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The Impact of Incarceration on Obesity: Are Prisoners with Chronic Diseases Becoming Overweight and Obese during Their Confinement?

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The Impact of Incarceration on Obesity: Are Prisoners with Chronic Diseases Becoming Overweight and Obese during Their Confinement?

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Introduction: The association between incarceration and weight gain, along with the public health impact of former prisoners who are overweight or obese, warrants more investigation to understand the impact of prison life. Studies regarding incarceration's impact on obesity are too few to support assertions that prisons contribute to obesity and comorbid conditions. This study examined a statewide prison population over several years to determine weight gain. Methods: Objective data for weight, height, and chronic diseases, along with demographics, were extracted from an electronic health record. These data were analyzed statistically to determine changes over time and between groups. Results: As a total population, prisoners not only gained weight, but also reflected the distribution of BMIs for the state. There were differences within the population. Male prisoners gained significantly less weight than females. The population with chronic diseases gained less weight than the population without comorbid conditions. Prisoners with diabetes lost weight while hypertension's impact was negligible. Conclusion: This study found that weight gain was a problem specifically to females. However, this prison system appears to be providing effective chronic disease management, particularly for prisoners with diabetes and hypertension. Additional research is needed to understand the impact incarceration has on the female population.
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Research Article
The Impact of Incarceration on Obesity:
Are Prisoners with Chronic Diseases Becoming Overweight and
Obese during Their Confinement?
Madison L. Gates1and Robert K. Bradford2
1Institute of Public and Preventive Health, Georgia Regents University, 1120 15th Street, CJ-2300, Augusta, GA 30912, USA
2Georgia Correctional HealthCare, Georgia Regents University, 1499 Walton Way, HS 3507, Augusta, GA 30912, USA
Correspondence should be addressed to Madison L. Gates; mgates@gru.edu
Received  October ; Accepted  March 
Academic Editor: Aron Weller
Copyright ©  M. L. Gates and R. K. Bradford. is is an open access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
Introduction. e association between incarceration and weight gain, along with the public health impact of former prisoners who
are overweight or obese, warrants more investigation to understand the impact of prison life. Studies regarding incarcerations
impact on obesity are too few to support assertions that prisons contribute to obesity and comorbid conditions. is study examined
a statewide prison population over several years to determine weight gain. Methods. Objective data for weight, height, and chronic
diseases, along with demographics, were extracted from an electronic health record. ese data were analyzed statistically to
determine changes over time and between groups. Results. As a total population, prisoners not only gained weight, but also reected
the distribution of BMIs for the state. ere were dierences within the population. Male prisoners gained signicantly less weight
than females. e population with chronic diseases gained less weight than the population without comorbid conditions. Prisoners
with diabetes lost weight while hypertensions impact was negligible. Conclusion. is study found that weight gain was a problem
specically to females. However, this prison system appears to be providing eective chronic disease management, particularly for
prisoners with diabetes and hypertension. Additional research is needed to understand the impact incarceration has on the female
population.
1. Introduction
Obesity is a pandemic that is impacting health and healthcare
costs of populations around the globe []. Prisoners, oen
referred to as oenders in the United States, belong to a
population that spans the globe and share social, environ-
mental, and health characteristics associated with the obesity
pandemic [,]. For example, many oenders, regardless of
the country where they are incarcerated, have the following
characteristics: low socioeconomic status, limited access to
healthcare prior to incarceration, substance use disorders,
and greater probability of having infectious and chronic
diseases [,]. Studies have discussed similar health issues
and problems with the provision of care in all types of cor-
rectional systems, including high and low income countries,
as well as a range of countries dened by dierent systems
of government; dierences oen pertain more to magnitude
and extent instead of type of problems [,]. However, there
are variations in the state and quality of healthcare that
correctional systems provide.
