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Modeling Respiratory Disease Modeling Respiratory Disease Clusters in Central Appalachia Clusters in Central Appalachia
Timothy S. HareTimothy S. HareIRAPP, MSUIRAPP, MSU414C Bert Combs Building414C Bert Combs BuildingTel. 606-783-9436Tel. 606-783-9436E-mail: E-mail: [email protected]
Matt Laurin - Matt Laurin - Morehead State UniversityMorehead State UniversityE-mail: E-mail: [email protected] Aaron Pierce - Aaron Pierce - Morehead State UniversityE-mail: Morehead State UniversityE-mail: [email protected]@moreheadstate.edu
Study Area
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!(
!(!(
!(
!(
!(
!(
!(
!(
!(
!(
!( !(
!(
!(
!(
!(!(
000
GeorgiaSouth Carolina
AlabamaMississippi
Missouri
Illinois
Indiana
OhioOhio
Virg
inia
Virg
inia
North C
arolina
North C
arolina
PennsylvaniaPennsylvania
Raleigh
Columbus
Baltimore
Charlotte
Lexington
Nashville
WashingtonCincinnati
Louisville
Virginia Beach
Asheville
Knoxville
CharlestonHuntington
Pittsburgh
Chattanooga
Johnson CityWinston-Salem
³0 10050Miles
Appalachian Cities
!( Over 50,000
Non-Appalachian Cities
!( Over 250,000
Northern Appalachia
Central Appalachia
Southern Appalachia
Mortality Rates - All Causes
LISA Cluster Map – Mortality - All Causes – Moran’s I 0.520***
Age-Adjusted Mortality for All Causes
55
LISA Cluster Map of Mortality for All Causes
66
000
GeorgiaSouth Carolina
AlabamaMississippi
Missouri
Illinois
Indiana
OhioOhio
Virg
inia
Virg
inia
North C
arolina
North C
arolina
PennsylvaniaPennsylvania
³0 10050Miles
Male Mortality LISA Cluster MapNot Significant
High - High
High - Low
Low - High
Low - Low
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Research Questions
Are mortality rates due to major respiratory conditions distributed evenly across central Appalachia?
What factors are associated with elevated mortality rates due to major respiratory conditions?
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Key Tasks
Identify meaningful clusters of high mortality rates.
Use techniques of ESDA to characterize associated factors.
Examine the effects of factors on spatial clusters of high mortality rates.
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Data & Methods
Data SeerStat Health Care Services
– AHA Annual Survey
– Additional surveys & questionnaires
– Area Resource File
Socioeconomic Data - Census
Travel – Kentucky State Transportation Model (KYSTM)
Methods Spatial Aggregation County-level (598) Mortality Rates Temporal Aggregation Direct Standardization Age-Adjusted Rates Year 2000 U.S. Standard
Population Travel time calculations
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Analysis Techniques
Map visualization Univariate & Bivariate Moran’s I Local Indicators of Spatial
Autocorrelation Spatial Regression (lag & error
models) Geographically Weighted
Regression
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Research Question 1
A. Are mortality rates due to lung cancer distributed evenly across central Appalachia?
B. Are Mortality rates due to COPD distributed evenly across central Appalachia?
Age-Adjusted Mortality for Lung Cancer
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LISA Cluster Map of Lung Cancer Mortality
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Age-Adjusted Mortality for Lung Cancer by Sex
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LISA Cluster Map of Lung Cancer Mortality by Sex
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Age-Adjusted Mortality for COPD
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LISA Cluster Map of COPD Mortality
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Age-Adjusted Mortality for COPD by Sex
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LISA Cluster Map of COPD Mortality by Sex
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Female vs. Male Lung Cancer Mortality Multivariate LISA Cluster Map Moran’s I = 0.3700 p < 0.001
2020
Female vs. Male COPD Mortality Multivariate LISA Cluster Map Moran’s I = 0.3368 p < 0.001
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Spatial Autocorrelation: Moran’s I
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Univariate Moran's I P-Value
Total Mortality Lung Cancer 0.53673 ***Female Mortality Lung Cancer
0.35862 ***
Male Mortality Lung Cancer 0.50032 ***Total Mortality COPD 0.52669 ***Female Mortality COPD 0.38812 ***Male Mortality COPD 0.45586 ***
