Experience of clinical services shapes attitudes to mental health data sharing: Findings from a UK-wide survey BackgroundRoutinely-collected mental health data could deliver novel insights for mental health research. However, patients willingness to share their mental health data remains largely unknown. We investigated factors influencing likelihood of sharing these data for research purposes amongst people with experience of mental illness.

MethodsWe collected responses from a diverse sample of UK National Health Service (NHS) users (n = 2187) of which about half (n = 1087) had lifetime experience of mental illness. Ordinal logistic regression was used to examine the influence of demographic factors, clinical service experience, and primary mental illness on willingness to share mental health data, contrasted against physical health data.

ResultsThere was a high level of willingness to share mental (89.7%) and physical (92.8%) health data for research purposes. Higher levels of satisfaction with the NHS were associated with greater willingness to share mental health data. Furthermore, people with personal experience of mental illness were more willing than those without to share mental health data, once the effect of NHS satisfaction had been controlled for. Of the mental illnesses recorded, people with depression, obsessive-compulsive disorder (OCD), personality disorder or bipolar disorder were significantly more likely to share their mental health data than people without mental illness.

ConclusionsThese findings suggest that positive experiences of health services and personal experience of mental illness are associated with greater willingness to share mental health data. NHS satisfaction is a potentially modifiable factor that could foster public support for increased use of NHS mental health data in research.
Measurement of the extent of Anxiety and Depression that has occurred in college students due to the COVID 19 pandemic: A Survey based cross-sectional study. OVERVIEWThe ongoing Pandemic because of the Coronavirus disease 2019 (COVID-19) has caused all the educational institutes including colleges to be closed for a very long time. As a result the students are compelled to remain in their homes for this time. Prolonged stay at home along with excess use of social media and other modes to "kill" the time are quite famous to cause certain health issues in a person, specially the teenagers and adolescents. Mental wellbegin, being a dimension of health as per WHO should not be ignored at all specially in these situations.

METHOD OF STUDYAn Online Questionnaire is prepared based of the ZUNG Self Rating Anxiety and Self Rating Depression Scale (Pre-validated Scales). The Form is circulated digitally among the people and then we have collected the data in excel. Based on the result we have prepared our statistical chart.

RESULTQuite a significant number of candidates were suffering due to the pandemic situation. 17.091% were suffering from mild to moderate anxiety, 1.785% had marked to severe anxiety levels(Constituting approximately 18.9% of the total). On the other hand, 8.673% of the students had mild depression, while 1 candidate (0.255%) had moderate depression and 1 (0.255%) had severe depression, (Constituting approximately 9.20% of the total). We found that candidates in the age group of 23-24 years had the maximum prevalence of depression, it was followed by candidates with age between 21-22 years. We found that the candidates with age between 23 to 24 years were having highest prevalence of significant anxiety levels which is closely followed by candidates having age which lies between 22 years to 23 years.
Mitigating Covid-19 aerosol infection risk in school buildings: the role of natural ventilation, classroom volume, occupancy and face masks Issues linked to aerosol physics within school buildings and related infection risk still lack a proper recognition in school safety regulations. In this theoretical work we try to shed more light on the critical role of air ventilation, classroom volume, occupancy, and face mask types (surgical vs ffp2) in controlling airborne transmission risk in educational settings. Limited spaces available in many schools require to precisely assess the occupancy/volume ratio in each classroom and to investigate the specific risk levels from aerosolization of viral loads from infective sources. Moreover, most schools are not provided with mechanical HVAC systems. Fundamental questions are therefore: how the specific classroom volume affects the long-range contagion risk in a given classroom? is linear social distancing the right way to assess a volumetric risk problem? How effective are other countermeasures like reduced speaking volume or equipping teachers with microphones? We present here the results of a numerical analysis based on the Gammaitoni-Nucci infection risk model and the consolidated Wells-Riley like approach, with SARS-CoV2 input data and geometric data from a typical high-school classroom in Italy. We investigated separately the case of infective asymptomatic student and infective asymptomatic teacher as source of viral quanta, taking into account thermal gradient effects on the air ventilation rates. First recommendations based on the volumetric nature of aerosol risk are suggested to extend the linear social distancing approach applied so far. Finally we discuss the concept of "cumulative infection risk" over multiple lesson+break cycles in Wells-Riley-like infection models. We believe that any attempt to proper model infection risk in closed environments with cycled changes of the source and the susceptible individuals, should carefully consider this point, particularly when modelling air ventilation breaks in classrooms. Complementary frailty and mortality prediction models on older patients as a tool for assessing palliative care needs BackgroundPalliative care (PC) has demonstrated benefits for life-limiting illnesses. Cancer patients have mainly accessed these services, but there is growing consensus about the importance of promoting access for patients with non-malignant disease. Bad survival prognosis and patients frailty are usual dimensions to decide PC inclusion.

