despair, drugs and death: understanding spatial...
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Despair, Drugs and Death:Understanding Spatial Differences in U.S.
‘Stress-Related’ Mortality
Shannon M. MonnatPenn State University
smm67@psu.edu
Research Support and Collaborators• USDA Economic Research Service Cooperative Agreement
(58-6000-6-0028);– Collaborators: David McGranahan and Tim Parker
• USDA Agricultural Experiment Station Multistate Project: W-3001, “The Great Recession, its Aftermath, and Patterns of Rural and Small Town Demographic Change”;
• Penn State Population Research Institute (NICHD Center Core Funding: R24-HD041025);
• Penn State Dept. of Agricultural Economics, Sociology, and Education
The views expressed in this presentation are mine and do not necessarily represent the views of the USDA or other supporting organizations.
Trends in U.S. Drug Overdose Mortality
Source: Park, Haeyoun and Matthew Bloch. “How the Epidemic of Drug Overdose Deaths Ripples Across America.” New York Times, Jan 19, 2016. http://www.nytimes.com/interactive/2016/01/07/us/drug-overdose-deaths-in-the-us.html?_r=0
The U.S. Drug Overdose “Spread”
Increase in Mortality Driven by Rural Women?
Theoretical Grounding• Economic restructuring
– Globalization and technology– Declines in decent paying manual labor jobs
and the gender dynamics that follow
• Social ties, social capital, and anomie – Community and family breakdown and
disinvestment– Out-migration
• Neoliberalism & Devolution– Big pharma runs wild– Medical industry embraces consumer culture– Massive hole in gov’t “safety” net
“There is no group of Americans more pessimistic than working-class whites.” – J.D. Vance, Hillbilly Elegy
Objectives1. Describe differences in U.S. ‘stress-
related’ mortality rates (drug-related, alcohol-related, and suicide) along the rural-urban continuum.
2. Identify how economic distress, inequality & mobility, and social capital contribute to differences in ‘stress-related’ mortality.
Data Source: CDC Multiple Cause of Death Files. CDC Wonder
Data Source: U.S. Centers for Disease Control and Prevention. 2015. CDC Wonder Multiple Cause of Death Files, 1999-2014. Note: Excludes intentional self-poisoning by exposure to drugs, excludes tobacco-related mortality
Data Source: U.S. Centers for Disease Control and Prevention. 2015. CDC Wonder Multiple Cause of Death Files, 1999-2014.
Stress-Related Mortality, 2006-14
Data Source: CDC Wonder. 2015. Multiple Cause of Death Files, 1999-2014. ‘Stress-Related’ includes drug-related, alcohol-related, and suicide. Excludes tobacco-related.Rate is per 100,000 (age-adjusted)
White, NH (44.6)Black, NH (31.6)Asian (11.1)American Indian (91.9)Hispanic (27.2)
Overall Mortality, 2006-14
Data Source: CDC Wonder. 2015. Multiple Cause of Death Files, 1999-2014. Rate is per 100,000 (age-adjusted)
Stress-Related (age-adjusted), 2006-14
Overall (age-adjusted), 2006-14
• Some overlap (r = 0.339)• Appalachia• Native American territories• New York• Texas• Grain Belt
• Significant divergence• Black Belt• Florida• New England• Pacific Northwest• Mountain West• Southwest
KEY VARIABLES (County) DATA SOURCEEconomic Distress/Instability: pct. poverty (ages 18-64); aged 25+ w/<HS diploma; aged 16-19 dropped out of school; NILF; unemployed; w/work disability; HH w/public asst., families w/children that are single-parented;w/o health insurance (ages 18-64)[YEARS: 2010/14 (α=.83); 2000 (α=.87); 1990 (α=.88)]
ACS 2010-14Decennial Census 2000Decennial Census 1990
Income Inequality: Gini coefficient; Share of parent income accruing to top 1 percent of tax filers; Share of parents w/ national family income rank between 25th and 75th (inverse) (α=.78)
Economic Mobility: Expected rank of children whose parents are at the 25th percentile of the national income distribution• 1980-1982 birth cohorts, measured children’s income in
2011-12 when they are approx. 30
Chetty et al. (2014). "Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States.” QJE 129(4).
N=2,698; excludes all counties in Alaska and Hawaii and counties missing on variables of interest
KEY VARIABLES CONT. DATA SOURCESocial Capital:Four -factor index (2009, 1997)• Associations: religious; civic & social; business;
political; professional; labor; bowling centers; physical fitness facilities; public golf courses; sports
• Voter turnout (2008, 1996)• Census response rate (2010, 2000)• Number of non-profit organizations per 10,000 pop
Persistent Population Loss, 1970-2000 (county)
Northeast Regional Center for Rural Development: http://aese.psu.edu/nercrd/community/social-capital-resourcesRupasingha, A., Goetz, S. J., & Freshwater, D. (2006).
USDA ERSCONTROL VARIABLES
County-Level• Pct. White (non-Hisp), pct. Native American, pct.
foreign born, pct. age 65+, pct. veterans• Ratio of population to primary care providers, 2013• Medicare Part D opioid Rx claims per enrollee
State-Level• Opioid/benzo Rx rates (overall opioid Rx, high dose
opioid Rx, ext. release opioid Rx, benzo Rx)• Medicaid coverage for substance abuse counseling
ACS, 2010/14
RWJF County Health RankingsCenters for Medicare & Medicaid
Paulozzi et al. (2014), MMWR
Amer. Society of Addiction Med.
