Thomas et al. Health and Justice (2018) 6:11
https://doi.org/10.1186/s40352-018-0069-2

Health and Justice

RESEARCH ARTICLE

Open Access

The validity of open-source data when
assessing jail suicides
Amanda L. Thomas* , Jacqueline Scott and Jeff Mellow

Abstract
Background: The Bureau of Justice Statistics’ Deaths in Custody Reporting Program is the primary source for jail
suicide research, though the data is restricted from general dissemination. This study is the first to examine whether
jail suicide data obtained from publicly available sources can help inform our understanding of this serious public
health problem.
Methods: Of the 304 suicides that were reported through the DCRP in 2009, roughly 56 percent (N = 170) of those
suicides were identified through the open-source search protocol. Each of the sources was assessed based on how
much information was collected on the incident and the types of variables available. A descriptive analysis was
then conducted on the variables that were present in both data sources. The four variables present in each data
source were: (1) demographic characteristics of the victim, (2) the location of occurrence within the facility, (3) the
location of occurrence by state, and (4) the size of the facility.
Results: Findings demonstrate that the prevalence and correlates of jail suicides are extremely similar in both open-source
and official data. However, for almost every variable measured, open-source data captured as much information as official
data did, if not more. Further, variables not found in official data were identified in the open-source database, thus allowing
researchers to have a more nuanced understanding of the situational characteristics of the event.
Conclusions: This research provides support for the argument in favor of including open-source data in jail suicide
research as it illustrates how open-source data can be used to provide additional information not originally found in
official data. In sum, this research is vital in terms of possible suicide prevention, which may be directly linked to being
able to manipulate environmental factors.
Keywords: Jail suicide, Open-source data, Official data, DCRP

Background
Approximately 12 million individuals cycle through U.
S. local jails each year with an estimated 721,300 daily
jail confinements (Bureau of Justice Statistics 2016;
Subramanian et al. 2015). Research into this population demonstrates that jail inmates have a much
higher rate of mental and substance abuse disorders
than that found in state and federal prisoners. It is
estimated, for example, that more than 64% of jail inmates have a mental health problem, compared to 56
and 45%, respectively, of state and federal prisoners
(Bureau of Justice Statistics 2006). It is also important
to note that statistics on jail inmates may be grossly
* Correspondence: amthomas@jjay.cuny.edu
John Jay College of Criminal Justice, 524 W 59th St., Haaren Hall, Rm. 631,
New York, NY 10019, USA

~ Springer Open

underestimated due to the high turnover rate of the
general jail population throughout the year (Tartaro
and Ruddell 2006).
Though suicide is not in itself a mental illness, it may
often be the result of undiagnosed or untreated mental
health disorders (Baillargeon et al. 2009; Hanson 2010;
He et al. 2001; Novick and Remmlinger 1978). Jails are
operating as de facto mental health facilities without adequate behavioral health resources and are experiencing
growing suicide rates (Hanson 2010). Suicide is the
leading cause of death in local jails, accounting for more
than 30% of inmate deaths (Goss et al. 2002; Hayes 1997;
Noonan 2016; Noonan et al. 2015). By 2013 the rate of jail
suicides (46 per 100,000 inmates) was higher than suicide
rates in state prison populations (15 per 100,000) and in
the community (13 per 100,000) (Noonan et al. 2015).

© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
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Thomas et al. Health and Justice (2018) 6:11

Even within jails, suicide rates vary drastically by size, with
smaller jails (e.g., those with less than 100 beds), reporting
a suicide rate roughly five times higher than the larger jails
(Tartaro and Ruddell 2006).
The epidemic proportion of suicides in local jails has
sparked an increased interest in researching the correlates of jail suicides and analyzing why jails, unlike
prisons, pose such a high suicide risk to its population
(Bonner 2000; Dillon 2013). Studies have identified
several suicide risk factors including the lack of suicidal
ideation screening protocols and the psychological and
environmental distress of being incarcerated for the first
time (Bonner 2000; Hanson 2010; Hayes 2012, 2013;
Schaefer et al. 2016; Tartaro and Ruddell 2006). What
these various studies have in common is their reliance
on administrative data from federal prisons, state prisons,
and local jails, specifically data from few primary sources:
the US Bureau of Justice Statistics Deaths in Custody
Reporting Program (DCRP) (see Hanson 2010; Hayes
2013) and state- and/or local-sources (see Dillon 2013;
Goss et al. 2002; Winter 2003).
Relying solely on administrative data, in of itself, is not
necessarily problematic. However, there are several important challenges which present themselves when using
this data for research, including missing data, reporting/
recording problems, limited access to data, the time lag
between data collection and release, and the limited
number of variables collected (see Hampton 2016; Zeng
et al. 2016). In 2016, Zeng and colleagues assessed some
of these concerns by comparing the data from the DCRP
and the National Death Index (NDI) from 2007 through
2010. Their research identified several discrepancies related to administrative data collection (Zeng et al. 2016,
p. 1); for example, a significant anomaly occurred in
2008 when more than 20% of DCRP data was missing in
the cause of death field for that year (Zeng et al. 2016).
Another study of official records of in-custody deaths in
Oklahoma found multiple inconsistencies between the
number of deaths reported via federal and state records
(Hampton 2016). A closer review of the data noted that
the discrepancies in the number of inmates who died in
custody, as they were reported to state and federal agencies, were found to have occurred in 2011, 2013, and
2014 in Cleveland County Jail (Hampton 2016). Despite
the fact that there is a difference between state and federal systems, there should be no discrepancies found in
the number of “deaths in custody” that are reported to
either agency. Hampton’s (2016) research concluded that
this difference might be a result of how a death in custody is defined and with who is tasked with the responsibility of submitting these reports.
While there has been a continued effort in the field of
corrections to critically examine the phenomenon of jail
suicides, researchers have yet to incorporate open-source