ObjectivesThe main aim of this work is to design and evaluate three quantitative models based on machine learning approaches to predict frailty and mortality on older patients in the context of supporting PC decision making: one-year mortality, survival regression and one-year frailty classification.

MethodsThe dataset used in this study is composed of 39,310 hospital admissions for 19,753 older patients (age >= 65) from January 1st, 2011 to December 30th, 2018. All prediction models were based on Gradient Boosting Machines. From the initial pool of variables at hospital admission, 20 were selected by a recursive feature elimination algorithm based on the random forests GINI importance criterion. Besides, we run an independent grid search to find the best hyperparameters in each model. The evaluation was performed by 10-fold cross-validation and area under the receiver operating characteristic curve and mean absolute error were reported. The Cox proportional-hazards model was used to compare our proposed approach with classical survival methods.

ResultsThe one-year mortality model achieved an AUC ROC of 0.87 {+/-} 0.01; the mortality regression model achieved an MAE of 329.97 {+/-} 5.24 days. The one-year frailty classification reported an AUC ROC of 0.9 {+/-} 0.01. The Spearmans correlation between the admission frailty index and the survival time was -0.1, while the point-biserial correlation between one-year frailty index and survival time was -0.16.

ConclusionsOne-year mortality model performance is at a state-of-the-art level. Frailty Index used in this study behaves coherently with other works in the literature. One-year frailty classifier demonstrated that frailty status within the year could be predicted accurately. To our knowledge, this is the first study predicting one-year frailty status based on a frailty index. We found mortality and frailty as two weakly correlated and complementary PC needs assessment criteria. Predictive models are available online at http://demoiapc.upv.es. SARS-CoV-2 Acquisition and Immune Pathogenesis Among School-Aged Learners in Four Diverse Schools BackgroundUnderstanding SARS-CoV-2 infection in children is necessary to reopen schools safely.

MethodsWe measured SARS-CoV-2 infection in 320 learners [10.5 {+/-} 2.1(sd); 7-17 y.o.] at four diverse schools with either remote or on-site learning. Schools A and B served low-income Hispanic learners; school C served many special-needs learners; and all provided predominantly remote instruction. School D served middle- and upper-income learners, with predominantly on-site instruction. Testing occurred in the fall (2020), and 6-8 weeks later during the fall-winter surge (notable for a tenfold increase in COVID-19 cases). Immune responses and mitigation fidelity were also measured.

ResultsWe found SARS-CoV-2 infections in 17 learners only during the surge. School A (97% remote learners) had the highest infection (10/70, 14.3%, p<0.01) and IgG positivity rates (13/66, 19.7%). School D (93% on-site learners) had the lowest infection and IgG positivity rates (1/63, 1.6%). Mitigation compliance [physical distancing (mean 87.4%) and face covering (91.3%)] was remarkably high at all schools. Documented SARS-CoV-2-infected learners had neutralizing antibodies (94.7%), robust IFN-{gamma}+ T cell responses, and reduced monocytes.

ConclusionSchools can implement successful mitigation strategies across a wide range of student diversity. Despite asymptomatic to mild SARS-CoV-2 infection, children generate robust humoral and cellular immune responses.

Key PointsO_LISuccessful COVID-19 mitigation was implemented across a diverse range of schools.
C_LIO_LISchool-associated SARS-CoV-2 infections reflect regional rates rather than remote or on-site learning.
C_LIO_LISeropositive school-aged children with asymptomatic to mild SARS-CoV-2 infections generate robust humoral and cellular immunity.
C_LI Development of machine learning models for the prediction of complications after colorectal and small intestine surgery in psychiatric and non-psychiatric patient collectives (P-Study) Psychiatric and psychosomatic diseases are an increasingly cumbersome burden for the medical system. Indeed, hospital costs associated with mental health conditions have been constantly on the rise in recent years. Moreover, psychiatric conditions are likely to have a negative effect on the treatment of other medical conditions and surgical outcomes, in addition to their direct effects on the overall quality of life. Our study aims to investigate the impact of preoperative Risk factor, psychiatric and psychosomatic diseases on the outcomes of colorectal surgery and length of hospital stay via predictive modeling techniques.

MethodPatient data will be collected from several participating national and international surgical centers The machine learning models will be calculated and coded, but also published according to the TRIPOD guidelines (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis).

