As of March 2021, three COVID-19 vaccines had been authorized by the U.S. Food and Drug Administration (FDA) for use in the United States. Each has substantial efficacy in preventing COVID-19. However, as efficacy from trials was <100% for all three vaccines, disease in vaccinated people is expected to occur. We created a spreadsheet-based tool to estimate the number of symptomatic COVID-19 cases among vaccinated people (vaccine breakthrough infections) based on published vaccine efficacy (VE) data, percent of the population that has been fully vaccinated, and average number of COVID-19 cases reported per day. We estimate that approximately 199,000 symptomatic vaccine breakthrough infections  occurred in the United States during January–July 2021 among >156 million fully vaccinated people. With high SARS-CoV-2 transmission and increasing numbers of people vaccinated in the United States, vaccine breakthrough infections will continue to accumulate. Understanding expectations regarding number of vaccine breakthrough infections enables accurate public health messaging to help ensure that the occurrence of such cases does not negatively affect vaccine perceptions, confidence, and uptake. Widespread uptake of safe and effective vaccines is critical to controlling the COVID-19 pandemic. Three COVID-19 vaccines have been authorized or approved by the U.S. Food and Drug Administration (FDA) for use in the United States, including the 2-dose Pfizer-BioNTech BNT162b2 mRNA and Moderna mRNA-1273 vaccines and the single-dose adenovirus-based Johnson & Johnson/Janssen Ad.26.COV2.S vaccine , 5. AmonAs no vaccine is 100% effective at preventing illness, COVID-19 occurring among vaccinated people, often referred to as vaccine breakthrough infections, are expected. Amid increases in vaccination coverage in the setting of widespread SARS-CoV-2 transmission, the numbers of COVID-19 cases among vaccinated people could be substantial. We estimated the number of symptomatic vaccine breakthrough infections expected in the United States based on published vaccine efficacy (VE) data, percent of the population that had been fully vaccinated, and reported COVID-19 case counts.© to estimate the expected number of symptomatic COVID-19 cases among vaccinated persons per day in the United States using publicly available data. Inputs are the 7-day moving average for daily numbers of COVID-19 cases in the United States as reported to the Centers for Disease Control and Prevention (CDC), the cumulative number of persons fully vaccinated with each vaccine as reported to CDC as of 14 days prior to each 7-day average case count, and VE data from phase 3 trials of the three vaccines authorized in the United States ), where RR is the ratio of confirmed symptomatic SARS-CoV-2 infections per 1000 person-years among those receiving vaccine in phase 3 trials divided by those receiving placebo. The Janssen RR1 was 0.331 (VE1 = 0.669), the Pfizer-BioNTech and Moderna RR2 was 0.054 (VE2 = 0.946), while RR0 was defined as 1 (VE0 = 0) for people who are unvaccinated or not fully vaccinated. The expected number of symptomatic vaccine breakthrough infections per day is calculated as:The number of symptomatic vaccine breakthrough infections expected per day is a function of VE and vaccination coverage in the population. For these calculations, Variance was calculated based on available phase 3 clinical trial data for the Pfizer-BioNTech and Moderna vaccine trials using Poisson regression models . The pooThe first persons in the United States to be vaccinated against SARS-CoV-2 completed their 2-dose series during the week of January 4, 2021. Therefore, we began calculating the expected number of symptomatic vaccine breakthrough infections 14 days later, the week beginning January 17. We calculated weekly estimates using approximated average case counts among the vaccine eligible population and vaccination coverage as of the Sunday beginning each week and then multiplied the daily estimate by seven. We estimated per-week symptomatic breakthrough infections through the last week of July. Incorporation of Janssen vaccine into the estimates began in mid-March. Case counts and eligible population included persons aged 12yearss during the week beginning May 30. We calculated cumulative expected counts to date by summing weekly expected vaccine breakthrough case counts. We derived 95% confidence intervals (CI) around cumulative counts by summing weekly variances as described above into standard CI calculations.To understand the relative influence of community transmission and VE in determining the number of expected vaccine breakthrough infections, we calculated expected cumulative counts during the same time period under two hypothetical scenarios: 1) doubling the average daily COVID-19 case counts each week; and 2) modifying population vaccination coverage during January–July such that it entirely reflected VE of 67%, VE associated with the Janssen vaccine.Nearly 12 million COVID-19 cases were reported in the United States during January–July 2021 . The numAs of the end of July, we estimate that a total of 198,840 symptomatic vaccine breakthrough infections  occurred in the United States among >156 million fully vaccinated people . On averThe expected number of vaccine breakthrough infections varied substantially under different hypothetical scenarios reflective of 1) doubling daily average case counts, and 2) all vaccination occurring at VE of 67% . The relWe created a spreadsheet-based calculator to estimate the number of symptomatic vaccine breakthrough infections in the United States based on the average number of COVID-19 cases, percent of the population fully vaccinated, and published efficacies of the three FDA-authorized vaccines. Using this tool, we estimate that approximately 200,000 symptomatic SARS-CoV-2 vaccine breakthrough infections occurred by the end of July among the over 156 million people fully vaccinated in the United States by the middle of July. Vaccine breakthrough infections occur in only a small fraction of all vaccinated persons. Understanding the number of expected vaccine breakthrough infections is important for accurate public health messaging to help ensure that the occurrence of such cases does not negatively affect vaccine perceptions, confidence, and uptake.We developed this tool incorporating ideal VE scenarios. Real-world vaccine effectiveness may be lower, particularly for people who are older or have underlying health conditions , 14. LowCOVID-19 vaccines have demonstrated the ability to mitigate risk of severe disease, hospitalization, and death among persons infected following vaccination , 13, 17.The analytic approach described here is based on several assumptions and limitations that affect how our results should be interpreted. First, this approach is based on reported case counts and does not account for the population at-risk, susceptibility of persons previously infected, or duration of immunity following vaccination. Second, these calculations assume that vaccinated and unvaccinated people have the same risk of exposure to SARS-CoV-2, which may not be true at the population level. Third, we define vaccine breakthrough infections as those occurring more than two weeks after completion of vaccination; these figures do not include people who may become infected following partial vaccination or prior to 14 days following completion of vaccination. Fourth, reported COVID-19 case counts stratified by patient age are not available from all states . Our assRisk reduction provided by any vaccination is inherently relative, and the number of cases among vaccinated persons assuming equal exposure as unvaccinated persons is directly linked to vaccination coverage and disease incidence. Even with highly effective vaccines, given the large number of people being vaccinated in the United States and high levels of SARS-CoV-2 transmission in many parts of the country, hundreds of thousands of symptomatic vaccine breakthrough infections are expected, and will continue to accumulate amid high-levels of SARS-CoV-2 circulation. The methods described here can be used by public health officials to determine if the frequency of vaccine breakthrough infections reported in their jurisdictions are consistent with expectations based on vaccine efficacy from clinical trials. Furthermore, public health messaging regarding expected vaccine breakthrough infections is important to assure the public that this is expected, is not cause for alarm, and does not indicate that vaccines are not preventing severe COVID-19.Vaccine breakthrough infections are expected to continue to accumulate amid ongoing widespread community transmission of SARS-CoV-2 and high vaccination coverage. However, the number of COVID-19 cases, hospitalizations, and deaths prevented among vaccinated persons will far exceed the numbers of vaccine breakthrough infections. CDC continues to collaborate with public health officials nationwide to monitor COVID-19 trends among vaccine persons and to identify unexpected trends in characteristics of people with vaccine breakthrough infections or patterns associated with infecting strains.S1 File(DOCX)Click here for additional data file.S2 File(XLSX)Click here for additional data file.S3 File(XLSX)Click here for additional data file.


This cross-sectional study examines rates of cognitive impairment among patients who survived COVID-19 and whether the care setting was associated with cognitive impairment rates. The prevalence of post–COVID-19 cognitive impairment and the association with disease severity are not well characterized. Previous studies on the topic have been limited by small sample sizes and suboptimal measurement of cognitive functioning.STROBE) reporting guideline.We analyzed data in this cross-sectional study from April 2020 through May 2021 from a cohort of patients with COVID-19 followed up through a Mount Sinai Health System registry. Study participants were 18 years or older, spoke English or Spanish, tested positive for SARS-CoV-2 or had serum antibody positivity, and had no history of dementia. Participant demographic characteristics  were collected via self-report. Cognitive functioning was assessed using well-validated neuropsychological measures: Number Span forward (attention) and backward (working memory), Trail Making Test Part A and Part B , phonemic and category fluency (language), and the Hopkins Verbal Learning Test–Revised . The Mount Sinai Health System Institutional Review Board approved this study, and informed consent was obtained from study participants. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology , adjusting for race and ethnicity, smoking, body mass index, comorbidities, and depression. The threshold for statistical significance was α = .05, and the tests were 2-tailed. Analyses were performed using SAS, version 9.4 (SAS Institute).We calculated the frequency of impairment on each measure, defined as a The mean (IQR) age of 740 participants was 49 (38-59) years, 63% (n = 464) were women, and the mean (SD) time from COVID-19 diagnosis was 7.6 (2.7) months . ParticiIn adjusted analyses, hospitalized patients were more likely to have impairments in attention , executive functioning , category fluency , memory encoding , and memory recall  than those in the outpatient group. Patients treated in the ED were more likely to have impaired category fluency  and memory encoding  than those treated in the outpatient setting. No significant differences in impairments in other domains were observed between groups.4 and has considerable implications for occupational, psychological, and functional outcomes. It is well known that certain populations  may be particularly susceptible to cognitive impairment after critical illness5; however, in the relatively young cohort in the present study, a substantial proportion exhibited cognitive dysfunction several months after recovering from COVID-19. The findings of this study are generally consistent with those of research on other viruses .6In this study, we found a relatively high frequency of cognitive impairment several months after patients contracted COVID-19. Impairments in executive functioning, processing speed, category fluency, memory encoding, and recall were predominant among hospitalized patients. The relative sparing of memory recognition in the context of impaired encoding and recall suggests an executive pattern. This pattern is consistent with early reports describing a dysexecutive syndrome after COVID-19Limitations of this study include a potential sampling bias, as some participants may have presented to Mount Sinai Health System because of health concerns. Future studies should investigate long-term post–COVID-19 cognitive trajectories and the association with neuroimaging findings to assess potential mechanisms.The association of COVID-19 with executive functioning raises key questions regarding patients’ long-term treatment. Future studies are needed to identify the risk factors and mechanisms underlying cognitive dysfunction as well as options for rehabilitation.


