Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, Neuherberg, Bayern, Germany.
German Center for Diabetes Research, Neuherberg, Bayern, Germany.
Med Decis Making. 2020 Feb;40(2):156-169. doi: 10.1177/0272989X20905809.
Causal effect estimates for the association of obesity with health care costs can be biased by reversed causation and omitted variables. In this study, we use genetic variants as instrumental variables to overcome these limitations, a method that is often called Mendelian randomization (MR). We describe the assumptions, available methods, and potential pitfalls of using genetic information and how to address them. We estimate the effect of body mass index (BMI) on total health care costs using data from a German observational study and from published large-scale data. In a meta-analysis of several MR approaches, we find that models using genetic instruments identify additional annual costs of €280 for a 1-unit increase in BMI. This is more than 3 times higher than estimates from linear regression without instrumental variables (€75). We found little evidence of a nonlinear relationship between BMI and health care costs. Our results suggest that the use of genetic instruments can be a powerful tool for estimating causal effects in health economic evaluation that might be superior to other types of instruments where there is a strong association with a modifiable risk factor.
肥胖与医疗保健成本之间的关联的因果效应估计可能会受到反向因果关系和遗漏变量的偏差影响。在这项研究中，我们使用遗传变异作为工具变量来克服这些限制，这种方法通常被称为孟德尔随机化（MR）。我们描述了使用遗传信息的假设、可用方法和潜在陷阱，以及如何解决这些问题。我们使用来自德国观察性研究和已发表的大规模数据的信息来估计体重指数（BMI）对总医疗保健成本的影响。在对几种 MR 方法的荟萃分析中，我们发现使用遗传工具的模型确定 BMI 每增加 1 个单位，每年额外增加 280 欧元的成本。这比没有工具变量的线性回归估计值高出 3 倍以上（75 欧元）。我们几乎没有发现 BMI 和医疗保健成本之间存在非线性关系的证据。我们的结果表明，使用遗传工具可以成为健康经济评估中估计因果效应的有力工具，这可能优于与可改变的风险因素密切相关的其他类型的工具。
