Purpose: Diabetes is one of the most prevalent and fastest-growing adverse health conditions in the United States and disproportionately affects those demographic and socioeconomic groups that are also more likely to be involved with the criminal justice (CJ) system. This study examines the prevalence and correlates of diabetes among CJ-involved individuals in the United States.
Methods: Using traditional statistical modeling and modern machine learning methods, data from the National Study on Drug Use and Health were analyzed to compare the correlates and predictive interactions of diabetes diagnosis among those respondents on probation and parole to a sample, matched by age and gender, who were not.
Results: Subjects involved in the CJ system were 15% more likely (1.66% vs. 1.44%, P = .015) to report a past-year diagnosis of diabetes than a sample of noninvolved individuals matched by age and sex, although this association was not statistically significant after adjusting for demographic and behavioral confounders. Similar trends in diabetes prevalence emerged for the non-CJ and CJ groups with regard to income, depression (OR of 2.38 and 1.65 for the CJ and non-CJ groups, respectively) and attainment of college education (OR of 0.64 and 0.30 for the CJ and non-CJ groups, respectively, compared with those with less than a high school education). Results also suggested that a generally high propensity toward risk taking had a negative effect on diabetes for the non-CJ group (OR 0.78; 95% CI 0.69-0.87), yet increased the odds of diabetes (OR 1.38; 95% CI 1.02-1.85) for the CJ group.
Conclusions: Involvement in the U.S. CJ system is correlated with a higher prevalence of diabetes and differing risk factors for diabetes diagnosis. Further research is necessary, however, to unpack the precise causal pathways that underlie the associational trends in the current analysis.
Keywords: Diabetes; Machine learning; Parolees; Probationers; Regression trees.
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