To evaluate the predictive validity of multimodal clinical assessment outcomes and quantitative measures of in- and off-laboratory mobility for fall-risk estimation in patients with different forms of neurological gait disorders.The occurrence, severity, and consequences of falls were prospectively assessed for 6 months in 333 patients with early stage gait disorders due to vestibular, cerebellar, hypokinetic, vascular, functional, or other neurological diseases and 63 healthy controls. At inclusion, participants completed a comprehensive multimodal clinical and functional fall-risk assessment, an in-laboratory gait examination, and an inertial-sensor-based daily mobility monitoring for 14 days. Multivariate logistic regression analyses were performed to identify explanatory characteristics for predicting the (1) the fall status , (2) the fall frequency , and (3) the fall severity  of patients.40% of patients experienced one or frequent falls and 21% severe fall-related injuries during prospective fall assessment. Fall status and frequency could be reliably predicted  primarily based on patients' retrospective fall status. Instrumented-based gait and mobility measures further improved prediction and provided independent, unique information for predicting the severity of fall-related consequences.Falls- and fall-related injuries are a relevant health problem already in early stage neurological gait disorders. Multivariate regression analysis encourages a stepwise approach for fall assessment in these patients: fall history taking readily informs the clinician about patients' general fall risk. In patients at risk of falling, instrument-based measures of gait and mobility provide critical information on the likelihood of severe fall-related injuries.The online version contains supplementary material available at 10.1007/s00415-021-10504-x. Gait disturbances and associated falls are common complications in neurological diseases, and compromise patients' mobility, functional independence, and quality of life . The preDifferent algorithms for the assessment of fall risk in neurological gait disorders have been evaluated. Disease-specific clinical scales frequently fail to reliably predict falls in respective clinical cohorts , 5. HoweComplementary approaches that focus on instrument-based measures of gait stability more reliably identify fall risk in central and peripheral neurological –9 and geThe aim of this study was to systematically evaluate and compare the validity of different multimodal clinical assessment tools as well as quantitative measures of in- and off-laboratory mobility for the prediction of prospectively assessed occurrence, severity, and consequences of falls in 333 patients with different forms of neurological gait impairments and 63 healthy controls.Patients were recruited for the cross-sectional Prospective Assessment of Falls and Mobility—study  in the period from June 2015 to February 2018 at the Department of Neurology and German Center for Vertigo and Balance Disorders (DSGZ) of the University Hospital Munich. Inclusion criteria were the presence of a chronic gait disorder due to (1) a peripheral vestibular disorder ,  and was conducted according to the Declaration of Helsinki.All participants completed a standardized interview with one of the authors , which included a survey of the following information: ambulatory status, functional status, medication, and falls within the preceding 6 months. Medication status was categorized into ‘hypnotics’, ‘antipsychotics’, ‘antidepressants’, ‘non-opoid pain relievers’, ‘opoids’, ‘anticonvulsivants’, ‘cardiovascular drugs’, ‘cholinergic agents’, ‘anticoagulants’, and ‘hypoglycemic agents’. Retrospective fall assessment included information on: fall status (yes or no), fall frequency , and fall severity. A fall was defined as an unexpected event in which the person comes to rest on the ground, floor, or lower level . A near Each participant was provided with falls diary covering a follow-up period of 6 months. The diary included a brief definition of different fall events and a German description of the HFGS. Participants were asked to document fall events on a daily basis (at evening) with information on: (1) the time, (2) the environmental circumstances, (3) the fall mechanisms , (4) the duration of the post-fall lying phase, and (5) the related HFGS of each fall event . Each paBased on the prospective fall assessment, participants were categorized with respect to fall status (yes or no), fall frequency , and fall severity (according to the HFGS).®, CIR System, Sparta, NJ, USA) with a sampling rate of 120 Hz. Participants walked over the carpet at their self-chosen walking speed for in total four times. Each walk was started 1.5 m in front of the mat and continued for 1.5 m beyond it to allow steady-state locomotion. Gait assessment was conducted without additional ambulatory aids. If necessary, the examiner walked beside the patient (approximately 0.3 m behind) to prevent falls. During each assessment, a sufficient number of at least 40 step events were collected.In-laboratory gait assessment was performed on a 6.7-m-long pressure-sensitive carpet  of base of support, CV of stride time, CV of stride length, gait asymmetry index, and phase synchronization index .®, PAL Technologies, Glasgow), which recorded the sequence and period of time of individual bouts of ambulatory, sedentary, and sleeping behavior at a sample rate of 10 Hz. The inertial sensor was placed at the thigh of the dominant leg approximately 0.1 m cranial and 0.05 m lateral of the patella. Participants were advised to continue their daily activities as usual and not to change their routine. Upon completion of the recording period, participants removed the sensor independently and sent it back via postal service.Following the initial visit, monitoring of daily mobility was undertaken for 14 days. Participants wore an inertial-sensor-based activity monitor (ActivPALThe following parameters (expressed as average daily estimates) were computed from the ActivPAL data in accordance to previously described procedures , 33 to rF value of the ANOVA was less than 0.05. To avoid collinearity, relationships among parameters were examined using Pearson's correlations. If two parameters were strongly correlated with one another (r > 0.7), only the one most strongly associated with the dependent measure was retained. Further input variables into the fall regression models were the presence of previous falls (yes/no) and the ten categories of the medication status (yes/no). Statistical analysis was performed using SPSS .Descriptive statistics are reported as mean ± SD. In a first step, analysis of variance (ANOVA) with Bonferroni post hoc analysis and Chi square tests were used to test differences of metric and categorical parameters from clinical assessment, in-laboratory gait examination, off-laboratory mobility assessment, and retro- and prospective fall assessment between clinical groups . In a second step, a series of multivariate backward logistic regression analyses  was performed to identify independent predictors associated with three dependent fall measures of interest: (I) non-faller vs. faller, (II) occasional vs. frequent faller , and (III) non-severe (defined as HFGS 1 or 2) vs. severe falling . For each of the three models, the classificatory accuracy, sensitivity, and specificity were computed. Initially, for all parameters from clinical assessment and in- and off-laboratory mobility recordings, an ANOVA was conducted for three dependent fall measures of interest. Parameters were entered into the respective regression model if the significance level of their Data reported in this article will be shared with any appropriately qualified investigator on request after pseudonymization.F6,365 = 2.1, p = 0.043). In agreement with the recruitment focus on independent ambulatory patients, functional mobility scores  of patients were in average only slightly affected, with more pronounced differences in patients with cerebellar  and hypokinetic disorders . All disease groups showed increased fear of falling  and a lowered balance confidence  compared to HC. The SF-12 as marker for disease-related reduction of the quality of life was impaired in the physical function domain for all patient subgroups compared to HC . In the psychological domain of the SF-12, only patients with hypokinetic  and patients with functional gait disorders  showed a reduction.A total of 333 patients from six different disease groups and 63 HC were recruited. Demographics, clinical characteristics, and outcomes from retrospective fall assessment are presented in Table p < 0.001). Most severe consequences of falling were reported in patients with hypokinetic disorders, followed by cerebellar and vestibular disorders. In contrast, fall-related events in patients with functional disorders were most often characterized as 'near-falls' (p < 0.001).Retrospective fall assessment revealed disease-specific differences of patients' fall status. Patients with hypokinetic and cerebellar disorders most often reported frequent falls within the last 6 months, followed by patients with vestibular and vascular disorders. Patients with functional disorders reported equally seldom frequent falling than HC but considerably more often occasional falling (p < 0.001). Gait