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The effect of weight on labor market outcomes: An application of genetic instrumental variables.

ABSTRACT: This paper contributes to the literature on the labor market consequences of obesity by using a novel instrument: genetic risk score, which reflects the predisposition to higher body mass index (BMI) across many genetic loci. We estimate instrumental variable models of the effect of BMI on labor market outcomes using Finnish data that have many strengths, for example, BMI that is measured rather than self-reported, and data on earnings and social income transfers that are from administrative tax records and are thus free of the problems associated with nonresponse, reporting error or top coding. The empirical results are sensitive to whether we use a narrower or broader genetic risk score, and to model specification. For example, models using the narrower genetic risk score as an instrument imply that a one-unit increase in BMI is associated with 6.9% lower wages, 1.8% fewer years employed, and a 3 percentage point higher probability of receiving any social income transfers. However, when we use a newer, broader genetic risk score, we cannot reject the null hypothesis of no effect. Future research using genetic risk scores should examine the sensitivity of their results to the risk score used.

SUBMITTER: Bockerman P

PROVIDER: S-EPMC6585973 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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The effect of weight on labor market outcomes: An application of genetic instrumental variables.

Böckerman Petri P Cawley John J Viinikainen Jutta J Lehtimäki Terho T Rovio Suvi S Seppälä Ilkka I Pehkonen Jaakko J Raitakari Olli O

Health economics 20180921 1

This paper contributes to the literature on the labor market consequences of obesity by using a novel instrument: genetic risk score, which reflects the predisposition to higher body mass index (BMI) across many genetic loci. We estimate instrumental variable models of the effect of BMI on labor market outcomes using Finnish data that have many strengths, for example, BMI that is measured rather than self-reported, and data on earnings and social income transfers that are from administrative tax ...[more]

PMID: 30240095

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Project description:BackgroundThis article demonstrates a means of assessing long-term intervention cost-effectiveness in the absence of data from randomized controlled trials and without recourse to Markov simulation or similar types of cohort simulation.MethodsUsing a Mendelian randomization study design, we developed causal estimates of the genetically predicted effect of bladder, breast, colorectal, lung, multiple myeloma, ovarian, prostate, and thyroid cancers on health care costs and quality-adjusted life-years (QALYs) using outcome data drawn from the UK Biobank cohort. We then used these estimates in a simulation model to estimate the cost-effectiveness of a hypothetical population-wide preventative intervention based on a repurposed class of antidiabetic drugs known as sodium-glucose cotransporter-2 (SGLT2) inhibitors very recently shown to reduce the odds of incident prostate cancer.ResultsGenetic liability to prostate cancer and breast cancer had material causal impacts on either or both health care costs and QALYs. Mendelian randomization 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 anticancer indication.ImplicationsOur new causal estimates of cancer exposures on health economic outcomes may be used as inputs into decision-analytic models of cancer interventions such as screening programs or simulations of longer-term outcomes associated with therapies investigated in randomized controlled trials with short follow-ups. Our method allowed us to rapidly and efficiently estimate the cost-effectiveness of a hypothetical population-scale anticancer intervention to inform and complement other means of assessing long-term intervention value.HighlightsThe article demonstrates a novel method of assessing long-term intervention cost-effectiveness without relying on randomized controlled trials or cohort simulations.Mendelian randomization was used to estimate the causal effects of certain cancers on health care costs and quality-adjusted life-years (QALYs) using data from the UK Biobank cohort.Given causal data on the association of different cancer exposures on costs and QALYs, it was possible to simulate the cost-effectiveness of an anticancer intervention.Genetic liability to prostate cancer and breast cancer significantly affected health care costs and QALYs, but the hypothetical intervention using SGLT2 inhibitors for prostate cancer may not be cost-effective, depending on the drug's price for the new anticancer indication. The methods we propose and implement can be used to efficiently estimate intervention cost-effectiveness and to inform decision making in all manner of preventative and therapeutic contexts.

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The effect of weight on labor market outcomes: An application of genetic instrumental variables.

Publications

The effect of weight on labor market outcomes: An application of genetic instrumental variables.

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