Showing posts with label Pennsylvania. Show all posts
Showing posts with label Pennsylvania. 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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Sunday, June 5, 2022

County Health Rankings Statistics and COVID Vaccination Rates


I have talked in the last few posts about COVID rates and County Health Rankings (CHR). I flagged significant correlations between the rates and the rankings (greater than 0.632 or less than -0.632 for 10 counties). While the rankings are easy to explain and are often quoted in the media, the individual statistics (more than 60 or them) used to create the rankings provides more specific information about the variables that predict local COVID rates.

This post focuses on COVID vaccination rates and individual CHR statistics.  The graph above shows the correlation between Vaccination rates (for those who have received the first 2 shots) and COVID case mortality rates. It shows a negative relationship between the two where every 1 % increase in the vaccination rate yields a predicted 0.046% decrease in the case mortality rate. This relationship accounts for 54% of the variability in the Case mortality rate. Next I will summarize the CHR statistics that are significant with the vaccination rates.
















There were 8 CHR variables that were significantly associated with the vaccination rate. The first that was significant was the average number of mentally unhealthy days (a quality of life statistic). This model states that for every one day increase in mentally unhealthy days, there is a predicted 19.3% decrease in the vaccination rate. This relationship accounts for 42.18% of the variability in the vaccination rate. 

To save space, I will summarize verbally the remaining significant correlations. The next significant correlation is the % of the county who are physically inactive which is also negative. This relationship accounts for 42.3% of the variability. Paradoxically, the % with access to exercise opportunities was positively correlated with the vaccination rate accounting for 49% of the variability.

The next significant correlation with the vaccination rate was with the primary care physician rate in the county. These rates were positively associated accounting for 42.3% of the variability. Likewise the flu vaccination rate was positively associated accounting for 49% of the variability. The high school completion rate and the % completing at least some college were both positively associated with the vaccination rate accounting for 44.9% and 39.7% of the variability respectively. Finally the income amount in the 80th percentile in the county was positively associated accounting for 39.7% of the variability. 

Next week I will summarize case mortality correlations.

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2022 County Health Rankings are Out: Cambria Still Ranks Low



Saturday, May 21, 2022

2022 PA Primary Post Mortem
























The 2022 Pennsylvania primary is almost in the books. State Sen. Doug Mastriano and Lt. Gov. John Fetterman won their respective primaries handily for Governor and U.S. Senator. Attorney General Josh Shapiro was unopposed for the Democratic Nomination for Governor while Dr. Mehmet Oz and and David McCormick are separated by roughly 1,000 votes for the GOP nod for the U.S. Senate.

The graphic above for the Real Clear Politics polling average for the GOP Governors race shows that Mastriano's lead began on April 15 and the gap began to widen on May 4. His actual percentage of the vote was 44% which was 9.7% higher than his final RCP average of 34.5%. The other candidates performed fairly close to their polling averages which suggests that the undecided voters broke for Mastriano in the last days of the campaign. This is partially explained by Trump's last minute endorsement of Mastriano. Jake Corman and Melissa Hart dropping out of the race to endorse Barletta had no effect.














The Senate race in PA has received a lot more scrutiny than the governors race. McCormick received a bigger surge in the last days of the campaign. Oz's percentage of the vote was 4.4% higher than his polling average while McCormick's was 10.5% higher. Kathy Barnette performed almost exactly the same as her polling average. My cousin Casey Contres is biting his nails over this one with the upcoming recounts. Trump's endorsement of Oz did not carry the same weight in this race.

On a local note, State Rep Frank Burns defeated challenger Michael Cashaw 72% to 28%. There were no exit polls in this primary which makes it harder to determine what the voters were thinking. I also have yet to see head to head polling for the Democratic and the GOP candidates for statewide offices for the fall. One bright spot for progressives in PA is that Summer Lee has won her primary for congress in Pittsburgh despite a smear campaign from outside groups. 



