Johns Hopkins University Research Articles

Influence of age and gender on the epidemic of COVID-19 : Evidence from 177countries and territories-an exploratory, ecological study.

BackgroundThe pandemic of coronavirus disease 2019 (COVID-19) is spreading worldwide with an uncertain ultimate impact on every aspect of human society. Recognizing the groups with higher susceptibility and fatality are in urgent need.MethodsWe retrieved the total number of confirmed incident and death cases of COVID-19 in 177 countries/territories from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). Data of age and gender composition were collected from the United Nations Department of Economic and Social Affairs. Spearman’s correlation analysis was used to explore the association between the composition of age and gender and the incidence rate (IRC), case fatality rate (CFRC), and mortality rate of COVID-19 (MRC). Multiple comparisons were adjusted by the Bonferroni method, and the threshold p-value was set as p < 0.01. Software SPSS 23.0, ArcGIS 10.6, and GraphPad Prism 8.0 were used to generate our results.ResultsMedian age was positively correlated to IRC, CFRC and MRC (rs = 0.60; rs = 0.27; rs = 0.61, p < 0.0001 for all). The age-dependent correlation between people over 65 years of age with IRC was higher in females, while the correlation between age distribution and CFRC as well as MRC was higher in males (p < 0.0001 for all). Besides, we found the age-gender-dependent differences were correlated to IRC in places with high income and associated with CFRC in non-high income countries/territories.ConclusionThe correlation between the composition of age and gender and the epidemic characteristics of COVID-19 confirmed previous points that females are more susceptible to COVID-19. The results remind us that more attention should be paid to male patients, particularly those over 65 years old for enhanced clinical management.Supplementary InformationThe online version of this article (10.1007/s00508-021-01816-z) contains supplementary material, which is available to authorized users.

Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: \u2013 evidence from Baidu index

BackgroundNew coronavirus disease 2019 (COVID-19) has posed a severe threat to human life and caused a global pandemic. The current research aimed to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19.MethodsWe collected the number of COVID-19 confirmed cases between January 11, 2020, and April 22, 2020, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The search index values of the most common symptoms of COVID-19 (e.g., fever, cough, fatigue) were retrieved from the Baidu Index. Spearman’s correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptom and the number of confirmed cases. Regional distributions among 34 provinces/ regions in China were also analyzed.ResultsDaily growth of confirmed cases and Baidu index values for each COVID-19-related symptom presented robust positive correlations during the outbreak (fever: rs=0.705, p=9.623× 10− 6; cough: rs=0.592, p=4.485× 10− 4; fatigue: rs=0.629, p=1.494× 10− 4; sputum production: rs=0.648, p=8.206× 10− 5; shortness of breath: rs=0.656, p=6.182× 10–5). The average search-to-confirmed interval (STCI) was 19.8 days in China. The daily Baidu Index value’s optimal time lags were the 4 days for cough, 2 days for fatigue, 3 days for sputum production, 1 day for shortness of breath, and 0 days for fever.ConclusionThe searches of COVID-19-related symptoms on the Baidu search engine were significantly correlated to the number of confirmed cases. Since the Baidu search engine could reflect the public’s attention to the pandemic and the regional epidemics of viruses, relevant departments need to pay more attention to areas with high searches of COVID-19-related symptoms and take precautionary measures to prevent these potentially infected persons from further spreading.

Homologous recombination deficiency (HRD) score in germline BRCA2- versus ATM-altered prostate cancer.

