Abstract

Previous research has suggested that incarceration has negative implications for individuals’ well-being, health, and mortality. Most of these studies, however, have not followed former prisoners over an extended period and into older adult ages, when the risk of health deterioration and mortality is the greatest. Contributing to this literature, this study is the first to employ the Panel Study of Income Dynamics (PSID) to estimate the long-run association between individual incarceration and mortality over nearly 40 years. We also supplement those analyses with data from the National Longitudinal Survey of Youth 1979 (NLSY79). We then use these estimates to investigate the implications of the U.S. incarceration regime and the post-1980 incarceration boom for the U.S. health and mortality disadvantage relative to industrialized peer countries (the United Kingdom).

Introduction

The growth in the incarcerated population is a widely acknowledged fact of contemporary American life that has been increasingly studied by social scientists (National Research Council 2014). Research has shown that incarceration is negatively associated with a host of outcomes for individuals themselves as well as their kin and close relationships, including employment and wages (Pager 2003; Western 2002), union stability (Massoglia and Warner 2011; Turney 2015), and children’s health, behavior, and development (Foster and Hagan 2014; Wildeman 2009). Furthermore, an emerging line of research has considered the consequences of incarceration for individual’s health and well-being. Recent studies found that those with an incarceration history report higher chronic health problems (Schnittker and John 2007), lower self-reported health (Massoglia 2008a), higher obesity (Houle and Martin 2011), more infectious diseases, stress-related illness (Massoglia 2008b), and psychological disorders (Massoglia and Pridemore 2015).

Despite increasing interest in understanding the impact of incarceration on health, few studies have investigated its long-term consequences for individuals’ mortality risks, mostly because of data limitations (Massoglia and Pridemore 2015). This is an important shortcoming in the current body of research. First, the health and mortality impacts of incarceration on the U.S. population are quite different if these health-related effects are experienced in the short run instead of the long run: short-term effects are likely to involve a much younger subpopulation than long-term effects. Furthermore, from a public health perspective, the distinction is crucial given that the nature of preventative measures and associated health costs are entirely different in each case.

Previous research on the relationship between incarceration and health primarily has relied on a handful of data sets, including the Fragile Families and Child Wellbeing Study, the National Longitudinal Survey of Youth 1979 and 1997 (NLSY), the National Longitudinal Study of Adolescent Health (Massoglia and Pridemore 2015), or administrative data (Binswanger et al. 2007; Patterson 2010, 2013). Although these studies make significant advances, they are limited in that they do not follow individuals over long spans of time and thus cannot estimate effects that unfold during the older adult phase of an individual’s life course, when the risk of health deterioration and mortality is the greatest.

Importantly, few extant individual-level analyses of the association between incarceration and health have examined the population-level implications of their estimates, which is unfortunate for two reasons. First, it is impossible to assess accurately the ultimate health consequences of shifts in federal policies regulating incarceration. This is not trivial given that incarceration affects a particularly vulnerable subpopulation. Second, ignoring the population-level consequences of ill-health and excess mortality brought about by incarceration rules out any study of the potential contribution of incarceration to at least well-known but underexplained phenomena: the persistent U.S. Black-White health and mortality disparities, and the perplexing U.S. health and mortality disadvantage relative to peer countries. What is the role of the recent expansion of the criminal justice system in the expanding (contracting) Black-White disparities? How much of the recent increase in the adult health and mortality gap between the United States and West European countries can be due to shifts in federal incarceration policies? Although data limitations prevent us from addressing the first question, we are able to investigate the second one.

Thus, this study makes two key contributions to this area of research. First, we estimate the long-term association between incarceration and adult mortality using two data sets: the Panel Study of Income Dynamics (PSID) (main analysis) and the National Longitudinal Survey of Youth 1979 (NLSY79) (sensitivity analysis). To our knowledge, no previous studies used the PSID to explore the relationship of interest over such an extensive period (including the time interval before the incarceration boom). NLSY79, however, has been used previously (e.g., Massoglia et al. 2014), but that work considered only premature deaths. We expand those analyses using the new NLSY79 data available to explore longer periods after incarceration.

Our second contribution is to shed light on the macro-level impact of U.S. mass incarceration on adult mortality. We use empirical estimates of excess mortality among formerly incarcerated adult populations to compute low bounds for the magnitude of the contribution of U.S. incarceration to the U.S. adult mortality disadvantage relative to peer countries (the United Kingdom). Although tentative, these estimates highlight the significance of U.S. incarceration policies and practices for adult U.S. mortality levels, trends, and patterns, thus expanding previous research based on aggregate data (Wildeman 2016). The analysis demonstrates that the investigation of excess mortality among those incarcerated has much larger implications than those associated with the population affected.

Incarceration and Heath: Causal Pathways

A number of mechanisms can explain the association between incarceration and long-term adult health and mortality. Figure 1 summarizes those most notable pathways discussed in the literature. The pathways in the figure are represented by lines connecting personal and contextual factors both to mortality and to incarceration.