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Pennsylvania's Primary: Stakes Higher Because of Roe v. Wade



Saturday, May 14, 2022

A look at new Racial Disparity Numbers in Cambria County

Group

Cambria County

Pennsylvania

U.S.

Overall

75.8

78.1

77.3

White

76.1

78.4

77.6

African American

66.9

73.3

71.8

Hispanic

97.1

82.2

78.8


Two weeks ago I wrote about the new County Health Ranking (CHR) numbers for this year in Cambria County. As before, there was a big discrepancy in life expectancy between African Americans, Whites and Hispanics. The life expectancy estimate for Hispanics may be unreliable as they are a small percentage of the county population. The state life expectancy and racial U.S. life expectancy numbers are are from before the pandemic began.

As before I will go through the other CHR statistics to search for clues for this disparity:
  • The percentage of low birthweight babies was 9% for the county and 8% for the state. It was 17% for African Americans, 13% for Hispanics, and 8% for whites.
  • The overall child mortality rate was 50/100,000 which was the same as the state rate. It was 160/100,000 for African Americans and 40/100,000 for whites. The African Americans rate was down from 210/100,000 last year and 240/100,000 in 2020.
  • The overall infant mortality rate for the county was 7/1,000 live births which was 1% higher than the state rate. For African Americans it was 38/1,000 live births while for whites it was 5/1,000 live births.
  • The teen birth rate was 18/1,000 females age 15-19 for the county and 15/1,000 for the state. For African Americans it was 55/1,000, for Hispanics it was 17/1,000 and for whites it was 16/1,000.
  • The overall drug overdose mortality rate was 50/100,000 for the county while for the state it was 36/100,000. For African Americans the rate was 117/100,000 while for whites it was 47/100,000. According to overdose free PA, African Americans accounted for 9% of the deaths while they were only 3% of the county population. Whites still accounted for 89% of the overdose deaths.
  • The rate of preventable hospital stays was 5,518/100,000 for the county and for the state it was 3,966/100,000. For African Americans it was 7,326/100,000 while for whites it was 5,505/100,000.
  • The percentage of female Medicare enrollees receiving a mammogram was 45% for the county and 47% for the state. For Asian Americans it was 23%, it was 38% for African Americans, it was 57% for Hispanics, and it was 45% for whites.
  • Forty three percent of the county Medicare enrollees received a flu vaccination while for the state it was 54%. For Asian Americans it was 20%. For African Americans it was 36%. For Hispanics it was 46% and for whites it was 43%.
  • The overall child poverty rate for the county was 16% while for the state it was 14%. For Asians it was 15%, for African Americans it was 57%, for Hispanics it was 35% and for whites it was 19%.
  • The injury death rate was 133/100,000 for the county while it was 93/100,000 for the state. For African Americans it was 220/100,000 and for whites it was 131/100,000.
  • The average 3rd grade reading test level was 3.1 grade for the county which was the same as the state rate. For African Americans it was at the 2.2 grade level and at the 3.2 grade level.
  • The average math test level for 3rd graders was also at the 3.1 grade level which was also the same as the state rate. For African Americans it was at the 2.0 grade level while for whites it was at the 3.2 grade level.
  • Median household income for the county was $48,700 while for the state it was $64,900. For Asian Americans it was $100,100, for African Americans it was $28,500, for Hispanics it was $40,200, and for whites it was $48,900.
  • The homicide rate for the county was 5/100,000 while for the state it was 6/100,000. For African Americans it was 48/100,000 and for whites it was 3/100,000.
  • Eighty one percent of the county drives alone to work while for the state it was 74%. For African Americans it was 59%, for Hispanics it was 62% and for whites it was 83%.
Each year CHR provides more detailed information to explain the racial disparities in the health of the area. The next step is to take action to remedy these disparities. 

Coronavirus numbers for the cases and deaths county were not broken down by race at the county level. However, case rates are provided at the zip code level. The vast majority of African Americans in the county live in and around the city of Johnstown. Case rates are higher outside of Johnstown than in the city and its suburbs (zip codes 15901 to 15909) as can be seen in the graph below.

