JMIR Publications

ABSTRACT

Background:

The Centers for Disease Control and Prevention (CDC) track influenza-like illness (ILI) using information on patient visits to health care providers through the Outpatient Influenza-like Illness Surveillance Network (ILINet). Because participation in this system is voluntary, the composition, coverage, and consistency of healthcare reports varies from state to state, leading to different measures of ILI activity between regions. The degree to which these measures reflect actual differences in influenza activity or systematic differences in the methods used to collect and aggregate the data is unclear.

Objective:

We qualitatively and quantitatively compare national and region-specific ILI activity in the United States (US) across four data sources: CDC ILINet, Flu Near You (FNY), athenahealth, and HealthTweets.org to determine whether these data sources, commonly used as input in influenza modeling efforts, show geographical patterns that are similar to those observed in CDC ILINet’s data. We also compare the yearly percent of FNY participants who sought health-care for ILI symptoms across geographical areas.

Methods:

We compare the national and regional 2018 ILI activity baselines, calculated using non-influenza weeks from previous years, for each surveillance data source. We also compare measures of ILI activity across geographical areas during three influenza seasons, 2015-2016, 2016-2017, and 2017-2018. Geographical differences in weekly ILI activity within each data source are assessed using relative mean differences and time series heatmaps. National and regional age-adjusted health-care seeking percents are calculated for each influenza season by dividing the number of FNY participants who sought medical care for ILI symptoms by the total number of ILI reports within an influenza season.

Results:

We observe consistent differences in ILI activity across geographical areas for CDC ILINet and athenahealth data. ILI activity for FNY displayed little variation across geographical areas, while differences in ILI activity for HealthTweets.org appear to be associated with the total number of Tweets within a geographical area. The percent of FNY participants seeking health-care for ILI symptoms differs slightly across geographical areas. Specifically, regions with higher health-care seeking percentages correspond to regions with higher CDC ILINet and athenahealth ILI activity.

Conclusions:

Our findings suggest that differences in ILI activity across geographical areas as reported by a given surveillance system may not accurately reflect true differences in the prevalence of ILI. Instead, these differences may reflect systematic collection and/or aggregation biases that are particular to each system and consistent across influenza seasons. These findings are potentially relevant in the real-time analysis of the influenza season and in the definition of unbiased forecast models.