First, the experience of imprisonment may increase the risk of contracting physical or mental illness. This, in interaction with post-imprisonment exposures, could increase accumulated adult mortality risks. Second, even in the absence of after-effects of exposures while imprisoned, released individuals may face disadvantageous residential, labor, and familial conditions detrimental to their physical and mental well-being. An important factor that may worsen conditions is the powerful social stigma of incarceration. Third, for some individuals at least, prison offers safety features they cannot enjoy outside of it. Thus, routine access to medical care may reduce some health risks both during and after life in prison. More frequent contact with health facilities and medical personnel may increase knowledge and vigilance leading to reduced exposures long after individuals are released from prison.

These three mechanisms—exposure to risks while incarcerated, increase of disadvantage after release, and protection from external risks due to imprisonment—involve a direct causal impact of imprisonment on long-term adult health status and mortality. The first mechanism is not mediated but could be modified by post-prison release environments. The second explanation is mediated by stigma and factors directly related to it that are the result of life in prison, whereas the third mechanism depends on specific characteristics of the prison environment that individuals experience while incarcerated and then outside prison.

Fourth, individual traits that attenuate (or enhance) the risk of imprisonment may also attenuate (or enhance) the risk of adult ill-health, thus producing an association between imprisonment experiences and long-term mortality that is not due to a direct causal effect. Thus, for example, individual propensity to violence associated with antecedent mental illness could translate both to behaviors that result in imprisonment and to personal injury after release. Conversely, individuals who enter prison may be, on average, healthier than those of similar characteristics who do not and could experience lower ill-health and mortality risks after release by virtue of these antecedent traits.

Finally, strong empirical evidence suggests that prisoners share characteristics (e.g., social disadvantage, family instability) that are related both to criminal activities and involvement in the criminal justice system and to individual health and mortality. In this case, the observed association between imprisonment and ill-health/mortality would be spurious.

In this study, we are interested in assessing the total magnitude of effects (first, second, and third pathways with solid lines in Fig. 1) and seek to estimate these with rich longitudinal panel data. We acknowledge, however, that the relations portrayed in Fig. 1 are complicated and that in the absence of randomized controlled trials, it is very difficult to rule out contamination of estimates attributable to selection or spuriousness. As a result, empirical estimates of the magnitude of the effect in this study, as in other population-based studies, must be interpreted with caution.1

Effects During Incarceration

Previous research has suggested that prison populations have characteristics that increase exposure to various health risks and mortality during the prison term served. Prisoners are often held in overcrowded and unsanitary conditions that are conducive to the spread of communicable diseases, which may account for the fact that incarcerated populations experience higher rates of infectious diseases (National Research Council 2014). According to the Centers for Disease Control and Prevention (CDC 2011), prevalence of syphilis, gonorrhea, and chlamydia are higher among inmates in correctional facilities than in the general population. These are all conditions that, in the absence of timely screening and adherence to treatment, may impair the health of individuals years after they leave the prison.

Similarly, prison environments expose individuals to chronic and acute stress as well as promote unhealthy behaviors, trigger mental health problems, and physical injury. This could explain drug abuse, depression, chronic anxiety, poor self-rated health, and plummeting life satisfaction. Additionally, extreme exposures, such as solitary confinement, increase the risk of fatal self-harm (e.g., hanging, or ingesting poison).

As is the case of communicable diseases, unhealthy behaviors and ill mental health may influence individuals during their prison term but could also have strong lagged effects over an individual’s post-prison adult life. The impact of communicable diseases, mental illness, and unhealthy behaviors during life in prison and its post-release effects may become worse or better depending on the prison setting and, importantly, on whether it offers routine access to prevention and treatment programs.

Effects After Incarceration

Increases in health risk during incarceration may have a long reach and influence exposures, choices, and behaviors after release. Individuals who experience incarceration spend, on average, far more time out of prison than in it: their exposure out of prison is nearly six times as long as their exposure as prison inmates (Wildeman and Wang 2017). Although short positive shocks (i.e., health care access, protective environment) might have long-term and cumulative consequences for health, former inmates face challenges that may outweigh transient benefits associated with imprisonment. They are often released without medications or follow-up appointments, and they are less likely to have a primary care physician. They have no or only precarious housing, employment, and family support, and they experience discrimination when applying for jobs and housing.

Consistent with these challenges, empirical evidence has shown a deleterious impact of incarceration experiences on health. For example, after adjusting for age, sex, and ethnicity, Binswanger et al. (2007) found that the mortality rate for recently released inmates is 3.5 times higher than the rate for the general population during an interval of two to five years since release. Similarly, Rosen et al. (2008), after adjusting North Carolina male administrative data for age, race (White/Black), and educational attainment, concluded that ex-prisoners experienced more deaths than expected among other residents: standardized mortality ratios (SMR) for White and Black former prisoners were 2.08 and 1.03, respectively (see also Spaulding et al. 2011).

