Impact of natural selection on global patterns of genetic variation, and association with clinical phenotypes, at genes involved in SARS-CoV-2 infection. We investigated global patterns of genetic variation and signatures of natural selection at host genes relevant to SARS-CoV-2 infection (ACE2, TMPRSS2, DPP4, and LY6E). We analyzed novel data from 2,012 ethnically diverse Africans and 15,997 individuals of European and African ancestry with electronic health records, and integrated with global data from the 1000GP. At ACE2, we identified 41 non-synonymous variants that were rare in most populations, several of which impact protein function. However, three non-synonymous variants were common among Central African hunter-gatherers from Cameroon and are on haplotypes that exhibit signatures of positive selection. We identify strong signatures of selection impacting variation at regulatory regions influencing ACE2 expression in multiple African populations. At TMPRSS2, we identified 13 amino acid changes that are adaptive and specific to the human lineage. Genetic variants that are targets of natural selection are associated with clinical phenotypes common in patients with COVID-19.
Longitudinal intronic RNA-Seq analysis of Parkinson Disease patients reveals disease-specific nascent transcription Transcriptomic studies usually focus on either gene or exon-based annotations, and only limited experiments have reported changes in reads mapping to introns. The analysis of intronic reads allows the detection of nascent transcription that is not influenced by steady - state RNA levels and provides information on actively transcribed genes. Here we describe substantial intronic transcriptional changes in Parkinsons Disease (PD) patients compared to healthy controls (CO) at two different timepoints; at the time of diagnosis (BL) and three years later (V08). We used blood RNA-Seq data from the Parkinsons Progression Markers Initiative (PPMI) cohort and identified significantly changed transcription of intronic reads only in PD patients during this follow up period. In CO subjects, only nine transcripts demonstrated differentially expressed introns between visits. However, in PD patients 4,873 transcripts had differentially expressed introns at visit V08 compared to BL, many of them in genes previously associated with neurodegenerative diseases, such as LRRK2, C9orf72, LGALS3, KANSL1AS1 and ALS2. In addition, at the time of diagnosis (BL visit) we identified 836 transcripts (e.g. SNCA, DNAJC19, PRRG4) and at visit V08 2,184 transcripts (e.g. PINK1, GBA, ALS2, PLEKHM1) with differential intronic expression specific to PD patients. In contrast, reads mapping to exonic regions demonstrated little variation indicating highly specific changes only in intronic transcription. Our study demonstrated that Parkinsons disease is characterized by substantial changes in the nascent transcription and description of these changes could help to understand the molecular pathology underpinning this disease.

Impact statementTranscriptomic studies in most cases describe the steady state changes of the cellular RNA combined with signals from newly synthesised RNA or nascent RNA. Nascent RNA reflects dynamic alterations in the cellular transcriptome and improves the resolution of RNA-Seq analysis. In the present study, we describe the changes in nascent RNA transcription in Parkinsons disease by using intronic RNA-Seq analysis. We compared transcriptome changes at the time of diagnosis and 3 years after the initial diagnosis. As a result, we were able to describe disease-specific time-dependent alterations in the nascent transcription in the blood of Parkinsons patients illustrating another layer of the blood-based biomarkers that could be diagnostic of both risk and progression of Parkinsons disease.
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. Three-dimensional cephalometric landmarking and Frankfort horizontal plane construction: reproducibility of conventional and novel landmarks ObjectivesTo assess manual landmarking repeatability and reproducibility (R&R) of a set of three-dimensional (3D) landmarks and to evaluate R&R of vertical cephalometric measurements using two Frankfort Horizontal (FH) planes as references for horizontal 3D imaging reorientation.

MethodsThirty-three landmarks, divided into "conventional", "foraminal" and "dental", were manually located twice by 3 experienced operators on 20 computed tomography (CT) scans of orthognathic surgery patients. R&R of the landmark localization were computed according to the ISO 5725 standard. These landmarks were then used to construct 2 FH planes: a conventional FH plane (orbitale left, porion right and left) and a newly proposed FH plane (midinternal acoustic foramen, orbitale right and left). R&R of vertical cephalometric measurements were computed using these 2 FH planes as horizontal references for CT reorientation.

ResultsLandmarks showing a 95% confidence interval (CI) of repeatability and/or reproducibility > 2mm were found exclusively in the "conventional" landmarks group. Vertical measurements showed excellent R&R (95% CI < 1mm) with either FH plane as horizontal reference. However, the 2 FH planes were not found to be parallel (absolute angular difference of 2.41{degrees}, SD 1.27{degrees}). The average time needed to landmark one CT scan was 14 {+/-} 3 minutes.

