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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.
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.
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.
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%,
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.
Influenza infections are associated with thousands of deaths each year in the United States [
Over the past decade, Internet-based biosurveillance systems have developed as a way to provide an informal, complementary approach to traditional (syndromic) surveillance methods. These systems have the potential to reach a wider population and provide real-time access to users’ symptom reports because they leverage alternative data sources, such as Google [
We focused on FNY, which is administered by HealthMap of Boston Children’s Hospital in partnership with the Skoll Global Threats Fund [
Any resident of the United States or Canada can register as a user through the FNY website [
Following registration, FNY users are asked to submit brief weekly reports where they can report any symptoms that they or any registered household members had during the previous week (Monday through Sunday). The symptoms in the report include fever, cough, sore throat, shortness of breath, chills or night sweats, fatigue, nausea or vomiting, diarrhea, headache, and body ache. If a user did not have any of these symptoms, he or she can also choose “I did not have any of the listed symptoms.” However, if a user reports any of these symptoms, he or she is asked to provide the date of symptom onset. In addition, users are asked if they have received an influenza vaccination for the current flu season. Users are sent a reminder to complete the symptom report every Monday through either an email with a survey link or a push notification on their mobile phone. Although data are collected throughout the year, users have the option to suspend symptom reporting during the summer.
We obtained national estimates of sex and age from the United States Census Bureau’s 2014 annual estimates of resident population [
Although FNY includes participants from the United States, Canada, and US territories, such as Puerto Rico, the majority of users reside within the 50 US states. As a result, the participant population used in this analysis included all registered users and household members residing within the 50 US states with complete sex, date of birth, and zip code information who submitted at least one symptom report during the 2014-2015 flu season, as defined by CDC weeks 40 (week ending October 4, 2014) through 20 (week ending May 23, 2015). We addressed the representativeness of sex (male and female) and age groups (<5, 5-14, 15-29, 30-39, 40-49, 50-59, 60-69, 70-79, ≥80 years) of FNY participants compared with the general US population using a 2-sided chi-square goodness-of-fit test. We compared the county-level distribution of HDI scores of FNY participants with that of the general US population using a 2-sample Kolmogorov-Smirnov test. For sensitivity purposes, we also performed these calculations using FNY data from the 2012-2013 and 2013-2014 flu seasons.
For this analysis, we considered only users who reported their own information, completed at least one symptom report during the 2014-2015 flu season during or before CDC week 17, and provided sex information at registration. In addition, we chose only residents of the United States between ages 13 and 80 years at their registration date because users must be at least 13 years of age to register. A limit of 80 years of age was used to account for possible errors in date of birth input at user registration. Users who met these criteria were classified as either a high-participation user or a low-participation user based on the number of symptom reports they submitted during the 2014-2015 flu season. Users who completed more than 3 symptom reports during the 2014-2015 flu season were identified as high-participation users.
The demographic factors used in this analysis were sex (male or female), age group (13-29, 30-39, 40-49, 50-59, 60-69, and 70-79 years), and HDI as a continuous variable (see above for a description of methods used to calculate HDI). In addition, we included whether or not an ILI, as defined by the CDC, was reported at first entry. Although we did not examine information from individual household members in the analysis, we also included whether or not primary participants reported on behalf of other household members.
We analyzed associations between these demographic and behavioral factors and the level of participant follow-up using multivariable logistic regression. For odds ratio (OR) comparisons among age groups, we used 50-59 years as the reference group because it had the largest number of users. The demographic and behavioral factors were treated as independent variables, while level of follow-up was a dichotomous outcome (high-participation user vs low-participation user). We dichotomized the outcome because the distribution of number of reports was not normally distributed, and we determined the cutoff value of 3 empirically by assessing the histogram of number of reports. Sensitivity analyses confirmed the robustness of our findings. These additional analyses were conducted using more- and less-stringent definitions of high-participation users—specifically, more than 10 entries and more than 1 entry, respectively—for the 2012-2013, 2013-2014, and 2014-2015 flu seasons. Data were analyzed using R for Mac OS X version 3.1.1 (R Foundation).
To supplement data from user registrations, the FNY team conducted end-of-season user surveys in June 2015 and May 2016. The surveys were administered through SurveyGizmo online survey software (Widgix LLC), with survey invitations sent via email to all active FNY users. A completed survey entered the user into a raffle for incentives that included an iPad and US $100 gift cards. Users were asked a variety of questions designed to better understand their interest in and motivations for reporting to FNY. Here we report on responses to a subset of questions (
How did you first hear about Flu Near You?
Why did you sign up for Flu Near You?
What is your primary motivation for continuing to use Flu Near You?
Do you consider yourself more or less likely to report to Flu Near You when you have symptoms of illness to report?
When you report to Flu Near You, do you report on any symptoms you’ve experienced during the week, or only symptoms that you think may be linked to the flu?
What symptoms do you think are the primary symptoms of a flu infection?
What is your industry/occupation?
What is your level of education?
We exported survey responses to Excel 2016 (Microsoft Corporation) and Stata version 13 (StataCorp LP) for analysis and tabulation of descriptive statistics.
