SPEM · Semi-Parametric Econometric Models: Health, Obesity and Patient Expenditures

Semi-Parametric Econometric Models: Health, Obesity and Patient Expenditures

What is the problem/issue being addressed? Econometric modelling of healthcare costs serves many purposes: to obtain key parameters in economic evaluation of new technologies based on their cost-effectiveness; to implement risk adjustment in public and private insurance systems; and to examine the health care costs attributable to risk factors such as smoking and obesity. Modelling healthcare costs is challenging because the distribution of cost data are typically non-normal, with heavy tails and a highly skewed distribution. It becomes even more challenging when the relationship between key predictors and costs is complex. Econometric methods that can accommodate these features are scarce and often strong assumptions need to be made to facilitate the data analysis. This may lead to biased results. Why is it important for society? To model the relationship between key predictors and costs (as well as other health outcome variables) reliably is crucial for policy making in areas such as economic evaluation and risk adjustment. Biased or imprecise estimates can lead to misleading evidence and hence incorrect decisions and subsequently loss of social welfare. What are the overall objectives? The primary objective of SPEM is to develop semi-parametric methods that build upon Generalised Linear Models (GLMs). GLMs are the benchmark approach for analysing medical costs and many other types of health data. SPEM will apply the new methods to produce accurate and robust estimates of the relationship between childhood obesity and healthcare costs, which are influential in the design and evaluation of government programmes aimed at treating and preventing childhood obesity.

Data: CORDIS, © European Union