Justice Quarterly

ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/rjqy20

Opening the Black Box of Solitary Confinement
Through Researcher–Practitioner Collaboration:
A Longitudinal Analysis of Prisoner and Solitary
Populations in Washington state, 2002–2017
David Lovell , R. Tublitz , K. Reiter , K. Chesnut & N. Pifer
To cite this article: David Lovell , R. Tublitz , K. Reiter , K. Chesnut & N. Pifer (2020) Opening the
Black Box of Solitary Confinement Through Researcher–Practitioner Collaboration: A Longitudinal
Analysis of Prisoner and Solitary Populations in Washington state, 2002–2017, Justice Quarterly,
37:7, 1303-1321
To link to this article: https://doi.org/10.1080/07418825.2020.1853800

Published online: 21 Dec 2020.

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JUSTICE QUARTERLY
2020, VOL. 37, NO. 7, 1303–1321
https://doi.org/10.1080/07418825.2020.1853800

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Opening the Black Box of Solitary Confinement Through
Researcher–Practitioner Collaboration: A Longitudinal
Analysis of Prisoner and Solitary Populations in
Washington state, 2002–2017
David Lovella, R. Tublitzb, K. Reiterb, K. Chesnutb,c and N. Piferd
a

School of Nursing, University of Washington, Seattle, WA, USA; bDepartment of Criminology, Law &
Society, University of California, Irvine, CA, USA; cVera Institute of Justice, New York, NY, USA;
d
Criminology & Criminal Justice Program, University of Rhode Island, Kingston, RI, USA
ABSTRACT

ARTICLE HISTORY

This article presents a rare longitudinal analysis of solitary confinement use in one state prison system: spanning 2002–2017 in the
Washington Department of Corrections (DOC). An ongoing partnership with DOC officials facilitated methodological and conceptual improvements, allowing us to construct a dataset that
provides a rich description of who is in solitary confinement, for
how long, and why. Operationalizing solitary confinement as the
intersection of the most serious custody status with the most
restrictive housing location, we describe significant changes in
ethnic composition and behavioral profiles of people in solitary
confinement and in frequency and duration of solitary confinement use. These results suggest how particular policy interventions have affected the composition, numbers, and lengths of
stay in solitary confinement. Combining longitudinal analysis and
iterative engagement with DOC officials, we provide a roadmap
for better understanding solitary confinement use in the United
States now and in the future.

Received 2 June 2020
Accepted 11 November 2020
KEYWORDS

Restrictive housing; solitary
confinement; prison; gangs

Tens of thousands of prisoners across the United States experience solitary confinement
annually (Association of State Correctional Administrators & the Arthur Liman Public
Interest Program and Yale Law School (ASCA-Liman), 2015, 2018; Beck, 2015). Prisoners
generally spend no more than an hour per day outside of cells the size of a wheelchairaccessible bathroom stall, and eat cold meals alone, with limited access to natural light,
phones, family visits, or any human touch. Prisoners live not days, but months and years
under such conditions. In tandem with mass incarceration, the use of solitary confinement
expanded drastically across the United States in the 1980s and 1990s, often in modern,
hyper-secure, “supermax” facilities (Reiter, 2016; Riveland, 1999; Sakoda & Simes, 2019).
Though integral to incarceration since the prison was “born” and perpetually controversial
(Foucault, 1977; Haney & Lynch, 1997; Rubin & Reiter, 2018; Smith, 2006), solitary
CONTACT K. Reiter

reiterk@uci.edu

ß 2020 Academy of Criminal Justice Sciences

1304

D. LOVELL ET AL.

Estimates of how many people experience solitary confinement annually range from
68,000 prisoners to 18% of all prisoners in the United States, or over 250,000 people
(ASCA-Liman, 2015; Beck, 2015). To address definitional debates underlying conflicting
estimates, Mears et al. recently suggested a four-dimensional conceptual framework –
goal, duration, quality, and intentionality – to describe the constellation of factors that
make up solitary confinement (or “restrictive housing”) practices (2019, p. 1434). The
operational focus of our alternative approach allows us to bypass arguments about
how to define solitary confinement, a conceptually and ethically controversial practice.
Rather, our operational definition applies the near-universal correctional functions of
classification and movement to identify the sites and subjects of solitary confinement
from correctional tracking records. These methods permit consistent, robust analyses
of who is subjected to solitary confinement and the association of this experience
with institutional misconduct and other factors.

Trajectories of solitary confinement placement

confinement has come under renewed scrutiny in the last decade (Reiter, 2018; ASCALiman, 2015). Federal and state correctional systems have begun to experiment with mitigation and alternative programs. Here, we focus on a 15-year period during which the
Washington Department of Corrections (DOC) attempted to confront these issues and ask
whether and how a prison system might reduce its use of solitary confinement.
The question of whether a prison system might change direction, including how
the practice of solitary confinement might be constrained, has animated criminological
scholarship over decades (Jacobs, 1977; Liebling, 1999; Petersilia, 1991; Reiter, 2016;
Rhodes, 2004; Rubin & Reiter, 2018). A longitudinal, quantitative dataset with which to
assess these questions, however, is rare. Our dataset, analyzed in collaboration with
practitioner partners, allows us to look both at individual factors, such as how many
gang members with violent infraction histories are placed in solitary confinement for
how long in any given year, and at institutional factors, including demographic shifts
and policy changes, which influence behavioral patterns (Haney, 2018; Liebling, 1999;
Toch, 1977; Toch, Adams, & Grant, 1989).
Where scholars have used point-in-time datasets to examine the relationship
between individual and institutional factors in understanding the use and effects of
solitary confinement, controversies abound over how to define and operationalize the
practice (Kurki & Morris, 2001; Mears et al., 2019; Naday et al., 2008; Reiter, 2016). We
identify which prisoners are subjected to the aversive conditions described above in
terms of two factors: (1) whether they are living in units engineered to lock them
down (location) and (2) the rules governing how long they stay, their conditions of
confinement, and movement (custody status). Here, these measurement principles are
applied to a rich administrative dataset to ask: (1) Who is in solitary confinement, for
how long, and why? (2) How, if at all, do their individual characteristics, including ethnicity, gang status, and behavioral profiles change over time? (3) What patterns
emerge from this analysis? We show how the distribution and extent of solitary confinement use in Washington has shifted with institutional vicissitudes in demographics,
capacity, gang management policies, programming, and classification systems.

