Question:
You are given the full text of a scientific research paper.

    Identify and summarize the **policy problem** that this research addresses. Your response should:
    - Clearly state the societal, environmental, or strategic issue motivating the study
    - Be written in accessible, policy-brief style sentences
    - Focus on the core challenge the research aims to solve
    - Avoid excessive detail or technical jargon unless necessary for clarity

    Write a short paragraph suitable for inclusion under the “Policy Problem” section of a policy brief.

    Scientific Research Paper:
    https://doi.org/10.1177/00221465211052568
Journal of Health and Social Behavior
2021, Vol. 62(4) 460–476
© American Sociological Association 2021
DOI: 10.1177/00221465211052568
jhsb.sagepub.com
Article
Mass incarceration has increasingly been recog-
nized as a pressing public health challenge. Nearly
6.5 million adults—1 in 40—are under correctional
supervision in the United States (Maruschak and
Minton 2020). More than 2.1 million are currently
serving sentences in prisons and jails. An additional
4.9 million individuals in the population were for-
merly imprisoned (Shannon et  al. 2017). This
decades-long expansion of the penal system has
occurred without the structures in place to facilitate
reintegration. Although the policies of mass incar-
ceration have been far-reaching, their imprint has
been concentrated among marginalized groups.
Black adults account for 33% of individuals incar-
cerated in prisons—almost triple their share of the
adult population—and they are incarcerated at rates
that are three to five times higher than whites
(Carson 2020; Zeng 2020). Noting these disparities,
some scholars have argued that incarceration is so
concentrated among disadvantaged groups that it
“has become a normal stage of the life course”
(Wildeman and Wang 2017:1465).
The deleterious effects of incarceration for
individual well-being have been the subject of a
growing body of interdisciplinary research at the
intersection of medicine and the social sciences.
1052568 HSBXXX10.1177/00221465211052568Journal of Health and Social BehaviorBerg et al.
research-article2021
1University of Iowa, Iowa City, IA, USA
2University of Georgia, Athens, GA, USA
Corresponding Author:
Mark T. Berg, Department of Sociology and
Criminology, University of Iowa, 400H North Hall, Iowa
City, IA 52242, USA.
Email: mark-berg@uiowa.edu
Losing Years Doing Time:
Incarceration Exposure and
Accelerated Biological
Aging among African
American Adults
Mark T. Berg1
, Ethan M. Rogers1, Man-Kit Lei2,
and Ronald L. Simons2
Abstract
Research suggests that incarceration exposure increases the prevalence of morbidity and premature
mortality. This work is only beginning to examine whether the stressors of the incarceration experience
become biologically embedded in ways that affect physiological deterioration. Using data from a longitudinal
sample of 410 African American adults in the Family and Community Health Study and an epigenetic index
of aging, this study tests the extent to which incarceration accelerates epigenetic aging and whether
experiences with violence moderate this association. Results from models that adjust for selection effects
suggest that incarceration exposure predicted accelerated aging, leaving formerly incarcerated African
American individuals biologically older than their calendar age. Direct experiences with violence also
exacerbated the effects of incarceration. These findings suggest that incarceration possibly triggers a stress
response that affects a biological signature of physiological deterioration.
Keywords
African Americans, epigenetic aging, health, incarceration

Berg et al.
461
Prisons and jails are known vectors for the trans-
mission of chronic infectious diseases, such as
tuberculosis and pneumonia (Hammett, Harmon,
and Rhodes 2002). Another line of work developed
in the psychoimmunology literatures maintains that
incarceration triggers an upregulated, acute stress
response that leads to physiological deterioration
(Massoglia and Pridemore 2015). Empirical research
indeed finds that formerly incarcerated persons
have elevated levels of morbidity, especially for
stress-related illnesses, and heightened risk for
premature mortality (e.g., Boen 2020).
Despite important advances in recent years, this
body of literature remains in a nascent stage owing
to several substantive and methodological chal-
lenges (Massoglia and Remster 2019). First, the
possibilities of selection bias and statistical con-
founding complicate efforts to isolate the causal
effects of incarceration exposure on health out-
comes. Second, the extent to which physical health
is affected by witnessing or experiencing interper-
sonal violence while detained has received scant
attention in the quantitative empirical research.
This is an important omission because periods of
confinement often involve unwanted, uncontrolla-
ble, and threatening experiences that conceivably
govern the effects of incarceration on postrelease
health (Porter 2019). As such, prior work has
largely considered incarceration a homogeneous
experience, thus ignoring the effects of varied expe-
riences. It is therefore important to assess whether
specific stress-inducing experiences during incar-
ceration are differentially related to poor health.
Third, with few exceptions, most studies examine
self-reported health outcomes, including perceived
health, illnesses, and chronic conditions, rather than
objective biomarkers obtained from independent
assessments (Wildeman and Wang 2017:1468).
Although useful, such indices of self-reported health
provide limited information on the biological pro-
cesses that precede the manifestations of disease.
Asymptomatic conditions go unmeasured. Furthermore,
subjective reports of health confound “physiological
with psychological and emotional well-being”
(Harris and Schorpp 2018:362). Because of the reli-
ance on self-report instruments, the extent to which
incarceration exposure becomes biologically embed-
ded or “gets under the skin” is not well understood.
Although objective biomarkers have been featured in
some studies of incarceration and health, this work
remains scarce (e.g., Boen 2020).
Accelerated aging is a “predominant risk factor
for most diseases and conditions that limit health
span” (Kennedy et al. 2014:709). The basis of healthy
aging is a focus of modern medicine (Sinclair 2019).
Newly developed epigenetic indices (“clocks”) of
biological aging have been shown to be powerful pre-
dictors of premature mortality and age-related dis-
eases (Ryan et al. 2020). How social adversities affect
biomarkers of accelerated aging has been the subject
of burgeoning research on the global burden of age-
related disease (Moffitt et al. 2017; Sinclair 2019).
Despite its promise for understanding the biology of
disease and illnesses, this work has not been inte-
grated into the research on incarceration.
Using a longitudinal sample of African American
adults from the Family and Community Health
Study (FACHS), this study examines whether incar-
ceration exposure and experiences with violence
while incarcerated predict accelerated biological
aging. Note that because the sample is limited to a
sample of African American adults, the study does
not assess whether incarceration contributes to
racial disparities in aging. We posit that individuals
exposed to incarceration will be biologically older
than their numerical age and even more so among
those who experienced violence while serving time.
The study will inform efforts to understand the
extent to which incarceration becomes biologically
embedded to affect physiological changes impli-
cated in the onset of chronic illness and disease.
Background
Incarceration and Stressors
Although relative differences in mortality between
black and white individuals is significantly lower in
prison compared to the differences in the general
population—a pattern largely attributable to deaths
from firearms and motor vehicle accidents (Patterson
2010)—formerly incarcerated individuals are at
greater risk of dying and suffering from disease fol-
lowing release. Incarceration leaves a negative
imprint on the physical health of this population. The
total health effects of incarceration are a product of
the transition from arrest to confinement, the time
spent in prison, and the period following release
(Massoglia and Pridemore 2015). The initial transi-
tion from the community to prison is a disruptive
event partly because individuals must learn to assim-
ilate into the prison environment (Irwin 1980).
Moreover, confinement removes individuals from
their primary social networks, who are sources of
critical emotional and psychological support. During
spells of incarceration, personal relationships often
become strained and difficult to nurture from a
distance.

