BACKGROUND:Viral respiratory illnesses are the most common acute illnesses experienced and generally follow a predicted pattern over time. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic interrupted that pattern. METHODS:The HIVE (Household Influenza Vaccine Evaluation) study was established in 2010 to follow a cohort of Southeast Michigan households over time. Initially focused on influenza, surveillance was expanded to include other major respiratory pathogens, and, starting in 2015, the population was followed year round. Symptoms of acute illness were reported, and respiratory specimens were collected and tested to identify viral infections. Based on the known population being followed, virus-specific incidence was calculated. RESULTS:From 2015 to 2022, 1755 participants were followed in HIVE for 7785 person-years with 7833 illnesses documented. Before the pandemic, rhinovirus (RV) and common cold human coronaviruses (HCoVs) were the viruses most frequently identified, and incidence decreased with increasing age. Type A influenza was next but with comparable incidence by age. Parainfluenza and respiratory syncytial viruses were less frequent overall, followed by human metapneumoviruses. Incidence was highest in young children, but infections were frequently documented in all age groups. Seasonality followed patterns established decades ago. The SARS-CoV-2 pandemic disrupted these patterns, except for RV and, to a lesser extent, HCoVs. In the first 2 years of the pandemic, RV incidence far exceeded that of SARS-CoV-2. CONCLUSIONS:Longitudinal cohort studies are important in comparing the incidence, seasonality, and characteristics of different respiratory viral infections. Studies documented the differential effect of the pandemic on the incidence of respiratory viruses in addition to SARS-CoV-2.

Disparities in coronavirus disease 2019 mortality are driven by inequalities in group-specific incidence rates (IRs), case fatality rates (CFRs), and their interaction. For emerging infections, such as severe acute respiratory syndrome coronavirus 2, group-specific IRs and CFRs change on different time scales, and inequities in these measures may reflect different social and medical mechanisms. To be useful tools for public health surveillance and policy, analyses of changing mortality rate disparities must independently address changes in IRs and CFRs. However, this is rarely done. In this analysis, we examine the separate contributions of disparities in the timing of infection-reflecting differential infection risk factors such as residential segregation, housing, and participation in essential work-and declining CFRs over time on mortality disparities by race/ethnicity in the US state of Michigan. We used detailed case data to decompose race/ethnicity-specific mortality rates into their age-specific IR and CFR components during each of 3 periods from March to December 2020. We used these estimates in a counterfactual simulation model to estimate that that 35% (95% credible interval, 30%-40%) of deaths in black Michigan residents could have been prevented if these residents were infected along the timeline experienced by white residents, resulting in a 67% (61%-72%) reduction in the mortality rate gap between black and white Michigan residents during 2020. These results clearly illustrate why differential power to "wait out" infection during an infectious disease emergency-a function of structural racism-is a key, underappreciated, driver of inequality in disease and death from emerging infections. Data from Michigan demonstrate how structural racism drove inequality in COVID-19 mortality via its impact on the time ordering of infection, due to differential risks of exposure across racial and ethnic groups when case fatality rates were high.

This cross-sectional study compares the completion of the primary COVID-19 vaccine series in Michigan residents with vs without sickle cell disease and by age group.

The annual reappearance of respiratory viruses has been recognized for decades. COVID‐19 mitigation measures taken during the pandemic were targeted at respiratory transmission and broadly impacted the burden of acute respiratory illnesses (ARIs).

The 10 years between the last influenza pandemic and start of the severe acute respiratory syndrome coronavirus 2 pandemic have been marked by great advances in our ability to follow influenza occurrence and determine vaccine effectiveness (VE), largely based on widespread use of the polymerase chain reaction assay. We examine the results, focusing mainly on data from the United States and inactivated vaccines. Surveillance has expanded, resulting in increased ability to characterize circulating viruses and their impact. The surveillance has often confirmed previous observations on timing of outbreaks and age groups affected, which can now be examined in greater detail. Selection of strains for vaccines is now based on enhanced viral characterization using immunologic, virologic, and computational techniques not previously available. Vaccine coverage has been largely stable, but VE has remained modest and, in some years, very low. We discuss ways to improve VE based on existing technology while we work toward supraseasonal vaccines.