ResultIt is conceivable to arrive at generalizable models predicting the abovementioned endpoints through large amounts of data from several centers. The models will be subsequently deployed as a free-to-use web-based prediction tool using the Shiny environment [47]. The cost for the hosting server and digital infrastructure will be covered by Dr. Anas Taha. The sponsor will store the data for ten years. Substance Use Onset in High-Risk 9-13 Year-Olds in the ABCD Study AimA key aim of the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study is to document substance use onset, patterns, and sequelae across adolescent development. However, substance use misreporting obscures accurate drug use characterization. Hair toxicology tests provide objective historical substance use data, but are rarely investigated in youth. Here, we compare objective hair toxicology results with self-reported substance use in youth.

MethodsA literature-based substance use risk algorithm identified 696 ABCD Study(R) participants for hair sample collections between baseline and 2-year follow-up (spanning ages 9-13) for laboratory analysis. Chi-square and t-tests assessed differences between participants demographics, positive and negative hair tests, risk algorithm scores, and self-reported substance use.

ResultsHair testing confirmed that 17% of at-risk 9-13 year-olds had evidence of past 3-month use of one (n=99), two (n=17), three (n=3), or four (n=2) drug classes. After considering prescribed medication use, 11% had a positive test incongruent with self- or parent-report. No participant with a positive result self-reported substance use consistent with their toxicology results. Participants with positive tests under-reported use (p<.001), reported less sipping of alcohol (p<.001), and scored higher on the risk algorithm (p<.001) than those with negative hair toxicology.

ConclusionsAn alarming 11% of tested samples in at-risk 9-13 year-olds were positive for at least one unreported substance, suggesting underreporting in this population when participating in a national healthy development study. The degree of underreporting cannot yet be calculated, as at-risk samples were prioritized for assays. Expanded toxicology testing is key to characterize substance use in youth. A Cross-Sectional Examination of Conflict-of-Interest Disclosures of Physician-Authors Publishing in High-Impact US Medical Journals ObjectiveTo assess the accuracy of self-reported financial conflict-of-interest (COI) disclosures in the New England Journal of Medicine (NEJM) and Journal of the American Medical Association (JAMA) within the requisite disclosure period prior to article submission.

DesignCross-sectional investigation.

Data SourcesOriginal clinical-trial research articles published in NEJM (n = 206) or JAMA (n = 188) from January 1 to December 31, 2017; self-reported COI disclosure forms submitted to NEJM or JAMA with the authors published articles; Open Payments website (from database inception; latest search: August 2019).

Main outcome measuresFinancial data reported to Open Payments from 2014 to 2016 (time period that included all subjects requisite disclosure windows) were compared to self-reported disclosure forms submitted to the journals. Payments were defined as those not associated with a research study or formal research funding. Payment types were categorized as "disclosed," "undisclosed," "indeterminate," or "unrelated."

ResultsThirty-one articles from NEJM and 31 articles from JAMA met inclusion criteria. The physician-authors (n = 118) received a combined total of $7.48 million. Of the 106 authors (89.8%) who received payments, 86 (81.1%) received undisclosed payments. The top 23 most highly compensated received $6.32 million, of which $3.00 million (47.6%) was undisclosed. Disclosure rates were the equivalent between the top 23 and the entire sample.

ConclusionsHigh payment amounts, as well as high proportions of undisclosed financial compensation, regardless of amount received, comprised potential COIs for two influential US medical journals. Further research is needed to explain why such high proportions of general payments were undisclosed and whether journals that rely on self-reported COI disclosure need to reconsider their policies. Deceased donor kidney degradomics indicates cytoskeletal proteolytic alterations impacting post-transplant function. BackgroundIn brain death, cerebral injury contributes to systemic biological dysregulation, causing significant cellular stress in donor kidneys that adversely impacts the quality of grafts. Here, we hypothesized that DBD kidneys may undergo proteolytic processes that renders grafts susceptible to post-transplant dysfunction.

Material & MethodsUsing mass spectrometry and immunoblotting analyses, we profiled degradation patterns of cytoskeletal proteins in deceased (n=55) and living (n=10) donor kidneys.

ResultsWe found that in DBD kidneys, key podocyte cytoskeletal proteins had been proteolytically cleaved. Generated degradation profiles were not associated to donor related demographic and clinical factors but were associated to suboptimal post-transplant function. Strikingly, -actinin -4 and Talin-1 degradation profiles were not observed in circulatory-death or living-donor kidneys. As Talin-1 is a specific proteolytic target of Calpain-1, we investigated a potential trigger of Calpain activation and Talin-1 degradation using ex-vivo precision-cut human kidney slices and in-vitro immortalised human podocytes. Notably, we found that Transforming-Growth Factor-{beta} (TGF-{beta}) activated Calpain-1 and proteolytically cleaved Talin-1 to generate distinct peptide fragments. These peptide fragments were of similar size and matched the degradation patterns observed in DBD kidneys. Talin-1 degradation was prevented in vitro by Calpain-1 inhibition.