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databases in their analyses. Also referred to as publicly
available data, open-source search protocols have been
frequently utilized to develop datasets to study a diverse
range of social science phenomena (see Ackerman and
Pinson 2016; Chandra et al. 2014; Gruenwald et al. 2013),
particularly those surrounding controversial social issues
(Ackerman and Pinson 2016). However, it is critical that
researchers building open-source datasets explicitly engage in a rigorous evaluation process of both the source(s)
and information gathered (Ackerman and Pinson 2016).
Furthermore, these researchers also posit that transparency is critical and “that any procedures should be …
explicitly coded into the database itself” (Ackerman and
Pinson 2016, p. 623).
Most of the published research on jail suicides in the
last two decades utilizes administrative data collected by
individual facilities that are then submitted to the US
Bureau of Justice Statistics Deaths in Custody Reporting
Program (DCRP) (Hanson 2010; Hayes 2013). The
DCRP was established following the passage of the
Death in Custody Reporting Act of 2000 (Public Law
106–297) and collects data annually on inmates’ deaths
from roughly 2900 local jails (Kaeble and Glaze 2016).
By law, the Act requires the individual state to furnish the
federal government on a quarterly basis with “information
regarding the death of any person who is in the process of
arrest, is en route to be incarcerated, or is incarcerated at
a municipal or county jail, State prison, or other local or
State correctional facility (including any juvenile facility)”
(Legal Institute Information n.d.). To date, it is the only
federal government survey to collect annual data on jail
deaths, including jail suicides.
According to the Act, states are only required to collect
the following individual-level suicide information: “(A) the
name, gender, race, ethnicity, and age of the deceased; (B)
the date, time, and location of death; and (C) a brief
description of the circumstances surrounding the death”
(Legal Institute Information n.d.). In compliance with the
Act, the Bureau of Justice Statistics developed the Death
in Custody Quarterly Report on Inmates Under Jail
Jurisdiction in 2000 (Kaeble and Glaze 2016). In addition
to the required data points, this 19-item survey also
collects individual information (i.e., inmate confined at
any time to a mental health unit or facility since admissions), criminal justice characteristics of the deceased (i.e.,
length of stay prior to death; convicted, probation/parole
violator, or detainees’ legal status; and criminal offense
type) and detailed circumstances of the death (i.e., death
location, medical examiner verification of cause of death,
cause of suicide death, and time of death) (Form CJ-9 n.
d.). Seven different death locations are listed in the survey
(e.g., general housing, segregation housing) with the
option of writing in a location. The cause of deaths
question gives three examples (i.e., hanging, knife/cutting

