Abstract
BackgroundExisting studies have suggested that internet-based participatory surveillance systems are a valid sentinel for influenza-like illness (ILI) surveillance. However, there is limited scientific knowledge on the effectiveness of mobile-based ILI surveillance systems. Previous studies also adopted a passive surveillance approach and have not fully investigated the effectiveness of the systems and their determinants.ObjectiveThe aim of this study was to assess the efficiency of a mobile-based surveillance system of ILI, termed FluMob, among health care workers using a targeted surveillance approach. Specifically, this study evaluated the effectiveness of the system for ILI surveillance pertaining to its participation engagement and surveillance power. In addition, we aimed to identify the factors that can moderate the effectiveness of the system.MethodsThe FluMob system was launched in two large hospitals in Singapore from April 2016 to March 2018. A total of 690 clinical and nonclinical hospital staff participated in the study for 18 months and were prompted via app notifications to submit a survey listing 18 acute respiratory symptoms (eg, fever, cough, sore throat) on a weekly basis. There was a period of study disruption due to maintenance of the system and the end of the participation incentive between May and July of 2017.ResultsOn average, the individual submission rate was 41.4% (SD 24.3%), with a rate of 51.8% (SD 26.4%) before the study disruption and of 21.5% (SD 30.6%) after the disruption. Multivariable regression analysis showed that the adjusted individual submission rates were higher for participants who were older (<30 years, 31.4% vs 31-40 years, 40.2% [P<.001]; 41-50 years, 46.0% [P<.001]; >50 years, 39.9% [P=.01]), ethnic Chinese (Chinese, 44.4% vs non-Chinese, 34.7%; P<.001), and vaccinated against flu in the past year (vaccinated, 44.6% vs nonvaccinated, 34.4%; P<.001). In addition, the weekly ILI incidence was 1.07% on average. The Pearson correlation coefficient between ILI incidence estimated by FluMob and that reported by Singapore Ministry of Health was 0.04 (P=.75) with all data and was 0.38 (P=.006) including only data collected before the study disruption. Health care workers with higher risks of ILI and influenza such as women, non-Chinese, allied health staff, those who had children in their households, not vaccinated against influenza, and reported allergy demonstrated higher surveillance correlations.ConclusionsMobile-based ILI surveillance systems among health care workers can be effective. However, proper operation of the mobile system without major disruptions is vital for the engagement of participants and the persistence of surveillance power. Moreover, the effectiveness of the mobile surveillance system can be moderated by participants’ characteristics, which highlights the importance of targeted disease surveillance that can reduce the cost of recruitment and engagement.
Highlights
Seasonal influenza is an acute respiratory infection that causes an estimated 290,000-650,000 deaths worldwide [1]
The Pearson correlation coefficient between influenza-like illness (ILI) incidence estimated by FluMob and that reported by Singapore Ministry of Health was 0.04 (P=.75) with all data and was 0.38 (P=.006) including only data collected before the study disruption
This study examined the following research questions: (1) What is the level of participation of FluMob among health care workers, and what factors would moderate the participation rate? (2) What is the level of ILI surveillance power of FluMob among health care workers, and what factors would moderate the ILI surveillance power?
Summary
Seasonal influenza is an acute respiratory infection that causes an estimated 290,000-650,000 deaths worldwide [1]. Since the influenza A (H1N1) outbreak in 2009, virological and clinical surveillance systems have been set up nationwide based on outpatients with influenza-like illness (ILI) visiting public health care institutions [2,6]. These methods can be less efficient due to the delay of visiting doctors or the culture of self-medication. Despite the strength of existing surveillance systems, the maintenance cost can be high [10] These limitations highlight the importance of a complementary surveillance system that is robust, in real-time, and cost-effective. Previous studies adopted a passive surveillance approach and have not fully investigated the effectiveness of the systems and their determinants
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