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1305

Previous studies have reached conflicting conclusions about whether solitary confinement has a disparate impact on groups defined by race or ethnicity. Studies focusing on
patterns in disciplinary infractions and solitary confinement placements over four to six
years tend to find minimal disparities (Cochran, Toman, Mears, & Bales, 2018; Tasca &
Turanovic, 2018), while point-in-time comparisons of demographics of solitary confinement
units with general population (GP) units consistently find non-white prisoners over-represented in solitary confinement (Reiter, 2012; Schlanger, 2012). A recent study analyzed a
survey that asked state prison systems to self-report solitary confinement and gang-affiliated populations; prisoners classified as gang members were over-represented in solitary
confinement across the United States (Pyrooz & Mitchell, 2020). The study does not mention race, but others have noted the longstanding ties between race and gangs in US prisons (Berger 2014; Bloom & Martin, 2013; Reiter 2016), strengthening Pyrooz and Mitchell’s
recommendation to “integrate measures of gang affiliation into correctional research”
(2019, p. 22), as we do in our analysis.
The relationship between solitary confinement and institutional order is also contested (Briggs, Sundt, & Castellano, 2003; Lovell, Johnson, & Cain, 2007). One recent
study among men in a three-year cohort in a mid-western DOC found that disciplinary
segregation was associated with a greater probability of misconduct (Labrecque &
Smith, 2019), but another study, among men in a two-year cohort in the Oregon DOC,
found that disciplinary segregation was not a significant predictor of subsequent institutional misconduct (Lucas & Jones, 2019). Our dataset permits an evaluation of longer-term patterns of misconduct, in and out of solitary settings.
One recent study expanded the usual short periods of analysis described in preceding studies about both race and misconduct, using nearly a decade (1987–1996) of
data from Kansas: a prison system small enough (5–7000 prisoners) to allow tracing of
bed-level data to examine individual correlates of solitary confinement placement,
such as race, and also patterns in frequency and duration of solitary confinement over
time (Sakoda & Simes, 2019). Our study takes an even broader scale approach: examining populations in and out of solitary confinement over 15 years, with 15,000 or more
prisoners per cohort, following particular individuals and groups over decades of criminal and correctional history.
Attending to broader institutional forces at play over our study period is critical to
our approach. Lynch recently argued that in studies of sentencing, findings are often
“operationalized as a single end-stage outcome that is unmoored from the social,
organizational, and institutional forces that help produce a class of defendants to be
sentenced” (Lynch, 2019, p. 1159). This critique could just as readily be applied to
studies of solitary confinement (Cochran et al., 2018; Logan et al., 2017) in which disparities in outcomes and differences in personal and behavioral characteristics of prisoners are analyzed with limited attention to institutional patterns such as fluctuations
in bed capacity, shifts in demographic make-up, and reforms or retrenchments in policies governing solitary confinement placement and release. Our longitudinal
dataset allows us to generate individual-level and aggregate statistics on histories and
outcomes during incarceration, and to place findings in the context of broader institutional forces shaping those patterns.

JUSTICE QUARTERLY

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D. LOVELL ET AL.

1
In a timely example of how relevant the analysis in the instant study is, DOC research staff recently noted that
they “had some concerns” with these numbers as originally reported and have revised them upwards, re-calculating
that, in 2015, 3.4% of the state prison population was in “restrictive housing” according to the ASCA-Liman
Definition, and, in 2017, 4.1% of the state prison population was in “restrictive housing” by this definition. E-mail
communication with DOC Department of Research, dated Sept. 25 and Sept. 28, 2020, on file with authors. The
ASCA-Liman report defines “restrictive housing” as “separating prisoners from the general population and holding
them in cells for an average of 22 or more hours per day for 15 continuous days or more.”

This analysis draws on a longitudinal administrative record set of the entire DOC
population on six evenly-spaced snapshot intervals (July 1, 2002, 2005, 2008, 2011,
2014, and 2017): subject-level demographic records (N ¼ 57,130), and event-level
records of admissions and releases (266,266), prison sentences (230,833), custody
assignments (1.2 million), infractions (630,088), and inter-facility movements (2.4 million). Discussions with DOC research office partners about how best to meet the data

Data and methods

The administrative dataset analyzed here was collected as part of a multi-method project, also using ethnographic, interview, and archival data, to evaluate solitary confinement
use over time in Washington DOC (Reiter et al., 2020). This project extends a decades-long
collaborative relationship between researchers and DOC: first between the University of
Washington (UW) and DOC through the Mental Health Collaboration (Allen, Lovell, &
Rhodes, 2001); later in a UW-led multi-method systematic survey of Washington’s solitary
confinement population in 1999–2000 (Lovell, 2008; Lovell, Cloyes, Allen, & Rhodes, 2000;
Rhodes, 2004); and finally, in this study, replicating and extending the 2000 study in collaboration with an original member of both previous studies.
In rates of overall incarceration and solitary confinement use, Washington DOC is below
average: it has the 12th lowest rate of incarceration among the states (Kaeble & Cowhig,
2018), and as of 2018, its reported proportion of population in “restrictive housing” (2.3%)
was half the national average (4.5%) (ASCA-Liman, 2018, p. 13).1 In terms of willingness to
collaborate with researchers, however, Washington DOC is above average: current and former DOC leadership have agreed there are knowledge gaps around solitary confinement,
invited scholars and advocates alike to analyze and critique policies in order to address
these gaps, and participated actively in collaborations: both facilitating access to the
administrative data underlying the analyses presented here and helping to interpret
results. In particular, Eldon Vail and Dan Pacholke, nationally recognized correctional policy
experts, led Washington DOC during part of our study period and consulted with us on
interpretation of findings.
Research about solitary confinement use has been produced through practitioner–researcher collaborations in a number of states, including Colorado (O’Keefe et al.
2011), Florida (Mears & Bales, 2009), Kansas (Sakoda & Simes, 2019), and Oregon
(Pyrooz, Labrecque, Tostlebe, & Useem, 2020). Few, however, have attempted the
quantitative and qualitative depth of this project, which is more comparable to the
New York studies of Toch and colleagues (Toch, 1977; Toch et al., 1989), conducted as
the new “supermax” era was coming upon us in the 1980s, or the California studies by
Petersilia on re-entry and community supervision (Petersilia 2009). Ours represents an
intergenerational academic–practitioner collaboration spanning both eras.

