Hannah E Segaloff’s research while affiliated with Centers for Disease Control and Prevention and other places

Background: Little is known about how symptoms or symptom clusters of Post-COVID Conditions (PCC) impact an individual's return to pre-COVID health. Methods: We used four state-level COVID-19 case reporting systems and patient-reported survey data to identify patients with PCC and associations with an individual's return to pre-COVID health after laboratory-confirmed SARS-CoV-2 infection. Participants had a positive SARS-CoV-2 test between March-December 2020. Weighted regression models were used to 1) estimate prevalence of PCC; 2) identify risk factors associated with developing PCC; and 3) examine associations between PCC symptom clusters and return to pre-COVID health. Factor analysis was used to statistically identify post-COVID symptom clusters. Findings: Prevalence of PCC in this population-based sample was 29·9% for persons with SARS-CoV-2 infection, during the pre-delta variant period (March-December 2020); 77·2% of persons experiencing PCC had not returned to pre-COVID health within 8-60 weeks after infection. Female sex, acute COVID-19 illness severity, and number of pre-existing comorbidities were significant risk factors associated with PCC. Myalgic encephalomyelitis/chronic fatigue syndrome-like symptoms, upper-respiratory symptoms, and gastrointestinal symptoms were significantly associated with not returning to pre-COVID health. Interpretation: Understanding PCC symptom clustering may provide insight into pathophysiology, severity of PCC, and management for patients who have not returned to their usual state of health after SARS-CoV-2 infection. Tracking PCC can help measure the impact of COVID-19 vaccination and acute COVID-19-specific treatments on reducing PCC in the US.

We investigated a blastomycosis cluster among humans and canines in a neighborhood in Wisconsin, United States. We conducted interviews and collected serum specimens for Blastomyces antibody testing by enzyme immunoassay. Although no definitive exposure was identified, evidence supports potential exposures from the riverbank, riverside trails or yards, or construction dust.

During responses to outbreaks, the collection and analysis of data on employed case patients’ industry and occupation are necessary to better understand the relationship between work and health outcomes. The occurrence of mpox by occupation and industry has not previously been assessed in the context of the 2022 outbreak. We analyzed employment data from 2548 mpox cases reported to the U.S. Centers for Disease Control and Prevention from surveillance systems in seven U.S. jurisdictions and population-based reference data on employment patterns from the U.S. Bureau of Labor Statistics to describe the differential proportionate distribution of cases across occupation and industry groups using the proportionate morbidity ratio. In gender-specific analyses, we found that men employed in certain occupations and industries had a higher relative risk of mpox than others. While occupational transmission cannot be ruled out, it is more likely that individuals with personal and behavioral risk factors for mpox were more likely to work in these occupations and industries. This analysis provides an example of collecting and analyzing occupation and industry data in case reports to understand possible differences in risk by occupation and industry in infectious disease outbreak investigation and help inform resource allocation, messaging, and response.

We report on five SARS-CoV-2 congregate setting outbreaks at U.S. Operation Allies Welcome Safe Havens/military facilities. Outbreak data were collected, and attack rates were calculated for various populations. Even in vaccinated populations, there was rapid spread, illustrating the importance of institutional prevention and mitigation policies in congregate settings.

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.

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's 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 C171 had 5.6 times the odds of mechanical ventilator use than those in C172 (95% CI: 1.49, 21.1) and a significantly longer mean hospital length of stay than those in both C172 (mean 1.5 days longer, 95% CI: 0.2, 2.7) and C173 (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.

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: 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 hours of the hospital stay. K-medoids clustering was used to determine groups of individuals based on these values. Multivariable linear regression or Firth’s 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 C171 had 5.6 times the odds of mechanical ventilator use than those in C172 (95%CI: 1.49,21.1) and a significantly longer mean hospital length of stay than those in both C172 (mean 1.5 days longer, 95%CI: 0.2,2.7) and C173 (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.

The SARS-CoV-2 Delta Variant of Concern is highly transmissible and contains mutations that confer partial immune escape. The emergence of Delta in North America caused the first surge in COVID-19 cases after SARS-CoV-2 vaccines became widely available. To determine whether individuals infected despite vaccination might be capable of transmitting SARS-CoV-2, we compared RT-PCR cycle threshold (Ct) data from 20,431 test-positive anterior nasal swab specimens from fully vaccinated (n = 9,347) or unvaccinated (n = 11,084) individuals tested at a single commercial laboratory during the interval 28 June– 1 December 2021 when Delta variants were predominant. We observed no significant effect of vaccine status alone on Ct value, nor when controlling for vaccine product or sex. Testing a subset of low-Ct (<25) samples, we detected infectious virus at similar rates, and at similar titers, in specimens from vaccinated and unvaccinated individuals. These data indicate that vaccinated individuals infected with Delta variants are capable of shedding infectious SARS-CoV-2 and could play a role in spreading COVID-19.