Showing posts with label Child Abuse. Show all posts
Showing posts with label Child Abuse. Show all posts

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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Sunday, April 21, 2019

A look at the Number of PA Abuser Priests Identified by the Grand Jury

Diocese
# of Abuser Clergy
Catholic population
Per capita Abusers
Grand Jury Year
Allentown
37
    258,997
14.29
2018
Erie
41
    221,508
18.51
2018
Harrisburg
45
    230,000
19.57
2018
Pittsburgh
99
    632,138
15.66
2018
Scranton
59
    348,600
16.92
2018
Greensburg
20
    137,641
14.53
2018
Philadelphia
63
  1,188,225
5.30
2003
Altoona-Johnstown
50
       98,284
50.87
2016
Statewide
414
  3,115,393
13.29

Last summer Pennsylvania Attorney General Josh Shapiro announced the results of the grand jury investigation into sexual abuse by priests and other clergy in the Catholic Church in six of the eight dioceses in Pennsylvania.  The other two, Philadelphia and Altoona-Johnstown were investigated by grand juries in 2003 and 2016 respectively.  The number of abuser clergy along with the catholic population and population adjusted numbers in abusers per 100,000 population in each diocese are summarized in the above table.  
Profile of victims in the six county 2018 grand jury report

Adjusting the number of abusive clergy for the population that they serve allows for comparing one diocese to another and one investigation to another.  Of the six counties in the 2018 investigation, Harrisburg and Erie had the most abusers identified.  Greensburg and Allentown had the fewest.  The report provided the size of the population each of the six dioceses they served except the Harrisburg Diocese.  I found the 230,000 number on the Harrisburg Diocese website.  


Philadelphia had the smallest number in the state at 5.3 per 100,000 in the 2003 investigationAltoona-Johnstown had by far the highest number in the state at 50.87/100,000 and easily the smallest Catholic population at 98,284 in 2016.  This low population number for this diocese is surprising since it covers eight counties

The Philadelphia investigation was conducted by the Philadelphia County DA Lynne Abraham which may not have had the same resources as the state AG.  The Altoona-Johnstown was focused solely on that Diocese with the smallest population in the state and was conducted by the state AG, possibly as a trial run.  



My classmate from St. Clements School, Shaun Dougherty has been crusading for victims of Catholic Clergy abuse.  Below is a video of him and a friend confronting the priest that abused him a few months ago.  Buona Pasqua a' tutti.

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Saturday, August 4, 2018

Wuthering Depths

Emily Bronte 1818-1848
I write here when I am inspired by something and when life does not get in the way.  This past Monday was the 200th anniversary of the birth of Emily Bronte, the author of the novel Wuthering Heights.  For this blog I considered the title Wuthering Depths as a tongue in cheek pun on the novel but went with CSI without Dead Bodies as the TV show was still popular when the blog began.  




The details of Bronte's early life can be seen in the image above.  She had a younger sister Charlotte who wrote Jane Eyre and Anne who wrote Agnes Grey.  She had a brother Branwell who struggled with alcoholism and his image was removed from the portrait above with his sisters.
Laurence Olivier as Heathcliff and Merle Oberon as Catherine in the most famous movie adaptation of Wuthering Heights from 1939

Emily was reserved and quiet but she knew the people around her well.  In Wuthering Heights (her only novel written shortly before she died in 1848) the main characters are Heathcliff, an orphan from Liverpool brought to the Moors by the father of Catherine Earnshaw, the other main character.  After the father dies Catherine's brother Linton shuns him and treats him like an indentured servant.  Heathcliff and Catherine have a complicated love affair where she marries a rich man and he becomes cruel and vindictive as a result.  The next generation on the Moors is able to overcome their hatred and be happy and the protagonists are finally reunited in death.




I first read the novel in high school.  It didn't grab me until a few years later as I sympathized with Heathcliff's struggles in a harsh landscape.  Yes he was a product of his environment where the right guidance might have produced a very different outcome.  We all struggle with these issues in life and we all need the right guidance to overcome.  The depths that this blog searches are the patterns that others might miss.  


