The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations. Single-cell RNA sequencing (scRNA-seq) is a powerful technology that has already shown great potential and the field is rapidly expanding ,2. In anRaceID is an excellent tool developed by Grün and colleagues to perform different aspects of scRNA-seq data analysis, including preprocessing, sub-population identification by k-means clustering, outlier cells detection and assessing differences in gene expression across clusters ,20. On tHence, we developed DIscBIO (DIscovery of single-cell transcriptomics BIOmarkers). DIscBIO is an open-source, multi-algorithmic pipeline that provides an opportunity to analyze scRNA-seq data for any organism with a taxonomy ID. It allows biomarker discovery using decision trees and gene enrichment analysis in a network context from single-cell sequencing read counts through clustering and differential analysis. DIscBIO is implemented as an R package  publishehttps://github.com/ocbe-uio/DIscBIO. Its R package can be downloaded from https://cran.r-project.org/package=DIscBIO. To fully harness the potential of transcriptome analysis in deciphering complex diseases at the single-cell level, we showcase DIscBIO over two scRNA-seq datasets. The first dataset consists of circulating tumor cells (CTCs) from patients with breast cancer, the second one is from a myxoid liposarcoma cell line. In the first case, we investigate the connection between the Golgi apparatus and oncogenesis of breast cancer to demonstrate how our analysis pipeline can be used to study this link based on current knowledge. In the second case, we focus on defining the molecular signature of the sub-populations of myxoid liposarcoma cells in association with their cell cycle phases. Additionally, we showcase the analysis of a mouse scRNA-seq dataset with accession number GSE41265 [DIscBIO is open source, and it is freely available at GSE41265  to demonGSE41265 . The conDIscBIO is a multi-algorithmic pipeline for an easy, fast and efficient analysis of sub-populations and the molecular signatures that characterize them. The pipeline consists of four successive steps: (1) data pre-processing; (2) cellular clustering and pseudo-temporal ordering; (3) determining differentially expressed genes (DEGs); and (4) biomarker identification, including decision trees, networking and gene enrichment analysis . Below, Prior to applying data analysis, normalization and gene filtering are used to pre-process the raw read counts that resulted from the sequencing. To account for RNA composition and sequencing depth among samples (single cells), the normalization method “median of ratios” is used through RaceID. This method makes it possible to compare the normalized counts for each gene equally between samples because it takes the ratio of the gene instantaneous median to the total counts for all genes in that cell. The key idea of gene filtering is to highlight the genes that manifest high variation across samples. DIscBIO provides three gene filtering techniques: noise filtering, expression filtering and designed filtering. In case the data include ERCC spike-ins, genes can be filtered based on variability in comparison to a noise level estimated from the ERCCs using an algorithm developed by Brennecke et al. . For datt-distributed stochastic neighbor embedding (tSNE). DIscBIO uses the “tsne” package through RaceID to plot the clusters in tSNE maps [Cellular clustering is performed according to gene expression profiles to detect cellular sub-populations with unique features. DIscBIO allows k-means clustering , using tSNE maps ,44. AddiSNE maps . OutlierDifferences in gene expression between clusters were identified using a significance analysis of the sequencing data (SAMseq)  from theThe biomarker discovery approach starts with a list of DEGs. Next, protein–protein interaction (PPI) networks and gene enrichment analyses are implemented to further explore the hub genes with the most interactions in gene modules ,51. The https://nbviewer.jupyter.org/github/ocbe-uio/DIscBIO/blob/dev/notebook/DIscBIO-CTCs-Binder-Leiden-Clustering.ipynb. Importantly, the features of DIscBIO can be expanded in future work to include other methods, such as SCDE and scTyper. SCDE is an alternative tool for differential analysis [Currently, DIscBIO provides limited options for normalization, clustering, differential analysis and cell type identification. However, its open, versatile and dynamic structure enables users with programming skills to fully edit the pipeline and include other methods. Through the Jupyter notebook, we showcase how additional methods, such as Leiden clustering, can be added to the pipeline. The Leiden clustering algorithm has the ability of identifying high-quality partitions ,53. It danalysis . It was analysis . On the analysis . IncorpoTo showcase DIscBIO, we analyzed two scRNA-seq datasets: one consisting of circulating tumor cells (CTCs) from patients with breast cancer, and a second dataset from a myxoid liposarcoma cell line. Additionally, we illustrate how to use the conquer repository by loading and analyzing one of the available mice scRNA-seq datasets. For each case study, we provide a Jupyter notebook that includes the complete analysis and can be used as a guide to examine other datasets.https://nbviewer.jupyter.org/github/ocbe-uio/DIscBIO/blob/dev/notebook/DIscBIO-CTCs-Notebook.ipynb. CTCs are a rare subset of cells found in the blood of cancer patients and arise as a consequence of tumors shedding cancer cells into blood vessels. They preserve primary tumor heterogeneity and imitate tumor characteristics [Here we analyze a dataset consisting of single (not clustered) CTCs collected from patients with breast cancer and obtained from several studies ,63,64,65eristics . CTCs maeristics . Due to To characterize sub-populations of CTCs, the dataset was filtered based on the median expression of all the genes in the dataset. Genes with less expression than the overall median expression in at least 10% of the cells were excluded. Cells with less than 1000 read counts were removed. In total, 215 genes and 1448 cells were used for further analysis. After filtering and computing gap statistics through RaceID, four clusters were obtained based on k-means clustering a. The clESRP1) and mitochondrial transcription factor A (TFAM). Both genes were downregulated in Cluster 4 compared to their expression in Cluster 1  have reported that metastasizing cells from a highly aggressive tumor exhibit a higher frequency of mitochondrial defects. To investigate the mitochondrial defects across the clusters, we plotted the expressions of two genes reported by Guha et al.: epithelial splicing regulatory protein  is an endocrine factor with overexpression, associated with low survival of patients with lung cancer [PPBP) is a member of the CXC subfamily of chemokines. PPBP was reported as an enhancer of the invasive ability of breast cancer cells [ITGA2B) is a member of the integrin family that regulates a diverse set of cellular processes and is crucial to the initiation, progression and metastasis of solid tumors, including breast cancer. The over-expression of ITGA2B promotes proliferation and invasion in breast cancer [FERMT3 gene encodes a protein involved in integrin activation. FERMT3 is known to enhance breast cancer progression and metastasis [SELP) was found to be important in organ-specific metastatic dissemination of breast cancer [The networking analysis suggested five genes, namely, ub nodes a. The plg cancer . The Proer cells . Integrit cancer . The FERtastasis . Selectit cancer . MALAT1) was detected to be a marker for Cluster 4. MALAT1 has been described as a long non-coding gene with contradictory functionality. Previous studies demonstrated that MALAT1 promotes cell proliferation, migration, tumor growth, metastasis and chemoresistance. On the other hand, a recent study by Kim and colleagues showed that MALAT1 levels inversely correlate with breast cancer progression and metastatic ability in transgenic, xenograft and syngeneic models [MALAT1, especially in Cluster 4  is 15 min, whereas the approximate time for running through the Jupyter notebook, the analysis described above, using an Intel Core i5-8300H laptop, is about 3 h. The time difference is due to structural deviation. Some objects, such as SC and DATAforDT, were precomputed and inserted into the Binder notebooks. Moreover, binomial differential expression was performed to detect differentially expressed markers for each cluster. The metastasis associated lung adenocarcinoma transcript-1  has been validated to cause a condensed Golgi phenotype . The Pre T Cell Antigen Receptor Alpha (PTCRA) has recently been nominated as a biomarker in breast cancer [KRT18) was reported as significantly less expressed in all basal-like cell lines, which are highly aggressive compared with luminal cell lines [KRT18 expression in breast cancer has been suggested to correlate with poor prognosis and to be a marker for epithelial–mesenchymal transition (EMT) [KRT18 in Cluster 3 could indicate that cells in this cluster are at earlier stages of EMT and that these cells have a compact Golgi due to low DGKD.Both pseudo-time ordering b and there KRT18 d,f. Intet cancer . Keratinll lines . Low KRTon (EMT) ,87. ElucRPS27A, TUBA1B, RPL4, RPL34 and RPS12) have been classified as significantly upregulated in Cluster 3, whereas 748 genes were significantly upregulated in Cluster 4  were relatively upregulated in Cluster 3 comparing to Cluster 4. The expression profiling of these 97 genes that fragment the Golgi showed two main Golgi fragmentation subsets within the Golgi genes. The first GF-subset of genes (56 genes) demonstrated upregulation in Cluster 3. The second GF-subset (15 genes) demonstrated upregulation in Cluster 4. The remaining genes (26 genes) showed either a stable low expression or unclear expression pattern across all clusters a. This cluster 4 . During luster 4 b. These CTNNB1, ILK, TGFB1 and ZEB2) were classified as significantly upregulated in Cluster 4; these genes exhibit similar expression patterns to the genes in the second GF subset. TGFB1 is a major inducer of EMT and is commonly used to induce EMT for research purposes [TGFB1 is highly expressed in all clusters except Cluster 3. Interestingly, we found a fairly strong negative correlation  between TGFB1 and KRT18, which is in line with previous findings [MMD and Zeb1 [MMD and Zeb1 . This discrepancy highlights the complexity of the link between Golgi structural alterations and cancer progression and indicates the need for more experimental efforts to clarify the role of the Golgi in cancer. The time for running the DIscBIO–CTCs–Binder notebooks (Parts 4 and 5) was 12 min, whereas the approximate time for running through the DIscBIO package, the analysis described above, using an Intel Core i5-8300H laptop, is about 2 h. The time difference is due to structural deviation. Some objects, such as fg and FgcdiffBinomial, were