upward or downward.  

As always, one should be careful about inferring cause and effect relationships.  These statistics from County Health Rankings were compiled before the coronavirus pandemic began.  Next week I will look at the association of county health ranking measures with Trump's % of the vote.

**Related Posts**

The Seven Counties in PA that are Worse than Cambria in COVID Case Mortality



Thursday, November 25, 2021

Pat, Lara, Kevin, Gabby and I: Working for Influence

On Tuesday, I went to a talk by local author Pat Farabaugh on his book Disastrous Floods and the Demise of Steel in Johnstown.  He gave a review of the 3 major floods in Johnstown's history and why the steel mills pulled out of Johnstown a few years after the 1977 flood.  In the interest of full disclosure, my mother's maiden name is also Farabaugh.  We are distant cousins.  We had a nice discussion about our books.  It's always nice to meet other authors.  Here is a link to his book on Amazon.
 
 

Also, the Out D' Coup podcast from Raging Chicken Press (I was interviewed by Kevin Mahoney in 2016) features Lara Putnam, professor of history at the University of Pittsburgh.  Like me, she has written on political shifts in the rust belt.  You can hear her interview with Kevin Mahoney above.  

Finally, I will comment on Gabby Petito.  I confess to being fascinated with this case.  Occasionally, I am taken in by social media feeding frenzies. The news came out on Tuesday about her former fiancé committing suicide by gunshot which was no surprise.  His notebook may hold more interesting revelations about what happened between the two.  I have travelled to the Grand Teton national park as seen in the photo below.

Like Pat, Lara, Kevin, and I, she was an aspiring social media influencer.  She has received much more attention than she would have if she lived.  The case has given me great examples to use in my psychology class on how appearances can be deceiving.  The contrasts between her Instagram posts where her and her fiancé seemed happy and the way she appeared in the Moab Police video where she was eager to excuse his behavior.  He made no effort to excuse her behavior.  Her family appears to be sublimating their grief over her death by creating the Gabby Petito Foundation to assist missing persons and victims of domestic violence.

Me driving through the Tetons in 2016

Other youtubers have put there two cents in on the case.  I have little to add to their commentary.  In addition to making a statement, they are also looking for views and likes of their work.  The competition is omnipresent.  

I admit that the influencing the others and I do is different than what Gabby was planning on doing.  Next week I will be back to posting on COVID in the hopes influencing people to get the vaccine.  Happy Thanksgiving everyone.

**Related Posts**



Tuesday, November 16, 2021

County Health Ranking Factors that Predict COVID Case Mortality in PA

This is the 3rd installment of my posts on case mortality in Pennsylvania.  Before I explained case mortality and looked at counties with the highest rates.  This time I'm looking at which County Health Ranking factors predict COVID Case Mortality at the county level.  This is done to see which health issues occurring in these counties prior to the pandemic predict mortality rates.

County Health Ranking Variable

Correlation with Case mortality (*Significant)

Length of Life

0.30*

Quality of Life

0.17

Health Behaviors

0.29*

Critical Care

0.16

Social and Economic

0.28*

Physical Environment

-0.10

Trump %

0.33*


The above table presents Spearman correlation coefficients for 6 county health ranking sub-measures and Trump % of the vote in 2020 with county case mortality rates.  The length of life, health behaviors, social and economic and Trump% of the vote coefficients were statistically significant and positive.  Scatterplots for these relationships are presented below.




























The above plot shows the relationship between the length of life z score (lower is better) for all 67 counties in Pennsylvania and COVID Case mortality.  This relationship accounts for 5.3% of the variability in case mortality.  Sullivan and Juniata counties with high case mortality rates but average length of life scores.




























The next graph shows the relationship of health behaviors (lower is better) with COVID case mortality.  This relationship accounts for 6.7% of the variability in case mortality.  As can be seen there is considerable spread in the data.



Social and economic factors (a smaller score is better) was a significant predictor of case mortality but only accounted for 3.1% of the variability in case mortality. Sullivan and Juniata Counties are still outliers for each measure.




























Trump's % of the vote is not a factor in County Health Rankings but there is a lot of data showing that counties where Trump is popular having higher COVID rates.  This relationship is stronger than any of the county health ranking factors accounting for 6.9% of the variability in case mortality.  


In a multiple regression model, only the health behaviors measure remains a significant predictor of case mortality.  These county health ranking sub-measures are themselves composites of over 60 individual statistics.  The next step will be to see which of these statistics are significant predictors of case mortality.