 

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Saturday, May 7, 2022

Pennsylvania's Primary: Stakes Higher Because of Roe v. Wade


Much has been made of the leaked opinion written by Supreme Court Justice Samuel Alito overturning the Roe v. Wade decision. The SCOTUS (or more accurately SCROTUS) decision will be made official in late June. If it hadn't been leaked, demonstrations, like the one above, would be erupting in June instead of now.

If this decision remains in June, the battle over abortion rights moves to the states making the upcoming elections even more critical. Pennsylvania's senate race was already receiving national attention with Pat Toomey retiring and celebrities like Mehmet Oz and John Fetterman running to replace him. The battle over state offices (Governor, Senate, and House) should become more heated as well. 

This week, Franklin and Marshall College published a poll showing Dr. Oz with a 2% lead over David McCormick in the Republican primary for Senate. This difference is within the margin of error of 6.9% and and 39% of GOP voters are still undecided. This poll was conducted before the SCOTUS opinion was leaked. Only 4% of voters listed "Women's Rights: Pro-life/Pro-choice" as the most important issue for which candidate they will support in the GOP primary. Donald Trump's endorsement of J.D, Vance in the Ohio GOP Senate primary helped propel him to victory this week.

John Fetterman has a big lead in the same poll on the Democratic side with 53% of the vote over Conor Lamb with 14%. Among the Democrats, only 2% of the voters listed "Women's Rights: Pro-life/Pro-choice" as their important issue. I have a feeling these percentages have increased on both sides since the SCOTUS leak.

Among the GOP, Trump still has 70% of respondents saying that they have strongly of somewhat favorable views of him. On this metric, Dr. Oz has 29% favorability and McCormick has 31%. Likewise, Biden has 80% favorability ratings on the Democratic side. Fetterman has 77%, Conor Lamb has 46%, and Gubernatorial candidate Josh Shapiro (running unopposed) has 62%.

Of all survey respondents, 31% said that abortion should be legal in all circumstances, 54% said it should be legal under certain circumstances, and 14% said it should be illegal in all circumstances. Since June 2009, there was a 13% increase in those who said it should be legal in all circumstances while, over the same period, there was an 8% decrease in those who wanted it illegal in all circumstances. The generic ballot for US/state house races shows 43% favoring the GOP, 39% favoring Democrats and 18% undecided.

It remains to be seen how the SCOTUS decision will affect the race. There are infinite possibilities for what could happen between now and November. Certainly passions will be higher on both sides. Dr. Oz's campaign manager happens to be my cousin, Casey Contres.

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Friday, March 25, 2022

New Hate Group Estimates Are Out for the U.S.



The County Health Rankings number will not be released until April 27.  I do have new numbers from the Southern Poverty Law Center for hate groups for each state in the U.S. The current map is displayed above.  They are currently tracking 733 groups in the U.S.

The graph at the left shows that the number of groups that they are tracking has decreased steadily since 2018 when there was an all time high in the raw number of groups at 1,020 (3.12/million).  Adjusting for population, the all time high year was 2011 at 3.18 groups per million.  These 2 peaks occurred during the first years of the Obama and Trump presidencies.  They did not track the numbers in 2005 and 2006.

The table at the bottom of this post shows the current number of hate groups for each state, their populations, population adjusted numbers, their rank, and 2020 rates.  The District of Columbia has had by far the highest population adjusted rates at 24.65 groups per million residents.  Many would argue that these 17 groups are national headquarters these groups and do not organize locally.  While this is true for 9 of these groups (like the Family Research Council), 8 of them have a local presence.  Adjusting these 8 local groups for population the rate is 11.60 which would still be the highest in the U.S.