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Tuesday, May 2, 2017

Education the Best Predictor for Trump Support in PA Counties

Trump marked the first 100 days milestone of his campaign with a rally in Harrisburg the State Capitol touting his achievements and his plan to repeal and replace the Affordable Care Act.  In my last post I looked at uninsured trends in the state.  This time I will look at uninsured, poverty rates, educational attainment rates and changes in substantiated child abuse rates and how they correlate with Trump's % of the vote in each county in 2016.


Map showing changes in substantiated child abuse cases from 2011 to 2015 in PA Counties.  From CNHI News and the PA DHS Child Abuse Report (The map was wrinkled in my folder)
The map above shows changes in substantiated Child abuse cases from 2011 to 2015 with the counties in red showing a 275% or more increase in cases.  Statewide there was an increase of 23.3% in cases.  This variable plus the other variables were considered in the correlation table below.  The numbers in the table below are correlation coefficients with significant ones in bold.  A positive coefficient of means that as one variable increases the other has a corresponding increase.  A negative correlation of means that for every increase in one variable there is a corresponding decrease in the other.  


Corr w Philly
Uninsured: %
% in poverty
% increase in Child Abuse
Bachelors
Some college
HS Grad
HS Only
LT High School
HS comp
Trump %
Uninsured: %
1









% in poverty
0.34
1








% increase in Child Abuse
-0.03
0.10
1







Bachelors
-0.41
-0.45
-0.16
1






Some college
-0.49
-0.47
-0.13
0.86
1





HS Grad
-0.73
-0.45
0.03
0.63
0.78
1




HS Only
0.35
0.42
0.18
-0.85
-0.97
-0.61
1



LT High School
0.73
0.45
-0.03
-0.63
-0.78
-1.00
0.61
1


HS comp
0.03
0.03
-0.04
-0.06
-0.13
0.00
0.17
0.00
1

Trump %
0.14
0.03
0.19
-0.77
-0.66
-0.31
0.71
0.31
0.28
1


The variables that are most strongly associated with Trump's % of the vote in each county are the educational attainment variables.  The strongest negative correlation with Trump's vote is the % of the county with a bachelor's degree or higher with a coefficient of -0.77.  The scatter plot shows that as the % with a bachelor's or higher increases, Trump's % of the vote decreases accounting for 58.4% of the variability in this relationship (if 100% of variability were accounted for, all of the counties would form a perfect straight line).  The one county that the model does not fit very well is Philadelphia which had 15% of the vote for Trump but had 25% with a bachelor's degree or higher which is close to the state rate.  To look at the effect that Philadelphia County has I recreated the table and scatterplot with it excluded.  27 pairwise correlations are significant above.



The correlation table below shows more significant relationships for Trump's % of the vote with the uninsured and poverty rates having a a weakly positive relationship of 0.33 and 0.30 respectively.  Looking at the % with a high school (HS) degree or higher there is a weak negative relationship (-0.52) but looking at the high school completion rate (those who enter HS in the 9th grade who finish) there is a positive relationship.  Doing some subtraction to find the % in each county who have a HS degree only and those with less than a HS degree the % in the county with a HS degree only has a strong positive relationship with a coefficient of 0.74 accounting for 55% of the variability.  Adding Philadelphia to the data had little effect on the correlation as can be seen in the scatterplot below.



Corr wo Philly
Uninsured: 
%
% in 
poverty
% increase in 
Child Abuse
Bachelors
Some 
college
HS 
Grad
HS 
Only
LT High 
School
HS 
comp
Trump %
Uninsured: %
1









% in poverty
0.24
1








% increase in Child Abuse
0.00
0.15
1







Bachelors
-0.44
-0.52
-0.15
1






Some coll
-0.52
-0.54
-0.13
0.86
1





HS Grad
-0.70
-0.39
0.01
0.67
0.82
1




HS Only
0.41
0.54
0.17
-0.86
-0.98
-0.68
1



LT High School
0.70
0.39
-0.01
-0.67
-0.82
-1.00
0.68
1


HS comp
0.04
0.04
-0.04
-0.05
-0.13
0.00
0.16
0.00
1

Trump %
0.33
0.30
0.17
-0.84
-0.73
-0.52
0.74
0.52
0.31
1


Philadelphia is an outlier for PA counties in much the same way as DC is for the rest of the states.  Child abuse case increases were not correlated with any other variables.  In the next post I will look at these variables in a multiple regression model.  Below is the raw data for the analysis. 

