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Dissertation Defense: Samantha Zhang
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Dissertation Defense: Samantha Zhang

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Schenley Place
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"Mediation and Moderation of Influenza Vaccination on the Perceived Severity of Acute Respiratory Illness", Department of Epidemiology, School of Public Health. Committee G.K. Galasubramani, (advisor) EPICatherine L. Haggerty, EPIMarnie Bertolet, EPIRichard K. Zimmerman, Family MedicineMary Patricia Nowalk, Family MedicineBrendan Flannery, CDCAbstract Background: Influenza causes substantial outpatient morbidity and healthcare utilization each year. Influenza outcomes are shaped by complex relationships among host characteristics, vaccination, infection, viral load, symptoms, and care-seeking behavior. Although annual influenza vaccination is the primary strategy for reducing influenza-associated illness and complications, vaccine protection is incomplete and varies across seasons, viral subtypes, and population groups. A large body of prior research has evaluated influenza vaccine effectiveness against laboratory-confirmed infection and medically attended illness. However, less is known about how vaccination relates to patient-reported illness severity among people seeking outpatient care for acute respiratory illness. In addition, the relationships among influenza vaccination, influenza infection, viral load, demographic characteristics, and perceived illness severity remain incompletely understood. Methods: Outpatients with acute respiratory illness (ARI) were enrolled across nine influenza seasons, from 2011–2012 through 2019–2020, through the U.S. Influenza Vaccine Effectiveness Network. Respiratory specimens were collected for influenza testing by RT-PCR. Influenza viral load was assessed using cycle threshold (Ct) values, with lower Ct values indicating higher viral load. Perceived illness severity was assessed at enrollment using the EQ-5D-5L Visual Analog Scale. Influenza vaccination status was ascertained from verified records and participant report. Demographic characteristics and symptoms were collected at enrollment. Linear regression, logistic reg
Sources: pitt_events

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