Patterson (2013) followed a cohort of parolees (released in 1989–1993) for about 10 years and found that an additional year in prison increases the odds of dying by 16% (about a two-year decline in adult life expectancy) and that the risk is highest early after release but decreases over time. Finally, Massoglia et al. (2014) reported a significant association between incarceration and premature mortality for women but not for men in the NLSY79.

Selection Effects

Some empirical research has shown salubrious effects of life in prison—in particular, reduced mortality and physical morbidity among inmates, especially Black male prisoners, during incarceration. According to Mumola (2007), mortality rates for state prisoners were about 20% lower than the resident population in 2001–2004. Patterson (2010) further bolstered these findings using data from the U.S. Bureau of Justice Statistics (BJS) and the U.S. Census Bureau for 1985–1998, showing that mortality rates decline while in prison, especially for African American males.

One explanation of this pattern is the healthy prisoner hypothesis, which argues that individuals who engage in criminal behavior are, on average, in better health than the general population. This conjecture is based on the healthy worker hypothesis, according to which productive and active individuals (i.e., those more likely to enter the workforce) have a lower risk of mortality and morbidity in comparison with those unemployed. Research by Baćak and Wildeman (2015), however, showed little support for the healthy prisoner hypothesis and suggested instead that the association between incarceration and health is attributable to factors emerging during and after imprisonment. Another explanation of lower mortality among those in prison is that imprisonment offers a protective environment, and spending time in prison may decrease the risk of death by violence or accidents, reduce access to illicit drugs and alcohol, and improve health care access. These mechanisms, however, may hold mostly for Black male prisoners but less so for other groups. For instance, there might be heterogeneous effects due to both the variability in the quality of medical care across prisons and differences in imprisonment experiences (Wildeman and Wang 2017). Finally, it is also possible that inmates with particularly poor health and close to death are systematically released (i.e., compassionate release), resulting in changes in the composition of prisoners by health status. Figure 1 represents compassionate release by connecting frailty and release.

Although our discussion here refers to effects of imprisonment on health and mortality during imprisonment, some of the factors on which these effects depend may enhance (or depress) health and mortality after release and are thus part of a different pathway.

Long-Term Mortality Effects of Incarceration and Its Implications

Because of data limitations, previous studies of incarceration and post-release mortality have focused mainly on short-term effects of incarceration on mortality (Massoglia and Pridemore 2015). The first goal and contribution of our study is to use two rich panel data sets to estimate the total association between individual incarceration experiences and long-term adult mortality (e.g., the association produced by pathways with solid lines in Fig. 1) and manifested after incarceration experiences. Our conjecture is that because of exposure during life in prison and/or due to negative post-imprisonment familial, residential, economic, and emotional challenges with strong influence of health and well-being, mortality rates will be consistently higher among former inmates than among individuals with no imprisonment experiences despite any protective effect that incarceration might have. We are not arguing, though, that the effect of incarceration is higher later than earlier after release from prison. Instead, we suggest that both immediate effects (closer to release) and long-term consequences of imprisonment (later after release) might have significant detrimental impacts in population health and that proper estimation of cumulative effects of incarceration on mortality requires examining long-term associations and extensive longitudinal data.2

The second goal and contribution of our study is to generate preliminary estimates of the influence of excess mortality among formerly incarcerated adults on the U.S. mortality disadvantage relative to peer countries in Western Europe (the United Kingdom). We conjecture that owing to the relative size of the population of interest in the United States and the expected excess mortality, post-prison mortality patterns will have nontrivial impacts of roughly the same order of magnitude as that of other candidate mechanisms.

Data and Measures

Our main analysis employs the PSID. The PSID is one of the most widely used population surveys, and one of its strengths is the longevity of the sample design. The PSID follows a nationally representative sample of 15,000 individuals living in 5,000 families in the United States, beginning in 1968 with annual follow-up interviews through 1997 and biannual thereafter. Moreover, we use the NLSY79 to undertake sensitivity analysis and verify whether our results from examination of NLSY are similar to those obtained from PSID. For more details, see the section Sensitivity Analysis.

Mortality

Our dependent variable is mortality (year of death) and is retrieved from information from the National Death Index (NDI) and PSID nonresponse records. Between 1968 and 2013, 6,863 respondents died during the observation period (9% of respondents with at least one valid record).

