Jessie R Chung1, Brendan Flannery2, Sara S Kim3, Manjusha Gaglani4, Chandni Raiyani5, Edward A Belongia6, Huong Q McLean7, Mary Patricia Nowalk8, Richard K Zimmerman9, Michael L Jackson10, Lisa A Jackson11, Emily T Martin12, Arnold S Monto13, Manish Patel14. 1. Centers for Disease Control and Prevention, Influenza Division, Atlanta, GA, USA. Electronic address: jchung@cdc.gov. 2. Centers for Disease Control and Prevention, Influenza Division, Atlanta, GA, USA. Electronic address: bif4@cdc.gov. 3. Centers for Disease Control and Prevention, Influenza Division, Atlanta, GA, USA. Electronic address: omy1@cdc.gov. 4. Texas A&M University College of Medicine, Temple, TX, USA; Baylor Scott & White Health, Temple, TX, USA. Electronic address: manjusha.gaglani@bswhealth.org. 5. Baylor Scott & White Health, Temple, TX, USA. Electronic address: chandni.raiyani@bswhealth.org. 6. Marshfield Clinic Research Institute, Marshfield, WI, USA. Electronic address: belongia.edward@mcrf.mfldclin.edu. 7. Marshfield Clinic Research Institute, Marshfield, WI, USA. Electronic address: mclean.huong@marshfieldresearch.org. 8. University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. Electronic address: tnowalk@pitt.edu. 9. University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. Electronic address: zimmrk@upmc.edu. 10. Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA. Electronic address: michael.l.jackson@kp.org. 11. Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA. Electronic address: lisa.a.jackson@kp.org. 12. University of Michigan, Ann Arbor, MI, USA. Electronic address: etmartin@umich.edu. 13. University of Michigan, Ann Arbor, MI, USA. Electronic address: asmonto@umich.edu. 14. Centers for Disease Control and Prevention, Influenza Division, Atlanta, GA, USA. Electronic address: aul3@cdc.gov.
Abstract
INTRODUCTION: Mid-season influenza vaccine effectiveness (VE) estimates are a useful tool to help guide annual influenza vaccine strain selection, vaccine policy, and public health messaging. We propose using a sample size-driven approach with data-driven inputs for publication of mid-season influenza VE. METHODS: We used pooled inputs for VE by (sub)type and average vaccine coverage by age groups using data from eight seasons of the US Influenza VE Network to calculate sample sizes needed to estimate mid-season VE. RESULTS: We estimate that 135 influenza-positive cases would be needed to detect an overall VE of 40% with 55% vaccine coverage among test-negative controls. Larger sample sizes would be required to produce reliable estimates specifically against influenza A/H3N2 and for older age groups. CONCLUSION: Using an existing network, most of the recent influenza seasons in the US would facilitate valid mid-season VE estimates using the proposed sample sizes for broad age groupings. Published by Elsevier Ltd.
INTRODUCTION: Mid-season influenza vaccine effectiveness (VE) estimates are a useful tool to help guide annual influenza vaccine strain selection, vaccine policy, and public health messaging. We propose using a sample size-driven approach with data-driven inputs for publication of mid-season influenza VE. METHODS: We used pooled inputs for VE by (sub)type and average vaccine coverage by age groups using data from eight seasons of the US Influenza VE Network to calculate sample sizes needed to estimate mid-season VE. RESULTS: We estimate that 135 influenza-positive cases would be needed to detect an overall VE of 40% with 55% vaccine coverage among test-negative controls. Larger sample sizes would be required to produce reliable estimates specifically against influenza A/H3N2 and for older age groups. CONCLUSION: Using an existing network, most of the recent influenza seasons in the US would facilitate valid mid-season VE estimates using the proposed sample sizes for broad age groupings. Published by Elsevier Ltd.
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