Fink David S, Keyes Katherine M, Calabrese Joseph R, Liberzon Israel, Tamburrino Marijo B, Cohen Gregory H, Sampson Laura, Galea Sandro
Am J Epidemiol. 2017 Aug 15;186(4):411-419. doi: 10.1093/aje/kww230.
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.
研究表明,战区部署与酒精使用量增加有关;然而,研究部署对酒精使用的影响面临两个复杂问题。首先,军方在决定是否部署一名军人之前会考虑多种因素,这就造成了一种不可忽视的暴露情况以及不平衡的比较组,这不可避免地使关于部署本身作用的推断变得复杂。其次,回归分析假定单一的效应估计值能够近似总体在部署后酒精使用量的变化,而这忽略了先前的研究,这些研究记录了受访者在部署后往往表现出不同的饮酒行为。因此,在这项针对美国陆军国民警卫队的前瞻性队列研究(2009 - 2014年)中,我们使用倾向得分匹配来平衡两个比较组(部署组和未部署组)的基线协变量,随后采用面向变量的差异分析方法来处理混杂因素,并采用面向个体的方法,即使用潜在增长混合模型来处理对部署的异质性反应。我们观察到,部署后第一年估计每月饮酒量有不显著的增加,在部署两年后又回归到部署前的饮酒水平。我们发现一个四类模型最适合这些数据,这表明常见的回归分析可能掩盖了部署后酒精使用行为中个体间的显著异质性。