A few studies have suggested that the social and structural
environment of prisons contribute to obesity, exacerbate
chronic diseases, and are an obstacle for oenders to either
maintain or improve their health [,]. ese studies and the
state of correctional health around the globe are the reasons
why public health professionals, researchers, and educators
should have an interest in corrections and oenders. e
health risks for individuals who are overweight or obese are
clear, in that, these individuals are at much greater risk of
developing conditions, such as hypertension, type  diabetes,
Hindawi Publishing Corporation
Journal of Obesity
Volume 2015, Article ID 532468, 7 pages
http://dx.doi.org/10.1155/2015/532468
Journal of Obesity
coronary heart disease (CHD), and stroke, as well as mental
health problems, such as depression, compared to their nor-
mal weight peers [,].
Studies have proposed that the design of correctional
facilities themselves, which control oenders’ freedom of
movement and options for caloric intake, is a contributor to
weight gain []. For example, caloric intake in corrections
seldom includes fresh fruits, vegetables, or low fat and low
sodium options; security concerns regarding oender and
sta safety necessitate restricting and controlling movement
[,]. Despite these potential contributors to obesity, there
is a paucity of correctional health research investigating
weight gain, despite the impact on public health (i.e., most
oenders return to their communities when they complete
their sentences).
e health of oenders has an impact on public health
when they are reintegrated into the general population, which
occurs on a daily basis []. Unmanaged obesity and risk
factors, such as hypertension, heart problems, and diabetes
in corrections ultimately impact public health resources
and communities to which oenders return. e oender
population should be considered as a vulnerable population,
since the vast majority of those incarcerated oenders have
had limited access to a healthcare system, largely due to
limitedornonancialresourcesandinadequateornohealth
insurance [].elimitedornoaccesstohealthcarehas
resulted in many oenders having a poor health history when
they became incarcerated.
National correctional health data from  indicated
that a large percent of oenders had hypertension (.%),
heart problems (.%), and diabetes (.%) and these medical
conditions were oen compounded by the fact that many
oenders had a history of alcohol abuse (.%) and drug
dependency (.%) []. Also, many oenders prior to
incarceration had limited access to health care, engaged in
unhealthy behaviors, and had high rates of chronic and infec-
tious diseases []. Limited access to healthcare, low health
literacy, and unhealthy behaviors contribute to oenders
being a vulnerable population; as a group, they experience
health disparities (poorer health outcomes and greater inci-
dence of diseases compared to other populations) in terms
of mental and behavioral health, substance use disorders,
infectious diseases, and chronic diseases.
eaimofthisstudywastocontributetoourunder-
standing of incarceration and obesity and to investigate the
impact that corrections have on oenders, particularly the
population that has comorbid diseases, such as hypertension,
hyperlipidemia, and diabetes.
2. Methods
2.1. Procedures. is retrospective longitudinal study for
– was approved by an institutional review board at
an academic health center and conducted with a statewide
department of corrections (DOC) in the east south central
region of the United States. Data for this study were extracted
from a statewide department of corrections’ electronic health
record (EHR) and oender management system (OMS),
T  : Va r i a b les.
Age
Beginning weight
Category of primary oense
Chronic disease: ICD- code
Diabetes: 
Hyperlipidemia: .
Hypertension: , ., ., and .
Date of incarceration
Education level
Ending weight
Gender
Height
Race
Security level
which contains demographic and nonhealth related infor-
mation, such as type of oense committed, parole date, and
education level.
e EHR that the DOC uses is a complete health record
and includes physical, mental, and dental health informa-
tion,aswellaspharmacy,laboratory,andvitalstatistics.
Health information, such as diagnoses and vital statistics,
was entered into the system by health care professionals
and data, such as pharmacy and laboratory values, and were
transmitted to the EHR from an external source. All OMS
data were entered into the system by correctional ocers. e
populationforthisDOCiscomprisedofmaleandfemale
oenders who reside in facilities located throughout the state.
AlloendershaveanOMSrecordandanEHR,whichremain
active until the oender dies or is released back into the
community. All oenders with an active EHR between 
and , two or more valid observations for weight, height,
andincarcerationdurationgreaterthanzerowereincluded
in the study.
2.2. Analysis. Diagnoses of hypertension, type  diabetes,
and hyperlipidemia and risk factors for obesity and being
overweight were extracted and linked to the patient’s phar-
macy record to determine who had active prescriptions. e
beginning weight and height and last recorded weight during
thestudyperiodwereextractedandusedtocalculatethe
beginning and ending body mass index (BMI). See Table  for
a complete list of variables collected.