462
Journal of Health and Social Behavior 62(4)
Adding to the stressors of adjustment, individu-
als face a variety of harsh and traumatic experi-
ences during incarceration (Massoglia and Remster
2019). Direct experiences with violent events are
not uncommon in jails and prisons (Labrecque,
Scherer, and McCafferty 2018). For instance,
prison surveys have found that upward of 21% of
both men and women report attacks by other incar-
cerated individuals, and nearly 25% and 8%,
respectively, report attacks by correctional staff
(Wolff et al. 2007). National estimates indicate that
approximately 13% of incarcerated individuals
are injured in a violent encounter (Teasdale et al.
2016). The loss of personal security—one “pain of
imprisonment”—is exacerbated by these distinct
moments of danger (Maier and Ricciardelli 2019;
Sykes 1958). Secondary, or witnessed, violence
also increases fear, hypervigilance, and general
unease with the social environment (Novisky and
Peralta 2020). Concerns about safety and hostile
interactions with other incarcerated individuals and
correctional staff create an action readiness of being
“on point” that contributes to an “overwhelming
sense of anxiety” (Porter 2019:5). Altogether,
encounters with violence may “contribute to the sus-
tained experience of emotional distress . . . up to and
beyond release from prison and jail” (Boxer,
Middlemass, and Delorenzo 2009:802). Studies have
reported associations between violent experiences
and the worsening of psychological health during
incarceration and after release (Wolff and Shi 2009;
Zweig et al. 2015).
Several well-documented barriers to reintegra-
tion add to the burden of stressors associated with
incarceration (Semenza and Link 2019). Many
individuals reenter society with limited resources
and narrow employment prospects because of
exclusionary hiring practices and weak ties to con-
ventional networks (Petersilia 2003). Safe and
secure housing is also a “hurdle in the steeplechase
of reentry” because some are barred from renting
because of their criminal records (LeBel 2017:889).
For instance, in a sample of formerly incarcerated
individuals, only half were unemployed 6 months
after release, and more than one-third stayed in
marginal or temporary housing (Western et  al.
2015). Harding and colleagues (2014:450) found
more than one-quarter of formerly incarcerated per-
sons shifted between “extreme desperation and sur-
vival when they were not in custody.” These
difficulties are compounded by substance abuse
and mental health problems (Tyler and Brockmann
2017). Furthermore, even with the implementation
of the Affordable Care Act, formerly incarcerated
individuals are more likely to be uninsured and less
likely to utilize preventive health care relative to the
general population (Widdowson and Fisher 2020;
Winkelman, Choi, and Davis 2017).
Incarceration, Stress Response,
and Physical Health
According to the neuroendocrine and immunology
literatures, the stressors resulting from social and
environmental adversities, such as the incarceration
experience, trigger the circuitry of the autonomic
and parasympathetic nervous systems in addition to
the hypothalamic–pituitary–adrenal axis (Miller,
Chen, and Parker 2011; Snyder-Mackler et  al.
2020). Activation of each system is necessary for
survival and reproductive success. However, the
prolonged and elevated stimulation of these systems
has a gradual “wear and tear” effect on the body’s
adaptive capacities, producing impairments that are
expressed in surrogate end-point biomarkers (Lu
et  al. 2019; McEwen and Gianaros 2011).
Physiological adaptations in response to adversity
may also contribute to the programming of the
stress response via alternations in histone acetyla-
tion (Meaney and Szyf 2005). Moreover, phases of
neuroinflammation disrupt the brain’s capacity to
monitor and control pathogen invasion, which is
detrimental to cerebral functioning (Danese and
Baldwin 2017). Geronimus and colleagues (2006)
argue that the relatively extensive social adversities
experienced by African Americans promotes a type
of “biological weathering.” Specifically, the weath-
ering work contends that African Americans are
burdened by material hardships and marginalization
encountered in a race stratified society (Geronimus
et al. 2006). The accumulation of these adverse con-
ditions contributes to physiological deterioration,
including accelerated aging (Simons et al. 2021).
Life course theories of the stress process
(Pearlin et al. 2005), the psychoimmunology litera-
tures (Miller et al. 2011), and the biological weath-
ering model (Geronimus et  al. 2006) all would
contend that incarceration engenders a harmful
stress response. Empirical research has indeed
shown that incarceration exposure is associated
with stress-related illnesses, such as hypertension,
diabetes, obesity, heart problems, and functional
limitations (Baquero et  al. 2020; Houle 2011;
Massoglia 2008; Schnittker and John 2007).
However, a variety of substantive and methodologi-
cal issues complicate efforts to fully understand the
nature of the relationship between incarceration
exposure and morbidity.