alterations were most pronounced in patients with hypokinetic and cerebellar disorders . Off-laboratory monitoring of daily-life mobility revealed a general reduction of daily energy expenditure , ambulatory bout number , and step count  in patients compared to HC. The pattern of ambulatory activity was generally less variable and heterogeneous in patients . Mobility data of 26 participants were not available due to technical problems of data recording or extraction: three sensors were lost during the postal return process; for eight sensors, data could not be extracted. In ten participants, a sufficient (> 50% of daytime) measurement was possible in less than 7 days (not a fully week of mobility tracking), and five datasets were withdrawn from analysis due to multiple interruptions of more than 6 h daily.Descriptive statistics of in- and off-laboratory mobility assessment can be found in Tables p < 0.001). Most severe consequences of falling were found in patients with hypokinetic disorders (p < 0.001), whereas the majority of fall events in functional disorders was categorized as 'near-falls'.Falls diary information from 16% of participants was considered invalid and excluded from further analysis, due to either missing telephone contact or considerable discrepancies between falls information from the diary and monthly phone interviews. Descriptive statistics of prospective falls data from the remaining participants is presented in Table Direct comparisons of retrospective and prospective fall data revealed occasional shifts in patients' fall status. In patients that were classified as non-fallers from retrospective assessment, 11.2% experienced occasional and 5.9% frequent falls during follow-up assessment. In patients originally classified as occasional fallers, 53.6% did not fall and 17.8% fell frequently during follow-up assessment. Finally, in patients classified as frequent fallers during retrospective assessment, 25.7% did not fall and 21.6% only occasionally fell during the follow-up period.Characteristics and between-group differences of patients with respect to the categories fall status, fall frequency, and fall severity from prospective fall assessment are presented in supplementary Tables A–C.The predictive model for fall status  was obtained after 9 iteration steps and yielded a correct prediction of 78% . The final model included four predictive parameters from sociodemographic and in-laboratory mobility assessment, with the most important being retrospective fall status and temporal gait variability Table a. AccordThe predictive model for fall frequency  was obtained after seven iteration steps and achieved a correct prediction of 92% . The final model comprised nine predictive variables from sociodemographic, clinical, and in- and off-laboratory mobility assessment with the most influential being retrospective fall status, temporal gait variability, and intensity of daily activity  was obtained after ten iteration steps and yielded a correct prediction of 91% . This model also considered near falls events corresponding to HFGS 1. The final model only considered two parameters from in- and off-laboratory mobility assessment, namely a reduced gait velocity and a decreased ambulatory bout alpha Table c.We performed a clinical and instrument-based screening of health and mobility status and prospectively monitored the occurrence and consequences of falling in a comprehensive cohort of patients with different neurological gait disorders. The main focus of this study was to identify sociodemographic, clinical, and instrument-based explanatory factors that might afford a reliable prediction of the risk of falling and fall-related consequences in this study cohort. The study focused on neurological patients with an independent ambulatory status, predominantly from a population at working age, which was reflected in only moderately impaired outcomes from clinical as well as in- and out-laboratory mobility assessment. Nonetheless, fall assessment revealed that frequent falling and severe fall-related injuries are a prevalent and relevant health problem already in the early stage of neurological gait impairments. As a direct accompanying effect of increased risk of falling, patients reported a generally lowered balance confidence and an increased risk to fall.In agreement with previous retrospective reports , 34, proTo identify explanatory variables that allow to predict the occurrence, frequency, and severity in the dataset of prospectively assessed fall events, we performed multiple regression analyses considering a wide set of sociodemographic characteristics, outcomes from clinical and self-report-based assessment and parameters from in- and off-laboratory gait and mobility recordings. The classificatory model on the general fall status identified fallers vs. non-fallers with an accuracy of 78%. The model included predictive factors from sociodemographic assessment and in-laboratory gait examination without, however, any contribution from off-laboratory mobility measures. A positive retrospective fall status of patients was the single most influential predictor in accordance to fall-risk assessment guidelines from the geriatric population . Gait stThe classificatory model on fall frequency identified frequent vs. occasional fallers at an accuracy of 92%. Similar to the first model on fall status, prediction of fall frequency primarily relied on information on patients' retrospective fall status and instrument-based measures of dynamic gait instability, i.e., in particular the temporal variability and asymmetry of walking. However, in contrast to the first model, the frequency of fall events during follow-up assessment was further associated with measures from off-laboratory mobility assessment. In particular, a higher intensity of daily-live activity was predictive for experiencing recurrent falls during follow-up assessment. This corresponds to the previous reports that suggested that a higher amount of physical activity, especially related to household activities, is associated to an increased risk of falling in the elderly population , 43. SimTaken together, these findings suggest that in particular patients with early stage gait impairments that yet maintain near-to-normal levels of daily activity are at a high risk of experiencing recurrent falls during ambulation. This risk will decrease not until advanced stages of disease that are linked to considerable reduced levels of mobility and daily activity . HoweverThe third classificatory model on fall severity identified those patients who experienced falls with severe consequences  during follow-up assessment with an accuracy of 91%. Fall severity classification was only based on two outcome measures from in- and off-laboratory mobility assessment. An impaired pattern of daily ambulatory activity  had the highest influence on predicting severe consequences of falling. Accordingly, patients susceptible to severe falls exhibited a lower variance of ambulatory activity with a reduced number of by tendency longer ambulatory bouts. This might involve an impaired ability to adjust ambulation activity to altered surrounding conditions. In line with this, a reduced variance in motor performance has been generally associated with age- or disease-related functional decline and maladaptive responses to changing environmental demands . In addiThe findings of this study should be interpreted with respect to certain limitations: first, patients with hypokinetic disorders were in average older than other patients and controls, which might at least partly explain their apparently lower levels of physical activity. Second, falls, gait, and mobility impairments in middle-aged patients with vascular encephalopathy (CADASIL) in the subgroup of vascular disorders are likely less pronounced compared to advanced-aged patients with sporadic vascular encephalopathy . HoweverTaken together, this prospective fall assessment study yielded three classificatory models that allow to predict patients' general fall status, their frequency of falling, and the severity of fall-related injuries at a high accuracy between 78 and 91%. Importantly, all three models were found to be independent of the specific disease group and thus equally apply for neurological gait disorders of central, peripheral, and functional pathogenesis. Clinical and instrument-based outcome measures had a differential impact on outcome prediction within and between the three classificatory models. These differential contributions encourage and provide guidelines for a multi-level, stepwise approach for fall-risk assessment in patients with neurological gait disorders that may help adjusting fall prevention strategies. Accordingly, basic index information that is readily available from medical history taking allows a good estimation on the general risk of falling and may promptly inform the clinician which patient would or would not benefit from a more in-depth examination. For those patients at a general risk of falling, a more elaborate, instrument-based gait and mobility examination provides additional, unique information with respect to the severity of their fall susceptibility and the likelihood for the occurrence of severe fall-related injuries.Supplementary file1 (DOCX 37 KB)Below is the link to the electronic supplementary material.


