Jaithri Ananthapavan1,2, Phuong K Nguyen3,4, Steven J Bowe5, Gary Sacks4, Ana Maria Mantilla Herrera6, Boyd Swinburn4,7, Vicki Brown3,4, Rohan Sweeney3,4,8, Anita Lal3,4, Claudia Strugnell4, Marj Moodie3,4. 1. Deakin Health Economics, Institute for Health Transformation, Faculty of Health, Deakin University, Geelong, VIC, Australia. jaithri.ananthapavan@deakin.edu.au. 2. Global Obesity Centre (GLOBE), Institute for Health Transformation, Faculty of Health, Deakin University, Geelong, VIC, Australia. jaithri.ananthapavan@deakin.edu.au. 3. Deakin Health Economics, Institute for Health Transformation, Faculty of Health, Deakin University, Geelong, VIC, Australia. 4. Global Obesity Centre (GLOBE), Institute for Health Transformation, Faculty of Health, Deakin University, Geelong, VIC, Australia. 5. Deakin Biostatistics Unit, Faculty of Health, Deakin University, Geelong, VIC, Australia. 6. School of Public Health, University of Queensland, Brisbane, QLD, Australia. 7. School of Population Health, University of Auckland, Auckland, New Zealand. 8. Centre for Health Economics, Monash Business School, Monash University, Melbourne, VIC, Australia.
Abstract
OBJECTIVES: The objective of this study is to examine, from a limited societal perspective, the cost-effectiveness of community-based obesity prevention interventions (CBIs)-defined as a programme of community-level strategies to promote healthy eating and physical activity for Australian children (aged 5-18 years). METHODS: The effectiveness of CBIs was determined by undertaking a literature review and meta-analysis. Commonly implemented strategies to increase physical activity and improve nutrition were costed (in 2010 Australian dollars) to determine the average cost of a generic programme. A multiple cohort Markov model that simulates diseases associated with overweight and obesity was used to estimate the health benefits, measured as health-adjusted life years (HALYs) and healthcare-related cost offsets from diseases averted due to exposure to the intervention. Health and cost outcomes were estimated over the lifetime of the target population. Monte-Carlo simulation was used to assess second-order uncertainty of input parameters to estimate mean incremental cost-effectiveness ratios (ICER) with 95% uncertainty intervals (UIs). Scenario analyses tested variations in programme intensity, target population, and duration of effect. RESULTS: The meta-analysis revealed a small but significant difference in BMI z-score (mean difference of - 0.07 (95% UI: - 0.13 to - 0.01)) favouring the CBI community compared with the control. The estimated net cost of implementing CBIs across all local government areas (LGAs) in Australia was AUD426M (95% UI: AUD3M to AUD823M) over 3 years. This resulted in 51,792 HALYs gained (95% UI: 6816 to 96,972) over the lifetime of the cohort. The mean ICER was AUD8155 per HALY gained (95% UI: AUD237 to AUD81,021), with a 95% probability of being cost-effective at a willingness to pay threshold of AUD50,000 per HALY. CONCLUSIONS: CBIs are cost-effective obesity prevention initiatives; however, implementation across Australia will be (relatively) expensive when compared with current investments in preventive health.
OBJECTIVES: The objective of this study is to examine, from a limited societal perspective, the cost-effectiveness of community-based obesity prevention interventions (CBIs)-defined as a programme of community-level strategies to promote healthy eating and physical activity for Australian children (aged 5-18 years). METHODS: The effectiveness of CBIs was determined by undertaking a literature review and meta-analysis. Commonly implemented strategies to increase physical activity and improve nutrition were costed (in 2010 Australian dollars) to determine the average cost of a generic programme. A multiple cohort Markov model that simulates diseases associated with overweight and obesity was used to estimate the health benefits, measured as health-adjusted life years (HALYs) and healthcare-related cost offsets from diseases averted due to exposure to the intervention. Health and cost outcomes were estimated over the lifetime of the target population. Monte-Carlo simulation was used to assess second-order uncertainty of input parameters to estimate mean incremental cost-effectiveness ratios (ICER) with 95% uncertainty intervals (UIs). Scenario analyses tested variations in programme intensity, target population, and duration of effect. RESULTS: The meta-analysis revealed a small but significant difference in BMI z-score (mean difference of - 0.07 (95% UI: - 0.13 to - 0.01)) favouring the CBI community compared with the control. The estimated net cost of implementing CBIs across all local government areas (LGAs) in Australia was AUD426M (95% UI: AUD3M to AUD823M) over 3 years. This resulted in 51,792 HALYs gained (95% UI: 6816 to 96,972) over the lifetime of the cohort. The mean ICER was AUD8155 per HALY gained (95% UI: AUD237 to AUD81,021), with a 95% probability of being cost-effective at a willingness to pay threshold of AUD50,000 per HALY. CONCLUSIONS: CBIs are cost-effective obesity prevention initiatives; however, implementation across Australia will be (relatively) expensive when compared with current investments in preventive health.
Authors: Arantzazu Arrospide; Oliver Ibarrondo; Iván Castilla; Igor Larrañaga; Javier Mar Journal: Med Decis Making Date: 2021-10-11 Impact factor: 2.583
Authors: Jaithri Ananthapavan; Marj Moodie; Andrew Milat; Lennert Veerman; Elizabeth Whittaker; Rob Carter Journal: Health Res Policy Syst Date: 2021-12-19