Russian Saber Rattling over NATO?

All this talk of war with Russia and Ukraine is the last thing we need.  With climate change and coronavirus we need more cooperation not more death and destruction.  Katrina vanden Heuvel of the Nation magazine gives a good review of the history behind this standoff. 

 

It started at the end of the cold war in 1990.  In return for dissolving the Soviet Union and the Communist bloc in Eastern Europe and allowing Germany to reunify, Gorbachev was promised by George HW Bush that NATO (the western military alliance formed after WWII) would not be expanded eastward toward Russia's border.

This promise was broken when Clinton took office as can be seen in the above map.  Poland, the Czech Republic, Hungary were admitted to NATO in 1999.  This continued under Bush, Obama, and Trump right up to Russia's borders with the admission of Baltic states (Estonia, Latvia, and Lithuania) and other former Iron Curtain state.  Any possible addition of Ukraine into NATO is totally unacceptable for Russia.

With Russia's annexation of the Crimean Peninsula in 2014, they were excluded from G8 summit of economic powers.  They also received many other economic sanctions including their athletes not being allowed to compete in the Olympics under their flag.  With Russia's history of paranoia of foreign invaders from the Mongols to Hitler, it should be easy to understand their obstinance over NATO expansion.

We have already tried the stick in dealing with Russia with little success.  Maybe it's time for a carrot to improve relations.  Let's come back from the brink.

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PA County Factors that Predict Full COVID Vaccination Rates

Last month I posted on which county health ranking variables were most strongly correlated with COVID case mortality rates.  Granted these posts were made just as the omicron variant was arriving on the scene.  This month, I thought I would take a look at which variables were the best predictors of full vaccination rates.  Full vaccination means receiving the first two shots.  As of today, only 51.8% of the state population has received the first two shots.  This includes Philadelphia County and individuals who are ineligible to receive the shots.

Univariately, the average number of mentally unhealthy days had the strongest negative correlation with the full vaccination rate.  Not surprisingly, the flu vaccination rate at the county level had the highest positive correlation.  These and other variables were entered into a multiple regression model to find the most robust predictors.  The flu vaccination rate was not significant in the presence of other variables but others were and are presented below.  These variables accounted for 62% of the variability in the full vaccination rate.  Philadelphia county was a problematic outlier and was excluded from the data set.

Full vaccination rate = 0.585 - 0.006(social assoc rate) - 0.044(avg # mentally unhealthy days) + 0.003(% with access to exercise opportunities) + 0.001(Primary Care Physician Rate)

The rate of social associations in a county was negatively associated with the full vaccination rate.  In the model, for every unit increase in the association rate there is a 0.6% decrease in the full vaccination rate.  Univariately this relationship accounts for 22.8% of the variability in this relationship.  Montour county, with the highest vaccination rate in the state is an outlier for this relationship as well as other variables.

The average number of mentally unhealthy days in the last month is also negatively associated with the full vaccination rate.  For every increase of one day in this variable there is a predicted 4.4% decline in the full vaccination rate.  Univariately this relationship accounts for 34.9% of the variability in the rate.  

Access to exercise opportunities is positively associated with the full vaccination rate.  For every 1% increase in the rate, there is a predicted 0.3% increase in the full vaccination rate.  Univariately, this variable accounts for 33.9% of the variability in the full vaccination rate.

The fourth variable is the primary care physician rate in the county which is positively associated with the full vaccination rate.  For every unit increase in the physician rate, there is a predicted 0.1% increase in the vaccination rate.  Univariately this variable accounts for 20.8% of the variability in the vaccination rate.  Montour County (where Geisinger Hospital is located), is influential in this relationship but not poorly fit.

When I looked at which County Healthy Ranking variables predicted COVID case mortality rate, the social association rate, the rate of mentally unhealthy days, and access to exercise opportunities were significantly associated with the outcome.  The PCP rate was not significant for case mortality and the % smoking did not hold up in variable selection for the vaccination rate.  

The correspondence is high for the models for these two dependent variables.  These predictors may provide clues as to crafting strategies for improving vaccination rates and thus decreasing COVID mortality.

**Related Posts**

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

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Changes in How Cases and Deaths in PA are Reported Due to the Omicron Variant

  

This week Anthony Fauci reported on ABC's This Week with George Stephanopoulus that cases were not as important as hospitalizations and deaths in measuring the severity of the coronavirus pandemic under the omicron variant.  This is because new Omicron variance of COVID-19 is believed to be more contagious but less severe.  With the advent of the new year, the Pennsylvania Department of Health has changed how it reports COVID cases and deaths at the county level.  

Instead of providing daily pdfs with case and death counts for each county which can easily be entered into a spreadsheet, these numbers are only provided on the state's dashboard.  This makes it more difficult for me to enter statewide numbers into a spreadsheet.  Having to enter it manually makes it easier to make errors of entry.  

I am still able to enter numbers for just Cambria and Somerset Counties from the dashboard and show they trend as in the above graph.  Here, the 7 day average for new cases (198.14) is the highest it has been since Dec 12, 2020.  So far there has not been a corresponding increase in hospitalizations or deaths (the dotted black line in the graph above).  If this increase in cases is due to the omicron variant, then there should not be a similar rise in deaths in a few weeks.  

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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.

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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**

County Health Ranking Factors that Predict COVID Case Mortality in PA

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

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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

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County Health Rankings and Corona Virus Cases: Lower ranked counties have fewer cases in PA (except for Philly)

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.

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