Mental health interventions for individuals with serious mental illness in the criminal legal system: a systematic review

BACKGROUND

Globally, individuals with mental illness get in contact with the law at a greater rate than the general population. The goal of this review was to identify and describe: (1) effectiveness of mental health interventions for individuals with serious mental illness (SMI) who have criminal legal involvement; (2) additional outcomes targeted by these interventions; (3) settings/contexts where interventions were delivered; and (4) barriers and facilitating factors for implementing these interventions.

METHODS

A systematic review was conducted to summarize the mental health treatment literature for individuals with serious mental illness with criminal legal involvement (i.e., bipolar disorder, schizophrenia, major depressive disorder). Searches were conducted using PsychINFO, Embase, ProQuest, PubMed, and Web of Science. Articles were eligible if they were intervention studies among criminal legal involved populations with a mental health primary outcome and provided description of the intervention.

RESULTS

A total of 13 eligible studies were identified. Tested interventions were categorized as cognitive/behavioral, community-based, interpersonal (IPT), psychoeducational, or court-based. Studies that used IPT-based interventions reported clinically significant improvements in mental health symptoms and were also feasible and acceptable. Other interventions demonstrated positive trends favoring the mental health outcomes but did not show statistically and clinically significant changes. All studies reported treatment outcomes, with only 8 studies reporting both treatment and implementation outcomes.

CONCLUSION

Our findings highlight a need for more mental health research in this population. Studies with randomized design, larger sample size and studies that utilize non-clinicians are needed.

Using publicly available data to predict recreational cannabis legalization at the county-level: A machine learning approach

BACKGROUND

There is substantial geographic variability in local cannabis policies within states that have legalized recreational cannabis. This study develops an interpretable machine learning model that uses county-level population demographics, sociopolitical factors, and estimates of substance use and mental illness prevalences to predict the legality of recreational cannabis sales within each U.S. county.

METHODS

We merged data and selected 14 model inputs from the 2010 Census, 2012 County Presidential Data from the MIT Elections Lab, and Small Area Estimates from the National Surveys on Drug Use and Health (NSDUH) from 2010 to 2012 at the county level. County policies were labeled as having recreational cannabis legal (RCL) if the sale of recreational cannabis was allowed anywhere in the county in 2014, resulting in 92 RCL and 3002 non-RCL counties. We used synthetic data augmentation and minority oversampling techniques to build an ensemble of 1000 logistic regressions on random sub-samples of the data, withholding one state at a time and building models from all remaining states. Performance was evaluated by comparing the predicted policy conditions with the actual outcomes in 2014.

RESULTS

When compared to the actual RCL policies in 2014, the ensemble estimated predictions of counties transitioning to RCL had a macro f1 average score of 0.61. The main factors associated with legalizing county-level recreational cannabis sales were the prevalences of past-month cannabis use and past-year cocaine use.

CONCLUSION

By leveraging publicly available data from 2010 to 2012, our model was able to achieve appreciable discrimination in predicting counties with legal recreational cannabis sales in 2014, however, there is room for improvement. Having demonstrated model performance in the first handful of states to legalize cannabis, additional testing with more recent data using time to event models is warranted.

Cannabis Policy in the 21st Century: Mandating an Equitable Future and Shedding the Racist Past

The cannabis legalization movement in the United States has experienced unprecedented success in the past decade due to a wave of grassroots reforms in states across the country. The current legalization movement began in 2012, when Colorado and Washington became the first two states to legalize the use and sale of cannabis for adults aged ≥ 21 years. Since then, the use of cannabis has been legalized in 21 states, Guam, the Northern Mariana Islands, and Washington, DC. Many of these states have explicitly framed the law change as a rejection of the War on Drugs and its harms, felt disproportionately in Black and Brown communities. However, racial inequities in cannabis arrests have increased in states that have legalized cannabis for adult use. Moreover, states working to implement social equity and community reinvestment programs have made little progress toward their goals. This commentary describes how US drug policy, racist in intention, gave way to drug policy that perpetuates racism, even when its ostensible or stated goal is equity. As the United States prepares for national legalization of cannabis, it is critical that we break away from past legislation and mandate equity in cannabis policy. Developing meaningful mandates will require us to acknowledge our history of using drug policy for the racist goals of social control and extortion, study the experience of states that are trying to implement social equity programs, listen to Black leaders and other leaders of color who have developed guidance for equity-focused cannabis policy, and commit to a new paradigm. If we are willing to do these things, we may be able to legalize cannabis in an anti-racist way that will stop causing harm and enable us to effectively implement reparative practices.

