Improving Accuracy of Influenza-Associated Hospitalization Rate Estimates - Volume 21, Number 9—September 2015 - Emerging Infectious Disease journal - CDC

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Improving Accuracy of Influenza-Associated Hospitalization Rate Estimates - Volume 21, Number 9—September 2015 - Emerging Infectious Disease journal - CDC

Volume 21, Number 9—September 2015
THEME ISSUE
Emerging Infections Program

Emerging Infections Program

Improving Accuracy of Influenza-Associated Hospitalization Rate Estimates

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• Methods
• Results
• Discussion
• Suggested Citation

Figures

• Figure 1
• Figure 2
• Figure 3

Tables

• Table

Technical Appendicies

• Technical Appendix

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Alexander J. Millman , Carrie Reed, Pam Daily Kirley, Deborah Aragon, James I. Meek, Monica M. Farley, Patricia Ryan, Jim Collins, Ruth Lynfield, Joan Baumbach, Shelley Zansky, Nancy M. Bennett, Brian Fowler, Ann Thomas, Mary L. Lindegren, Annette Atkinson, Lyn Finelli, and Sandra Chaves

Author affiliations: Centers for Disease Control and Prevention, Atlanta, Georgia, USA (A.J. Millman, C. Reed, L. Finelli, S.S. Chaves); California Emerging Infections Program, Oakland, California, USA (P. Daily Kirley); Colorado Department of Public Health and Environment, Denver, Colorado, USA (D. Aragon); Connecticut Emerging Infections Program, New Haven, Connecticut, USA (J. Meek); Emory University School of Medicine, Atlanta (M.M. Farley); Atlanta Veterans Administration Medical Center, Atlanta (M.M. Farley); Maryland Department of Health and Mental Hygiene, Baltimore, Maryland, USA (P. Ryan); Michigan Department of Health and Human Services, Lansing, Michigan, USA (J. Collins); Minnesota Department of Health, St. Paul, Minnesota, USA (R. Lynfield); New Mexico Department of Health, Santa Fe, New Mexico, USA (J. Baumbach); New York State Department of Health, Albany, New York, USA (S. Zansky); University of Rochester School of Medicine and Dentistry, Rochester, New York, USA (N.M. Bennett); Monroe County Department of Public Health, Rochester (N.M. Bennett); Ohio Department of Health, Columbus, Ohio, USA (B. Fowler); Oregon Public Health Division, Portland, Oregon, USA (A. Thomas); Vanderbilt University School of Medicine, Nashville, Tennessee, USA (M.L. Lindegren); Utah Department of Health, Salt Lake City, Utah, USA (A. Atkinson)

Suggested citation for this article

Abstract

Diagnostic test sensitivity affects rate estimates for laboratory-confirmed influenza–associated hospitalizations. We used data from FluSurv-NET, a national population-based surveillance system for laboratory-confirmed influenza hospitalizations, to capture diagnostic test type by patient age and influenza season. We calculated observed rates by age group and adjusted rates by test sensitivity. Test sensitivity was lowest in adults >65 years of age. For all ages, reverse transcription PCR was the most sensitive test, and use increased from <10% during 2003–2008 to ≈70% during 2009–2013. Observed hospitalization rates per 100,000 persons varied by season: 7.3–50.5 for children <18 years of age, 3.0–30.3 for adults 18–64 years, and 13.6–181.8 for adults >65 years. After 2009, hospitalization rates adjusted by test sensitivity were ≈15% higher for children <18 years, ≈20% higher for adults 18–64 years, and ≈55% for adults >65 years of age. Test sensitivity adjustments improve the accuracy of hospitalization rate estimates.

In the United States, surveillance for influenza-associated hospitalizations relies on laboratory-confirmed diagnostic testing (1–3). Influenza testing modalities have expanded from traditional viral culture to include rapid influenza diagnostic tests (RIDTs) and molecular assays, such as reverse transcription PCR (RT-PCR) (4,5). RIDTs are point-of-care tests that provide results within 30 minutes; however, with reported sensitivities of 10%–80%, negative test results can be unreliable (6–9). RT-PCR exceeds viral culture in sensitivity for detecting influenza, but its widespread use is limited by cost and complexity of the assay (10,11).

Researchers have examined rates of influenza-associated hospitalization during different influenza seasons (1,2,12,13). However, comparing rates between seasons can be inaccurate without accounting for changes in the sensitivity of diagnostic testing used. In particular, after the 2009 influenza A(H1N1) pandemic, hospitals and state public health laboratories expanded diagnostic capabilities with high-sensitivity molecular assays to better detect influenza viruses and other respiratory pathogens (5). Particularly for nationally based surveillance, the use of different testing platforms by health care facilities and the variability in sensitivity of these diagnostic tests could lead to underestimation of rates of influenza-associated hospitalization and limit comparisons of severity across influenza seasons (3,4,6,7).

Dr. Millman is a physician and an Epidemic Intelligence Service Officer assigned to the Influenza Division, CDC. His research interests include influenza, emerging infections surveillance, and advanced molecular diagnostics.

Acknowledgments

We thank the following persons for their contributions at CDC and the FluSurv-NET sites: Alejandro Perez, Michelle Leon, Hallie Randel, Maria Rosales, Darcy Fazio, John Palumbo, Kimberly Yousey-Hindes, Olivia Almendares, Delmar Little, Kyle Openo, May Monroe; Dave Boxurd, Craig Morin, Sara Vetter, Minnesota Team Flu, Meghan Fuschino, Nancy Spina, Kirsten St. George, Gary Hollick, Maria Gaitan; Matthew Laidler, Patricia Newman, Katie Dyer, and Karen Leib.

FluSurv-NET is a collaboration of state health departments, academic institutions, and local partners and is funded by CDC. This publication was supported in part by cooperative agreement nos. CDC-RFA-CK12-1202 and 5U38HM000414.

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Figures

• Figure 1. Distribution of influenza diagnostic tests among identified cases in the Centers for Disease Control and Prevention Influenza Hospital Surveillance Network (FluSurv-NET), 2003–2013. RT-PCR, reverse transcription PCR; DFA, direct fluorescent...
• Figure 2. Observed and adjusted rates of influenza-associated hospitalizations per 100,000 population identified in the Centers for Disease Control and Prevention Influenza Hospital Surveillance Network (FluSurv-NET), 2003–2013. A) Children <18 years...
• Figure 3. Underestimation of rates of influenza-associated hospitalization after adjustment for test sensitivity, by patient age group, Centers for Disease Control and Prevention Influenza Hospital Surveillance Network (FluSurv-NET), 2003–2013.

Table

• Table. Influenza diagnostic test sensitivity range, by patient age group (years), FluSurv-NET, 2003–2013

Technical Appendix

• Technical Appendix. FluSurv-NET coverage area and results, influenza diagnostic sensitivity range from literature review, and applied probability distribution by age group.  313 KB

Suggested citation for this article: Millman AJ, Reed C, Daily Kirley P, Aragon D, Meek J, Farley MM, et al. Improving accuracy of influenza-associated hospitalization rate estimates. Emerg Infect Dis. 2015 Aug [date cited]. http://dx.doi.org/10.3201/eid2109.141665

DOI: 10.3201/eid2109.141665