already precomputed and inserted into the Binder notebooks.However, four genes  cell line. The MLS data, including ERCC spike-ins and 94 single MLS cells with 59838 genes, are available in the ArrayExpress database at EMBL-EBI with accession number E-MTAB-6142 . The MLSUS-DDIT3 , with feUS-DDIT3 ,97. The US-DDIT3 . The oriUS-DDIT3 . Here, wed model . The MLSed model . ClusterTYMS) as a hub gene in addition to a set of highly connected genes involved in DNA synthesis and replication. TYMS has been reported to be associated with the well-differentiated subtype of liposarcoma [TYMS is upregulated in Cluster 3 (AURKA) and Cell Division Cycle 27 (CDC27). AURKA is involved in self-renewal of breast cancer stem cells [CDC27 is known to induce metastasis, invasion and sphere-formation in colorectal and gastric tumors [PLK1, CDC20, KIF2C, AURKA, BUB1, AURKB and PTTG1 as potential biomarkers for Cluster 2 cells. The overall impact of our findings reported here is that cells in Cluster 2, which are mainly in the G2 phase, seem to exhibit aggressive and stem-like properties and further computational and experimental investigations should validate these biomarkers candidates in myxoid liposarcoma. The approximate time for running the DIscBIO–MLS–Binder notebook is 15 min. The same time is needed to run the DIscBIO package for the analysis described above using a laptop (Intel Core i5-8300H).To further define the molecular signature of the clusters, SAMseq and binomial differential expression analyses were performed (FDR < 0.05). Significant DEGs were highlighted in the volcano plots a. Moreovosarcoma . TYMS isluster 3 c. To ideluster 3 d includeem cells . CDC27 ic tumors ,103. Cluc tumors . Severalc tumors ,106. G2 c tumors . The netMany of the tools used in DIscBIO were benchmarked before ,100,101.https://nbviewer.jupyter.org/github/ocbe-uio/DIscBIO/blob/dev/notebook/DIscBIO_VS_Granatum_%20Notebook.ipynb.We performed a side-by-side evaluation of the performance of DiscBIO and Granatum using the CTC dataset . With GrTo reduce the complexity of single-cell transcriptomics analyses using a combination of specific computational tools for scRNA-seq analysis, we developed DIscBIO as a step-wise approach. It is available as an R package, Jupyter notebook and Binder cloud version. This makes it a convenient tool for a variety of researchers to facilitate the exploration of their scRNA-seq data. The open, versatile and dynamic structure of DIscBIO enables users with programming skills to fully edit the pipeline and extend it to include other methods. Furthermore, and due to its sequential narrative design and explanatory structure, DIscBIO can be used for teaching and training purposes. Using DIscBIO we were able to identify the CTCs with highly aggressive characteristics of invasion and metastasis in breast cancer and also gain insights about the link between the alterations of the Golgi apparatus and cancer progression. Furthermore, DIscBIO enabled us to identify a small subset of cells with possible aggressive and stem-like properties in myxoid liposarcoma.


Background: Compared with those born at term gestation, infants with complex congenital heart defects (CCHD) who were delivered before 37 weeks gestational age and received neonatal open-heart surgery (OHS) have poorer neurodevelopmental outcomes in early childhood. We aimed to describe the growth, disability, functional, and neurodevelopmental outcomes in early childhood of preterm infants with CCHD after neonatal OHS. Prediction models were evaluated at various timepoints during hospitalization which could be useful in the management of these infants.Study Design: We studied all preterm infants with CCHD who received OHS within 6 weeks of corrected age between 1996 and 2016. The Western Canadian Complex Pediatric Therapies Follow-up Program completed multidisciplinary comprehensive neurodevelopmental assessments at 2-year corrected age at the referral-site follow-up clinics. We collected demographic and acute-care clinical data, standardized age-appropriate outcome measures including physical growth with calculated z-scores; disabilities including cerebral palsy, visual impairment, permanent hearing loss; adaptive function (Adaptive Behavior Assessment System-II); and cognitive, language, and motor skills . Multiple variable logistic or linear regressions determined predictors displayed as Odds Ratio (OR) or Effect Size (ES) with 95% confidence intervals.Results: Of 115 preterm infants  with CCHD and OHS, there were 11(10%) deaths before first discharge and 21(18%) deaths by 2-years. Seven (6%) neonates had cerebral injuries, 7 had necrotizing enterocolitis; none had retinopathy of prematurity. Among 94 survivors, 9% had cerebral palsy and 6% had permanent hearing loss, with worse outcomes in those with syndromic diagnoses. Significant predictors of mortality included birth weight z-score , single-ventricle anatomy , post-operative ventilation days , and cardiopulmonary resuscitation ; for adverse functional outcome in those without syndromic diagnoses, birth weight 2,000–2,499 g , post-conceptual age , post-operative lowest pH , and sepsis .Conclusions: Our findings suggest preterm neonates with CCHD and early OHS had significant mortality and morbidity at 2-years and were at risk for cerebral palsy and adverse neurodevelopment. This information may be important for management, parental counseling and the decision-making process. Infants with complex congenital heart defects (CCHD) who have open-heart surgery (OHS) during the neonatal period are at risk for mortality and neurodevelopmental morbidity , 2. AdvaDespite the adverse outcome compared with that of term infants, there is little information available regarding “gestation-related details” and predictors of outcomes in early childhood of preterm neonates with CCHD and early OHS at “different time points during hospitalization.” This information is important for management of the infants, parental counseling and informing the decision-making process. We therefore aimed to describe the growth, disability, functional, and neurodevelopmental outcomes in early childhood of preterm infants with CCHD who had OHS with cardiopulmonary bypass (CPB) by 6 weeks corrected age. The secondary aims of the study included the identification of risk factors for mortality and adverse functional outcomes at each of five time points that we believe are important moments of clinical decision-making and family counseling: (i) before and at birth, (ii) pre-operative, (iii) day 1 post-operatively, (iv) post-operatively day 5, and (v) at first hospital discharge.We studied all preterm infants with CCHD who received OHS with CPB at 6 weeks of corrected age or less at the Stollery Children's Hospital in Edmonton, Alberta, Canada, between 1996 and 2016. Demographic, clinical and outcomes data were obtained from the Western Canadian Complex Pediatric Therapies Follow-up Program. This program maintains a prospectively collected registry and database including demographic, acute-care clinical and long-term neurodevelopmental outcomes in all infants who have complex cardiac surgery (CCS) at 6 weeks of age or less . The cliz-scores  determined child functional abilities . The ABACertified pediatric psychologists and psychometrists administered the Bayley Scales of Infant and Toddler Development, third edition (Bayley-III) . The BayAnonymous data were returned to the central site for adjudication by one of the investigators . MaternaCCHD were coded according to cardiac anatomy, particularly the presence of single ventricle shown to be predictive of perioperative mortality , and perObjective 1: First, we conducted a descriptive analysis to explore the distribution of mortality and each neurodevelopmental outcome using mean, SD, median, and interquartile range (IQR). Student t, Fisher's Exact and Chi-square tests were used to compare groups.Objective 2: To determine potential predictors of adverse outcomes , we used univariate and multiple variable regression analyses.Univariate analyses: Using logistic regression, we compared the demographic and clinical variables at each time point between the surviving and non-surviving groups of infants during the first hospital stay for OHS. Using linear regression, we analyzed predictors of the ABAS-II GAC scores as a continuous variable. Following these comparisons, risk factors with P < 0.1 were selected for the multiple variable regression models.a. Multiple variable analyses: The effect of risk factors for mortality and ABAS-II GAC scores was further examined in multiple variable models using five clinical time points for analyses. We chose these five periods as they are important time points informing clinical management decisions and family counseling. The five time points were: (i) before and at birth, (ii) pre-operative, (iii) post-operative day 1, (iv) post-operative day 5, and (v) before hospital discharge. Predictive analyses were performed at each time point with the retainment of risk factors which had P < 0.1 in the previous clinical period. Because of the large association of chromosomal abnormalities with adverse outcome, the prediction of the ABAS-II GAC scores was further completed with the non-syndromic 76 children. Only potential predictor variables that occurred in 10% of more of the total population were entered in the regressions. In view of the small number of infants in some GAs, we also analyzed the effects of GA on outcomes using three different categorical bands .b. P ≤ 0.05. Data analyses were performed using R software V3.6.1  or medians (IQR). Odds Ratio (OR) and Effect Size (ES) are reported with 95% confidence intervals [CI]. Significance was determined as e V3.6.1 .From September 1996 to December 2016, all 115 preterm infants with CCHD who received OHS at ≤6 weeks corrected age at the Stollery Children's Hospital were registered. These 115 children made up 10.7% of all children at age 6 weeks and under having complex cardiac surgery with CPB at this institution during this time. The demographic and clinical characteristics are shown in z-scores and primary cardiac defect of single ventricle were associated with death and these factors remained significant when including variables from all time points. Other risk factors included the lowest arterial pH at the pre-operative time point, lowest arterial pH and highest lactate level post-operatively, and cardiopulmonary resuscitation (CPR) and days of post-operative ventilation before discharge. GA, postnatal age and the post-conceptual age of surgery were not statistically significantly different between survivors and non-survivors  and without  syndromic diagnoses are shown in the All 94 (100%) surviving infants received neurodevelopmental evaluation at 2 years corrected age. The demographic and clinical characteristics of these surviving preterm infants with (n = 6). Of the birthweight groups, those with a weight of 2,000–2,499 g (n = 26) had ABAS-II GAC scores significantly below the others (n = 