®

1307

In Washington DOC policy (2020: 320.250), maximum custody status is the highest
level of custody classification. Maximum custody prisoners are assessed in formal hearings to pose a sufficient risk to safety – whether their own or others’ – to warrant
holding them for an extended period in a maximum-security location, isolated by
architecture, procedure, and staffing. As legal expert Fred Cohen notes, maximum custody is a risk-based classification, justified as a preventive measure rather than a punitive sanction (Cohen, 2008). In Washington DOC, prisoners first enter solitary
confinement through short-term administrative segregation (Ad-Seg) placements, usually awaiting adjudication following an infraction. Infraction of a specific prison rule
may result in a disciplinary hearing and the sanction of a disciplinary segregation (DSeg) placement. Alternatively, multiple infractions, other behavior patterns, or an
extended stay in administrative segregation may lead to a re-classification as maximum
custody (Max).

Terminology

needs of our study, exemplifying our academic–practitioner collaboration, led to two
major expansions of the scope and power of this dataset.
First, to assess how solitary confinement populations had changed since the 2000
UW study, we requested archival information on prisoners in any form of solitary confinement on our snapshot dates. Lacking ready capacity to identify these prisoners,
DOC offered to provide data for all prisoners in custody on these dates, leaving it to
us to identify who was in solitary confinement and when. Our willingness to pick our
own apples from the DOC data tree led to a 30-fold expansion of our subject pool,
permitting longitudinal comparisons between solitary confinement and GP prisoners.
Second, DOC provided us all Washington prison sentences in the entire history of prisoners in our vastly expanded dataset, rather than only the index offense data we had
requested. Although information about currently active convictions accompanies prisoners as they move through DOC, retrospectively retrieving links between court and
correctional records is complicated by the multiplicity of charges, sentencing policies,
and admission statuses that may apply. Recognizing a systematic problem when we
showed them a pattern of missing data, DOC provided the entire prison conviction history for the 57,000 prisoners in our expanded subject population, allowing us both to
identify the most serious current offense and to provide a consistent measure of prisoners’ criminal histories.
Source data were compiled cohort by cohort, applying uniform coding procedures
to compile event-level data into a subject-level dataset. We computed the facility location and custody status of every prisoner in the system throughout each admission,
length of stay (LOS) at each location, and subject-level summaries of numbers and
rates of relevant events, such as infractions. Compilation codes were tested and modified until they yielded consistent and plausible counts and summary statistics (e.g. no
negative values for LOS or rates) across all prisoners in six snapshot cohorts. We also
use some inferential statistics (e.g. chi-square and t-tests) in the analyses we present
to test for differences across cohorts and groups.

JUSTICE QUARTERLY

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1308

D. LOVELL ET AL.

2
Intra-facility housing changes and periods spent in recently decommissioned internal solitary confinement units are
better captured in our related, intensive field study dataset of 106 solitary confinement prisoners (Reiter
et al., 2020).

To contextualize findings on the size and characteristics of Washington’s solitary
confinement population, we first describe overall patterns in the state prison population between 2002 and 2017. Table 1 displays counts and demographic, crime type,
sentence length, and gang affiliation characteristics for the entire prison population
incarcerated on each of the six snapshot dates. Washington State’s prison population
grew by 13%, despite changes in sentencing policy (SHB2338, 2002) that were
expected to reduce imprisonment by lessening penalties and providing treatment

Results

In DOC, Intensive Management Units (IMUs) are the most secure housing facilities.
The term “supermax” is not a category of institution in DOC; instead the state has five
IMUs, located at Clallam Bay Corrections Center (CC), Monroe CC, Washington CC
(“Shelton”), Stafford Creek CC, and the Washington State Penitentiary (called “Walla
Walla” or the “concrete mama” (Hoffman & McCoy, 2018)). IMUs feature distinct security perimeters with advanced technology for controlling entrances, gates, and doors;
strict procedures for prisoner movement; and no normal occasions for prisoners to
share space with others unless shackled. Though exact conditions (like cell size and
degree of access to natural light) vary across IMUs, the uniformly restrictive conditions
impose intense isolation (often for extended periods of time) comparable to conditions in other state supermaxes. IMUs are adjacent to the “main institution” (a correctional center or complex may have multiple facilities, or stand-alone buildings, sharing
a common Superintendent) to allow escorting prisoners on foot without delay.
As a Lieutenant at Shelton said during a prison visit: “Nothing happens fast around
here except going to the IMU.”
Transfers between facilities are recorded in DOC’s movement records, allowing us to
identify who was placed in IMUs and for how long. Transfers in and out of cells within
a facility, however, are recorded as housing changes: likely 50 million in number for
our subjects, vastly exceeding our and DOC’s ability to retrieve and compile, absent
unlimited resources.2 Therefore, inter-facility movement records in our data do not
capture prisoners isolated on Ad-Seg or D-Seg status (Ad/DSeg status) inside a main
institution. Importantly, Ad/DSeg prisoners, who were living under comparably
stringent conditions as IMU-Max prisoners, in two decrepit segregation units
within the main institutions at two of Washington’s oldest prisons – Walla Walla and
Monroe – are not captured in our data. These two units, with a combined capacity of
250, closed in 2011, but were replaced (and then some) by 200 new IMU beds at each
prison. Our inability to identify all such Ad/DSeg prisoners through movement records
requires caution in how the terms “IMU” versus “solitary confinement” are used in our
findings. Because of this limitation, we center our trend and comparative analyses
on the maximum custody group, who are reliably identified over the entire course
of our study period and whose long-term presence in maximum security settings
raises the sharpest ethical issues (Lovell, 2014).

®

63%
19%
10%
8%

8%
92%

19%
33%
29%
20%

2005

44%
20%
18%
18%
0%

62%
19%
11%
9%

8%
92%

17%
32%
28%
23%

2008

46%
20%
19%
15%
0%

60%
19%
12%
9%

8%
93%

16%
34%
25%
25%

2011

46%
20%
20%
14%
0%

61%
18%
13%
9%

8%
92%

13%
35%
26%
27%

2014

JUSTICE QUARTERLY

42%
17%
17%
23%
1%

5%
10%
9%
2%
74%
17,625

101.7
120.4

6%
10%
8%
2%
75%
17,288

99.8
117.3

6%
9%
6%
2%
78%
17,308

94.8
112.1

5%
9%
5%
1%
80%
16,852

89.1
107.1

Cohort

Table 1. Washington DOC population characteristics, 2002–2017.
2002
Age at snapshot (in years)
18–25
21%
26–35
33%
36–45
29%
Over 45
17%
Gender
Female
7%
Male
93%
Race/ethnicity
White, Non-Hispanic
60%
Black, Non-Hispanic
21%
Hispanic
12%
Other/Unknown
7%
Most serious offense at conviction
Violent, non-sex
41%
Sex
17%
Property
15%
Drug/other
25%
Missing
2%
Sentence length (in months)
Mean
87.9
Standard deviation
104.8
Gang affiliation by racial/ethnic STG
White
5%
Black
9%
Hispanic
4%
Other
1%
No gang affiliation
81%
Total prison population
15,907

