Estimating the causal effect of genetic liability to prevalent disease on hospital costs using Mendelian Randomization
BACKGROUND Accurate measurement of the effects of disease status on healthcare cost is important in the pragmatic evaluation of interventions but is complicated by endogeneity biases due to omitted variables and reverse causality. Mendelian Randomization, the use of random perturbations in germline genetic variation as instrumental variables, can avoid these limitations. We report a novel Mendelian Randomization analysis of the causal effect of liability to disease on healthcare costs. METHODS We used Mendelian Randomization to model the causal impact on inpatient hospital costs of liability to six highly 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 using data from UK Biobank, a large prospective cohort study of individuals linked to records of hospital care. We assessed potential violations of the instrumental variable assumptions, particularly the exclusion restriction (i.e. variants affecting costs through alternative paths). We also conducted new genome wide association studies of hospital costs within the UK Biobank cohort as a further split sample sensitivity analysis. RESULTS We analyzed data on 307,032 individuals. Genetic variants explained only a small portion of the variance in each disease phenotype. Liability to coronary heart disease had substantial impacts (mean per person per year increase in costs from allele score Mendelian Randomization models: 712 pounds sterling (95% confidence interval: 238 pounds to 1,186 pounds)) on inpatient hospital costs in causal analysis, but other results were imprecise. There was concordance of findings across varieties of sensitivity analyses, including stratification by sex, and those obtained from the split sample analysis. CONCLUSION A novel Mendelian Randomization analysis of the causal effect of liability to disease on healthcare cost demonstrates that this type of analysis is feasible and informative in this context. There was concordance across data sources and across methods bearing different assumptions. Selection into the relatively healthy UK Biobank cohort and the modest proportion of variance in disease status accounted for by the allele scores reduced the precision of our estimates. We therefore could not exclude the possibility of substantial costs due to these diseases.
- Downloaded 388 times
- Download rankings, all-time:
- Site-wide: 87,280
- In health economics: 119
- Year to date:
- Site-wide: 75,343
- Since beginning of last month:
- Site-wide: 83,746
Downloads over time
Distribution of downloads per paper, site-wide
- 27 Nov 2020: The website and API now include results pulled from medRxiv as well as bioRxiv.
- 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
- 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
- 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
- 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
- 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
- 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
- 22 Jan 2019: Nature just published an article about Rxivist and our data.
- 13 Jan 2019: The Rxivist preprint is live!