Berg et al.
463
First, selection bias and confounding threaten the
validity of causal estimates of the effects of incar-
ceration exposure on health. Because adverse physi-
cal health conditions and criminal justice contact
share many common correlates—social class, child-
hood adversities, and self-control—it is plausible
that preexisting risk factors confound the effects
of incarceration exposure on health outcomes
(Schnittker, Massoglia, and Uggen 2012). Any
unique effect attributable to incarceration might
instead result from the endowments that individuals
bring to prison. This possibility requires adjustments
for preexisting confounds, including adverse child-
hood experiences and childhood disadvantage,
which are reliable determinants of adult morbidity
and mortality (Miller et al. 2011). Due to preexisting
tendencies to have poor health and to engage in
behaviors that result in confinement, it is also neces-
sary for observational research to adjust for selec-
tion effects (Massoglia and Pridemore 2015).
Prospective longitudinal data are a powerful tool in
this regard because they allow for the implementa-
tion of pretreatment (i.e., incarceration) measures.
Second, there is a shortage of research on the
extent to which violent and hostile social interac-
tions during incarceration contribute to the health
of released individuals. As noted earlier, these
experiences are not uncommon based on survey
and qualitative accounts. The psychological burden
of these experiences could encumber the process of
reintegration, engendering additional or secondary
stressors that are expressed in the postrelease set-
ting. There are good theoretical reasons to expect
that exposure to violence, whether direct or indi-
rect, might regulate the effects of incarceration on
physical health outcomes. In this way, incarceration
is not a homogeneous experience; rather, the extent
to which incarceration affects the health of released
individuals is determined by their differential expe-
riences with social stressors during the period of
confinement.
Third, most research on postrelease morbidity
examines measures of general health, chronic con-
ditions, illnesses, and diseases ascertained from
survey self-reports. Self-report measures provide
valuable information about one’s perceived health
burden, and some predict mortality (e.g., Jylhä
2009), but these tools are known to suffer from
threats of validity and reliability, especially among
marginalized sociodemographic groups (Assari,
Lankarani, and Burgard 2016; Zajacova and Dowd
2011). Importantly, self-reports of various health
conditions are unlikely to capture cases in which
underlying illnesses have not yet manifested as
obvious symptoms (Boen 2020; Morton and
Ferraro 2020). For instance, respondents who report
no evidence of illness but have undetected pathol-
ogy would be considered healthy based on self-
reports when they are not, thereby producing
misclassification errors. The possibility of these
types of errors is heightened among younger popu-
lations. Should formerly incarcerated individuals
not report symptoms of illness or disease on self-
reports, despite underlying pathology, this would
thus bias estimates of the incarceration–health asso-
ciation. Incarcerated persons may also be better
aware of their own health conditions than if they
had not been incarcerated because of their prison
medical assessments. If so, this could increase the
association between incarceration and self-reported
health conditions (Massoglia and Remster 2019).
Objective biomarkers are not vulnerable to such
subjective recall and reporting errors and, further-
more, they capture predisease pathology regardless
of whether individuals exhibit symptoms.
As such, there have been recent calls to integrate
biomarkers into research on incarceration and
health to understand how incarceration exposure
becomes biologically embedded (see Massoglia
and Remster 2019; Wildeman and Wang 2017).
Notably, some researchers have modeled objective
biomarkers, but there have been mixed results.
Using longitudinal data from the Coronary Artery
Risk Development in Young Adults study, Wang
and colleagues (2009) found that prior incarcera-
tion was associated with hypertension and left ven-
tricular hypertrophy among adults ages 23 to 35.
More recently, scholars have leveraged the National
Longitudinal Study of Adolescent to Adult Health
(Add Health) study to investigate the association
between incarceration and health biomarkers
among young adults (Boen 2020; Esposito et  al.
2017). For instance, Esposito and colleagues (2017)
found no effect of incarceration on hypertension
after adjusting for selection effects. Similarly, Boen
(2020) found a relationship between incarceration
and inflammation, but this effect was reduced after
applying treatment weights to adjust for selection.
Additionally, incarceration was only associated
with inflammation in that study when the period of
confinement was a year or longer. Other work with
Add Health has found associations between incar-
ceration and cardiovascular disease risk—an index
derived from objective and subjective self-report
items—using models that did not adjust for threats
of selection bias (e.g., Semenza et al. 2021), which
raises questions about the internal validity of the
findings.

464
Journal of Health and Social Behavior 62(4)
Incarceration and Epigenetic Aging
Research on the social determinants of health has
increasingly focused on the speed of cellular aging.
This work is consequential because aging is the
“most important risk factor for many human dis-
eases” (Ryan et al. 2020:481). Conditions including
cognitive decline, heart disease, and cancers are doc-
umented products of biological aging (Sinclair
2019:67). Epigenetic aging involves the “accumula-
tion of damage” responsible for cellular and organis-
mal decline (Song and Johnson 2018:1). Researchers
have studied mechanisms of aging with measures of
telomere length and pubertal timing. Since the 1960s,
researchers have been aware of the strong association
between age and deoxyribonucleic acid (DNA)
methylation (Koch and Wagner 2011). DNA methyl-
ation is a form of epigenetic modification—or chemi-
cal alteration to the genome that affects gene
activity—whereby a methyl group attaches to a seg-
ment of DNA at a CpG site, which can inhibit gene
expression. Through this process, the activity of a
DNA segment can change without an adjustment to
its base sequence. Many of these sites are positioned
near genes that trigger age-related conditions, includ-
ing cancer, oxidative stress, respiratory diseases, and
Alzheimer’s disease (Hannum et al. 2013; Yoon, Jin,
and Sin 2019). Some have argued that cellular aging
is caused by epigenetic signalers—which repair dam-
aged DNA—becoming overtaxed from cellular
insults, including those present in the external envi-
ronment (Sinclair 2019:47–49). Site-specific patterns
in DNA methylation are integral to the aging process
among humans (Ryan et al. 2020).
Such findings have prompted the development
of various epigenetic indices or “clocks” of biologi-
cal aging (Murabito et al. 2018). These clocks assess
the discrepancy between a person’s chronological
age and the age predicted by the epigenetic index,
which indicates whether a person is aging at an
accelerated or decelerated pace. A recently devel-
oped clock known as the GrimAge index examines
methylation at 1,030 sites and has a superior ability
to predict time to mortality and age-related illnesses
(Lu et al. 2019) relative to the other available epi-
genetic clocks (see McCrory et al. 2021). Research
indicates that accelerated GrimAge is associated
with social adversities, including material hardship,
discrimination (Simons et  al. 2021), and trauma
(Yang et al. 2020).
The extent to which incarceration exposure
predicts accelerated biological aging has not been
examined. As noted previously, the substantive liter-
ature implies that formerly incarcerated persons con-
ceivably age at a faster pace than nonincarcerated
persons. If so, this would provide an important clue
about how incarceration might give rise to health
disparities.
Current Study
Although there is consistent evidence that incarcer-
ation exposure undermines the physical health of
individuals, important gaps remain in this research.
Using data on a sample of African American adults,
the present study addresses two objectives. First,
using the newly developed GrimAge index, it
assesses the extent to which incarceration exposure
predicts accelerated biological aging. Second, the
study examines whether experiences with violence,
both direct and secondary, during confinement
exacerbate the effect of incarceration on accelerated
biological aging.
Data and Methods
Sample
We tested hypotheses for the current study by
employing data from the Family and Community
Health Study (FACHS). FACHS is designed to
investigate the factors associated with the health and
well-being of African American adults (see Lei
et al. 2018; Simons et al. 2019). The FACHS sam-
pling strategy includes African American families
with a child who was in fifth grade at the time of
recruitment. Families were intentionally recruited
from neighborhoods that varied on demographic
and economic characteristics based on 1990 census
data. For 259 census block groups (115 in Georgia
and 144 in Iowa), households were randomly
selected from rosters of fifth graders in the public
school system. Households were then contacted
until the required number of households were
recruited (84% response rate).
The data for the first wave of FACHS were col-
lected in 1997 and 1998 (for a description of FACHS
waves, see Appendix A in the online version of the
article). The sample consisted of 889 African
American fifth-grade children who were an average
age of 10.6 years and resided in Iowa (n = 467) or
Georgia (n = 422). The second through sixth waves
of data were collected between 1999 and 2012, cap-
turing information when the target children were
12.5, 15.7, 18.8, 21.6, and 23.6 years. The seventh
wave of data was completed between 2015 and
2016 when the target children had an average age of
28.8 years. By Wave 6, approximately 49% of
respondents resided in Georgia, 40% in Iowa, and
the remaining 10% were located in other states.