1309

2017
11%
34%
27%
28%
8%
92%
60%
18%
14%
9%
48%
19%
19%
13%
0%
100.9
124.6
5%
10%
9%
2%
74%
17,943

3
General crime types were derived from DOC codes in the administrative data. Violent, non-sex offenses include
murder, manslaughter, robbery, and assault; sex offenses include rape, sexual assault, child molestation, and failure
to register as a sex offender; property crimes include arson, burglary, theft, forgery, trafficking, and possession of
stolen property; drug crimes include manufacturing, delivering or possession with intent to distribute, and
possession of a controlled substance.
4
To avoid confusion, we follow DOC’s terminology with the term “Hispanic,” which DOC codes separately from race
as “Hispanic Origin” (Y/N); but we apply these data to define mutually exclusive categories: “White, non-Hispanic”
includes any individual whose race is listed as White and who is not classified as Hispanic Origin; “Black, nonHispanic” includes any individual whose race is listed as Black and not identified as Hispanic; “Hispanic” includes any
individual whose ethnicity is listed as Hispanic or Latino, regardless of any other racial identification; “Other/
Unknown” includes any individual whose race is listed as Asian/Pacific Islander, Native American/American Indian,
Other, Unknown and whose ethnicity is not Hispanic.

alternatives for drug-related offenses. The proportion of prisoners incarcerated for
drug or other offenses declined substantially, while those incarcerated for violent,
non-sexual offenses increased by nearly 17% between 2002 and 2017 (p < 0.001).3
Reflecting the shift toward more violent offenses, average sentence lengths increased
significantly, as did the average age of prisoners. The proportion of Hispanic prisoners
increased by 17%, while the proportion of Black, non-Hispanic prisoners decreased by
16% (p < 0.001), and White, non-Hispanic representation remained stable.4
Affiliation with security threat groups (STG) or prison gangs, increased as well: in
2017, over one in four prisoners (26%) was identified as a member of an STG, up from
19% in 2002. The growth of gang affiliation was not equally distributed across racial

Source: Authors’ calculations. Washington State Department of Corrections.

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1310

D. LOVELL ET AL.

5
Rates of gang affiliation by racial/ethnic group were generated by dividing the total number of members in each
racial/ethnic group identified as an STG member by the total number of prisoners of each racial/ethnic group. Table
1 displays the STG membership by racial/ethnic affiliation of STGs, grouped from detailed STG data provided by
DOC. STGs identified as “White” affiliated included Biker, Skinhead, White Supremacist and Security Threat Concern;
~o,
“Black” affiliated included Black Gangster Disciples, Blood, Crip, and Vice Lord; “Hispanic” affiliated included Norten
~o, Paisas, La Fuma, Cuban, and Hispanic-Other; “Other” affiliated included Asian and Other.
Suren
6
Our original analysis identified an even larger proportion of prisoners in this “Other-Max” group; our practitioner
collaborators thought more than 10% was an unlikely proportion of prisoners to be assigned max custody status
but still awaiting placement in an IMU or similar facility. We then further evaluated whether some of those “OtherMax” prisoners were housed out-of-state. Indeed, when we examined individual cases in the original movement
files, we found this was true, leading us to better specify and exclude those prisoners in our sample, of any custody
status, who were housed out of state.

Table 2 presents trends in solitary confinement use by both custody status (classification) and location (facility). We distinguish four groups either classified at the highest
custody level (Maximum, labeled “Max”), or located in the most restrictive locations
(IMUs). At the center of our analysis are prisoners both classified Max and housed in
IMUs (denoted by IMU-Max). Next are prisoners who have not been reclassified Max,
but are housed in IMUs for administrative or disciplinary segregation (IMU-Ad/DSeg).
Third, for treatment purposes, some Max prisoners are housed at the special offender
unit (SOU) at Monroe, designed to address serious behavioral health needs, or at the
inmate transitional pod (ITP) at Clallam Bay, a program-focused unit for prisoners
transitioning out of solitary confinement (denoted by Max-Tx). Finally, a residual group
of Max prisoners could not be assigned a facility type because, on the snapshot date,
they were on hospital or court release, or awaiting transfers to an IMU, SOU, or
ITP (Other-Max).6
Solitary confinement use (in IMU-Max, IMU-Ad/Dseg, and Total IMU) far outpaces
population growth over our study period in the state, growing at least 130% (in IMUMax), compared to a 13% growth in the state prison population. As explained earlier,
IMU-Max represents a clearly defined population, with reliable snapshot counts for
prisoners subjected to long-term solitary confinement over the entire study period,
but it excludes prisoners in Ad/DSeg either in the IMU, or in other within-facility units,
not identifiable in the between-facility movement records we analyze. Figure 1 illustrates differences in rates and patterns of growth in IMU-Max and total prison populations, accompanied by changes in average LOS for the IMU-Max group on their
snapshot date assignments.
One-day counts capture those physically held in IMUs on snapshot dates, and demonstrate that a small, but increasing proportion of Washington’s prison population
was held in solitary confinement across snapshots, in both IMU-Max and IMU-Ad/DSeg
groups. One-day counts, however, do not account for movement in and out of IMUs

Disentangling the solitary population

and ethnic groups.5 While rates of gang affiliation for White, non-Hispanic prisoners
remained relatively low over the 15-year period, gang affiliation among prisoners of
color increased substantially: between 2002 and 2017, the proportion of Black, nonHispanic prisoners classified as gang-affiliated rose from 35% to 41%; for Hispanic prisoners, from 28% to 53%, a sharp increase with substantial consequences for solitary
confinement practices.

®

Table 2. Solitary confinement in Washington State, 2002–2017.
Cohort
2002

2005

2008

2011

2014

2017

%

Num.

%

Num.

%

Num.

%

Num.

%

Num.