Background and Objective: Childcare attendance is a common risk factor for acute respiratory illness (ARI) in young children. Our goal was to better understand the specific respiratory viruses that predominate in childcare, which may support the development of tailored illness prevention and intervention strategies in childcare settings. Methods: Using data from a prospective household cohort of ARI surveillance, we assessed specimen from 1418 ARIs reported by 359 childcare-aged children over 6 study seasons (2012/2013 through 2017/2018). Respiratory swabs were tested by polymerase chain reaction for 9 respiratory viruses. A mixed-effect logistic regression model was used to compare odds of various viral detection outcomes. The Shannon’s Diversity index was used to compare the richness (ie, number of species) and diversity (ie, relative species abundance) associated with respiratory viruses detected in both groups. Results: At least 1 virus was detected in 75.5% of childcare-associated ARIs and in 80.1% of homecare ARIs. Compared with illnesses among homecare children, childcare illnesses were associated with significantly higher odds of detected adenovirus (odds ratio = 1.86, 95% confidence interval = 1.05–3.28) and human metapneumovirus (odds ratio = 1.76, 95% confidence interval = 1.03–3.0). The pool of viruses associated with childcare ARI was found to be significantly richer and more diverse than that of viruses associated with homecare ARI ( P < 0.0001). Conclusions: Children attending childcare experience a higher risk of adenovirus and human metapneumovirus infection and are regularly exposed to a rich and diverse pool of respiratory viruses in childcare environments. Our results underscore the necessity of thorough and multifaceted viral prevention strategies in childcare settings.

Background:Some studies conducted before the Delta and Omicron variant-dominant periods have indicated that influenza vaccination provided protection against COVID-19 infection or hospitalization, but these results were limited by small study cohorts and a lack of comprehensive data on patient characteristics. No studies have examined this question during the Delta and Omicron periods (08/01/2021 to 2/22/2022).Methods:We conducted a retrospective cohort study of influenza-vaccinated and unvaccinated patients in the Corewell Health East(CHE, formerly known as Beaumont Health), Corewell Health West(CHW, formerly known as Spectrum Health) and Michigan Medicine (MM) healthcare system during the Delta-dominant and Omicron-dominant periods. We used a test-negative, case-control analysis to assess the effectiveness of the influenza vaccine against hospitalized SARS-CoV-2 outcome in adults, while controlling for individual characteristics as well as pandameic severity and waning immunity of COVID-19 vaccine.Results:The influenza vaccination has shown to provided some protection against SARS-CoV-2 hospitalized outcome across three main healthcare systems. CHE site (odds ratio [OR]=0.73, vaccine effectiveness [VE]=27%, 95% confidence interval [CI]: [18-35], p<0.001), CHW site (OR=0.85, VE=15%, 95% CI: [6-24], p<0.001), MM (OR=0.50, VE=50%, 95% CI: [40-58], p <0.001) and overall (OR=0.75, VE=25%, 95% CI: [20-30], p <0.001).Conclusion:The influenza vaccine provides a small degree of protection against SARS-CoV-2 infection across our study sites.

Mathematical models have come to play a key role in global pandemic preparedness and outbreak response: helping to plan for disease burden, hospital capacity, and inform nonpharmaceutical interventions. Such models have played a pivotal role in the COVID-19 pandemic, with transmission models—and, by consequence, modelers—guiding global, national, and local responses to SARS-CoV-2. However, these models have largely not accounted for the social and structural factors, which lead to socioeconomic, racial, and geographic health disparities. In this piece, we raise and attempt to clarify several questions relating to this important gap in the research and practice of infectious disease modeling: Why do epidemiologic models of emerging infections typically ignore known structural drivers of disparate health outcomes? What have been the consequences of a framework focused primarily on aggregate outcomes on infection equity? What should be done to develop a more holistic approach to modeling-based decision-making during pandemics? In this review, we evaluate potential historical and political explanations for the exclusion of drivers of disparity in infectious disease models for emerging infections, which have often been characterized as “equal opportunity infectors” despite ample evidence to the contrary. We look to examples from other disease systems (HIV, STIs) and successes in including social inequity in models of acute infection transmission as a blueprint for how social connections, environmental, and structural factors can be integrated into a coherent, rigorous, and interpretable modeling framework. We conclude by outlining principles to guide modeling of emerging infections in ways that represent the causes of inequity in infection as central rather than peripheral mechanisms.

