The impact of obesity and timely antiviral administration on severe influenza outcomes among hospitalized adults

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141541/1/jmv24946.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141541/2/jmv24946_am.pd

The impact of obesity and timely antiviral administration on severe influenza outcomes among hospitalized adults

Obesity was identified as a risk factor for severe influenza during the 2009 influenza A(H1N1)pandemic, but evidence of this association has been mixed since. Post-pandemic antiviral treatment guidelines may have increased antiviral treatment among obese individuals. A prospective study of adults hospitalized with laboratory-confirmed influenza in Detroit, Michigan in 2011-2012 and 2012-2013 was conducted. Patient information was collected from interviews and medical chart abstraction. Obese (BMI ≥ 30) and non-obese (BMI \u3c 30) participants were compared. Late antiviral treatment (\u3e2 days from symptom onset), obesity (30 ≤ BMI \u3c 40), and morbid obesity (BMI ≥ 40) were evaluated as predictors of lower respiratory tract disease (LRD), ICU admission, and length of stay (LOS) using logistic regression and inverse probability weighted models. Forty-eight participants were included in the study after exclusions and all patients received antiviral treatment. Participants who were obese were significantly more likely to have a cough and to take steroids than non-obese participants, and had a shorter time from hospital admission to antiviral treatment (median time from admission to treatment of 0 days for obese patients and 1 day for non-obese patients [P = 0.001]). In all models, late antiviral treatment was associated with increased odds of LRD (OR: 3.9 [1.1,15.9] in fully adjusted model). After adjustment for treatment timing, the odds of ICU admission (OR: 6.4 [0.8,58.2] to 7.9 [0.9, 87.1]) and LRD (OR: 3.3 [0.5, 23.5] to 4.0 [0.6, 35.0]) associated with morbid obesity increased. Obese individuals were treated with antivirals earlier than others. Late antiviral treatment was associated with severe influenza in the hospital

Distinct influenza surveillance networks and their agreement in recording regional influenza circulation: Experience from Southeast Michigan

IntroductionIn 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.MethodsAll 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.ResultsAcross 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.ConclusionInfluenza 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.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/172297/1/irv12944.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/172297/2/irv12944_am.pd

Influenza Vaccine Effectiveness in the Inpatient Setting; Evaluation of Potential Bias in the Test Negative Design by use of Alternate Control Groups

The test negative design is validated in outpatient but not inpatient studies of influenza vaccine effectiveness. The prevalence of chronic pulmonary disease among inpatients may lead to nonrepresentative controls. Test negative design estimates are biased if vaccine administration is associated with incidence of non-influenza viruses. We evaluated whether control group selection and effects of vaccination on non-influenza viruses biased vaccine effectiveness in our study. Subjects were enrolled at the University of Michigan and Henry Ford hospitals during the 2014-15 and 2015-16 seasons. Patients presenting with acute respiratory infection were enrolled and tested for respiratory viruses. Vaccine effectiveness was estimated using three control groups: influenza negative, other respiratory virus positive, and pan-negative individuals; it was also estimated for other common respiratory viruses. In 2014-15, vaccine effectiveness was 41.1% (95% CI: 1.7%, 64.7%) using influenza negative, 24.5% (95% CI: -42.6%, 60.1%) using other-virus positive, and 45.8% (95% CI: 5.7%, 68.9%) using pan-negative controls. In 2015-16, vaccine effectiveness was 68.7% (95% CI: 44.6%, 82.5%) using influenza negative, 63.1% (95% CI: 25.0%, 82.2%) using other-virus positive, and 71.1% (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

K-medoids clustering of hospital admission characteristics to classify severity of influenza virus infection

BACKGROUND: Patients are admitted to the hospital for respiratory illness at different stages of their disease course. It is important to appropriately analyse this heterogeneity in surveillance data to accurately measure disease severity among those hospitalized. The purpose of this study was to determine if unique baseline clusters of influenza patients exist and to examine the association between cluster membership and in-hospital outcomes. METHODS: Patients hospitalized with influenza at two hospitals in Southeast Michigan during the 2017/2018 (n = 242) and 2018/2019 (n = 115) influenza seasons were included. Physiologic and laboratory variables were collected for the first 24 h of the hospital stay. K-medoids clustering was used to determine groups of individuals based on these values. Multivariable linear regression or Firth\u27s logistic regression were used to examine the association between cluster membership and clinical outcomes. RESULTS: Three clusters were selected for 2017/2018, mainly differentiated by blood glucose level. After adjustment, those in C(17)1 had 5.6 times the odds of mechanical ventilator use than those in C(17)2 (95% CI: 1.49, 21.1) and a significantly longer mean hospital length of stay than those in both C(17)2 (mean 1.5 days longer, 95% CI: 0.2, 2.7) and C(17)3 (mean 1.4 days longer, 95% CI: 0.3, 2.5). Similar results were seen between the two clusters selected for 2018/2019. CONCLUSION: In this study of hospitalized influenza patients, we show that distinct clusters with higher disease acuity can be identified and could be targeted for evaluations of vaccine and influenza antiviral effectiveness against disease attenuation. The association of higher disease acuity with glucose level merits evaluation