Berg et al.
465
Only members of the sample residing in Georgia,
Iowa, or a contiguous state were identified as
eligible for the blood draws needed for the current
research due to the logistical challenges of schedul-
ing home visits by phlebotomists. After also
excluding persons who were deceased (n = 12),
incarcerated at the time (n = 15), hard refusals
(n = 19), unidentifiable phone number (n = 46), or
otherwise unreachable (n = 99), 556 respondents
agreed to be interviewed, 470 of whom successfully
provided blood. Of these respondents, successful
assaying for methylation was achieved for 449
individuals. In the current study, analyses were based
on the 410 African American respondents who were
successfully assayed and for whom data were avail-
able on all study measures—13 respondents were
missing data on incarceration history (2.9%), and an
additional 26 respondents were missing data on the
covariates in the model (5.8%).1
Procedures
Questions were administered to families in their
homes using audio-enhanced, computer-assisted, and
self-administered interviews. Respondents answered
questions on a computer as they were presented visu-
ally on the screen. In Wave 7, participants were also
asked to provide a blood sample. The phlebotomist
drew four tubes of blood (30 mL) from each partici-
pant; these were shipped on the same day to a labora-
tory at a public university for preparation. Blood
samples were assessed for genome-wide methylation
status using standard protocols. The Illumina
Infinium HumanMethylationEPIC 850 BeadChip
was used to assay genome-wide DNA methylation.
Participants were randomly assigned to 16 sample
“slides/chips” with groups of eight slides being bisul-
fite converted in a single plate, resulting in two
“batches/plates.” A replicated sample of DNA was
included in each plate to aid in assessment of batch
variation. Methylation data were filtered according to
the following guidelines: (a) Samples containing 1%
of CpG sites with detection p < .05 were removed, (b)
sites were removed if a bead count of <3 was present
in 5% of samples, and (c) sites with a detection p <
.05 in 1% of samples were removed.
Measures
Accelerated aging.  To calculate the speed of aging,
we used an epigenetic clock known as the GrimAge
index (Horvath and Raj 2018). The index is called
GrimAge—after the Grim Reaper—because an
accelerated score is grim news. This measure of
aging has been shown to be a more robust predictor
of morbidity and mortality among different racial
groups than other epigenetic clocks currently avail-
able (Lu et  al. 2019; McCrory et  al. 2021). The
index was developed by identifying a set of plasma
protein predictors of mortality and then using these
protein predictors to identify CpG sites that could
predict time to death. As expected, the GrimAge
index has a weak to moderate correlation with
chronological age in the FACHS sample (r = .22),
indicating a discrepancy between biological and
calendar ages. Following standard procedures, we
regressed GrimAge on chronological age to create
a speed of aging score or “accelerated aging.” The
resulting speed of Grim aging measure was thus
adjusted to correlate with chronological age at 0. A
positive value on this variable indicates, in years,
accelerated epigenetic aging, whereas a negative
value indicates, in years, decelerated aging.
Incarceration exposure.  To assess the prevalence
of incarceration, we used reports from Waves 5, 6,
and 7. If respondents reported being arrested (since
age 18 in Wave 7), respondents were asked to report
their frequency of being to jail and their frequency of
being to prison (since age 18 in Wave 7). If respon-
dents reported being in jail or prison at least once, the
measure was coded as 1 = formerly incarcerated. If
the respondent never reported being in jail or prison
at Waves 5, 6, or 7, the measure was coded as 0 =
never incarcerated. “Don’t know” responses were
treated as missing. Although most studies assess
incarceration using a lifetime prevalence measure, it
may be that the experience in itself does not fully
capture how incarceration exposure is associated
with individual health. For this reason, it is important
to conduct comprehensive tests of the varieties of
exposure. To probe the nature of exposure effects,
we also measured the timing and frequency of incar-
ceration. Past 4 years was coded as 1 if the respon-
dent reported being in jail or prison in the past 4
years at Wave 7 (average age at Wave 7 was 28.8
years). This 4-year measure was included in the
Wave 7 survey to capture spells of incarceration
since Wave 6, which was administered approxi-
mately 4 years earlier. The measure of incarceration
in the past 4 years is intended to assess whether the
effect of incarceration exposure on accelerated aging
extends beyond lifetime prevalence to recent spells
of incarceration. More than once was coded as 1 if
the respondent reported being in jail or prison two or
more times. A frequency measure of incarceration is
intended to assess the effect of exposure to multiple
spells of incarceration on accelerated aging.

466
Journal of Health and Social Behavior 62(4)
Violence during incarceration.  We created two
measures to examine experiences with violence dur-
ing incarceration. To measure direct violence,
respondents were asked to report during Waves 6
and 7 how often the following experiences happened
during jail or prison: property was taken from them
using force or intimidation, they were threatened
with violence, they were assaulted with an object
used as a weapon, or they were involved in physical
fights with others incarcerated. If respondents
reported experiencing any of these violent events,
direct violence was coded as 1 (0 otherwise). To
measure secondary violence, respondents reported
how often they witnessed other incarcerated indi-
viduals being assaulted with a weapon or involved in
a physical fight (Waves 6 and 7). If respondents
reported experiencing either of these events, second-
ary violence was coded as 1 (0 otherwise).
Confounds.  We controlled for the following
demographic and socioeconomic characteristics at
Wave 7: male, high school education, income, and
married or cohabiting (for description of the mea-
surement of the confounds, see Appendix B in the
online version of the article). These measures were
concurrent to the outcome. Income was assessed by
asking respondents to report their income in the past
year and was log-transformed to reduce skew. The
average income in the analytical sample was
$21,474.26. We also adjusted for past-year health
insurance at Wave 7. To account for preincarcera-
tion health and health risk behaviors, we included
measures of smoking, drinking, diet, exercise, and
BMI. This class of measures was included either on
or before the wave of the incarceration measure
based on their availability in the data. Smoking was
measured as the prevalence of cigarette smoking in
the past year, and binge drinking was measured as
the frequency of consuming three or more drinks of
alcohol during the past year (Waves 1 to 4). Two
items indicating the frequency of eating fruits and
vegetables were used to measure a healthy diet
(Waves 4 to 5), and two items indicating the fre-
quency of physical activity for at least 30 minutes
were used to measure exercise (Waves 3 to 5). BMI
was calculated using the respondents’ recorded
height and weight (Waves 1 to 2, 4).
Additional preincarceration conditions were
included as potential confounds (see Appendix B
in the online version of the article). To measure
community disorder (Waves 1 to 4), participants
were asked about the frequency of criminal acts in
their neighborhood. Mental health is an important
confound because it is related to both criminal
behavior and physical health (Beyers and Loeber
2003). A battery from the Diagnostic Interview
Schedule for Children, Version 4 (DISC-IV), was
used to measure depression (Waves 1 to 4; Shaffer
et al. 1993). We summed all responses to create a
count variable scored from 0 (no symptoms) to 22
(all symptoms). A measure of self-control (Waves
1 to 2, 4) was measured using an 11-item scale
from the Kendall and Wilcox (1979) inventory of
self-constraint (e.g., “you usually think before you
act”). We also adjusted for a measure of delin-
quency (Waves 1 to 4) using the conduct disorder
section of the DISC-IV. We summed responses to
past-year involvement in 15 acts to create a count
variable scored from 0 (no acts) to 15 (all acts).
Two measures accounted for childhood charac-
teristics implicated in criminal justice contact and
poor health (see Appendix B in the online version
of the article). To assess childhood adversities,
respondents were asked at Wave 1 to report whether
they experienced various negative events during the
past year (see Berg et  al. 2020). Each item was
summed to create a total childhood adversity score
ranging from 0 to 14. Childhood socioeconomic
status was assessed by summing caregivers’ reports
of education and income and averaging the reports
across Waves 1 and 2.
Analytical Strategy
We first conducted descriptive analyses by examin-
ing the means and percentages of all variables for
the total sample and by incarceration exposure. We
then used a series of ordinary least squares regres-
sion models to test our hypothesis regarding the
association between incarceration and our continu-
ous speed of aging variable, GrimAge, adjusting
for a series of key confounds. Given that respon-
dents were not randomly assigned to incarceration,
we then estimate the analysis using an inverse prob-
ability of treatment weighting (IPTW) scheme to
adjust for possible threats of selection bias. For the
IPTW procedures, we first created propensity
scores by calculating the probabilities of incarcera-
tion conditional on our confounds (Rosenbaum and
Rubin 1983). We then reran our regression analysis
weighted by the propensity scores. By doing so,
the IPTW method balances the distribution of
confounds across the treated and untreated groups
and allows for less biased estimates of the potential
causal effect of incarceration (Robins, Hernán, and
Brumback 2000). For both the treatment and outcome
models, we used the same set of measures. For the
raw and weighted mean scores and standardized