ConclusionsThe "dental" and "foraminal" landmarks tended to be more reliable than the "conventional" landmarks. Despite the poor overall reliability of the landmarks orbitale and porion, the construction of the conventional FH plane using 3 landmarks provided a reliable horizontal reference for 3D craniofacial CT scan reorientation. Impact of HIV Infection on COVID-19 Outcomes Among Hospitalized Adults in the U.S. BackgroundWhether HIV infection is associated with differences in clinical outcomes among people hospitalized with COVID-19 is uncertain.

ObjectiveTo evaluate the impact of HIV infection on COVID-19 outcomes among hospitalized patients.

MethodsUsing the American Heart Associations COVID-19 Cardiovascular Disease registry, we used hierarchical mixed effects models to assess the association of HIV with in-hospital mortality accounting for patient demographics and comorbidities and clustering by hospital. Secondary outcomes included major adverse cardiac events (MACE), severity of illness, and length of stay (LOS).

ResultsThe registry included 21,528 hospitalization records of people with confirmed COVID-19 from 107 hospitals in 2020, including 220 people living with HIV (PLWH). PLWH were younger (56.0+/-13.0 versus 61.3+/-17.9 years old) and more likely to be male (72.3% vs 52.7%), Non-Hispanic Black (51.4% vs 25.4%), on Medicaid (44.5% vs 24.5), and active tobacco users (12.7% versus 6.5%).

Of the study population, 36 PLWH (16.4%) had in-hospital mortality compared with 3,290 (15.4%) without HIV (Risk ratio 1.06, 95%CI 0.79-1.43; risk difference 0.9%, 95%CI -4.2 to 6.1%; p=0.71). After adjustment for age, sex, race, and insurance, HIV was not associated with in-hospital mortality (aOR 1.13; 95%CI 0.77-1.6; p 0.54) even after adding body mass index and comorbidities (aOR 1.15; 95%CI 0.78-1.70; p=0.48). HIV was not associated with MACE (aOR 0.99, 95%CI 0.69-1.44, p=0.91), severity of illness (aOR 0.96, 95%CI 0.62-1.50, p=0.86), or LOS (aOR 1.03; 95% CI 0.76-1.66, p=0.21).

ConclusionHIV was not associated with adverse outcomes of COVID-19 including in-hospital mortality, MACE, or severity of illness.

Condensed AbstractWe studied 21,528 patients hospitalized with COVID-19 at 107 hospitals in AHAs COVID-19 registry to examine the association between HIV and COVID-19 outcomes. More patients with HIV were younger, male, non-Hispanic Black, on Medicaid and current smokers. HIV was not associated with worse COVID-19 in-hospital mortality (Risk ratio 1.06, 95%CI 0.79-1.43; p=0.71) even after adjustment (aOR 1.15; 95%CI 0.78-1.70; p=0.48). HIV was also not associated with MACE (aOR 0.99, 95%CI 0.69-1.44, p=0.91) or severity of illness (aOR 0.96, 95%CI 0.62-1.50, p=0.86. Our findings do not support that HIV is a major risk factor for adverse COVID-19 outcomes. Factors affecting first return to work following arthroscopic rotator cuff repair: A retrospective observational study in an occupational medicine clinic and single-surgeon practice BackgroundReturn to work (RTW) following arthroscopic rotator cuff repair (aRCR) within New South Wales (NSW), Australia, under compensable schemes has not been reported.

AimsDetermine factors affecting RTW status and time in patients treated with aRCR under state-based compensation schemes, compared to those outside such schemes.

MethodsPatients undergoing aRCR by one surgeon with minimum 1-year follow-up were grouped into those under (CP) or outside (non-CP) workers or vehicle accident compensation schemes, matched by age and gender. RTW status and time were assessed using chi-square analysis and multivariable linear regression.

ResultsOf 1054 available patients, 90 CP patients were identified with 29 consented and matched to non-CP (N=29). A higher proportion of CP patients (17.2 vs 0%, P<0.001) never returned to work and a lower proportion resumed pre-injury duties at first RTW (3 vs 52%, P<0.01). Median time to first RTW did not differ between CP and non-CP groups (5.1 vs 4.4, P=0.86). Smoking (P=0.007) and post-injury activity level (P=0.004) were significantly associated with longer time to first RTW, whereas compensation status was not.