BMI was calculated as weight in kilograms divided by
height in meters squared (bmi =weight (kg)/height (m)2).
is study used the Centers for Disease Control and Preven-
tion (CDC) ranges for BMI, as shown in Table .
e rate of change in BMI (ΔBMI) was calculated as
the dierence in BMI divided by the beginning BMI. is
measure (ΔBMI) was calculated to evaluate whether or not
oenders gain weight during their incarceration (a primary
aim of this study). e rate of change in BMI measures the rate
in which BMI changes, which allows for comparison across
BMI ranges; for example, a . rate of change in BMI can be
Journal of Obesity
T  : C D C BMI ran g e s f or adu l t s .
BMI Weight status
Underweight <.
Normal .–.
Overweight .–.
Obese .
detected and compared to those who are underweight, as well
as oenders who are obese. Duration of incarceration was
calculated using the date of incarceration and the end date
for the study.
Using SAS ., dierences in beginning and ending
weight and BMI were examined using paired t-test to deter-
mine whether or not ending weight minus beginning weight
were signicantly dierent from zero. e one sample t-test
was used to investigate dierences in ΔBMI. Correlations
between BMI, ΔBMI, age, and incarceration duration also
were conducted to determine what relationships, if any,
existed between variables. To make group comparisons, such
as race, the nonparametric Kruskal-Wallis rank sum test was
used to examine whether or not variance between groups was
equal.
3. Results
e population for this statewide department of corrections
(DOC) in  was  oenders. Ta ble  describes the
population for the DOC in which the majority of oenders
were male, white, had a twelh grade education, had a
primary oense of larceny, and were classied as medium
level security. However, % or more of the observations
for education level, primary oense, and security level were
missing.
Observations that could not be paired for beginning
weight, date for beginning weight, ending weight, and date
for ending weight were excluded from the study, as were
the observations that had an incarceration duration equal
to zero. As a result of the exclusions, there were  valid
observations. ere were  (%) males and  (%)
females, and the race distribution was comprised of 
(%) whites,  (%) African Americans, and  (%) of
all other races, which represented the actual distribution of
race for the DOC in . e percent of female oenders
was less than the actual .%. e large percent of missing
data for education level, primary oense, and security level
precluded any descriptive or inferential analyses with these
variables. Hypertension (, %) emerged as the most
prevalent chronic disease followed by dyslipidemia (, %)
and diabetes (, %).
3.1. Population Changes in Weight. e mean age for oend-
ers included in this study was , which was greater than
theactualmeanageofformalesandforfemales,
and the mean length of incarceration was  years (See
Table ). Tabl e  shows that oenders entered corrections
overweight and that there was a modest increase in ending
T : Population demographics.
Males Females Total
Race
African American   
Asian   
Latino   
Native American  
Pacic Islander
White   
Unknown   
Total 9767 1074 10841
Education level
Primary school  
Middle school   
Less than high school   
Twe lh gr ade   
Some college   
-year degree   
Bachelor degree  
Graduate degree 
Doctoral degree
Subtotal 4028 516 4544
Missing   
Category of primary oense
Drugs   
Homicide   
Larceny   
Nonviolent   
Sex   
Violent   
Subtotal 330 9 197 3506
Missing   
Security level
Community (level )   
Minimum (level )   
Medium (level )   
Close (level )   
Maximum (level )  
Subtotal 3477 208 3685
Missing   
Total 9767 1074 10841
BMI. Oenders also had a positive rate of change in BMI
(ΔBMI) during their incarceration.
e mean weight change for the population was an
increase of . kg and the mean change in BMI was .,
as shown in Table . In other words, oenders gained weight
during their incarceration.
e one sample t-test revealed signicant population
dierences for the ΔBMI, which was used to standardize
weight change. e one sample t-test indicated that the mean
Journal of Obesity
T : BMI changes during incarceration (total population).
Min Max Median Mean
Age . . . .
Beginning weight (kg) . . . .
