Saturday, April 10, 2021

African Americans Still Lag Behind in Life Expectancy in Cambria County

Group

Cambria County

Pennsylvania

U.S.

Overall

76.1

78.4

78.7

Caucasian

76.4

78.9*

78.6

African American

67.3

73.4*

74.7

Hispanic

91.3

85.3*

81.8

*Pennsylvania life expectancy by race numbers are from 2010 (Measure of America, 2013) 

In my book, Wuthering Depths in Johnstown: By the Numbers.  I wrote extensively on the 2020 County Health Rankings for Cambria County (where Johnstown is located).  The number that jumped out at me most was racial disparities in mortality data in Cambria county where African Americans had a life expectancy of 65.6 years, Caucasians 76.2 years, and Hispanics 89.0 years.  

This year's life expectancy numbers from the 2021 County Health Rankings are displayed above in the table above for the years 2017-2019 (corona virus deaths are not included).  Cambria County's overall life expectancy increased from 75.6 to 76.1 years (63rd in PA).  Among counties with life expectancies by race, African American life expectancy increased by 1.7 years to 67.3 (the lowest in PA).  Caucasians increased by 0.2 years to 76.4 (3rd lowest ).  Hispanics increased the most by 2.3 years to 91.3 (5th highest in the state).  

Counties with miniscule racial/ethnic minority populations do not have a breakdown for life expectancy by race.  African Americans comprise 3.5% (10.9% of PA) of the county and Hispanics make up 1.8% (7.8% of PA).  Caucasians comprise 92.6% of the county (75.7% of the state).  Approximately 59% of the African Americans in the county live inside the city of Johnstown

Overall the county is 2.6 years behind the US life expectancy.  Caucasians are 2.2 years behind, African Americans are 7.4 years behind, and Hispanics are 9.5 years ahead of the US rate.  Life expectancy is not included in the overall County Health Rankings for Health Outcomes.  Mortality Measured as Years of Potential Life Lost is factored into the rankings which is a measure related to life expectancy.

Years of potential life lost (YPLL) is the number of years lost if someone dies before age 75.  For example, if someone dies at age 25, they have 50 years for potential life lost.  The overall YPLL rate is 9,700 years per 100,000 for the county.  It is 19,900 years per 100,000 for African Americans and 9,400 years per 100,000 for Caucasians.  

The premature age adjusted death rate for the county is the number of deaths under age 75 per 100,000 for the years 2017-2019.  For Cambria it is 440 deaths/100,000.  For African Americans it is 840 deaths/100,000 and for Caucasians it is 430 deaths/100,000.  For Pennsylvania, the overall rate is 350 deaths/100,000.  This measure is provided by County Health Rankings but not factored into the rankings.

The most shocking mortality rate is the child mortality rate which is the number of deaths under the age of 18 per 100,000 for the years 2016-2019.  For Cambria the rate is 50 deaths/100,000 which is the same as PA's rate.  For African Americans in the county, it is 210 deaths/100,000 (the highest in PA) while for Caucasians it is 40 deaths/100,000.  The rate is more than 5 times higher for African Americans. 

The health factors data for the county provides some clues for this discrepancy.  I will discus it in my next post.

**Related Posts**

Why do African Americans live to be 64.8 years in Cambria County?



Friday, April 2, 2021

The 2021 County Health Rankings are Out

 

The 2021 County Health Rankings are now out for every county in the US.  The rankings use more than 60 government statistics to assess the overall health of each county.  Above is a video description of their model.  They have two overall rankings for the state: Health Outcomes (mortality and quality of life data) and Health Factors (things that contribute to the health outcomes).

The maps above show the rankings for each county in Pennsylvania.  The darker green counties have lower health outcomes rankings while the counties with darker color blue have lower health factors ranking in the map on the right.  Cambria County ranks 62nd in health outcomes (up 2 spots from last year which is not cause for celebration) and 47th on health factors which is down 3 spots since last year.

For me the overall rankings are not as interesting as the underlying statistics used to determine them.  Two individuals can have the exact some IQ score but one can have a high verbal aptitude while another can have an equally high math ability. The graph below shows the trend in mortality data (measured as years of potential life lost or YPLL).

In my book, I went in depth into the rankings to explain why last year's rankings were so low.  It was mostly racial disparities in life expectancy in the county.  In the next few posts I will go in depth into this years numbers.  Corona virus numbers are not yet factored into the rankings as 2019 is the most recent year for mortality data.  The graph above shows that Cambria's rate, though decreasing, is still higher than the state and U.S. rates. 