Are rates of severe influenza disease associated with race and ethnicity?In this cross-sectional study of influenza-associated outcomes among 113 352 patients hospitalized with influenza over the course of 10 influenza seasons, Black, Hispanic, and American Indian or Alaska Natives persons had higher rates of hospitalization and intensive care unit admission, even after adjusting for age. The greatest disparities were found in the youngest age groups.These findings suggest that targeted prevention and intervention efforts, such as improved influenza vaccine coverage and early use of antiviral treatment, could improve influenza-associated outcomes among racial and ethnic minority groups identified in this study as having higher rates of severe influenza disease. This cross-sectional study examines rates of influenza-associated hospitalization, intensive care unit (ICU) admission, and in-hospital death by race and ethnicity in the US from 2009 to 2019. Racial and ethnic minority groups, such as Black, Hispanic, American Indian or Alaska Native, and Asian or Pacific Islander persons, often experience higher rates of severe influenza disease.To describe rates of influenza-associated hospitalization, intensive care unit (ICU) admission, and in-hospital death by race and ethnicity over 10 influenza seasons.This cross-sectional study used data from the Influenza-Associated Hospitalization Surveillance Network (FluSurv-NET), which conducts population-based surveillance for laboratory-confirmed influenza-associated hospitalizations in selected counties, representing approximately 9% of the US population. Influenza hospitalizations from the 2009 to 2010 season to the 2018 to 2019 season were analyzed. Data were analyzed from October 2020 to July 2021.The main outcomes were age-adjusted and age-stratified rates of influenza-associated hospitalization, ICU admission, and in-hospital death by race and ethnicity overall and by influenza season.Among 113 352 persons with an influenza-associated hospitalization , 70 225 persons (62.3%) were non-Hispanic White (White), 24 850 persons (21.6%) were non-Hispanic Black (Black), 11 903 persons (10.3%) were Hispanic, 5517 persons (5.1%) were non-Hispanic Asian or Pacific Islander, and 857 persons (0.7%) were non-Hispanic American Indian or Alaska Native. Among persons aged younger than 75 years and compared with White persons of the same ages, Black persons were more likely to be hospitalized  and to be admitted to an ICU . Among persons aged younger than 50 years and compared with White persons of the same ages, American Indian or Alaska Native persons were more likely to be hospitalized  and to be admitted to an ICU . Among children aged 4 years or younger and compared with White children, hospitalization rates were higher in Black children , Hispanic children , American Indian or Alaska Native children , and Asian or Pacific Islander children , as were rates of ICU admission . In this age group and compared with White children, in-hospital death rates were higher among Hispanic children , Black children , and Asian or Pacific Islander children . Few differences were observed in rates of severe influenza-associated outcomes by race and ethnicity among adults aged 75 years or older. For example, in this age group, compared with White adults, hospitalization rates were slightly higher only among Black adults . Overall, Black persons had the highest age-adjusted hospitalization rate  and ICU admission rate .This cross-sectional study found racial and ethnic disparities in rates of severe influenza-associated disease. These data identified subgroups for whom improvements in influenza prevention efforts could be targeted. Prior research has shown that owing, in part, to limitations of public health infrastructure and disparities in health care, pandemic influenza outbreaks are likely to disproportionately affect socially disadvantaged persons in the United States.8 During the 2009 influenza A H1N1 pandemic, racial and ethnic disparities in influenza-associated hospitalizations and pediatric deaths were identified,9 and non-Hispanic American Indian or Alaska Native persons were found to have 3- to 8-fold higher hospitalization and death rates associated with influenza A(H1N1)pdm09 compared with other racial and ethnic groups.10 Since that time, non-Hispanic American Indian or Alaska Native persons have been considered by the Advisory Committee on Immunization Practices to be at increased risk for influenza-associated complications.11 Disparities in seasonal influenza-associated hospitalizations have also been observed12; non-Hispanic Black persons have been reported to have 1.7-fold higher adjusted odds of hospitalization, and Hispanic or Latino persons have been reported to have 1.2-fold higher adjusted odds of hospitalization compared with non-Hispanic White persons in a study conducted during the 2009 to 2010 through 2013 to 2014 influenza seasons.13 However, recent comprehensive data on racial and ethnic differences in rates of influenza-associated hospitalizations in the United States are lacking. We analyzed data from the Centers for Disease Control and Prevention (CDC)’s Influenza-Associated Hospitalization Surveillance Network (FluSurv-NET) to estimate rates of influenza-associated hospitalization, intensive care unit (ICU) admission, and in-hospital death by race and ethnicity over 10 influenza seasons (2009-2010 through 2018-2019).The burden of influenza in the United States is substantial, and annual influenza epidemics result in 140 000 to 810 000 hospitalizations and 12 000 to 61 000 deaths each year.STROBE) reporting guideline.FluSurv-NET surveillance activities were reviewed by CDC and were conducted consistent with applicable federal law and CDC policy . Sites participating in FluSurv-NET obtained approval from their respective state and local institutional review boards, as applicable. The requirement for informed consent was waived per 45 CFR 46. This study is reported following the Strengthening the Reporting of Observational Studies in Epidemiology , and had a positive influenza test no more than 14 days prior to or during hospitalization. Influenza testing was ordered at the discretion of health care practitioners, and laboratory confirmation was defined by a positive test result from rapid antigen diagnostic testing, molecular assay, indirect or direct fluorescent antibody assay, or viral culture. Trained surveillance staff conducted medical record abstractions using a standardized data collection form to obtain information on demographics, including age, sex, race and ethnicity, underlying medical conditions, antiviral treatment, and outcomes, including ICU admission and in-hospital death.Race and ethnicity data were obtained from multiple sources, including notifiable disease, laboratory, and hospital databases. In most cases, race and ethnicity is self-reported, but the source could not be confirmed in every case. We categorized race and ethnicity according to the National Center for Health Statistics categories as non-Hispanic White, non-Hispanic Black, Hispanic or Latino, non-Hispanic Asian or Pacific Islander, and non-Hispanic American Indian or Alaska Native. People of more than 1 race and ethnicity or unknown race and ethnicity are captured by surveillance but were excluded from this analysis, since population denominators were not available for these groups. People of any race (including unknown race) but of Hispanic ethnicity were classified as Hispanic. If ethnicity was unknown (16.9% of included persons) but race was available, persons were classified based on reported race but were assumed to be of non-Hispanic ethnicity.15 Medical record abstractions were conducted for all patients younger than 50 years and all patients of any age who died during their hospitalization. Of 14 surveillance sites included during these 2 seasons, 7 opted to implement a sampling strategy during the 2017 to 2018 season and 6 used the sampling strategy during the 2018 to 2019 season  was reported for all persons across all seasons. During the 2009 to 2010 through 2016 to 2017 influenza seasons, medical record abstractions were also performed for all persons. Because of high counts of laboratory-confirmed influenza-associated hospitalizations during the 2017 to 2018 and 2018 to 2019 seasons, an age- and site-stratified random sampling scheme was used to obtain detailed clinical data through medical record abstraction for a representative sample of adults aged 50 years or older in 2017 to 2018 and adults aged 65 years or older in 2018 to 2019, as previously described.9 season . Race anFor all seasons, sampled data were weighted using survey procedures in SAS statistical software version 9.4 (SAS Institute), in which the weight was the inverse probability of selection of a person for medical record abstraction. For seasons prior to 2017 to 2018, sample weights were equal to 1. For the 2017 to 2018 season, sampling rates ranged from 25% to 100% by age group and site, while in 2018 to 2019, sampling rates ranged from 50% to 100% , yieldin16 These files have postcensal and intercensal estimates of the US resident population for the indicated years, by county, age, bridged race, Hispanic origin, and sex. For the 2010 to 2011 through 2018 to 2019 seasons, Vintage 2010 to 2018 postcensal estimates were applied. For the 2009 to 2010 season, the 2009 intercensal estimate was applied.Population denominators used for rate estimation were obtained from the National Center for Health Statistics bridged-race population estimates.P values were 2-sided, and statistical significance was set at α = .05. Data were analyzed from October 2020 to July 2021.Unadjusted rates were calculated by dividing the weighted number of hospitalizations, ICU admissions, or in-hospital deaths by the total catchment population. Unadjusted rates were stratified by age group  and race and ethnicity. Unadjusted rates by race and ethnicity and age group were multiplied by the age distribution of the total FluSurv-NET catchment population to obtain age-adjusted rates; the age groups referenced previously were used for the age adjustment. We calculated 95% CIs for rates and rate ratios (RRs) assuming a simple random sample design and a normal distribution via the SAS STDRATE procedure. All analyses were conducted using SAS software. Across all seasons, 131 908 persons hospitalized with influenza were identified, and of those 123 882 persons (93.9%) were sampled. Among the 123 882 sampled persons, 10 037 (8.1%) were excluded because race and ethnicity were unknown and 493 (0.4%) were excluded because they were classified as having more than 1 race and ethnicity. Unadjusted age-specific hospitalization  and ICU Compared with White persons of the same age group, unadjusted death rates were higher among Black adults aged 50 to 64 years , American Indian or Alaska Native adults aged 18 to 49 years , and American Indian or Alaska Native children aged 5 to 17 years  . Among cAge-adjusted rates per 100 000 population of hospitalization, ICU admission and in-hospital death are displayed in Age-adjusted rates of hospitalization, ICU admission, and in-hospital death by race and ethnicity differed by surveillance site. While all sites experienced higher hospitalization and ICU admission rates in Black persons compared with White persons, only a few sites  experienced higher hospitalization and ICU admission rates in American Indian or Alaska Native persons compared with White persons , and census tract–level factors included