Ending weight (kg) . . . .
Height (m) . . . .
Beginning BMI . . . .
Ending BMI . . . .
ΔBMI (%) . . . .
Duration (days) .  . .
T : Paired sample 𝑡-test: weight and BMI.
Mean C condence interval 𝑡df Sig.
Lower Upper
Weight . . . .  .
BMI . . . .  .
ΔBMI was ., which was signicantly dierent from zero,
𝑡(2931) =.,𝑃 = 0.000,and%CI[0.68, 1.35].
Gender and hypertension were the only variables where there
was a signicant change in BMI (𝑃 < 0.001). Diabetes
(𝑃 = 0.058)wasslightlygreaterthan𝑃 = 0.05 level of
signicance, but there were no other signicant dierences
to include hyperlipidemia, race, length of incarceration, and
age. Interestingly, age was not meaningfully correlated with
BMI (See Figure )orΔBMI; older oenders were neither
more overweight/obese than younger oenders nor were they
gaining more weight.
3.2. Population Dierences for Changes in Weight. Further
analyses of gender revealed that female oenders had a signif-
icantly greater rate of change in BMI (ΔBMI) during incarcer-
ationthanmales.emeanΔBMI for female oenders was
. (CI [., .]) during their incarceration compared to
. (CI [., .]) for males, as shown in Figure .e
ΔBMI for female oenders was . times that of males.
e results of this study indicated that oenders (males
and females) gained weight during incarceration; female o-
enders gained signicantly more weight than males. How-
ever, oenders with diabetes (ΔBMI = .) and hyperten-
sion (ΔBMI=.)didnotgainmoreweightthanoenders
who did not have these chronic diseases.
4. Discussion
is study found that oenders gained weight and increased
their BMI during incarceration, as other studies have indi-
cated. Surprisingly, chronic diseases, such as diabetes and
hypertension, were not explanatory for individuals who were
overweight or obese. Race also was not a factor, despite the
fact that African Americans adults have the highest rate of
obesity compared to other groups, such as Mexican Amer-
icans and whites []. Despite the many nonsignicant
20
30
40
50
60
70
80
90
Change in BMI
20 10 0 10 20 30
Age
F : Age distribution for change in BMI.
Rate of change in BMI (%)
100
75
50
25
0
−25
−50
Male Female
Distribution of rate by gender
F : Rate of change in BMI during incarceration.
ndings, gender was highly relevant; female oenders gained
more weight than their male counterparts.
In a meta-analysis, Herbert et al. []foundobesityto
be prevalent among female oenders. Herbert et al. []
evaluated multiple oender studies throughout the world and
found dierences between oenders and nonoenders and
disparities between high and low income countries. Male and
female oenders were more likely to be overweight or obese
compared to their nonoender counterparts and high income
countries had a higher prevalence of obesity than low income
countries [].
Clarke and Waring []conductedastudyinauniedjail
and prison for women and found that % of the population
was overweight and % were obese. During a median of
-week period, % of oenders experienced weight gain
during their incarceration. In fact, oenders had a mean
weight gain of . kg per week (SD = . kg, % CI
[., .]) []. However, oenders who were incarcerated
 weeks or less had greater weight gain (. kg) compared
to women with longer periods of incarceration (. kg) [].
Journal of Obesity
Shorter durations resulted in about . times more weight
gain than longer durations []. However, this study found no
relationship between being overweight or obese and length
of incarceration; that is, there were no dierences between
recently incarcerated oenders and oenders who have been
incarcerated for several years.
is study, unlike others, was conducted institution-wide
for a -year period and found that female oenders were
more likely to gain weight and to be overweight or obese
compared to male oenders, which raised a number of issues
forcorrectionalhealth,suchastheimpactthatimprisonment
may be having on female oenders. ere have been a few
reports and studies that have described or explored the dier-
ences in services and programs provided to female oenders
[]. e suggestion is that female oenders are pro-
vided inadequate services, programs (e.g., work release),
and recreational activities compared to male counterparts
and that these dierences have an adverse eect on their
physical and mental health []. e weight gain disparity
between males and females for this statewide department of
corrections (DOC) may only be limitedly explained by the
opportunities men have for work release and recreational
and physical activities compared to women. Similar to other
correctional systems, this DOC had signicantly fewer female
oenders than males and as such had smaller facilities and
fewer programs; in other words, female oenders have more
sedentary lifestyles.