Note: *** P < 0.001, ** P < 0.01, * P < 0.05
Multivariate: Female vs. Male
Moran's I P-Value
Lung Cancer 0.3700 ***COPD 0.3368 ***
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Research Question 1
A. Are mortality rates due to lung cancer distributed evenly across central Appalachia? - No
B. Are Mortality rates due to COPD distributed evenly across central Appalachia? - No
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Research Questions 2
A. What factors are associated with elevated mortality rates due to Lung Cancer?
B. What factors are associated with elevated mortality rates due to Lung Cancer?
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Population Density Population Density (people/mile(people/mile22))
Median Household Income
LISA Cluster Map – Median Income – Moran’s I 0.681***
% with High School Education
LISA Cluster Map – High School – Moran’s I 0.582***
LISA Cluster Map – College – Moran’s I 0.532***
Total Household Health Care Expenditures
LISA Cluster Map - Household Health Care – Moran’s I 0.579***
Health Care Spending (% of Total Household Spending)
000
Illinois
³0 10050Miles
Health Care Spending7.06% - 7.37%6.72% - 7.05%6.31% - 6.71%5.83% - 6.3%5.09% - 5.82%
Health Insurance Spending
LISA Cluster Map - Insurance – Moran’s I 0.589***
Health Insurance Spending (% of Total Expenditures)
000
Illinois
³0 10050Miles
% Health Insurance Spending3.6% - 3.8%3.4% - 3.5%3.1% - 3.3%2.9% - 3%2.6% - 2.8%
Total Household Education Spending
LISA Cluster Map – Total Education – Moran’s I 0.680***
Persistent Pattern: Rx Drug Spending
LISA Cluster Map - Rx Drug Spending – Moran’s I 0.419***
Bivariate Moran’s I vs. Total Mortality for Lung Cancer
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Variables Moran's I P-Value
Median Household Income -0.3550***Unemployment Rate 0.3355***Mining as Percent of Total Employment 0.3027***Percent With High School Diploma or Equivalent
-0.3833***
Health Care as Percent of Total Household Spending
0.2529***
Health Insurance as Percent of Total Household Spending
0.2335***
Hospital as Percent of Total Household Spending
0.3242***
Non-Prescription Drugs as Percent of Total Household Spending
0.3124***
Prescription Drugs as Percent of Total Household Spending
0.3110***
Education as Percent of Total Household Spending
-0.3324***
Note: *** P < 0.001, ** P < 0.01, * P < 0.05
Bivariate Moran’s I vs. Total Mortality for COPD
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Variables Moran's I P-Value
Median Household Income -0.3920***Unemployment Rate 0.3217***Mining as Percent of Total Employment 0.3027***Percent With High School Diploma or Equivalent
-0.3432***
Health Care as Percent of Total Household Spending
0.2940***
Health Insurance as Percent of Total Household Spending
0.2739***
Hospital as Percent of Total Household Spending
0.3434***
Non-Prescription Drugs as Percent of Total Household Spending
0.3506***
Prescription Drugs as Percent of Total Household Spending
0.3507***
Education as Percent of Total Household Spending
-0.3506***
Note: *** P < 0.001, ** P < 0.01, * P < 0.05
OLS Regression Model 1
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Variable Coefficient Std.Error t-Statistic ProbabilityCONSTANT 75.963630 9.848863 7.712934***Unemployment Rate 1.007752 0.196690 5.123532 ***% Employed in Mining 0.649820 0.143670 4.522988 ***% High School Graduates -0.509364 0.072799 -6.996775 ***% Household Health Spending -6.462413 2.344200 -2.756767 **% Household Hospital Spending 256.6613 71.9874 3.565364 ***
Note: *** P < 0.001, ** P < 0.01, * P < 0.05
Adjusted R-squared: 0.328321Akaike info criterion: 4677.76
Spatial Regression Model 1 (Lag)
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Variable Coefficient Std.Error z-value ProbabilityLag of Total Lung Cancer Mortality
0.5101864 0.04091244 12.4702 ***
CONSTANT 39.61875 8.884213 4.459455 ***Unemployment Rate 0.6780908 0.1712365 3.959966 ***% Employed in Mining 0.332557 0.1255439 2.648929 **
% High School Graduates2.02509 0.0645824 -
4.601721 ***
% Household Health Spending-4.60215 2.02509 -
2.272565 *
% Household Hospital Spending
162.1175 62.41695 2.597331 **Note: *** P < 0.001, ** P < 0.01, * P < 0.05
R-squared: 0.49796 Akaike info criterion: 4575.46
Comparison of OLS & Spatial Lag Regression
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VariableOLS
CoefficientOLS
ProbabilitySpatial Lag Coefficient
Spatial Lag Probability
Lag of Total Lung Cancer Mortality na na
0.51019 ***
CONSTANT 75.963630*** 39.61875***Unemployment Rate 1.007752 *** 0.67809 ***% Employed in Mining 0.649820 *** 0.33256**% High School Graduates -0.509364 ** -0.29719***% Household Health Spending -6.462413*** -4.60215*
% Household Hospital Spending 256.6613 * 162.1175
0 **
Note: *** P < 0.001, ** P < 0.01, * P < 0.05
R-squared: 0.49796 Akaike info criterion: 4575.46
Adjusted R-squared: 0.328321Akaike info criterion: 4677.76
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Results
1. Mortality rates due to major respiratory conditions are not distributed evenly across central Appalachia.
2. All examined factors are associated with elevated mortality rates due to major respiratory conditions.
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Future Investigations
Further explore differences by sex Deal with multicollinearity
– Create composite deprivation index Examine service utilization patterns Examine
– Tobacco use– Air quality
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Questions?!Questions?!
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Institute for Regional Analysis and Institute for Regional Analysis and Public PolicyPublic Policy
(IRAPP)(IRAPP)
Center of Excellence, MSUCenter of Excellence, MSUAA Kentucky Program of DistinctionKentucky Program of Distinction
http://irapp.morehead-st.edu
Thank YouThanks to:The National Center for Health Statistics for the mortality data.
This Research is supported in part by:An MSU Faculty Research GrantKBRIN-NIH Research GrantThe Institute for Regional Analysis and Public PolicyBooth Endowment Research Grant