ConclusionsCP patients undergoing aRCR in NSW are at risk of not returning to work. For those that return, there is no significant difference compared to non-CP in time to first RTW. Particularly, patient and management factors associated with extended time to first RTW have been identified. Interventions aimed at modifiable factors such as smoking cessation and increasing preoperative activity may improve future outcomes.

What is already known about this subjectO_LIWork-related injury and disease present a significant financial burden to the Australian economy ($AUD61.8billion per annum), with shoulder conditions due to body stressing accounting for 7% of this total cost (>$AUD4.2billion)
C_LIO_LIPatients receiving compensation appear to have poorer outcomes and take longer to return to work than their non-compensated counterparts, but the degree to which this occurs is variably reported in the literature
C_LIO_LIThe effect of modifiable factors on return to work status and time remain unclear, and have not previously been reported for the Australian population under the New South Wales workers compensation scheme
C_LI

What this study addsO_LIPatients receiving arthroscopic rotator cuff repair under a compensable scheme in New South Wales are at increased risk of not returning to work compared to patients presenting outside of a compensable scheme and patients in equivalent schemes internationally
C_LIO_LIPatients reporting a longer duration of symptoms and smokers are at increased risk of extended time to first return to work
C_LI

What impact this may have on practice or policyO_LIThe study has identified smoking and pre-surgery activity levels as key targets for intervention in patients receiving arthroscopic rotator cuff repair under a workers compensation scheme in New South Wales
C_LIO_LIBy further investigating whether interventions designed to modify these factors are able to favourably affect patient outcomes, there is potential to return patients to their preoperative level of work sooner
C_LI Machine Learning Approaches for Binary Classification to Discover Liver Diseases using Clinical Data For a medical diagnosis, health professionals use different kinds of pathological ways to make a decision for medical reports in terms of patients medical condition. In the modern era, because of the advantage of computers and technologies, one can collect data and visualize many hidden outcomes from them. Statistical machine learning algorithms based on specific problems can assist one to make decisions. Machine learning data driven algorithms can be used to validate existing methods and help researchers to suggest potential new decisions. In this paper, Multiple Imputation by Chained Equations was applied to deal with missing data, and Principal Component Analysis to reduce the dimensionality. To reveal significant findings, data visualizations were implemented. We presented and compared many binary classifier machine learning algorithms (Artificial Neural Network, Random Forest, Support Vector Machine) which were used to classify blood donors and non-blood donors with hepatitis, fibrosis and cirrhosis diseases. From the data published in UCI-MLR, all mentioned techniques were applied to find one better method to classify blood donors and non-blood donors (hepatitis, fibrosis, and cirrhosis) that can help health professionals in a laboratory to make better decisions. Our proposed ML-method showed a better accuracy score (e.g. 98.23% for SVM). Thus, it improved the quality of classification. I wanna live and not think about the future; What place for advance care planning for people living with severe multiple sclerosis and their families? A qualitative study BackgroundLittle is known about how people with multiple sclerosis and their families comprehend advance care planning (ACP) and its relevance in their lives.

AimTo explore under what situations, with whom, how, and why do people with MS and their families engage in ACP

MethodsWe conducted a qualitative study comprising interviews with people living with MS and their families followed by an ethical discussion group with five health professionals representing specialties working with people affected by MS and their families. Twenty-seven people with MS and 17 family members were interviewed between June 2019 and March 2020. Interviews and the ethical discussion group were audio-recorded and transcribed verbatim. Data were analysed using the framework approach.

ResultsParticipants narratives focused on three major themes :(i) planning for an uncertain future; (ii) perceived obstacles to engaging in ACP that included uncertainty concerning MS disease progression, negative previous experiences of ACP discussions and prioritising symptom management over future planning; (iii) Preferences for engagement in ACP included a trusting relationship with a health professional and that information then be shared across services. Health professionals accounts from the ethical discussion group departed from viewing ACP as a formal document to that of an ongoing process of seeking preferences and values. They voiced similar concerns to people with MS about uncertainty and when to initiate discussions. Some shared concerns of a lack of confidence when having ACP discussions.