Herbert et al. []proposedthathighincomecountries
made no distinction between the energy intake provided to
males and females, even though females require less. e
energy intake issue was compounded by the fact that the
most high income countries provided foods that exceeded
dietary recommendations for sodium and fat []. Along with
energy intake, atypical antipsychotic medications may be
explanatory for the dierences in weight gain between female
and male oenders. Atypical antipsychotic medications have
been associated with weight gain, because they can disrupt
metabolic regulation []. e female population for this
DOCutilizedmentalhealthservicesandwasprescribed
more atypical antipsychotic medications than the male pop-
ulation.
e gender dierences for mental health problems in
this DOC are consistent with the national data. Glaze and
James [] reported that % of female oenders in prison
had mental health problems compared to % of males and
the number of oenders who are prescribed medication
increased from % to % during the same time period [].
However, we do not suggest that a mental health diagnosis
alone contributes to obesity and there is no evidence that all
antipsychotic medications are associated with weight gain.
endingthatchronicdiseaseswerenotassociatedwith
weight gain for oenders (male and female) was surprising.
e rate of change in BMI (ΔBMI) for oenders with diabetes
or hypertension was signicantly dierent from the ΔBMI for
oenders who did not have these diseases. In fact, oenders
with diabetes and a prescribed medication had a mean ΔBMI
of . compared to nondiabetics at .. e nding for
oenders with hypertension who were prescribed antihyper-
tensive medication was similar, in that oenders who not
diagnosed with hypertension had a ΔBMIof.comparedto
a . for oenders with hypertension. Initially, these ndings
appeared counterintuitive. Yet, there are explanatory reasons
why there is a “positive” health disparity in favor of patients
with chronic diseases.
Unlike primary care clinics for the nonincarcerated pop-
ulation, correctional health has more access to information
about its patients than the general public and has been able
to overcome many of the obstacles that complicate care for
the nonincarcerated population. ere are no transportation
issues (primary care for this DOC is conducted in the prison).
() Correctional clinics have a robust reminder system to
minimize missed appointments.
() Correctional health is a managed care organization
and has more or less eliminated issues regarding
access to primary care, as mandated by the  U.S.
SupremeCourtEstellev.Gambledecision.
() ere is a strong continuity of care, since patients, in
eect, belong to one health care practice, which shares
andhasaccesstothesamehealthinformation.
Correctional institutions also provide what they call chronic
care clinics. When an oender is diagnosed with a chronic
disease, the patient is assigned to the appropriate clinic and
scheduled for recurrent visits based on national standards.
Oenders with chronic diseases typically are seen more
oen by a primary care provider than oenders who do
not have chronic diseases, which may be explanatory for
theweightgaindisparitybetweenthosewithandwithout
diabetes and hypertension. In other words, their primary care
provider has more opportunities to intervene in the oender’s
health, such as placing oenders on special diets. Oenders
who do not belong to a chronic care clinic may only be seen
by a primary care provider once a year for an annual physical.
e relationship between weight gain and becoming obese or
overweight and having a chronic disease may be an indicator
of the success primary care providers in corrections have with
chronic diseases and the availability of health data, such as
vital statistics and lab values, accessed and managed via an
electronic health record (EHR).
is study did not nd a statistical dierence in BMI or
ΔBMI between African Americans and whites, which does
not coincide with the national obesity data. In -,
Flegal et al. []intheirestimateoftheprevalenceforobesity
among adults found that African Americans had the highest
age adjusted rates of obesity compared to Hispanics and
whites. African American and Mexican American women
hadgreaterincreasesinprevalenceofobesityduringthe
-year period that ended in  than other populations
[]. With respect to race, this study found no weight gain
disparities, despite the overrepresentation of African Ameri-
cans as oenders and the greater prevalence of diabetes and
hypertension.
e data that emerged from this study indicated that
chronic diseases were not explanatory or a factor for the
prevalence of obesity or weight gain among oenders. In fact,
correctional health may have a management model that can
be exported or adapted to the general population with respect
Journal of Obesity
to managing weight gain and preventing the onset or mini-
mizing the eect of chronic diseases and excess weight gain.
