Nuffield Department of Primary Care Health Sciences, University of Oxford, UK.
MRC Integrative Epidemiology Unit, University of Bristol, UK.
Nicotine Tob Res. 2024 Oct 22;26(11):1521-1529. doi: 10.1093/ntr/ntae089.
Knowledge of the impact of smoking on health care 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.
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 39-72 years) in the UK Biobank. We followed individuals up for a mean of 6 years.
Genetic liability to initiate smoking (ever vs. 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.
Our findings have implications for the potential cost-effectiveness of smoking interventions.
We report the first Mendelian Randomization analysis of the causal effect of smoking on health care costs. Using two 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 antismoking interventions and to understand the scale of externalities associated with this behavior.
了解吸烟对医疗保健成本的影响对于确定吸烟的外部效应以及评估旨在改变这种行为的政策非常重要。由于忽略变量偏差、反向因果关系和测量误差，对这种关联的常规分析存在困难。
我们使用孟德尔随机化研究设计来解决这些挑战；将与吸烟行为相关的遗传变异作为工具变量模型中的自变量，将住院医疗费用（根据电子健康记录计算）作为因变量。我们进行了全基因组关联研究，以确定与吸烟开始和复合吸烟指数（反映吸烟对健康的累积影响）相关的遗传变异，该研究纳入了多达 300045 名个体（平均基线年龄为 57 岁，范围为 39-72 岁）。我们对个体进行了平均 6 年的随访。
遗传易感性与吸烟开始（曾经吸烟与从不吸烟）估计使每位患者每年的住院医疗费用平均增加 477 英镑（95%置信区间（CI）：187 至 766 英镑）。复合吸烟指数的遗传易感性每增加一个单位（范围：0-4.0），住院医疗费用就会增加 204 英镑（95%CI：105 至 303 英镑）。有一些证据表明，复合吸烟指数因果模型违反了工具变量假设，所有孟德尔随机化模型的估计都存在很大的不确定性。基于风险容忍度的模型在弱工具偏差下并不稳健。
我们的发现对吸烟干预的潜在成本效益有影响。
我们报告了吸烟对医疗保健成本因果影响的第一项孟德尔随机化分析。使用两种吸烟表型，我们确定了吸烟对住院医疗费用的重大影响，尽管因果模型存在很大的不确定性。这些结果可以与其他关于吸烟影响的证据一起用于评估反吸烟干预的成本效益，并了解与这种行为相关的外部性的规模。
