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Activity Number: 442 - Disease Prediction, Statistical Methods for Genetic Epidemiology and Mis
Type: Contributed
Date/Time: Thursday, August 12, 2021 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #318169
Title: Missing Data Matters in Participatory Syndromic Surveillance Systems: Comparative Evaluation of Missing Data Methods When Estimating Incidence Rate
Author(s): Kristin Baltrusaitis* and Craig Dalton and Sandra Carlson and Laura White
Companies: Harvard T.H. Chan School of Public Health and Hunter New England Population Health and Hunter New England Population Health and Boston University School of Public Health
Keywords: Missing Data; Missing Not at Random; Multiple Imputation; Digital Epidemiology; Participatory Syndromic Surveillance; Influenza Surveillance

Traditional surveillance methods have been enhanced by the emergence of online participatory systems that collect health-related digital data. However, not every volunteer consistently completes surveys. We assess how five missing data methods: available case, complete case, assume missing is non-case, multiple imputation (MI), and delta (?) MI, which is a flexible and transparent MI method under Missing Not at Random (MNAR) assumptions, affect Influenza-Like Illness (ILI) incidence rate (IR) estimates. We evaluate these methods using simulated and Flutracking data. In simulations, the ?-MI method has the smallest normalized root mean square error under MNAR models (NRMSE range: 0.8-12.5), and in sensitivity analyses, the ?-MI method outperforms other methods, under modest changes in the degree of MNAR. For Flutracking, 2018 IR estimates range from 30 to 35 ILI reports per 1000 person-weeks. Missing data is an important problem in participatory systems, and we show that accounting for missingness using statistical approaches leads to different inferences from the data.

Authors who are presenting talks have a * after their name.

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