living in areas with higher levels of poverty and household crowding. However, outside of these earlier FluSurv-NET analyses and studies conducted during the 2009 influenza A H1N1 pandemic,21 data are relatively limited on the association of race and ethnicity with recent rates of severe influenza-associate disease. To our knowledge, this is the largest analysis to date to examine disparities in rates of multiple severe influenza-associated outcomes across all racial and ethnic groups.Across all age groups, we found that Black persons had the highest rates of hospitalization and ICU admission, followed by American Indian or Alaska Native persons and Hispanic persons. Our findings are similar to prior studies that have identified disparities in influenza-associated hospitalizations in children during 2002 through 2009 using data from the New Vaccine Surveillance Network,9 which found that the proportion of H1N1pdm09-associated pediatric deaths was disproportionately higher among Hispanic children compared with the distribution of Hispanic children in the US population. A study by Quinn et al21 found significant racial/ethnic disparities in exposure risk, susceptibility to complications, and access to care during the 2009 pandemic. Generally, it has been recognized that a pandemic influenza outbreak in the United States would likely disproportionately impact socially disadvantaged groups, and pandemic planning must consider exposure, susceptibility, and treatment of high-risk populations.8In our analysis, while rates of in-hospital death were similar across racial and ethnic groups when combining seasons, during the 2009 influenza A H1N1 pandemic season, rates of in-hospital mortality were highest in Hispanic persons, followed by American Indian or Alaska Native persons. Our findings are consistent with a 2009 H1N1 pandemic study by Dee et al,22 Multiple studies have shown that racial and ethnic disparities associated with COVID-19 are likely due to inequities in social determinants of health among racial and ethnic minority groups. Contributing factors include lack of access to quality health care, disproportionate representation in essential occupations, and crowded living conditions, which may all increase exposure to COVID-19.26 Future work should investigate whether racial and ethnic disparities in influenza-associated hospitalizations and outcomes are influenced by similar factors.Striking racial and ethnic disparities in COVID-19–associated hospitalization rates have been observed during the ongoing COVID-19 pandemic, with differences observed between non-Hispanic White persons and other racial and ethnic groups being far more pronounced than for both seasonal and 2009 H1N1 pandemic influenza. Compared with rates among White persons, cumulative COVID-19–associated hospitalization rates are approximately 3-fold higher in Hispanic persons aged 17 years or younger, 6-fold higher in American Indian or Alaska Native persons aged 18 to 49 years, 4-fold higher among American Indian or Alaska Native persons aged 50 to 64 years, and 2-fold higher among Black persons 65 years or older.30 which might partially explain why racial and ethnic minority groups, such as Black, Hispanic, Asian or Pacific Islander, and American Indian or Alaska Native populations, are disproportionately affected by severe influenza and also helps to identify critical gaps in influenza vaccination that need to be filled. Over multiple influenza seasons through 2015, vaccination coverage in the general population has been lower among Black and Hispanic adults compared with White adults, and Black and Hispanic children had lower rates of full vaccination coverage compared with White children30; our findings in hospitalized patients of all ages showed similar disparities. Vaccination coverage has been shown to correlate with socioeconomic status,31 access to health care, and birth country, where those with greater access to care had higher vaccination coverage, and those born outside the United States had lower vaccination coverage compared with US-born persons.29Seasonal influenza vaccination coverage disparities by race and ethnicity have been well-described,32 and may also vary by race and ethnicity. Third, approximately 16% of persons were missing ethnicity and were classified based only on their reported race. Fourth, death rates reflect persons who died during their hospitalization, and our analysis did not capture influenza-associated deaths that occurred after hospital discharge. Preliminary analyses from FluSurv-NET indicate that deaths occurring within 30 days after discharge could account for a substantial burden of seasonal influenza-associated mortality, particularly among older adults.33 Fifth, because these data were not available for the underlying source population, this analysis did not adjust rates for other factors, such as vaccination status, underlying medical conditions, receipt of antivirals, or illness severity at hospital admission, all of which could potentially impact findings.This study has several limitations. First, FluSurv-NET conducts surveillance in selected counties within the United States, and findings may not represent the entire US population. Second, laboratory-confirmed influenza-associated hospitalizations reported to FluSurv-NET are identified by clinician-directed testing. Thus, hospitalization rates may be underestimated, since they have not been adjusted for testing practices, which are known to differ by surveillance site, age group, and timing during influenza seasons,This cross-sectional study identified racial and ethnic disparities in rates of influenza-associated hospitalization, ICU admission, and in-hospital death from the 2009 to 2010 through 2018 to 2019 influenza seasons, with the widest disparities occurring among young children from racial/ethnic minority groups, such as Black, Hispanic, and Asian or Pacific Islander children. These data could help target prevention and intervention efforts, such as improved influenza vaccine coverage and early use of antiviral treatment, to improve influenza-associated outcomes among racial and ethnic minority groups.


To accelerate progress toward the UNAIDS 90-90-90 targets, US Centers for Disease Control and Prevention Nigeria country office (CDC Nigeria) initiated an Antiretroviral Treatment (ART) Surge in 2019 to identify and link 340,000 people living with HIV/AIDS (PLHIV) to ART. Coronavirus disease 2019 (COVID-19) threatened to interrupt ART Surge progress following the detection of the first case in Nigeria in February 2020. To overcome this disruption, CDC Nigeria designed and implemented adapted ART Surge strategies during February–September 2020.Adapted ART Surge strategies focused on continuing expansion of HIV services while mitigating COVID-19 transmission. Key strategies included an intensified focus on community-based, rather than facility-based, HIV case-finding; immediate initiation of newly-diagnosed PLHIV on 3-month ART starter packs (first ART dispense of 3 months of ART); expansion of ART distribution through community refill sites; and broadened access to multi-month dispensing (MMD) (3–6 months ART) among PLHIV established in care. State-level weekly data reporting through an Excel-based dashboard and individual PLHIV-level data from the Nigeria National Data Repository facilitated program monitoring.During February–September 2020, the reported number of PLHIV initiating ART per month increased from 11,407 to 25,560, with the proportion found in the community increasing from 59 to 75%. The percentage of newly-identified PLHIV initiating ART with a 3-month ART starter pack increased from 60 to 98%. The percentage of on-time ART refill pick-ups increased from 89 to 100%. The percentage of PLHIV established in care receiving at least 3-month MMD increased from 77 to 93%. Among PLHIV initiating ART, 6-month retention increased from 74 to 92%.A rapid and flexible HIV program response, focused on reducing facility-based interactions while ensuring delivery of lifesaving ART, was critical in overcoming COVID-19-related service disruptions to expand access to HIV services in Nigeria during the first eight months of the pandemic. High retention on ART among PLHIV initiating treatment indicates immediate MMD in this population may be a sustainable practice. HIV program infrastructure can be leveraged and adapted to respond to the COVID-19 pandemic. In 2019, Nigeria had an estimated 1.8 million people living with HIV (PLHIV), of whom 1.1 million (61%) were receiving antiretroviral treatment (ART) . As partThe coronavirus disease 2019 (COVID-19) epidemic in Nigeria threatened to disrupt the progress of the ART Surge. The Nigeria Federal Ministry of Health confirmed the first COVID-19 case in Nigeria on February 27, 2020, and Nigeria Centre for Disease Control (a domestic agency distinct from CDC Nigeria) activated a national Emergency Operations Center the same day . AdditioAlthough COVID-19 was detected in Nigeria in late February 2020, CDC Nigeria, in conjunction with its implementing partners and state-based ART Surge consortia, began developing COVID-19-related adaptations to ART Surge strategies in early February, and had implemented these adaptations by early April. The overarching principle in these adapted strategies was to maintain and expand access to HIV services, including HIV testing, ART initiation, and retention on ART, while mitigating COVID-19 transmission among PLHIV, other health care recipients, and health services staff. In practice, and because facility-based services were more limited, application of this principle meant increasing focus on community-based, rather than facility-based, services, and decreasing the frequency of face-to-face interactions between PLHIV and health staff, both in facility-based and community-based services, without compromising the quality of provided services. In implementing these strategies, we devised corresponding process variables to track their dissemination through time.Modifying HIV testing to intensify focus on community-based testing. From the start of the ART Surge, a key HIV case-finding strategy was conducting targeted community-based testing., 8 In thIncreasing the number of daily doses in ART “starter packs” provided to newly-diagnosed PLHIV from 30 to 90 days. During the ART Surge, prior to COVID-19, newly-diagnosed PLHIV, including those diagnosed in the community, were provided an immediate ART “starter pack” in line with the national Test and Treat strategy.