Thomas et al. Health and Justice (2018) 6:11

instrument, intentional drug overdose) and requires a
write-in response (Form CJ-9 n.d.).
Despite the fact that the DCRP has been responsible for
collecting data on inmate deaths since 2000, questions still
exist regarding the validity and reliability of this program,
particularly because DCRP forms are completed and
submitted by a wide range of correctional professionals
(Zeng et al. 2016). To better understand this issue, Zeng
et al. (2016) compared the cause of death findings
reported via the DCRP and the National Death Index
(NDI) for 2007 through 2010. This research found that
the DCRP had less missing data relating to the cause of
death (1.9%) compared to the NDI (6.3%). However,
within the DCRP there was more missing data on the
cause of death in jails (6.3%) than in state prisons (0.7%).
The DCRP also reported slightly higher rates of jail suicide
deaths (31%) between 2007 and 2010 than the NDI (29.
6%). Zeng et al. (2016) recommends incorporating both
the NDI with the DCRP in order to obtain “more accurate
and comprehensive COD data” (p. 13).
Recognizing the methodological limitation of the DCRP
data, several researchers (e.g., Hayes 2013; Tartaro and
Ruddell 2006; Winter 2003) supplement the DCRP data
with a secondary survey to the facilities concerned to
follow up with a more detailed review of the critical incident. For example, in Winter’s (2003) study, the follow-up
questionnaire consisted of more in-depth questions surrounding the inmate’s demographic characteristics, arrest
history and current charges, as well as questions that
assessed the inmate’s physical and mental health prior to
the incident. Other studies have collected jail suicide data
at the state- (e.g., Winter 2003; Woog 2016) or local-level
(e.g., Goss et al. 2002; Woog 2016). Woog (2016), for
example, analyzed suicides in Texas jails using Texas
Commission on Jail Standards’ county jail death data.
Winter (2003) identified the suicides that occurred within
local jail facilities via administrative records obtained from
the State Office of Detention Facilities for one Midwestern
state, then followed up with a secondary questionnaire in
order to obtain the most accurate information available.
Goss et al.’s (2002) study used members of the King
County, Washington jail psychiatric staff to collect all
pertinent data surrounding jail suicides.
Despite their differences, all of the published
sources reviewed attempt to examine suicides in correctional facilities by identifying the individual-, situational-, and facility-level risk factors for jail suicides,
and the majority of research conducted in this field
focused on studying this phenomenon quantitatively.1
As such, the following section reviews the empirical
literature on jail suicides by characteristic type as they
relate directly to this study, focusing specifically on
individual-level and facility-level characteristics of jail
suicides.

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Individual-level characteristics of jail suicides

Hayes’ (2010) review of 464 jail suicides found that individuals who were described as single, white men who were
around the age of 35 experienced the highest rates of jail
suicides. Furthermore, approximately one-third of the
inmates who committed suicide had a history of mental
illness (38%) and [or] a history of suicidal behavior (34%).
In this study, 24% of jail suicides occur within the first 24 h
of arrest [or intake] with another 27% from day two to 2
weeks, and suicides were more likely to occur around the
times when inmates were expected to appear for court
proceedings (Hayes 2010).
Subsequent to Hayes’ (2010) seminal piece on correctional suicide, Hanson (2010) specifically focused on
examining “clean” jail suicides. A “clean” suicide is one
that is committed by an individual who has “no prior psychiatric history” (Hanson 2010, p. 7). Hanson (2010) argued that individual characteristics (e.g., experiencing
more life stressors and relationship troubles) and environmental factors (e.g., violent behavior within the institution,
institutional overpopulation, and institutional instability)
might be directly related to the rate of suicide in jail facilities. His research found that some inmates who commit
suicide escape detection during the intake process because
they have never experienced (or reported) a mental illness
(or episode) prior to incarceration (Hanson 2010). As
such, these individuals prove to be especially difficult to
identify during intake.
The majority of state and local-level jail suicide studies
(see Dillon 2013; Goss et al. 2002; Winter 2003) are
consistent with Hayes’ (2010), Hayes’ (2013) and Hanson’s (2010) findings that jail suicide victims are predominantly white males, with an average age of 30, and
currently detained for a violent felony charge with a
history of previous arrests. Furthermore, the research
consistently finds that inmates who commit suicide are
more likely to be single-celled and kill themselves within
the first month of their detention (Dillon 2013; Goss et
al. 2002; Winter 2003).

Facility-level characteristics of jail suicides

The role of facility-level characteristics also plays a role
in jail suicides. Research suggests that the extremes of
jail housing, overcrowding and single-cell or isolated
housing, is a critical factor in jail suicides (Dye 2010).
While single-cell occupancy may appear to be beneficial
in a jail setting, research has shown that there are also
significant, unintended drawbacks (Reeves and Tamburello 2014). The DCRP data reports that the majority
(80%) of correctional suicides occur in the victim’s cell
(Mumola 2005). Bonner’s (2000) meta-analysis of prison
and jail suicides identified single-cell housing as a “common denominator” among the research in explaining

