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HE@Cam: Padraig Dixon - The causal effect of BMI on inpatient hospital costs: Mendelian Randomization analysis of the UK Biobank cohort

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Health Economics at Cambridge (HE@Cam) are happy to present Dr Padraig Dixon, University of Bristol, for a talk on the use of new methods for estimating the causal effects of body mass index (BMI) upon inpatient hospital costs.

Almost all evidence of the association between obesity and inpatient costs is based on observational research prone to bias because of reverse causation, measurement error, and residual confounding. During his talk, Padraig will discuss the first use of genetic variants in a Mendelian Randomization framework to estimate the causal effect of BMI (or any other disease/trait) on healthcare costs. This type of analysis can be used to inform the cost-effectiveness of interventions and policies targeting the prevention and treatment of overweight and obesity, and for setting research priorities.

More information and full abstract can be found below. More information about Health Economics at Cambridge (HE@Cam) can be found at our website

Time: 15:00 – 16:00 Monday 3rd December 2018 Venue: Large seminar Room, Institute of Public Health, Cambridge

All welcome. Part of the HE@Cam seminars 2017/18 series. For any questions, please contact healtheconomics@medschl.cam.ac.uk

Background High adiposity as measured by body mass index (BMI) is associated with increased healthcare costs. Understanding this association is important for the formulation and evaluation of healthcare policies targeting overweight and obesity, for identifying research priorities, and for planning future healthcare budgets. However, almost all evidence of this association is based on observational research prone to bias because of reverse causation, measurement error, and residual confounding.

Methods We used germline genetic variants as instrumental variables (IVs) – a method known as Mendelian Randomization – to obtain causal estimates of the effect of BMI on inpatient hospital costs. These variants – pieces of the genetic code that differ between individuals – are precisely measured, are generally independent of confounders and are not affected by reverse causation. We estimated IV models of the marginal causal effect of BMI using 79 variants robustly associated with BMI in the largest and most recent genome-wide association study of BMI . The association of these variants with inpatient costs was modelled in a two-sample Mendelian Randomization analysis using data from UK Biobank, a large prospective cohort study (n=502,617) linked to records of inpatient hospital care. We assessed potential violations of the instrumental variable assumptions, particularly the exclusion restriction via pleiotropy (i.e. variants affecting costs through paths other than BMI ) using median-based IV methods (which are consistent if no more than 50% of instruments are invalid), and mode-based IV models (which clusters IVs into groups based on similarity of causal effects). We investigated potential non-linear effects of BMI on hospital costs.

Results Preliminary analysis suggests that the causal Mendelian Randomization effect sizes are almost twice as large as the observational effect sizes. These effects attenuated under median and mode-based sensitivity analyses, but effect sizes remained larger than observational estimates. There was some evidence for modest non-linear effects.

Conclusions This paper is the first to use genetic variants in a Mendelian Randomization framework to estimate the causal effect of BMI (or any other disease/trait) on healthcare costs. This type of analysis can be used to inform the cost-effectiveness of interventions and policies targeting the prevention and treatment of overweight and obesity, and for setting research priorities.

This talk is part of the Health Economics @ Cambridge series.

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