Introduction In Southeast Michigan, active surveillance studies monitor influenza activity in hospitals, ambulatory clinics, and community households. Across five respiratory seasons, we assessed the contribution of data from each of the three networks towards improving our overall understanding of regional influenza circulation. Methods All three networks used case definitions for acute respiratory illness (ARI) and molecularly tested for influenza from research-collected respiratory specimens. Age- and network-stratified epidemic curves were created for influenza A and B. We compared stratified epidemic curves visually and by centering at seasonal midpoints. Results Across all seasons (from 2014/2015 through 2018/2019), epidemic curves from each of the three networks were comparable in terms of both timing and magnitude. Small discrepancies in epidemics recorded by each network support previous conclusions about broader characteristics of particular influenza seasons. Conclusion Influenza surveillance systems based in hospital, ambulatory clinic, and community household settings appear to provide largely similar information regarding regional epidemic activity. Together, multiple levels of influenza surveillance provide a detailed view of regional influenza epidemics, but a single surveillance system-regardless of population subgroup monitored-appears to be sufficient in providing vital information regarding community influenza epidemics.

Background. The evidence that influenza vaccination programs regularly provide protection to unvaccinated individuals (ie, indirect effects) of a community is lacking. We sought to determine the direct, indirect, and total effects of influenza vaccine in the Household Influenza Vaccine Evaluation (HIVE) cohort. Methods. Using longitudinal data from the HIVE cohort from 2010-11 through 2017-18, we estimated direct, indirect, and total influenza vaccine effectiveness (VE) and the incidence rate ratio of influenza virus infection using adjusted mixed-effect Poisson regression models. Total effectiveness was determined through comparison of vaccinated members of full or partially vaccinated households to unvaccinated individuals in completely unvaccinated households. Results. The pooled, direct VE against any influenza was 30.2% (14.0-43.4). Direct VE was higher for influenza A/H1N1 43.9% (3.9 to 63.5) and B 46.7% (17.2 to 57.5) than A/H3N2 31.7% (10.5 to 47.8) and was higher for young children 42.4% (10.1 to 63.0) than adults 18.6% (-6.3 to 37.7). Influenza incidence was highest in completely unvaccinated households (10.6 per 100 personseasons) and lower at all other levels of household vaccination coverage. We found little evidence of indirect VE after adjusting for potential confounders. Total VE was 56.4% (30.1-72.9) in low coverage, 43.2% (19.5-59.9) in moderate coverage, and 33.0% (12.1 to 49.0) in fully vaccinated households. Conclusions. Influenza vaccines may have a benefit above and beyond the direct effect but that effect in this study was small. Although there may be exceptions, the goal of global vaccine recommendations should remain focused on provision of documented, direct protection to those vaccinated.

Background. As of 1 November 2020, there have been >230 000 deaths and 9 million confirmed and probable cases attributable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the United States. However, this overwhelming toll has not been distributed equally, with geographic, race/ethnic, age, and socioeconomic disparities in exposure and mortality defining features of the US coronavirus disease 2019 (COVID-19) epidemic. Methods. We used individual-level COVID-19 incidence and mortality data from the state of Michigan to estimate age-specific incidence and mortality rates by race/ethnic group. Data were analyzed using hierarchical Bayesian regression models, and model results were validated using posterior predictive checks. Results. In crude and age-standardized analyses we found rates of incidence and mortality more than twice as high than for Whites for all groups except Native Americans. Blacks experienced the greatest burden of confirmed and probable COVID-19 (age-standardized incidence, 1626/100 000 population) and mortality (age-standardized mortality rate, 244/100 000). These rates reflect large disparities, as Blacks experienced age-standardized incidence and mortality rates 5.5 (95% posterior credible interval [CrI], 5.4-5.6) and 6.7 (95% CrI, 6.4-7.1) times higher than Whites, respectively. We found that the bulk of the disparity in mortality between Blacks and Whites is driven by dramatically higher rates of COVID-19 infection across all age groups, particularly among older adults, rather than age-specific variation in case-fatality rates. Conclusions. This work suggests that well-documented racial disparities in COVID-19 mortality in hard-hit settings, such as Michigan, are driven primarily by variation in household, community, and workplace exposure rather than case-fatality rates.

