Document Type

Journal Article

Publication Date

7-29-2014

Journal

PLoS ONE

Volume

9

Issue

7

Inclusive Pages

Article number e102429

DOI

10.1371/journal.pone.0102429

Keywords

Electronic Health Records--statistics & numerical data; Influenza, Human--epidemiology; Population Surveillance

Abstract

Introduction

Fine-grained influenza surveillance data are lacking in the US, hampering our ability to monitor disease spread at a local scale. Here we evaluate the performances of high-volume electronic medical claims data to assess local and regional influenza activity.

Material and Methods

We used electronic medical claims data compiled by IMS Health in 480 US locations to create weekly regional influenza-like-illness (ILI) time series during 2003–2010. IMS Health captured 62% of US outpatient visits in 2009. We studied the performances of IMS-ILI indicators against reference influenza surveillance datasets, including CDC-ILI outpatient and laboratory-confirmed influenza data. We estimated correlation in weekly incidences, peak timing and seasonal intensity across datasets, stratified by 10 regions and four age groups (

Results

Regional IMS-ILI indicators were highly synchronous with CDC's reference influenza surveillance data (Pearson correlation coefficients rho≥0.89; range across regions, 0.80–0.97, P

Conclusion

Medical claims-based ILI indicators accurately capture weekly fluctuations in influenza activity in all US regions during inter-pandemic and pandemic seasons, and can be broken down by age groups and fine geographical areas. Medical claims data provide more reliable and fine-grained indicators of influenza activity than other high-volume electronic algorithms and should be used to augment existing influenza surveillance systems.

Comments

Reproduced with permission of PLoS ONE.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

Peer Reviewed

1

Open Access

1

Download

Share

COinS