[Expansion of ART distribution through community refill sites. During the ART Surge, prior to COVID-19, PLHIV on ART living in rural or poorly-accessible areas could receive ART refills via community drug distribution. In the cExpansion of availability of multi-month dispensing (MMD) of 3–6 months ART for PLHIV established in HIV care. MMD of 3–6 months ART, as opposed to the monthly dispensation of ART, is a key component of PEPFAR-supported HIV services, although in Nigeria, the availability of MMD was limited to PLHIV considered “stable” in care, as indicated by being on ART for at least 6 months and having documented viral load suppression. In the cKey adapted strategies, and their corresponding process variables, were as follows:In addition to the program process variables described, we examined two program outcome variables key to program growth and quality: the number of PLHIV initiated on ART per month and their retention in HIV care 6 months after initiation.As part of ART Surge monitoring and evaluation, both health facilities and community-based HIV service teams reported weekly numbers of newly-diagnosed PLHIV initiating ART in an Excel-based dashboard described elsewhere , 8. UponUsing ART Surge dashboard data, we aggregated and examined the reported number of newly-diagnosed PLHIV initiating ART across the nine focus states by month during February–September 2020, and we documented the proportion diagnosed in community settings. Using this time period permitted 6 months of follow-up after ART Surge COVID-19-related adaptations were implemented in April 2020. Also using dashboard data collected during April–September 2020, we examined the number of PLHIV initiating ART each month with an ART “starter pack” of fewer than 90 daily doses or at least 90 daily doses . For PLHIV established in care, defined as being a diagnosed PLHIV who had initiated care prior to a given reporting week, we examined the proportion receiving on-time ART refills, defined as receiving an ART dispense on or before the end date of the previous dispense, by month during April–September 2020. This variable was reported in aggregate as the proportion of the number of PLHIV who received a refill over the number of PLHIV expected to receive a refill. Thus, in a given month, the proportion of PLHIV receiving on-time refills could exceed 100%, since PLHIV could pick up a refill prior to when they were due for a refill.Using NDR data, we examined the ART refill interval for PLHIV established in care by month during February–September 2020 as less than 3-month MMD or at least 3-month MMD. Finally, among monthly cohorts of PLHIV initiating ART during the same time period, we examined their retention in HIV care 6 months after initiation, defined as having received an ART dispense expected to last through the sixth month after initiation, or receiving an ART dispense within 28 days after the drug pickup expected at six months after initiation. Of note, this definition of retention aligns with PEPFAR guidance..Because this activity consisted of monitoring and improvement of a public health program, using de-identified data and no perceived ethical risk to participants, no informed consent was obtained. This public health program activity review was covered under a non-research determination from the CDC Center for Global Health.During February–September 2020, the reported monthly number of PLHIV initiated on ART in the nine focus states was 11,407 in February and increased to a peak of 25,560 in September  model is widely promoted within PEPFAR-supported HIV programs as a more sustainable approach than traditional monthly clinical visits, but to date MMD has generally been made available to PLHIV considered “stable in care” , 22, excThe described COVID-19-related adaptations entailed fewer face-to-face clinical interactions, so health staff used virtual interactions to maintain clinical contact with PLHIV. While some PLHIV support activities, specifically tracking and tracing of PLHIV who had interruptions in treatment, were already conducted virtually, COVID-19 obligated the standardization of a programmatic approach to these activities, and these processes remain in place as best practices in the ART Surge. Other activities, like ART adherence counseling, were converted from in-person to virtual activities; a 2015 meta-analysis, including a study from Nigeria, found that technology-supported adherence counseling could achieve desirable clinical outcomes , 24. Of While the ART Surge’s wide reach, unique structural components, and flexibility allowed for its continued operations during the COVID-19 pandemic, the ART Surge in turn facilitated COVID-19 surveillance and mitigation efforts in Nigeria. CDC Nigeria and its implementing partners’ staff provided technical input to the national and state-level Emergency Operations Centers on mitigation policies and provided data analysis for situation reports. HIV viral load sample transport networks and instrument platforms were used for COVID-19 testing. Advancing the ART Surge while mitigating the COVID-19 pandemic demonstrates that capacities developed specifically for HIV services have strengthened health systems generally, which can be leveraged to address other pressing public health needs, including future pandemics.The report has several limitations. The described interventions were of a programmatic design, as opposed to an experimental design, precluding causal inference between the interventions and observed results. Numbers of PLHIV initiating ART reported by HIV service teams in the weekly dashboard were based on self-report from the PLHIV, so duplication of counts of this variable within the weekly dashboard is possible; NDR deduplication algorithms reduce that likelihood in the retention cohort data. Refill data was collected as an aggregate indicator, so individual-level refill timing and interruptions in treatment were not assessed for PLHIV established in care. For analysis of retention, we focused solely on cohorts of PLHIV initiating ART; retention of PLHIV established in care and its relationship with MMD needs further analysis. The ART Surge’s ability to facilitate expansion of community-based services depended on work of implementing partners and community-based organizations, and other national HIV programs without these networks in place may not find the described approach replicable. Although partnerships with community-based organizations facilitated program implementation, formal consultation and evaluation of community perspectives on these described adapted strategies were not conducted. Collecting these perspectives, and using them to ensure services are PLHIV-centered, represent an important next step for the ART Surge. Finally, this report examines aggregate CDC data from nine states and thus may not be representative across all demographic groups, or representative of sites in the nine states outside the CDC-supported site network or in other states.A rapid and flexible HIV program response, focused on reducing facility-based and frequent interactions while ensuring delivery of lifesaving ART, was critical in overcoming COVID-19-related service disruptions to the ART Surge. With key COVID-19-specific strategic adaptations in place, CDC Nigeria and its implementing partners were able to expand access to critical HIV services across nine states during the pandemic. High retention on ART among PLHIV initiating treatment indicates immediate MMD in this population may be a sustainable practice. Virtual health system engagement with PLHIV was strengthened. The Nigeria ART Surge infrastructure can be used and adapted to monitor and respond to the COVID-19 pandemic and other public health emergencies, providing an example for how other public health programs could adapt operations in the face of emerging or changing public health program disruptions.


The Belun total sleep time (bTST) and PSG-TST had a high correlation coefficient . The accuracy, sensitivity, specificity in categorizing AHI ≥ 15 were 0.808 , 0.931 , and 0.735 , respectively. The use of beta-blocker/calcium-receptor antagonist and the presence of comorbidities did not negatively affect the sensitivity and specificity of BSP in predicting OSA. A diagnostic algorithm combining STOP-Bang cutoff of 5 and bAHI cutoff of 15 events/h demonstrated an accuracy, sensitivity, specificity of 0.938 , 0.944 , and 0.933 , respectively, for the diagnosis of moderate to severe OSA. BSP is a promising testing tool for OSA assessment and can potentially be incorporated into clinical practices for the identification of OSA.Many wearables allow physiological data acquisition in sleep and enable clinicians to assess sleep outside of sleep labs. Belun Sleep Platform (BSP) is a novel neural network-based home sleep apnea testing system utilizing a wearable ring device to detect obstructive sleep apnea (OSA). The objective of the study is to assess the performance of BSP for the evaluation of OSA. Subjects who take heart rate-affecting medications and those with non-arrhythmic comorbidities were included in this cohort. Polysomnography (PSG) studies were performed simultaneously with the Belun Ring in individuals who were referred to the sleep lab for an overnight sleep study. The sleep studies were manually scored using the American Academy of Sleep Medicine Scoring Manual (version 2.4) with 4% desaturation hypopnea criteria. A total of 78 subjects were recruited. Of these, 45% had AHI < 5; 18% had AHI 5–15; 19% had AHI 15–30; 18% had AHI ≥ 30. The Belun apnea-hypopnea index (bAHI) correlated well with the PSG-AHI (Trial registration: ClinicalTrial.org NCT03997916https://clinicaltrials.gov/ct2/show/NCT03997916?term=belun+ring&draw=2&rank=1 Obstructive Sleep Apnea (OSA) is a challenging sleep disorder associated with increased cardiovascular and metabolic morbidities as well as increased mortality –7. In thThe current gold standard for the diagnosis of OSA requires overnight multi-channel polysomnography (PSG) in the sleep lab. Type 3 home sleep apnea testing (HSAT) devices are now widely used despite concerns of false negative results –14. TechWearable devices now exert a significant impact in medicine and healthcare , 19. ManA recent proof-of-concept study in adult subjects not taking heart-rate affecting medications and having no significant comorbidities showed a good correlation between the Belun apnea-hypopnea index (bAHI) and the PSG-AHI as well as good sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) in categorizing AHI ≥ 15 events/h. The Belun total sleep time (bTST) also correlated well with the PSG-TST .In this current study, we hypothesized that BSP can reliably identify moderate to severe OSA in a sleep lab patient population including those who take heart rate-affecting medications and have non-arrhythmic comorbidities. We specifically studied the accuracy of bAHI and bTST by comparing them directly to concurrent in-lab PSG-AHI and PSG-TST. We also investigated the performance of a diagnostic algorithm incorporating STOP-Bang to predict moderate to severe OSA. STOP-Bang was selected in this study over Berlin Questionnaire (BQ) and Epworth Sleepiness Score (ESS) as studies have shown that STOP-Bang is more accurate than ESS and BQ in predicting OSA of various severity as ESS only assesses sleepiness and BQ focuses primarily on OSA-related symptoms as opposed to STOP-Bang questionnaire, which includes demographic and anthropometric characteristics such as BMI, age, neck circumference, and gender –35. We hA total of 336 consecutive subjects who were referred to the American Academy of Sleep Medicine (AASM)-accredited University Hospitals Cleveland Medical Center labs  for evaluation of sleep disorders were screened; 206 adult subjects met the eligibility criteria; 113 signed informed consent and participated in the study. Seventy-eight subjects who passed the 2-step ring size optimization protocol and had a recording time ≥ 4 hours were enrolled  32]. Pa. Pa32]. The inclusion criteria were adults age 18–80 with a valid email address who are willing to provide written informed consent and complete STOP-Bang questionnaire. The exclusion criteria included subjects who were referred for positive airway pressure titration study; those with atrial fibrillation, pacemaker, defibrillator, left ventricular assist device, or status post-cardiac transplantation; patients on home O2, non-invasive ventilator, diaphragmatic pacing, or any form of nerve stimulator; patients with hospitalization in the prior 30 