The homologous recombination deficiency (HRD) score integrates three DNA-based measures of genomic instability, and has been understudied in prostate cancer. Given the recent FDA-approval of two PARP inhibitors for prostate cancer, HRD score analysis could help to refine treatment selection. We assessed HRD score (defined as the sum of loss-of-heterozygosity, telomeric allelic imbalance, and large-scale state transitions) in three cohorts of primary prostate cancer, including a Johns Hopkins University (JHU) cohort with germline mutations in BRCA2, ATM or CHEK2 (n=64), the TCGA cohort (n=391), and the PROGENE cohort (n=102). In the JHU cohort, tumors with germline BRCA2 mutations had higher HRD scores (median=27) than those with germline ATM or CHEK2 mutations (median=16.5 [p=0.029] and 9 [p<0.001], respectively). For TCGA tumors without underlying HR pathway mutations, the median HRD score was 11, significantly lower than ovarian carcinoma lacking BRCA1/2 mutations (median=28). In the absence of HR gene mutations, the median HRD score was unexpectedly higher among prostate cancers with TP53 mutations versus those without (17 vs 11; p=0.015); this finding was confirmed in the PROGENE cohort (24 vs 16; p=0.001). Finally, among eight BRCA2-altered patients who received olaparib, progression-free survival trended longer in those with HRD scores above versus below the median (14.9 vs 9.9 months). We conclude that HRD scores are low in primary prostate cancer and higher in cases with germline BRCA2 or somatic TP53 mutations. Germline BRCA2-altered cases have significantly higher HRD scores than germline ATM-altered or CHEK2-altered cases, consistent with the lower efficacy of PARP inhibitors among the latter.

SARS/CoV-2: Behavioral Host Manipulation.

Though it has not been extensively studied, host manipulation has been documented for various pathogens. Examples of this phenomenon can be seen in cases of toxoplasmosis, rabies, and the influenza virus. An examination of the possible means by which SARS/CoV-2 alters the behavior of its host to spread among populations is elaborated. Indirect evidence that serves as indicators of this phenomenon is presented. This is primarily a theoretical document. Many of the ideas raised are not amenable to direct testing due to ethical concerns. However, several indirect means by which to test the hypothesis are discussed. Primary data from cell phones regarding miles traveled, number of times leaving home, etc., are among the possible indirect measures. The rapid ability of the SARS/CoV-2 virus to spread through society suggests that it may cause behavioral changes of the host to increase its transmission. Numerous cases of super spreader events are noted that have provided meaningful measures of host manipulation. In the case of SARS/CoV-2, the largest advantage of the pathogen is likely that between 50% and 70% of those infected are asymptomatic (John's Hopkins Coronavirus Resource Center, John's Hopkins University Corona Virus Resource Center. Available at https://coronavirus.jhu.edu/map.html , 2020). This component is a threat to elderly individuals and those immunocompromised who are more likely to have severe complications from the virus and die. To spread within these groups, a seemingly healthy host is necessary to carry the virus to them. The goal of the virus is not to kill the host, but to survive and reproduce.

Smokeless Tobacco Use and Oral Potentially Malignant Disorders Among People Living with HIV (PLHIV) in Pune, India: Implications for Oral Cancer Screening in PLHIV

Background: In India, smokeless tobacco (SLT) is a predominant form of tobacco used among people living with HIV (PLHIV). Despite SLT being a risk factor for oral potentially malignant disorders (OPMDs), no prior studies have quantified the association of OPMDs with SLT use among PLHIV. This limits the planning of preventive and control strategies for oral cancer among PLHIV, who are at higher risk for the disease. Methods: We enrolled 601 PLHIV and 633 HIV-uninfected individuals in an oral cancer screening study at BJ Government Medical College, Pune, India. Oral cavity images were collected using an m-Health application and reviewed by three clinicians. Participants with two clinician positive diagnoses were deemed to have suspected OPMDs. Prevalence ratios (PRs) were used to quantify the association between suspected OPMDs and SLT use among PLHIV. PRs for current SLT users, across HIV status and use duration were also estimated. Corrected PRs were obtained by modifying the maximum likelihood estimation. Models were adjusted for age, smoking, alcohol use and CD4 counts. Findings: Of those enrolled, 61% were men, median age was 36 years (IQR: 28-44), and 33% currently use SLT. Proportion of current SLT users was similar across PLHIV and HIV-uninfected groups but use duration for current SLT use was higher among PLHIV(p<0.05). Among PLHIV, current users had a 5-times (95% CI:3·1-7·0) higher prevalence of suspected OPMDs, compared to non-users. Relative to HIV uninfected individuals with the same SLT use duration, significant associations with suspected OPMDs were seen for PLHIV with<10 use years (PR: 3·5, 95% CI: 1·5 – 8·1) but not for PLHIV with≥10 use years (PR: 1·3, 95% CI: 0·9 – 1·8). Interpretation: PLHIV that are current SLT users are at high risk of OPMDs and potentially oral cancer. The development of strategies for screening, early detection, and management of OPMDs must be considered for this group. Funding: This work was supported by amfAR, The Foundation for AIDS Research, with support from the U.S. National Institutes of Health’s National Institute of Allergy and Infectious Diseases, the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Cancer Institute, the National Institute of Mental Health, the National Institute on Drug Abuse, the National Heart, Lung, and Blood Institute, the National Institute on Alcohol Abuse and Alcoholism, the National Institute of Diabetes and Digestive and Kidney Diseases, and the Fogarty International Center, as part of the International Epidemiology Databases to Evaluate AIDS (IeDEA; U01AI069907) and the NIH-funded Johns Hopkins Baltimore-Washington-India Clinical Trials Unit for NIAID Networks [UM1AI069465]. Declaration of Interest: The authors have no competing interests to declare. Ethical Approval: The Institutional Review Board of Johns Hopkins University (JHU) and Ethics Committee of BJGMC-JHU Clinical Research Site approved the project.