N=2,698; excludes all counties in Alaska and Hawaii and counties missing on variables of interest
Factors Associated with Stress-Related Mortality
Factor(Unadjusted Models)
β(rate per 100,000)
SE
Economic Distress
2010-14 7.406*** 0.325
2000 6.799*** 0.316
1990 7.117*** 0.316
Income Inequality -0.047 0.312
Economic Mobility -5.552*** 0.406
Social Capital
2009 -1.773*** 0.433
1997 -2.830*** 0.473
Persistent Population Loss 2.619** 0.824
Multilevel Models (counties within states); Unadjusted Weighted by log of county population, 2010-14 All factors except persistent population loss are standardized (represent standard deviation units)
N=2,698; excludes all counties in Alaska and Hawaii and counties missing on variables of interest
Factors Associated with Stress-Related Mortality
Factor(Unadjusted Models)
Unadj. Fully Adjusted
Economic Distressa 7.406*** 6.173***
Income Inequality -0.047 1.267***
Economic Mobility -5.552*** -3.295***
Social Capitalb -1.773*** -0.292
Persistent Population Loss 2.619** 2.895***
a Results robust to using 2000 and 1990 economic distress indicesb Results robust to using 1997 social capital index
Multilevel Models (counties within states);Weighted by log of county population; All factors except persistent population loss are standardized (represent standard deviation units)Fully adjusted models control for metro status, racial/ethnic and foreign-born composition, pct age 65+, pct veterans, opioid Rx claims per Med Part D enrollee, primary care provider supply, state Medicaid coverage for substance abuse counseling, and state Rx score
N=2,698; excludes all counties in Alaska and Hawaii and counties missing on variables of interest
Regression ResultsRural-Urban Differences in Stress-Related Mortality, 2006-14
0.0Model 1
Diff
eren
ce in
Age
-Adj
uste
d M
orta
lity
Rat
e Small UrbanNon-Met MicropolitanNon-Met Noncore
N=2,698; weighted by log of county population, 2010-14*statistically significant difference, p<.05Model 1 is unadjusted
Ref=large urban
Regression ResultsRural-Urban Differences in Stress-Related Mortality, 2006-14
**
*
0.0
5.0
10.0
15.0
20.0
Model 1
Diff
eren
ce in
Age
-Adj
uste
d M
orta
lity
Rat
e Small UrbanNon-Met MicropolitanNon-Met Noncore
N=2,698; weighted by log of county population, 2010-14*statistically significant difference, p<.05Model 1 is unadjusted
Ref=large urban
Regression ResultsRural-Urban Differences in Stress-Related Mortality, 2006-14
**
*
-5.0
0.0
5.0
10.0
15.0
20.0
Model 1 Model 2
Diff
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Age
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orta
lity
Rat
e Small UrbanNon-Met MicropolitanNon-Met Noncore
N=2,698; weighted by log of county population, 2010-14*statistically significant difference, p<.05Model 2 adjusts for demographic characteristics, health care variables, and Rx variables
Ref=large urban
Regression ResultsRural-Urban Differences in Stress-Related Mortality, 2006-14
**
*
-10.0
-5.0
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Model 1 Model 2 Model 3 Model 4
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e Small UrbanNon-Met MicropolitanNon-Met Noncore
N=2,698; weighted by log of county population, 2010-14*statistically significant difference, p<.05Model 3 integrates economic distress (2010-14), inequality, mobility, social capital (2009), and persistent population loss
Ref=large urban
** *
Regression ResultsRural-Urban Differences in Stress-Related Mortality, 2006-2014
**
*
-10.0
-5.0
0.0
5.0
10.0
15.0
20.0
Model 1 Model 2 Model 3 Model 4
Diff
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ce in
Age
-Adj
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Rat
e Small UrbanNon-Met MicropolitanNon-Met Noncore
N=2,698; weighted by log of county population, 2010-14*statistically significant difference, p<.05Model 4 uses economic distress index for 2000 and social capital index for 1997
Ref=large urban
** *
** *
uses 2010-14 economic distress and 2009 social
capital
uses 2000 economic distress and 1997 social
capital
Summary• Its not just about the drugs.• Not a universal national problem – clustered in pockets
of economic disadvantage/despair (except black belt)• Economic distress, mobility, and social capital (including
persistent population loss) all associated with stress-related mortality;
• Stress-related mortality is higher in small urban and nonmetro counties than in large urban counties;– Explained by both demographic composition
differences and greater economic distress and persistent population loss in small urban and nonmetro counties.
• Results robust to economic distress and social capital measures from earlier time points, suggesting long-term dynamic.
Additional Sources• Monnat, Shannon M. 2016. “Drugs, Death, and Despair in New England.”
Communities & Banking Magazine. Federal Reserve Bank of Boston. https://www.bostonfed.org/publications/communities-and-banking/2016/fall/drugs-death-and-despair-in-new-england.aspx.
• Monnat, Shannon M. and Khary K. Rigg. 2016. “Rural Adolescents More Likely than their Urban Peers to Abuse Prescription Painkillers.” National Fact Sheet #32. Carsey School of Public Policy. University of New Hampshire. https://carsey.unh.edu/publication/prescription-painkiller-abuse.
• Monnat, Shannon M. and Khary K. Rigg. 2016. “Examining Rural/Urban Differences in Prescription Opioid Misuse among U.S. Adolescents.” Journal of Rural Health. http://www.ncbi.nlm.nih.gov/pubmed/26344571.
• Rigg, Khary K. and Shannon M. Monnat. 2015. “Comparing Characteristics of Prescription Painkiller Misusers and Heroin Users in the U.S.” Addictive Behaviors 51:106-112. http://www.ncbi.nlm.nih.gov/pubmed/26253938
• Rigg, Khary K. and Shannon M. Monnat. 2015. “Urban vs. Rural Differences in Prescription Opioid Misuse among Adults in the United States: Informing Region Specific Drug Policies and Interventions.” International Journal of Drug Policy 26:484-491. http://www.ncbi.nlm.nih.gov/pubmed/25458403.
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