There is a bidirectional relationship between homelessness and incarceration. After incarceration, people have an increased rate of homelessness, and incarceration leads to increased housing vulnerability due to loss of housing during incarceration, decreased eligibility for employment and public housing, and disrupted community ties.The average age of single adults experiencing homelessness in the US has increased; the proportion age 50 or older is growing.The US criminal justice population is also aging.Adults 55 or older in prison increased by 366% between 1999 and 2016,and in jails, by 278% between 1996 and 2008.The prevalence of, and risk factors for, incarceration among older adults experiencing homelessness remains unexamined. Most studies examining incarceration and housing instability are retrospective or cross-sectional analyses including all ages.In the general population, incarceration decreases with age, though rates remain high among older homeless adults. Understanding older adults’ unique risk factors for incarceration is critical to target interventions to prevent criminal justice involvement. This is increasingly important as the COVID-19 pandemic has had a disproportionate impact on people living in congregate settings.Understanding movement between homelessness and the criminal justice system can inform interventions to prevent SARS COV-2 transmission, particularly among older adults who are at increased risk of severe disease and death.Therefore,vertical outdoor farming in a prospective cohort of older adults who were homeless at study entry, we examined factors associated with subsequent incarceration over the multi-year study period including sociodemographic, social, housing, and health factors.
We hypothesized that continued homelessness, substance use, mental illness, and cognitive impairment would be associated with incident incarceration.We completed a descriptive analysis using all variables from baseline. Variables included only in the descriptive analysis were income, illicit income sources, duration of homelessness, hospitalizations, and history of incarceration. To identify risk factors for incarceration, we selected independent variables based on our hypotheses. We assessed bivariable associations between a priori independent variables and incarceration using an extended Cox hazard model to incorporate multiple, independent events and time-varying covariates. We used 6-month intervals as the period of measure for time-to-event outcomes. In the hazard models, we included demographics , life history , and cognitive impairment as time-constant variables assessed at baseline. We included all other variables as time varying. We estimated our multi-variable model by including variables with bivariable type III p-values < 0.20. If a categorical variable had more than two levels, we included all levels in our multi-variable model if any type III pvalue was < 0.20. We reduced the model using backward elimination by retaining variables with type III p-values < 0.05 in our final model. We conducted our analysis in SAS using complete case analysis and robust confidence intervals. In a sensitivity analysis, we estimated models without the probation and parole variables. We estimated models separately for homelessness based on HEARTH and nights unsheltered to assess the role of unsheltered homelessness and rehousing on incarceration. First, we estimated models including HEARTH and covariates. Then, we replaced HEARTH with any nights unsheltered.In this prospective study of older adults experiencing homelessness, almost one-quarter experienced at least one incarceration event during follow-up.
We found several risk factors for incarceration that are associated in the general adult population, including substance use, and being on parole or probation. Building on prior data about the association between homelessness and incarceration, we found that individuals who continued to experience homelessness at follow-up had an elevated risk of incarceration compared to those who exited homelessness. We found that having a larger social network is associated with incarceration, which has not been reported previously. Though the median age of the cohort at baseline was 58, participants had a burden of disease and disability commensurate with adults aged 15–20 years older.Individuals experiencing homelessness, like those in prisons, experience an early onset of geriatric conditions and are considered “older” by age 50.Despite this population’s relative frailty, study participants continued to experience incarceration. Incarceration presents health risks, particularly for older adults; these threats have intensified during the COVID-19 pandemic.Housing status was dynamic and approximately half exited homelessness during study follow-up;remaining homeless was independently associated with risk of incident incarceration. Homelessness can increase the risk for incarceration via increased visibility to law enforcement, via increased illicit economic behaviors , via participation in criminalized survival behaviors or via barriers to completing court-mandated interventions . The direction of the association may be reversed; it is possible that individuals who experienced incarceration may have faced additional barriers to accessing housing, prolonging homelessness. Substance use was common and increased the risk of incarceration. Alcohol and amphetamine intoxication can lead to impulsive behavior and impaired judgment, which may increase illegal activity or visibility to law enforcement.
For individuals on parole or probation, substance use may be monitored and any use may result in time in jail or prison. Our prior research showed that only one-in-eight older individuals experiencing homelessness with need for substance use treatment received such treatment, highlighting an unmet need.Counter to our hypothesis, social support was not associated with decreased risk of incarceration, instead a higher number of confidants was associated with a higher risk. Those with larger social networks may have more peers who are involved in the criminal justice system, increasing the risk of incarceration.There is a need to understand the nature of social support that is associated with an increased risk of arrest in order to interrupt this cycle, either by encouraging social networks with positive outcomes or by disrupting cycles of arrests. In this study, Black race was not associated with incarceration, although it is well established that Black Americans are disproportionately incarcerated due to structural racism.Black Americans are significantly more likely to become homeless due to structural racism so there may be lower rates of individual risk factors for incarceration .Thus, non-Black participants may have individual risk factors that elevated their risk of incarceration in a way that we did not account for. Future studies should be conducted to better understand this finding. As continued homelessness is associated with incarceration,cannabis vertical farming it is possible that rehousing older adults experiencing homelessness could reduce this risk. A recent randomized controlled trial of permanent supportive housing for chronically homeless adults did not find a reduction in jail use. This may have been explained by police having an increased ability to serve outstanding warrants to people upon rehousing.Given the high rates of substance use, expansion of substance use treatment programs might reduce older homeless adults’ risk of incarceration. Among all variables that we tested, parole and probation had the highest hazard ratio.There are movements to reform probation and parole because they may perpetuate incarceration.Reform efforts include shortening supervision sentences, reducing conditions and cost, limiting incarceration for violations, and providing specialty community supervision programs which use probation officers with health-focused expertise who incorporate a treatment-oriented approach in collaboration with community resources.Future areas for research include whether reducing or tailoring supervision programs to the needs and risk factors of older homeless adults decreases recidivism.
Another innovation is specialty courts , which emphasize connection to treatment, though there is mixed data on their impact on incarceration and recidivism.It is possible that we under counted incarceration events because we relied on participant self-report and manual review of custodial records; participants may have under reported visits, or jail and prison stays may have occurred outside the window in which we checked, or outside of the correctional facilities that we were able to monitor. Such under counting could have made it more difficult for us to find associations. Participants who spent longer times in custody were less likely to experience multiple incarcerations; we did not control for length of incarceration. We did not have detailed data on incarceration events, including the cause of arrest,duration of incarceration, or whether the participants were charged or prosecuted.Cannabidiol self-medication is becoming increasingly common in the United States with surveys showing that the percent of individuals who have tried CBD has increased from 14% in 2019 to at least 33.3% in 2020.Although a very-high-dose CBD is associated with elevations in liver tests in children being treated for epilepsy and in normal adults, all other studies of CBD use have found no such association.With the sole exception of diarrhea, all of the adverse outcomes in childhood epilepsy studies were limited to instances where CBD may have interacted with other medications, such as clobazam and/or sodium valproate.In a phase 1 trial of 70 normal adults who consumed 1500 mg/day of CBD along with various epileptic drugs, not a single individual developed abnormal LT, however, in a similar phase 1 trial of adults consuming only CBD at the same dose/day, 7 of 16 individuals had elevated LT with 5 having levels meeting criteria for drug-induced liver injury.Why such vast differences in findings exist is unknown at this time, however, these very high daily doses of CBD are much greater than the daily dosage typically consumed by CBD self-medicating users in the United States. Laboratories set the 97.5% value of LT in adult individuals with no disease as the upper limit of normal . However, the prevalence of elevated LT in the general adult populations is estimated at between 10% and 20% and has been rising over the years.The prevalence of elevated alanine aminotransferase and elevated aspartate aminotransferase in the United States in years 1999–2002 were 8.9% and 4.9%, respectively.7 Using any LT by itself as a screening instrument for liver disease or damage is not as useful as using multiple LTs, that is, ALT, AST, alkaline phosphatase , and total bilirubin .This study was undertaken to determine the prevalence of elevated levels of these four LTs in an adult population of self-medicating CBD users.Adults, 18–75 years of age across the United States, known to be taking CBD orally for a minimum of 30 days, were recruited from consumers of 12 individual CBD product companies to participate in this decentralized, observational, IRB-approved study in accordance with the ethical standards on human experimentation. An app-based, 21CFR Part 11 decentralized clinical study platform was used to securely automate consent inclusion/exclusion criteria. Exclusion criteria included individuals with known liver disease, liver function impairment, allergies to CBD, or taking the following: valproate, Vitamin A, clobazam, cyclosporin, phenytoin, fluvoxamine, isoniazid, ritonavir, clarithromycin, diltiazem, erythromycin, grapefruit juice, itraconazole, ketoconazole, nefazodone, ritonavir, telithromycin, or verapamil. Individuals selected were sent their standard CBD regimen from the company of their choice. Demographic information, medical history, reasons for taking, dosage, and current medications were collected through the app, along with daily journaling information on dosage, adverse effects, and efficacy. At the end of 30 days of journaling, blood draws were performed locally and serum was sent to one of two national laboratories, where LTs were performed. The different laboratories had different ULN values for their populations. To normalize the ALT data from the different laboratories, ALT values were adjusted to the percentile of the ULN for their laboratory. All individuals with elevated ALT values were contacted and offered a follow-up LT. Medical history, medication history, and cannabinoid use history data were collected for the time period between the testing points. Individuals were encouraged to provide information on LT ordered by personal medical providers, regardless of whether they consented to the follow-up testing. Quantitative data were analyzed using the Wilcoxon rank-sum or signed-rank tests and qualitative data by the chi-square test for homogeneity or a binomial test using an exact p-value calculation. Nonlaboratory variables analyzed include weight, height, body mass index , sex, age, form of CBD, composition of CBD, alcoholic drinks per day, number of medical conditions, number of prescribed drugs, number of other therapies, number of over-the-counter treatments, and number of other CBD treatments being used. Different laboratories had different ULN values for their populations. To normalize the ALT data from the different laboratories, ALT values were adjusted to the percentile of the ULN for their laboratory. A forward stepwise linear regression was used to identify predictors of LT values. The algorithm was validated by repeated application to sub-samples. A total of 28,121 individuals from across the United States were invited to participate in this study. One thousand four hundred seventy-five were enrolled, and 839 fully completed the study with blood draws for LTs.