Estimating the causal effect of genetic liability to prevalent disease on hospital costs using Mendelian Randomization

Accurate measurement of the effects of disease status on healthcare costs is important in the pragmatic evaluation of interventions but is complicated by endogeneity bias. Mendelian Randomization, the use of random perturbations in germline genetic variation as instrumental variables, can avoid these limitations. We used Mendelian Randomization to model the causal impact on inpatient hospital costs of liability to six prevalent diseases: asthma, eczema, migraine, coronary heart disease, type 2 diabetes, and major depressive disorder. We identified genetic variants from replicated genome-wide associations studies and estimated their association with inpatient hospital costs on over 300,000 individuals. Liability to coronary heart disease had substantial impacts on costs, but other results were imprecise. There was concordance of findings across varieties of sensitivity analyses, including stratification by sex and methods robust to violations of the exclusion restriction.