Showing posts with label Poverty. Show all posts
Showing posts with label Poverty. Show all posts

Sunday, June 19, 2022

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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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.

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African Americans Still Lag Behind in Life Expectancy in Cambria County


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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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Tuesday, December 31, 2019

Top Ten Posts of 2019

As 2019 comes to a close I thought I would imitate other media news outlets and look at the top 10 posts on this site in terms of the number of page views.  The majority of the posts came in the early part of this year and in the later part.  Posts made before 2019 are included in this list.

1. 


This post was on changes in enrollment and in those needing special education services in the Greater Johnstown, Westmont, and Richland School Districts.  It was posted in November.  Enrollment has decreased in all three districts while African-American enrollment has increased in Johnstown.





2. 

This post was made in November 2018 to introduce the poll for the greatest nonfiction book of all time.  So far the Origin of Species is leading.


3. 



This post from May of this year presents a documentary on the migratin of African Americans to Johnstown.


4. 



This post from October looks at the propaganda campaign against the local candidate for Cambria County Commissioner, Jerry Carnicella.  He lost.











5.  


This post is from May 2011 the oldest post on this list.  It talks about how the conflict in the US is reflected in similar conflicts in Italy, Germany and Japan.








6.  

I took a look at the numbers of abuser priests identified by Pennsylvania grand juries and adjusting them for the size of the populations that they serve in each of their respective Dioceses.  Cambria County had by far the most adjusting for population.





7.  

This post from a few weeks ago showed how endemic the problem of poverty has become in the Greater Johnstown School District (GJSD) compared to the neighboring districts of Westmont, Richland, and Ferndale.  Nationally it ranks 84th out of 13,207 school districts.




8.  


This post is a follow up to the second ranked post on this list.  It is from November and gives the results up to Nov 24 of this year.











9.  


This is the first post of 2019.  It compared elite coaches in the NFL who played in the NFL (ie. Chuck Noll, Don Shula, and Tom Landry) to ones who did not (Vince Lombardi, Bill Belichick, and Jimmy Johnson).  It found that they were equal in the regular season but the one who did not play had better records in the playoffs and won more championships.

10.  

This is the next to last post of the year.  It was a follow up post to the seventh most popular post on this list.  It looked at trends in the city, county, state, and US in poverty and median income.  The gap between the county and the city and the state and the US is growing.





For this list, two of the posts were made before 2019 and six of them were on the City of Johnstown and the surrounding area.  Of the eight posts made this year, three were made in the first half of the year.  This blog almost completely covers the 2010s.  In September I will have the tenth anniversary post looking at it's all time most popular posts.

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Friday, December 27, 2019

Holiday Poverty Estimates for Johnstown and Cambria County

In addition to the school district estimates that I posted about two weeks ago the Census bureau has produced estimates for every county and municipality in the US just in time for the holidays.  For Johnstown the poverty rate increased to 38.7% while it decreased slightly for Cambria County, Pennsylvania and the US. This is according to the federal definition of poverty which is about $30,000 for a family of four.


The census bureau also produced median household income estimates for the same counties and municipalities in the US for 2018.  For Johnstown there was a slight increase of $658 in the median income which is still well below the county, state, and US medians. This increase was within the $1,770 margin of error so we must conclude that there is really negligible change in the median income.

The median for Cambria County did decrease in 2018 by $844 in 2018 while the state and US medians increased by $1,786 and $1,601 respectively.  The PA and US increases were outside their margins of error while Johnstown's and Cambria's were not.

On Christmas Eve PBS Frontline rebroadcast a documentary on the effect of poverty on children in the US.  It's not just a Johnstown problem.  Unfortunately they do not allow me to embed it here but you can watch it at the link here.  Merry Christmas to all.

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Clairton HS vs. Bishop Guilfoyle HS: A Contrast in Poverty