Berg et al.
467
differences between the formerly and never incar-
cerated samples, see Appendix C in the online ver-
sion of the article. We also conducted sensitivity
analyses often recommended for the proper use of
IPTW: restricting to common support and trimming
(Cole and Hernán 2008). It is worth noting that the
quality of estimates—the extent to which they min-
imize selection bias—derived from propensity
score-based methods depends on the extent to
which the observed variables not only exhaust
sources of possible selection but also if the vari-
ables have good validity and reliability.
Finally, we applied an internal moderator
approach to assess if the timing, frequency, and
exposure to violence determine the health effects of
incarceration on GrimAge. Timing, frequency, and
exposure to violence during incarceration are condi-
tionally relevant measures—they were only asked
of individuals who have been incarcerated. The
internal moderator approach explicitly considers
that these hypothesized moderators only apply to the
respondents who experienced incarceration, provid-
ing a “contingent” interaction effect (Mirowsky
2013). In other words, this approach allowed us to
compare those who were formerly incarcerated with
those who were not while simultaneously assessing
if the qualities of the situation—the incarceration
spell—determine the effects of being in that situa-
tion (Ross and Mirowsky 1992).
Results
Means and percentages for the total sample and by
incarceration exposure are displayed in Table 1.
Over half of this African American sample reported
being formerly incarcerated (52.2%), over one-third
were incarcerated in the past 4 years (35.0%), and
two-thirds were incarcerated more than once
(65.9%). These descriptive results are consistent
with literature indicating the mass incarceration of
young low-income black adults (see Wildeman and
Wang 2017).2 Among the formerly incarcerated indi-
viduals, 12.0% experienced direct violence during
incarceration, and 23.4% experienced secondary
violence. GrimAge scores indicate that, on average,
formerly incarcerated individuals experience accel-
erated aging (x = 1.179), whereas those who were
never incarcerated experience decelerated aging (x =
–1.349, p < .001). Table 1 also reveals differences by
incarceration exposure across demographic charac-
teristics, preincarceration health behaviors, and
childhood adversity.
Incarceration Exposure and Accelerated
Biological Aging
Regression models predicting the speed of Grim
aging are shown in Table 2. Model 1 presents the esti-
mates of the association between incarceration expo-
sure and the speed of aging net of the full array of
confounds. The results show that incarceration is
associated with accelerated epigenetic aging in this
African American sample, with exposure predicting a
greater accelerated aging score by about 2 years (b =
1.970; p ≤ .001). After applying IPTW (Model 2) to
the model, the association between incarceration
exposure and the speed of aging is slightly reduced
but remains statistically significant. Exposure to
incarceration is associated with a 1.719 difference
(approximately 1 year and 9 months) in accelerated
aging (p ≤ .001). This finding is illustrated in
Figure 1—formerly incarcerated individuals are
roughly 11 months (12 × .902) older biologically than
expected based on their chronological (or calendar)
age, whereas those who have never been incarcerated
are 10 months younger biologically (12 × –.816) than
expected based on their chronological age. In Model
3, we restricted the sample to respondents with pro-
pensity scores that fall within the range of propensi-
ties calculated for both the incarcerated and never
incarcerated respondents. Similarly, in Model 4, we
exclude all respondents with atypical propensities—
propensity scores within the fifth percentiles. Both
models reveal a stronger association between incar-
ceration exposure and the speed of Grim aging, indi-
cating a conservative estimate in Model 2.3 Together,
the evidence from Table 2 indicates that among the
African American respondents in our sample, incar-
ceration exposure is associated with an acceleration
in biological aging by 21 to 28 months.
To probe the effects of incarceration exposure,
we examine whether the timing and frequency of
incarceration operate as internal moderators of the
association between incarceration exposure and the
speed of aging (see Models 1 and 2 of Table 3).
Results suggest that the timing of incarceration may
not moderate the association—formerly incarcer-
ated individuals did not experience greater acceler-
ated aging if they were incarcerated in the past 4
years (Model 1; see also, Boen 2020). Results also
show that the effects of incarceration exposure did
not vary by the frequency of incarceration spells
(Model 2).4 These findings align with past studies in
which incarceration exposure or prevalence mat-
tered more for health outcomes than dosage (e.g.,
Schnittker and John 2007; but see Boen 2020).

