INTRODUCTION: Smoking remains a significant public health problem and is a leading cause of preventable morbidity and mortality around the world. By combining the behavioral intervention principles used in specialist services with the high reach rates of public health interventions, personal tailored self-help cessation intervention provides a potential economical method for improving reach and effectiveness. METHODS: Cost-effectiveness analysis is performed alongside a randomized controlled trial to compare the computer-tailored self-help intervention with a generic self-help intervention in smoking cessation. A Markov model was developed to extrapolate lifetime cost-effectiveness by combining trial data with estimates from the literature. FINDINGS: In the short term, smokers in the intervention group gained 0.0006 (95% CI = -0.0024 to 0.0036) quality-adjusted life years (QALYs) more than those in the control group at an increased cost of about £9 per person (95% CI = £5-£12). This yielded an incremental cost-effectiveness ratio (ICER) of £14,432/QALY. Precision of the ICER estimates was assessed by 5,000 bootstrapping replications. The probability that the intervention was cost effective was 54% (58%) at a cost-effectiveness threshold of £20,000(£30,000) per QALY. The Markov model showed that simulating lifetime outcomes improved the cost-effectiveness ratio (£9,700/QALY) in favor of the tailored intervention. The intervention would have a 55%-57% chance of being more cost effective than nontailored intervention at the willingness-to-pay threshold of £20,000-30,000/QALY. CONCLUSIONS: The computer-tailored intervention appears slightly more likely to be cost effective than the generic self-help intervention in smoking cessation, in both the short term and the long term, but caution is required given the considerable uncertainty surrounding the estimates.
INTRODUCTION: Smoking remains a significant public health problem and is a leading cause of preventable morbidity and mortality around the world. By combining the behavioral intervention principles used in specialist services with the high reach rates of public health interventions, personal tailored self-help cessation intervention provides a potential economical method for improving reach and effectiveness. METHODS: Cost-effectiveness analysis is performed alongside a randomized controlled trial to compare the computer-tailored self-help intervention with a generic self-help intervention in smoking cessation. A Markov model was developed to extrapolate lifetime cost-effectiveness by combining trial data with estimates from the literature. FINDINGS: In the short term, smokers in the intervention group gained 0.0006 (95% CI = -0.0024 to 0.0036) quality-adjusted life years (QALYs) more than those in the control group at an increased cost of about £9 per person (95% CI = £5-£12). This yielded an incremental cost-effectiveness ratio (ICER) of £14,432/QALY. Precision of the ICER estimates was assessed by 5,000 bootstrapping replications. The probability that the intervention was cost effective was 54% (58%) at a cost-effectiveness threshold of £20,000(£30,000) per QALY. The Markov model showed that simulating lifetime outcomes improved the cost-effectiveness ratio (£9,700/QALY) in favor of the tailored intervention. The intervention would have a 55%-57% chance of being more cost effective than nontailored intervention at the willingness-to-pay threshold of £20,000-30,000/QALY. CONCLUSIONS: The computer-tailored intervention appears slightly more likely to be cost effective than the generic self-help intervention in smoking cessation, in both the short term and the long term, but caution is required given the considerable uncertainty surrounding the estimates.
Authors: Dina Jankovic; Laura Bojke; David Marshall; Pedro Saramago Goncalves; Rachel Churchill; Hollie Melton; Sally Brabyn; Lina Gega Journal: Appl Health Econ Health Policy Date: 2021-01 Impact factor: 2.561
Authors: Lina Gega; Dina Jankovic; Pedro Saramago; David Marshall; Sarah Dawson; Sally Brabyn; Georgios F Nikolaidis; Hollie Melton; Rachel Churchill; Laura Bojke Journal: Health Technol Assess Date: 2022-01 Impact factor: 4.014
Authors: Natale Salvatore Bonfiglio; Maria Lidia Mascia; Stefania Cataudella; Maria Pietronilla Penna Journal: Int J Environ Res Public Health Date: 2022-09-08 Impact factor: 4.614