days or unstable cardiopulmonary status judged to be unsafe for a sleep study by the sleep tech or the on-call sleep physician on the night of the study; those with heart rate outside the 50–100 per minute range at the time of testing, and those unable to complete the required study. Patients with atrial fibrillation were excluded from this study as the BSP analysis algorithm has not been tested in this specific population.In this study, the neural network algorithm used in the BSP is the same as that used in the proof-of-concept study . The BSPSTOP-Bang data were collected on the night of the sleep study. There are seven different ring sizes  available. A 2-step ring selection approach was used to ensure the appropriate ring size as described in the proof-of-concept study . Once thAttended in-lab PSG  was performed in all patients in a standard fashion. The PSG montage includes EEG leads , right electrooculogram, left electrooculogram, chin electromyogram, nasal pressure airflow, thermistor airflow, chest and abdominal respiratory efforts, pulse oximetry, left leg electromyogram, right leg electromyogram, and electrocardiogram. CPAP flow was also monitored for split night studies. All studies were manually scored in 30-sec epochs according to the AASM Scoring Manual, version 2.4 , 37. An Statistical analysis was performed using R (R Core Team) to assess the accuracy of BSP in predicting OSA . We usedThe study protocol was approved by the University Hospitals Institutional Review Board (STUDY20181042). All ongoing and related BSP trials are registered at ClinicalTrials.org. This trial was registered after recruitment of participants began due to clerical error.A total of 78 adult subjects were included. Baseline data including gender, race, BMI, STOP-Bang, AHI, CAI are shown in r = 0.888, P < 0.001; r = 0.967, P < 0.001; P = 0.192 for sensitivity; P = 0.474 for specificity). Comorbidities also did not affect the performance of BSP testing .The bAHI correlated well with the PSG-AHI  positive for moderate or severe OSA (STOP-Bang ≥ 5 with bAHI ≥ 15 events/h); (2) negative for moderate or severe OSA (STOP-Bang < 5 with bAHI < 15 events/h); and (3) indeterminate . With this combined diagnostic algorithm, 61% of the subjects need no further sleep testing . CompareThis study assessed the performance of BSP in detecting OSA in adult patients referred to sleep labs for in-lab PSG. Unlike the proof-of-concept study, this cohort included patients taking medications known to affect heart rate and those with non-arrhythmic comorbidities. The bAHI and bTST derived from BSP correlated well with PSG-AHI and PSG-TST with a correlation coefficient of 0.888 and 0.967, respectively. The use of heart rate-affecting medications did not negatively influence the performance of BSP testing. Despite the presence of a relatively low overall OSA prevalence and an unusually high female-male ratio uncharacteristic for an in-lab study population, the performance of BSP remains reasonably robust.This study again demonstrated that although BSP has a good overall performance, diagnostic discordance exists. BSP tends to overestimate AHI in individuals with AHI under 15 events/h and underestimate AHI in those with an AHI over 15 events/h . SimilarSeveral studies have investigated the performance of PAT/PPG-based HSAT. Choi et al. used WatchPAT-100 in 25 subjects and showed a sensitivity and specificity of 0.810 and 0.770 in a population with a prevalence of 68% for AHI ≥ 15 events/h . Li et aOverall, PAT/PPG-based testing has a sensitivity and specificity in the range of 0.810 to 0.970 and 0.610 to 0.830 for moderate to severe OSA and has the potential to significantly misclassify OSA severity , 44, 45.Our study demonstrated that another feasible approach to improve diagnostic precision is to simply combine PAT/PPG-based testing with STOP-Bang questionnaire, which is ubiquitously used for OSA screening in clinical practice. The concept of combining a prediction score and portable testing for diagnosis and screening of OSA is not new –50. MoraThis study is the first investigation to use a combination of a sleep questionnaire and a medical-grade wearable using neural network algorithm to identify OSA. One of the strengths of this study is the concurrent recording of both in-lab PSG and BSP. All sleep studies were manually scored based on the most current AASM scoring manual by one of our two senior registered sleep technicians and reviewed by an experienced board-certified sleep specialist. The scoring technicians and the sleep specialist reviewing the sleep studies were blinded to the BSP results.As far as the limitations are concerned, readers should take into account that this sampled population, unlike a typical sleep lab population with high OSA prevalence, skewed towards females and those with no OSA or mild OSA. This could have resulted from a high percentage of male patients with severe OSA declining to consent. This made the comparison of this study with other studies difficult, but nonetheless allows us an opportunity to project how BSP may perform in a non-sleep clinic where the prevalence of moderate to severe OSA is relatively low. Another limitation of this study is that this study was not performed in an unattended home setting but a sleep lab setting. Further investigation in the home setting in comparison to established HST devices is warranted. Finally, this diagnostic algorithm combining STOP-Bang and bAHI requires further validation in various patient populations and clinical settings.BSP is a promising wearable sleep technology that can potentially be incorporated into clinical practice for detecting OSA. Combining STOP-Bang cutoff of 5 with a bAHI cutoff of 15 events/h improves prediction precision and diagnostic concordance in detecting moderate or severe OSA, which may be used to streamline the clinical practice. Further clinical investigation for the diagnosis and screening of moderate to severe OSA using BSP with or without the combination with STOP-Bang is warranted.S1 Data(XLS)Click here for additional data file.


Epigallocatechin gallate (EGCG) is a major catechin found in green tea, and there is mounting evidence that EGCG is potentially useful for the treatment of coronavirus diseases, including coronavirus disease 2019 (COVID-19). Coronaviruses encode polyproteins that are cleaved by 3CL protease (the main protease) for maturation. Therefore, 3CL protease is regarded as the main target of antivirals against coronaviruses. EGCG is a major constituent of brewed green tea, and several studies have reported that EGCG inhibits the enzymatic activity of the coronavirus 3CL protease. Moreover, EGCG has been reported to regulate other potential targets, such as RNA-dependent RNA polymerase and the viral spike protein. Finally, recent studies have demonstrated that EGCG treatment interferes with the replication of coronavirus. In addition, the bioavailability of EGCG and future research prospects are discussed. The coronavirus disease 2019 (COVID-19) pandemic has impacted all aspects of society, leading to extensive investigations into remedies for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative virus of COVID-19. As of 2021, vaccines for SARS-CoV-2 are available, but effective antiviral medicines for COVID-19 are not yet available. Since the vaccination speed is limited, additional years will be required to achieve complete herd immunity. Moreover, it is expected that novel coronavirus diseases will emerge in the future. Therefore, numerous antiviral medicines should be developed to treat or alleviate coronavirus diseases.There are four coronavirus subfamilies: alpha, beta, gamma and delta; and seven coronaviruses are known to infect human . SARS-CoGreen tea has been a popular beverage for millennia, and many reports have shown that drinking green tea has various health benefits, such as cancer prevention and treatment of infectious diseases ,5. EpigaRecently, one study examined the relationship between green tea consumption and overall COVID-19 risk, including morbidity and mortality . AlthougMany antiviral medicines have been developed to treat viral diseases, and virus-specific enzymes are major targets for drug development. For example, human immunodeficiency virus (HIV) encodes reverse transcriptase and protease, and inhibitors of these enzymes are well-known antiviral drugs for acquired immune deficiency syndrome (AIDS) . LikewisCoronavirus-specific proteases are candidate targets for viral drug development . CoronavEGCG is a major constituent of green tea, and EGCG treatment has been reported to inhibit various viruses . PreviouBecause 3CL protease is essential for coronavirus maturation, potential inhibitors of 3CL protease were sought to develop antivirals for coronavirus. Initially, SARS 3CL protease was used to screen the inhibitors using a 720-compound library, and tea components, including tannic acid and 3-isotheaflavin-3-gallate (TF2B), showed inhibitory effects against SARS 3CL protease . Several50) of EGCG for SARS 3CL protease ranged from 25 μM to greater than 100 μM, and the IC50 for SARS-CoV-2 3CL protease ranged from 0.847 μM to 16.5 μM, according to our results  is a popular target of antiviral medicines . PolypheThe SARS-CoV-2 spike glycoprotein is known to interact with the host cell angiotensin-converting enzyme 2 (ACE2) protein to initiate viral infection. Therefore, SARS-CoV-2 infection can be prevented by blocking the interaction between the viral spike protein and ACE2. Several structural studies have suggested that EGCG interacts with the SARS-CoV-2 spike proteins ,50. MoreFinally, immune modulation can be a target of EGCG in the treatment of coronavirus diseases. Patients with COVID-19 can develop acute pneumonia, which can lead to the onset of a cytokine storm resulting from the upregulation of pro-inflammatory cytokines . Since ESince EGCG treatment showed inhibitory effects on 3CL protease and ACE2 binding in vitro, EGCG was expected to inhibit coronavirus replication. However, the handling of SARS or SARS-CoV-2 requires a BSL-3/BSL-4 lab facility, and the use of these facilities is limited. For this reason, there are limited experimental infection data for SARS or SARS-CoV-2 to support the role of EGCG in coronavirus replication . 50 of EGCG for coronavirus replication was relatively lower than that for 3CL protease inhibition. Since the coronavirus polyprotein contains multiple cleavage sites for 3CL protease, partial inhibition of polyprotein cleavage by EGCG may contribute to the inhibition of coronavirus replication [Bovine coronavirus is a causative virus for diarrhea in cattle, and pretreatment with EGCG significantly decreases the propagation of bovine coronavirus in host cells . Incubatlication . The inhAlthough EGCG efficiently inhibits coronavirus 3CL protease and replication in vitro, it is unknown whether EGCG can reach an effective concentration in vivo. For this reason, we conducted a literature review of the known in vivo distributions of EGCG. Although the respiratory tract is the primary site for SARS-CoV-2 infection, recent data indicate that SARS-CoV-2 also infects the gastrointestinal tract . Moreove50 for 3CL protease inhibition [Since SARS-CoV-2 is detected in both the respiratory and gastrointestinal tracts, the distribution of EGCG in the lung, intestine, and colon is summarized in hibition ,66. In phibition ,66. SincSaliva is important for the respiratory transmission of coronavirus because coronavirus is transmitted primarily through respiratory droplets, which contain saliva . When on50 values) required to effectively inhibit 3CL protease [Here, we have reviewed recent research progress on the therapeutic potential of EGCG for treating coronavirus diseases. EGCG is an abundant tea polyphenol in brewed green tea, and many reports provide evidence that EGCG can efficiently block 3CL protease, an essential enzyme for coronavirus replication ,39. Moreprotease ,66. In aprotease . Likewisprotease . These rBecause most of the EGCG data were obtained from in vitro studies, animal experiments or clinical tests are required to confirm the effects of EGCG on coronavirus diseases. Since green tea comprises EGCG as the main constituent, its extract can be used in in vivo experiments. As the safety of green tea has long been verified, an adequate amount of green tea can be directly used in in vivo experiments without toxicity concerns. These experiments will determine whether EGCG or green tea is useful for the treatment of coronavirus diseases. In addition, an epidemiological study would be useful for examining the effects of EGCG or green tea on coronavirus diseases. Although preliminary statistical results are available, the results of epidemiological studies, such as the correlation between personal green tea consumption and the risk of developing coronavirus disease, can be evaluated to determine the effects of green tea on coronavirus . We expe


