The Epidemic-Pandemic Impacts Inventory (EPII) was developed to assess pandemic-related adverse and positive experiences across several key domains, including work/employment, home life, isolation, and quarantine. Several studies have associated EPII-assessed pandemic-related experiences with a wide range of psychosocial factors, most commonly depressive and anxiety symptoms. The present study investigated the degree to which specific types of COVID-19 pandemic-related experiences may be associated with anxiety and depression risk, capitalizing on two large, independent samples with marked differences in sociodemographic characteristics. The present study utilized two adult samples: participants (N = 635) recruited online over a 4-week period in early 2020 (Sample 1) and participants (N = 908) recruited from the student body of a large Northeastern public university (Sample 2). We employed a cross-validated, least absolute shrinkage and selection operator (LASSO) regression approach, as well as a random forest (RF) machine learning algorithm, to investigate classification accuracy of anxiety/depression risk using the pandemic-related experiences from the EPII. The LASSO approach isolated eight items within each sample. Two items from the work/employment and emotional/physical health domains overlapped across samples. The RF approach identified similar items across samples. Both methods yielded acceptable cross-classification accuracy. Applying two analytic approaches on data from two large, sociodemographically unique samples, we identified a subset of sample-specific and nonspecific pandemic-related experiences from the EPII that are most predictive of concurrent depression/anxiety risk. Findings may help to focus on key experiences during future public health disasters that convey greater risk for depression and anxiety symptoms. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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流行病-大流行影响清单（EPII）：一项多样本研究，检查与大流行相关的经历及其与心理健康的关系。

流行病-大流行影响清单 (EPII) 旨在评估几个关键领域与大流行相关的不利和积极经历，包括工作/就业、家庭生活、隔离和检疫。几项研究将 EPII 评估的流行病相关经历与多种社会心理因素（最常见的抑郁和焦虑症状）联系起来。本研究利用社会人口学特征显着差异的两个大型独立样本，调查了特定类型的 COVID-19 大流行相关经历可能与焦虑和抑郁风险相关的程度。本研究使用了两个成人样本：2020 年初在 4 周内在线招募的参与者 (N = 635)（样本 1）和从东北部大型公立大学的学生群体中招募的参与者 (N = 908)（样本 2） ）。我们采用交叉验证的最小绝对收缩和选择算子 (LASSO) 回归方法以及随机森林 (RF) 机器学习算法，利用 EPII 中与大流行相关的经验来研究焦虑/抑郁风险的分类准确性。LASSO 方法在每个样本中分离出八个项目。来自工作/就业和情感/身体健康领域的两个项目在样本中重叠。RF 方法在样本中识别出相似的项目。两种方法都产生了可接受的交叉分类精度。通过对来自两个大型、社会人口学独特样本的数据应用两种分析方法，我们从 EPII 中确定了样本特异性和非特异性流行病相关经历的子集，这些经历最能预测并发抑郁/焦虑风险。研究结果可能有助于关注未来公共卫生灾难期间的关键经历，这些经历会带来更大的抑郁和焦虑症状风险。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。