Mendelian Randomization analysis of the causal effect of cigarette smoking on hospital costs

INTRODUCTION
Knowledge of the impact of smoking on healthcare costs is important for establishing the external effects of smoking and for evaluating policies intended to modify this behavior. Conventional analysis of this association is difficult because of omitted variable bias, reverse causality, and measurement error.

METHODS
We approached these challenges using a Mendelian Randomization study design; genetic variants associated with smoking behaviors were used in instrumental variables models with inpatient hospital costs (calculated from electronic health records) as the outcome. We undertook genome wide association studies to identify genetic variants associated with smoking initiation and a composite smoking index (reflecting cumulative health impacts of smoking) on up to 300,045 individuals (mean age: 57 years at baseline, range 39 to 72 years) in the UK Biobank. We followed individuals up for a mean of six years.

RESULTS
Genetic liability to initiate smoking (ever versus never smoking) was estimated to increase mean per-patient annual inpatient hospital costs by £477 (95% confidence interval (CI): £187 to £766). A one-unit change in genetic liability to the composite smoking index (range: 0-4.0) increased inpatient hospital costs by £204 (95% CI: £105 to £303) per unit increase in this index. There was some evidence that the composite smoking index causal models violated the instrumental variable assumptions, and all Mendelian Randomization models were estimated with considerable uncertainty. Models conditioning on risk tolerance were not robust to weak instrument bias.

CONCLUSIONS
Our findings have implications for the potential cost-effectiveness of smoking interventions.

IMPLICATIONS
We report the first Mendelian Randomization analysis of the causal effect of smoking on healthcare costs. Using two distinct smoking phenotypes, we identified substantial impacts of smoking on inpatient hospital costs, although the causal models were associated with considerable uncertainty. These results could be used alongside other evidence on the impact of smoking to evaluate the cost-effectiveness of anti-smoking interventions and to understand the scale of externalities associated with this behaviour.