Predicting panel attrition in longitudinal HRQoL surveys during the COVID-19 pandemic in the US

• Published in: Health and quality of life outcomes Vol. 20; no. 1; pp. 104 - 12
• Main Authors: Yu, Tianzhou, Chen, Jiafan, Gu, Ning Yan, Hay, Joel W, Gong, Cynthia L
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 06.07.2022; BioMed Central; BMC
• Subjects: Age; Age composition; Analysis; Bias; Coronaviruses; COVID-19; COVID-19 - epidemiology; Data collection; Demographic variables; Demographics; Demography; Disease control; Epidemics; Ethnicity; Health aspects; Health surveys; Hispanic people; Humans; Longitudinal Studies; Medical research; Mental disorders; Mental health; Minority & ethnic groups; Norms; Pandemics; Panel attrition; Polls & surveys; Population; Productivity; Quality of Life; Questionnaires; Socioeconomics; Surveys; Surveys and Questionnaires; Variables; Working hours; United States; United States > US; COVID-19; Panel attrition; Quality of life
• ISSN: 1477-7525; 1477-7525
• DOI: 10.1186/s12955-022-02015-8

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. 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. 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. 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.