Determinants of Participants' Follow-Up and Characterization of Representativeness in Flu Near You, A Participatory Disease Surveillance System
- PMID: 28389417
- PMCID: PMC5400887
- DOI: 10.2196/publichealth.7304
Determinants of Participants' Follow-Up and Characterization of Representativeness in Flu Near You, A Participatory Disease Surveillance System
Abstract
Background: Flu Near You (FNY) is an Internet-based participatory surveillance system in the United States and Canada that allows volunteers to report influenza-like symptoms using a brief weekly symptom report.
Objective: Our objective was to evaluate the representativeness of the FNY population compared with the general population of the United States, explore the demographic and behavioral characteristics associated with FNY's high-participation users, and summarize results from a user survey of a cohort of FNY participants.
Methods: We compared (1) the representativeness of sex and age groups of FNY participants during the 2014-2015 flu season versus the general US population and (2) the distribution of Human Development Index (HDI) scores of FNY participants versus that of the general US population. We analyzed associations between demographic and behavioral factors and the level of participant follow-up (ie, high vs low). Finally, descriptive statistics of responses from FNY's 2015 and 2016 end-of-season user surveys were calculated.
Results: During the 2014-2015 influenza season, 47,234 unique participants had at least one FNY symptom report that was either self-reported (users) or submitted on their behalf (household members). The proportion of female FNY participants was significantly higher than that of the general US population (n=28,906, 61.2% vs 51.1%, P<.001). Although each age group was represented in the FNY population, the age distribution was significantly different from that of the US population (P<.001). Compared with the US population, FNY had a greater proportion of individuals with HDI >5.0, signaling that the FNY user distribution was more affluent and educated than the US population baseline. We found that high-participation use (ie, higher participation in follow-up symptom reports) was associated with sex (females were 25% less likely than men to be high-participation users), higher HDI, not reporting an influenza-like illness at the first symptom report, older age, and reporting for household members (all differences between high- and low-participation users P<.001). Approximately 10% of FNY users completed an additional survey at the end of the flu season that assessed detailed user characteristics (3217/33,324 in 2015; 4850/44,313 in 2016). Of these users, most identified as being either retired or employed in the health, education, and social services sectors and indicated that they achieved a bachelor's degree or higher.
Conclusions: The representativeness of the FNY population and characteristics of its high-participation users are consistent with what has been observed in other Internet-based influenza surveillance systems. With targeted recruitment of underrepresented populations, FNY may improve as a complementary system to timely tracking of flu activity, especially in populations that do not seek medical attention and in areas with poor official surveillance data.
Keywords: community-based participatory research; crowdsourcing; digital disease detection; influenza, human; public health informatics; public health surveillance.
©Kristin Baltrusaitis, Mauricio Santillana, Adam W Crawley, Rumi Chunara, Mark Smolinski, John S Brownstein. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 07.04.2017.
Conflict of interest statement
Conflicts of Interest: None declared.
Figures
Similar articles
-
Flu Near You: Crowdsourced Symptom Reporting Spanning 2 Influenza Seasons.Am J Public Health. 2015 Oct;105(10):2124-30. doi: 10.2105/AJPH.2015.302696. Epub 2015 Aug 13. Am J Public Health. 2015. PMID: 26270299 Free PMC article.
-
Decreased Seasonal Influenza Rates Detected in a Crowdsourced Influenza-Like Illness Surveillance System During the COVID-19 Pandemic: Prospective Cohort Study.JMIR Public Health Surveill. 2023 Dec 28;9:e40216. doi: 10.2196/40216. JMIR Public Health Surveill. 2023. PMID: 38153782 Free PMC article.
-
Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation.JMIR Public Health Surveill. 2019 Sep 14;5(4):e13403. doi: 10.2196/13403. JMIR Public Health Surveill. 2019. PMID: 31579019 Free PMC article.
-
Screening for High Blood Pressure in Adults: A Systematic Evidence Review for the U.S. Preventive Services Task Force [Internet].Rockville (MD): Agency for Healthcare Research and Quality (US); 2014 Dec. Report No.: 13-05194-EF-1. Rockville (MD): Agency for Healthcare Research and Quality (US); 2014 Dec. Report No.: 13-05194-EF-1. PMID: 25632496 Free Books & Documents. Review.
-
The Landscape of Participatory Surveillance Systems Across the One Health Spectrum: Systematic Review.JMIR Public Health Surveill. 2022 Aug 5;8(8):e38551. doi: 10.2196/38551. JMIR Public Health Surveill. 2022. PMID: 35930345 Free PMC article. Review.
Cited by
-
COVID-19 Vaccine Acceptance and Uptake in Bangkok, Thailand: Cross-sectional Online Survey.JMIR Public Health Surveill. 2023 Apr 13;9:e40186. doi: 10.2196/40186. JMIR Public Health Surveill. 2023. PMID: 36811852 Free PMC article.
-
FluWatchers: Evaluation of a crowdsourced influenza-like illness surveillance application for Canadian influenza seasons 2015-2016 to 2018-2019.Can Commun Dis Rep. 2021 Sep 10;47(9):357-363. doi: 10.14745/ccdr.v47i09a02. eCollection 2021 Sep 10. Can Commun Dis Rep. 2021. PMID: 34650332 Free PMC article.
-
Participatory Surveillance for COVID-19 Trend Detection in Brazil: Cross-sectional Study.JMIR Public Health Surveill. 2023 Apr 26;9:e44517. doi: 10.2196/44517. JMIR Public Health Surveill. 2023. PMID: 36888908 Free PMC article.
-
Knowledge barriers in a national symptomatic-COVID-19 testing programme.PLOS Glob Public Health. 2022 Jan 19;2(1):e0000028. doi: 10.1371/journal.pgph.0000028. eCollection 2022. PLOS Glob Public Health. 2022. PMID: 36962066 Free PMC article.
-
A mixed methods analysis of participation in a social contact survey.Epidemics. 2022 Dec;41:100635. doi: 10.1016/j.epidem.2022.100635. Epub 2022 Sep 22. Epidemics. 2022. PMID: 36182804 Free PMC article.
References
-
- U.S. Centers for Disease Control and Prevention Estimates of deaths associated with seasonal influenza --- United States, 1976-2007. MMWR Morb Mortal Wkly Rep. 2010 Aug 27;59(33):1057–62. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm5933a1.htm mm5933a1 - PubMed
-
- Yuan Q, Nsoesie EO, Lv B, Peng G, Chunara R, Brownstein JS. Monitoring influenza epidemics in china with search query from baidu. PLoS One. 2013;8(5):e64323. doi: 10.1371/journal.pone.0064323. http://dx.plos.org/10.1371/journal.pone.0064323 PONE-D-13-00331 - DOI - DOI - PMC - PubMed
-
- Signorini A, Segre AM, Polgreen PM. The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic. PLoS One. 2011 May 04;6(5):e19467. doi: 10.1371/journal.pone.0019467. http://dx.plos.org/10.1371/journal.pone.0019467 PONE-D-10-02464 - DOI - DOI - PMC - PubMed
Grants and funding
LinkOut - more resources
Full Text Sources
Other Literature Sources