ConclusionsHere, we provide initial evidence that DBD kidneys are susceptible to cytoskeletal protein degradation that impacts posttransplant kidney function. Subsequent studies should aim to further investigate the link between brain death and activation of proteolytic pathways exploring new therapeutic opportunities. Generalized Prediction of Hemodynamic Shock in Intensive Care Units Early prediction of hemodynamic shock in the ICU can save lives. Several studies have leveraged a combination of vitals, lab investigations, and clinical data to construct early warning systems for shock. However, these have a limited potential of generalization to diverse settings due to reliance on non-real-time data. Monitoring data from vitals can provide an early real-time prediction of Hemodynamic shock which can precede the clinical diagnosis to guide early therapy decisions. Generalization across age and geographical context is an unaddressed challenge. In this retrospective observational study, we built real-time shock prediction models generalized across age groups (adult and pediatric), ICU-types, and geographies. We trained, validated, and tested a shock prediction model on the publicly available eICU dataset on 208 ICUs across the United States. Data from 156 hospitals passed the eligibility criteria for cohort building. These were split hospital-wise in a five-fold training-validation-test set. External validation of the model was done on a pediatric ICU in New Delhi and MIMIC-III database with more than 0.23 million and one million patient-hours vitals data, respectively. Our models identified 92% of all the shock events more than 8 hours in advance with AUROC of 86 %(SD= 1.4) and AUPRC of 93% (SD =1.2) on the eICU testing set. An AUROC of 87 % (SD =1.8), AUPRC 92 % (SD=1.6) were obtained in external validation on the MIMIC-III cohort. The New Delhi Pediatric SafeICU data achieved an AUROC of 87 % (SD =4) AUPRC 91% (SD=3), despite being completely different geography and age group. In this first, we demonstrate a generalizable model for predicting shock, and algorithms are publicly available as a pre-configured Docker environment at https://github.com/tavlab-iiitd/ShoQPred. Reports of myocarditis and pericarditis following mRNA COVID-19 vaccines: A systematic review of spontaneously reported data from the UK, Europe, and the US and of the literature ObjectivesTo bring together spontaneously reported data from around the world to estimate the reporting rate in different countries and better understand the risk factors for myocarditis and pericarditis following exposure to COVID-19 mRNA vaccines.

DesignSystematic review of the literature and systematic review of spontaneously reported data from the United Kingdom (UK), United States (US), and European Union/European Economic Area (EU/EEA).

Data sourcesSpontaneously reported data for COVID-19 Vaccine Pfizer/BioNTech (Comirnaty) and COVID-19 Vaccine Moderna (Spikevax) were obtained using outputs from the UKs Yellow Card scheme (covering the period 09/12/2020 - 20/10/2021), the Vaccine Adverse Event Reporting System (VAERS; US; covering the period 23/08/2021 - 21/10/2021), and EudraVigilance (EU/EEA; covering the period 21/12/2020 - 21/10/2021). Systematic searches of PubMed and Embase literature databases were performed with a datalock point of 11 October 2021.

Eligibility criteriaAll publicly available spontaneous reporting data from the three regions of interest were included. Data on events labelled "Myocarditis" and "Pericarditis" were included. No restrictions on age or gender were applied. For the systematic literature review, all pharmacoepidemiological studies of observational design investigating myocarditis and pericarditis following mRNA COVID-19 vaccines identified by our search were included. No restrictions on language or date were applied. Pre-print manuscripts were included if no peer-reviewed version was available. Studies of non-observational design, case reports, and case series were excluded. Study quality was assessed using relevant Critical Appraisal Skills Programme (CASP) tools. All studies were considered of sufficient quality for inclusion.

Data extraction and synthesisOne researcher extracted data from spontaneous reporting systems and published pharmacoepidemiological studies. Spontaneously reported events of myocarditis and pericarditis were presented for each individual data source, and stratified by vaccine, age, sex, and dose where this information was available. Vaccination data were obtained to the same date, and reporting rates were calculated for the events of myocarditis and pericarditis for each population of interest. For published pharmacoepidemiological studies, the study design, participant characteristics, and study results were tabulated.