COVID-19 vaccination is being rapidly rolled out in the US and many other countries, and it is crucial to provide fast and accurate assessment of vaccination coverage and vaccination gaps to make strategic adjustments promoting vaccine coverage. We reported the effective use of real-time geospatial analysis to identify barriers and gaps in COVID-19 vaccination in a minority population living in South Texas on the US-Mexico Border, to inform vaccination campaign strategies. We developed 4 rank-based approaches to evaluate the vaccination gap at the census tract level, which considered both population vulnerability and vaccination priority and eligibility. We identified areas with the highest vaccination gaps using different assessment approaches. Real-time geospatial analysis to identify vaccination gaps is critical to rapidly increase vaccination uptake, and to reach herd immunity in the vulnerable and the vaccine hesitant groups. Our results assisted the City of Brownsville Public Health Department in adjusting real-time targeting of vaccination, gathering coverage assessment, and deploying services to areas identified as high vaccination gap. The analyses and responses can be adopted in other locations. Over 90% of the population is Mexican–American, with high prevalence of Type 2 diabetes and obesity (27% and 50%), respectively leading risk factors to nearly twice the national mortality from COVID-194. The population is historically underserved and socioeconomically disadvantaged with limited healthcare access 2.COVID-19 vaccination is being rapidly rolled out in the US and many other countries, but barriers in disadvantaged and highly vulnerable communities are significant. In this analysis, we reported the effective use of real-time geospatial analysis to identify barriers and gaps in COVID-19 vaccination in a minority population to inform rollout strategies and vaccination campaigns. The study region was the City of Brownsville , the largest city within Cameron County  in South Texas on the US-Mexico Border5. It is therefore crucial to provide fast and accurate assessment of vaccination coverage to identify and remedy local gaps. Since the COVID-19 vaccine first became available in COB in early February 2021, researchers from the University of Texas School of Public Health at Brownsville have been working closely with the COB Public Health Department, to provide real-time geospatial analysis to assess vaccination gaps. We developed a real-time evaluation to quantify vaccination needs, as well as identify and locate gaps at census tract level. Our assessment has provided the COB Public Health Department with the needed information to reach vulnerable groups, improve overall vaccination uptake, and decide where to deploy their ‘boots-on-the-ground’ vaccination campaigns.Despite the disproportionally high impact of COVID-19 on minorities and underserved populations, a recent study reported lower COVID-19 vaccination coverage in socially vulnerable populations 6. All three rates were much higher than the national cumulative case rate of 8.9%, case fatality rate of 1.82% and overall mortality rate of 0.16% 7. This population is also among the most vulnerable populations, with the social vulnerability index (SVI) for Cameron County ranked among the lowest 5% in the US  8. Our analysis included all 48 census tracts that COB encompasses.The minority population in the COB has been disproportionally affected by COVID-19, with cumulative COVID-19 case rate of 9.13%, case fatality rate (COVID-19 death among COVID-19 cases) of 3.42% and mortality rate  of 0.31%, between March 19th, 2020 (first case reported from COB) and March 13th, 2021, reported from Cameron County at the time of this analysis COB Public Health Department functioned as a local COVID-19 vaccination hub provider held four mass vaccinations clinics, with first dose clinics conducted on February 5th, February 13th, February 26th, 2021, and a second dose clinic on March 5th, 2021. The Moderna mRNA-1273 COVID-19 vaccine  was administered during these mass vaccination clinics. COB also held a small clinic the week of March 9th, 2021 with the Janssen (Ad.26.COV2.S) vaccine , at the Fire Station for home-bound individuals and transportation-restricted individuals. The initial rollout of the COVID-19 vaccination eligibility was based on Texas Department of State Health Services vaccine rollout plan for front-line health workers (Phase 1A), those aged 65 years and older, and/or those aged 16 years and older with at least one medical condition with increased COVID-19 risk (Phase 1B). COVID-19 vaccination data in this report were obtained from the Texas Immunization Registry database (ImmTrac2) and encompass all those vaccinated in the city clinics since February 5th through March 13th, 2021. These data were used to assess vaccination coverage up to March 13th during the initial phase. We calculated the COVID-19 vaccination coverage as the population that has received at least one dose of vaccination per 10,000 population, for each census tract within the study region and during this initial rollout phase.9. We first ranked the census tracts by their vaccination priority from the lowest to the highest, and then ranked the census tracts by their actual vaccinations coverage rates also from the lowest to the highest. The difference between the two ranks was then used to quantify the vaccination gap.We followed the COVID-19 Vaccination Planning to assess local COVID-19 vaccination gaps 10. CCVI covered seven themes of COVID-19 risk factors including socioeconomic status, work environment, ethnicity and healthcare system. We averaged the theme-specific scores to obtain an overall CCVI composite score, which ranged from 0 to 1 with 0 the lowest vulnerability and 1 the highest. The overall composite score was used to rank the census tracts for vaccine priority.To assess the COVID-19 vaccination priority, different metrics can be used, and each may focus on a different aspect of COVID-19, such as the impact, social vulnerability, or priority population eligible for COVID-19 vaccination. Here we considered and compared the following four priority assessments. In Approach 1, we used census tract cumulative COVID-19 case rate reported above, and areas with high rate were considered priority. Approach 2 used SVI, and so areas with higher SVI were ranked as higher priority. Approach 3 used the census tract percentage of population aged 65 and older, so that areas with higher percentage of elderly were designated as higher priority. This approach accounted for the vaccination eligibility in the initial rollout phase, as the higher priority was given to the elderly. Finally in Approach 4, we used the COVID-19 community vulnerability index (CCVI) created by Surgo Ventures 11. To create the maps presented in this analysis, we first obtained the GIS shapefiles from 2019 TIGER/Line shapefiles from the U.S. Census Bureau12, and used R package “tmap” to create the choropleth maps13.The COVID-19 vaccination gap was assessed by each of the four approaches. Census tracts were identified as having vaccination gap if the rank of vaccination coverage rates was lower than the rank of priory; otherwise, they were identified as no gap. Among the census tracts with vaccination gaps, we further classified them into high, medium and low gap group by dividing the rank difference to 3 equal size groups. All analyses were performed in R Studio Table a), cumulative COVID-19 case rate between March 18th, 2020 and March 13th, 2021 (b), CDC SVI for year 2018 (c), percentage of population 65 years old and above (d) and CCVI composite score (e). These maps present somewhat different spatial patterns and identify different sets of census tracts as the priority areas.Figure Figure 14.The analysis has allowed COB Public Health Department to strategically target areas with high vaccination gaps through their “boots on the ground” campaign, while considering the population characteristics in these areas . For example, COB Public Health Department has developed and implemented a vaccination messaging approach utilizing door-to-door outreach in both English and Spanish languages in identified high gap areas with older populations, with additional messaging via the city’s social media platforms and the weekly COVID update targeting high gap areas with higher young populationsIn this analysis, we reported the first phase of COVID-19 vaccination rollout in the COB between February 5th, 2021 and March 13th, 2021. We developed four rank-based approaches to evaluate the vaccination gap, which considered COVID-19 impact, population vulnerability and vaccination eligibility. The rank-based approach, despite being simple, can accommodate the fast-changing dynamics of COVID-19 vaccination. For example, starting March 29th, 2021, vaccination eligibility for COVID-19 vaccination in Texas will include all population aged 16 years and older. Eligibility criteria should be accounted for when assessing vaccination gaps. As COVID-19 vaccine distribution plans evolve, together with COVID-19 herd immunity accumulating, we need a flexible approach such as the ones developed in this analysis to enable rapid vaccine strategy adaptations.15. Our analyses have assisted the COB Public Health Department in adjusting real-time targeting of vaccination, gathering coverage assessment, and the deployment of services to areas with the largest gaps. Along with low education and low income levels, Hispanics showed higher COVID-19 vaccine hesitancy compared with their counterparts15. Our real-time geospatial analysis incorporating SVI, for example, would provide the information on vaccine hesitant groups overlapping with high SVI. The effective use of real-time geospatial analysis can assist local public health departments, particularly those with limited resources, to develop effective, as well as cultural and language appropriate strategies to reach communities and increase vaccination uptake.Real-time geospatial analysis to identify vaccination gaps is critical to increase vaccination uptake in the vulnerable and vaccine hesitant groups