50), . This birth weight of 2,000–2,499 g was associated with the ABAS-II GAC at each of the 5 different time points. The adjusted R2 of the variance in ABAS-II GAC scores at 2 years accounted for in the multiple logistic models was 0.263 before discharge from the hospital  −13.8], we sought possible predictors of outcome including only the 76 infants without syndromic abnormalities . We did hospital . The expThis study adds “gestation-related information” to the mortality and developmental outcome information of preterm neonates with CCHD , 24, 25,The preterm neonates of this study with CCHD , show a high mortality (18.3%) at 2 years of corrected age. When compared with preterm infants of lower GA (31–32 weeks) in the Canadian Neonatal Network, those with CCHD have more neonatal short-term complications including brain injuries  and necrotizing enterocolitis (6.1 vs. 1%), but not of retinopathy of prematurity (0 vs. 0% of Stage 3/4/5) . The prez-scores was critical for this prediction. As part of the consideration of termination of pregnancy for a fetus with CCHD, it is important to examine fetal growth rather than GA alone, in addition to the compounding effect of CCHD. Postnatal age and post-conceptual age of surgery were not related to mortality. This finding should be interpreted with caution because of the small study population. We speculate that postnatal transition of cardiopulmonary systems may affect the tolerance of OHS, a pathophysiological phenomenon which is similar to the delay of surgery in infants with congenital diaphragmatic hernia  included birth weight 2,000–2,499 g, older post-conceptual age of surgery, post-operative acidosis, and sepsis. These findings may aid clinicians in discussing outcomes with families. While parents are anxious and stressed regarding the prognosis of these infants with CCHD, it may be difficult to explain to the parents and counsel them regarding the impact of birth weight category and post-conceptual age on the outcomes. Nevertheless, the information may help explain the management of these infants including the timing of delivery and surgery. Future studies may assist in providing better counseling. Furthermore, these findings emphasize the importance of long-term follow-up of survivors to detect neurodevelopmental concerns that can be addressed by early intervention programs, ensuring these vulnerable children can reach their full potential.Preterm neonates with CCHD and early OHS had significant mortality and morbidity. Early outcomes suggest more cerebral palsy and lower functional and neurodevelopmental scores than occurring with prematurity alone. In this preterm group, predictive factors for mortality included birthweight The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Institutional ethics boards at all six follow-up sites . Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.P-YC conceptualized and designed the study, drafted the initial manuscript, reviewed, and revised the manuscript. CR designed the data collection instruments, conceptualized and designed the study, supervised data collection, critically reviewed, and revised the manuscript. MH and ID carried out the statistical analyses, and critically reviewed and revised the manuscript. HS and AJ conceptualized and designed the study, supervised data collection, and critically reviewed the manuscript for important intellectual content. GB conceptualized and designed the study, coordinated data collection, and critically reviewed the manuscript for important intellectual content. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Does the presentation of comorbidities in patients with coronavirus disease 2019 (COVID-19) differ by race/ethnicity, and is there a difference in case fatality rates among ethnic and racial groups when controlling for key risk factors?In a cohort study of 5902 patients with positive COVID-19 diagnosis treated at a single academic medical center in New York, non-Hispanic Black and Hispanic patients had a higher proportion of more than 2 medical comorbidities and were more likely to test positive for COVID-19 compared with their non-Hispanic White counterparts. However, their survival outcomes were at least as good as those of their non-Hispanic White counterparts when controlling for age, sex, and comorbidities.In this study, non-Hispanic Black and Hispanic patients experienced similar outcomes as their non-Hispanic White counterparts after COVID-19 infection; this is critical to further understanding the observed population differences in mortality by race/ethnicity reported elsewhere. This cohort study investigates whether presenting comorbidities in patients with coronavirus disease (COVID-19) in New York City differed by race/ethnicity and whether case fatality rates varied among different ethnic and racial groups controlling for presenting comorbidities and other risk factors. As of May 11, 2020, there have been more than 290 000 deaths worldwide from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). Risk-adjusted differences in outcomes among patients of differing ethnicity and race categories are not well characterized.To investigate whether presenting comorbidities in patients with COVID-19 in New York City differed by race/ethnicity and whether case fatality rates varied among different ethnic and racial groups, controlling for presenting comorbidities and other risk factors.This cohort study included 5902 patients who presented for care to the Montefiore Medical Center, a large urban academic medical center in the Bronx, New York, between March 14 and April 15, 2020, and tested positive for SARS-CoV-2 on reverse transcription quantitative polymerase chain reaction assay. Final data collection was April 27, 2020.Patient characteristics, including self-identified ethnicity/race, age, sex, socioeconomic status, and medical comorbidities, were tabulated.2 tests, and the association with survival was assessed using univariable and multivariable Cox proportional hazards regression, based on time from positive COVID-19 test.Overall survival. Associations between patient demographic characteristics, comorbidities, and race/ethnicity were examined using χP = .25). Hispanic and non-Hispanic Black patients had a higher proportion of more than 2 medical comorbidities with 654 (34.3%) and 764 (39.5%), respectively, compared with 147 (28.9%) among non-Hispanic White patients (P < .001). Hispanic and non-Hispanic Black patients were also more likely to test positive for COVID-19 than White patients, with 1905 of 2919 Hispanic patients (65.3%), 1935 of 2823 non-Hispanic Black patients (68.5%), and 509 of 960 non-Hispanic White patients (53.0%) having positive test results for SARS-CoV-2 (P < .001). While controlling for age, sex, socioeconomic status and comorbidities, patients identifying as Hispanic  or non-Hispanic Black  had slightly improved survival compared with non-Hispanic White patients.Of 9268 patients who were tested, 5902 ethnically diverse patients (63.7%) had SARS-CoV-2. Of these, 3129 patients (53.0%) were women, and the median (interquartile range) age was 58 (44-71) years. A total of 918 patients (15.5%) died within the study time frame. Overall, 1905 patients (32.3%) identified as Hispanic; 1935 (32.8%), non-Hispanic Black; 509 (8.6%), non-Hispanic White; and 171 (2.9%), Asian; the death rates were 16.2% (309), 17.2% (333), 20.0% (102), and 17.0% (29), respectively (In this cohort study of patients with COVID-19 who presented for care at the same urban medical center, non-Hispanic Black and Hispanic patients did not experience worse risk-adjusted outcomes compared with their White counterparts. This finding is important for understanding the observed population differences in mortality by race/ethnicity reported elsewhere. By contrast, non-Hispanic White patients made up 45% of the hospitalized sample but 59% of the residents in the sampled area.7The CDC recently reported on a sample of 1482 patients with confirmed COVID-19 who were hospitalized from March 1 to 30, 2020, with race/ethnicity information for 580 of them.These and other observations have led to much speculation regarding why patients from minority ethnic and racial groups appear to be particularly susceptible to infection with and complications from this new pathogen. Factors such as living conditions, including housing density and residential segregation; working conditions, especially acknowledging the overrepresentation of these populations among essential workers; the prevalence of underlying health conditions; and access to quality health care may affect this perceived association. As the number of patients with COVID-19 rapidly increases, understanding how ethnicity and race play into the outcomes of this disease is crucial. Health systems located in New York City are uniquely positioned to contribute to this evolving portrait because of the high volume of cases and the racial and ethnic diversity of the populations in this largest of US metropolitan areas. In particular, by examining the experience of an ethnically diverse sample of patients all cared for at a single institution, we can hold constant the associations of care received with outcomes and focus on whether race/ethnicity are independently associated with outcomes of patients with COVID-19.Here we present a retrospective analysis of patients with COVID-19 who presented to a large urban medical center serving an ethnically and racially diverse population. The overall aim of the study was to characterize the presentation of patients with laboratory confirmed COVID-19 infections cared for at the Montefiore Medical Center located in the Bronx, New York, especially regarding differences among ethnic and racial groups. Because age is known to be the most potent factor associated with outcomes for COVID-19 infections, we posed three questions, as follows: (1) did the presentation of comorbidities differ by race/ethnicity, (2) controlling for age and comorbidities, did case fatality rates differ by race/ethnicity, and (3) did the association between comorbidities and survival vary by race/ethnicity?International Classification of Diseases, Ninth Revision (ICD-9) or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) classification. Race/ethnicity were based on patient self-identification and categorized as Hispanic, non-Hispanic Black, non-Hispanic White, Asian, other  or unknown/declined. SES was calculated by a representative summary score based on home address, using neighborhood information taking into account median household income, median value of housing units, occupation and education of inhabitants, and percentage of households receiving interest or net rental income.8 The SES score is a relative measure and should be interpreted as such, in which a lower SES score reflects lower socioeconomic status.In this cohort study, we used the electronic medical record to identify all patients who were tested for COVID-19 between March 14 and April 15, 2020, and presented for care to the Montefiore Medical Center, a large academic medical center located in the Bronx, New York, whether or not they were admitted as inpatients. SARS-CoV-2 positive status was determined based on reverse transcription quantitative polymerase chain reaction assay. Patients who were tested multiple times were categorized as positive if any of their tests indicated positivity. Patient characteristics, race/ethnicity, socioeconomic status (SES), comorbidities, and clinical outcomes were tabulated for patients who tested positive for COVID-19, with final data collection on April 27, 2020. Individual comorbidities included in the Charlson Comorbidity Index were collected as well as body mass index  and hypertension. Comorbidities were included based on either STROBE) reporting guideline for cohort studies.This study was approved by the institutional review board of the Albert Einstein College of Medicine, and informed consent was waived because this was a retrospective cohort study and because patient data were deidentified. