Influenza is an important cause of morbidity and mortality, and influenza vaccination is a key component of influenza prevention. Unlike many other infections, influenza viruses are constantly evolving and the vaccine components are modified to account for these changes. As a result, the effectiveness of influenza vaccine varies from year to year, and it can vary by subtype, antigenic match, and age group. Annual assessment of clinical vaccine effectiveness is needed to evaluate the impact of national vaccine recommendations and policies, understand the relationship between antigenic changes in viruses and clinical protection, and prepare for vaccine assessment during a pandemic. Interim, mid-season estimates of vaccine effectiveness are useful for public health agencies and physicians. To estimate vaccine effectiveness, patients with acute respiratory illness (fever, feverishness or cough) will be enrolled from a predefined population cohort during the influenza season. Enrollment will occur during or after an outpatient, urgent care or emergency department encounter. Most enrollments will occur at the point of care, but some patients will be contacted by phone and screened for eligibility on the day after receiving a diagnosis for acute respiratory illness. After obtaining informed consent, nose and throat swabs will be tested for influenza A and B using a nucleic acid amplification test (RT-PCR). Viral cultures will be performed on positives, and a subset will be sent to CDC for antigenic characterization. Influenza immunization status will be determined by a validated immunization registry. Vaccine effectiveness will be calculated using a case control approach where cases include participants with RT-PCR confirmed influenza and controls include study participants with noninfluenza respiratory illness (negative RT-PCR). Severity will be assessed by obtaining data on hospital admissions and x-ray confirmed pneumonia episodes. Data will provided to CDC for combined analyses with other participating sites, including mid-season data for interim analysis of effectiveness. Separate estimates of vaccine effectiveness will be calculated for different age groups, for each influenza type/subtype, and for high risk individuals. We will estimate the population-based attack rate for medically attended influenza in vaccinated and unvaccinated individuals. The proposed research will 1) generate data to support and inform physician practice and public health agency recommendations for influenza prevention, 2) increase our understanding of antigenic distance and its relationship to clinical vaccine effectiveness, and 3) provide a platform to measure VE and study the epidemiology of influenza during a pandemic.
Influenza vaccine effectiveness can vary from year to year because influenza viruses are always changing and vaccine components are routinely updated. It is important to measure vaccine effectiveness each season to determine the impact of the vaccination program for preventing influenza-related health care visits. Timely information on vaccine effectiveness will be useful for public health agencies and physicians, particularly during seasons with more severe illness or antiviral medication resistance. The capacity to rapidly measure vaccine effectiveness will also be critical during a pandemic when vaccines are produced on a short timeline and field studies have not been performed.
|Zimmerman, Richard K; Nowalk, Mary Patricia; Chung, Jessie et al. (2016) 2014-2015 Influenza Vaccine Effectiveness in the United States by Vaccine Type. Clin Infect Dis 63:1564-1573|
|Thompson, Mark G; Clippard, Jessie; Petrie, Joshua G et al. (2016) Influenza Vaccine Effectiveness for Fully and Partially Vaccinated Children 6 Months to 8 Years Old During 2011-2012 and 2012-2013: The Importance of Two Priming Doses. Pediatr Infect Dis J 35:299-308|
|Dinis, Jorge M; Florek, Nicholas W; Fatola, Omolayo O et al. (2016) Deep Sequencing Reveals Potential Antigenic Variants at Low Frequencies in Influenza A Virus-Infected Humans. J Virol 90:3355-65|
|Zimmerman, Richard K; Balasubramani, G K; Nowalk, Mary Patricia et al. (2016) Classification and Regression Tree (CART) analysis to predict influenza in primary care patients. BMC Infect Dis 16:503|
|Petrie, Joshua G; Cheng, Caroline; Malosh, Ryan E et al. (2016) Illness Severity and Work Productivity Loss Among Working Adults With Medically Attended Acute Respiratory Illnesses: US Influenza Vaccine Effectiveness Network 2012-2013. Clin Infect Dis 62:448-55|
|Chung, Jessie R; Flannery, Brendan; Thompson, Mark G et al. (2016) Seasonal Effectiveness of Live Attenuated and Inactivated Influenza Vaccine. Pediatrics 137:e20153279|
|McLean, Huong Q; Chow, Brian D W; VanWormer, Jeffrey J et al. (2016) Effect of Statin Use on Influenza Vaccine Effectiveness. J Infect Dis 214:1150-8|
|Gaglani, Manjusha; Pruszynski, Jessica; Murthy, Kempapura et al. (2016) Influenza Vaccine Effectiveness Against 2009 Pandemic Influenza A(H1N1) Virus Differed by Vaccine Type During 2013-2014 in the United States. J Infect Dis 213:1546-56|
|Havers, Fiona; Flannery, Brendan; Clippard, Jessie R et al. (2015) Use of influenza antiviral medications among outpatients at high risk for influenza-associated complications during the 2013-2014 influenza season. Clin Infect Dis 60:1677-80|
|McLean, Huong Q; Thompson, Mark G; Sundaram, Maria E et al. (2015) Influenza vaccine effectiveness in the United States during 2012-2013: variable protection by age and virus type. J Infect Dis 211:1529-40|
Showing the most recent 10 out of 13 publications