Navigating the COVID-19 pandemic: Initial impacts and responses of the Organ Procurement and Transplantation Network in the United States.

COVID‐19 has been sweeping the globe, hitting the United States particularly hard with a state of emergency declared on March 13, 2020. Transplant hospitals have taken various precautions to protect patients from potential exposure. OPTN donor, candidate, and transplant data were analyzed from January 5, 2020 to September 5, 2020. The number of new waiting list registrations decreased, with the Northeast seeing over a 50% decrease from the week of 3/8 versus the week of 4/5. The national transplant system saw near cessation of living donor transplantation (−90%) from the week of 3/8 to the week of 4/5. Similarly, deceased donor kidney transplant volume dropped from 367 to 202 (−45%), and other organs saw similar decreases: lung (−70%), heart (−43%), and liver (−37%). Deceased donors recovered dropped from 260 to 163 (−45%) from 3/8 compared to 4/5, including a 67% decrease for lungs recovered. The magnitude of this decrease varied by geographic area, with the largest percent change (−67%) in the Northeast. Despite the pandemic, discard rates across organ has remained stable. Although the COVID‐19 pandemic continues to evolve, OPTN data show recent evidence of stabilization, an indication that an early recovery of the number of living and deceased donors and transplants has ensued.

Registration quality filtering improves robustness of voxel-wise analyses to the choice of brain template

MotivationMany clinical and scientific conclusions that rely on voxel-wise analyses of neuroimaging depend on the accurate comparison of corresponding anatomical regions. Such comparisons are made possible by registration of the images of subjects of interest onto a common brain template, such as the Johns Hopkins University (JHU) template. However, current image registration algorithms are prone to errors that are distributed in a template-dependent manner. Therefore, the results of voxel-wise analyses can be sensitive to template choice. Despite this problem, the issue of appropriate template choice for voxel-wise analyses is not generally addressed in contemporary neuroimaging studies, which may lead to the reporting of spurious results. ResultsWe present a novel approach to determine the suitability of a brain template for voxel-wise analysis. The approach is based on computing a “distance” between automatically-generated atlases of the subjects of interest and templates that is indicative of the extent of subject-to-template registration errors. This allows for the filtering of subjects and candidate templates based on a quantitative measure of registration quality. We benchmark our approach by evaluating alternative templates for a voxel-wise analysis that reproduces the well-known decline in fractional anisotropy (FA) with age. Our results show that filtering registrations minimizes errors and decreases the sensitivity of voxel-wise analysis to template choice. In addition to carrying important implications for future neuroimaging studies, the developed framework of template induction can be used to evaluate robustness of data analysis methods to template choice.

Performance Estimation of Machine Learning Algorithms in the Factor Analysis of COVID-19 Dataset

