1. Abstract #363481
Predictors of active participation in an online self-reported influenza surveillance system among
an university population
Marie-Claude Couture, Ph.D, Department of Population Health Sciences/School of Nursing and Health
Professions, University of San Francisco, 2130 Fulton st., San Francisco, CA 94117, Andy Lalka,
Population Health Sciences, University of San Francisco, 2130 Fulton st., San Francisco, CA 94117,
Adam Crawley, MPH, Pandemics, Skoll Global Threats Fund, 1808 Wedemeyer St Suite 300, San
Francisco, CA 94129 and Dhrubajyoti Bhattacharya, JD, MPH, LLM, School of Nursing and Health
Professions, University of San Francisco, University of San Francisco, 920 Old Mason Street, San
Francisco, CA 94129
Abstract Text:
Background: Existing disease surveillance systems are inadequate to fully understand the spread and
effect of influenza. Participatory surveillance systems using online self-reporting of flu symptoms by the
public overcome some of these limitations and can be a powerful complementary tool to track and fight
influenza in real-time. Objective: We aimed to examine the predictors of active participation in Flu Near
You (FNY), an online participatory influenza surveillance tool in the United States. Methods: A cross-
sectional study was conducted in 2014 among students, staff and faculty registered to FNY at the
University of San Francisco (n=114). Participants were recruited through emails and data collected using
online surveys. Logistic regression was conducted to examine predictors of active FNY participation
(filling weekly surveys), focusing on the Health Belief Model (HBM) constructs. Results: The majority of
the participants (88.0%) had been vaccinated for influenza during the year prior the study. Half (47.4%)
were considered active FNY users. Active users were more likely to be older, male, had been vaccinated
for influenza and had lower perceived barriers to use FNY. In multivariate models, predictors of active
participation were being male (OR=4.13: 95%CI: 1.13-15.04) and having lower perceived barriers
(OR=1.54; 95%CI: 1.28-1.85). Conclusions: Low perceived barriers to FNY use was a significant predictor
of active participation. The findings of active users more often being males was also consistent with prior
studies showing that women more often engage in information-seeking behaviors while men display
higher rates of internet usage. Strategies to reduce perceived barriers and address gender disparity may
increase participation in an online participatory surveillance tool. The effectiveness of these tools may
serve as the foundation for a broader network of platforms to determine the incidence of influenza in real-
time for an epidemic forecast infrastructure.