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology  were missing information for BMI and 676 (11.5%) were missing SES scores, this could produce biased or inefficient results when analyzing only patients with complete information for all covariates. Therefore, missing values of BMI were imputed based on sex and age, and missing values of SES were imputed using sex, age, and area code. Imputations were performed using ordered logistic regression for BMI categories and SES quartiles, using 20 multiple imputation data sets.P < .05, and all tests were 2-tailed.Subgroup analyses of patients with known ethnicity identification were performed to compare the proportion of medical comorbidities among racial and ethnic groups in patients who were admitted to the hospital or those admitted to the intensive care unit (ICU). Stratified multivariable Cox regression analyses were performed to examine whether risk factors for death were different within each race/ethnicity strata. All statistical analyses were performed using Stata version 14.2 (StataCorp). Statistical significance was set at P < .001). We are reporting the outcomes of 5902 patients (63.7%) who tested positive at any time in the study period. The median (interquartile range) age was 58 (44-71) years, and 3129 patients (53.0%) were women. Overall, 1905 patients (32.3%) identified as Hispanic; 1935 (32.8%), non-Hispanic Black; 509 (8.6%), non-Hispanic White; 171 (2.9%), Asian; 792 (13.4%), other; and 590 (10.0%) had unknown race/ethnicity or declined to answer  died within the study time frame, after allowing for at least a 12-day window between the last day of COVID positivity and final data collection, with death rates broken down by patient demographic characteristics (eTable 1 in the P < .001), and older age was strongly associated with the risk of death . In multivariable analysis, while controlling for age, sex, SES, and comorbidities, patients who identified as Hispanic  and non-Hispanic Black  had somewhat improved survival compared with non-Hispanic White patients. Medical comorbidities found to be risk factors significantly associated with death were morbid obesity , cardiovascular disease, diabetes, kidney disease, and dementia.Risk factors associated with survival after COVID-19 infection are shown in eTable 2 in the P = .25). The eFigure in the Death rates among the different ethnic and racial groups were 20.0% for non-Hispanic White patients (102 of 509), 17.2% for non-Hispanic Black patients (333 of 1935), 16.2% for Hispanic patients (309 of 1905), and 17.0% for Asian patients (29 of 171) (P < .001). Differences in management setting by race/ethnicity are further described in eTable 3 in the Comparing the number of medical comorbidities across ethnic and racial groups, Hispanic and non-Hispanic Black patients had a higher proportion of multiple medical comorbidities , and theRisk factors associated with survival, broken down by ethnic and racial grouping, are presented in 9 Can we reconcile the findings with reports of elevated case fatality rates for Black and Hispanic patients relative to White patients?In this sample of 5902 ethnically diverse patients with COVID-19 who were treated at a single large academic medical center in the Bronx, New York, non-Hispanic Black and Hispanic patients, despite presenting with higher numbers of comorbidities and being more likely to require inpatient hospitalization, had outcomes at least as good as, and maybe even marginally superior to, their non-Hispanic White counterparts after controlling for age, sex, and comorbidities. This finding that Black and Hispanic patients did not experience worse outcomes compared with their White counterparts when presenting for care at the same urban medical center is important for understanding the observed population differences in mortality by race/ethnicity reported elsewhere. Do the findings reported here conflict with observations that the population-based mortality rates for COVID-19 are higher for Black and Hispanic populations than for White populations?Our findings that non-Hispanic Black and Hispanic patients with COVID-19 had mortality rates at least as good as their White counterparts does not speak directly to the elevated burden of this disease among minority populations. To the extent that more non-Hispanic Black and Hispanic individuals contracted the infection during the period under study, the observed population-based mortality rates would be elevated relative to white populations even if non-Hispanic Black and Hispanic patients with infection had similar outcomes as White patients.Differences between our estimates of equality among racial and ethnic groups in case fatality rates for COVID-19 relative to other estimates may derive from differential testing for COVID-19 in our sample compared with the testing patterns in other samples reporting case fatality rates. If non-Hispanic Black and Hispanic patients not receiving care in a well-integrated health care delivery system are only tested when they show severe symptoms compared with their White counterparts, whose access to care permits a broader testing strategy, estimates of the case fatality rate for COVID-19 may be falsely elevated compared with the estimates that would be reported if testing regimens were equivalent across race/ethnicity classifications.Are the characteristics of our sample population comparable with those of other published series? Similar to other reports, we found that older age and multiple comorbidities, including obesity, cardiovascular disease, diabetes, kidney disease, and dementia, were associated with poor outcomes from COVID-19, and this remained consistent across ethnic and racial groups.10 reported on differences in COVID-19 testing, hospitalizations, and outcomes among the different New York City boroughs, showing higher rates of hospitalization and death in the Bronx. The authors reported only unadjusted rates for these metrics. Contrasting this to our study, the importance of controlling for risk factors, such as age and comorbidities, to accurately estimate outcome differences in ethnically and racially diverse populations, such as that of New York City, becomes clear.An April 2020 article4 In contrast, our hospitalized patients were slightly older, with a median age of 65, compared with 63 and 61 for patients hospitalized at Northwell and at Kaiser Permanente in Northern California,3 respectively. Further differences included a higher proportion of patients at Montefiore presenting with diabetes . The older age and higher rates of diabetes seen in our patients might account for the higher mortality in hospitalized patients.Comparing the demographic characteristics of hospitalized patients in our sample with other recent reports from the United States, we find important similarities between patients hospitalized at Montefiore and those from other institutions, with 88.6% of our hospitalized patients presenting with medical comorbidities compared with 94% patients hospitalized within the Northwell Health System in New York.11 from another large health care system in California showed that Black patients were more likely to be hospitalized with COVID-19, which agrees with the higher rates of inpatient admission seen among non-Hispanic Black patients in our cohort. A major difference between this cohort and our cohort is that Montefiore serves a large proportion of patients who belong to ethnic and racial minority groups, and despite a larger proportion of these patients requiring inpatient admission, they did not have worse outcomes in terms of mortality.A July 2020 reportThe stratified design of this study permitted a detailed presentation of comorbidities by race/ethnicity and fully interactive analytic approach that highlighted differences in outcomes among ethnic and racial groups while controlling for all comorbidities in addition to age and sex. Although non-Hispanic White patients were slightly older and had fewer comorbidities, stratified multivariate analysis indicated that outcomes from COVID-19 were not notably different compared with patients from other racial and ethnic groups. Another strength of this analysis is that only a small number of hospitalized patients (13.2%) were still admitted by end of data collection, with 86.8% of patients having known outcome disposition.This study has limitations, including its retrospective nature, with data availability limited to the electronic medical records at our institution. This study also did not address differences in the rates of COVID-19 positivity or access to health care among the different ethnic and racial groups, both of which could affect cumulative mortality numbers. Importantly, the case fatality rates presented here are conditional on patients having tested positive for COVID-19. If the likelihood of being tested varies systematically across race/ethnicity and SES or if potential differences in false-negative rates among different groups exist, the ratios of case fatality rates across race/ethnicity categories would be affected. Furthermore, approximately 4.5% of non-Hispanic White patients in this cohort were transferred from chronic nursing facilities; thus, these patients may not be representative of the general non-Hispanic White population in a variety of unobservable characteristics that could not be controlled for. However, excluding patients coming from nursing homes did not change the association between race/ethnicity and mortality.In this study, Black and Hispanic patients who tested positive for COVID-19 and received care at a large academic health system in a densely populated urban environment experienced similar outcomes as their White counterparts when controlling for age, sex, and comorbid conditions at presentation. These findings may provide some reassurance that access to the services available in comprehensive health care environments may attenuate, if not eliminate, racial/ethnic differentials in COVID-19 mortality rates.