**Related Posts**

The graph above shows that Cambria's rate, though decreasing, is still higher than the state and U.S. rates. 

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


Sunday, March 21, 2021

One Year of Corona Virus in Cambria County

March 23 will be the one year anniversary of the first case of coronavirus in Cambria County.  The graph above shows the trend in cases and vaccinations over the past year.  The graph below shows the trend in deaths.  The first death was reported on April 7.  The county now (as of this writing) has 12,059 cases, 402 deaths, and 19,664 full vaccinations.  As I update the numbers on Google sheets, the numbers on the graphs in this post will be updated.


I have been tracking the number of cases and deaths almost every day over the past year.  It has been a labor of love keeping track of the pandemic here.  The feedback has been mostly positive to this effort.  As more publicly available data has been released over the year, the effort has taken more and more of my time every day.  In addition to the daily posting of numbers, I have written 34 out of 47 posts on corona virus on this blog.  The more insights that I can provide for the public to guide decision making the better.

In This Together Cambria is putting together a slideshow showing the resilience of the area to the past years challenges.  Submissions are due by March 26.  One story I can add is the passing of Rosie Caeti, an old family friend who passed away last on March 13 from complications from Corona Virus at 87.  She used to babysit my dad when he was young.























Soon, the county health rankings for every county in the US will be released for 2021.  I do not yet know how or if coronavirus deaths will factor into the rankings.  

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Wednesday, March 10, 2021

Hate Groups do not Predict Trump % in 2020. It did in 2016

The Southern Poverty Law Center has come out with it's 2020 hate map showing the concentration of hate groups in the US as seen above.  The total number of groups that they follow nationally went down in 2020 to 838 from 940 in 2019.  The high point for hate groups during the Trump years was 2018 with 1,020.  You can see my summary of the numbers here


As before, I adjusted the number of hate groups in each state and expressed it as hate groups per million and correlated it with Trump's support in that state for that year.  As in previous years, the District of Columbia led the nation with 26.9 groups per million but he only received 5.4% of the vote in that state so it was excluded from the above graph.  Montana had the highest hate group rate at 5.61 per million.  New Mexico had the lowest at 0.48 per million.  Pennsylvania's raw number of hate groups was unchanged at 36 from 2019.

The above graph shows a weak positive correlation for Hate Group concentration in 2020 and Trump's % of the vote in last year's election accounting for 5.6% of the variability in his vote.  In 2016, hate group concentration accounted for 20.1% of the variability in his vote that year.  This correlation was almost twice as strong in 2016.  The weaker correlation could be due to greater minority mobilization in 2020.




























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Thursday, March 4, 2021

What other Variables Contribute to Nursing Home Mortality in PA?

 

 

% of death in LTCF

Case Mortality in LTCF

Case Mortality in County

% over 66

% Rural

Median Household Income

% of death in LTCF

1.00

Case Mortality in LTCF

0.38

1.00

Case Mortality in County

0.02

0.26

1.00

% over 66

-0.19

-0.30

0.33

1.00

% Rural

-0.13

-0.21

0.11

0.62

1.00

Median Household Income

0.15

0.38

-0.12

-0.48

-0.49

1.00


Two weeks ago I looked at the correlation between the percentage of residents over the age of 66 in the county with the percentage of total county deaths in a nursing home and the case mortality rate in the nursing home.  This week I'm looking at the the additional variables of the % of the county in a rural area and the median household income in the county.  Of these, the relationship between case mortality and median income was the strongest accounting for 14.9% of the variability in the rates.


While this is a relatively weak (though significant) positive correlation there is a stronger correlation between that may moderate this relationship.  There a stronger relationship between % rural and % over the age of 66 of 0.62 (seen below).  Both of these have a relatively strong negative correlation of -0.49 and -0.48 with median household income respectively.    


The % over the age of 66 has a stronger positive correlation with the case mortality in the county (0.33) that it does with case mortality in a nursing home (LTCF) (-0.30 which is smaller in absolute value than 0.33).  This suggests that the elderly who live in more rural counties are more likely to die outside of a nursing home from corona virus.  The more rural and more elderly counties are also more likely to have lower incomes.

**Related Posts**


Saturday, February 20, 2021

Why the New York COVID-19 Nursing Home Scandal Matters

 


Last week I posted on coronavirus mortality in nursing homes in Pennsylvania.  By coincidence, just to the north of PA is the state of New York where it's Governor, Andrew Cuomo, has come under fire for underreporting the number of deaths in nursing homes there.  In the above clip, NY Assemblyman Ron Kim claims that Cuomo called him to threaten him not to go public with the underreporting.  The state assembly will vote to curtail Cuomo's emergency powers.

