Project Dates: 2023-2024
People with serious mental Illness (SMI) have a much higher smoking prevalence ((60% vs. 15%)and die 25 years earlier than the general population, despite expressing interest in evidence-based cessation treatment. The inadequate provision of treatment in community mental health centers (CMHCs) contributes to the high smoking prevalence and related health consequences. Despite these tremendous health needs, recommendations to increase smoking cessation treatment in this population have had modest success because existing models involve many components that are not easily implemented or sustained in resource-limited settings such as CMHCs. There are multi-level barriers to delivering evidence-based tobacco treatment (EBTT) in CMHC settings, including practice cultural barriers (system-level), lack of time (provider-level), and low motivation (patient-level). There is a critical need to identify low-burden strategies to address these barriers in CMHC settings and efficiently promote effective tobacco treatment engagement.
- Conduct pre-implementation diagnostics by assessing tobacco treatment practices, patient needs, implementation barriers, and resources in CMHCs.
- Conduct framework-guided design of multilevel strategies, including decision support (system level), standardized protocol (provider level), and proactive outreach (patient level) in CMHC.
- Evaluate the preliminary effect of the multi-level strategies on reach and effectiveness as a foundation for a sizeable future trial.
Implications for Research and Practice:
The proposed project is innovative on several levels. Focus on CMHCs, an understudied setting for implementation science applied to smoking cessation treatment. This proposal targets mental health clinics, a novel and scalable channel for reaching low-income, disadvantaged populations with SMI who are at greater risk for smoking. Use of technology for patient assessment to facilitate broad reach, rapid adoption, and fidelity of implementation. The use of phone assessments may be an efficient way to capture patient-reported data for decision support in medical settings but has not been tested in CMHC settings This project aligns closely with the priority areas identified by the WU-ISC3 community partners and will actively engage both community and clinical stakeholders.
Project Contact: Chen Li-Shiun
Project Team: Nina Smock, James Reddy, Aimee James, Robert Schnoll