Thomas et al. Health and Justice (2018) 6:11

successful suicide attempts. Villarreal (2015) also found
that suicides are more likely to occur when an inmate is
not supervised and remains alone. Surprisingly, most
correctional suicide studies do not attempt to systematically uncover the “types of stresses that predispose toward or trigger carceral suicides” (Felthous 2011, p.
1550), though Bonner (2000) notes that correctional suicides are correlated with the levels of hopelessness and
depression experienced by inmates housed in single
cells.
Correctional overcrowding is another factor found to
be correlated with correctional suicides (Ciuhodaru et al.
2009; Dillon 2013; Jewkes 2011; Tartaro and Ruddell
2006; Villarreal 2015). Hanson (2010) found that institutional overcrowding combined with institutional instability and the presence of a violent setting may also lead to
an increase in correctional suicides. In further support,
Villarreal (2015) also found that suicides are more likely
to occur in maximum-security facilities that are overcrowded.
The size of the correctional facility is another environmental factor that has been linked to jail suicides (Dillon
2013; Tartaro and Ruddell 2006). Tartaro and Ruddell’s
(2006) research found that small-medium sized facilities
(e.g., those with less than 100 beds) were plagued with a
suicide rate two to five times higher than larger correctional facilities, with facilities with an average daily
population of less than 50 having the highest suicide
rates. While, Dillon’s (2013) research suggests that the
suicides rate for smaller facilities is actually six times
higher than the suicide rate for larger correctional facilities. Tartaro and Ruddell (2006) determined that less
than one-fifth (approximately 355) of the small-medium
facilities in their study failed to utilize formal suicide assessment during initial inmate intake, “and only slightly
more than one-half of these facilities provided annual
suicide training to jail officers” (p. 81).
Goss et al.’s (2002) research suggests that making
strategic decisions about structural changes to the actual
facility may help decrease suicides in correctional facilities (e.g., using Plexiglas barriers to prevent jumping).
Hayes (2013) also strongly supports the idea of utilizing
suicide-resistant architecture (e.g., fixtures that are
tamper-proof and fiberglass bunks) and “anti-suicide”
products (e.g., safety smocks). Additionally, Villarreal
(2015) suggests that penitentiary designers should aim to
reduce “over-institutionalization” of the facility and remove any aspect that may be used to facilitate self-harm.
All of this previous research suggests that researchers
are still trying uncover and understand all of the possible
correlates that may be associated with jail suicides
(Dillon, 2013; Goss et al. 2002; Hanson 2010; Hayes
2010, 2012, 2013; Tartaro and Ruddell 2006; Villarreal
2015; Winter 2003). Thus, by creating a database that

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contains ample event descriptors, researchers can begin
to explore different individual and situational factors
that may decrease the likelihood of jail suicides. In sum,
this study is crucial to the field, in that it may potentially
present researchers and practitioners with an alternative
outlet to consult for a more robust explanation as to
which correlates are associated with jail suicides. As
such, this study aims to determine the validity and
reliability of utilizing open-source jail suicide data to
improve understanding on the individual-, incident-, and
facility-level characteristics of jails suicides. Specifically,
the goal of this research is to determine if using an
open-source protocol can replicate the Bureau of Justice
Statistics’ jail suicide national prevalence rates, and add
to a more comprehensive understanding of jail suicide at
a national level.

Methods
The current study

This study aims to add to the existing literature surrounding jail suicides by examining the validity and reliability of
utilizing open-source jail suicide data in conjunction with
official jail suicide reports. There are two main goals of
conducting this type of research. The first is to evaluate
how the newly synthesized data can be used to better
inform the development of new correctional policies (e.g.,
implementing the use of “anti-suicide” products and/or
utilizing suicide-resistant architecture) surrounding jail
suicides. The second is to increase our understanding of
the correlates of jail suicide.
The present study is a retrospective, descriptive study
that examines whether open-source data can be used in
conjunction with official data to create a more robust
dataset on jail suicides. This was assessed by analyzing
the types of data presented in official Bureau of Justice
Statistics’ reports compared with data gathered via an
open-source protocol. This study used a sample of jail
suicides occurring in local correctional facilities within
the United States from January 1, 2009 to December 31,
2009. Of the 304 suicides that were officially reported
through the DCRP, 170 (almost 56%) suicides were identified through the open-source search protocol.
In this study, local correctional facilities were defined
as any city, municipal or county detention facility that
was responsible for detaining an individual either after
an arrest has been made or while they await sentencing
and/or trial (Bonner 2000). Here, the unit of analysis (i.e.
, jail suicides) was an event/incident that occurred at the
facility level. Furthermore, each of these incidents were
comparatively examined (e.g., official data and opensource data) via four modalities: (1) the demographics of
the victim, (2) the location of the occurrence within the
facility, (3) the location of the occurrence by state, and (4)
the size of the facility. Lastly, in regards to terminology,

Thomas et al. Health and Justice (2018) 6:11

the term validity refers to the accuracy of a measurement;
whereas, reliably refers to measurement consistency
(Maxfield and Babbie 2015).
Data sources