%

Custody and confinement level
IMU-Max
IMU-Ad/DSeg
Max-Tx
Other-Max
General population
Out of state/unknown
Total IMUb
Total maximum custodyc

149
105
18
34
15,499
102
254
201

0.9%
0.7%
0.1%
0.2%
97.4%
0.6%
1.6%
1.3%

228
144
50
55
16,270
105
372
333

1.4%
0.9%
0.3%
0.3%
96.5%
0.6%
2.2%
2.0%

338
337
44
11
16,438
140
675
393

2.0%
1.9%
0.3%
0.1%
95.0%
0.8%
3.9%
2.3%

472
177
35
27
16,440
137
649
534

2.7%
1.0%
0.2%
0.2%
95.1%
0.8%
3.8%
3.1%

283
291
42
20
16,893
96
574
345

1.6%
1.7%
0.2%
0.1%
95.8%
0.5%
3.3%
2.0%

342
260
52
18
17,121
150
602
412

1.9%
1.4%
0.3%
0.1%
95.4%
0.8%
3.4%
2.3%

Cumulative days spent in IMU (any
custody status)d
Mean (St. Dev.)
Not placed in IMU
1–45 days
46–90 days
91–365 days
366 days or more (>1 year)
At least 1 day in IMU

43.1
12,062
2128
499
728
490
3845

(211.5)
75.8%
13.4%
3.1%
4.6%
3.1%
24.2%

47.6
12,673
2344
487
755
593
4179

(230.3)
75.2%
13.9%
2.9%
4.5%
3.5%
24.8%

56.2
12,533
2606
583
890
695
4774

(256.8)
72.4%
15.1%
3.4%
5.1%
4.0%
27.6%

74.6
12,120
2535
610
1041
981
5167

(302.7)
70.1%
14.7%
3.5%
6.0%
5.7%
29.9%

80.4
11,863
2854
810
1050
1048
5762

(327.1)
67.3%
16.2%
4.6%
6.0%
5.9%
32.7%

82.4
11,847
2985
928
1075
1108
6096

(330.0)
66.0%
16.6%
5.2%
6.0%
6.2%
34.0%

(136.2)
(124.6)
15,907

306.0
116.9

(239.2)
(121.2)
16,852

283.9
90.6

(192.9)
(116.9)
17,308

347.7
127.8

(273.2)
(138.5)
17,288

325.8
66.4

(316.7)
(77.9)
17,625

214.0
70.9

Days in IMU by custody and confinement
level at snapshot date: Mean (St. Dev.)
IMU-Max
IMU-Ad/DSeg
Total prison population

227.0
114.7

(129.6)
(79.6)
17,943

®
1311

Source: Authors’ calculations. Washington State Department of Corrections.
Changes in the use of local segregation for disciplinary and administrative purposes (outside of IMUs, for prisoners classified lower than Max Custody) likely affect the counts of IMUAd/DSeg populations, particularly in early cohort years.
b
Total IMU is the sum of all prisoners living in IMU units on July 1st, including (i) IMU-Max, those on maximum custody housed in IMUs, and (ii) IMU-Ad/DSeg, those who are housed
in IMUs on lower custody levels, including administrative segregation, disciplinary segregation and awaiting hearings.
c
Total Maximum Custody consists of three groups, all classified as maximum custody: (i) those housed in IMUs (IMU-Max), (ii) those in SOU or ITP units (Max-Tx), and (iii) those located
elsewhere (Other-Max).
d
Days spent in IMU represents cumulative days spent in IMU until the snapshot date for all prisoners, regardless of custody classification, during their current prison admission.
a

JUSTICE QUARTERLY

Num.

1312

9

D. LOVELL ET AL.

350
300
0
"'
0

"'e

250

0

,l:

"C

OJ)

u"

200

.c

"

150

OJ)

~"
~

100

i:::
50
0
2002
(=100)

-

2005

2008

2011

2014

2017

Snapshot Year

!MU-Max Population

- - !M U-Max LOS

-

Total Prison Population

Figure 1. Percentage change in IMU-Max population, IMU-Max length of stay (LOS), and total
prison population (indexed at 2002), Washington DOC, 2002–2017.

at other points. To better understand both the prevalence and duration of placement
in solitary, we used event-level movement information to calculate the cumulative
amount of time each prisoner spent in solitary confinement from admission to snapshot date. Over the study period, a majority of prisoners in DOC in each snapshot
cohort were never placed in solitary confinement, but a substantial and growing proportion of prisoners had spent time in these units. The proportion of prisoners spending at least one day in an IMU between their prison admission and snapshot dates
had increased from 24.2% in 2002 to 34% in 2017. Prisoners in 2002 spent an average
of 6 weeks in IMUs from admission to snapshot; by 2017, time spent in IMU increased
significantly to an average of 12 weeks (p< 0.001). Changes in mean values are skewed
by a few outliers, who have spent their entire (long or life) prison sentences in an
IMU, beginning decades before and extending through the study period. To counter
the skew, we binned cumulative days in IMU into distinct groups: 0 days, 1–45 days,
46–90 days, 91 days to 1 year, and over 1 year.7
Pooling across all cohorts, we find that more than half of those who spent at least
one day in an IMU stayed for between 1 and 45 days, cumulatively. The second largest
group (18.6%) cumulatively spent between three months and one year in solitary confinement, and a substantial proportion (16.5%) of those placed in an IMU spent more
than 1 year there. The changing distribution of cumulative time spent in IMUs reinforces the finding that average time spent in solitary increased over the study period.
More prisoners spent at least one day in IMU, and proportions of prisoners in each
cumulative LOS group increased substantially, led by those spending between 46 and
90 days and those spending more than one year in IMU. In total, our data
7

Here, the 45-day cut point reflects institutionally-mandated administrative hearings required to extend or release an
individual from administrative segregation. Likewise, for those classified as Max, (re-)classification reviews only
happen every 6–12 months, as reflected in the overall longer mean lengths of stay for IMU-Max, as opposed to
IMU-Ad/DSeg groups. Both represent examples of policies driving patterns in lengths of stay.

