Article
A decision-analytic framework and model to evaluate the effectiveness and cost effectiveness of a community-based intervention to prevent obesity in Austria
Search Medline for
Authors
-
Jùlia Santamaria Navarro
- Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
-
Beate Jahn
- Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
-
Daniela Gallmetzer-Schmid
- Division for Quantitative Methods in Public Health and Health Services Research, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
-
Kieran Tuohy
- Nutrition and Nutrigenomics Unit; Research and Innovation Center - Fondazione Edmund March, San Micehele all'Adige, Italy; School of Food Science & Nutrition, University of Leeds, Leeds, United Kingdom
-
Michael Laxy
- Professorship of Public Health and Prevention, School of Medicine and Health, Technical University of Munich, Munich, Germany
-
Uwe Siebert
- Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria; Institute for Technology Assessment and Department of Radiology; Massachusetts General Hospital; Harvard Medical School, Boston, United States; Division of Health Technology Assessment and Bioinformatics; ONCOTYROL - Center for Personalized Cancer Medicine, Innsbruck, Austria; Center for Health Decision Science; Departments of Epidemiology and Health Policy & Management, Harvard T.H. Chan School of Public Health, Boston, United States
-
Nikolai Mühlberger
- Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall in Tirol, Austria
| Published: | September 6, 2024 |
|---|
Outline
Text
Introduction: In Austria, the prevalence of overweight and obesity among individuals over 15 years of age was 52% in 2019 and is projected to keep rising [1]. Individual behaviors and lifestyles are greatly influenced by the environment and social contexts that individuals live in [2]. Based on this rationale, we previously conducted a systematic review on community-based interventions (CBIs) showing the short-term impact of such interventions in the reduction of body mass index (BMI). CBIs are multidisciplinary holistic approaches that include a wide range of strategies implemented in multiple settings and sectors [2], [3]. To our knowledge, no full economic evaluations of CBIs have been conducted in Austria. Our study, funded by the EUREGIO – Environment, Food and Health project [4], aims to develop a decision-analytic model (DAM) to evaluate the benefits, harms, costs and cost effectiveness of a CBI program compared to no intervention in the context of Austria.
Methods: We developed a framework and decision-analytic model (DAM) [5] structure to assess the effect of a CBI in an Austrian community. The CBI was selected from our previously conducted systematic review, leading to a short-term reduction in the BMI. The DAM evaluates long-term changes in health such as quality-adjusted life years, life years, number of deaths, and economic outcomes such as resource utilization and costs. To identify the diseases most strongly associated with obesity to be included in the model, a review was performed. We used Austrian data, if available, to populate the model, otherwise international data were applied.
Results: We developed a Markov model that depicts the possible course of events/diseases (stroke, Type 2 Diabetes mellitus, colorectal cancer, coronary heart disease or death) based on weight status (normal weight, overweight or obesity) and age of a healthy (free-of-events) adolescent starting cohort. The modeled diseases were chosen based on the strength of association reported in a consensus paper [6] identified in the review. We assumed the absence of concomitant diseases and the condition that once individuals experience an event, they cannot return to a state completely free of events. Transition probabilities were calculated using relative risks, incidence and mortality rates. Utilities were assigned to each health state and, in a final step, costs of each disease from a societal perspective were added to the different health states. The intervention effect (BMI-z-score reduction: -1.005; 95% CI -0.1541, -0.0555) from the selected CBI [7] was applied at the beginning through a shift in the distribution of the cohort among the different weight status categories in the free-of-events health state. Sensitivity analyses for various parameters was performed to evaluate uncertainty, as well as scenario analyses for different intervention effect assumptions.
Conclusion: The framework is currently being implemented and it will enable to estimate the effectiveness and cost effectiveness of a CBI in comparison to no intervention in Austria. Our decision-analytic obesity model will allow us to comprehensively inform decision makers and provide the flexibility to evaluate alternative obesity prevention measures to inform evidence-based healthcare decision making.
The authors declare that they have no competing interests.
The authors declare that a positive ethics committee vote has been obtained.
References
- 1.
- Eurostat [Internet]. Overweight and obesity - BMI statistics European Commission 2014. Available from: https://ec.europa.eu/eurostat/statistics-explained/index.php/Overweight_and_obesity_-_BMI_statistics
- 2.
- Merzel C, D'Afflitti J. Reconsidering community-based health promotion: promise, performance, and potential. Am J Public Health. 2003;93(4):557-74.
- 3.
- Ananthapavan J, Sacks G, Moodie M, Carter R. Economics of obesity--learning from the past to contribute to a better future. Int J Environ Res Public Health. 2014;11(4):4007-25.
- 4.
- EUREGIO. Environment, Food and Health. 2018. Available from: https://eventi.fmach.it/eng/EFH/Mission
- 5.
- Caro JJ, Briggs AH, Siebert U, Kuntz KM. Modeling good research practices--overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--1. Value Health. 2012;15(6):796-803.
- 6.
- Schwander B, Hiligsmann M, Nuijten M, Evers S. Systematic review and overview of health economic evaluation models in obesity prevention and therapy. Expert Rev Pharmacoecon Outcomes Res. 2016;16(5):561-70.
- 7.
- Economos CD, Hyatt RR, Goldberg JP, Must A, Naumova EN, Collins JJ, et al. A community intervention reduces BMI z-score in children: Shape Up Somerville first year results. Obesity (Silver Spring). 2007;15(5):1325-36.