Novel Coronavirus Disease (COVID-19) is a communicable disease that originated during December 2019, when China officially informed the World Health Organization (WHO) regarding the constellation of cases of the disease in the city of Wuhan. Subsequently, the disease started spreading to the rest of the world. Until this point in time, no specific vaccine or medicine is available for the prevention and cure of the disease. Several research works are being carried out in the fields of medicinal and pharmaceutical sciences aided by data analytics and machine learning in the direction of treatment and early detection of this viral disease. The present report describes the use of machine learning algorithms [Linear and Logistic Regression, Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and SVM with Grid Search] for the prediction and classification in relation to COVID-19. The data used for experimentation was the COVID-19 dataset acquired from the Center for Systems Science and Engineering (CSSE), Johns Hopkins University (JHU). The assimilated results indicated that the risk period for the patients is 12–14 days, beyond which the probability of survival of the patient may increase. In addition, it was also indicated that the probability of death in COVID cases increases with age. The death probability was found to be higher in males as compared to females. SVM with Grid search methods demonstrated the highest accuracy of approximately 95%, followed by the decision tree algorithm with an accuracy of approximately 94%. The present study and analysis pave a way in the direction of attribute correlation, estimation of survival days, and the prediction of death probability. The findings of the present study clearly indicate that machine learning algorithms have strong capabilities of prediction and classification in relation to COVID-19 as well.

In-House Production of Dialysis Solutions to Overcome Challenges During the Coronavirus Disease 2019 Pandemic

In-House Production of Dialysis Solutions to Overcome Challenges During the Coronavirus Disease 2019 Pandemic

Prognosticating the Spread of Covid-19 Pandemic Based on Optimal Arima Estimators.

COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19 from the explicit data based on optimal ARIMA model estimators. Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and the Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to the number of autoregressive terms, d refers to the number of times the series has to be differenced before it becomes stationary, and q refers to the number of moving average terms. Results obtained from the ARIMA model showed a significant decrease in cases in Australia; a stable case for China and rising cases have been observed in other countries. This study predicted the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.

Experts and Encounters

In a 2018 piece for the chronicle of higher education, Michael Clune describes a “rude awakening” he once experienced when developing a new course as a graduate student at Johns Hopkins University. As was customary then at Johns Hopkins, he was required to submit a course proposal that underwent a review by a board of professors from different fields. Clune arrived at the interview ready to discuss a syllabus that, as he recalls, “purported to explain urban decay, novels, the nature of free-market economics, and the political history of the 1970s in one brilliant synthesis.” But the board was underwhelmed. As Clune cited the “illustrious figures” and “hallowed formulas” he had learned to incant in literary studies, the political scientists and historians sitting in front of him—people who spend their lives researching stuff like urban decay, free-market economics, and recent political history—responded with “withering skepticism” and did something he hadn't anticipated: they “actually asked difficult questions about the reasoning behind the stars' dicta,” unimpressed by “the heroes of literature-department economic, political, and historical thought.” Clune doesn't say whether he got to teach the course, but the episode was the first time he began to see literary studies as “a kind of twilight zone,” an “exploded discipline” with fading intellectual prestige.

PIN-like ductal carcinoma of the prostate has frequent activating RAS/RAF mutations.

Prostatic intraepithelial neoplasia-like (PIN-like) ductal carcinoma is a rare tumour characterised by often cystically dilated glands architecturally resembling high-grade PIN, but lacking basal cells. These tumours are frequently accompanied by grade group 1 acinar cancer and behave relatively indolently. In contrast, conventional ductal adenocarcinoma of the prostate is an aggressive variant comparable to grade group 4 acinar cancer. Here, we used targeted next-generation sequencing to molecularly profile PIN-like ductal carcinoma cases at radical prostatectomy. Five PIN-like ductal carcinoma samples at radical prostatectomy with sufficient tumour tissue available were analysed for genomic alterations by targeted next-generation sequencing using the Johns Hopkins University (JHU) solid tumour panel. DNA was captured using SureSelect for 640 genes and sequenced on the Illumina HiSeq platform. Three of five (60%) of the PIN-like ductal carcinomas showed activating mutations in the RAS/RAF pathways, which are extraordinarily rare in conventional primary prostate carcinoma (<3% of cases), including an activating hot-spot BRAF mutation (p.K601E), an activating hot-spot mutation in HRAS (p.Q61K) and an in-frame activating deletion in BRAF (p.T488_Q493delinsK). An additional two cases lacked BRAF or HRAS mutations, but harboured in-frame insertions of uncertain significance in MAP2K4 and MAP3K6. One case had sufficient acinar tumour for sequencing, and showed a similar molecular profile as the concurrent PIN-like ductal carcinoma, suggesting a clonal relationship between the two components. PIN-like ductal carcinoma represents a molecularly unique tumour, enriched for potentially targetable oncogenic driver mutations in the RAS/RAF/MAPK pathway. This molecular profile contrasts with that of conventional ductal adenocarcinoma, which is typically enriched for pathogenic mutations in the mismatch repair (MMR) and homologous recombination (HR) DNA repair pathways.

