A novel decision model to predict the impact of weight management interventions: The Core Obesity Model. (9th March 2021)

• Record Type: Journal Article
• Authors: Lopes, Sandra; Meincke, Henrik H.; Lamotte, Mark; Olivieri, Anamaria‐Vera; Lean, Michael E. J.
• Is Part Of: Obesity science and practice. Volume 7:Number 3(2021)
• Page Start: 269
• Page End: 280
• Publication Date: 2021-03-09
• Subjects: cost‐effectiveness -- health economics -- obesity therapy; Obesity -- Periodicals; 616.398005
• DOI: 10.1002/osp4.495
• Languages: English
• ISSNs: 2055-2238

Abstract

Abstract: Aims: Models are needed to quantify the economic implications of obesity in relation to health outcomes and health‐related quality of life. This report presents the structure of the Core Obesity Model (COM) and compare its predictions with the UK clinical practice data. Materials and methods: The COM is a Markov, closed‐cohort model, which expands on earlier obesity models by including prediabetes as a risk factor for type 2 diabetes (T2D), and sleep apnea and cancer as health outcomes. Selected outcomes predicted by the COM were compared with observed event rates from the Clinical Practice Research Datalink‐Hospital Episode Statistics (CPRD‐HES) study. The importance of baseline prediabetes prevalence, a factor not taken into account in previous economic models of obesity, was tested in a scenario analysis using data from the 2011 Health Survey of England. Results: Cardiovascular (CV) event rates predicted by the COM were well matched with those in the CPRD‐HES study (7.8–8.5 per 1000 patient‐years across BMI groups) in both base case and scenario analyses (8.0–9.4 and 8.6–9.9, respectively). Rates of T2D were underpredicted in the base case (1.0–7.6 vs. 2.1–22.7) but increased to match those observed in CPRD‐HES for some BMI groups when a prospectively collected prediabetes prevalence was used (2.7–13.1). Mortality rates in the CPRD‐HES were consistently higher than the COM predictions, especially in higher BMI groups. Conclusions: The COM predicts the occurrence of CV events and T2D with a good degree of accuracy, particularly when prediabetes is included in the model, indicating the importance of considering this risk factor in economic models of obesity.