ConclusionThese findings support the need for a whole systems strategic approach where information about the potential benefits of ACP in all its forms can be shared with people with MS. Moreover, they highlight the need for health professionals to be skilled and trained in engaging in ACP discussions and where information is contemporaneously and seamlessly shared across services. What We Learned from COVID-19: From endotheliitis to treatment ObjectiveCOVID-19 may yield a variety of clinical pictures, differing from pneumonitis to Acute Respiratory Distress Syndrome (ARDS) along with vascular damage in the lung tissue, named as endotheliitis. To date, no specific treatment strategy was approved by any authority for the prevention or treatment of COVID-19 in terms of endotheliitis-related comorbidities. Here, we present our experience of COVID-19 by evaluating 11,190 COVID-19 patients with the manifestations of endotheliitis in skin, lung, and brain tissues according to the different phases of COVID-19.

MethodsAfter a retrospective examination, patients were divided into three groups according to their repercussions of vascular distress, which were represented by radiological, histopathological, and clinical findings. (Group A: no or mild pulmonary involvement, Group B: moderate pulmonary involvement with clinical risk of deterioration, Group C: severe pulmonary involvement and respiratory failure). We presented the characteristics and disease course of seven representative and complicated cases which represents the different phases of the disease, and discussed the treatment strategies in each group. The current pathophysiological mechanisms responsible from SARS-CoV-2 infection, COVID-19 related respiratory failure and current treatment strategies were reviewed and discussed in detail.

ResultsAmong 11.190 patients, 9294 patients met the criteria for Group A, and 1376 patients were presented to our clinics with Group B characteristics. Among these patients, 1896 individuals(Group B and Group C) were hospitalized. While 1220 inpatients were hospitalized within the first 10 days after the diagnosis, 676 of them were worsened and hospitalized 10 days after their diagnosis. Among hospitalized patients, 520 of them did not respond to group A and B treatments and developed hypoxemic respiratory failure (Group C) and 146 individuals needed ventilator support and were followed in the intensive care unit, and 43 (2.2%) patients died.

ConclusionDistinctive manifestations in each COVID-19 patient, including non-respiratory conditions in the acute phase and the emerging risk of long-lasting complications, suggest that COVID-19 has an endotheliitis-centred thrombo-inflammatory pathophysiology. Endotheliitis can also explain the mechanism behind the respiratory failure in COVID-19, and the difference of COVID-19 related ARDS from ARDS seen in other critical conditions. In addition, use of early corticosteroid in patients with early symptoms and early tocilizumab in ICU helps to reduce mortality and progression of the disease. Endotheliitis-based pathophysiological mechanisms are known to be momentarily changing and difficut to manage due to their risk of sudden aggrevation. Hence, daily evaluation of clinical, laboratory and radiological findings of patients and deciding appropriate pathophysiological treatment would help to reduce the mortality rate of COVID-19. A collaborative maternity and newborn dashboard (CoMaND) for the COVID-19 pandemic: a protocol for timely, adaptive monitoring of perinatal outcomes in Melbourne, Australia BackgroundThe COVID-19 pandemic has resulted in a range of unprecedented disruptions to the delivery of maternity care globally and has been associated with regional changes in perinatal outcomes such as stillbirth and preterm birth. Metropolitan Melbourne endured one of the longest and most stringent lockdowns in 2020. This paper presents the protocol for a collaborative maternity dashboard project to monitor perinatal outcomes in Melbourne, Australia, during the COVID-19 pandemic.

MethodsDe-identified maternal and newborn outcomes will be collected monthly from all public maternity services in Melbourne, allowing rapid analysis of a multitude of perinatal indicators. Weekly outcomes will be displayed as run charts according to established methods for detecting non-random  signals in health care. A pre-pandemic median for all indicators will be calculated for the period of January 2018 to March 2020. A significant shift is defined as [&le;] six consecutive weeks, all above or below the pre-pandemic median. Additional statistical analyses such as regression, time-series, and survival analyses will be performed for an in-depth examination of maternal and perinatal outcomes of interests.

Ethics and DisseminationThis study has been registered as an observational study with the Australian and New Zealand Clinical Trials Registry (ACTRN12620000878976).