Rotator cuff tears are common among the elderly, and studies on the outcomes after rotator cuff repair in the elderly are limited. We carried out this meta-analysis with systematic literature search, aiming to clarify the outcomes after rotator cuff repair in the elderly as assessed by the American Shoulder and Elbow Surgeons (ASES) shoulder score.We conducted a literature search through October 2019 in PubMed and EMBASE databases and performed meta-analysis to calculate the summary mean difference comparing the post- and pre-operation ASES scores under both fixed-effect and random-effect models.p=0.88) and I2 statistic (0%). The ASES scores on average increased by 39.7  after rotator cuff repair, in both fixed-effect and random-effect models. No substantial publication bias was indicated.Among 4978 studies identified through literature search, four studies  were eligible for the meta-analysis, including 282 patients who were aged over 70 years. These studies had low heterogeneity as measured by Cochran’s Q test (Our findings suggest improved outcomes after rotator cuff repair in the elderly population as measured by the ASES score, and such improvements have been consistent in previous studies. Rotator cuff tears are common conditions in all age groups, but the risk increases with age ,2. PartiA few previous studies have evaluated the outcomes after rotator cuff repair in the elderly patients, and the results vary between populations, and particularly are dependent on the tools used for assessing the post-operation outcomes ,6-10. AmAiming to clarify the outcomes after the repair of rotator cuff tears in the elderly, specifically as measured by the ASES score, we conducted this meta-analysis of published original studies with a systematic literature search. We also evaluated the heterogeneity and potential publication bias in previous studies.We performed a systematic literature search in PubMed and EMBASE until October 2019 without language restriction. A search strategy combining the following keywords was used to identify potentially eligible publications: (injury OR injuries OR repair OR tears) AND rotator cuff. All these terms were searched in TITLE/ABSTRACT. We also reviewed the reference lists of eligible original articles and previous systematic reviews to identify additional studies -8,10.p-value).We considered studies meeting the following criteria as eligible: (1) cohort studies published as original articles, (2) measuring outcomes before and after rotator cuff repair by the ASES score, (3) participants including patients who were aged 70 years or older, and (4) providing information necessary to estimate the mean difference comparing post- and pre-operation scores, with a measure of statistical uncertainty  study design and characteristics , (2) distribution of sex and age at entry, (3) type of operation , (4) patients’ characteristics, and (5) main results.We assessed the quality of included studies with the help of the Newcastle-Ottawa Scale (NOS) for assessing the quality of non-randomized studies in meta-analyses. This scale assesses the study quality in terms of selection of participants, comparability, and outcome ascertainment. A score ranging from zero to nine is awarded to each study with higher scores indicating higher study quality .2 statistic. We considered a p-value <0.10, instead of <0.05, in the Q test as statistically significant, and this threshold is widely used in meta-analyses  in ASES score after rotator cuff repair in both fixed-effect and random-effect models (p<0.001) under both fixed-effect and random-effect models.All of the four studies individually reported improved outcomes comparing the post- and pre-operation ASES scores. Meta-analysis of these studies generated an average increase by 39.7  and I2 statistic (0%). No evident publication bias was indicated by visual inspection of funnel plot .The heterogeneity tests indicated very low heterogeneity across studies as measured by Cochran’s Q test , Simpler Shoulder Test, and others ,19-21. AIn summary, this systematic review and meta-analysis suggests improved outcomes after rotator cuff repair in the elderly population as measured by the ASES score, and such improvements have been consistent in previous studies. The available studies mainly involved arthroscopic surgery, and more studies are needed to assess the outcomes after open surgery for rotator cuff tears in the elderly.Fang Y and Zhang Q jointly conceived and designed the study. Fang collected and analyzed the data and drafted the manuscript. Zhang Q contributed to the interpretation of the results and critically review of the manuscript.


Returning university students represent large-scale, transient demographic shifts and a potential source of transmission to adjacent communities during the COVID-19 pandemic.In this prospective longitudinal cohort study, we tested for IgG antibodies against SARS-CoV-2 in a non-random cohort of residents living in Centre County prior to the Fall 2020 term at the Pennsylvania State University and following the conclusion of the Fall 2020 term. We also report the seroprevalence in a non-random cohort of students collected at the end of the Fall 2020 term.Of 1313 community participants, 42 (3.2%) were positive for SARS-CoV-2 IgG antibodies at their first visit between 07 August and 02 October 2020. Of 684 student participants who returned to campus for fall instruction, 208 (30.4%) were positive for SARS-CoV-2 antibodies between 26 October and 21 December. 96 (7.3%) community participants returned a positive IgG antibody result by 19 February. Only contact with known SARS-CoV-2-positive individuals and attendance at small gatherings  were significant predictors of detecting IgG antibodies among returning students .Despite high seroprevalence observed within the student population, seroprevalence in a longitudinal cohort of community residents was low and stable from before student arrival for the Fall 2020 term to after student departure. The study implies that heterogeneity in SARS-CoV-2 transmission can occur in geographically coincident populations. Despite high seroprevalence observed within the student population, seroprevalence in a longitudinal cohort of community residents remained low and stable from before student arrival for the Fall term to after their departure, implying limited transmission between these subpopulations. Demographic shifts, high population densities, and population mobility are known to impact the spread of infectious diseases –5. WhileDuring the Fall 2020 term, many universities in the US experienced high rates of COVID-19 cases among students , with a This investigation reports the results of a longitudinal serosurvey of community residents in Centre County, Pennsylvania, USA, which is home to The Pennsylvania State University (PSU), University Park (UP) campus. The return of approximately 35,000 students to the UP campus in August 2020 represented a nearly 20% increase in the county population . During This human subjects research was conducted with PSU Institutional Review Board approval and in accordance with the Declaration of Helsinki. The study uses a longitudinal cohort design, with two separate cohorts: community residents and returning students. We report on measures from the first two clinic visits for the community resident cohort and the first clinic visit for the returning student cohort.To assist with recruitment into studies under the Data4Action (D4A) Centre County COVID Cohort Study umbrella, a REDCap survey was distributed to residents of Centre County where respondents could indicate interest in future study participating and provide demographic data. Returning students received a similar survey and were also recruited through cold-emails and word-of-mouth.Individuals were eligible for participation in the community resident cohort if they were: 18 years old, residing in Centre County at the time of recruitment (June through September 2020); expecting to reside in Centre County until June 2021; fluent in English; and capable of providing their own consent. PSU students who remained in Centre County through spring and summer university closure were eligible for inclusion in the community resident cohort as they experienced similar geographic COVID-19 risks as community residents. Participants were eligible for inclusion in the returning student cohort if they were: ≥18 years old; fluent in English; capable of providing their own consent; residing in Centre County at the time of recruitment (October 2020); officially enrolled as PSU UP students for the Fall 2020 term; and intended to be living in Centre County through April 2021. In both cohorts, individuals were invited to participate in the survey-only portion of the study if they were: lactating, pregnant, or intended to become pregnant in the next 12 months; unable to wear a mask for the clinic visit; demonstrated acute COVID-19 symptoms within the previous 14 days; or reported a health condition that made them uncomfortable with participating in the clinic visit.Upon enrollment, returning students were supplied with a REDCap survey to examine socio-behavioural phenomena, such as attendance at gatherings and adherence to non-pharmaceutical interventions, in addition to their travel history and contact with individuals who were known or suspected of being positive for SARS-CoV-2. Community residents received similar surveys with questions relating to potential SARS-CoV-2 household exposures. All eligible participants were scheduled for a clinical visit at each time interval where blood samples were collected.The primary outcome was the presence of S/RBD IgG antibodies, measured using an indirect isotype-specific (IgG) screening ELISA developed at PSU . Furtherprevalence package in R. We calculated a 95% binomial confidence interval for test sensitivity of the IgG assay for detecting prior self-reported positive tests in the returning student cohort (students had high access to testing from a common University provider) with a uniform prior distribution between these limits. Prevalence estimates were calculated across possible values of specificity between 0.85 and 0.99. Estimates were not corrected for demographics as participants were not enrolled using a probability-based sample. We assessed demographic characteristics of the tested participants relative to all study participants to illustrate potential selection biases.Community resident and returning student cohorts’ seroprevalence are presented with binomial 95% confidence intervals. We estimated each subgroup’s true prevalence, accounting for imperfect sensitivity and specificity of the IgG assay, using the mice and finalfit packages, and two-sided Chi-squared tests for raw odds ratios (OR), and present 95% confidence intervals. We considered the following variables a priori to be potential risk factors as they increase contact with individuals outside of a participants’ household [Missing values were deemed “Missing At Random” and imputed, as described in the ousehold –27: closa priori: being a PSU employee; and the amount of contact with PSU students when “Stay at home” orders are not in place . BIC and AIC were used to evaluate the contribution of the variables to the model.We estimated the aOR of IgG positivity at either time point in the community subgroup, with the following risk factors determined targets package.All statistical analyses were conducted using R version 4.1.1 (2021-08-10), with a pipeline created using the A total of 9299 community residents were identified through an initial REDCap survey that collected eligibility, demographic, and contact information. 1531 were eligible, indicated willingness to participate, and were enrolled. 1462 completed a first clinic visit between 07 August and 02 October 2020, and 1313 of those completed a second clinic visit between 30 November and 19 February 2020 and for whom both visit 1 and visit 2 samples were analysed. 