Abstract
Objective:
To investigate sleep quality among individuals incarcerated in a rural county jail, by housing status before incarceration.
Methods:
Using cross-sectional survey methods, 194 individuals incarcerated in jail reported sleep quality prior to and during incarceration on a Likert scale and pre-incarceration housing status (i.e., house, apartment, motel, group living, or homeless). Prevalence ratios (PR) were estimated using log binomial regression to determine associations between housing status before incarceration and changes in sleep quality.
Results:
Participants in non-permanent housing before incarceration had a lower prevalence of worsening sleep quality while incarcerated (compared to stable or improving) compared to those in permanent housing before incarceration (PR=1.69, 95% CI: 1.03, 2.77).
Conclusions:
Pre-incarceration housing is associated with change in sleep quality among individuals incarcerated in jail. Jail may be an important point of intervention to improve sleep quality during incarceration and through connecting individuals to more stable living conditions.
Keywords: Housing status, jail, health equity, sleep quality, incarceration
INTRODUCTION
Compared with the general population, individuals incarcerated in jail have a higher burden of mental health problems,1,2 physical health problems,3–7 and death.7 Chronic disease, impaired immune function, poor mental health, and death are all impacted by poor sleep quality.8 Individuals who are incarcerated report poor sleep quality while incarcerated.9–12 However, there is little research on sleep among incarcerated populations. Of the little research done on sleep among individuals incarcerated, most is conducted in prison, long-term correctional facilities that house sentenced persons for more than one year, with relatively little research conducted in jails.13 In 2019, there were about 10 million admissions to jail,14 facilities that house individuals awaiting adjudication or serving sentences <1 year; however, the impact of incarceration on sleep among individuals in jails has not been well studied.
Although the average length of stay in jails is short (2–10 days),15 short-term impacts on sleep quality may prove detrimental to an already unhealthy population. Short-term consequences of poor sleep include increased stress responsivity, psychiatric symptoms, substance use, emotional distress, mental health problems, and behavioral problems.8 Impacts of incarceration, including exacerbated or new mental health symptoms,16,17 emotional distress,18 change in sleep environment,19 and sedentary behavior20,21 can cause sleep disruptions. Alternatively, when jail provides a more stable environment compared to living situations before incarceration in which food and a place to sleep is guaranteed, individuals may have improved sleep quality while incarcerated.
Understanding the potential impact of incarceration in jail on sleep may be a significant public health opportunity that could lead to meaningful interventions to improve short- and long-term health. Thus, the objective of the proposed study was to determine sleep quality prior to and during incarceration among individuals incarcerated in a rural county jail.
PARTICIPANTS AND METHODS
Study Population
During 2017–2018, individuals housed at the Coconino County Detention Facility (CCDF) in Flagstaff, Arizona were recruited using a non-randomized sampling strategy to participate in a cross-sectional health survey. Individuals were eligible if they were ≥18 years and able to read English. Individuals housed in juvenile, administrative confinement, severe mental illness, and administration dorms were excluded. We additionally excluded 5 individuals missing housing status. Detailed study procedures are available elsewhere.22
The study was approved by Northern Arizona University Institutional Review Board and participants gave written, informed consent.
Demographics
Age, sex, race/ethnicity, education, income, health insurance coverage, and housing status were self-reported. Length of stay at time of survey was determined from date of arrest. Participants self-reported if they had previously spent time as an adult or juvenile in a prison, jail, or correctional facility.
Sleep Quality
Participants self-reported sleep quality before incarceration by responding to, “In the 7 days before admission to jail, my sleep quality was:” on a Likert scale (very good, good, fair, poor, very poor). Participants then self-reported sleep quality while incarcerated by responding to, “In jail, my usual sleep quality is:” on the same Likert scale. Change in sleep quality from before admission to during incarceration was determined and categorized as improved, stayed the same, or worsened.
Housing
Participants self-reported where they were living before being admitted to jail. “Permanent housing” included those living in a house (n=62), apartment (n=39), or trailer or mobile home (n=35) before incarceration. “Temporary housing included those living in a hotel or motel (n=10) or group living (n=9) before incarceration. “Experiencing homelessness” included those living on the street or a homeless shelter (n=39) before incarceration. Participants residing in non-permanent housing or experiencing homelessness were collapsed into one group (non-permanent housing).