Among states, California had the largest number of participants for the 2014-2015 flu season (n=6595), while Wyoming had the fewest (n=89) (see
During the 2014-2015 influenza season, 47,234 unique participants had at least one symptom report that was either self-reported or submitted on their behalf. Of these participants, 28,906 (61.20%) were female and 18,328 (38.80%) were male. The proportion of female FNY participants was significantly overrepresented when compared with the general US population (51.1% female,
Distribution of participants for 2014- 2015 flu season by state (A) unadjusted distribution (B) adjusted distribution by state population.
(A) Sex and (B) age profiles of Flu Near You (FNY) participants and comparison with the general population. (C) Distribution and (D) cumulative distribution of Human Development Index (HDI) scores by county for FNY and US populations and (E) difference between US and FNY distributions.
Summary of adjusted odds ratios (ORs) of being a high-participation user of Flu Near You.
Variable | Reference group | Adjusted OR | 95% CI | ||
Sex: female | Male | 0.75 | 0.71-0.79 | <.001 | |
Household members: yes | No | 3.29 | 3.12-3.35 | <.001 | |
Human Development Index | N/Aa | 1.12 | 1.09-1.14 | <.001 | |
Influenza-like illness status at first survey: yes | No | 0.22 | 0.19-0.25 | <.001 | |
13-29 | 50-59 | 0.67 | 0.61-0.74 | <.001 | |
30-39 | 50-59 | 0.54 | 0.49-0.58 | <.001 | |
40-49 | 50-59 | 0.70 | 0.64-0.75 | <.001 | |
60-69 | 50-59 | 1.14 | 1.07-1.23 | <.001 | |
70-79 | 50-59 | 1.23 | 1.11-1.36 | <.001 |
aN/A: not applicable.
Flowchart of user enrollment.
In 2015 the FNY user survey received 3217 responses from users, and the total numbers of users responding to the survey increased to 4850 in 2016. In both years the largest proportion of users identified as being retired (878/3217, 27.29%, in 2015; 1651/4850, 34.04%, in 2016), followed by users employed in the fields of health care and social assistance (620/3217, 19.27%, in 2015; 902/4850, 18.59%, in 2016), professional, scientific, and technical services (370/3217, 11.50%, in 2015; 453/4850, 9.34%, in 2016), and educational services (309/3217, 9.61%, in 2015; 409/4850, 8.43%, in 2016). These 4 categories accounted for 67.67% (2177/3217) of survey respondents in 2015 and 70.41% (3415/4850) in 2016. These results suggest that FNY relies heavily on retirees and those employed in the health, education, and social services sectors.
In addition, our user surveys indicated that the majority of respondents had achieved a bachelor’s degree or higher (2322/3217, 72.18%, in 2015; 3315/4850, 68.35%, in 2016), while less than 1% had not graduated high school. Approximately one-quarter of respondents had attained a master’s degree in each survey year (845/3217, 26.27%, in 2015; 1181/4850, 24.35%, in 2016), while some respondents held doctoral or other advanced degrees (435/3217, 13.52%, in 2015; 602/4850, 12.41%, in 2016). These results support trends seen in participants’ HDI scores, suggesting that the FNY participant population may have a higher educational attainment than the general US population (
Distributions of county-level Human Development Index (HDI) scores and individual user-specific HDI scores in Flu Near You (FNY) survey participants.
Our results, in combination with previous work [
Overall, females were overrepresented in our participant population (61.20%). This overrepresentation is consistent with findings from other participatory surveillance systems. During the 2011-2012 flu season, Influenzanet participants were more likely to be female than in the general population (56.8% vs 50.9%,
FNY participants also had a higher HDI than that of the US population. This finding aligns with the results of our user survey conducted in 2015 and 2016. When comparing county-level HDI estimates with the user-specific HDI estimates within the population of FNY users who completed the 2016 survey, we found that for most survey participants, the county-level HDI underestimated the user-specific HDI, which further supports our initial findings that FNY participants have a higher HDI than the US population. These relatively high levels of HDI in the FNY population can be in part due to patterns in Internet penetration. Studies from the Pew Research Center have shown that Americans with high education levels and those in relatively affluent households have high Internet penetration [
Nor was the FNY population representative of the general US population in terms of age. Both younger populations (ages <30 years) and older populations (ages ≥80 years) were underrepresented, while the age groups between 40 and 80 years were overrepresented. As with sex, patterns of age representations were similar for both Influenzanet and FluTracking participants [
We found that higher participation in follow-up symptom reports was associated with sex (male), reporting for household members, higher HDI score, not reporting an ILI at the first survey, and older age. These findings were consistent using both more-stringent (>10 entries) and less-stringent (>1 entry) definitions of good users (
The biases intrinsic within the FNY population are consistent with biases found in other Internet-based influenza surveillance systems. Despite these biases in the sociodemographic characteristics of the population, previous studies have shown that FNY and traditional disease surveillance systems capture similar trends of ILI rates at the national level [
Odds ratios of high-participation users, by sex, influenza-like illness (ILI), Human Development Index (HDI), and age range, for (A) different levels of follow-up during the 2014-2015 flu season and (B) the 2012-2013, 2013-2014, and 2014-2015 flu seasons using more than 3 entries to define high-participation users.
Centers for Disease Control and Prevention
Flu Near You
Human Development Index
influenza-like illness
odds ratio
KB, MSa, AWC, RC, MSm, and JSB conceived the research. KB and AWC conducted the statistical analysis. KB, MSa, AWC, and RC drafted the manuscript. All authors contributed to the final version of the paper. Research supported by the NIH/GMS: T32GM074905 (to KB).
None declared.