Records for individuals whose deaths took place between 1979 and 2013 were matched to the NDI to obtain precise dates at death. For deaths that took place before 1979, we use the PSID year of death (for details, see Freedman et al. 2016). When the PSID provides a range for the year of death, we impute an exact date as the middle point of the interval (e.g., 1980–1986 = 1983). In total, 76% of deaths in our analysis were matched to the NDI.3

Incarceration Indicators

Our key independent variable is incarceration and is measured using two indicators from PSID sources (for more details, see the online appendix). First, we use nonresponse information on whether a member of a household was incarcerated at the time of the interview. According to this variable, 627 individuals were in prison at any wave. The major disadvantage of this indicator is that it misses short spells of imprisonment and, therefore, could generate downward biases in estimates of the association between incarceration and mortality. Figure 2 shows weighted and unweighted proportions of respondents in prison by year using the nonresponse incarceration variable. As shown in the figure, the trend of observed imprisonment is consistent with significant increases in the number of people behind bars since 1972 in the United States. The magnitude of the incarceration rate from PSID, however, is somewhat lower than figures reported by the BJS (National Research Council 2014). This suggests that, as conjectured before, the PSID nonresponse incarceration records underreport imprisonment.4

Our second indicator of incarceration uses a set of questions about involvement with the criminal justice system. One of them asked respondents in 1995 whether they had been in jail or prison. According to this variable, 1995 incarceration, 838 individuals were in prison before 1996.

Finally, we use a combination of these two indicators. Given that mortality and imprisonment are rare events, combining both measures maximizes the statistical power of our estimates. To do so, we create a time-varying incarceration variable for year t that attains a value of 1 if there was information on respondent’s imprisonment by year t, and 0 otherwise. For 1995 incarceration, we use information about the year of the last release to define an imprisonment event (every period after release was coded as 1). We then combine both variables; to avoid survival selection bias, we exclude those who died before 1996. According to the combined indicator, 1,285 persons (1.7% of respondents) were ever in jail/prison at any time during the observation period. Although both incarceration measures may be affected by measurement error, most errors will be due to underreporting caused by either short spells of incarceration or desirability bias.

Control Variables

To minimize contamination by selection and confounding, all models adjust for individual and household characteristics. A major concern, however, is to avoid adjusting for variables measured after the event of interest (i.e., incarceration). We adjust for covariates, such as age, gender, race, education attainment, household income, and self-reported health. We prioritize relevant and well-measured variables that cover most of the observation period per respondent. Age is included as a fixed covariate, and its value is set at the beginning of the observation (time = 0). Race and ethnicity is available only for household heads and their spouses. For the remaining population, we impute race using the household head’s race. Educational attainment, originally measured in years of education, is recoded into four categories (less than high school, high school, some college, and college). Household income is adjusted for inflation (2013) and then log-transformed. Finally, self-rated health, a time-varying covariate measured since 1986, is recorded using a five-point scale (excellent, very good, good, fair, and poor) and applied to household heads and their spouses. For the remaining household members, we know only whether they were in poor health or not (binary variable). To establish comparability, we recode the variable and use the last two categories of the five-point scale (fair and poor) to create a poor health single binary variable.

Analytic Data Set

We arrange the PSID data set in a person-year format, so that records represent observations from time 0 (when respondents started at the PSID) to time t, the last time a respondent was interviewed. Because PSID interviews have been conducted biannually since 1997, we expand those intervals to create an annual data set. We define an observation as valid when respondents were present in a family unit (FU), were in an institution, or died during that year. That is, we include both sample and nonsample respondents according to PSID’s terms. Sample respondents are those who belong to the original 1968 family for the core PSID sample or the original 1997 family for the Immigrant sample. Nonsample members are those respondents added later who were not related by blood or adoption to any PSID sample member.

The data set for analysis (excluding deaths before 1995) includes 48,340 respondents (48.7% males). The average age at the start of PSID was 28 years (median = 23), and the average exposure (or observation window) was 13 years (median = 7), with a minimum of 1 and maximum of 46 years. More than one-half (56%) of the sample is White, and 34% African American. A total of 2,719 deaths were registered (after records with missing data are removed), and 73 respondents were in prison and died during the observation period. Among these, the average number of years between the last spell of incarceration recorded and the year of death was 14 (median = 12). Those deaths occurred on average at age 47 (median = 49), and 22% occurred during the first five years after release at the average age of 38. Because our indicator of release from prison is based on nonresponse records in the PSID, it is subject to error and should not be taken as a precise measure of date of release.

To ensure an internally consistent analytic data set devoid of coding errors, we employ a simple multistate simulation model with arbitrary parameters and verify that simulated outcomes obtained from the analytic data set and the arbitrary parameters reproduce the input parameters (see the online appendix). This analytic data set has shortcomings. First, the number of respondents who were both in prison and died is small, preventing us from identifying precisely differences by gender, race, cause of death, age, and cohort. As a consequence, throughout our analyses, we focus on the gross association between imprisonment experiences and mortality and do not establish fine-tuned contrasts between gender, races, or ages. Second, given the sample size, our estimates might be upwardly biased because of Type M error: namely, conditional on statistical significance, the noisier the estimate is, the higher the chance of overestimating the magnitude of the effect (Gelman and Carlin 2014). One way to address this issue is to use additional data and prior information to evaluate the plausibility of our estimates. We briefly discuss this point in the final section of this article and assess the magnitude of our estimates relative to those found in previous literature.

