Among different investigators studying the same exposures and outcomes, there may be a lack of consensus about potential confounders that should be considered as matching, adjustment, or stratification variables in observational studies. Concerns have been raised that confounding factors may affect the results obtained for the alcohol-ischemic heart disease relationship, as well as their consistency and reproducibility across different studies. Therefore, we assessed how confounders are defined, operationalized, and discussed across individual studies evaluating the impact of alcohol on ischemic heart disease risk. For observational studies included in a recent alcohol-ischemic heart disease meta-analysis, we identified all variables adjusted, matched, or stratified for in the largest reported multivariate model (i.e. potential confounders). We recorded how the variables were measured and grouped them into higher-level confounder domains. Abstracts and Discussion sections were then assessed to determine whether authors considered confounding when interpreting their study findings. 85 of 87 (97.7%) studies reported multivariate analyses for an alcohol-ischemic heart disease relationship. The most common higher-level confounder domains included were smoking (79, 92.9%), age (74, 87.1%), and BMI, height, and/or weight (57, 67.1%). However, no two models adjusted, matched, or stratified for the same higher-level confounder domains. Most (74/87, 85.1%) articles mentioned or alluded to “confounding” in their Abstract or Discussion sections, but only one stated that their main findings were likely to be affected by residual confounding. There were five (5/87, 5.7%) authors that explicitly asked for caution when interpreting results. There is large variation in the confounders considered across observational studies evaluating the impact of alcohol on ischemic heart disease risk and almost all studies spuriously ignore or eventually dismiss confounding in their conclusions. Given that study results and interpretations may be affected by the mix of potential confounders included within multivariate models, efforts are necessary to standardize approaches for selecting and accounting for confounders in observational studies.

中文翻译：

---

评估饮酒对缺血性心脏病风险的流行病学研究中的混杂因素。

在研究相同暴露和结果的不同研究人员中，可能对在观察性研究中应被视为匹配、调整或分层变量的潜在混杂因素缺乏共识。有人担心混杂因素可能会影响酒精-缺血性心脏病关系的结果，以及它们在不同研究中的一致性和可重复性。因此，我们评估了在评估酒精对缺血性心脏病风险影响的各个研究中如何定义、操作和讨论混杂因素。对于最近的酒精缺血性心脏病荟萃分析中包含的观察性研究，我们确定了在最大报告的多变量模型（即潜在混杂因素）中调整、匹配或分层的所有变量。我们记录了如何测量变量并将它们分组到更高级别的混杂域中。然后评估摘要和讨论部分，以确定作者在解释他们的研究结果时是否考虑混淆。87 项研究中有 85 项 (97.7%) 报告了酒精与缺血性心脏病关系的多变量分析。最常见的高级混杂域包括吸烟 (79, 92.9%)、年龄 (74, 87.1%) 和 BMI、身高和/或体重 (57, 67.1%)。但是，没有两个模型针对相同的更高级别的混杂域进行调整、匹配或分层。大多数（74/87，85.1%）文章在摘要或讨论部分提到或暗示了“混杂”，但只有一篇文章表示他们的主要发现可能会受到残留混杂的影响。有五个 (5/87, 5. 7%) 在解释结果时明确要求谨慎的作者。在评估酒精对缺血性心脏病风险影响的观察性研究中，考虑的混杂因素存在很大差异，几乎所有研究都虚假地忽略或最终驳回了结论中的混杂因素。鉴于研究结果和解释可能会受到多变量模型中潜在混杂因素的影响，因此有必要努力使观察性研究中选择和解释混杂因素的方法标准化。