Skip to navigation Skip to main content Skip to footer

Approved research

Mendelian Randomization analysis of the causal effect of obesity and coronary artery disease on healthcare costs

Principal Investigator: Dr Padraig Dixon
Approved Research ID: 29294
Approval date: July 31st 2017

Lay summary

Accurate information on the healthcare costs associated with health conditions is required by healthcare funders. Observational studies that describe associations between health conditions and cost are prone to bias because of reverse causation, measurement error, and residual confounding. This proposal will use information on the relationship between genetic variants (single nucleotide polymorphisms) and two health conditions (obesity and coronary artery disease) to address the problems of conventional observational studies. The proposal will use genetic data linked to Hospital Episode Statistics to produce the first causal cost estimates of the effect of obesity and coronary artery disease using Mendelian Randomization methodology. The ultimate aim of the proposed research is to improve population health by increasing the quality of evidence available to decision makers responsible for the allocation of scarce healthcare resources. Evidence on the costs associated with healthcare conditions is relevant to healthcare policy evaluation, cost-effectiveness analysis, research prioritization and private sector decision-making such as in relation to the setting of healthcare insurance premiums. Moreover, a clearer appreciation of the consequences of health conditions for costs is likely to be of considerable interest to patients and to their families and carers. The effects of obesity and coronary artery disease on healthcare cost will be estimated by using data on certain types of genetic variants that are associated with these conditions. We will use data from replicated genome-wide association studies to obtain robust evidence of the associations between genetic variants and health conditions. We will obtain data on the association of variants and costs from the Biobank. Combining information on the effect of genetic variants on long-term conditions with their relationship to costs will allow important, new evidence to be generated concerning the cost impact of long term conditions. The full cohort will be eligible for inclusion in the analysis.