Statistical Analysis
Demographic characteristics and sleep quality were presented as frequencies and relative frequencies, overall and by housing status before incarceration. Because the proportional odds assumption was not met, prevalence ratios (PR) and 95% confidence intervals (CI) were estimated using log-binomial regression to determine the association between housing status (permanent vs. non-permanent housing) and change in sleep quality (improved vs. stayed the same or worsened and worsened vs. stayed the same or improved). The model was adjusted for age, sex, race/ethnicity, income, and time incarcerated. Analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC).
RESULTS
Among the 194 participants, 58 (30.0%) were in non-permanent housing before incarceration. Most were under 45 years old (79.4%), male (78.4%), American Indian/Alaska Native (Indigenous, 58.3%), had a high school diploma, GED, or greater (70.1%), and an income of less than $10,000 USD (45.0%, Table 1). Participants in non-permanent housing were older, more likely to be Indigenous, and have a lower income compared to those with permanent housing before incarceration. Over three-quarters were previously incarcerated. Time incarcerated ranged from 1 to 898 days with a median length of incarceration at time of questionnaire of 46 days. Most study participants were housed in minimum or medium security housing units (76.8%). Incarceration characteristics were similar by housing status before incarceration. Regarding health status, more participants reported fair or poor health (36.3%) compared to excellent or very good health (28.9%). A higher proportion of participants who indicated that they were in non-permanent housing (49.1%) before incarceration reported fair or poor health compared to those in permanent housing (31.1%). Additionally, more than one-quarter of participants reported being previously diagnosed with anxiety (37.0%), depression (33.5%), or post-traumatic stress disorder (26.8%) by a healthcare professional with no difference by housing status before incarceration.
Table 1.
Descriptive characteristics of individuals incarcerated in Coconino County Detention Facility, by housing status prior to incarceration, 2017–2018
| Characteristic | All Participants (n = 194) | Housing Status Prior to Incarceration | ||||
|---|---|---|---|---|---|---|
| Permanent (n = 136) | Non-Permanent a (n = 58) | |||||
| N | % | N | % | N | % | |
| Age | ||||||
| 18–24 | 24 | 12.4 | 21 | 15.4 | 3 | 5.2 |
| 25–34 | 71 | 36.6 | 57 | 41.9 | 14 | 24.1 |
| 35–44 | 59 | 30.4 | 36 | 26.5 | 23 | 39.7 |
| 45–54 | 28 | 14.4 | 16 | 11.8 | 12 | 20.7 |
| ≥55 | 12 | 6.2 | 6 | 4.4 | 6 | 10.3 |
| Sex | ||||||
| Female | 42 | 21.7 | 24 | 17.7 | 18 | 31.0 |
| Male | 152 | 78.4 | 112 | 82.3 | 40 | 69.0 |
| Race/Ethnicity b | ||||||
| American Indian or Alaska Native | 113 | 58.3 | 70 | 51.5 | 43 | 74.1 |
| Latino/Latina | 25 | 12.9 | 23 | 16.9 | 2 | 3.5 |
| White | 46 | 23.7 | 35 | 25.7 | 11 | 19.0 |
| Other c | 10 | 5.2 | 8 | 5.9 | 2 | 3.5 |
| Education | ||||||
| Less than a high school diploma of General Educational Development Test (GED) | 58 | 29.9 | 37 | 27.2 | 21 | 36.2 |
| High school diploma or GED | 73 | 37.6 | 53 | 39.0 | 20 | 34.5 |
| Some college or greater | 63 | 32.5 | 46 | 33.8 | 17 | 29.3 |
| Income | ||||||
| $0–9,999 | 85 | 45.0 | 49 | 36.8 | 36 | 64.3 |
| $10,000–19,999 | 19 | 10.1 | 16 | 12.0 | 3 | 5.4 |
| $20,000–29,999 | 14 | 7.4 | 9 | 6.8 | 5 | 8.9 |
| $30,000–39,999 | 13 | 6.9 | 12 | 9.0 | 1 | 1.8 |