Name
Uninsured
%
% in
poverty
% increase in
Child Abuse
Bachelors
Or higher
Some
college
HS
Grad
HS
Only
LT High
School
HS
comp
Trump
%
Pennsylvania
7.6
13.1
23.30%
28.6
63
89
26.2
10.8
85
48.58
Adams County, PA
7.9
8.5
-40.3
21.7
53
87.3
34.8
12.7
92
66.17
Allegheny County, PA
6
12.2
14.8
37.8
77
93.5
16.3
6.5
89
39.91
Armstrong County, PA
7.2
12.6
16
15
54
88.9
34.6
11.1
89
74.27
Beaver County, PA
5.5
13.1
37.5
22.8
66
91.7
25.6
8.3
74
57.64
Bedford County, PA
8.5
13.9
275
13.5
47
86.0
38.7
14.0
92
82.72
Berks County, PA
8.6
12.6
17.7
23.2
57
85.1
28.4
14.9
84
52.78
Blair County, PA
6.4
15.3
12.5
19.6
54
90.5
36.2
9.5
88
71.27
Bradford County, PA
8.1
13
-4.3
17.6
46
88.5
42.7
11.5
89
70.57
Bucks County, PA
5.8
6.3
-31.7
37.4
72
93.5
21.7
6.5
92
47.74
Butler County, PA
4.9
9.5
-19.2
32.3
72
93.0
20.8
7.0
95
66.37
Cambria County, PA
6.6
14.9
65.2
19.1
58
89.6
31.6
10.4
90
67
Cameron County, PA
7.4
13.3
100
15.7
59
89.5
30.5
10.5
72.69
Carbon County, PA
7.1
11.5
9.5
15.5
52
88.7
36.9
11.3
81
65.13
Centre County, PA
7.9
16.1
110
41.4
72
93.2
21.3
6.8
92
46.32
Chester County, PA
6.4
6
-20.3
49.1
75
92.7
17.5
7.3
89
43.2
Clarion County, PA
8.1
17.4
322.2
20.1
56
88.7
33.1
11.3
94
71.72
Clearfield County, PA
7.3
16.6
41.9
13.2
45
87.1
41.7
12.9
89
72.75
Clinton County, PA
7.6
16
66.7
16.9
52
87.1
35.5
12.9
93
65.1
Columbia County, PA
6.7
15
18.8
21.1
54
88.5
34.0
11.5
88
63.78
Crawford County, PA
9.8
15.3
-21.2
19.8
52
87.7
35.4
12.3
90
66.65
Cumberland County, PA
6.8
7.3
102
32.7
66
91.5
26.0
8.5
90
56.8
Dauphin County, PA
7.4
13.6
146
28.4
63
88.8
25.8
11.2
84
46.51
Delaware County, PA
6.8
10.4
27
36
69
92.2
23.0
7.8
76
37.18
Elk County, PA
6
9.3
220
16.8
53
91.1
38.1
8.9
91
69.49
Erie County, PA
6.9
17.1
-18.3
26.1
59
90.8
31.3
9.2
87
48.57
Fayette County, PA
7.5
20.1
67.5
14.2
48
86.8
38.7
13.2
79
64.33
Forest County, PA
8.3
24.3
66.7
8.5
16
80.5
64.7
19.5
70.1
Franklin County, PA
9.4
9.4
23.4
19
50
86.0
35.8
14.0
86
71.37
Fulton County, PA
7.8
11.6
42.9
13.1
46
85.1
39.5
14.9
86
84.09
Greene County, PA
6.5
15.1
300
17.7
45
86.9
42.0
13.1
85
68.82
Huntingdon County, PA
7
14.3
80
14.3
44
88.8
45.1
11.2
89
73.55
Indiana County, PA
8.7
18.2
-4.3
22.6
58
88.5
30.4
11.5
94
65.89
Jefferson County, PA
8.8
14.6
58.3
14.6
49
88.6
39.3
11.4
92
78
Juniata County, PA
9.9
12.8
-81.3
13.1
37
81.7
44.8
18.3
94
79.14
Lackawanna County, PA
7.8
15.3
46.6
25.9
63
89.9
