Abstract
AimsModels 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 methodsThe 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.ResultsCardiovascular (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.ConclusionsThe 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.
Highlights
Rates of type 2 diabetes (T2D) were underpredicted in the base case (1.0–7.6 vs. 2.1–22.7) but increased to match those observed in Clinical Practice Research Datalink (CPRD)‐Hospital Episode Statistics (HES) for some body mass index (BMI) groups when a prospectively collected prediabetes prevalence was used (2.7–13.1)
The Core Obesity Model (COM) predicts the occurrence of CV events and T2D with a good degree of accuracy, when prediabetes is included in the model, indicating the importance of considering this risk factor in economic models of obesity
These models have been used to assess the cost‐utility of orlistat[9] and compare the cost‐effectiveness of orlistat, sibutramine, and rimonabant,[8] and to assess the cost‐effectiveness of the LighterLife weight management program[10] and the Weight Action Program[11] these previous models can be refined and improved upon; given the multifactorial nature of overweight and obesity and the range of associated complications, the incorporation of additional comorbidities and risk factors offers the potential to improve the accuracy of predictions
Summary
A recently published external validation of the COM showed that it reliably predicts the occurrence of obesity‐related complications.[41] The aim of this analysis was to assess how baseline glycemic status impacts model predictions using event rates sourced from a large analysis of merged patient data from the Clinical Practice Research Datalink (CPRD), Hospital Episode Statistics (HES), and the Office for National Statistics examining associations between BMI and obesity‐ related complications in a cohort of more than 2.9 million individuals followed up for a median of 11.4 years.[42]. The predicted rates remained relatively constant across groups (4.9–5.2), indicating that mortality rate predictions by the COM may be insensitive to changes in BMI This underprediction was confirmed by linear regression analysis (OLS LRL slope: 0.445; Table 9); the negative R2 value obtained from this analysis (−26.840; Table 9) limited the ability to fully interpret the result. All‐cause mortality predictions were consistent in the scenario analysis (Table 8), suggesting that prediabetes prevalence did not significantly affect mortality during the modeled time horizon
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