Background: There are limited data on influenza vaccine effectiveness (IVE) in preventing laboratory confirmed influenza illness among healthcare personnel (HCP). Methods: HCP with direct patient contact working full-time in hospitals were followed during three influenza seasons in Israel (2016-2017 to 2018-2019) and Peru (2016 to 2018). Trivalent influenza vaccines were available at all sites, except during 2018-2019 when Israel used quadrivalent vaccines; vaccination was documented by electronic medical records, vaccine registries, and/or self-report (for vaccinations outside the hospital). Twice-weekly active surveillance identified acute respiratory symptoms or febrile illness (ARFI); self-collected respiratory specimens were tested by real-time reverse transcription polymerase chain reaction (PCR) assay. IVE was 100 x 1-hazard ratio (adjusted for sex, age, occupation, and hospital). Results: Among 5,489 HCP who contributed 10,041 person-seasons, influenza vaccination coverage was 47% in Israel and 32% in Peru. Of 3,056 ARFIs in Israel and 3,538 in Peru, A or B influenza virus infections were identified in 205 (7%) in Israel and 87 (2.5%) in Peru. IVE against all viruses across seasons was 1% (95% confidence interval [CI] =-30%, 25%) in Israel and 12% (95% CI =-61%, 52%) in Peru. Conclusion: Estimates of IVE were null using person-time models during six study seasons in Israel and Peru. (c) 2021 Published by Elsevier Ltd.

Introduction: Healthcare personnel (HCP) have an increased risk of exposure to influenza and other respiratory pathogens. Increased presenteeism, decreased absenteeism, and low uptake of the influenza vaccine can contribute to the spread of influenza among HCP in healthcare settings. We used a mixed methods approach to investigate attitudes and behaviors of HCP in Israel towards influenza vaccination, presenteeism, and absenteeism. Methods: The study took place over three influenza seasons (2016-2017, 2017-2018, 2018-2019) at the largest hospital in southern Israel. We administered a Knowledge, Attitudes and Practices (KAP) questionnaire and conducted semi-structured interviews with HCP who had been recently ill with respiratory symptoms. The KAP questionnaire included closed-ended questions about attitudes and behaviors regarding influenza, working while sick, and influenza vaccination. The interviews investigated HCP's perceptions of influenza infection and attitudes about absenteeism, presenteeism, and the influenza vaccine. Results: We conducted 74 semi-structured interviews over three influenza seasons. Four HCP were interviewed twice, in separate seasons for different illness episodes. The 70 individuals interviewed included 16 physicians, 45 nurses or technicians, and 9 administrative staff. The median age was 42.5 years (range: 2560), and most (79%) were female. Half (50%) got vaccinated against influenza before their illness episode. In interviews, most HCP said they come to work while sick (presenteeism) due to a strong personal work ethic and an institutional culture that discourages taking sick leave (absenteeism). HCP expressed skepticism about the effectiveness of the influenza vaccine as well as concern that the influenza vaccine causes severe illness. Discussion: Over three influenza seasons in Israel, HCP cited a number of reasons for working while sick, and doubted the usefulness of influenza vaccine. Addressing reasons for presenteeism and vaccine hesitancy among HCP is crucial to protect HCP and patients from influenza virus infection and other viral respiratory illnesses, such as COVID-19. (C) 2021 Elsevier Ltd. All rights reserved.

Background. As part of the Household Influenza Vaccine Evaluation (HIVE) study, acute respiratory infections (ARI) have been identified in children and adults from 2010 to 2018. Methods. Annually, 890 to 1441 individuals were followed and contacted weekly to report ARIs. Specimens collected during illness were tested for human coronaviruses (HCoV) types OC43, 229E, HKU1, and NL63. Results. In total, 993 HCoV infections were identified during the 8 years, with OC43 most commonly seen and 229E the least. HCoVs were detected in a limited time period, between December and April/May and peaked in January/February. Highest infection frequency was in children <5 years (18 per 100 person-years), with little variation in older age groups (range, 7 to 11 per 100 person-years). Overall, 9% of adult cases and 20% of cases in children were associated with medical consultation. Of the 993 infections, 260 were acquired from an infected household contact. The serial interval between index and household-acquired cases ranged from 3.2 to 3.6 days and the secondary infection risk ranged from 7.2% to 12.6% by type. Conclusions. Coronaviruses are sharply seasonal. They appear, based on serial interval and secondary infection risk, to have similar transmission potential to influenza A(H3N2) in the same population.

