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首页> 美国卫生研究院文献>American Journal of Epidemiology >Deployment and Alcohol Use in a Military Cohort: Use of Combined Methods to Account for Exposure-Related Covariates and Heterogeneous Response to Exposure
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Deployment and Alcohol Use in a Military Cohort: Use of Combined Methods to Account for Exposure-Related Covariates and Heterogeneous Response to Exposure

机译：军事队列中的部署和酒精使用：使用组合方法解决与暴露有关的协变量和对暴露的异质反应

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摘要

Studies have shown that combat-area deployment is associated with increases in alcohol use; however, studying the influence of deployment on alcohol use faces 2 complications. First, the military considers a confluence of factors before determining whether to deploy a service member, creating a nonignorable exposure and unbalanced comparison groups that inevitably complicate inference about the role of deployment itself. Second, regression analysis assumes that a single effect estimate can approximate the population's change in postdeployment alcohol use, which ignores previous studies that have documented that respondents tend to exhibit heterogeneous postdeployment drinking behaviors. Therefore, we used propensity score matching to balance baseline covariates for the 2 comparison groups (deployed and nondeployed), followed by a variable-oriented difference-in-differences approach to account for the confounding and a person-oriented approach using a latent growth mixture model to account for the heterogeneous response to deployment in this prospective cohort study of the US Army National Guard (2009–2014). We observed a nonsignificant increase in estimated monthly drinks in the first year after deployment that regressed to predeployment drinking levels 2 years after deployment. We found a 4-class model that fit these data best, suggesting that common regression analyses likely conceal substantial interindividual heterogeneity in postdeployment alcohol-use behaviors.
机译：研究表明，战斗区的部署与酒精使用量的增加有关。然而，研究部署对饮酒的影响面临2个复杂问题。首先，军方在确定是否部署服务人员之前考虑了各种因素的影响，形成了不可忽视的影响力和不平衡的比较小组，这不可避免地使对部署本身作用的推断变得复杂。其次，回归分析假设单个影响估计值可以近似估计人口在部署后饮酒方面的变化，而忽略了先前的研究，该研究已记录了受访者倾向于表现出不同的部署后饮酒行为。因此，我们使用倾向得分匹配来平衡两个比较组（部署和非部署）的基线协变量，然后采用变量导向的差异方法解决混杂问题，并采用以人为导向的方法使用潜在增长混合物该模型在美国陆军国民警卫队的这项前瞻性队列研究（2009-2014年）中解释了对部署的异类反应。我们观察到，部署后第一年的每月估计饮料量没有显着增加，但在部署两年后恢复到部署前的饮水量。我们发现了一个最适合这些数据的4类模型，表明常见的回归分析可能掩盖了部署后酒精使用行为中大量个体之间的异质性。

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