The data used in this study was secondary data from
two main sources – official and open-source. The
Deaths in Custody Reporting Program (DCRP) (discussed above) supplied the official data, which was publicly available from the US Bureau of Justice Statistics’
webpage (see https://www.bjs.gov/index.cfm?ty=tp&tid=1). The open-source suicide data was obtained from
the Jail Correctional Incident Database (JCID). The JCID
is a database of critical incidents (e.g., escapes, suicides,
riots) that have occurred in U.S. local jails, from 2009 on
(Peterson et al. 2016). This database was originally
developed to gather detailed information on inmate escapes, but was then expanded to include information on
other violent incidents (i.e., suicides and riots). Furthermore, the original research team that was involved in
sourcing this database was comprised of Professor Jeff
Mellow and eight volunteer research assistants. Therefore, as part of an ongoing project, this study was approved as exempt from needing ethics approval by the
Human Research Protections Program at John Jay College of Criminal Justice.
The JCID used a multi-state, open-source, internetbased search protocol to identify and code information
on jail suicides. A number of strategies were used to
effectively and efficiently source and search for jail
suicides online. First, a custom date range of January 1,
2009 through January 31, 2010 was included for all
searches to ensure that jails suicides committed at the
end of December 2009 had time to be identified and
reported in the media. Secondly, jail suicides were
searched using Boolean search keywords (e.g., “jail”,
“detention”, “inmate”, “killed”, “die*”, “suicid*”, “hang*”,
“hung”) and Boolean operators (“and”, “or”, “not”, “*”). A
search for “suicid*”, for example, returns the terms
“suicide” and “suicidal” while “hang*” also includes
“hanged.” Finally, jail suicides were searched by individual states as a way to filter a large number of search
results to review at a time. Suicide incidents were
collected based on the following criteria: (1) the suicide
was a confirmed inmate suicide (not an attempted
suicide), (2) the suicide occurred in a local U.S. jail, (3)
the suicide occurred in 2009, and (4) the information
was sourced online using Google Search, Google News,
and Corrections.com, a national serial correctional news
archive.
Once the incidents were collected, each identified incident was assigned to a specific researcher who systematically searched the incident in online search engines
(Microsoft, Firefox, and Google) to uncover all publicly

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available materials on it. In this way, each incident was
treated as a case study with the goal of compiling as
much open-source information as possible. Additional
jail suicide cases uncovered during the second stage
were treated as separate incidents and added to the
database. Finally, the sourced information was coded
into a relational database according to variables identified by the research team from the literature. These
included facility- (18 variables), incident- (22 variables),
and suicide victim- (24 variables) level variables (see
Additional file 1). Additionally, facility-level data (e.g.,
jail size and daily rated capacity) was also gathered from
the American Correctional Association’s National Jail
and Adult Detention Directory (2012), which were
supplemented where necessary by jail and county webpages. These variables were coded into a testable form
for future analysis.
Methodological procedures
Combining and comparing variables

While both data sources provided critical information
pertaining to jail suicides, they often diverged on how
much information was collected and the types of variables that were available (see Table 1).
The most striking difference was found in the level
of detail included in the open-source database as
compared to official data. For example, the opensource database had significantly more information on
the facility (e.g., the year it was built, its capacity, the
percent over capacity, and annual admission) and on
the incident (e.g., the day of the week that it occurred
on, the suicide type, how the suicide was completed
and with what, and the exact location within the facility that it occurred). Furthermore, official data reports jail suicide data aggregately, making the
examination of any single year or variable difficult.
Despite these differences in individual-, facility-, and
incident-level variables, there were four variables that
were consistent in both data sources, which included
demographics, location within the facility, location by
state, and facility size.
Regardless of this overlap, not all variables were classified the same (see Additional file 1). For example, the
Table 1 Type of Variables Collected by Each Source
Variable Type

