Abstract Background Substance use disorders (SUDs) represent major public health concerns and are linked to enhanced risk of legal consequences. Unresolved legal issues may prevent individuals with SUD from completing treatment. Interventions aimed at improving SUD treatment outcomes are limited. Filling that gap, this randomized controlled trial (RCT) tests the ability of a technology-assisted intervention to increase SUD treatment completion rates and improve post-treatment health, economic, justice-system, and housing outcomes. Methods A randomized controlled trial with a two-year administrative follow-up period will be conducted. Eight hundred Medicaid eligible and uninsured adults receiving SUD treatment will be recruited at community-based non-profit health care clinics in Southeast, Michigan, USA. Using an algorithm embedded in a community-based case management system, we randomly assign all eligible adults to one of two groups. The treatment/intervention group will receive hands-on assistance with a technology aimed at resolving unaddressed legal issues and the control group receives no treatment. Upon enrollment into the intervention, both treatment ( n = 400) and control groups ( n = 400) retain traditional options to resolve unaddressed legal issues, such as hiring an attorney, but only the treatment group is targeted the technology and offered personalized assistance in navigating the online legal platform. To develop baseline and historical contexts for participants, we collect life course history reports from all participants and intend to link those in each group to administrative data sources. In addition to the randomized controlled trial (RCT), we used an exploratory sequential mixed methods and participatory-based design to develop, test, and administer our life course history instruments to all participants. The primary objective is to test whether targeting no-cost online legal resources to those experiencing SUD improves their long-term recovery and decreases negative health, economic, justice-system, and housing outcomes. Discussion Findings from this RCT will improve our understanding of the acute socio-legal needs faced by those experiencing SUD and provide recommendations to help target resources toward the areas that best support long-term recovery. The public health impact includes making publicly available a deidentified, longitudinal dataset of uninsured and Medicaid eligible clients in treatment for SUD. Data include an overrepresentation of understudied groups including African American and American Indian Alaska Native persons documented to experience heightened risk for SUD-related premature mortality and justice-system involvement. Within these data, several intended outcome measures can inform the health policy landscape: (1) health, including substance use, disability, mental health diagnosis, and mortality; (2) financial health, including employment, earnings, public assistance receipt, and financial obligations to the state; (3) justice-system involvement, including civil and criminal legal system encounters; (4) housing, including homelessness, household composition, and homeownership. Trial registration Retrospectively registered # NCT05665179 on December 27, 2022.
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Contexts shaping misdemeanor system interventions among people with mental illnesses: qualitative findings from a multi-site system mapping exercise
People with mental illnesses are disproportionately entangled in the criminal legal system. Historically, this involvement has resulted from minor offending, often accompanied by misdemeanor charges. In recent years, policymakers have worked to reduce the footprint of the criminal legal system. This paper seeks to better understand how misdemeanor systems intervene in the lives of people with mental illnesses.
System mapping exercises were conducted with misdemeanor system stakeholders from the jurisdictions of Atlanta, Chicago, Manhattan, and Philadelphia. Narrative detail on decision-making and case processing, both generally and in relation to specific types of behavior, including trespassing, retail theft/shoplifting, and simple assault, were coded and analyzed for thematic patterns. Based on the qualitative analysis, this paper offers a conceptual diagram of contexts shaping misdemeanor system interventions among people with mental illnesses.
All four sites have been engaged in efforts to reduce the use of misdemeanor charges both generally and in relation to people with mental illnesses. Decision-makers across all sites experience contexts that shape how, when, and where they intervene, which are: (1) law and policy environments; (2) location of the behavior; (3) expectations of stakeholders; (4) knowledge of mental illnesses; and (5) access to community resources. Law and policy environments expand or constrain opportunities for diversion. The location of offending is relevant to who has a stake in the behavior, and what demands they have. Clinical, experiential, and system-level knowledge of mental illnesses inform a chain of decisions about what to do. The capacity to address mental health needs is contingent on access to social services, including housing.
People making decisions along the criminal legal continuum are critical to illuminating the dynamic, inter-related contexts that facilitate and frustrate attempts to address defendants’ mental health needs while balancing considerations of public safety. Multi-sector, scenario-based or case study exercises could help identify concrete ways of improving each of the contexts that surround whole-of-system decisions.
- NSF-PAR ID:
- 10405187
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Health & Justice
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2194-7899
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Policy Points Promoting healthy public policies is a national priority, but state policy adoption is driven by a complex set of internal and external factors.
This study employs new social network methods to identify underlying connections among states and to predict the likelihood of new firearm‐related policy adoption given changes to this interstate network.
This approach could be used to assess the likelihood that a given state will adopt a specific new firearm‐related law and to identify points of influence that could either inhibit or promote wider diffusion of specific laws.
Context US states are largely responsible for the regulation of firearms within their borders. Each state has developed a different legal environment with regard to firearms based on different values and beliefs of citizens, legislators, governors, and other stakeholders. Predicting the types of firearm laws that states may adopt is therefore challenging.
Methods We propose a parsimonious model for this complex process and provide credible predictions of state firearm laws by estimating the likelihood they will be passed in the future. We employ a temporal exponential‐family random graph model to capture the bipartite state law–state network data over time, allowing for complex interdependencies and their temporal evolution. Using data on all state firearm laws over the period 1979–2020, we estimate these models’ parameters while controlling for factors associated with firearm law adoption, including internal and external state characteristics. Predictions of future firearm law passage are then calculated based on a number of scenarios to assess the effects of a given type of firearm law being passed in the future by a given state.
Findings Results show that a set of internal state factors are important predictors of firearm law adoption, but the actions of neighboring states may be just as important. Analysis of scenarios provide insights into the mechanics of how adoption of laws by specific states (or groups of states) may perturb the rest of the network structure and alter the likelihood that new laws would become more (or less) likely to continue to diffuse to other states.
Conclusions The methods used here outperform standard approaches for policy diffusion studies and afford predictions that are superior to those of an ensemble of machine learning tools. The proposed framework could have applications for the study of policy diffusion in other domains.