Abstract 813: Doublecortin-like kinase 1 (DCLK1) and NOTCH1 pathways lead to poor prognosis in HNSCC

Abstract Introduction: Despite advancements in the field, the 5 year survival rate of Head and Neck Squamous Cell Carcinoma (HNSCC) still hovers at 60%. DCLK1 has been shown to regulate epithelial-to-mesenchymal transition (EMT) as well as serving as a cancer stem cell marker in colon, pancreatic and renal cancer. In previous studies, we showed that DCLK1 expression is associated with NOTCH pathways in HNSCC tumors obtained from publicly available databases as well as influences NOTCH signaling in HNSCC cell lines. Although it was reported that DCLK1 is associated with poor prognosis in oropharyngeal cancers and HNSCC lymph node metastasis, very little is known about the clinical implication of DCLK1 signaling in promoting HNSCC tumorigenesis. Methods and Materials: We stained 303 HNSCC and normal mucosa samples in two independent tissue microarray (TMA) assays: a commercially available US biomax HN483a and an in-house TMA built in Johns Hopkins University (JHU). TMAs were stained with antibodies against cleaved (active) NOTCH1 and DCLK1 which were previously optimized on HNSCC cell lines. The TMA tissue staining was scored by an experienced surgical pathologist. Statistical analysis was performed using Graph Pad Prism software. For overall survival (OS), Kaplan Meier Log-rank (Mantel-Cox) tests were performed. For comparison studies, we utilized the Mann Whitney test. Results: The JHU cohort consisted of 245 HNSCC samples and 13 normal tissues. Mean age of diagnosis was 59.7 year old, mean survival was 74.5 months and mean follow up time was 45.5 months. There were 35, 29, 49 and 132 patients with stages 1, 2, 3 and 4 respectively. 110 patients presented with lymph node metastasis. 47 patients were HPV+. In this TMA, DCLK1 staining was higher in tumors vs normal samples (p&amp;lt;0.05) and cleaved NOTCH1 expression followed this pattern; higher in tumors vs normal (p&amp;lt;0.0001). There was a significant correlation between the expression levels of these two proteins. The TMA was further stratified to primary tumor samples and recurrent samples as well as histological subsites of HNSCC. In primary tumors, there was a trend towards poorer OS for patients with high DCLK1 expression (p&amp;lt;0.0665), whereas in recurrent patients the OS was significantly poorer for patients with high DCLK1 expression (p&amp;lt;0.04). When analyzing HNSCC subsites, oral cavity tumors with high DCLK1 expression had poorer OS (p&amp;lt;0.0394). When we further characterized samples based on both DCLK1 and NOTCH staining, we found that the group of DCLK-/NOTCH- patients had lower mortality rate (54% vs 73%), independent of stage.Although the patients' survival data was not available for the HN468a TMA, staining on 45 HNSCC tumors indicates that 45% of the tumor samples are positive for DCLK1 compared to 90% DCLK1 negative staining in normal oral tissue. Conclusion: Overall, our results demonstrate a strong link between DCLK1 and NOTCH expression levels and poor prognosis in HNSCC. Citation Format: Esther Channah Broner, Lisa Rooper, Tejaswini Subbannayya, Aditi Chatterjee, David Sidransky, Evgeny Izumchenko. Doublecortin-like kinase 1 (DCLK1) and NOTCH1 pathways lead to poor prognosis in HNSCC [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 813.

Evaluating the process of partnership and research in global health: reflections from the STRIPE project