Models and Estimation Strategy

We use both parametric (Gompertz) and semiparametric (Cox) survival models to estimate the association between incarceration and mortality. Parametric models allow us to validate the data setup and to verify consistency of the adult mortality pattern we find in the data (constant and slope of the Gompertz model) with U.S. national data. Semiparametric models are used to test sensitivity of estimates to alternative specifications and examine the association between incarceration and mortality using different sampling weights, handling alternative missing data strategies, modeling unobserved heterogeneity specifications, and employing marginal structural models (MSMs). The semiparametric models yield bounds and ranges rather than point estimates and thus lead to more conservative inferences (see the online appendix for more details).

We first estimate a baseline model that adjusts only for demographic characteristics, such as age, gender, and race/ethnicity. We then add two time-dependent covariates: the centered log of household income, and education attainment by time t. Finally, we add poor health to adjust for any potential confounding between health, incarceration, and mortality. Given the time-dependent nature of our key independent variable (incarceration) and confounders (e.g., income, education, and poor health), we estimate MSMs by using stabilized inverse probability weights (van der Wal and Geskus 2011). See the online appendix for more details.

Results

We begin by estimating Gompertz models using the PSID data and comparing the slope of those models with the estimates obtained using the U.S. vital statistics and census population. This exercise provides a good benchmark to judge the accuracy of the data and suitability of the model. Table 1 displays Gompertz models using different controls and no sampling weights. The Gompertz model includes a parameterized baseline hazard that depends on two parameters: a constant, α; and a slope (or shape), γ. The parameter γ is a measure of the rate of increase of mortality rates at older age, whereas the constant is a measure of levels of mortality at approximately age 18 when all covariates are set to 0. Covariates influence the magnitude of the constant only, and we constrain the slope to be invariant across subpopulations.5

The value of γ for the male 2010 U.S. life table is 0.095 (Arias et al. 2017). Our baseline models in Table 1 lead to an estimate of γ in the neighborhood of 0.13, within the expected human range but somewhat higher than the one in the U.S. life table. Comparisons of the values of the (exponentiated) estimate of the Gompertz constant with the U.S. mortality rate at near age 20 are less meaningful because our estimates are based on very few events in the vicinity of ages 18–25. Moreover, because it is a measure of mortality levels in the left-out groups (all covariates set to 0), it is not an average measure of mortality and therefore is not directly comparable to the U.S. life table. In contrast, the estimate of the slope reflects experiences during a stage of the life cycle when mortality increases rapidly, and the number of events on which the estimate is based is larger than the points of support available for the estimate of Gompertz’s constant.

The effects of imprisonment in Table 1 ranges between 0.74 and 0.97 in the models with demographic adjustments: that is, hazard rates for former prisoners are 2.10 to 2.64 times higher than hazard rates for those who have not been in prison by time t (after other covariates are adjusted for), with standard errors considerably smaller than the size of coefficients. These differences in mortality rates translate into differences in life expectancy at age 45 of (approximately) four to five years or roughly 14% of the value of U.S. male life expectancy at age 50.

Sensitivity Analysis

We assess the sensitivity of estimates obtained by varying model specification, using different strategies for panel attrition and missingness, and using a different data set.

Model Specification and Panel Attrition

To help assessment of the effects of panel attrition, we estimate additional Cox models and MSMs with various specifications. First, Tables 2 and 3 show results of Cox models using both unweighted and weighted samples and four model specifications. We define two sets of models: (1) adjusting only for sociodemographic covariates, and (2) adjusting for sociodemographic covariates and health. Within these two sets, we estimate models with and without marginal structural effects. Coefficients in Tables 2 and 3 are about the same order of magnitude as those in Table 1 (hazard ratios from 1.88 to 2.69), and they behave as expected. Income and education coefficients are consistently negative; poor health (measured since 1986) is, not surprisingly, highly associated with mortality. Figure 3 shows cumulative mortality by incarceration status (probability that an individual died by time x) at ages 18 and 30 using Model 1 in Table 2. An individual who started out as a PSID member at age 18 (time or year = 0) and is incarcerated will experience a probability of dying after 40 years of about .37; someone who was not incarcerated will experience an accumulated probability of dying of about .20.

Second, the marginal structural models use two scaled weights: one correcting for attrition (drop out), and the other adjusting for time-varying confounders. The estimates from the MSM yield slightly smaller hazard ratios associated with imprisonment (from 1.88 to 2.01). Models with sampling weights (Table 3) leads to higher hazard ratios: former prisoners experience mortality rates between 2.41 and 2.69 times higher than those for the population that has not been in prison by time t. Standard errors from weighted models are, as expected, higher, and regression estimates are more uncertain. Although unweighted models have coefficients about 4.3 times larger than standard errors, weighted models show a slightly lower ratio (3.5).

