Breanne Hobden1,2,3, Mariko Carey1,2,3, Rob Sanson-Fisher1,2,3, Andrew Searles3, Christopher Oldmeadow3, Allison Boyes1,2,3. 1. Health Behaviour Research Collaborative, School of Medicine and Public Health, Faculty of Health and Medicine, University of Newcastle, Callaghan, NSW, Australia. 2. Priority Research Centre for Health Behaviour, University of Newcastle, Callaghan, NSW, Australia. 3. Hunter Medical Research Institute, New Lambton Heights, NSW, Australia.
Abstract
BACKGROUND: This study aimed to illustrate the potential utility of a simple filter model in understanding the patient outcome and cost-effectiveness implications for depression interventions in primary care. METHODS: Modelling of hypothetical intervention scenarios during different stages of the treatment pathway was conducted. RESULTS: Three scenarios were developed for depression related to increasing detection, treatment response and treatment uptake. The incremental costs, incremental number of successes (i.e., depression remission) and the incremental costs-effectiveness ratio (ICER) were calculated. In the modelled scenarios, increasing provider treatment response resulted in the greatest number of incremental successes above baseline, however, it was also associated with the greatest ICER. Increasing detection rates was associated with the second greatest increase to incremental successes above baseline and had the lowest ICER. CONCLUSIONS: The authors recommend utility of the filter model to guide the identification of areas where policy stakeholders and/or researchers should invest their efforts in depression management.
BACKGROUND: This study aimed to illustrate the potential utility of a simple filter model in understanding the patient outcome and cost-effectiveness implications for depression interventions in primary care. METHODS: Modelling of hypothetical intervention scenarios during different stages of the treatment pathway was conducted. RESULTS: Three scenarios were developed for depression related to increasing detection, treatment response and treatment uptake. The incremental costs, incremental number of successes (i.e., depression remission) and the incremental costs-effectiveness ratio (ICER) were calculated. In the modelled scenarios, increasing provider treatment response resulted in the greatest number of incremental successes above baseline, however, it was also associated with the greatest ICER. Increasing detection rates was associated with the second greatest increase to incremental successes above baseline and had the lowest ICER. CONCLUSIONS: The authors recommend utility of the filter model to guide the identification of areas where policy stakeholders and/or researchers should invest their efforts in depression management.
Authors: Brett D Thombs; James C Coyne; Pim Cuijpers; Peter de Jonge; Simon Gilbody; John P A Ioannidis; Blair T Johnson; Scott B Patten; Erick H Turner; Roy C Ziegelstein Journal: CMAJ Date: 2011-09-19 Impact factor: 8.262
Authors: Alize J Ferrari; Fiona J Charlson; Rosana E Norman; Scott B Patten; Greg Freedman; Christopher J L Murray; Theo Vos; Harvey A Whiteford Journal: PLoS Med Date: 2013-11-05 Impact factor: 11.069