1410 returning students were recruited using volunteer sampling and 725 enrolled; of these, 684 completed clinic visits for serum collection between 26 October and 21 December 2020.Among participants with serum samples: the median age community residents was 47 years (IQR: 36–58), with 86.5% between the ages 18–65 years, and for the returning students the median age was 20 years (IQR: 19–21), with 99.7% between the ages 18–65 years; 66.9% of the community residents identified as female and 32.3% as male; 64.5% of the returning students identified as female and 34.6% as male; 92.9% of the community residents identified as white, as did 81.9% of the students. Similar proportions were seen in those enrolled without samples, and among the initial survey respondents ; Table 2Of the returning student participants, 673 (92.8%) had at least one test prior to enrollment in the study; of these, 107 (15.9%) self-reported a positive result . Of thesOf the returning students with a self-reported prior positive SARS-CoV-2 test, 93.1% (95% CrI: 86.4–97.2%) had positive IgG antibodies; this was used as an estimate of sensitivity of the IgG assay for detecting previously detectable infection see .Among the returning students, only close proximity to a known SARS-CoV-2-positive individual  and attending small gatherings in the past 3 months  were significantly associated with a positive ELISA classification in the multivariable model . AttendiBoth the returning students and community residents self-reported high masking compliance; 86.7% and 75.9%, respectively, reported always wearing mask or cloth face covering when in public . Less thThe return of students to in-person instruction on the PSU UP campus was associated with a large increase in COVID-19 incidence in the county, evidenced by over 4,500 student cases at PSU . In a saet al. [Within the student group, presence of SARS-CoV-2 antibodies was significantly associated with close proximity to known SARS-CoV-2-positive individuals and attendance of small events. No other risk factors were correlated with an increase in IgG test positivity, aligning with other research . It is net al.  identifiNeither the resident nor the student participants were selected using a probability-based sample. Thus, these participants may not be representative of the populations. Those who chose to participate in this study may have been more cognizant and compliant with public health mitigation measures. Specifically, the resident participants disproportionately lived in the townships immediately surrounding the UP campus, where extensive health messaging  and prevThough the participants reflect a convenience sample, the large differences in SARS-CoV-2 seroprevalence suggest that the cohorts did not experience a synchronous, well-mixed epidemic despite their close geographic proximity. College campuses have been observed to have high COVID-19 attack rates, and counties containing colleges and universities have been observed to have significantly higher COVID-19 incidence than demographically matched counties without such institutions . Thus, wWhile SARS-CoV-2 transmission between the student and community resident populations is likely to have occurred (perhaps multiple times), the large difference in seroprevalence between the student and resident participants after the Fall term are consistent with either rare or non-persistent transmission events between students and residents, or both. This suggests that it is possible to minimize risks brought about by sub-populations with high SARS-CoV-2 incidence using behavioural interventions. This observation may have implications for outbreak management in other high risk, highly mobile populations . However, we note that this was achieved in the context of disproportionate investment in prevention education, testing, contact tracing, and infrastructure for isolation and quarantine by PSU in the high-prevalence sub-population (students).With respect to the health behaviours measured, both students and community residents reported high masking rates (>75%) and low distancing rates in public (<30%). However, students had significantly higher masking and gathering rates than community residents, thus a next step is to identify factors that may explain these differences. Minimizing risk, however, may come at significant social, psychological, educational, economic, and societal costs . Thus, oSupplement 1


Prevalence of light daily smoking, <10 cigarettes per day (CPD), and non-daily smoking has increased in the US population. This analysis examined the heterogeneity in past-year smoking behavior, current tobacco use behaviors, and smoking cessation behaviors among light and/or non-daily smokers.Current adult (≥18 years old) smokers (N=26196) participated in the 2010–2011 US Current Population Survey – Tobacco Use Supplement, which reported current (T1) and past 12-month (T0) smoking behaviors. Responses were categorized by intensity (light ≤10 CPD vs heavy >10 CPD) and frequency (non-daily vs daily). Combinations of T0 and T1 smoking behaviors resulted in 15 smoking trajectories ending in light/non-daily smoking and a 16th category of heavy daily smokers at T1. Differences in demographics, tobacco use, and smoking cessation behaviors were assessed by using weighted multivariable regression models.Overall, 46.1% of US smokers were heavy smokers, 24.6% remained light daily smokers and 12.5% remained light non-daily smokers between T0 and T1. Current cigar, smokeless tobacco, and pipe use differed by smoking trajectories (p<0.05). All light and/or non-daily smokers were more likely than heavy daily smokers to have made a quit attempt (p<0.05) but use of cessation treatments varied. Smokers in many light and/or non-daily smoking trajectories were less likely than heavy daily smokers to be aided by healthcare providers for smoking cessation (p<0.05).Among heavy daily smokers who became light non-daily smokers, the mismatch between intent to quit (80.9%) and receiving advice to set a quit date (33.7%) is one example of a potential opportunity for a clinical intervention. Additionally, the decline in the prevalence of non-daily smoking  was slower than the decline in prevalence of daily smoking in the US population, resulting in an increase in the proportion of non-daily smoking among current smokers, and has varied by racial/ethnic group over the last decade2. Specifically, among non-Hispanic White smokers the prevalence of non-daily smoking has decreased from 11% to 10%, among non-Hispanic Black smokers the prevalence has decreased from 13% to 11%, and among Hispanic smokers the prevalence has decreased from 13% to 8%2. Despite the decline in both daily and non-daily smoking, racial/ethnic minorities are more likely to be light or non-daily smokers compared to White smokers4. Previous research suggests that intermittent smokers score lower on the Fagerström test for nicotine dependence (FTND), which indicates that these smokers are less dependent on nicotine than daily smokers7.From 2005 through 2016 the prevalence of heavy daily smoking, >10 cigarettes per day (CPD), and more specifically ≥20 CPD among the US population decreased, while the prevalence of light daily smoking (≤10 CPD) among the US population increased8. Additionally, despite the differences between daily smokers and intermittent smokers, approximately 70% to 80% of the quit attempts of intermittent smokers are still unsuccessful, while over 85% of daily smoker attempts are unsuccessful9. Prior research suggests that nicotine dependence is lower among light non-daily smokers compared to light daily smokers10. Nonetheless, among smokers who smoke their first cigarette within 5 minutes after waking (indication of nicotine dependence), 25% are light non-daily smokers10. An additional limitation in the literature is that most of the previous reports on frequency and intensity of cigarette smoking are based on current use, while light daily smoking and non-daily smoking could be a transitional stage for those who just started smoking, or for those who are trying to quit smoking by cutting back cigarette consumption8. No studies, to date, have examined how light/heavy and daily/non-daily smoking are associated with receiving advice from healthcare providers to quit smoking and cessation treatment use. Additionally, light and intermittent smoking still carry significant health risks, therefore understanding this increasingly prevalent behavior among smokers can inform strategies to reduce tobacco induced diseases.Much less is known about the intersection between frequency of smoking (i.e. daily/non-daily) and intensity of smoking (i.e. heavy/light). For example, some intermittent smokers used to be daily smokers while others never were daily smokers12 and overall mortality13) associated with light daily smoking and non-daily smoking, better understanding of tobacco use and cessation behaviors among light daily smokers and non-daily smokers compared to heavy daily smokers may help to identify potential interventions that could lead to better health outcomes.Consequently, the current analysis aim is to provide a detailed examination of light daily smoking and non-daily smoking. Specifically, the first aim of this analysis is to estimate the prevalence of different smoking groups that resulted in light daily smoking and non-daily smoking. The second aim is to examine the changes in cigarette consumption over a year within these groups as well as compare them with those in heavy daily smokers. The third aim is to examine the use of other tobacco products by these groups. Lastly, the fourth aim is to examine the associated smoking cessation history and receipt of smoking cessation advice by these groups. Given the health risks (e.g. for cardiovascular disease and cancerData were obtained from the Tobacco Use Supplement to the Current Population Survey (TUS-CPS). TUS-CPS is sponsored by the National Cancer Institute and is conducted along with the Current Population Survey (CPS) by the Census Bureau, which provides information on demographic characteristics and employment. The sampling frame of the TUS-CPS is the non-institutionalized civilian population of the United States. Regarding the sampling process of the CPS, the United States is divided into 2025 primary sampling units (PSU), which are then grouped into 824 strata. Afterwards, within each of the strata one PSU is chosen and households within the chosen PSUs are surveyed. This analysis was based on data from the 2010–2011 TUS-CPS, which was composed of about 83000 respondents, representing the non-institutionalized population of civilians aged ≥18 years. We did not use more recent data (e.g. 2014–2015 TUS-CPS) because they did not assess interactions with healthcare providers about smoking cessation and use of cessation treatment. The analysis was limited to self-respondents because proxy respondents did not provide detailed tobacco use information. Of those, 68% of the self-responses were from telephone interviews, while 32% of the self-responses were from personal interviews, with an overall response rate of 59.8%. Because the research questions focused on smoking behaviors, the overall analytic sample was further restricted to current smokers (reported smoking every day or some days) at the time of survey who had smoked at least 100 cigarettes in their lifetime (n=26196).14, if current smokers reported smoking ≤10 CPD, they were classified as light smokers, whereas if they smoked >10 CPD they were classified as heavy smokers. The combination of daily/non-daily and light/heavy smoking status yielded four main smoking categories at the time of survey (T1): light daily (LD), heavy daily (HD), light non-daily (LND) and heavy non-daily (HND) smoking. Respondents also reported their smoking status  and CPD 12 months prior to the survey (T0). Smoking status and CPD at T0 were used to create 5 smoking categories of non-smoking (NS), LD, HD, LND, and HND. Finally, smoking categories at T0 and T1 were used to create 15 groups from T0 to T1 that resulted in light and/or intermittent smoking at T1, and a 16th group of respondents that represented current heavy daily smokers at T1, which served as the reference group. Most smokers (90%) in the reference group were heavy daily smokers at T0 as well . To calculate CPM at T0 and T1, among every-day smokers, CPD was multiplied by 30 days; among non-daily smokers, CPD was multiplied by number of days smoked in the past 30 days. To calculate changes in cigarettes smoked per month, CPD at T1 was subtracted from CPD at T0. Regarding non-cigarette tobacco product use, respondents reported if they used the following products every day, some days, or not at all, at the time of survey: regular cigars, cigarillos, regular pipes, hookah, or smokeless tobacco products. Respondents who reported every-day or some-day use were classified as current users of that product.In terms of smoking cessation, respondents reported if they attempted