Missingness

Multiple imputation does not change imprisonment estimates. Point estimates of hazard ratios in Tables S1 and S2 in the online appendix range from 1.86 to 2.36. Standard errors, though, are much higher, especially when sampling weights are used. The average ratio between coefficients and standard errors is 3.49 without weights and 2.75 with weights. Thus, missing data augment the uncertainty of our estimates even though it does not appear to alter their mean values.

NLSY79

We estimate parameters using the complete NLSY79 cohort through 2014 (584 deaths), thus including a 34-year follow-up period. We employ MSMs to adjust for time-varying confounders while avoiding overcontrolling for them (Hernán and Robins 2006). We follow the same analytic approach used when analyzing the PSID data but are able to include a much richer set of covariates.

Simple hazard imprisonment coefficients are close to 0 (or negative) and with large uncertainty bounds (see Tables S3 and S4 in the online appendix). The covariates are properly signed: males have a higher risk of mortality; income, education, working status, and married are associated with lower risks; and poor (self-reported) health is a strong predictor of mortality. The MSMs, on the other hand, show positive and more accurate coefficients for incarceration (hazard ratios ranging from 1.73 to 1.77). Thus, results from the NLSY and PSID are consistent despite the fact that these data sets include completely different cohorts and cover periods that only partially overlap. This strengthens our confidence on estimates from the PSID data.

Implications of Excess Mortality Due to Incarceration

It is a relatively simple exercise to translate the estimates of excess mortality disclosed in the previous section into a more transparent metric, such as the life expectancy at age 40. As mentioned earlier, we estimate that incarceration’s adult mortality excess translates into a loss of between four and five years of life expectancy at age 40. Although this is an improvement over the more opaque metric of relative risks used before, it still does not render the full significance of the estimates. To do so, one needs to investigate further their broader implications. That some of these implications are quite significant is demonstrated by results of previous research that attempted to assess the impact of individuals’ incarceration on their own well-being, health, and mortality and that of related individuals (close kin, spouses and children, and neighbors). A small body of that research evaluated the impact of aggregate levels of incarceration on U.S. national levels of health and mortality (Wildeman 2016). In this section, we contribute to this literature by examining the implications of U.S. incarceration experiences for recent adult U.S. mortality. In particular, we explore the possibility that past U.S. incarceration policies and practices may have contributed significantly to the persistent adult mortality disadvantage of the United States relative to peer countries. We transform the estimates obtained from our models into (expected) excess adult mortality in the U.S. population attributable to past incarceration experiences. These transformations amount to computing counterfactual differences—for example, differences between current mortality levels in the U.S. total male population (or some other of its subpopulations) and what we would observe if incarceration had no mortality effects or if the rates of incarceration were 0.

Background: The National Research Council Report

Recent evidence has shown that the United States does not rank well among peer countries in terms of adult health and mortality. In particular, health status indicators and mortality rates among young adult males and females (20–44 years old) and older adults (45–69) (but not the very old) rank close to the bottom of the distributions (National Research Council 2014). Could it be that part of the observed disparities are accounted for by differential incarceration experiences and excess mortality risks? To what extent is the U.S. health and mortality disadvantage among young and older adults accounted for by disparities in the composition of populations by incarceration experiences? In light of both differences in rates of incarceration in the United States vis-à-vis other countries (Wildeman 2016) and of qualitative and quantitative evidence showing that imprisonment experiences increase health risks and mortality, these are not far-fetched questions. Furthermore, over the last 40 years, the U.S. penal system has experienced a large expansion (National Research Council 2014), and this, jointly with excess health and mortality risks of the population that experiences imprisonment, is consistent with a gradual trend of increased U.S. deterioration relative to peer countries. We ask the following question: how much of the gap in mortality between the United States and peer countries can be attributable to differential imprisonment experiences?

Estimation

To approximately assess the contribution of the association between incarceration and mortality to the poor U.S. health and mortality ranking, we decompose the difference of mortality between the United States and the United Kingdom and estimate what fraction of that difference could be attributed to differences in rates of incarceration in both countries. This provides a first approximation to the question of how much differential incarceration regimes contribute to the U.S. adult mortality disadvantage relative to peer countries. We implement a very simple procedure and compute the fraction of the difference of mortality rates in two age ranges between the United States and the United Kingdom. The procedure rests on two simplifications. First, a more systematic tests of the conjecture should be based on comparisons with multiple populations. However, the requisite data for the exercise are available only for the United Kingdom. Further, along with Canada, the United Kingdom resembles the U.S. on a number of dimensions such as demographic profiles, ethnic diversity, culture, and language.6

Second, we employ only two age groups: 20–45 and 20–70. The choice of the wider older age group is justified on the grounds that the mortality effects of imprisonment we estimate include the population older than 45, and it is this older population that could experience the brunt of excess mortality risks. The choice of the younger age group serves to constrain the computations to a subpopulation that is more likely to be vulnerable in the future: their imprisonment experiences are surely more recent, and the consequences have not yet fully unfolded. These estimates are preliminary and should be interpreted with caution.