ResultsOverall, there were 435 reports of myocarditis and 327 reports of pericarditis reported to the UKs Yellow Card scheme up to 20 October 2021. To 21 October 2021, in the US, 1936 reports of myocarditis and 1271 reports of pericarditis had been submitted to VAERS; there had been 2924 reports of myocarditis and 1855 reports of pericarditis submitted to EudraVigilance to the same date. Most reports (73.3%) had been submitted following Comirnaty (Pfizer/BioNTech). Males represented 74.9% of reports of myocarditis and 56.9% of pericarditis overall (for US and EU/EEA populations only), and the majority of reports concerned vaccinees ages under 40 years. Reports were more frequent following the second dose of mRNA COVID-19 vaccine. Reporting rates of myocarditis and pericarditis were consistent between the data sources. Seven pharmacoepidemiological studies were included; results of these studies were largely consistent with results of our spontaneous report analyses.

ConclusionsThis study provides evidence that younger vaccinees more frequently report myocarditis and pericarditis following mRNA COVID-19 vaccines compared with older vaccinees. These events had mild clinical course followed by full recovery in most cases. Results from published literature supported the results of our analyses.

Strengths and Limitations of the StudyO_LIThis is the first study to bring together spontaneously reported data from the United Kingdom, United States, and Europe on myocarditis and pericarditis following mRNA COVID-19 vaccines.
C_LIO_LIResults from this study provide evidence on the frequency of reported events of myocarditis and pericarditis following mRNA vaccines in different age groups, and by sex and vaccine dose. Analyses of spontaneous reports were consolidated with results of published literature, identified by systematic review.
C_LIO_LIResults may have been influenced by biases including different vaccination policies in each region examined, and publicity on events of myocarditis and pericarditis following mRNA vaccines.
C_LIO_LIThe study relied on outputs from spontaneous reporting systems in which the level of detail differed between the systems examined; furthermore, it is not possible to estimate incidence rates using spontaneous reports due to the lack of data on the exposed population, and there is no unvaccinated comparison group.
C_LIO_LIThere is an urgent need for further pharmacoepidemiological studies to be conducted to provide more accurate estimates of the frequency, clinical course, long term outcome, effects of treatment and impact on quality of life, to address many of the limitations of spontaneous reporting.
C_LI A general computational framework for COVID-19 modelling with applications to testing varied interventions in education environments We construct a spatially-compartmental individual-based model of the spread of Covid-19 in indoor spaces. The model can be used to predict the infection rates in a variety of locations when various non-pharmaceutical interventions (NPIs) are introduced. Tasked by the Welsh Government, we apply the model to secondary schools and Further and Higher Education environments. Specifically, we consider student populations mixing in a classroom and in halls of residence. We focus on assessing the potential efficacy of Lateral Flow Devices (LFDs) when used in broad-based screens for asymptomatic infection or in  test-to-release scenarios in which individuals who have been exposed to infection are released from isolation given a negative result. LFDs are also compared to other NPIs; we find that, although LFD testing can be used to mitigate the spread of Covid-19, it is more effective to invest in personal protective equipment, e.g. masks, and in increasing ventilation quality. In addition, we provide an open-access and user-friendly online applet that simulates the individual-based model, complete with user tutorials to encourage the use of the model to aid educational policy decisions as input infection data evolves (https://bit.ly/CV19_INTER_IBM). Hyperreflective foci, OCT progression indicators in age-related macular degeneration, include transdifferentiated retinal pigment epithelium PurposeBy optical coherence tomography (OCT) imaging, hyperreflective foci (HRF) indicate progression risk for advanced age-related macular degeneration (AMD) and are in part attributable to ectopic retinal pigment epithelium (RPE). We hypothesized that ectopic RPE are molecularly distinct from in-layer cells and that their cross-retinal course follows Muller glia.

MethodIn clinical OCT (61 eyes, 44 AMD patients, 79.4 {+/-} 7.7 years; 29 female; follow-up = 4.7 {+/-} 0.9 years) one HRF type, RPE plume, was reviewed. Twenty eyes of 20 donors characterized by ex vivo OCT were analyzed by histology (normal, 4; early/intermediate AMD, 7; geographic atrophy (GA), 6; 3 neovascular AMD (nvAMD). Cryosections were stained with antibodies to retinoid (RPE65, CRALPB) and immune (CD68, CD163) markers. In published RPE cellular phenotypes, red immunoreactivity was assessed semi-quantitatively by one observer (none, some cells, all cells).

ResultsTrajectories of RPE plume and cellular debris paralleled Muller glia, whether ordered or subsident, near atrophy borders. RPE corresponding to HRF lost immunoreactivity for retinoid markers and gained immunoreactivity for immune markers. Aberrant immunoreactivity appeared in individual in-layer RPE cells and extended to all abnormal phenotypes. Muller glia remained CRALBP-positive. Plume cells approached and contacted retinal capillaries.

ConclusionGain- and loss-of-function starts with individual in-layer RPE cells and extends to all abnormal phenotypes. Down-regulated RPE retinoid handling may impair rod vision while Muller glia sustain cone vision. Evidence for RPE transdifferentiation, possibly due to ischemia, supports a proposed process of epithelial-mesenchyme transition. Data can propel new biomarkers and therapeutic strategies for AMD.