JUSTICE QUARTERLY

1313

8
This analysis uses the person (in custody as of the snapshot date) as the unit of analysis. Even if a single person
has multiple stays in an IMU during the current admission up to the snapshot date, they would be counted only
once as “having spent at least one day in an IMU.” We further examined the average percentage of days spent in
an IMU out of the total number of days in prison up to the snapshot date for each cohort, finding an increasing
proportion of prison time spent in IMUs across the cohorts. While not presented here in detail, this finding
reinforces the trends in the cumulative time spent in IMU and average LOS analyses.
9
Unlike the cumulative days in IMU calculations, the average length of stay by classification and confinement levels
presented here do not cumulate days in IMU facilities. Here, each placement in a distinct IMU facility is analyzed as
a separate placement term. Thus, if one prisoner is placed in IMU facility A, and subsequently moved to IMU facility
B, the length of stay in each placement will be counted separately. (To the extent individuals have consecutive stays
across multiple IMUs, then, these numbers might undercount average lengths of total stay.) Length of stay is
calculated from admission date in the current incarceration up until the snapshot date.
10
The general population (GP) excludes: prisoners housed in IMUs, prisoners with a max custody classification held
in other locations (i.e. those in SOU, ITP, or “Other Locations”), prisoners held out of state, and prisoners whose
locations or custody statuses were unknown.

Table 3 compares demographic, criminal history, gang status, and behavioral histories
of IMU-Max and GP prisoners across snapshots,10 showing significant differences
between these groups. In both populations, White, non-Hispanic prisoners represented
the largest group. However, compared to the GP, prisoners of Hispanic ethnicity were
substantially over-represented in IMU-Max, while White, non-Hispanic prisoners are
under-represented (p < 0.001). Black, non-Hispanic people were slightly underrepresented among IMU-Max prisoners, relative to their presence in the GP. These
disparities diverge over time: the proportion of Hispanic prisoners in the IMU-Max
population increased by nearly 34% between 2002 and 2017, while the proportions
of all other racial and ethnic groups decreased.
IMU-Max prisoners have more serious conviction and in-prison misconduct
histories than GP prisoners. Across cohorts, nearly three-quarters (73%) of IMU-Max
prisoners were convicted of non-sexual violent offenses, compared with just 44% of
GP prisoners. The IMU-Max group were also first convicted of prison-eligible offenses
at a younger age, on average, than those in the GP (p < 0.001). Further, in-prison misconduct rates were higher and more serious for the IMU-Max group: annual infraction
rates for these prisoners were more than double GP rates, and IMU-Max prisoners
committed far more violent infractions and staff assaults than those in GP

The maximum custody IMU population

demonstrate a greater prevalence of IMU placement across the population over time,
and an increasing proportion of prison time spent in IMUs.8
In addition to examining cumulative days spent in IMU for the full prison population, we also calculated mean lengths of stay (LOS) in IMUs for both the IMU-Max and
IMU-Ad/DSeg groups.9 Both groups spent substantial amounts of time in IMU settings,
although, as expected, those in IMU-Max had markedly longer stays in IMU than the
IMU-Ad/DSeg group. Across the study period, average time in IMU-Max ranged from 7
to 12 months, compared to 2 to 4 months for the IMU-Ad/DSeg group. The mean LOS
for IMU-Max fluctuated: generally increasing until 2011, followed by a decline through
2017 to a level just below the mean LOS in 2002 (Figure 1). For the IMU-Ad/DSeg
group, mean LOS dropped even more substantially after 2011. Changes in average
LOS for both groups were a factor in periods of growth in total IMU populations prior
to 2008, as well as in declines of IMU populations after 2011.

®

1314

Table 3. Comparison of IMU-Max and General Prison populations, Washington DOC, 2002–2017.
Cohort
2002

Background characteristics
Age at snapshot (years)a
18–25
36%
26–35
40%
36–45
17%
Over 45
7%
Race/ethnicitya
Black, non-Hispanic
19%
Hispanic
20%
Other/unknown
13%
White, non-Hispanic
48%
Most serious offense at convictiona
Violent, non-Sex
68%
Sex
15%
Property
8%
Drug/other
9%
Missing
1%
Age of first conviction (years) a
Under 18
12%
18–25
69%
Over 25
20%
In-prison behavioral profile
Gang affiliation by racial/ethnic STGa
White
14%
Black
22%
Hispanic
21%
Other
3%
No gang affiliation
40%
Annual infraction ratea
Mean
8.3
St. Dev.
7.6
Violent infractionsa
Mean
4.0
St. Dev.
5.8
Staff assaultsa
Mean
1.2
St. Dev.
3.3
Total population
149

2005
Gen. Pop.

IMU-Max

2008
Gen. Pop.

IMU-Max

2011
Gen. Pop.

IMU-Max

2014
Gen. Pop.

IMU-Max

®

2017
Gen. Pop.

IMU-Max

Gen. Pop.

21%
33%
29%
17%

24%
40%
22%
13%

19%
32%
29%
20%

31%
43%
15%
12%

16%
32%
29%
23%

24%
45%
18%
13%

15%
34%
26%
25%

19%
41%
20%
19%

13%
34%
26%
27%

20%
47%
20%
13%

11%
34%
27%
29%

21%
11%
7%
60%

16%
22%
8%
55%

19%
10%
8%
63%

15%
30%
6%
49%

19%
10%
9%
62%

20%
29%
7%
44%

19%
12%
9%
61%

14%
37%
5%
44%

18%
12%
9%
62%

17%
27%
9%
47%

18%
13%
9%
60%

41%
17%
16%
25%
2%

66%
14%
10%
9%
0%

42%
17%
17%
23%
1%

70%
9%
14%
7%
0%

43%
20%
19%
18%
0%

74%
11%
11%
4%
0%

45%
21%
19%
16%
0%

78%
8%
10%
4%
0%

45%
20%
20%
14%
0%

75%
7%
11%
7%
0%

48%
20%
20%
13%
0%

4%
45%
51%

9%
69%
22%

3%
45%
52%

10%
69%
21%

3%
45%
52%

10%
65%
25%

3%
46%
51%

8%
67%
25%

3%
46%
51%

8%
69%
23%

3%
45%
52%

4%
9%
4%
1%
81%

21%
14%
22%
1%
43%

5%
9%
4%
1%
81%

20%
12%
39%
1%
28%

5%
9%
5%
2%
79%

15%
14%
33%
3%
36%

5%
10%
7%
2%
76%

15%
11%
40%
4%
31%

5%
10%
8%
2%
75%

14%
16%
32%
4%
33%

4%
10%
8%
2%
76%

1.3
2.4

5.1
7.8

1.1
1.8

5.3
5.4

1.1
2.0

4.2
4.9

1.0
1.7

4.7
5.9

1.0
1.8

4.9
6.7

1.1
1.9

0.5
1.5

3.3
4.5

0.4
1.4

3.3
4.2

0.5
1.5

3.0
4.0

0.5
1.6

3.3
4.3

0.5
1.6

3.0
3.4

0.5
1.6

0.1
0.4
15,499

0.7
2.2
228

0.0
0.4
16,270

0.7
2.0
338

0.0
0.4
16,438

0.7
2.1
472

0.1
0.5
16,440

0.8
2.5
283

0.1
0.5
16,893

0.6
2.0
342

0.1
0.5
17,121

Source: Authors’ calculations. Washington State Department of Corrections.
a
Statistically significant differences between IMU-Max and general population (Gen. Pop.) at p < 0.001 (for categorical, chi square; for numeric, and t-test).