Seasonal and pandemic respiratory viruses such as influenza and the novel coronavirus (SARS-COV-2) currently sweeping the globe have often been described as ‘equal opportunity infectors’, implying little socioeconomic disparity in susceptibility. However, early data from the COVID-19 pandemic has underscored that the burden of respiratory viruses actually reflect and magnify existing socioeconomic inequalities. We review the literature on socioeconomic and racial disparities in acute respiratory infection (ARI), as well as ARI-associated hospitalization and mortality. Our goal is to identify key principles of the relationship between socioeconomic inequality and ARI outcomes, as well as highlighting poorly understood areas that need to be addressed by research and policy in the wake of the COVID-19 pandemic. We find that there has been descriptive work in this area, but that there is a distinct lack of cohesive methodology in the literature exploring social determinants and ARI. We propose the fundamental cause theory is a useful framework for guiding future research of disparities in ARI and for the design of interventions to alleviate these disparities.

Background As of August 5, 2020, there were more than 4.8M confirmed and probable cases and 159K deaths attributable to SARS-CoV-2 in the United States, with these numbers undoubtedly reflecting a significant underestimate of the true toll. Geographic, racial-ethnic, age and socioeconomic disparities in exposure and mortality are key features of the first and second wave of the U.S. COVID-19 epidemic. Methods We used individual-level COVID-19 incidence and mortality data from the U.S. state of Michigan to estimate age-specific incidence and mortality rates by race/ethnic group. Data were analyzed using hierarchical Bayesian regression models, and model results were validated using posterior predictive checks. Findings In crude and age-standardized analyses we found rates of incidence and mortality more than twice as high than Whites for all groups other than Native Americans. Of these, Blacks experienced the greatest burden of confirmed and probable COVID-19 infection (Age-standardized incidence = 1,644/100,000 population) and mortality (age-standardized mortality rate 251/100,000). These rates reflect large disparities, as Blacks experienced age-standardized incidence and mortality rates 5.6 (95% CI = 5.5, 5.7) and 6.9 (6.5, 7.3) times higher than Whites, respectively. We also found that the bulk of the disparity in mortality between Blacks and Whites is driven by dramatically higher rates of COVID-19 infection across all age groups, particularly among older adults, rather than age-specific variation in case-fatality rates. Interpretation This work suggests that well-documented racial disparities in COVID-19 mortality in hard-hit settings, such as the U.S. state of Michigan, are driven primarily by variation in household, community and workplace exposure rather than case-fatality rates. Funding This work was supported by a COVID-PODS grant from the Michigan Institute for Data Science (MIDAS) at the University of Michigan. The funding source had no role in the preparation of this manuscript.

The test-negative design is validated in outpatient, but not inpatient, studies of influenza vaccine effectiveness. The prevalence of chronic pulmonary disease among inpatients can lead to nonrepresentative controls. Test-negative design estimates are biased if vaccine administration is associated with incidence of noninfluenza viruses. We evaluated whether control group selection and effects of vaccination on noninfluenza viruses biased vaccine effectiveness in our study. Subjects were enrolled at the University of Michigan and Henry Ford hospitals during the 2014-2015 and 2015-2016 influenza seasons. Patients presenting with acute respiratory infection were enrolled and tested for respiratory viruses. Vaccine effectiveness was estimated using 3 control groups: negative for influenza, positive for other respiratory virus, and pan-negative individuals; it was also estimated for other common respiratory viruses. In 2014-2015, vaccine effectiveness was 41.1% (95% CI: 1.7, 64.7) using influenza-negative controls, 24.5% (95% CI: -42.6, 60.1) using controls positive for other virus, and 45.8% (95% CI: 5.7, 68.9) using pan-negative controls. In 2015-2016, vaccine effectiveness was 68.7% (95% CI: 44.6, 82.5) using influenza-negative controls, 63.1% (95% CI: 25.0, 82.2) using controls positive for other virus, and 71.1% (95% CI: 46.2, 84.8) using pan-negative controls. Vaccination did not alter odds of other respiratory viruses. Results support use of the test-negative design among inpatients.

