Kimber P Richter1, Jamie J Hunt2, A Paula Cupertino1, Byron J Gajewski3, Yu Jiang4, Janet Marquis5, Peter D Friedmann6. 1. a Department of Preventive Medicine and Public Health , University of Kansas Medical School , Kansas City , Kansas , USA. 2. b School of Nursing and Health Studies, University of Missouri Kansas City , Kansas City , Missouri , USA. 3. c Department of Biostatistics , University of Kansas Medical School , Kansas City , Kansas , USA. 4. d Division of Epidemiology, Biostatistics, and Environmental Health , School of Public Health, University of Memphis , Memphis , Tennessee , USA. 5. e The Schiefelbusch Institute for Life Span Studies, University of Kansas , Lawrence , Kansas , USA. 6. f Office of Research and Department of Medicine , University of Massachusetts-Baystate and Baystate Health , Springfield , Massachusetts , USA.
Abstract
BACKGROUND: Although people with mental illness, including substance use disorders, consume 44% of cigarettes in the United States, few facilities provide tobacco treatment. This study assesses staff- and facility-level drivers of tobacco treatment in substance use treatment. METHODS: Surveys were administered to 405 clinic directors selected from a comprehensive inventory of 3800 US outpatient facilities. The main outcome was the validated 7-item Index of Tobacco Treatment Quality. Other measures included the validated Tobacco Treatment Commitment Scale and indicators of facility resources for providing tobacco treatment. RESULTS: Stepwise model selection was used to determine the relationship between capacity/resources and treatment quality. The final model retained 7 items and had good fit (adjusted R2 = 0.43). Four capacities significantly predicted treatment quality. Structural equation modeling (SEM) was used to test the impact of staff commitment on treatment quality; the model had good fit and the relationship was significant (comparative fit index [CFI] = 0.951, root mean square error of approximation [RMSEA] = 0.054). Adding the 7 capacity/resources maintained similar model fit (CFI = 0.922, RMSEA = 0.053). Staff commitment was slightly strengthened in this model, with a rise in parameter estimate from 0.449 to 0.560. All resource/capacity items were also significant predictors of treatment quality; the strongest was receiving training in how to provide tobacco treatment (0.360), followed by dedicated staff time (0.279) and having a policy that requires staff to offer treatment (0.272). CONCLUSIONS: Staff commitment to providing tobacco treatment was the strongest predictor of tobacco treatment quality, followed by resources for providing treatment. Interventions to change staff attitudes and improve resources for tobacco treatment have the strongest potential for improving quality of care.
BACKGROUND: Although people with mental illness, including substance use disorders, consume 44% of cigarettes in the United States, few facilities provide tobacco treatment. This study assesses staff- and facility-level drivers of tobacco treatment in substance use treatment. METHODS: Surveys were administered to 405 clinic directors selected from a comprehensive inventory of 3800 US outpatient facilities. The main outcome was the validated 7-item Index of Tobacco Treatment Quality. Other measures included the validated Tobacco Treatment Commitment Scale and indicators of facility resources for providing tobacco treatment. RESULTS: Stepwise model selection was used to determine the relationship between capacity/resources and treatment quality. The final model retained 7 items and had good fit (adjusted R2 = 0.43). Four capacities significantly predicted treatment quality. Structural equation modeling (SEM) was used to test the impact of staff commitment on treatment quality; the model had good fit and the relationship was significant (comparative fit index [CFI] = 0.951, root mean square error of approximation [RMSEA] = 0.054). Adding the 7 capacity/resources maintained similar model fit (CFI = 0.922, RMSEA = 0.053). Staff commitment was slightly strengthened in this model, with a rise in parameter estimate from 0.449 to 0.560. All resource/capacity items were also significant predictors of treatment quality; the strongest was receiving training in how to provide tobacco treatment (0.360), followed by dedicated staff time (0.279) and having a policy that requires staff to offer treatment (0.272). CONCLUSIONS: Staff commitment to providing tobacco treatment was the strongest predictor of tobacco treatment quality, followed by resources for providing treatment. Interventions to change staff attitudes and improve resources for tobacco treatment have the strongest potential for improving quality of care.
Entities:
Keywords:
Health services; substance use disorder; tobacco use disorder
Authors: Jamie J Hunt; Byron J Gajewski; Yu Jiang; A Paula Cupertino; Kimber P Richter Journal: Am J Public Health Date: 2013-08-15 Impact factor: 9.308