Assessment of Anti-SARS-CoV-2 antibody levels among university students vaccinated with different COVID-19 primary and booster doses — fall 2021, Wisconsin

Abstract Background University students commonly received COVID-19 vaccinations before returning to U.S. campuses in the Fall of 2021. Given likely immunologic variation among students based on differences in type of primary series and/or booster dose vaccine received, we conducted serologic investigations in September and December 2021 on a large university campus in Wisconsin to assess anti-SARS-CoV-2 antibody levels. Methods We collected blood samples, demographic information, and COVID-19 illness and vaccination history from a convenience sample of students. Sera were analyzed for both anti-spike (anti-S) and anti-nucleocapsid (anti-N) antibody levels using World Health Organization standardized binding antibody units per milliliter (BAU/mL). Levels were compared across categorical primary COVID-19 vaccine series received and binary COVID-19 mRNA booster status. The association between anti-S levels and time since most recent vaccination dose was estimated by mixed-effects linear regression. Results In total, 356 students participated, of whom 219 (61.5%) had received a primary vaccine series of Pfizer-BioNTech or Moderna mRNA vaccines and 85 (23.9%) had received vaccines from Sinovac or Sinopharm. Median anti-S levels were significantly higher for mRNA primary vaccine series recipients (2.90 and 2.86 log [BAU/mL], respectively), compared with those who received Sinopharm or Sinovac vaccines (1.63 and 1.95 log [BAU/mL], respectively). Sinopharm and Sinovac vaccine recipients were associated with a significantly faster anti-S decline over time, compared with mRNA vaccine recipients (P <.001). By December, 48/172 (27.9%) participants reported receiving an mRNA COVID-19 vaccine booster, which reduced the anti-S antibody discrepancies between primary series vaccine types. Conclusions Our work supports the benefit of heterologous boosting against COVID-19. COVID-19 mRNA vaccine booster doses were associated with increases in anti-SARS-CoV-2 antibody levels; following an mRNA booster dose, students with both mRNA and non-mRNA primary series receipt were associated with comparable levels of anti-S IgG

Infectious SARS-CoV-2 detected in the majority of fully vaccinated individuals with low Ct values.

Infectiousness was determined for a subset of N1 Ct-matched specimens with Ct (TIF)</p

Comparisons of Ct values between vaccine types.

Vaccination had negligible effects on mean Cts in vaccinated as compared with unvaccinated individuals, regardless of the vaccine manufacturer. (DOCX)</p

Concordance between self-reported vaccination status and records in public health vaccine registries.

Individuals were considered fully vaccinated based on vaccine registry (WIR/WEDSS) data if the registries indicated receipt of a final vaccine dose at least 14 days prior to submitting the sample used in our analysis. For individuals whose vaccination status could not be verified in the registry, self-reported data collected at the time of testing were used. Individuals were considered unvaccinated based on self-report only if there was an explicit declaration of unvaccinated status in the self-reported data. Individuals were considered fully vaccinated based on self-report if they fulfilled all of the following criteria: (1) indicated that they had received a COVID vaccine prior to testing; (2) indicated that they did not require another vaccine dose; and (3) reported a date of last vaccine dose that was at least 14 days prior to testing. Specimens lacking data on vaccination status were excluded from the study. Specimens from partially vaccinated individuals (incomplete vaccine series, or A. Of 20,431 specimens with vaccination status available from at least one source, 5,078 specimens had data available from both sources. Under-reporting of full vaccination status in self-reports 1,064/6,142 or 17%) was more common than over-reporting (409/5,487 or 7.4%). B. N1 Ct values for SARS-CoV-2-positive specimens grouped by vaccination status for individuals whose vaccination status was determined by vaccine registry or by self-reported data. (TIF)</p

Vaccinated vs. Unvaccinated.

Vaccinated vs. Unvaccinated.</p