27.2
10.1
83
46.77
Lancaster County, PA
11.1
10.6
0
25.2
53
84.5
31.0
15.5
90
57.2
Lawrence County, PA
7.1
17.5
-16.7
19.7
60
89.2
29.5
10.8
93
62.4
Lebanon County, PA
8.7
11.6
89.7
19.6
52
85.7
33.5
14.3
86
65.53
Lehigh County, PA
8.6
12.1
18.8
28.5
62
87.4
25.0
12.6
84
45.77
Luzerne County, PA
7.8
15.1
52.1
21.4
59
88.9
29.8
11.1
87
58.29
Lycoming County, PA
7.2
14.8
46.6
20.4
57
88.0
31.3
12.0
87
70.46
Mc Kean County, PA
7.2
16.9
-20.7
16.7
48
89.7
41.7
10.3
90
71.4
Mercer County, PA
7.9
14.2
76.2
21.5
56
89.1
33.2
10.9
93
60.3
Mifflin County, PA
10.7
15.4
57.9
11.6
43
82.2
39.2
17.8
89
75.77
Monroe County, PA
8.5
12.7
12.7
23
64
89.7
26.2
10.3
89
47.86
Montgomery County, PA
5.3
6.6
8.6
46.9
78
93.8
15.8
6.2
94
37.44
Montour County, PA
5
9
50
28.8
61
89.4
28.3
10.6
88
61.8
Northampton County, PA
6.9
8.8
-10.7
27.2
66
89.8
23.6
10.2
73
49.98
Northumberland County, PA
7.3
13.1
23.8
14.6
45
85.6
40.6
14.4
87
69.43
Perry County, PA
9.3
9.6
0
16
55
88.5
33.4
11.5
91
73.81
Philadelphia County, PA
11
25.4
-9.2
25.4
58
82.0
23.7
18.0
88
15.37
Pike County, PA
8.6
10.9
200
23.7
63
91.0
28.3
9.0
70
61.51
Potter County, PA
7.8
14.3
0
15.7
51
87.8
37.2
12.2
92
80.31
Schuylkill County, PA
7.9
13
15.8
15.4
51
87.5
36.1
12.5
69.99
Snyder County, PA
10.5
11.7
38.9
16.5
45
82.4
37.7
17.6
89
71.66
Somerset County, PA
8.7
14.4
-36
15.3
47
86.5
39.8
13.5
87
76.54
Sullivan County, PA
8.9
13.5
300
16.1
47
89.6
42.7
10.4
93
73.05
Susquehanna County, PA
9.3
12.6
-17.2
17.1
49
89.1
40.5
10.9
68.34
Tioga County, PA
8.6
13.1
72.2
19.6
55
88.4
33.0
11.6
95
74.29
Union County, PA
8.9
12.4
45.5
20.5
46
83.9
37.5
16.1
88
60.81
Venango County, PA
7.3
13.5
22.2
15.8
50
89.0
39.0
11.0
95
68.62
Warren County, PA
7.1
12.2
-30.8
18.1
55
90.5
35.4
9.5
90
67.68
Washington County, PA
5.7
10.1
29.2
27.4
67
91.3
24.5
8.7
89
60.51
Wayne County, PA
8.1
13.3
-13.6
19.7
50
89.3
39.7
10.7
90
67.63
Westmoreland County, PA
5.9
11.3
-1.5
26.4
68
92.7
25.0
7.3
94
64.01
Wyoming County, PA
6.9
11.1
200
18.2
50
91.2
40.9
8.8
91
67.23
York County, PA
7.1
10.4
21.9
22.8
57
88.3
31.8
11.7
94
62.4
 

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2014 Income and Poverty Update for Pennsylvania and Cambria County

 

Hate Groups and Trump's Vote%: Predictive effect present when education and poverty are considered

 

More Hate Groups in States Where Trump and Clinton Win (and in DC Where He Lost)