D. LOVELL ET AL.

IMU-Max

JUSTICE QUARTERLY

1315

11
Violent infractions include seven infraction types: aggravated assault on another offender, fighting, possession of a
weapon, aggravated assault on a staff member, sexual assault of a staff member, assault on another offender, sexual
assault of another offender, and assault on a staff member.

Our findings draw on an especially robust dataset, including: (1) multiple individual
characteristics, like gang status and infraction rates, each one of which has constituted
the sole focus of previous analyses; (2) snapshot data that covers both the entire
prison population and each individual’s entire criminal and incarceration history; and
(3) a 15-year period of analysis over six snapshot dates, a longer time period than in
previous studies of solitary confinement. Such a rich dataset makes a succinct analysis
of a subset of findings challenging to present. Here, we focus on our analytic methods, an overview of the characteristics of people in and out of solitary confinement,
and overall patterns in solitary confinement use.
First, we measure the sites, subjects, and varieties of solitary confinement in terms
of the intersection of location and custody status. This operational taxonomy, along
with the prisoner characteristics associated with solitary confinement placements, was
achieved by developing an extensive population analysis script that compiled a correctional dataset tracking events, movements, and dispositions into an analytic dataset
permitting analysis of patterns of prisoner behavior and facility placements over time.
Our multi-generational researcher-practitioner collaboration with Washington DOC
facilitated both obtaining and interpreting this data. In turn, we hope our operational
taxonomy will facilitate more precise measurements of solitary confinement use,
applicable and comparable across the vicissitudes of different correctional systems’
varied labels for security levels, housing locations, and solitary confinement practices
(Mears et al., 2019).
Second, we provide an overview and comparison of characteristics of people in solitary confinement, focusing on the specifically targeted IMU-Max group to provide a

Discussion

(p < 0.001).11 Nevertheless, serious misconduct appeared to decline substantially across
IMU-Max prisoner snapshots (but not for GP), with average annual infraction rates
among IMU-Max prisoners falling from 8.3 in 2002 to 4.9 in 2017 (p < 0.001), average
numbers of violent infractions decreasing from 4 to 3 (p < 0.05), and average numbers
of staff assaults decreasing from 1.2 to 0.6 (p < 0.05).
Gang members were substantially over-represented in IMU-Max compared to GP
(66% to 22%, pooled across all snapshot years). While the prevalence of gang membership grew in both groups over time, patterns of gang affiliation across racial-ethnic
sub-categories behaved differently within the IMU-Max and GP groups. Among GP
prisoners, the proportion of those affiliated with Hispanic gangs grew by 118% from
2002 to 2017; among IMU-Max prisoners, Hispanic gang membership grew substantially (55%), but at a lower rate than in the GP. Black gang membership, on the other
hand, grew by just 7% in the GP, but fell by 24% among IMU-Max prisoners.
Explaining these patterns is outside the scope of the present analysis, but the scale of
divergence in patterns across both racial-ethnic sub-categories of gang affiliates and
GP and IMU-Max populations merits future attention.

®

1316

D. LOVELL ET AL.

clear contrast to GP prisoners. Over time, the average IMU-Max prisoner was increasingly likely to be older, Hispanic, convicted of a violent offense, and gang affiliated,
but decreasingly likely to have assaulted a staff member. Like Pyrooz and Mitchell
(2020), we find gang members over-represented in solitary confinement relative to
their representation in the general prison population. We also find that Hispanic prisoners are increasingly over-represented in solitary confinement, providing evidence of
the racially disproportionate impact of solitary confinement (Reiter, 2012; Sakoda &
Simes, 2019; Schlanger, 2012). Our longitudinal analysis shows this disproportion
steadily increasing over time, at a faster rate than gang membership in the general
prison system, which increased only slightly over our period of analysis. As in other
studies finding misconduct associated with solitary confinement placement (Labrecque
& Smith, 2019), we find that prisoners in solitary confinement have significantly and
consistently higher annual infraction, violent infraction, and staff assault rates than GP
prisoners. However, all three measures of infractions, despite remaining fairly stable
throughout the system, generally declined in IMU-Max over time.
Rendering population patterns visible also renders visible new questions about what
combination of individual behavior patterns and institutional policies produce the changes
we see. Have IMU-Max prisoners become less violent and dangerous? Have institutional
policies about identifying gang members and behavioral or affiliation criteria for max custody changed? When the UW solitary confinement study was conducted 20 years ago, pioneering experiments in relaxing the stringency of solitary confinement conditions and
supporting prisoners in changing course had begun at Shelton (Rhodes, 2004); at that
time, Washington DOC leaders justified IMU placements as a necessary response to White
Supremacist groups, and IMU reforms focused on mitigating organized attacks and challenges to correctional authority by these groups. The late 2010s brought another round of
reforms attempting to relax the stringent conditions of solitary confinement; this time factional rivalries among gang-affiliated Hispanic prisoners first justified IMU placements and
then became the focus of reform efforts (Warner, Pacholke, & Kujath, 2014). This relationship between shifts in prison population demographics, behavior patterns, and correctional
attention to specific sub-categories of gangs perceived as particularly dangerous deserves
further analysis, but identifying the relevant trends, as we do here, is a first step.
Third, we see changing patterns in solitary confinement use over time. Overall, the
prevalence and duration of solitary confinement grew across Washington’s prison
population between 2002 and 2017. The raw numbers and rates of both Max custody
status prisoners and prisoners in IMU locations more than doubled from 2002 to 2017.
And an increasing proportion of people throughout the system experienced solitary
confinement: in 2017, more than 1 in 3 prisoners had spent at least a day in solitary
compared to 1 in 4 in 2002. This trend echoes and quantifies Sakoda & Simes’ argument that solitary confinement is a “normal event during imprisonment” (2019: 2).
Although rates of solitary confinement use increased overall, average LOS in solitary
confinement (which peaked in 2011 in tandem with the peak years of solitary confinement use in Washington) decreased. By 2017, average LOS on IMU-Max and IMU-Ad/
DSeg (along with the standard deviations) were the shortest they had been in the
state since 2002. This analysis reveals that Washington DOC had some success in

