Addressing survey fatigue bias in longitudinal social contact studies to improve pandemic preparedness - Shozen Dan; Zhi Ling; Yu Chen; Joshua Tegegne; Veronika K. Jaeger; André Karch; Swapnil Mishra; Oliver Ratmann; The Machine Learning &Global Health network (Scientific Reports)
Abstract Social contact surveys are an important tool to assess infection risks within populations, and the effect of non-pharmaceutical interventions on social behaviour during disease outbreaks, epidemics, and pandemics. Numerous longitudinal social contact surveys were conducted during the COVID-19 era, however data analysis is plagued by survey fatigue, a phenomenon whereby the average number of social contacts reported declines with the number of repeat participations and as participants’ engagement decreases over time. Using data from the German COVIMOD Study between April 2020 to December 2021, we demonstrate that survey fatigue varied considerably by sociodemographic factors and was consistently strongest among parents reporting children contacts (parental proxy reporting), students, middle-aged individuals, those in full-time employment and those self-employed. We find further that, when using data from first-time participants as gold standard, statistical models incorporating a simple logistic function to control for survey fatigue were associated with substantially improved estimation accuracy relative to models with no survey fatigue adjustments, and that no cap on the number of repeat participations was required. These results indicate that existing longitudinal contact survey data can be meaningfully interpreted under an easy-to-implement statistical approach addressing survey fatigue confounding, and that longitudinal designs including repeat participants are a viable option for future social contact survey designs.