Examining the Rela.onship between Incarcera.on Rates and - - PowerPoint PPT Presentation

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Examining ¡the ¡Rela.onship ¡ ¡ between ¡Incarcera.on ¡Rates ¡and ¡ Popula.on ¡Health ¡at ¡the ¡County ¡ Level

• Prof. ¡Jen ¡Schultz, ¡PhD ¡| ¡@RepJenSchultz ¡| ¡#ARM19 ¡

University ¡of ¡Minnesota ¡

June 3, 2019

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Overview

• Purposes of study
• Add to the growing body of knowledge

about the collateral consequences of mass incarceration

• Examine the relationship between level
• f incarceration and health outcomes at

the county level

@RepJenSchultz | #ARM19 | @AcademyHealth

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Context “The individual-level effects of incarceration… ripple outward” (Clear, 2008, p. 99)

• The incarcerated individual à

members of their families à the communities they are from

@RepJenSchultz | #ARM19 | @AcademyHealth

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Examining the Link between Incarceration and Health Outcomes

• Individual

– Individuals with a history of incarceration report more chronic health problems post-incarceration(Schnittker & John, 2007) – Those who had been incarcerated disproportionately suffer from infectious diseases (Massoglia, 2008) – Women (but not men) with a history of incarceration are more likely to die earlier than those without such a history (Massoglia et al., 2014)

• Family

– Many of the harmful effects of incarceration on children’s health operate indirectly through other mechanisms (Massoglia & Pridemore, 2015)

@RepJenSchultz | #ARM19 | @AcademyHealth

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Community / Population Health Outcomes

• Given its unequal distribution across demographic

groups in the population, incarceration may have implications for class /ethnic inequalities in children’s health at the population level (Turney, 2014; Wildeman & Wang, 2017)

@RepJenSchultz | #ARM19 | @AcademyHealth

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Incarceration & Health: State-Level Analyses

• Wildeman (2011) found that imprisonment is

significantly associated with poorer population health, defined as infant mortality and female life expectancy

• Wildeman (2012) found a positive association

between imprisonment rate and the total infant mortality rate, black infant mortality rate, and black- white inequality in infant mortality

@RepJenSchultz | #ARM19 | @AcademyHealth

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The Present Study…

• …examines the relationship between incarceration

rates and two indicators of health outcomes at the county level

@RepJenSchultz | #ARM19 | @AcademyHealth

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Analytic Strategy

Rate of incarceration is an endogenous variable

• Many of the same factors that affect level of

incarceration also affect population health – If endongenity is not accounted for, parameter estimates will be biased,

• verestimating the effect of incarceration

rates on public health outcomes

@RepJenSchultz | #ARM19 | @AcademyHealth

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Analytic Strategy

To address the endongenity of incarceration rate, we use a 2-stage modeling strategy

1.

Estimate predicted incarceration rate in a multivariate model including the exogenous “instrumental” variable per capita expenditures on incarceration

• Exogenous variable – a factor that predicts

incarceration rate, but not population health

2.

Use the predicted value of incarceration rate from the first model in a multivariate model predicting health

• utcomes

@RepJenSchultz | #ARM19 | @AcademyHealth

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Data Sources

This study is based on information from three county-level sources

1.

Vera’s In-Our-Backyards data set, for 2015

• Incarceration rates, urbanicity, UCR Index crime rate

2.

County Health Rankings & Roadmaps (CHR&R) data, for 2015

• Public health factors and outcomes

3.

U.S. Census Bureau data on expenditures, 2012

• Per capita spending on corrections and community and

public health care

• A 3-year lag period was used to account for

hypothesized effects on correctional populations and health outcomes

@RepJenSchultz | #ARM19 | @AcademyHealth

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Model 1, Predicting Incarceration Rate

• Per capita correctional spending was one of

8 independent variables in this OLS model

• The coefficient for correctional spending

was positive and significant (p < .001)

@RepJenSchultz | #ARM19 | @AcademyHealth

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Health Outcome Models: Description of Variables

• Dependent Variables: Health Outcomes

Item ¡ Mean ¡

Years ¡of ¡potenAal ¡life ¡lost ¡(YPLL) ¡(rate ¡per ¡100,000) ¡ 7,996.3 ¡ Percentage ¡of ¡populaAon ¡reporAng ¡fair ¡or ¡poor ¡health ¡ 17.3 ¡

@RepJenSchultz | #ARM19 | @AcademyHealth

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Descriptive Statistics: Health Outcome Model Variables

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OLS Regression Results: Health Outcome Models—Dep. Var. = YPLL

@RepJenSchultz | #ARM19 | @AcademyHealth

Variable b SE B t Sig. Adult smoking 89.380 5.779 0.240 15.468 0.000 Adult obesity 81.462 9.986 0.157 8.158 0.000 Glucose testing

• 41.458

5.715

• 0.108
• 7.254

0.000 Uninsured 7.789 12.288 0.017 0.634 0.526 Poverty index 968.348 72.872 0.324 13.288 0.000 Rural county 472.201 65.068 0.102 7.257 0.000 Public health spending (logged)

• 23.003

24.806

• 0.014
• 0.927

0.354 Predicted incarceration rate 0.823 0.211 0.088 3.895 0.000

Note: CT, DE, HI and RI are excluded because of missing data.

R

2 = .685

F (53, 2,176) = 89.471 (p < .001) N = 2,229

! !

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OLS Regression Results: Health Outcome Models—Dep. Var. = Fair/Poor Health

@RepJenSchultz | #ARM19 | @AcademyHealth

Variable b SE B t Sig. Adult smoking 0.256 0.017 0.260 15.22 0.000 Adult obesity 0.235 0.029 0.172 8.233 0.000 Glucose testing 0.018 0.016 0.017 1.082 0.279 Uninsured 0.236 0.035 0.190 6.793 0.000 Poverty index 0.013 0.002 0.168 6.367 0.000 Rural county 0.009 0.002 0.071 4.641 0.000 Public health spending (logged)

• 0.001

0.001

• 0.032
• 1.899

0.058 Predicted incarceration rate 0.002 0.000 0.090 3.694 0.000

Note: CT, DE, HI, MA and RI are excluded because of missing data.

R

2 = .639

F (53, 2,119) = 70.916 (p < .001) N = 2,172

! !

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Discussion

• Implication
• Reducing the level of incarceration

within a county can potentially have the collateral benefit of improved health

• utcomes among the county’s

population

@RepJenSchultz | #ARM19 | @AcademyHealth