PrecisHyperreflective foci (HRF) are OCT progression risk indicators in age-related macular degeneration. Abnormal RPE cells including some that correspond to HRF lose immunoreactivity for retinoid markers and gain immunoreactivity for immune markers, indicating molecular transdifferentiation. When efficacy and adherence conflict: preferences and patterns of response to mask-wearing and social distancing public health advice during the COVID-19 pandemic With recurring waves of the Covid-19 pandemic, a dilemma facing public health leadership is whether to provide public advice that is medically optimal (e.g., most protective against infection if followed), but unlikely to be adhered to, or advice that is less protective but is more likely to be followed. To provide insight about this dilemma, we examined and quantified public perceptions about the tradeoff between (a) the stand-alone value of health behavior advice, and (b) the advices adherence likelihood. In a series of studies about preference for public health leadership advice, we asked 1,061 participants to choose between (1) strict advice that is medically optimal if adhered to but which is less likely to be broadly followed, and (2) relaxed advice, which is less medically effective but more likely to gain adherence - given varying infection expectancies. Participants preference was consistent with risk aversion. Offering an informed choice alternative that shifts volition to advice recipients only strengthened risk aversion, but also demonstrated that informed choice was preferred as much or more than the risk-averse strict advice. mRNA COVID-19 vaccine booster fosters B and T cell responses in immunocompromised patients SARS-CoV-2 vaccination has proven effective in inducing an immune response in healthy individuals and is progressively allowing to overcome the pandemic. Recent evidence has shown that response to vaccination in some vulnerable patients may be diminished, and it has been proposed a booster dose. We tested the kinetic of development of serum antibodies to the SARS-CoV-2 Spike protein, their neutralizing capacity, the CD4 and CD8 IFN-{gamma} T cell response in 328 subjects, including 131 immunocompromised individuals (cancer, rheumatologic, and hemodialysis patients), 160 healthcare workers (HCW) and 37 subjects older than 75 yo, after vaccination with two or three doses of mRNA vaccines. We stratified the patients according to the type of treatment. We found that immunocompromised patients, depending on the type of treatment, poorly respond to SARS-CoV-2 mRNA vaccines. However, an additional booster dose of vaccine induced a good immune response in almost all of the patients except those receiving anti-CD20 antibody. Similarly to HCW, previously infected and vaccinated immunocompromised individuals demonstrate a stronger SARS-CoV-2 specific immune response than those who are vaccinated without prior infection.

Summary blurbImmunocompromised patients poorly respond to two doses of SARS-CoV-2 mRNA vaccines. However, an additional booster dose elicits a strong humoral and cellular immune response in these subjects. Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis BackgroundElectroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, mono-center studies indicate that both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers.

ObjectiveTo develop and validate neuroimaging biomarkers for ECT outcome in a multi-center setting.

MethodsMultimodal data (i.e., clinical, sMRI and resting-state fMRI) was collected from seven centers of the Global ECT-MRI Research Collaboration (GEMRIC). We used data from 189 depressed patients to evaluated which data modalities or combinations thereof could provide the best predictions for treatment response ([&ge;]50% symptom reduction) or remission (HAM-D score [&le;]7) using a support vector machine classifier.

ResultsRemission classification using a combination of gray matter volume and functional connectivity led to good performing models with average 0.82-0.83 area under the curve (AUC) when trained and tested on samples coming from the three largest centers, and remained acceptable when validated using leave-one-site-out cross-validation (0.70-0.73 AUC).

ConclusionsThese results show that multimodal neuroimaging data is able to provide good prediction of remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. This suggests that these biomarkers are robust, indicating that future development of a clinical decision support tool applying these biomarkers may be feasible. Declines and pronounced state disparities in prescription opioid distribution in the United States BackgroundThe United States (US) opioid epidemic is a persistent and pervasive public health emergency as it continues to claim the lives of Americans through addiction and overdose. There have been sustained efforts to reverse this iatrogenic crisis over the past decade. This study analyzed the changes in prescription opioid distribution for pain and identified regional differences between 2010 and 2019.