BJS

JCID

N

%

N

%

Individual-level

7

36.8

24

37.5

Facility-level

1

5.3

18

28.12

Incident-level

10

52.6

22

34.38

Other

1

5.3

0

0

Total

19

100

64

100

Thomas et al. Health and Justice (2018) 6:11

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JCID provided the exact age of the victim, whereas the
official data (DCRP) provided the ages of suicides victims
in ranges. As such, the JCID data had to be collapsed to fit
the DCRP data classifications. Furthermore, the JCID
referred to inmate conviction status as “sentenced vs.
detained,” whereas DCRP data classified legal status as
“convicted or unconvicted.” Thus, the JCID data had to be
reclassified appropriately to conduct the analyses. Specifically, “sentenced” was reclassified as “convicted” and
“detained” was reclassified as “unconvicted.”
There were also differences in the classification of the
location where the suicide occurred. The JCID provided
more detailed information on this variable (18 different
classifications), whereas DCRP data only had 7 classifications. Again, in order to compare the data, the opensource JCID categories were collapsed to match the
DCRPs categories of: general housing, segregation unit,
medical unit, mental health unit, in transit, or elsewhere/outside. Lastly, we also included one additional
classification utilized by JCID – missing.
Finally, there were some issues with comparing the
facility size using official data and open source data.
Here, DCRP data reports the amount of suicides that
occurred in facilities by their size (e.g., the smallest
facilities have less than 50 beds, then there are facilities that have 50 to 99 beds, 100 to 149 bed) for
only 1 year (i.e., 2002). However, the JCID does not
provide that information. Instead, information pertaining to facility size (i.e., average daily population and rated
capacity) was located through the American Correctional
Association’s National Jail and Adult Detention Directory
(2012). Despite these inconsistencies, the data was still
compared to determine whether there was a significant
change in where jail suicides occurred based on facility
size.
Table 2 Demographic Characteristics of the Deceased
Total
Sex

Age

Legal Status

BJS

JCID

4508

170

Male

4122

91%

155

91%

Female

385

9%

15

9%

≤ 17

48

1%

3

2%

18–24

827

18%

31

18%

Results
Of the 304 jail suicides reported in the DCRP for 2009,
170 of them (almost 56%) were located in the JCID.
With the 170 matching results, descriptive analyses were
conducted on the two sources of data.
Official demographic data for local jail suicides is not
publicly available, likely for reasons of confidentiality; therefore, aggregate data from 2000 to 2014 (N: 4508) was compared with 2009 JCID data (N: 170). However, despite these
diverging date ranges and differences in overall counts, the
demographic makeup of the deceased in jail suicides were
nearly identical between the two data sources, with an overall correlation of 0.99. As shown in Table 2, in both data
sources, 91% of the deceased were male and 9% were female. Age distributions were also nearly identical, with
higher proportions of the deceased aged 25–34 and 35–44
and low proportions in the older and younger age ranges.
However, race and ethnicity were not widely available in
the JCID and therefore were not included in the comparison. Finally, the majority of the deceased were detained/
unconvicted (82% in DCRP data and 89% in JCID
data) as opposed to sentenced/convicted in both data
sources (17% in DCRP data and 9% in JCID data).
Jail suicide location statistics were found to be generally
similar in rank but divergent in proportion2 in the two
datasets, with a correlation coefficient of 0.82. Table 3
shows the largest proportion of the reported suicides in
both data sources occurred in general housing (47% in
DCRP data, 77% in JCID data), followed by segregation
units (21.3% in DCRP data and 5.9% in JCID data) and
medical units (23.6% in DCRP data and 3.5% in JCID
data). A discussion of the potential reasons for these divergences is detailed below.
Again, location by state was generally similar between
the two data sources (see Table 4). Interestingly, the same
three states (i.e., Florida, Texas and California) ranked in
the top three for percent of total suicides for the year 2009
and state-level correlation was found to be 0.82. These
similarities continued when the state-level data was aggregated to U.S. Division and U.S. Region, with 0.78 and 0.89
correlations respectively. Of course, a significant factor in
Table 3 Location of Jail Suicidesa2
BJS

JCID

46.9%

77.1%

25–34

1399

31%

46

27%

General housing

35–44

1291

29%

49

29%

Segregation unit

21.3%

5.9%

45–54

699

16%

26

15%

Medical unit

23.6%

3.5%

Mental health unit

1.2%

1.2%

55 ≥

237

5%

3

2%

Missing

7

0%

12

7%

In transit

0.4%

0.0%

Elsewhere

6.6%

1.2%

Missing

0.0%

11.2%

Convicted / sentenced

715

17%

16

9%

Unconvicted / detained

3400

82%

151

89%

Missing

19

0%

3

2%

a

Only the percentages were reported in the official data sets (BJS) and did not
include confidence intervals

Thomas et al. Health and Justice (2018) 6:11

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Table 4 Division-Level Suicide Proportionsa
BJS

JCID

East North Central

14%

14%

East South Central

7%

11%

Mountain

11%

9%

Middle Atlantic

10%

12%

New England

4%

3%

Pacific

15%

8%

South Atlantic

021%

22%

West North Central

7%

11%

West South Central

12%

10%

a

Only the percentages were reported in the official data sets (BJS)

these correlations is likely to be the population base of
these states; for example, Florida, Texas and California
had the highest proportions of suicides and are the most
populous in the country, while New England states had
lower proportions and have lower populations.
As indicated in Table 5, facility size is where the two
datasets diverged the most. While in official statistics,
nearly a third of reported suicides occurred in small facilities (i.e., those under 50 beds (32.5%)) and nearly half in
facilities under 100 beds (46.6%); however, the JCID’s data
demonstrates a wider distribution of incidents across facility sizes with higher proportions reported in larger facilities between 250 and 1500 beds. It is unclear whether
official data was utilizing Average Daily Population (ADP)
or Rated Capacity (RC) to determine jail facility size, and
both variables were available in the JCID with varying
levels of availability (95 jails had ADP while 160 had RC).
Therefore, both were included with similar results.