Strengths and weaknesses of this study This project is the first clinician-led, multi-centre perinatal data collection system for metropolitan Melbourne.
 It complements the state government data collection, with the significant benefits of more timely and flexible reporting of outcomes, and granular detail on emerging areas of concern.
 The study relies on primary source coding of exposure and outcomes from each hospital that have not been internally validated during the study period.
 Data from private maternity hospitals, containing 25% of Melbourne births, are not available.
 This resource will support data-informed hospital pandemic responses through to the end of 2022. A Hybrid Machine Learning Framework for Enhancing the Prediction Power in Large Scale Population Studies: The ATHLOS Project The ATHLOS cohort is composed of several harmonized datasets of international cohorts related to health and aging. The healthy aging scale has been constructed based on a selection of particular variables from 16 individual studies. In this paper, we consider a selection of additional variables found in ATHLOS and investigate their utilization for predicting the healthy aging. For this purpose motivated by the datasets volume and diversity we focus our attention upon the clustering for prediction scheme, where unsupervised learning is utilized to enhance prediction power, showing the predictive utility of exploiting structure in the data by clustering. We show that imposed computation bottlenecks can be surpassed when using appropriate hierarchical clustering within a clustering for ensemble classification scheme while retaining prediction benefits. We propose a complete methodology which is evaluated against baseline methods and the original concept. The results are very encouraging suggesting further developments in this direction along with applications in tasks with similar characteristics. A strait-forward open source implementation is provided for the R project. Pausing methotrexate improves immunogenicity of COVID-19 vaccination in elderly patients with rheumatic diseases ObjectiveTo study the effect of methotrexate (MTX) and its discontinuation on the humoral immune response after COVID-19 vaccination in patients with autoimmune rheumatic diseases (AIRD).

MethodsNeutralising SARS-CoV-2 antibodies were measured after second vaccination in 64 rheumatic patients on methotrexate therapy, 31 of whom temporarily paused medication without a fixed regimen. The control group consisted of 21 AIRD patients without immunosuppressive medication.

ResultsMTX patients showed a significantly lower median antibody response compared to AIRD patients without immunosuppressive therapy (p< 0.001). Young age (<60 years) and MTX-hold after vaccination were found to be the main factors influencing antibody response after vaccination, while BMI or MTX dose demonstrated no effect. For patients taking MTX, age correlated negatively with immune response (r=-0.49; p<0.001) and all patients with antibody levels (14 %) below the cut-off were older than 60 years. Patients who held MTX during at least one vaccination showed significantly higher median neutralising antibody levels after second vaccination, compared to patients who continued MTX therapy during both vaccinations (68.82 %, 92.73 %, p=<0.001). This effect was particularly pronounced in patients older than 60 years (p=0.0016). The impact of the time period after vaccination was greater than of the time before vaccination with the critical cut-off being 10 days.

ConclusionMTX reduces the immunogenicity of SARS-CoV-2 vaccination in an age-dependent manner. Our data further suggest that holding MTX for at least 10 days after vaccination significantly improves the antibody response in patients over 60 years of age. 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. 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. Trends and clinical characteristics of COVID-19 vaccine recipients: a federated analysis of 57.9 million patients primary care records in situ using OpenSAFELY. BackgroundOn December 8th 2020, NHS England administered the first COVID-19 vaccination as part of an ambitious vaccination programme during a global health emergency.

AimsTo describe trends and variation in vaccine coverage by key clinical and demographic groups; to create a framework for near-real-time monitoring of vaccine coverage in key subgroups.

MethodsWorking on behalf of NHS England we analysed 57.9 million patient records in situ and in near-real-time within the infrastructure of the Electronic Health Record (EHR) software vendors EMIS and TPP using OpenSAFELY. We describe vaccine coverage and time trends across a range of demographic and fine-grained clinical subgroups in eight Joint Committee on Vaccination and Immunisation (JCVI) priority cohorts.

Results20,852,692 patients (36%) received a COVID-19 vaccine between December 8th 2020 and March 17th 2021. Of patients aged [&ge;]80 not in a care home (JCVI group 2) 94.7% received a vaccine, but with substantial variation by ethnicity (White 96.2% vaccinated, Black 68.3%) and deprivation (least deprived 96.6%, most deprived 90.7%). Overall, patients with pre-existing medical conditions were equally or more likely to be vaccinated with two exceptions: severe mental illness (89.5% vaccinated) and learning disability (91.4%). 275,205 vaccine recipients were identified as care home residents (priority group 1; 91.2% coverage). 1,257,914 (6.0%) recipients have had a second dose. Detailed characteristics of recipients in all cohorts are reported.

ConclusionsThe NHS in England has rapidly delivered mass vaccination. We were able to deploy a data monitoring framework using publicly auditable methods and a secure, in-situ processing model, using linked but pseudonymised patient-level NHS data on 57.9 million patients with very short delays from vaccine administration to completed analysis. Targeted activity may be needed to address lower vaccination coverage observed among certain key groups: ethnic minorities, those living in deprived areas, and people with severe mental illness or learning disabilities. Survey of Contaminated Percutaneous Injuries in Anesthesia Practitioners BackgroundAnesthesia practitioners are at inherent risk for percutaneous injuries by blood-contaminated needles and sharp objects. These exposures may result in transmission of HIV and hepatitis viruses. Data about this occupational hazard from contaminated needles and sharp devices is limited and decades old. We conducted a web-based survey to assess the occurrence, reporting, characteristics, and outcome of contaminated percutaneous injuries (CPI) in anesthesia residents, fellows, and attendings.