Methods and FindingsOpioid production data was obtained from the DEAs annual production quotas. Total opioid production has decreased 41.5% from 2013 (87.6 morphine mg equivalent metric tons) to 2019 (51.3). Opioid distribution from 2010 to 2019 was collected for ten prescription opioids (codeine, fentanyl, hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, and tapentadol) from the US Drug Enforcement Administrations Automation of Reports and Consolidated Orders System. Regional variance was expressed as the ratio of the 95th to 5th percentiles of opioid distribution per state, corrected for population. The peak year for all ten prescription opioids was identified, individually, as between 2010 and 2013, except for codeine (2015). There was a 51.96% overall decrease in opioid distribution per capita with the largest decrease in Florida (-61.61%) and smallest in Texas (-18.64%). The largest quantities of opioid distribution were observed in Tennessee (520.70 morphine mg equivalent or MME per person) and Delaware (251.45) in 2011 and 2019. The smallest was Nebraska (153.39) in 2011 and Minnesota (90.49) in 2019. The highest to lowest state ratio of total opioid use, corrected for population, was sizable in 2011 (5.25) and 2019 (2.78). Similarly, the mean 95th/5th ratio was relatively stable in from 2011 (4.78 +0.70) and 2019 (5.64+0.98). The 95th/5th ratio in 2019 was greatest for methadone (10.23) and oxymorphone (10.09) and smallest for morphine (2.20) and fentanyl (2.12). Southern states (e.g MS, TN, AL, AK, DE, and NC) had the highest per capita distribution for eight of the ten opioids in 2019. The strength of the correlation between per capita hydrocodone and codeine, and hydromorphone and codeine increased significantly from 2011 to 2019

ConclusionsThis study found a decline in total production and distribution for ten prescription opioids during the last half-decade. However, distribution was non-homogeneous at a state level. Analysis of regional differences revealed a three-fold difference in the 95th:5th percentile ratio between states which was unchanged over the past decade. Future research focused on identifying factors contributing to the observed regional variability could prove valuable to understanding, and potentially remediating, the pronounced disparities in prescription opioid use in the US. An Accurate Model for SARS-CoV-2 Pooled RT-PCR Test Errors PCR testing is an important tool to mitigate outbreaks of infectious diseases. One way of increasing testing throughput is by simultaneously testing multiple samples for the presence of a pathogen, a technique known as pooling. During the current COVID-19 pandemic, rapidly testing individuals for the presence of SARS-CoV-2 is conducted in large amounts. Since testing is often a bottleneck in mitigating the spread of SARS-CoV-2, pooling is increasing in popularity. Most analyses of the error rates of pooling schemes assume that including more than a single infected sample in a pooled test does not increase the probability of a positive outcome. We challenge this assumption with experimental data and suggest a novel probabilistic model for the outcomes of pooled tests. As an application, we analyze the false-negative rates of one common pooling scheme known as Dorfman pooling. We show that the false-negative rates of Dorfman pooling increase when the prevalence of infection decreases. However, low infection prevalence is exactly the condition under which Dorfman pooling achieves highest throughput. We therefore implore the cautious use of pooling and development of pooling schemes that consider correctly accounting for tests error rates. Detection of COVID-19 and age-dependent dysosmia with paired crushable odorant ampules BackgroundSigns of anosmia can help detect COVID-19 infection when testing for viral positivity is not available. Inexpensive mass-produced disposable olfactory sensitivity tests suitable for worldwide use might serve not only as a screening tool for potential infection but also to identify cases at elevated risk of severe disease as anosmic COVID-19 patients have a better prognosis.

Methods and FindingsWe adopted paired crushable ampules with two concentrations of a standard test odorant (n-butanol) as standard of care in several clinics as community prevalence of COVID-19 infection waxed and waned. This was not a clinical trial; a chart review was undertaken to evaluate the operating characteristics and potential utility of the test device as RT-PCR testing became routine. The risk of anosmia was greater in COVID-19 patients. Olfactory sensitivity was concentration-dependent and decreased with aging. Hyposmia was detected across a wider age range than expected from the literature, and tests can be optimized to characterize different age groups.

Conclusionsn-Butanol at 0.32 and 3.2% in crushable ampules can be used to characterize olfactory function quickly and inexpensively and thus has potential benefits in pandemic screening, epidemiology, and clinical decision-making. Spatial Allocation of Scarce COVID-19 Vaccines The COVID-19 Vaccines Global Access (COVAX) is an initiative led by the World Health Organization (WHO) and other partners that aims for an equitable access of COVID-19 vaccines. Despite a potential heterogeneous disease burden across space, countries receiving allotments of vaccines via COVAX may want to follow WHOs allocation rule and distribute vaccines to their jurisdictions based on the jurisdictions relative population size. Utilizing economic-epidemiological modeling, we benchmark the performance of this ad hoc allocation rule by comparing it to the rule that minimizes the economic damages and expenditures over time, including a penalty cost representing the social costs of deviating from the ad hoc allocation. Under different levels of vaccine scarcity and different demographic characteristics, we consider scenarios where length of immunity and compliance to travel restrictions vary, and consider the robustness of the rules when assumptions regarding these factors are incorrect. The benefits from deviating are especially high when immunity is permanent, when there is compliance to travel restrictions, when the supply of vaccine is low, and when there is heterogeneity in demographic characteristics. Interestingly, a lack of compliance to travel restrictions pushes the optimal allocations of vaccine towards the ad hoc and improves the relative robustness of the ad hoc rule, as the mixing of the populations reduces the spatial heterogeneity in disease burden.