Discussion
While there is an abundance of research on jail suicides,
the fact that jail suicide rates continue to rise suggests that
Table 5 Facility Sizea
JCID – ADP

JCID – RC

BJSb

< 50

8%

9%

32%

50–99

11%

10%

14%

100–149

3%

8%

9%

150–249

12%

12%

9%

250–499

20%

15%

10%

500–999

16%

20%

6%

1000–1499

17%

15%

8%

1500–1999

4%

4%

6%

≥ 2000

9%

8%

6%

a

Only the percentages were reported in the official data sets (BJS)
b
It was unclear whether BJS used average daily population (ADP) or rated
capacity (RC)

we may have become too reliant on using the limited
number of variables collected through official data to
explain jail suicides (Hayes 2013). The current study
demonstrates the potential usefulness of open-source data
to examine suicides in jail facilities and, as the demand for
evidence-based policy and programming increases in the
field of corrections, so too does this opportunity for
integrating quality sourced publicly available datasets into
current research and discourse.
It is important to note that some information was more
readily available via official documentation than in the
open-source database. In particular, individual race and
ethnicity were widely included in official data, yet were
frequently not reported in open-source media and/or
news, and, where they were reported, were questionable
regarding their reliability. The most comparable variables
between the two data sets were found in the individualand incident-level data consisting of other demographic
data (i.e., age, sex, and legal status) and location (i.e.,
where the incident occurred). While the majority of the
official data variables were collected in the open-source
data, the latter was found to have significantly more
range and diversity in the variables collected on all
three levels (individual-, facility-, and incident-level)
(see Additional file 1).
Incorporating open-source data such as that contained
in the JCID can improve upon the overall understanding
of the individual-, incident-, and facility-level characteristics of jail suicides. Expanding upon the current data
collection process would enable researchers to create a
more robust dataset, enrich the overall quality of the
variables collected, and allow for a more nuanced understanding of the correlates of jail suicide. While this data
also suggests that the JCID and other open-source data
could be used as a check for official data, more research
needs to be conducted in order to fully determine how
this can be accomplished. In sum, the major findings
from this study are that open-source protocol can be
used to supplement government data in both meaningful
and significant ways.
Limitations

Despite adding meaning to the jail suicide correlates
research, the inclusion of open-source data did not come
without several limitations. First, there was missing data
in the location field for the JCID and location categories
had to be collapsed in the JCID to match with the DCRP
location categories. Other categories also differed across
data sources and some of the available through the Bureau of Justice Statistics categories were not completed in
the JCID (e.g., race in the demographics section).
Second, the reported years differed between the two
datasets. For example, in the JCID, researchers are able
to examine jail suicides occurring in a single year alone

Thomas et al. Health and Justice (2018) 6:11

(e.g., 2009), whereas the majority of DCRP tables are
aggregated across several years. Presumably, the data is
presented in the aggregate for reasons of confidentiality;
yet, aggregating data at this level makes it hard to single
out year-to-year changes in the potential influential
factors leading to jail suicides for any given year (Reilly
2016). Furthermore, the DCRP does not include data on
any facility that is classified as a temporary lockup
(Noonan 2016; Reilly 2016), and several states fail to
submit data on jail deaths to the DCRP because their
jails and prisons are part of singular, fully integrated
system (i.e., CT, DE, HI, RI, and VT) (Noonan 2016).
The collection of open-source data itself also has some
limitations. First, an open-sourced database such as the
JCID is potentially biased in that it is based on selective
media output, where researchers are unable to control
what information is available compared to information
that may have been kept out of the public sphere. Second,
as this study shows, open-source data is often incomplete.
For example, we were only able to identify 170 of the 304
officially reported jail suicides (or 56%) which may have
been a result of how the media reports on these incidents
(e.g., what they consider newsworthy and whether jail size
effects news coverage) or how jails choose to make this
data available. Despite these differences however, we were
still able to produce meaningful and significant results.
It is important to note that there are other reasons that
make it difficult to determine the exact number of suicides
that occur within any given correctional facility. First, it is
almost impossible to acquire data on issues occurring
within correctional facilities that have been classified as a
“sensitive issue” (Hayes 1983), and it has been suggested
that releasing this type of information may discredit all of
the stakeholders involved, which may influence reported
suicide numbers (Hampton 2016; Tartaro and Ruddell
2006). Second, unintentional suicides (e.g., the individual
was trying to inflict self-harm (cutting), or they accidentally overdosed) could potentially be included in general
suicide data or suicides may be misclassified as “accidental” if the stakeholders involved are trying to avoid public
and legal repercussions (Hampton 2016; Hayes 1983).
Third, the place of occurrence for a successful suicide attempt may not be properly recorded if the victim was being treated, transferred, and removed from the facility
immediately following the incident occurrence (Hampton
2016; Hayes 1983). Lastly, Hampton (2016) also noted that
not all parties who are allowed to complete DCRP reports
have a comprehensive understanding of what constitutes
an actual death in custody.