BACKGROUND:Community-based studies of influenza and other respiratory viruses (eg, SARS-CoV-2) require laboratory confirmation of infection. During the current COVID-19 pandemic, social distancing guidelines require alternative data collection in order to protect both research staff and participants. Home-collected respiratory specimens are less resource-intensive, can be collected earlier after symptom onset, and provide a low-contact means of data collection. A prospective, multi-year, community-based cohort study is an ideal setting to examine the utility of home-collected specimens for identification of influenza. METHODS:We describe the feasibility and reliability of home-collected specimens for the detection of influenza. We collected data and specimens between October 2014 and June 2017 from the Household Influenza Vaccine Evaluation (HIVE) Study. Cohort participants were asked to collect a nasal swab at home upon onset of acute respiratory illness. Research staff also collected nose and throat swab specimens in the study clinic within 7 days of onset. We estimated agreement using Cohen's kappa and calculated sensitivity and specificity of home-collected compared to staff-collected specimens. RESULTS:We tested 336 paired staff- and home-collected respiratory specimens for influenza by RT-PCR; 150 staff-collected specimens were positive for influenza A/H3N2, 23 for influenza A/H1N1, 14 for influenza B/Victoria, and 31 for influenza B/Yamagata. We found moderate agreement between collection methods for influenza A/H3N2 (0.70) and B/Yamagata (0.69) and high agreement for influenza A/H1N1 (0.87) and B/Victoria (0.86). Sensitivity ranged from 78% to 86% for all influenza types and subtypes. Specificity was high for influenza A/H1N1 and both influenza B lineages with a range from 96% to 100%, and slightly lower for A/H3N2 infections (88%). CONCLUSIONS:Collection of nasal swab specimens at home is both feasible and reliable for identification of influenza virus infections.

Abstract Background Out-of-home child care (CC) is a risk factor for viral acute respiratory infection (ARI) in young children. Little is known, however, about differences in frequencies of viral infection between CC children and those cared for exclusively at home. Methods Using surveillance data from the HIVE household cohort in southeast Michigan from 2014–2018 (4 seasons), we analyzed 1022 illness cases from 354 children aged 0–6 years. Age groups were dichotomized as infants (aged <2 years) and toddlers/preschoolers (aged 2–6 years). Households were prospectively enrolled and nasal respiratory swabs were collected from children upon report of acute illness symptoms. We used real-time RT–PCR to test for 18 respiratory viruses. Results We detected at least one virus in 855 illness cases (83% of all illnesses reported). Age at first illness onset in all four seasons was significantly younger among CC children than homecare children (P < 0.001) across all 4 years (average difference = 1.25 years). CC children <2 years had slightly lower odds of viral detection during illness (OR = 0.89, 95% CI [0.49, 1.61]) but higher odds at ages 2–6y (1.07 [0.65, 1.76]); neither was statistically significant. Neither CC nor homecare children were significantly more or less at risk for any particular pathogen—expect for rhinovirus in the <2-year group, where odds of rhinovirus infection were 58% lower (OR = 0.42) in CC children compared with homecare counterparts (95% CI, 0.21–0.83). Conversely, CC attendees under 3 more frequently had influenza, RSV, hMPV, parainfluenza, and coronavirus; however, none of these associations were significant. Odds of coinfection (> 1 virus detected) were higher among CC children, but not significant (OR = 1.4 [0.63, 2.96] and 1.2 [0.77, 1.88] in <2 year and 2–6 year age groups, respectively). Among all children <7 year, the mean number of pathogens detected was not different between CC and homecare individuals (1.20 vs. 1.23, P = 0.16). Conclusion As expected, results indicated that CC attendees aged 0–6y experienced illness episodes earlier in life compared with homecare children. Our analysis also indicated that, compared with children cared for at home, CC children were less at risk for rhinovirus infection when young but could potentially be at higher risk for viruses of greater clinical concern. Disclosures All authors: No reported disclosures.

Data resource profile: Household Influenza Vaccine Evaluation (HIVE) Study Arnold S Monto,* Ryan E Malosh, Richard Evans, Adam S Lauring, Aubree Gordon, Mark G Thompson, Alicia M Fry, Brendan Flannery, Suzanne E Ohmit, Joshua G Petrie and Emily T Martin, On behalf of the HIVE Study Research Staff Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA, Division of Infectious Diseases, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA and Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA