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.As continued homelessness is associated with incarceration, 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,grow rack parole and probation had the highest hazard ratio. Recidivism may be driven by technical violations of probation or parole rather than new criminal offenses. 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.Subjects for these analyses were 397 men who completed baseline evaluations and all follow-ups from approximate ages 20 to 50 in the San Diego Prospective Study . These men represent 90.0% of 442 individuals who entered the study, were alive at the age 50 follow-up, and participated in all evaluations at 10 , 15 , 20 , 25 , and 30 years. At T1, these subjects responded to mailed questionnaires distributed annually between 1978 and 1988 to new groups of 18-to- 25-year-old Caucasian students and nonacademic staff at the University of California, San Diego, selecting those who indicated they had experience with alcohol but never developed an AUD or SUD. For the approximately 60% who responded, additional exclusion criteria included bipolar disorder, schizophrenia, or the report of physical problems that precluded alcohol challenges. The original protocol was limited to males to optimize the rate of expression of AUDs over time, and to Caucasians because few African-Americans attended UCSD and 40% of Asians had alcohol-related flushing that could affect their LR measures. The requirement that probands did not have an SUD or AUD excluded individuals with early onset substance-related disorders that often reflflect preexisting severe conduct or antisocial problems . Individuals were selected as matched pairs of sons of alcohol-dependent fathers and FH-negative controls who were similar on demography as well as alcohol and drug use histories. By T1, 64% of these probands had some experience with illicit substances.
Baseline data were gathered using a variation of the Renard Diagnostic Interview, and at follow-up using questions from the Semi-Structured Assessment for the Genetics of Alcoholism interview . The latter has 1-week retest reliabilities for SUDs and AUDs of approximately 0.75 . Follow-up information was gathered from subjects and resource persons who gave information using an interview similar to the probands, with the higher figure for an item from either informant used if the 2 sources disagreed. Follow-up data included the prior 5- year interval use of alcohol, illicit drugs, and cigarettes, as well as personal and FHs of DSM-IV substance-related and independent major depressive and anxiety disorders . The Family History Assessment Module was used to gather information regarding lifetime AUDs and SUDs in the proband’s biological parents . As described in detail elsewhere, baseline LRs for probands were determined through alcohol-related changes in subjective feelings of intoxication, standing steadiness, and hormones at breath alcohol concentrations of approximately 60 mg/dl, as measured by an Intoximeter . Across multiple sessions, all subjects consumed 0.75 ml of alcohol or placebo and were evaluated over 3 hours as their blood alcohol concentrations rose, peaked and decreased to close to zero. Z-scores were used to combine data into 1 overall LR score where lower values reflected lower LRs per drink. At T15, probands completed the Self-Report of the Effects of Alcohol questionnaire regarding the number of drinks required for effects during 3 life epochs . SRE5 and total scores incorporating all 3 epochs were generated by summing the number of drinks required for up to 4 effects , and dividing that by the number of the effects reported . Thus, higher SRE scores indicate more drinks needed for effects, or a lower LR per drink. The SRE Cronbach’s a is >0.90, with retest reliabilities of 0.8. Externalizing characteristics were first evaluated at T10 and T15 using the novelty seeking from the Tridimensional Personality Questionnaire, Sensation Seeking from the Zuckerman Questionnaire, and Impulsivity from the Karolinska Personality Questionnaire.
To evaluate the hypotheses, emphasis was placed on LR, externalizing characteristics and internalizing items along with the demographic and alcohol/drug variables likely to predict future alcohol and substance difficulties. Most analyses focused on 4 proband groups regarding substance-related diagnoses over the 30 years: men who developed both SUDs and AUDs over the follow-up ; those with follow-up SUDs only ; subjects who developed AUDs only ; and individuals developing neither diagnosis during the 3 decades . Comparisons regarding Hypotheses 2 to 4 used analysis of variance or chi-square across all 4 groups, with an emphasis on LR, externalizing, and internalizing characteristics along with demographic and earlier alcohol and drug use items. Significant differences were followed with a planned comparison to evaluate if the groups with AUD and/or SUD diagnoses differed from Group 4 with no diagnosis to identify relevant items to test regarding association with Groups 1, 2, and 3 separately. Those significant items were then entered into a series of backward elimination logistic regressions to evaluate if LR, externalizing, and/or internalizing items were still significantly related to each of the 3 groups with diagnoses when considered in the context of other significant items. Comparisons regarding Hypothesis 5 were limited to Groups 1 to 3 who had relevant diagnoses, where an overall significant difference across groups was followed by planned comparisons of how Groups 2 and 3 differed from the combined diagnosis Group 1. For all evaluations, missing data were handled through a maximum likelihood procedure , including 6.8% who needed correction for 1 item and 0.8% for 2 items.As demonstrated by the distribution of the 4 groups in Table 1, over the 30 years, 41.3% of the 397 men met criteria for an AUD,microgreens shelving while 20.6% fulfilled criteria for an SUD. The SUDs included 51.2% of the 397 men with a cannabis use disorder only, 25.6% with an amphetamine and/or cocaine diagnosis only, 14.6% with combined cannabis and stimulant diagnoses, and 8.5% with an SUD related to cannabis or stimulants combined with other drug conditions. Consistent with Hypothesis 1 , in this prospective study of men at high risk of AUDs, the rate of a second substance-related diagnosis was almost 2-fold higher among individuals who had either SUDs or AUDs. Thus, 62 of the 164 probands with an AUD in Groups 1 plus 3 also had an SUD, and 62 of the 82 men with an SUD in Groups 1 plus 2 also had an AUD. The 4 groups of subjects in Table 1 differed on a broad range of early life characteristics. The results indicated significant differences for earlier life LR and externalizing characteristics, but not for having seen a mental health worker or reporting depressive syndromes . There were also differences for several demographic, alcohol, and drug use items. All significant alcohol, drug, and externalizing items in that table also differentiated between the combined Groups 1 through 3 versus the no diagnosis Group 4. In Table 2, further analyses were then carried out to more directly evaluate how items from Table 1 that were significantly different across the combined Groups 1 to 3 versus Group 4 performed when each of Groups 1, 2, and 3 was evaluated for differences from Group 4.
This approach used a series of backward elimination logistic regressions to identify the odds ratios for optimal combinations of items for each group that best differentiated it from the no diagnosis Group 4. In Table 2, the regression predicting Group 3 from among Group 3 plus the no diagnosis Group 4 identified 6 items including: a lower LR per drink; a higher score on an externalizing questionnaire; 2 alcohol use characteristics; 1 drug use item; and lower T1 education. Five of these 6 items also related to the combined diagnosis Group 1 compared with Group 4, with the sixth characteristic similar to Group 3 in that it involved an externalizing questionnaire score. The regression relating to Group 2 from among Groups 2 plus 4 had contributions from 3 items: a higher score on an externalizing questionnaire, 1 alcohol, and 1 drug item . All 3types of items also related to Group 1 membership, but with a different externalizing questionnaire score. Thus, 5 of the 8 items that contributed to the regression for Group 1 versus Group 4, also related to Group 2 and/or Group 3, with 1 of the additional items relating to an externalizing score from a different questionnaire. Similar results for these 3 regressions were seen if hierarchical logistic regressions were used focusing on blocks of demographic items, alcohol, and drug use items, and then LR and externalizing items, with the exception that the equation relating to the block of externalizing characteristics and LR regarding the small Group 2 was a trend . In summary regarding Hypotheses 2 to 4 , externalizing characteristics related to all 3 diagnostic groups, while LR related only to groups with later AUDs. Overall, the characteristics that related to AUDs alone combined with those that best identified subjects with SUDs alone were associated with the combined diagnoses in Group 1, with few predictors that were uniquely related to Group 1. Table 3 addresses Hypothesis 5 . Here, planned comparisons were used because some items were only relevant to groups with AUDs and some were only appropriate for probands with SUDs. Beginning with demography, the only overall significant difference across the 3 groups at age 50 was the higher proportion of individuals in Group 3 who had ever been married, a finding that reflected higher proportions in Group 3 versus the comorbid diagnosis Group 1. The course of alcohol-related items for the 2 AUD groups indicated that members of the combined diagnosis Group 1 endorsed more AUD items, an earlier AUD onset, a higher risk of alcohol-induced mood disorders, and a greater proportion who received formal AUD treatment or who attended Alcoholics Anonymous meetings compared with Group 3. The drug-related items for the 2 SUD groups indicated that members of Group 1 were more likely to use tobacco and reported a higher number of SUD items. Mental health histories over the 30 years were generally similar across the 3 groups, except for a greater probability of having seen a mental health worker for the combined diagnosis Group 1 compared with the AUD diagnostic Group 3. The demographic and alcohol-related characteristics in Table 3 that differentiated between the AUD Groups 1 and 3 were likely to be correlated, so these 6 items were entered into a backward elimination logistic regression analysis to determine which variables remained significantly related to a combined AUD plus SUD outcome among individuals in Groups 1 plus 3. The results demonstrated significant ORs for Group 1 membership for the number of AUD items endorsed , an alcohol induced mood diagnosis , and having received formal AUD treatment . The 2 drug-related items and the SRET score that differentiated between Group 1 and Group 2 in Table 3 were entered into a logistic regression predicting Group 1 membership from among Groups 1 plus 2, with only the SRET contributing significantly .