JEL ClassificationC61, H12, H84, I18, Q54 Understanding Barriers to Diagnosis in a Rare, Genetic Disease: Delays and Errors in Diagnosing Schwannomatosis Diagnosis of rare, genetic diseases is challenging, but conceptual frameworks of the diagnostic process can be used to guide benchmarking and process improvement initiatives. Using the National Academy of Medicine diagnostic framework, we assessed the extent of, and reasons for diagnostic delays and diagnostic errors in schwannomatosis, a neurogenetic syndrome characterized by nerve sheath tumors and chronic pain. We reviewed the medical records of 97 people with confirmed or probable schwannomatosis seen in two U.S. tertiary care clinics. Time-to-event analysis revealed a median time from first symptom to diagnosis of 16.7 years (95% CI, 7.5-26.0 years) and median time from first medical consultation to diagnosis of 9.8 years (95% CI, 3.5-16.2 years). Factors associated with longer times to diagnosis included initial signs/symptoms that were intermittent, non-specific, or occurred at younger ages (p<0.05). Thirty-six percent of patients experienced a misdiagnosis of their underlying genetic condition (18.6%), pain etiology (16.5%) and/or tumor classification (11.3%). One-fifth (19.6%) of patients had a clear missed opportunity for genetics workup that could have led to an earlier schwannomatosis diagnosis. These results suggest that interventions in clinician education, genetic testing availability, expert review of pathology findings, and automatic triggers for genetics referrals may improve diagnosis of schwannomatosis. Genetic factors affecting immune phenotypes in type 1 diabetes Large inter-individual variability in immunological cell composition and function determines immune responses in general and susceptibility to immune-mediated diseases in particular. While much has been learned about the genetic variants relevant for type 1 diabetes, the pathophysiological mechanisms through which these variations exert their effects are unknown. In this study, we characterize the genetic factors influencing immune responses in patients with type 1 diabetes. Genetic variants that determine susceptibility to T1D significantly affect T cell composition. Specifically, the CCR5+ regulatory T cells associate with T1D through the CCR region, suggesting a shared genetic regulation. Genome-wide quantitative trait loci (QTL) mapping analysis of immune traits revealed 15 genetic loci that influence immune responses in T1D. Among them, 12 have never been reported in healthy population studies, implying a disease-specific genetic regulation. Altogether this study provides new insights into the genetic factors that affect immunological responses in T1D. Using controlled interrupted time series to estimate the population-level effects of Ontario's recently implemented overdose prevention sites and consumption and treatment services BackgroundOntario recently implemented overdose prevention sites and consumption and treatment services (OPS/CTS) to stem the harms of the opioid epidemic. We tested whether operating any site improved local opioid-related health service use and mortality rates.

MethodsWe used monthly counts of all opioid-related emergency department (ED) visits, hospitalizations, and deaths between January 2014 and December 2020 for our outcomes. For each public health unit (PHU) that implemented any OPS/CTS, we created a synthetic control as a weighted combination of unexposed PHU. We estimated the population-level effects of operating any site using controlled interrupted time series with segmented regression and adjusted for time varying confounders (i.e. OPS/CTS capacity, naloxone kits distributed, and persons receiving opioid agonist treatment per 100,000 population). We repeated the analysis using a multiple baseline approach to estimate province-wide effects.

ResultsBetween 2017 and 2020, nine out of 34 PHU implemented at least one OPS/CTS. ED visit (RR=0.96, 95%CI: 0.92 - 0.99) and hospitalization (RR=0.95, 95%CI: 0.92 - 0.98) trends declined faster among treated units. Improvements in local ED visit rate trends were observed for the majority of treated units. Hospitalization rates declined faster for London (RR=0.97, 95%CI: 0.95 - 0.99) and Niagara (RR=0.95, 95%CI: 0.92 - 0.98); while mortality rates declined faster for Hamilton (RR=0.93, 95%CI: 0.90 - 0.96), Niagara (RR=0.97, 95%CI: 0.94 - 0.99) and Guelph (RR=0.94, 95%CI: 0.88 - 1.00).

ConclusionAlthough OPS/CTS are not sufficient to stem the harms of the opioid overdose epidemic on their own, they play a critical role in local harm reduction strategies.