468
Journal of Health and Social Behavior 62(4)
Violence During Incarceration
Next, in Models 3 and 4 of Table 3, we examine
whether violence moderates the association
between incarceration exposure and the speed of
Grim aging. As shown in Model 3, direct violence
intensifies the association between incarceration
exposure and accelerated aging in this African
American sample (b = 1.388; p < .05). Figure 2
illustrates these patterns. As shown, formerly incar-
cerated individuals who did not report direct vio-
lence are roughly 9 months (12 × .776) older
biologically than expected based on their chrono-
logical age. Those who did report direct violence
during incarceration are approximately 2 years and
2 months (12 × 2.164) older biologically than
expected based on their chronological age. These
results indicate that the direct experiences with vio-
lence during incarceration moderate the association
between incarceration exposure and the speed of
aging. Secondary violence, however, does not mod-
erate the association (see Model 4), suggesting that
witnessing violence may not exacerbate the already
stressful incarceration experience for African
American adults.
Supplementary Analyses
The literature remains mixed regarding gender dif-
ferences in the physical health impacts of incarcera-
tion. Some studies find that the association between
incarceration and mortality is stronger for women
than men (e.g., Massoglia et  al. 2014; Patterson
2010), whereas studies of incarceration and morbid-
ity tend to find little variation (e.g., Baquero et al.
2020; Schnittker and John 2007; Wang et al. 2009).
Few studies, however, have examined gender differ-
ences among a sample of black individuals (cf.
Semenza et al. 2021). In supplementary analyses,
we explore whether the gender of respondents mod-
erates the association between incarceration expo-
sure and the speed of Grim aging (not shown in
tabular form). Approximately 66.9% of males and
43.4% of females were formerly incarcerated.
Table 1.  Means (Standard Deviations) and Proportions of Study Variables Stratified by Incarceration
Exposure.
Variable
Total Sample
(n = 410)
Never
Incarcerated
(n = 196)
Formerly
Incarcerated
(n = 212)
t Test
Accelerated GrimAge
–.030 (4.202)
–1.349 (3.708)
1.179 (4.272)
–6.373***
Incarceration exposure
.522
—
—
—
  Past 4 years
.183
—
.350
—
  More than once
.344
—
.659
—
  Direct violence
.063
—
.120
—
  Secondary violence
.122
—
.234
—
Male
.376
.260
.481
–4.731***
High school education
.915
.959
.874
3.119**
Income
7.908 (4.195)
8.446 (3.780)
7.415 (4.494)
2.502*
Married or cohabiting
.300
.306
.294
.258
Health insurance
.834
.867
.804
1.732
Smoking
.067 (.149)
.039 (.115)
.093 (.171)
–3.716***
Binge drinking
1.235 (.394)
1.162 (.306)
1.301 (.451)
–3.632***
Healthy diet
2.912 (.931)
2.906 (.908)
2.917 (.954)
–.124
Exercise
2.653 (.790)
2.615 (.782)
2.688 (.796)
–.939
BMI
24.452 (5.239)
25.047 (5.814)
23.906 (5.239)
2.090*
Depression
.309 (.730)
.343 (.748)
.278 (.714)
.896
Community disorder
1.264 (.247)
1.240 (.237)
1.287 (.255)
–1.951
Self-control
1.660 (.249)
1.662 (.266)
1.658 (.233)
.124
Delinquency
.255 (.510)
.184 (.387)
.320 (.595)
–2.725**
Childhood adversity
2.844 (2.735)
2.546 (2.632)
3.117 (2.805)
–2.120*
Childhood socioeconomic status
–.032 (1.449)
.138 (1.438)
–.189 (1.444)
2.293*
Note: Data from Family and Community Health Study, Waves 1997–2016.
*p < .05, **p < .01, ***p < .001, two-tailed.

Berg et al.
469
Supplementary models—available on request—
revealed that gender does not significantly moderate
the association between incarceration exposure and
accelerated aging in this African American sample
(b = .303, SE = .900; p = .736). Exposure to incar-
ceration is associated with a 1.904 difference
(approximately 1 year and 11 months) in acceler-
ated aging for African American men (p ≤ .001) and
a 1.602 difference (approximately 1 year and 7
months) in accelerated aging for African American
women (p ≤ .001).
Discussion
A growing literature traces the consequences of mass
incarceration for dimensions of individual-level
health and well-being. Some have argued that the
Table 2.  Ordinary Least Squares Regression Models Predicting Accelerated GrimAge.
Model 1a
Model 2b
Model 3c
Model 4d
Variable
b
SE
b
SE
b
SE
b
SE
Incarceration exposure
1.970***
(.408)
1.719***
(.415)
1.846***
(.429)
2.339***
(.446)
Male
2.041***
(.458)
2.186***
(.501)
2.142***
(.515)
1.981***
(.577)
High school education
–.666
(.841) –1.016
(.772)
–.976
(.786)
.069
(1.215)
Income
–.086
(.057)
–.063
(.058)
-.058
(.057)
–.103
(.065)
Married or cohabiting
–.627
(.417)
–.592
(.443)
–.500
(.434)
–.402
(.471)
Health insurance
.399
(.578)
.219
(.593)
.186
(.594)
.310
(.639)
Smoking
2.967*
(1.450)
4.856*
(1.906)
5.827**
(2.097)
3.503
(1.885)
Binge drinking
.179
(.576)
1.029
(.555)
.684
(.695)
.527
(.764)
Healthy diet
–.197
(.204)
.003
(.215)
–.068
(.216)
–.154
(.248)
Exercise
–.105
(.266)
–.028
(.276)
–.029
(.277)
–.295
(.331)
BMI
.086*
(.034)
.074*
(.036)
.083*
(.037)
.070*
(.039)
Depression
.148
(.306)
–.234
(.256)
–.060
(.268)
.101
(.346)
Community disorder
–.405
(.883)
–.922
(.902)
–.807
(.929)
–.092
(1.012)
Self-control
–.380
(.838)
–.356
(.838)
–.206
(.831)
–.211
(.920)
Delinquency
–.334
(.482)
–.426
(.475)
–.433
(.495)
–.571
(.502)
Childhood adversities
–.064
(.076)
–.010
(.081)
–.031
(.088)
–.043
(.087)
Childhood socioeconomic
status
–.040
(.161)
–.008
(.152)
.097
(.151)
.094
(.183)
Constant
–.989
(2.282) –1.641
(2.217) –1.767
(2.286) –2.235
(2.619)
Note: Data from Family and Community Health Study, Waves 1997–2016. Unstandardized coefficients (b) and robust
standard errors (SE) are presented.
aOrdinary least squares regression (n = 410).
bOrdinary least squares regression with inverse probability of treatment weighting (n = 410).
cOrdinary least squares regression with inverse probability of treatment weighting restricted to common support
(n = 402).
dOrdinary least squares regression with inverse probability of treatment weighting trimmed at the fifth percentiles
(n = 313).
*p < .05, **p < .01, ***p < .001, two-tailed.
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
Never Incarcerated
Formerly Incarcerated
la
cig
olo
n
o
r
h
C
m
o
rf
e
g
A
M
I
R
G
f
o
n
oitaiv
e
D
Age in Years
Figure 1.  Speed of GrimAge by Incarceration
Exposure.
Note: Data from Family and Community Health Study,
Waves 1997–2016. Marginal effects estimates derived
from regression Model 2 of Table 2. Vertical bars
indicate the 95% confidence interval; n = 410.