The mortality gap between the United States and a peer country is given by
$Dx=MUx−mux,$
where MU(x) is the mortality rate at age x in the United States, and mu(x) is the mortality rate in the peer country. The rates are defined as follows:
$MUx=MUox×Px×E−1+1=MUox×Hx$
mu(x) = muo(x) × (p(x) × (E − 1) + 1) = muo(x) × h(x),
where MUo(x) and muo(x) are the mortality rates at age x among those who have not been in prison in the United States and peer country, respectively; P(x) and p(x) are the fractions of the population not currently in prison but that have been in prison in the past in the United States and the peer country, respectively; and E is the relative risk of mortality of those who had been in prison. We assume throughout that the mortality excess of people who have been imprisoned is the same in the United States and peer country. The expression for the gap can be rewritten as follows:
$Dx=Ax+Bx,$
where
$Ax=Hx+hx2×MUox−muox$
$Bx=MUox+muox2×Hx−hx.$

A(x) is the contribution of differences in mortality in the population that have not experienced prison. The second term, B(x), is the contribution to the total difference of the population who have been in prison. The fraction of the difference attributable to imprisonment is B(x) / D(x) and the one due to other factors is A(x) / D(x). These quantities are computed assuming that the incarceration rate J(x) and j(x) are invariant with age, P(x) = r × J(x) and p(x) = r × j(x).

Table 4 displays estimates for both age groups using alternative estimates of the effects of prison on mortality from different Cox models. We use incarceration rates (per 100,000) for the United States and the United Kingdom 2011–2012 (National Research Council 2014:36) and mortality rates (per 1,000) for the different age groups in 2011 (Human Mortality Database n.d.). Because we lack information for the United Kingdom, we assume that p(x) = r × j(x). Estimates for P(x) in the United States are around 2%: 2.05% according to Bonczar (2003), 2.09% according to Shannon et al. (2017), and 1.86% found by Uggen et al. (2006). Throughout, we use the highest estimates. According to Table 4, the contribution of differential composition by imprisonment to the mortality gap in the age group 20–70, B(x) / D(x), ranges between 4% and 10%, depending on which estimate of mortality effects we choose. In the narrower age groups, the results are more muted, and the range for the proportionate contribution of imprisonment is between 3% and 6%.

Two caveats are in order. First, the National Research Council (2014) report, which discusses comparisons between the United States and peer countries mortality, uses a more fine-tuned aggregation of mortality rates by age than we do here. Ideally, our counterfactuals should be computed at the same level of aggregation as in the report. Unfortunately, this is not possible in our case because the sparsity of events across the life course in PSID enables us to estimate only a single level of mortality with a fixed age pattern. This weakness is, however, inconsequential because the U.S. age pattern of excess mortality is quite regular, at least between ages 20 and 69. It follows that the use of an even more simplified approach, with a single age group instead of two as we do here, would produce robust estimates.

Second, in an attempt to include a broader range of estimates of excess mortality among formerly incarcerated individuals, we gather information from several sources and build a set of estimates from which we sample to compute counterfactuals. However, the range of these estimates turned out to be a subset of the range we used earlier, and therefore could not possibly lead to different inferences.7

Caveats aside, we hasten to emphasize that the main takeaway message from this exercise should not be about precise magnitudes of target parameters but rather about order of magnitudes. The counterfactual estimates derived earlier rest on a number of somewhat fragile assumptions that, although plausible, must be ascertained with more accuracy than we are able to do here. Although they do not translate into massive contributions to the U.S.–U.K. disadvantage, the estimates of effects we compute are nontrivial and of roughly the same order of magnitude as contributions attributable to other factors routinely considered relevant for adult mortality disparities, such as race and education (National Center for Health Statistics (NCHS) 2018; Olshansky et al. 2012). Finally, note that these estimates rely on the highly conservative assumption that excess mortality risks due to past imprisonment are the same in the United Kingdom and the United States. This could misrepresent the possibly more benign social and economic environments for post-prison life of young adults in the United Kingdom and produce underestimates of the contribution of U.S. incarcerations to its own mortality disadvantage.

Conclusion and Discussion

In this study, we use the PSID to estimate the long-term association between imprisonment and mortality in the United States over a period of nearly 40 years. Our estimates point to a moderate association between incarceration and mortality, with relative risks ranging from 1.7 to 2.7. These mortality excesses translate into losses of life expectancy at age 45 of about four to five years, or 13% of current U.S. life expectancy at age 45. Moreover, the sensitivity analysis employing the NLSY79 shows associations in the same direction and of the same order of magnitude as those in PSID.