to quit smoking for any length of time during the 12 months prior to the time of survey (yes/no). Among those who reported making a quit attempt, respondents reported using of a variety of cessation treatment methods such as nicotine replacement therapy, prescription medication, and counseling. Further, among the respondents who reported seeing a doctor or a dentist during the 12 months prior to the time of survey, they reported if a doctor or dentist advised them to stop smoking, and if a doctor or a dentist recommended that they use nicotine replacement therapy, prescription medication, counseling programs, or set a specific quit date, to aid their smoking cessation attempt. Advice from both sources was combined to represent healthcare provider recommendations.Demographic information on age, sex, race, income, and education, was collected. Age was coded into three groups: 18–34, 35–49, and >49 years. Sex was coded as a binary variable with male or female options. Race was classified into 6 categories consisting of: Hispanic, non-Hispanic White, non-Hispanic Black, non-Hispanic American Indian/Alaskan Native/Hawaiian, non-Hispanic Asian, and Non-Hispanic-Multi race. Educational attainment was classified into 4 categories consisting of high school degree or less, some college, associate degree, and Bachelor’s degree or higher. Annual household income (US$) was grouped into 4 categories: < 25000; 25000–49999; 50000–74999; and ≥75000.15. This project only involved the use of de-identified data, which is considered ‘not human subjects research’ and requires no IRB review or approval per NIH policy and 45 CFR 46.Weighted multiple logistic regression models were used to assess associations between smoking groups and current cigar use, current pipe use, current hookah use, and current smokeless tobacco use, while controlling for race/ethnicity, income, age, education, and sex. Weighted logistic regression models were used to assess associations between smoking groups and use of cessation treatments among current smokers who made a quit attempt in the past 12 months, as well as the associations between smoking groups and current smokers who were advised by a healthcare provider to quit smoking during the past 12 months using a variety of cessation treatment methods. In all these logistic regression models, heavy daily smokers were used as reference group while controlling for race/ethnicity, income, education, age, and sex. The main analyses were conducted using SAS-callable SUDAAN 11.0.1 to account for the complex sample design, and TUS-CPS self-response replicate weights were used. Pairwise comparison results among smoking groups engaging in light smoking or non-daily smoking were reformatted into a compact letter display using the %MULT macro by PiephoMore than half (53.9%) of US adult smokers were light daily or non-daily smokers at the time of the survey. The most prevalent of the past 12-month groups among light and/or non-daily smokers was maintaining light daily smoking (24.6%), followed by maintaining light non-daily smoking (12.5%), heavy daily to light daily smoking (5.1%), non-smoking to light non-daily smoking (3.1%), light non-daily to light daily smoking (2.4%), light daily to light non-daily smoking (1.7%), non-smoking to light daily smoking (1.5%), heavy daily to light non-daily smoking (1.0%), maintaining heavy non-daily smoking (0.5%), heavy non-daily to light non-daily smoking (0.4%), heavy daily to heavy non-daily smoking (0.4%), heavy non-daily to light daily smoking (0.2%), light non-daily to heavy non-daily smoking (0.2%), light daily to heavy non-daily smoking (0.1%), and non-smoking to heavy non-daily smoking (0.1%). Less than half (46.1%) were heavy daily smokers.Tobacco use and cessation behaviors, as well as healthcare providers’ advice and cessation medication prescription varied significantly when comparing these variables across smoking groups engaged in light smoking or non-daily smoking at T1. For example, changes in cigarette per months differ significantly between most groups (p<0.05). Light non-daily to light non-daily smokers and non-smoking to light non-daily smokers were less likely to smoke their first cigarettes ≤30 minutes after waking up than other groups (p<0.05). Light daily to light daily smokers were one of the least likely groups to report seriously considering quitting smoking in the next six months and attempted to quit smoking in the past 12 months (p<0.05). Non-smoking to light daily smokers and non-smoking to light non-daily smokers were among the groups that were least likely to be advised to use nicotine replacement therapy. Heavy daily to light daily smokers, light non-daily to light daily smokers, heavy non-daily to light daily smokers, heavy daily to heavy non-daily smokers were more likely than other groups to be advised to set a quit date (p<0.05).Research on smokers who engaged in light and/ or non-daily smoking is limited, especially on the heterogeneity of smoking history within light daily and non-daily smokers, their use of non-cigarette tobacco products, and their cessation behaviors. This analysis advanced our understanding on light daily and non-daily smokers in the following ways.16, overall the literature is mixed because both outcomes are possible depending on the characteristics of the smokers involved. In our study, we found light and/or non-daily smoking to be largely stable within a year.First, among light daily and non-daily smokers, over two-thirds maintained light and/or non-daily use over a 12-month period . While some literature suggests that light daily and non-daily smoking may be a transient stage in the natural history of smokingSecond, our findings on changes in cigarette consumption help shed light on the nuances of light daily and non-daily smoking behavior with respect to health risks. For example, despite maintaining the same smoking frequency and quantity, those who maintained light daily and light non-daily smoking showed little changes in cigarette consumption, while those who maintained heavy non-daily smoking showed an increase in cigarette consumption. Further, it was previously unclear if changing from heavy non-daily to light daily smoking represents an increase or a decline in total cigarette consumption, given the group represents a decrease in quantity but an increase in frequency. Our finding clarified that this group was associated with a reduction of over 200 cigarettes per month.9 and to seriously consider quitting smoking. However, it is worth noting the heterogeneity by groups. Specifically, those who maintained light daily smoking were only slightly more likely than heavy daily smokers to seriously consider quitting smoking, perhaps due to their lower perceived risks of smoking-related illnesses17. Despite the intention to quit smoking, this analysis showed that smokers in several light daily and nondaily smoking groups were less likely than heavy daily smokers to be advised by healthcare providers to set a quit date, to use nicotine replacement therapy or behavioral programs, and/or be prescribed cessation medications. Given the elevated health risks associated with light daily and non-daily smoking13 and the effectiveness of brief smoking cessation interventions by healthcare providers18, it is important for healthcare providers to assess, advise, and assist light daily and non-daily smokers to quit smoking.Third, in agreement with previous literature, this analysis found that light daily and non-daily smokers were more likely than heavy daily smokers to have made a quit attempt19. Individuals who eventually quit smoking are less likely to have been Black or broken the rules during high school and more likely to have been Hispanic and have mothers who completed higher education19. Alternatively, individuals who are early and established smokers are less likely to have educated mothers and be Black or Hispanic, while being more likely to be out of school or employed19. Additional smoking trajectory literature, measuring cigarettes per day, suggests that continuous heavy smokers and occasional smokers are more likely to experience unemployment compared to non-smokers20. Other smoking trajectory research has suggested that high amounts of trouble at school and low education achievement also lead to an increased probability of heavy and continuous smoking21. These studies somewhat correlate with our findings that smokers with less education and less income were more likely to be light daily and non-daily smokers. However, these studies also suggest that smoking cessation interventions and outreach need to differ based on racial/ethnic group since racial groups do not all necessarily display the same trends in smoking behavior.Literature related to smoking trajectories demonstrates how light daily and non-daily smoking can vary between being a transient and stable stage, while clarifying some of the characteristics associated with smokers and helping to predict how the smoking behavior of individuals changes over time. Rather than using cigarettes smoked per day as our study did, some smoking trajectory literature has shown that days smoked per month is the best outcome variable for distinguishing between the characteristics of different smoker groups that are represented in trajectoriesWhile the strength of this analysis was a large sample size, which allowed examination of various smoking groups involving light daily and non-daily smoking, it also has limitations. The data were from the 2010–2011 TUS-CPS, which are slightly dated. Given the increasing trends in light daily and non-daily smoking in recent years, this analysis could have underestimated the prevalence of various groups. However, the 2010–2011 TUS-CPS was the only cycle that cessation-treatment use and healthcare provider advice were assessed, and therefore the only cycle that could answer our research questions. Nonetheless, this analysis is unable to show how the introduction of e-cigarettes in the tobacco marketplace may have influenced frequency and intensity of smoking as well as access to evidence-based cessation treatments. A threshold of 100 cigarettes as opposed to ‘ever use’ was used because smokers who did not meet this threshold in the survey were not asked detailed tobacco use questions. As a result, this threshold inadvertently might not have captured younger smokers who were just experimenting or initiating cigarette use. Another limitation of this study was the reliance on memory for baseline tobacco use status due to the absence of prospective data. Due to the cross-sectional nature of this analysis, it is important to refrain from making causal inferences based on our findings.22. Additionally, this phenomenon persists even after controlling for demographic covariates and other factors such as health status22. This finding mirrors our own results and highlights an area that physicians can improve in when treating their patients. Furthermore, educating healthcare providers about the harm of light daily and non-daily smoking may increase the smoking cessation effort among light daily and non-daily smokers.This analysis showed that light daily and non-daily smoking could be a result of heterogenous smoking groups, while the majority involved maintaining light smoking in a daily or non-daily fashion. It also showed that non-cigarette tobacco product use was more prevalent in only some light daily and non-daily smoking groups compared to heavy daily smoking. Further, despite intention to quit smoking, smokers in several light daily and non-daily smoking groups were less likely than heavy daily smokers to be assisted by healthcare providers for smoking cessation. Prior research has also demonstrated that non-daily smokers are less likely than daily smokers to be advised to quit smoking or asked about their tobacco use by their doctors, despite having a greater desire to quit smoking than daily smokersThe authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none was reported.This study was supported by the Division of Intramural Research at the National Institute on Minority Health and Health Disparities, National Institutes of Health. The views presented in this article do not necessarily reflect the views of the U.S. Government, Department of Health and Human Services, National Institutes of Health, or National Institute on Minority Health and Health Disparities.Not commissioned; externally peer reviewed.Click here for additional data file.