470
Journal of Health and Social Behavior 62(4)
pervasiveness and concentration of incarceration
among disadvantaged groups contributes to widening
health inequalities (Wildeman and Muller 2012:12).
Although research has reported associations between
incarceration exposure and various negative health
outcomes, important substantive and methodological
gaps remain in this body of work. These gaps reflect
special challenges in determining whether and why
formerly incarcerated individuals report an elevated
burden of chronic illnesses and diseases. The goal of
the current study was to extend this area of research
by examining whether incarceration exposure is
related to the pace of biological aging and if experi-
ences with violence during incarceration intensify
this association. To do so, the study employed data on
a sample of African American adults along with mea-
sures of several relevant confounds from different life
course domains, and it implemented adjustments for
selection.
The results of the analysis support two key sub-
stantive conclusions. First, incarceration exposure
significantly predicts accelerated biological aging
among African American adults, as assessed in the
GrimAge measure. Study participants who reported
a history of incarceration were, on average, biologi-
cally older than expected given their numerical age.
Notably, the estimates were robust to a host of
potential confounding factors obtained earlier in the
life course, including adverse childhood experi-
ences, social class, depression, and delinquency.
Even after implementing statistical adjustments,
the findings remained stable. This is noteworthy
because the proposition that any observed effects of
incarceration on physical health are due to selec-
tion, confounding, or both has a strong logical basis
(Schnittker et al. 2012). The results therefore pro-
vide reasonable evidence that the stressors of incar-
ceration exposure appear to contribute to the pace
of epigenetic aging.
This evidence is important for understanding the
physical health toll of incarceration among African
Americans because accelerated aging is a funda-
mental predisease process. This process reliably pre-
dicts mortality risk and the onset of chronic and
acute morbidities (Sinclair 2019). Research has
Table 3.  Ordinary Least Squares Regression Models Predicting Accelerated GrimAge from
Incarceration Timing, Frequency and Exposure to Violencea
Model 1
Model 2
Model 3
Model 4
Variable
b
SE
b
SE
b
SE
b
SE
Incarceration exposure
1.504***
(.452)
1.194*
(.546)
1.593***
(.424)
1.616***
(.441)
Internal moderators:

  × past 4 years
.693
(.613)
—
—
—

  × more than once
—
.854
(.624)
—
—

  × direct violence
—
—
1.388*
(.701)
—

  × secondary violence
—
—
—
.488
(.625)
Note: Data from Family and Community Health Study, Waves 1997–2016. Unstandardized coefficients (b) and robust
standard errors (SE) are presented.
aOrdinary least squares regression with inverse probability of treatment weighting (n = 410). Confound estimates
not shown in the table: male, high school education, income, married or cohabiting, health insurance, smoking, binge
drinking healthy diet, exercise, BMI, depression, community disorder, self-control, delinquency, childhood adversities,
childhood socioeconomic status.
*p < .05, *** p < .001, two-tailed.
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
Never Incarcerated
No Direct Violence
Direct Violence
 e
g
A
 la
cig
olo
n
o
r
h
C

m
o
rf e
g
A

m
ir
G
 f
o n
oitaiv
e
D
in Years
Figure 2.  Speed of GrimAge by Incarceration
Exposure and Direct Violence during
Incarceration.
Note: Data from Family and Community Health Study,
Waves 1997–2016. Marginal effects estimates derived
from multivariate regression model in Model 3 of
Table 3. Vertical bars indicate the 95% confidence
interval; n = 410.

Berg et al.
471
shown that accelerated aging is among the most
important drivers of adverse health conditions
already linked to incarceration, including hyperten-
sion and cancer (Massoglia 2008; Wang et al. 2009),
all of which diminish total health span (Murabito
et al. 2018; Yoon et al. 2019). Epigenetic aging may
be a biological pathway whereby the hardships of
incarceration exposure affect the onset of acute ill-
nesses and chronic conditions. Furthermore, the cur-
rent findings add to the burgeoning body of work
focused on how incarceration may “get under the
skin” to leave an imprint on bodily systems (e.g.,
Boen 2020; Esposito et al. 2017). That we uncov-
ered evidence of accelerated aging in a sample of
African American adults who were approximately
30 years of age is worth noting—this is a period in
the life course when age-related chronic diseases
and emerging pathology typically do not yet mani-
fest and register clinically. As Moffitt and colleagues
(2017) note, accelerated aging is not simply an
affliction of the elderly but a lifelong biological pro-
cess that begins early in the life span.
Second, exposure to violence during incarcera-
tion exacerbates the degree to which incarceration
exposure hastens the pace of Grim aging among
African American adults. Those who experienced
violence were more than 2 years older than their cal-
endar age. This effect, however, was exclusive to
direct incidents of violent threats, assaults with and
without weapons, and robbery. Interpersonal vio-
lence likely engenders a stress response that chroni-
cally activates immunological and nervous system
capacities. This evidence aligns with the literature
on prison violence and mental health (Zweig et al.
2015). Moreover, it accords with research indicating
that threatening social environments trigger adverse
physiological responses (Miller et al. 2011; Simons
et al. 2021). Perhaps violent victimization is espe-
cially stressful in the confined spaces of the prison
context where hostile interpersonal relations are par-
ticularly difficult to circumvent. As one formerly
incarcerated individual explained: “The whole men-
tal thing of me . . . trying to think of who I could have
a problem with or who might have a problem with
me all the time . . . is extremely stressful” (Porter
2019:9). Other potentially traumatic prison and jail
conditions are deserving of additional research on
the biological consequences of incarceration,
including discriminatory treatment and placement in
solitary confinement, the latter of which has been
linked to mental health (Strong et  al. 2020).
Similarly, researchers should study conditions dur-
ing postrelease that might buffer the link between
incarceration and adverse physiological deteriora-
tion, including relational and community supports.
The present study did not examine how incar-
ceration contributes to racial disparities in aging
given that the sample was limited to African
American individuals. But this work is needed.
Contact with the criminal justice system—stops,
arrests, and jail and prison stays—is disproportion-
ately felt by African Americans owing to a legacy
of discrimination and marginalization in a racial-
ized society. Whether the linkage between incar-
ceration and adverse health outcomes differs by
race is the focus of ongoing research (Boen 2020;
Semenza et al. 2021). The findings from this study
suggest ways forward with research on health dis-
parities, particularly biological markers. As noted,
epigenetic aging indices are thought to be direct
measures of the biological weathering conceptual-
ized in Geronimus and colleagues’ (2006) model of
health inequalities (see Simons et  al. 2021).
Perhaps, then, epigenetic aging serves as a biologi-
cal mechanism through which disparities in incar-
ceration contribute to racial differences in chronic
morbidity and excess mortality. Note, however, that
we are unable to test this possibility given our reli-
ance on an African American sample. Future
research should examine the validity of this
assumption given that it might hold important clues
about how mass incarceration and other race-
related social adversities combine to affect health
inequalities. Modern medicine often considers dif-
ferences in health risk behaviors (e.g., diet) as the
major cause of class and race disparities in chronic
illness (Milani and Lavie 2015). Some argue that
this view perhaps exaggerates the importance of
health risk behaviors as a cause of health inequali-
ties. Our findings show that regardless of diet, exer-
cise, and smoking, incarceration exposure is a
significant social determinant of accelerated aging.
The current study is not without limitations.
Despite utilizing a series of preincarceration mea-
sures of likely confounds, the analysis did not assess
the GrimAge of respondents prior to incarceration.
Rather, GrimAge was obtained at a single period—
Wave 7. Assessing a biomarker of aging at multiple
time points would allow for a more compelling
assessment of how incarceration affects changes
across the life course in the pace of biological aging.
This question should be a priority for future
research. Readers should also use caution when gen-
eralizing our findings to other health biomarkers,
including objective markers of inflammation and
cardiovascular disease risk. Furthermore, the IPTW

