Sadeghi Reza, Delavari Heravi Maryam, Naghibzadeh-Tahami Ahmad, Abadi Niloofar Ebrahim, Masoodi Mahmoud Reza, Mashayekhi Minoo, Mirzaei Maryam, Aryaie Mohammad
Department of Public Health, Sirjan School of Medical Sciences, Sirjan, Iran.
Department of Public Health Sciences. Neyshabur University of Medical Sciences, Neyshabur, Iran.
Tanaffos. 2022 Mar;21(3):330-335.
Unmeasured confounding is the primary obstacle to causal inference in observational research. We aimed to illuminate the association between exposure to influenza vaccination (IV) within six months before contracting the coronavirus disease (COVID-19) and COVID-19 hospitalization in relation to unmeasured confounding using the E-value method.
Information about 367 patients, 103 of whom (28.07 %) had received IV, and confounders included sex, age, occupation, cigarette smoking, opium, and comorbidities were collected. We estimated the interest association using the inverse probability weighted (IPW) method. There was no information on some potential unmeasured confounders, such as socioeconomic status. Therefore, we computed E-value as a sensitivity analysis, which is the minimum strength of unmeasured confounding to explain away an exposure-outcome association beyond the measured confounders completely.
IPW denoted 1.12 (95% CI: 0.71 to 1.29) times greater risk of COVID-19 hospitalization in patients exposed to IV than in unexposed individuals. Sensitivity analysis demonstrated that an E-value (95% CI) of 1.49 (1.90 to 2.15) is required to shift the RR and the corresponding confidence Interval (CI) lower and upper limits toward the null. Moreover, if they had been omitted, the most computed E-values for measured confounders were relatively larger than for unmeasured confounders.
According to the context of the measured confounders, if they had been omitted, an E-value of 1.16 to 1.76, a weaker confounding could fully explain away the reported association, suggesting that no relationship exists between IV and COVID-19 hospitalization.
未测量的混杂因素是观察性研究中因果推断的主要障碍。我们旨在使用E值方法阐明在感染冠状病毒病（COVID-19）前六个月内接种流感疫苗（IV）与COVID-19住院之间的关联以及未测量的混杂因素。
收集了367例患者的信息，其中103例（28.07%）接受了IV，混杂因素包括性别、年龄、职业、吸烟、吸食鸦片和合并症。我们使用逆概率加权（IPW）方法估计感兴趣的关联。没有关于一些潜在未测量混杂因素的信息，如社会经济地位。因此，我们计算E值作为敏感性分析，E值是未测量混杂因素的最小强度，以完全解释除测量的混杂因素之外的暴露-结局关联。
IPW表示，暴露于IV的患者COVID-19住院风险比未暴露个体高1.12倍（95%CI：0.71至1.29）。敏感性分析表明，需要E值（95%CI）为1.49（1.90至2.15）才能使RR及相应的置信区间（CI）的下限和上限向无效值移动。此外，如果遗漏了这些因素，测量的混杂因素计算出的大多数E值相对大于未测量的混杂因素。
根据测量的混杂因素情况，如果遗漏这些因素，E值为1.16至1.76，较弱的混杂因素就能完全解释所报告的关联，这表明IV与COVID-19住院之间不存在关系。