BackgroundThoughtful and equitable engagement with international partners is key to successful research. STRIPE, a consortium of 8 academic and research institutions across the globe whose objective is to map, synthesize, and disseminate lessons learned from polio eradication, conducted a process evaluation of this partnership during the project’s first year which focused on knowledge mapping activities.MethodsThe STRIPE consortium is led by Johns Hopkins University (JHU) in partnership with 6 universities and 1 research consultancy organization in polio free, at-risk, and endemic countries. In December 2018 JHU team members submitted written reflections on their experiences (n = 9). We held calls with each consortium member to solicit additional feedback (n = 7). To establish the partnership evaluation criteria we conducted preliminary analyses based on Blackstock’s framework evaluating participatory research. In April 2019, an in-person consortium meeting was held; one member from each institution was asked to join a process evaluation working group. This group reviewed the preliminary criteria, adding, subtracting, and combining as needed; the final evaluation criteria were applied to STRIPE’s research process and partnership and illustrative examples were provided.ResultsTwelve evaluation criteria were defined and applied by each member of the consortium to their experience in the project. These included access to resources, expectation setting, organizational context, external context, quality of information, relationship building, transparency, motivation, scheduling, adaptation, communication and engagement, and capacity building. For each criteria members of the working group reflected on general and context-specific challenges and potential strategies to overcome them. Teams suggested providing more time for recruitment, training, reflection, pre-testing. and financing to alleviate resource constraints. Given the large scope of the project, competing priorities, and shifting demands the working group also suggested a minimum of one full-time project coordinator in each setting to manage resources.ConclusionSuccessful management of multi-country, multicentered implementation research requires comprehensive communication tools (which to our knowledge do not exist yet or are not readily available), expectation setting, and institutional support. Capacity building activities that address human resource needs for both individuals and their institutions should be incorporated into early project planning.

A Study on Comparative Analysis of COVID-19 Datasets

Abstract In December 2019 a virus named COVID-19 appeared in China, precisely in the city of Wuhan. This virus was declared a global pandemic by the World Health Organization in March 2020. Since no adequate medical treatment has yet been discovered for this virus, many world institutions are committed to share with each other the data they collect and process in their laboratories. A large amount of these data is shared with citizens in order to inform about the risk that threaten us by virus COVID-19. Various credible world institutions such as the World Health Organization (WHO), Johns Hopkins University (JHU), the European Centre for Disease Prevention and Control (ECDC), etc., are providing various statistical data to address the issues raised by this emergent situation, but these reports in some cases are putting doubts on the completeness and the transparency of the data, which are not sufficiently processed and which then create confusion about the risks that we are facing. In this paper we are conducting a study of the quality of current global datasets from the must credible sources related to COVID-19. Also, we are comparing datasets collected from Republic of Kosovo and Republic of North Macedonia with corresponding data from WHO, ECDC and JHU datasets. To analyze datasets from different sources, we are using Power BI tool, making the improvement through the implementation of adequate dimensions and methods of improving the quality of datasets.

Leo Kanner’s Call for a Pediatric-Psychiatric Alliance

* Abbreviation: JHU — : Johns Hopkins University The “Menace of Psychiatry.”1 The title of pediatrician Joseph Brennemann’s 1931 article was inflammatory, and it was meant to be. Brennemann voiced concerns about psychiatry that many contemporary pediatricians shared at the time.2,3 Under “psychiatry” he included the work of an array of professionals in psychology, child development, and child guidance who, in his opinion, were negatively influencing parents’ attitudes toward their children’s health. These professionals, who were trained in psychoanalysis, mental testing, or the behaviorist psychology of John B. Watson, popularized standards, techniques, and theories that parents could not fully understand. In addition, they led parents to see any variation from a scientifically defined ideal of health as a problem. Brennemann argued that only pediatricians had sufficient knowledge of the specific and normal conditions of children’s lives to understand each child as an individual and to diagnose pathologic deviations. Yet, lacking the elaborate jargon of the psychoanalysts or the prestige of the behaviorists’ laboratory methods, pediatricians appeared to be losing the ongoing debate over a fundamental question: who is the expert on the mental health of children?4 Recognizing the validity of Brennemann’s concerns, psychiatrist Leo Kanner,5,6 head of the Division of Child and Adolescent Psychiatry at the Johns Hopkins University (JHU) School of Medicine, called for an alliance between child psychiatry and pediatrics. Here, we examine how Kanner came to that conviction through the influence of psychiatrist Adolf Meyer and pediatrician Edwards A. Park and argue that pediatrics deeply shaped Kanner’s work in … Address correspondence to Marga Vicedo, Institute for the History and Philosophy of Science and Technology, University of Toronto, Victoria College, Room 316, 91 Charles St W, Toronto, ON M5S 1K7, Canada. E-mail: marga.vicedo{at}utoronto.ca

Preparing for a COVID-19 surge.

