Showing posts with label elderly. Show all posts
Showing posts with label elderly. Show all posts

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.

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Saturday, October 28, 2017

Veterans, The Elderly, and Living Wage Cities/Counties

I asked others in the field of demographics on my last post on the percentage of veterans being a negative predictor of the amount of living wage enacted in the 38 cities/counties that have passed living wage ordinances.  One expert in the field suggested one variable that I hadn't considered.  




Chris Briem over at the blog Nullspace suggested I look at age as a possible variable that could mediate this relationship.  He stated that there are higher concentrations of veterans among the elderly.  This makes sense as the draft existed before 1970.  I did obtain the % of the population over the age of 65 for cities in the 2010 census and added it to the model seen below.


Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
14.70
1.58
9.30
0.00
11.49
17.91
% veteran
-0.60
0.17
-3.61
0.00
-0.94
-0.26
% over 65
0.03
0.13
0.22
0.83
-0.24
0.30

The % of veterans in the city/county still significantly negatively predicted the amount of the living wage passed while the % over the age of 65 did not predict it in either direction.  These cities did have lower percentage of veterans (mean=4.95%) than the US (6.22%).  Likewise these cities did have lower percentages of those over 65 (mean=11.77%) than the US (13.00%).  

I looked at the correlation between the % of veterans and the % over 65.  There was a non-significant positive correlation between the variables as can be seen in the graph below.  Only 8% of the variability in the % over 65 was accounted for by the % veterans for these cities.  There are cities with high elderly populations and low veteran populations such as Palo Alto and El Cerrito, CA



It may be more informative to look at the % of elderly veterans vs. younger veterans as a predictor of the amount of the living wage.  I'm not sure where that data is available but it is a good area of inquiry.

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What do Living Wage Cities Have in Common?





Veterans, the Living Wage, and the McNamara Fallacy