| $40,000–49,999 | 6 | 3.2 | 6 | 4.5 | 0 | |
| ≥ $50,000 | 20 | 10.6 | 18 | 13.5 | 2 | 3.6 |
| Don’t know | 32 | 16.9 | 23 | 17.3 | 9 | 16.1 |
| Missing | 5 | 3 | 2 | |||
| Prior Incarceration | ||||||
| No | 45 | 23.4 | 30 | 22.2 | 15 | 26.3 |
| Yes | 147 | 76.6 | 105 | 77.8 | 42 | 73.7 |
| Missing | 2 | 1 | 1 | |||
| Time incarcerated d | ||||||
| 1–16 days | 50 | 25.8 | 33 | 24.3 | 17 | 29.3 |
| 17–46 days | 49 | 25.3 | 34 | 25.0 | 15 | 25.9 |
| 47–92 days | 47 | 24.2 | 35 | 25.7 | 12 | 20.7 |
| >92 days | 48 | 24.7 | 34 | 25.0 | 14 | 24.1 |
| Security designation e | ||||||
| Maximum | 45 | 23.2 | 33 | 24.3 | 12 | 20.7 |
| Minimum or Medium | 149 | 76.8 | 103 | 75.7 | 46 | 79.3 |
| General health | ||||||
| Excellent | 16 | 8.4 | 14 | 10.4 | 2 | 3.6 |
| Very Good | 39 | 20.5 | 32 | 23.7 | 7 | 12.7 |
| Good | 66 | 34.7 | 47 | 34.8 | 19 | 34.6 |
| Fair | 54 | 28.4 | 34 | 25.2 | 20 | 36.4 |
| Poor | 15 | 7.9 | 8 | 5.9 | 7 | 12.7 |
| Missing | 4 | 1 | ||||
| Mental Health Conditions | ||||||
| Anxiety | 71 | 37.0 | 48 | 35.3 | 23 | 39.7 |
| Depression | 65 | 33.5 | 43 | 31.6 | 22 | 37.9 |
| Post-Traumatic Stress Disorder | 52 | 26.8 | 35 | 25.7 | 17 | 29.3 |
| Sleep Quality before Incarceration | ||||||
| Very Good | 15 | 7.7 | 12 | 8.8 | 3 | 5.2 |
| Good | 35 | 18.0 | 23 | 16.9 | 12 | 20.7 |
| Fair | 63 | 32.5 | 47 | 34.6 | 16 | 27.6 |
| Poor | 41 | 21.1 | 27 | 19.9 | 14 | 24.1 |
| Very Poor | 40 | 20.6 | 27 | 19.9 | 13 | 22.4 |
| Sleep Quality while Incarcerated | ||||||
| Very Good | 5 | 2.6 | 5 | 3.7 | 21 | 15.4 |
| Good | 38 | 19.6 | 19 | 14.0 | 37 | 27.2 |
| Fair | 76 | 39.2 | 54 | 39.7 | 54 | 39.7 |
| Poor | 48 | 24.7 | 37 | 27.2 | 19 | 14.0 |
| Very Poor | 27 | 13.9 | 21 | 15.4 | 5 | 3.7 |
Not permanent housing includes participants who reported living in a hotel, motel, group living, on the streets, or a homeless shelter
Not mutually exclusive
Other included participants who identified as Asian or Pacific Islander, Black, or other
At time of interview; quartiles; maximum time incarcerated = 898 days
Security designation at time of survey; inmates may be moved while incarcerated
Many participants reported poor or very poor sleep quality before incarceration (41.7%) and while incarcerated (38.6%, Table 1). Before incarceration, 39.8% of individuals in permanent housing and 46.5% of individuals in non-permanent housing before incarceration reported poor or very poor sleep quality. During incarceration, 42.6% of individuals in permanent and 17.7% of individuals in non-permanent housing before incarceration reported poor or very poor sleep quality. Overall, 28.9% of all participants’ sleep quality improved, 43.8% stayed the same, and 27.3% worsened during incarceration compared to before they were incarcerated (Figure 1). Findings differed by housing status before incarceration. A higher proportion of individuals in non-permanent housing (34.5%) indicated their sleep quality improved compared to those in permanent housing before incarceration (26.5%). Those in non-permanent housing before incarceration had a lower prevalence of worsening sleep quality while incarcerated (compared to stable or improving) compared to those in permanent housing before incarceration (PR=0.36, 95% CI: 0.19, 0.69). Similarly, participants in non-permanent housing before incarceration had a higher prevalence of improving sleep quality while incarcerated (compared to stable or worsening) compared to those in permanent housing before incarceration (PR=1.69, 95% CI: 1.03, 2.77).