5. Conclusion
Energy intake, programs, and atypical antipsychotic medica-
tion may be explanatory for the disparities between female
and male oenders; however, there are no indicators that
female oenders consume all the food they are provided.
In addition to institutionally provided foods, correctional
facilities typically have commissaries (markets) where oend-
ers may purchase goods via credit they have earned from
working in a correctional facility or funds they have received
from an outside source, such as family members or friends.
Goods from the commissaries include food items, many of
which are processed high sodium and high fat content foods.
Food purchases from the commissary also are only an
approximation of what oenders consume instead of, or in
addition to, their institutional meals. Oenders sometimes
engage in proxy purchases for other oenders or trade com-
missarygoodsasaformofcurrency.isstudydidnotcollect
data regarding oenders who were on special diets, what they
purchased from the commissary, what specic medications
oenders were prescribed, or whether or not they were
taking atypical antipsychotic medications. However, atypical
antipsychotic medications may be a factor related to the
signicant dierence in weight gain between female and male
oenders.
is study found health disparities within a statewide
department of corrections where female oenders are gaining
signicantly more weight than males. Other correctional sys-
temsaroundtheglobemayalsohavesignicantpopulation
dierences regarding health outcomes and statuses and this
study has discussed potential contributors, as well as positive
ndings, related to obesity that may be broadly applicable
to correctional systems and that transcend geography and
geopolitics.
Future studies will benet from understanding the rela-
tionship between obesity and social factors, such as educa-
tional level, jobs that oenders perform during their incarcer-
ation (a potential indicator of physical activity), security level
(e.g., minimum, medium, and maximum), primary oense,
andoutcomedata,suchasHbAc,bloodpressure,lipidpan-
els, and degree of disease control. Along with social factors
and health outcome data, interviews with a diverse sample of
oenders and former oenders, particularly female oenders,
maybehelpfultolearnwhatpsychosocialissuesmaybe
barriers to eectively addressing obesity and weight gain.
Understanding the impact these factors have on excess weight
gain will be instrumental in developing an intervention for
oenders and changing policy, especially regarding female
oenders, who are at greater risk of becoming overweight or
obese than their male counterparts.
Conflict of Interests
e authors declare that there is no conict of interests
regarding the publication of this paper.
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... A cross-sectional study, in which a specific clinical record was created using the Python programming language to include the sociodemographic, health and medical diagnoses data of 17,279 prisoners. E i g h t a r t i c l e s w e re fo u n d i n M e d l i n e / Pubmed (5,15,(17)(18)21,23,25,28) , seven in Scopus (16,(19)(20)22,24,29,30) , one in CUIDEN (26) and one in Web of Science (27) . All the articles had an evidence level of 4 (5,(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) . ...
... E i g h t a r t i c l e s w e re fo u n d i n M e d l i n e / Pubmed (5,15,(17)(18)21,23,25,28) , seven in Scopus (16,(19)(20)22,24,29,30) , one in CUIDEN (26) and one in Web of Science (27) . All the articles had an evidence level of 4 (5,(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) . The USA (5,19,(22)(23)29) presented more publications on the theme, followed by Spain (20)(21)26,30) , Mexico (15,18,28) , Australia (25) , Greece (27) , Ethiopia (16) , Italy (17) and France (24) . ...
... All the articles had an evidence level of 4 (5,(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) . The USA (5,19,(22)(23)29) presented more publications on the theme, followed by Spain (20)(21)26,30) , Mexico (15,18,28) , Australia (25) , Greece (27) , Ethiopia (16) , Italy (17) and France (24) . Regarding the language of the publications, English prevailed (5,(13)(14)(15)(16)(17)(20)(21)(22)(23)(25)(26)(27) , followed by Spanish (20)(21)26,30) . ...
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