**Related Posts**

Wednesday, November 10, 2021

The Seven Counties in PA that are Worse than Cambria in COVID Case Mortality

County

Cases

Deaths

Case Mortality %

Cases per death

Deaths per 100,000

Rank of Deaths per 100.000 in PA

% Fully Vaccinated

JUNIATA

3,009

120

3.99%

25.1

484.6

1

36%

SULLIVAN

607

24

3.95%

25.3

395.7

6

48%

WARREN

4,436

141

3.18%

31.5

359.8

10

41%

NORTHUMBERLAND

13,371

409

3.06%

32.7

450.2

3

51%

TIOGA

4,814

137

2.85%

35.1

337.5

14

39%

MIFFLIN

7,494

212

2.83%

35.3

459.5

2

43%

MONTOUR

2,599

73

2.81%

35.6

400.4

5

68%

CAMBRIA

20,420

535

2.62%

38.2

410.9

4

48%

PENNSYLVANIA

1,608,022

32,188

2.01%

50.0

251.4

 

51%

Last week I posted on Cambria County's high case mortality rate from COVID.  It has the eighth highest rate in the state.  The case mortality is an estimate of the probability of a person dying when they are diagnosed with the disease.  This week I thought that I would take a look at the seven counties with higher case mortality rates than Cambria.  You can find these states in the map below.



The table above shows the top 8 counties in case mortality from COVID. Juniata County has the worst rate in the state with the probability of dying is one out of 25.1 cases when diagnosed with the disease.  Close behind is Sullivan County with a probability of one in every 25.3 cases.  Cambria's is one in every 38.2 cases.  Pennsylvania's rate is one in every 50 cases.  All of the counties with rates higher than Cambria have populations less than 100,000.

Not surprisingly, the population adjusted mortality rates for each county are higher than the state rate of 251.4 deaths/100,000.  Except for Warren and Tioga, all of them are in the top 8 counties for this metric.  Looking at vaccination rates, 6 of the 8 counties have full vaccination rates lower than the state rate of 51%.  Montour County actually has the highest full vaccination rate in Pennsylvania at 68%.  

 
The rates in the above table are cumulative from the beginning of the pandemic in PA.  Case mortality can vary with time as cases and deaths fluctuate.  The dotted green line shows the 7 day case mortality rate which is the 7 day average for deaths divided by the 7 day average for cases.  A rise in cases precedes a rise in deaths which makes the case mortality decrease.  

Since the vaccine has been rolled out, the cumulative rates decreased from a high of 3.41% on Jan 21 in Cambria to 2.62% today.  Over the same period for Pennsylvania, it decreased from 2.55% to 2.01%.  In the U.S., the rate decreased from 1.67% to 1.62% during this period.  The 7 day case mortality is not a continuous line because there were periods in Cambria where there were no deaths.  

I have written on Montour and Sullivan Counties on this blog before.  In January, Montour had a high rate of vaccinations, hospitalizations and patients on a ventilator.  It still has high rates in these areas.  Sullivan County had one of the highest uninsured rates and the lowest median income in PA before the Affordable Care Act was passed.  

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Wednesday, November 3, 2021

Case Mortality in Cambria County Explained

I was thinking about writing about the Gabby Petito case but events here in Cambria County have trumped (no pun intended) that and put it on the back burner.  COVID deaths have been rising again in the county.  On Oct 23 the county reached 500 deaths.  Eleven days later we have reached 521 for an average of 1.91 deaths per day.

The graph above shows the case mortality rates for Cambria County, Pennsylvania, and the U.S.  The case mortality rate is simply the number of deaths divided by the number of cases.  The rates have remained fairly steady for all three entities as the rate of new cases have kept pace with the rate of new deaths.  

The current rate of 2.63% for Cambria may not seem that much larger than the corresponding PA rate of 2.01% and the U.S. rate of 1.62%.  It we take the reciprocal of these rates (cases divided by deaths) we see a different picture.  The reciprocal of Cambria's rate shows that 1 out of every 38 individuals who have COVID has died.  For PA it is 1 out of every 50 cases dying and for the U.S., it is one out of every 62 cases dying.  This means that someone is 31% more likely to die in Cambria than in PA as a whole and 62% nore likely to die than in the U.S. as a whole.

In my last post I talked about how more people are getting the 3rd booster show than are getting the first two.  To date only 48.06% of the county population has received the first two shots.  This number should rise with the approval of the vaccine for children aged 5-11 years old.  Let's hope the rate of vaccinations keeps pace with the rest of the U.S.  The lagging vaccination rate here suggests that most of the recent deaths were preventable.

**Related Posts**

COVID Deaths Rising in Cambria County and my 11th Anniversary Post