Figure 1.
Change in Sleep Quality Before Admission to Jail to During Incarceration among Individuals Incarcerated at Coconino County Detention Facility, by Housing Status Prior to Incarceration (2017–2018)
DISCUSSION
Over one-third of participants reported poor or very poor sleep before or during incarceration. How incarceration impacts sleep quality may differ by many factors, including where a person lived prior to being incarcerated. Similar to our findings, most participants in two studies among women incarcerated reported poor sleep quality during incarceration.9,11 Similarly, a recent systematic review of poor sleep quality among individuals incarcerated in prison (n=9 studies) found 43–88% of participants reported poor sleep quality.12 However, prior research did not collect information on change in sleep quality by asking about sleep before incarceration. Additionally, findings did not indicate differences by prior housing status. Findings from prisons, long-term correctional facilities, may not be appropriate to directly compare to our study among individuals incarcerated in jail, a more transient, short-term population.
Many people who experience incarceration come from low socioeconomic backgrounds. Most of our participants had a high school diploma or less and almost half made less than $10,000 in annual income. Individuals with challenging socioeconomic circumstances may experience poor sleep before incarceration and then continue to experience poor or very poor sleep due to the jail environment. Jails could provide opportunities to improve sleep health through physical activity and quality sleep environments with minimal lighting during sleeping hours, comfortable mattresses, controlled temperatures, decreased noise, and appropriate wake times.23 Additionally, jails may prove as an opportunity for reentry planning services, such as housing connections, eviction assistance, Medicaid applications, and job connections, to mitigate some of the profound effects of poverty on sleep and other health outcomes.
There are limitations to our study. Generalizability is limited as most participants were Indigenous. Further, there may be measurement error due to a reliance on self-reported sleep quality data. In particular, our measure of sleep quality before incarceration may induce recall bias for participants who were incarcerated for long periods of time. We conducted a sensitivity analysis in which we excluded participants with outlying incarceration times (longer than 206 days). Findings were not substantially different. Additionally, we asked participants to report their sleep quality in the week prior to being incarcerated. Depending on the circumstances around participants’ arrest and incarceration, the week before incarceration may be worse on average compared to their usual sleep quality, suggesting a smaller change in sleep quality upon admission to jail. Future studies should consider typical sleep quality prior to admission to jail.
Overall, the findings in this brief report highlight the vulnerable sleep health of individuals incarcerated in jail and the moderating role of housing before incarceration on changes in sleep health. Future research should investigate whether improving living and sleeping conditions both before and during incarceration will confer additional benefits to health and well-being of this at-risk population.
Acknowledgements:
The authors want to thank Dr. Robert Trotter II for being a leader in correctional health and making this work possible. The authors would also like to acknowledge the members of the Coconino County Criminal Justice Coordinating Council (CJCC) and James Brett (Program Coordinator for the Coconino County Detention Center) who provided key access and advice to the field staff during data collection. In addition, important contributions to the data collection were made by Robert T. Trotter II (NAU), Viacheslav Y. Fofanov (NAU), Bailey Kohlbeck (NAU), Nicola Williams (NAU), Kellie Rexroat (NAU), Luke Chiverton (NAU), Erin Comprosky (NAU), Omar Gomez (NAU), and Galen McCloskey (NAU).
Grant Funding:
This work was supported by the NARBHA Institute, Flagstaff Arizona, with additional support from the Northern Arizona University Center for Health Equity Research (CHER). Research reported in this publication was supported by the National Institute on Minority Health and Health Disparities of the National Institutes of Health under Award Number U54MD012388. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Footnotes
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Declaration of Conflicts of Interest: The authors have no conflicts of interest to disclose.
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