COVID-19 pneumonia has high mortality rates. The symptoms are undiagnostic, the results of viral nucleic acid detection method (PCR) can delay, so that chest computerized tomography is often key diagnostic test in patients with possible COVID-19 pneumonia. In this review, we discussed the main radiological findings of this infection. COVID-19 infection has been spreading globally. By 2 April 2020, there were 950,430 confirmed cases with COVID-19 infection in 203 countries [1]. COVID-19 is highly infectious and may lead severe pneumonia. The most common presenting clinical symptoms are fever and cough in addition to other nonspecific symptoms including dyspnoea, headache, muscle soreness, and fatigue [2]. Due to the symptoms and manifestations being nonspecific in COVID-19 pneumonia, special diagnostic tools have to be used. Viral nucleic acid detection using real-time polymerase chain reaction (RT-PCR) remains the standard test in the diagnosis of COVID-19 pneumonia. However, waiting-period for viral detection with RT-PCR can cause delayed diagnosis of pneumonia. Meanwhile, PT-PCR could result negative at the beginning of COVID-19 pneumonia. Sometimes, the interval between initial negative to positive RT-PCR can be 4–8 days . Therefore, thoracic radiology evaluation is often key diagnostic element in patients with suspected COVID-19 pneumonia [4].In this article, a wide spectrum from the preparation before the radiological test to the radiological findings on possible or confirmed COVID-19 cases are going to be discussed.The radiology department has an obligation to ensure patient-centred care while maintaining staff safety. Because radiology departments have the potential of being in the eye of spreading infection, plans to prevent nosocomial infection spread should be developed. The health authority of the authors  has developed a pandemic preparedness checklist for medical clinics that can be applied to radiology clinics [5].Preventing patient-to-staff transmission:It is advised to use barrier protections in reception and ensure appropriate distance between patients and staff. Personal protective equipment (PPE) for staff involved in direct patient care of potential COVID-19 cases should include gloves, surgical masks (airborne precautions including N95 masks are not indicated), eyeglasses, and gowns. Preventing patient-to-patient transmission: It is strongly recommended to supply patients with hand sanitizer and surgical masks at entrances. It is an obligation to separate the patients with respiratory symptoms from those without symptoms.Preventing staff/facility-to-patient transmission: Hand sanitizer can be handed out the staff. It is also an important point to ensure that, appropriate disinfection methods are used on clinical equipment including radiography equipment between uses, and the staff with respiratory symptoms should be off. Effective communication between the referring services and radiology department is crucial on limiting the spread of this highly contagious infection. The radiology department should be notified before transferring the suspected case so that appropriate infection control measures are in place beforehand. After imaging the suspected patient, the imaging room should not be used for 15 min [5]. In literature, the diagnostic value of chest x-ray is relatively low as 30–60% in COVID-19 pneumonia [6]. Although it is possible to see some abnormalities in viral pneumonia on chest x-rays, it cannot be excluded the disease if the chest x-ray is normal. Noncontrast chest CT should be considered for early diagnosis of viral disease in suspected patients with normal chest x-ray .Some typical radiological images can be demonstrated by computerized tomography (CT) in COVID pneumonia patients [9]. The predominant CT findings of COVID-19 infection are bilateral, peripheral, and basal predominant ground-glass opacity, consolidation, or both.There are many similar articles about CT findings on pandemic of COVID-19 infection which are written by experienced authors from China, Italy, UK, and Canada in the literature. The first confirmed COVID-19 case in Turkey was detected on 10th of March 2020. Our knowledge and experiences about COVID-19 have been rising from the day that the first case was declared. According to our own experience, the predominant CT findings in COVID-19 pneumonia included basically ground-glass opacification, consolidation, bilateral involvement, and peripheral and diffuse distribution .With the gradual recognition of radiological findings of COVID-19 pneumonia, guidelines, and reviews are written for rapid and correct diagnosis and treatment [9].Radiologists are trying to classify the findings of thoracic CT images in COVID-19 pneumonia. The developed chest CT table which is advised by British Society of Thoracic Imaging (BTSI) could make classifications at 4 stages. This classification has been adopted by many radiologists in Turkey (Table 1) .Another classification has also been recommended by partnership of Radiological Society of North America (RSNA), Society of Thoracic Radiology (STR), and American College of Radiology (ACR) (Table 2) . COVID-19 pneumonia series were firstly reported by China. Based on a prospective analysis of 1014 patients, the sensitivity of CT is estimated to be 97% in detecting COVID-19 infection. The sensitivity of the RT-PCR has been reported to be in the range of 60% to 70% [14]. Although PCR is the gold standard for confirming infection, chest CT has been shown to be more sensitive for the detection of COVID-19. Polymerase chain reaction–negative cases with positive CT findings and high clinical suspicion may benefit from repeated PCR testing [15]. In another Chinese series, the authors emphasized that COVID-19 pneumonia manifested with chest CT imaging abnormalities, even in asymptomatic patients, with rapid evolution from focal unilateral to diffuse bilateral ground-glass opacities that progressed to or coexisted with consolidations within 1–3 weeks. Combining assessment of imaging features with clinical and laboratory findings could facilitate early diagnosis of COVID-19 pneumonia [16]. The sensitivity of the CT was 97.2%, while the sensitivity of initial rRT-PCR was 83.3%. Patients with typical CT findings but negative rRT-PCR results should be isolated [17].In an Italian study from Rome, the value of chest CT imaging was analysed in patients with confirmed COVID-19 pneumonia. The typical pattern of COVID-19 pneumonia was peripherally ground-glass opacities with multilobe and posterior involvement, bilateral distribution, and subsegmental vessel enlargement (>3 mm). Chest CT sensitivity was high (97%) but with lower specificity (56%) [18].In our country, a national guideline was written in the light of the recent literature and COVID-19 pneumonia patients who recovered from COVID-19 pneumonia, 4 stages of lung involvement were defined on CT [19-22]: 1. Early stage : In this stage, ground-glass opacities (GGO) was the main radiological demonstration distributed subpleurally in the lower lobes unilaterally or bilaterally. 2. Progressive stage : In this stage, the infection rapidly aggravated and extended to a bilateral multi-lobe distribution with diffuse GGO, crazy-paving pattern, and consolidation.3. Peak stage : In this stage, the involved area of the lungs slowly increased to the peak involvement and dense consolidation became more prevalent. Findings included diffuse GGO, crazy-paving pattern, consolidation, and residual parenchymal bands.4. Absorption stage : In this stage, the infection was controlled and the consolidation was gradually absorbed. No crazy-paving pattern was present anymore. However, in this process, extensive GGO could be observed as the demonstration of the consolidation absorption.In conclusion, chest CT examination plays an important role in the initial diagnosis and follow-up examination of the novel coronavirus pneumonia. Large studies are needed to clarify the diagnostic value of initial radiological evaluation and serial measurements in COVID-19 pneumonia management. The authors  are the members of COVID-19 Advisory Committee of Ministry of Health of Turkey.