Focusing on Pennsylvania, the number of total groups has declined to 30 from 36 a year ago.  The most they have ever tracked there was 41 in 2013.  Of the current 30 being tracked, 10 of them have a statewide presence.  The local groups are presented in the map at the right.  The population adjusted rate in 2021 was 2.31/million (ranking 30th in the U.S.) down from 2.81 million last year.  The local group nearest to Johnstown is called Evergreen.  It is a white nationalist group located in Bedford, PA.  It may be a group worth investigating.

The events of January 6, 2021 suggest that these groups still are a threat.  Kicking these groups off of social media platforms makes it harder for them to spread their propaganda but it may also make it harder for them to be tracked by groups like the Southern Poverty Law Center and the Anti Defamation League to track them.

Row  Labels

Count of 

Hate Groups

Population

Hate Groups 

Per Million

Rank

Hate Groups 

Per Million 2020

Alabama

13

       5,024,279

       2.59

24

4.08

Alaska

1

           733,391

       1.36

46

1.37

Arizona

22

       7,151,502

       3.08

15

3.57

Arkansas

9

       3,011,524

       2.99

16

4.64

California

65

     39,538,223

       1.64

44

1.82

Colorado

18

       5,773,714

       3.12

13

2.95

Connecticut

7

       3,605,944

       1.94

37

1.68

Delaware

4

           989,948

       4.04

6

3.08

District of Columbia

17

           689,545

24.65        

1

26.92

Florida

53

     21,538,187

       2.46

27

3.17

Georgia

24

     10,711,908

       2.24

32

2.73

Hawaii

4

       1,455,271

       2.75

20

2.83

Idaho

6

       1,839,106

       3.26

11

3.92

Illinois

23

     12,812,508

       1.80

38

1.50

Indiana

14

       6,785,528

       2.06

33

2.82

Iowa

2

       3,190,369

       0.63

49

0.95

Kansas

4

       2,937,880

       1.36

47

0.69

Kentucky

9

       4,505,836

       2.00

35

2.91

Louisiana

15

       4,657,757

       3.22

12

3.44

Maine

4

       1,362,359

       2.94

17

2.23

Maryland

11

       6,177,224

       1.78

41

2.48

Massachusetts

14

       7,029,917

       1.99

36

1.74

Michigan

18

     10,077,331

       1.79

39

2.50

Minnesota

9

       5,706,494

       1.58

45

1.95

Mississippi

8

       2,961,279

       2.70

22

3.02

Missouri

17

       6,154,913

       2.76

19

3.10

Montana

4

       1,084,225

       3.69

7

5.61

Nebraska

9

       1,961,504

       4.59

2

4.65

Nevada

9

       3,104,614

       2.90

18

3.90

New Hampshire

6

       1,377,529

       4.36

4

4.41

New Jersey

12

       9,288,994

       1.29

48

1.80

New Mexico

1

       2,117,522

       0.47

51

0.48

New York

35

     20,201,249

       1.73

42

1.90

North Carolina

28

     10,439,388

       2.68

23

2.77

North Dakota

2

           779,094

       2.57

25

1.31

Ohio

20

     11,799,448

       1.69

43

1.80

Oklahoma

9

       3,959,353

       2.27

31

3.03

Oregon

10

       4,237,256

       2.36

28

2.61

Pennsylvania

30

     13,002,700

       2.31

30

2.81

Rhode Island

3

       1,097,379

       2.73

21

1.89

South Carolina

17

       5,118,425

       3.32

10

3.88

South Dakota

4

           886,667

       4.51

3

1.13

Tennessee

28

       6,910,840

       4.05

5

4.98

Texas

52

     29,145,505

       1.78

40

1.86

Utah

2

       3,271,616

       0.61

50

1.25

Vermont

2

          643,077

       3.11

14

1.60

Virginia

20

       8,631,393

       2.32

29

3.87

Washington

19

       7,705,281

       2.47

26

2.89

West Virginia

6

      1,793,716

       3.35

9

2.23

Wisconsin

12

       5,893,718

       2.04

34

2.23

Wyoming

2

           576,851

       3.47

8

1.73

US

733

   331,449,281

       2.21

2.55

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