As of May 12, 2020, over 4.2 million cases of COVID-19 across 187 countries have been reported, of which 1.3 million have occurred in the United States (US) alone [[1]Johns Hopkins. COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) [Internet]. 2020. Available from: https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6.Google Scholar]. Although many infected individuals will have mild symptoms, older patients are disproportionately more likely to progress to severe disease (US adults aged 65–84 are hospitalized at a rate of 31–59%, and infected adults over 85 at a rate of 31–70%) [[2]Center for Disease Control and Prevention. Coronavirus Disease 2019 (COVID-19): older Adults [Internet]. 2020. Available from: https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/older-adults.html.Google Scholar]. As hospital capacity and the population density of older adults vary considerably across the country and worldwide, understanding which regions will face disproportionate burden from a surge in hospitalizations is necessary for targeted preparedness efforts. We analyzed these data in the context of the US, mapping the hospital bed capacity in relation to the prevalence of older adults for all US counties (Fig. 1a). We also mapped case fatality rate (CFR) of COVID-19 for all US counties to understand which counties may be experiencing a greater burden of severe cases at the present (Fig. 1b). Our analysis documents substantial variation within states, but nonetheless, in Arizona, Connecticut, Rhode Island, New York, Nevada, Louisiana, Vermont, Michigan, Massachusetts, Indiana, Oregon, Hawaii, and Utah the average number of beds per 1000 individuals over the age of 65 is below 10. Findings are particularly alarming given that many of these states are some of the hardest hit by the COVID-19 epidemic to date. Connecticut, Michigan, Massachusetts, Louisiana, New Jersey, Ohio, Indiana, New York, Wisconsin, Oklahoma, and the District of Columbia have a CFR for COVID-19 that is greater than 4%, suggesting these states are experiencing a high burden of severe cases that require hospitalization. As half of these are also the states that have less than 10 beds per 1000 persons over 65, these states are more likely to require assistance given more limited capacity to handle a surge in cases. Furthermore, there is considerable variability of hospital beds per 1000 individuals over the age of 65 by county within these states, suggesting that state resources will also need to be strategically allocated to counties with the greatest need. These findings demonstrate the importance of understanding demographics and health infrastructure in the context of COVID-19 to prepare for surges in cases. As the pandemic shifts to more low resource settings, including low-to-middle income countries, where hospital beds and reliable data on hospital capacity are unavailable, obtaining useful data for preparedness will be more difficult. To that end, ensuring that across nations we have clarity on the types of data needed and resources put toward quality data collection should be viewed as fundamental preparedness activities for pandemics and other crisis situations. We may be too late to fully obtain and use data in this manner for this wave of the pandemic, but we can and should be better prepared in this manner for the inevitable second wave. Download .pdf (.51 MB) Help with pdf files Download .pdf (1.2 MB) Help with pdf files Download .pdf (1.2 MB) Help with pdf files Download .pdf (1.2 MB) Help with pdf files

Coronavirus Disease 2019 (COVID-19): Radiology Department Financial Impact and Planning for Post-COVID Recovery.

Coronavirus Disease 2019 (COVID-19): Radiology Department Financial Impact and Planning for Post-COVID Recovery.

COVID-19 in Spain: Transplantation in the midst of the pandemic.

Spain has been one of the most affected countries by the COVID‐19 outbreak. As of April 28, 2020, the number of confirmed cases is 210 773, including 102 548 patients recovered, more than 10 300 admitted to the ICU, and 23 822 deaths, with a global case fatality rate of 11.3%. From the perspective of donation and transplantation, the Spanish system first focused on safety issues, providing recommendations for donor evaluation and testing, and to rule out SARS‐CoV‐2 infection in potential recipients prior to transplantation. Since the country entered into an epidemiological scenario of sustained community transmission and saturation of intensive care, developing donation and transplantation procedures has become highly complex. Since the national state of alarm was declared in Spain on March 13, 2020, the mean number of donors has declined from 7.2 to 1.2 per day, and the mean number of transplants from 16.1 to 2.1 per day. Increased mortality on the waiting list may become a collateral damage of this terrible pandemic.

Commentary: Myths and facts on vitamin D amidst the COVID-19 pandemic

Commentary: Myths and facts on vitamin D amidst the COVID-19 pandemic