Only a handful of studies have examined incarceration and mortality with a long-term follow-up. Our estimates using the PSID and NLSY79, however, are in line with research showing a significant association between imprisonment and mortality. The magnitude of effects reported in previous studies ranges between 1.32 and 2.56 (Massoglia and Pridemore 2015), a range of values consistent with our estimates. This suggests that our coefficients are not exaggerating the association between incarceration and mortality because of small sample size and Type M error, but it might also mean that they suffer from the same biases as those in other studies. Further research will be need to clarify this sample size limitation.

Although we estimate the association between imprisonment and mortality in the United States over a period of nearly 40 years, we are not able to identify the precise magnitude of differences between early and later effects because of sample size limitations. It is likely that most of the effects operate both in the short term—inducing effects that are visible immediately after prison release (Patterson 2013)—and also in the long term, unfolding over long stretches of time and spread over a wide segment of an individual’s adult life. The identification of these effects is possible only through extended longitudinal studies with larger samples than those available to us.

In an attempt to uncover the substantive significance of our estimates, we investigate their implications for an important problem in social demography: namely, the U.S. mortality disadvantage relative to peer countries. Thus, we estimate that the fraction of the mortality gap between the United States and the United Kingdom that can be attributed to differential imprisonment experiences ranges from 3% to 10%. These contributions are not large and cannot reflect massive influences, but they are not trivial, either; in addition, they are of comparable order of magnitude as those attributed to better-known factors, such as education, health care, and race (NCHS 2018; Olshansky et al. 2012).

Our analyses and model specifications are limited by the PSID and NLSY79 design, including timing and frequency of some key questions, attrition, and missing data. We do make an effort to provide a range of estimates using different strategies and model specifications to explicitly disclose levels of uncertainty and of model dependency. Our models might also be seriously affected by selection: the paths linking personal and contextual characteristics to both incarceration and mortality shown in Fig. 1. As a consequence, the counterfactual computations associated with the fraction of mortality gaps between the United States and the United Kingdom attributable to incarceration are tentative and preliminary. Despite these limitations, our estimates of the contribution of incarceration to adult mortality gaps is useful because it provides a sense of the magnitude of the potential macro-level consequences of mass incarceration in the United States. This can be readily extended to the study of other problems involving mortality gaps between subpopulations (e.g., Black-White mortality disparities).

Our work represents a first step in a more comprehensive assessment of repercussions of the recent U.S. expansion of the criminal justice system. Future research in this area should focus on two fronts: (1) to obtain more precise and nuanced estimates of excess adult health and mortality due to incarceration experiences, including assessments by gender, race, and detailed age groups; and (2) to investigate the implications of such estimates for some of the most startling features of modern U.S. mortality patterns, such as recent time trends of disparities between adult White and Black male mortality.

Acknowledgments

The University of Wisconsin–Madison researchers are supported by core grants to the Center for Demography and Ecology, University of Wisconsin (R24 HD047873), and to the Center for Demography of Health and Aging, University of Wisconsin (P30 AG017266), as well as a small grant for research using PSID data through the National Institute on Aging (P01AG029409).

Notes

1

In the remainder of the text, we use the terms “effect” or “impact” of condition X on mortality as shorthand to mean the estimated regression coefficient of condition X on mortality. We do this to avoid terminological cluttering, not to equivocate. We invoke causal language only when we think it is legitimate to do so.

2

We explored whether the association between incarceration and mortality changes after the release of inmates (not shown). Our results were consistent with the literature and previous research (i.e., the risk of mortality is highest early after release and decreases over time). However, because our sample is too small to sustain robust inferences, we did not estimate the magnitude of the differences between long- and short-term effects. This issue should be explored in future research.

3

The distribution of the age of death is shown in Fig. S1 of the online appendix.

4

The distribution of the age of first imprisonment is shown in Fig. S1 of the online appendix.

5

The variability of the slope in humans is restricted to a somewhat narrow range (.05–.14) (Kirkwood 2015). We expect our estimates to be within that range. We opt for not letting the Gompertz slope be a function of covariates (some of them identical to those that modify the constant) because this leads to intractable identification problems, even in very large samples.

6

There are many and very strong reasons to use the United Kingdom and not France or Russia as a benchmark. First, in key studies of the U.S. adult health and mortality disadvantage, the United Kingdom is used as the preferred benchmark (Banks et al. 2006) because of similarity of culture and language and contrasts of medical health care system. Ideally, we would have liked to produce a full comparison with all countries included in the National Research Council report, but that is left for future work. In terms of the criminal justice system, comparisons between the United States and the United Kingdom are not rare, either. Garland (2002), for instance, claimed that there are strong similarities in the recent criminal policies and practices, and this alone makes the comparison interesting.

7

Had the range of values been broader, we would have proceeded differently via Bayesian computations, imposing a prior distribution on estimates. This exercise is left for future research.

The text of this article is only available as a PDF.

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