472
Journal of Health and Social Behavior 62(4)
procedure is intended to minimize threats of selec-
tion based on differences in observables, which pro-
vides some confidence in our estimates. However,
as noted earlier, the quality of these estimates is
dependent on the reliability and validity and scope
of the observed measures. Furthermore, the esti-
mates remain vulnerable to unobserved sources of
selection that are not controlled in the current IPTW
methods but may be addressed through fixed effects
estimators in longitudinal data.
Additionally, as noted, our sample consists of
African American adults under age 30, which limits
the generalizability of our findings to other groups.
Although an exclusive focus on an African American
sample is a strength given their high level of expo-
sure to incarceration and worse health profile, future
research should explore the extent to which incar-
ceration accelerates aging among other racial and
ethnic groups. Similarly, researchers should con-
sider the possibility that our findings could vary in a
sample of older adults with a more substantial health
burden. A sample that included older adults could
also allow for a detailed assessment of how the tim-
ing of incarceration may affect the speed of aging.
In addition, because the majority of FACHS respon-
dents reside in two states, this limits the generaliz-
ability of the findings to individuals located in other
states. The characteristics of prisons and jails and
correctional policies, including reentry program-
ming, in each of these states might uniquely affect
the biological health of the formerly incarcerated in
such a way that similar findings may not be observed
among those in other states.
The consequences of mass incarceration for
health and well-being have become a topic of inter-
est among policymakers and academics from mul-
tiple scientific disciplines for good reason: With so
many Americans cycling through prisons and jails
each year, the public health toll of the carceral state
is likely to mount, creating challenges for commu-
nities already beleaguered by accumulated hard-
ships. The findings would seem to have important
intervention and policy implications. At the very
least, they provide further support for decreasing
the scale of the carceral state and providing sound
alternatives to punitive penal policies for the sake
of reversing health inequalities.
Funding
The authors disclosed receipt of the following financial sup-
port for the research, authorship, and/or publication of this
article:This research was supported by the National Heart,
Lung, Blood Institute (R01 HL118045), the National Institute
on Child Health and Human Development (R01 HD080749),
the National Institute on Aging (R01 AG055393), the
National Institute on Drug Abuse (P30 DA027827), the
National Institute of Mental Health (R01 MH62699), and the
Center for Disease Control (1 R49 CE003095-01).
ORCID iDs
Mark T. Berg
 https://orcid.org/0000-0003-2709-0950
Ronald L. Simons
 https://orcid.org/0000-0002-
4902-0462
Suppplemental Material
Appendices A through C are available in the online ver-
sion of the article.
Notes
1.
We reestimated our analyses using a multiple impu-
tation strategy to recover missing values from the
confounding measures—this step increased the
sample size to 436 respondents. The results did not
substantively depart from the main findings and
conclusions.
2.
Furthermore, Iowa—where many in the Family
and Community Health Study (FACHS) sample
reside—incarcerates black residents at a rate that
exceeds most other states (Beck and Blumstein
2018). Compared to other states, Georgia’s rate of
incarceration (1,110 per 100,000 adult residents) is
higher than the average rate (750 per 100,000 adult
residents) in the United States (Maruschak and
Minton 2020). Iowa’s rate of incarceration (580 per
100,000 adult residents) is lower than the average in
the United States.
3.
In an additional sensitivity analysis, we use a regres-
sion adjustment estimator in addition to inverse
probability of treatment weighting (see Boen 2020).
When using a regression adjustment, we find a simi-
lar treatment effect (b = 1.836, SE = .386, p = .000).
4.
All formerly incarcerated individuals in the sample
spent time in jail; 25 (6.1%) spent time in prison in
addition to jail. There were no cases where individ-
uals spent time in prison but not jail. Supplemental
analyses revealed that exposure to prison did not
have a unique effect on the speed of aging among
those exposed to incarceration (b = –.348, SE =
.906, p = .701).
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Author Biographies
Mark T. Berg is a professor of sociology and criminology
and director of the Crime and Justice Policy Research
Program at the University of Iowa, where he is also a
Collegiate Scholar. His current research examines conflict

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Journal of Health and Social Behavior 62(4)
escalation and the life course determinants of biological
aging and cardiovascular disease risk.
Ethan Rogers is an assistant research scientist in the
Crime and Justice Policy Research Program at the
University of Iowa Public Policy Center. His research uti-
lizes quantitative analyses to explore interpersonal conflict
and violence, the spatial distribution of crime across com-
munities, trends in violence, and the health consequences
of incarceration.
Man-Kit Lei is an assistant professor of sociology and
affiliated with the Center for Family Research at the
University of Georgia. His research utilizes quantitative
methods to examine health-related outcomes such as
chronic inflammation and rate of biological aging. Recent
articles have investigated the impact, for better or worse,
of the social environment on biomarkers of health, espe-
cially among minority populations.
Ronald L. Simons is Regent’s professor in the Department
of Sociology and codirector of the Center on the Biological
Embedding of Social Events and Relationship at the
University of Georgia. He has been funded continuously
by National Institutes of Health for over 25 years. His
research utilizes longitudinal data to investigate avenues
whereby social events and circumstances such as racial
discrimination, economic hardship, neighborhood disor-
der, and relationship conflict become biologically embed-
ded and influence speed of aging, health, and mortality.

    Policy Problem:

Answer: The unprecedented scale of U.S. incarceration—encompassing roughly 6.5 million people under correctional control and disproportionately ensnaring Black Americans—now emerges as a direct driver of premature biological aging and, by extension, chronic disease. Long-term, longitudinal evidence shows that African American adults who spend time in jail or prison age epigenetically two or more years faster than their peers, even after accounting for childhood adversity, socioeconomic status, and health behaviors; exposure to violence behind bars magnifies this effect. Because accelerated “GrimAge” is a validated harbinger of cardiovascular disease, cancer, and early death, current penal practices are effectively embedding future medical crises into the bodies of already-marginalized populations while widening racial health gaps. Yet correctional health policy remains focused on short-term infection control and basic care, with little recognition of the lasting physiological toll of confinement, the compounding impact of prison violence, or the downstream costs to community health systems when people return home. In short, mass incarceration is not merely a criminal-justice issue—it is an unaddressed public-health emergency that transforms a carceral sentence into an accelerated aging sentence, demanding policy responses that both curtail unnecessary confinement and mitigate its biological harms.