Estimating the effects of legalizing recreational cannabis on newly incident cannabis use

Liberalized state-level recreational cannabis policies in the United States (US) fostered important policy evaluations with a focus on epidemiological parameters such as proportions [e.g., active cannabis use prevalence; cannabis use disorder (CUD) prevalence]. This cannabis policy evaluation project adds novel evidence on a neglected parameter–namely, estimated occurrence of newly incident cannabis use for underage (<21 years) versus older adults. The project’s study populations were specified to yield nationally representative estimates for all 51 major US jurisdictions, with probability sample totals of 819,543 non-institutionalized US civilian residents between 2008 and 2019. Standardized items to measure cannabis onsets are from audio computer-assisted self-interviews. Policy effect estimates are from event study difference-in-difference (DiD) models that allow for causal inference when policy implementation is staggered. The evidence indicates no policy-associated changes in the occurrence of newly incident cannabis onsets for underage persons, but an increased occurrence of newly onset cannabis use among older adults (i.e., >21 years). We offer a tentative conclusion of public health importance: Legalized cannabis retail sales might be followed by the increased occurrence of cannabis onsets for older adults, but not for underage persons who cannot buy cannabis products in a retail outlet. Cannabis policy research does not yet qualify as a mature science. We argue that modeling newly incident cannabis use might be more informative than the modeling of prevalences when evaluating policy effects and provide evidence of the advantages of the event study model over regression methods that seek to adjust for confounding factors.

An NIH investment in health equity - the economic impact of the Flint Center for Health Equity Solutions

Background

Health disparities are pervasive and are linked to economic losses in the United States of up to $135 billion per year. The Flint Center for Health Equity Solutions (FCHES) is a Transdisciplinary Collaborative Center for health disparities research funded by the National Institute of Minority Health and Health Disparities (NIMHD). The purpose of this study was to estimate the economic impact of the 5-year investment in FCHES in Genesee County, Michigan.

Methods

The estimated impacts of FCHES were calculated using a U.S.-specific input/output (I/O) model, IMPLAN, from IMPLAN Group, LLC., which provides a software system to access geographic specific data regarding economic sector interactions from a variety of sources. This allowed us to model the cross-sector economic activity that occurred throughout Genesee County, Michigan, as a result of the FCHES investment. The overall economic impacts were estimated as the sum of three impact types: 1. Direct (the specific expenditures impact of FCHES and the Scientific Research and Development Services sector); 2. Indirect (the impact on suppliers to FCHES and the Scientific Research and Development Services sector); and 3. Induced (the additional economic impact of the spending of these suppliers and employees in the county economy).

Results

The total FCHES investment amounted to approximately $11 million between 2016 and 2020. Overall, combined direct, indirect, and induced impacts of the total FCHES federal investment in Genesee County included over 161 job-years, over $7.6 million in personal income, and more than $19.2 million in economic output. In addition, this combined economic activity generated close to $2.3 million in state/local and federal tax revenue. The impact multipliers show the ripple effect of the FCHES investment. For example, the overall output of over $19.2 million led to an impact multiplier of 1.75 – every $1 of federal FCHES investment led to an additional $.75 of economic output in Genesee County.

Conclusions

The FCHES research funding yields significant direct economic impacts above and beyond the direct NIH investment of $11 million. The economic impact estimation method may be relevant and generalizable to other large research centers such as FCHES.