®

1317

The research presented here utilized a confidential data file from the Washington Department
of Corrections. This study would not have been possible without the support of the research
and correctional staff in the Washington DOC, especially Eldon Vail, Bernard Warner, Dan
Pacholke, Dick Morgan, Jody Becker-Green, Steve Sinclair, Paige Harrison, Vasiliki GeorgoulasSherry, Bruce Gage, Ryan Quirk, and Tim Thrasher. Formerly of the University of Washington,
Lorna Rhodes served as a project mentor, and L. Clark Johnson provided critical advice at early

Acknowledgments

reducing its use of solitary confinement from peak levels, and especially in shortening
LOS in these conditions. But what forces facilitated or constrained these reductions?
The dramatic shifts we document in both numbers of people in solitary confinement and durations of stays – without any associated dramatic shifts in the usually
assumed behavioral predictors of solitary confinement, like overall institutional rates of
gang membership or violent infractions – suggest the influence of other institutional
factors (cf. Lynch, 2019). While additional analysis is needed, we can, thanks to our
iterative conversations with DOC officials, suggest two institutional factors that
influenced rates and durations of solitary confinement use during periods of abrupt
change: bed capacity increases and local-level rehabilitative programming changes.
First, between 2000 and 2008, while DOC’s expanding capacity was continually
outpaced by population growth (despite legislative changes intended to reduce
imprisonment, Washington State Institute for Public Policy [WSIPP], 2006), IMU capacity in
Washington expanded by 520 beds. Three years later, in 2011, both IMU-Max counts and
average LOS peaked. Both then decreased in tandem with decreasing IMU capacity: down
212 beds as of 2017, as some units were re-purposed for other special groups, such as
parole violators, and managed with far less restrictive protocols. While the relationship
between capacity, IMU counts, and LOS deserves its own focused analysis, we have taken
the first step by identifying relevant trends. These findings suggest that constraining
capacity is likely a key to long-term reductions in solitary confinement, along with
reducing LOS and rate of assignments into maximum security settings like IMUs.
Second, between 2011 and 2014, Washington DOC built upon previous, local
initiatives at Clallam Bay and Walla Walla IMUs, embarking on an effort to “reinvent
what segregation can be”: partnering with Vera Institute of Justice, eliminating some
aversive disciplinary policies, and introducing facility-specific missions and group
rehabilitative programming across IMUs (Neyfakh, 2015). Both the temporary drop in
IMU-Max populations in 2014, and the more sustained decreases in average LOS for
this population between 2011 and 2017 are tied to these interventions.
The correctional population analysis presented in this study exemplifies an
approach to research and collaboration suited to improving the ability of corrections
systems to track changes in prisoner characteristics, LOS, and overall rates of placement in various forms of solitary confinement. Rendering such patterns visible
strengthens researcher–practitioner collaboration, revealing in Washington’s case what
is working, i.e. sustained reductions in lengths of solitary confinement stays; and what
is not working, i.e. less sustained reductions in rates of solitary confinement use. By
displaying institutional patterns, our collaborative research findings also suggest
avenues of analysis to improve outcomes for prisoners and in prison settings.

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D. LOVELL ET AL.

Natalie Pifer, JD, PhD, is an Assistant Professor of Criminology and Criminal Justice at the
University of Rhode Island, 510 Chafee Hall, Kingston, RI 02881. Email: npifer@uri.edu. She

Kelsie Chesnut, MA, ABD, is a Research Associate at the Vera Institute of Justice, Center on
Sentencing and Corrections, 634 S Spring Street, #300A, Los Angeles CA 90014. Email: kchesnut@vera.org. She studies criminal justice reform, the impact and translation of policy into practice, and is especially interested in correctional staff’s role in policy reform. Her work has
appeared in American Journal of Public Health, Injury Prevention, and The Annual Review of Law
and Social Science.

Keramet Reiter, JD, PhD is an Associate Professor, at the University of California, Irvine,
Department of Criminology, Law & Society, 3373 Social Ecology II, Irvine, CA 92697. E-mail: reiterk@uci.edu. She is the project PI, studies prisons, prisoners’ rights, and the impact of prison
and punishment policy on individuals, communities, and legal systems, especially the history
and uses of long-term solitary confinement in the United States and internationally. Her work
has appeared in the American Journal of Public Health, Law & Society Review, and Punishment &
Society, and she is the author of two books: 23/7: Pelican Bay Prison and the Rise of Long-Term
Solitary Confinement (Yale University Press, 2016), and Mass Incarceration (Oxford University
Press, 2017).

Rebecca Tublitz, MPP is a doctoral student at the University of California, Irvine, Department of
Criminology, 2340 Social Ecology II, Irvine, CA 92697. Email: rtublitz@uci.edu. She studies the
impact of criminal justice and corrections reforms and is particularly interested in how actors
across the criminal justice system respond to policy interventions.

David Lovell, PhD (philosophy, University of Wisconsin); MSW (social work, University of
Washington), Research Associate Professor Emeritus, University of Washington, Child, Family,
and Community Health Nursing. In 1982–1983, he was philosopher-in-residence with the
Connecticut Department of Correction. His writing on prisons, mental illness, solitary confinement, and ethics centers on processes and outcomes in prison and the community, and has
appeared in Psychiatric Services, Crime and Delinquency, Law and Human Behavior, and Criminal
Justice and Behavior as well as Correctional Mental Health Report and Correctional Law Reporter.
He is the author of the entry on solitary confinement in J. Bumgarner, C. Lewandowski (Eds.),
Criminal Justice in America: The Encyclopedia of Crime, Law Enforcement, Courts, and Corrections.

Notes on contributors

This work was supported by the Langeloth Foundation and approved by the Institutional
Review Board at the University of California, Irvine (HS 2016-2816).

Funding

None of the authors have conflicts of interest to declare.

Disclosure statement

stages of data compilation. At the University of California, Irvine, Keely Blissmer helped to compile the literature review; Dallas Augustine, Melissa Barragan, Pasha Dashtgard, Gabriela
Gonzalez, and Justin Strong all participated in data collection and analysis at various stages of
this project. Note: The views expressed here are those of the authors and do not necessarily represent those of the Washington DOC or other data file contributors. Any errors are attributable
to the authors.

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