Using Mendelian Randomisation to model the causal effect of cancer on health economic outcomes and to simulate the cost-effectiveness of anti-cancer interventions
Cancer is associated with significant economic impacts. Quantifying the scale of these impacts is challenged by confounding variables that jointly influence both cancer status and economic outcomes such as healthcare costs and quality of life. Moreover, the increasing costs attributed to cancer drug development complicate the cost-effective provision of cancer care.
Padraig Dixon addresses both challenges in this work by using germline genetic variation in the risk of incident cancer as instrumental variables in Mendelian Randomisation analyses of eight cancers. They developed causal estimates of the effect of bladder, breast, colorectal, lung, multiple myeloma, ovarian, prostate, and thyroid cancers on healthcare costs and quality adjusted life years (QALYs) using outcome data drawn from the UK Biobank cohort. They then used these results and methodologies to model the cost-effectiveness of a hypothetical population-wide preventative intervention based on a repurposed class of anti-diabetic drugs known as sodium-glucose co-transporter-2 (SGLT2) inhibitors very recently shown to reduce the odds of incident prostate cancer.
Genetic liability to prostate and breast cancers had material causal impacts on health economic outcomes. Mendelian Randomisation results for the less common cancers were associated with considerable uncertainty. SGLT2 inhibition was unlikely to be a cost-effective preventative intervention for prostate cancer, although this conclusion depended on the price at which these drugs would be offered for a novel anti-cancer indication.
These methods can be used to rapidly and efficiently estimate intervention cost-effectiveness for any disease or trait where Mendelian Randomisation is feasible.
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Padraig Dixon, University of Oxford
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