预测美国 COVID-19 大流行期间纵向 HRQoL 调查中的小组流失。
Predicting panel attrition in longitudinal HRQoL surveys during the COVID-19 pandemic in the US.
机构信息
Department of Pharmaceutical and Health Economics, School of Pharmacy, University of Southern California, Los Angeles, CA, USA.
School of Nursing and Health Professions, University of San Francisco, San Francisco, CA, USA.
出版信息
Health Qual Life Outcomes. 2022 Jul 6;20(1):104. doi: 10.1186/s12955-022-02015-8.
BACKGROUND
Online longitudinal surveys may be subject to potential biases due to sample attrition. This study was designed to identify potential predictors of attrition using a longitudinal panel survey collected during the COVID-19 pandemic.
METHODS
Three waves of data were collected using Amazon Mechanical Turk (MTurk), an online crowd-sourced platform. For each wave, the study sample was collected by referencing a US national representative sample distribution of age, gender, and race, based on US census data. Variables included respondents' demographics, medical history, socioeconomic status, COVID-19 experience, changes of health behavior, productivity, and health-related quality of life (HRQoL). Results were compared to pre-pandemic US norms. Measures that predicted attrition at different times of the pandemic were identified via logistic regression with stepwise selection.
RESULTS
1467 of 2734 wave 1 respondents participated in wave 2 and, 964 of 2454 wave 2 respondents participated in wave 3. Younger age group, Hispanic origin (p ≤ 0.001) and higher self-rated survey difficulty (p ≤ 0.002) consistently predicted attrition in the following wave. COVID-19 experience, employment, productivity, and limited physical activities were commonly observed variables correlated with attrition with specific measures varying by time periods. From wave 1, mental health conditions, average daily hours worked (p = 0.004), and COVID-19 impact on work productivity (p < 0.001) were associated with a higher attrition rate at wave 2, additional to the aforementioned factors. From wave 2, support of social distancing (p = 0.032), being Republican (p < 0.001), and having just enough money to make ends meet (p = 0.003) were associated with predicted attrition at wave 3.
CONCLUSIONS
Attrition in this longitudinal panel survey was not random. Besides commonly identified demographic factors that contribute to panel attrition, COVID-19 presented novel opportunities to address sample biases by correlating attrition with additional behavioral and HRQoL factors in a constantly evolving environment. While age, ethnicity, and survey difficulty consistently predicted attrition, other factors, such as COVID-19 experience, changes of employment, productivity, physical health, mental health, and financial situation impacted panel attrition during the pandemic at various degrees.
背景
在线纵向调查可能由于样本流失而存在潜在偏差。本研究旨在通过在 COVID-19 大流行期间收集的纵向面板调查，确定潜在的流失预测因素。
方法
使用亚马逊 Mechanical Turk（MTurk），一个在线众包平台，收集了三波数据。对于每一波，都是通过参考美国人口普查数据中基于年龄、性别和种族的全国代表性样本分布来收集研究样本。变量包括受访者的人口统计学特征、病史、社会经济地位、COVID-19 经历、健康行为变化、生产力和健康相关生活质量（HRQoL）。结果与大流行前的美国标准进行了比较。通过逐步选择的逻辑回归，确定了在大流行不同时间预测流失的措施。
结果
1467 名第 1 波受访者中有 964 名参加了第 2 波，2454 名第 2 波受访者中有 964 名参加了第 3 波。年轻的年龄组、西班牙裔（p≤0.001）和更高的自我报告调查难度（p≤0.002）始终预示着在下一波中流失。COVID-19 经历、就业、生产力和有限的体育活动是常见的与流失相关的变量，具体措施因时间段而异。从第 1 波开始，心理健康状况、平均每天工作时间（p=0.004）和 COVID-19 对工作生产力的影响（p<0.001）与第 2 波的更高流失率相关，除了上述因素外。从第 2 波开始，支持社会疏远（p=0.032）、共和党人（p<0.001）和刚刚够维持生计的钱（p=0.003）与第 3 波的预测流失相关。
结论
在这项纵向面板调查中，流失并不是随机的。除了通常确定的导致面板流失的人口统计学因素外，COVID-19 通过将流失与不断变化的环境中的其他行为和 HRQoL 因素相关联，为解决样本偏差提供了新的机会。虽然年龄、种族和调查难度始终预测流失，但其他因素，如 COVID-19 经历、就业变化、生产力、身体健康、心理健康和财务状况在大流行期间以不同程度影响了面板流失。
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