When I report on the Pennsylvania numbers, I and those that use the numbers trust that their reporting is accurate.  There is already a certain level of distrust of the federal, state, and local government's honesty.  Scandals like these only reinforce this mistrust.  It makes it harder to track and sound the alarm about the spreading of cases. 


Impeachment or some other accountability by the voters is warranted.  Cuomo will not be up for reelection for a fourth term until 2022.  His father Mario (once a potential Presidential candidate) was defeated for a fourth term by George Pataki in 1994.

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Thursday, February 11, 2021

Size of PA County Elderly Population Predicts Smaller Nursing Home COVID-19 Mortality

This week I thought I would take a look at mortality in nursing homes (or Long Term Care Facilities (LTCF)) in Pennsylvania.  Statewide, nursing home deaths account for 52.1% of the total coronavirus deaths.  Looking at the county level there is considerable variability in the rates where the 10 counties with the highest rates are seen below.  The rates for this group rage from 115.8% for Forest county to 67.2% for Delaware County.  Forest county actually has 3 more LTCF deaths than total deaths with one facility.  This suggests that at least 3 of their residents reside outside the county.  You can see the full list of counties here.

Facility County

Number of Facilities with Cases

Number of Cases Among Residents

Number of Cases Among Employees

Number of Deaths in LTCF

Total Deaths in the County

% of death in LTCF

Case Mortality in LTCF

FOREST

1

121

26

22

19

115.8%

15.0%

ERIE

35

1354

717

287

363

79.1%

13.9%

MCKEAN

9

372

110

47

60

78.3%

9.8%

CENTRE

16

675

146

155

203

76.4%

18.9%

BUTLER

29

1421

156

257

350

73.4%

16.3%

LACKAWANNA

35

1423

295

290

395

73.4%

16.9%

LYCOMING

19

922

283

165

232

71.1%

13.7%

MONTGOMERY

111

4723

186

1026

1456

70.5%

20.9%

CARBON

7

407

48

95

138

68.8%

20.9%

DELAWARE

59

3197

871

796

1184

67.2%

19.6%

The bottom 10 counties are listed below.  The percentage of deaths in the in LTCFs for this group rage from 35.7% in Sullivan County to 15.0% in Potter County.  Surprisingly, Philadelphia county is in this bottom tier with 26.1% of their deaths in LTCFs.  Another way of looking at mortality in LTCFs is the case mortality or the % of cases who have died.  Philadelphia ranks 11th in the state on this measure with Pike county ranking 1st at 27.0% and Cameron County ranking last at 4.3%.

Facility County

Number of Facilities with Cases

Number of Cases Among Residents

Number of Cases Among Employees

Number of Deaths in LTCF

Total Deaths in the County

% of death in LTCF

Case Mortality in LTCF

SULLIVAN

3

94

13

5

14

35.7%

4.7%

WAYNE

5

88

19

20

58

34.5%

18.7%

BEDFORD

5

269

29

43

126

34.1%

14.4%

FRANKLIN

26

799

94

106

313

33.9%

11.9%

CAMERON

1

36

10

2

7

28.6%

4.3%

SNYDER

2

124

31

20

73

27.4%

12.9%

PHILADELPHIA

85

4222

16

782

3001

26.1%

18.5%

ELK

3

122

50

8

34

23.5%

4.7%

CLINTON

6

112

48

9

53

17.0%

5.6%

POTTER

3

45

15

3

20

15.0%

5.0%


I looked at the percentage of the population in each county over the age of 65 and found some interesting patterns.  For the correlation with % of deaths in LTCFs, as the proportion of the population over 65 increases the % of deaths decreases as seen with the graph below.  Forest county is an outlier and is labeled green in the scatterplot.  With Forest county included, this relationship accounts for 6% of the variability in the data.  When Forest county is excluded, the relationship accounts for 11.6% of the variability in the data and is still negative.  I don't have a good explanation for this but will investigate further.


I then looked at the correlation between case mortality and the % of the county over 65.  This relationship was also negative accounting for 11.3% of the variability.  Pike county is a mild outlier.  With Pike excluded, this relationship accounts for 16.6% of the variability.  This pattern is similar to the one with the percentage of deaths in the county.  

 

Case mortality and % of deaths in LTCFs are positively associated but not strongly accounting for 15.3% of the variability.  There may be stronger relations ships among the variables not considered yet.  There also may be variables at the facility level to which I do not have access.  Investigating will continue.

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