Conclusion
The two main goals of this study were: (1) to determine
if open-source data was able to replicate official national jail suicides prevalence rates and (2) to determine

Page 8 of 10

if open-source data was able to increase our overall
understanding of the correlates of jail suicides. The
results from this study reveal support for the argument
in favor of including open-source data in jail suicide
research because it not only replicated official national
jail suicides prevalence rates but it also afforded
researchers a more nuanced understanding of the
potential correlates of jail suicides.
For almost every variable measured, open-source data
captured as much, if not more of the information presented in official data. Variables not available in official
data were identified in the open-source database, allowing for researchers to have a better understanding of the
situational characteristics of the event. Uncovering and
identifying the potential nuances within jail suicide data
may allow researchers to explore different situational
factors that may decrease the likelihood of jail suicides.
For example, researchers can continue to explore how
altering environmental factors can minimize the risk and
ability for inmates to commit suicide. Thus, prevention
may be directly linked to being able to manipulate environmental factors.
Suicide is a complex phenomenon that is the result of
the intersection of a multitude of factors (i.e., psychological, social, biological, environmental, and economic).
This study illustrates that correctional suicides are best
described as a serious public health issue (McMullan
2011), as suicide remains the leading cause of death for
jail inmates in the U.S. (Goss et al. 2002; Hayes 1997;
Noonan 2016; Noonan et al. 2015). Therefore, it is not
sufficient to continue to rely on national best-practice
correctional policy guidelines to deter suicide attempts,
as American jails remain highly decentralized. Even
though jails today are required to screen detainees
during intake for both physical and mental health issues,
every jail system adopts and develops its own operating
processes (McMullan 2011). Furthermore, it is also not
enough for departments/agencies to solely respond to
matters surrounding inmate suicide as a result of feared
litigation through liability cases, as doing so motivates
most facilities to implement policies which require
minimal suicide prevention provisions.
Hayes (2013) concludes that correctional suicide rates
can only be reduced when these facilities adopt a
comprehensive methodology, which has yet to be done
systematically within the U.S. This current study
attempts to demonstrate the usefulness of considering
other methods of data collection (i.e., open-source) to
examine suicides in jail facilities. Finally, with the rapid
growth of correctional critical incident information
reported and stored online, the issue is no longer the
lack of data, but how to efficiently and effectively access
the abundance of online data for research purposes.
Plus, with the advancement in software technology and

Thomas et al. Health and Justice (2018) 6:11

the advent of web data scraping software it is envisioned
that in the not too distant future data obtained from
texts from online webpages will be a dominant way of
collecting certain jail and prison related data.

Endnotes
1
One exception is Frank and Aguirre’s (2013) metasynthesis, which attempts to qualitatively examine preexisting research on correctional suicides (see Hayes
1997; Suto and Arnaut 2010).
2
Source of this location data was BJS’s DCRP Table 13:
Death location of local jail inmates, by cause of death,
2000–2014.
Additional file
Additional file 1: Figure S1. Variable Comparison – BJS to JCID. Figure
S2. JCID Suicide-Specific Variables. (DOCX 17 kb)
Abbreviations
ACA: American correctional association; ADP: Average daily population;
BJS: Bureau of Justice Statistics; DCRP: The U.S. Bureau of Justice Statistics’,
Deaths in custody reporting program; JCID: Jail correctional incident
database; NDI: National Death Index; RC: Rated capacity
Funding
There was no funding provided for the completion of this research study.
Availability of data and materials
The data is not available as collection of the JCID is ongoing.
Authors’ contributions
Study concept and design: JM, AT, and JS. Analysis of data: AT and JS.
Interpretation of data: JS, AT, and JM. Preparation of manuscript: AT, JS
and JM. All authors read and approved the final manuscript.
Ethics approval and consent to participate
This study (Project title: Jail Incident Database of Suicides, Protocol #2015–1323)
was approved as exempt by the Human Research Protections Program, John
Jay College of Criminal Justice.
Competing interests
The authors declare that they have no competing interests.

Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Received: 1 February 2018 Accepted: 30 April 2018

Published online: 09 May 2018
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