Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation

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JMIR Public Health Surveill. 2019 Sep 14;5(4):e13403. doi: 10.2196/13403. Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation.

Baltrusaitis K¹, Vespignani A², Rosenfeld R³, Gray J⁴, Raymond D⁴, Santillana M^5,6.
Author information

1 Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States. 2 Institute for Network Science, Northeastern University, Boston, MA, United States. 3 School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, United States. 4 athenaResearch at athenahealth, Watertown, MA, United States. 5 Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States. 6 Department of Pediatrics, Harvard Medical School, Boston, MA, United States.

Abstract

BACKGROUND:

The Centers for Disease Control and Prevention (CDC) tracks influenza-like illness (ILI) using information on patient visits to health care providers through the Outpatient Influenza-like Illness Surveillance Network (ILINet). As participation in this system is voluntary, the composition, coverage, and consistency of health care reports vary 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:

The objective of our study was to qualitatively and quantitatively compare national and region-specific ILI activity in the United States across 4 surveillance 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 compared the yearly percentage of FNY participants who sought health care for ILI symptoms across geographical areas.
METHODS:

We compared the national and regional 2018-2019 ILI activity baselines, calculated using noninfluenza weeks from previous years, for each surveillance data source. We also compared measures of ILI activity across geographical areas during 3 influenza seasons, 2015-2016, 2016-2017, and 2017-2018. Geographical differences in weekly ILI activity within each data source were also assessed using relative mean differences and time series heatmaps. National and regional age-adjusted health care-seeking percentages were 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. Pearson correlations were used to assess the association between the health care-seeking percentages and baselines for each surveillance data source.
RESULTS:

We observed consistent differences in ILI activity across geographical areas for CDC ILINet and athenahealth data. ILI activity for FNY displayed little variation across geographical areas, whereas differences in ILI activity for HealthTweets.org were associated with the total number of tweets within a geographical area. The percentage of FNY participants who sought health care for ILI symptoms differed slightly across geographical areas, and these percentages were positively correlated with CDC ILINet and athenahealth baselines.
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 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.
?Kristin Baltrusaitis, Alessandro Vespignani, Roni Rosenfeld, Josh Gray, Dorrie Raymond, Mauricio Santillana. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 14.09.2019.


KEYWORDS:

digital disease surveillance; disease modeling; influenza; participatory syndromic surveillance; surveillance

PMID: 31579019 DOI: 10.2196/13403