MethodsAfter institutional research board approval, an email was sent to 217 anesthesia practitioners requesting their participation in an online survey about contaminated percutaneous injuries. Responses were collected from February through March 2020. Results are reported as absolute numbers and proportions with 95% confidence interval (CI).

ResultsThe overall survey response rate was 51% (110/217). 59% (65/110) (95% CI, 50-68) of participants reported having one or more contaminated percutaneous injury during their years of anesthesia practice (42% (21/50) of residents, 50% (4/8) of fellows, 77% (40/52) of anesthesia attendings). Prevalence of injuries related to attendings years of anesthesia practice was 69% (95% CI, 44-94) for 5-10 years, 62.5% (95% CI, 29-96) for 10-15 years, and 79% (95% CI, 63- 95) for greater than 15 years of practice.

35% (95% CI, 26-44) of participants reported having one or more CPI within the last 5 years (40% of residents, 50% of fellows, 29% of attendings). Occurrence of CPI within the last 5 years based on attending anesthesiologist years of practice was 57% for less than 5 years, 37.5% for 10-15 years, and 20% for 15-20 years of practice. 75% (95% CI, 65-85) reported the incident at the time of injury. 59% (95% CI, 48-70) of injuries were due to hollow bore needles. 50% (95% CI, 39-61) of total injuries were high risk. 26% of injured anesthesia practitioners received post-exposure prophylaxis and there were zero seroconversions.

ConclusionMost anesthesiologists will sustain a contaminated percutaneous injury during their careers. Incidence of these injuries decreases with years of practice. Occurrence of these injuries is high among anesthesia residents, with the majority reporting their injuries. Half of the injuries are high risk with a quarter requiring postexposure prophylaxis. More education and interventions are needed to reduce percutaneous injuries and improve reporting. Evaluating a first fully automated interview grounded in Multiple Mini Interview (MMI) methodology: results from a feasibility study ObjectivesGlobal, Covid-driven restrictions around face-to-face interviews for healthcare student selection have forced admissions staff to rapidly adopt adapted online systems before supporting evidence is available. We have developed, what we believe is, the first fully automated interview grounded in Multiple Mini-Interview (MMI) methodology. This study aimed to explore test re-test reliability, acceptability and usability of the system.

Design, setting and participantsmixed-methods feasibility study in Physician Associate (PA) programmes from two UK and one US university during 2019 - 2020.

Primary, secondary outcomesFeasibility measures (test retest reliability acceptability and usability) were assessed using intra-class correlation (ICC), descriptive statistics, thematic and content analysis.

MethodsVolunteers took (T1), then repeated (T2), the automated MMI, with a seven-day interval (+/- 2) then completed an evaluation questionnaire. Admissions staff participated in focus group discussions.

ResultsSixty-two students and seven admission staff participated; 34 students and four staff from UK and 28 students and three staff from US universities.

Good-excellent test-retest reliability was observed with T1 and T2 ICC between 0.62-0.81 (p<0.001) when assessed by individual total scores (range 80.6-119), station total scores 0.6-0.91, p<0.005, individual site (all ICC[&ge;] 0.76 p<0.001) and mean test retest across sites 0.82 p<0.001 (95% CI 0.7-0.9).

Admissions staff reported potential to reduce resource costs and bias through a more objective screening tool for pre-selection or to replace some MMI stations in a  hybrid model. Maintaining human interaction through  touch points was considered essential.

Users positively evaluated the system, stating it was intuitive with an accessible interface. Concepts chosen for dynamic probing needed to be appropriately tailored.

ConclusionThese preliminary findings suggest that the system is reliable, generating consistent scores for candidates and is acceptable to end-users provided human touchpoints are maintained. Thus, there is evidence for the potential of such an automated system to augment healthcare student selection processes. Precision Diagnostic Approach to Predict 5-Year Risk for Microvascular Complications in Type 1 Diabetes BackgroundIndividuals with long standing diabetes duration can experience damage to small microvascular blood vessels leading to diabetes complications (DCs) and increased mortality. Precision diagnostic tailors a diagnosis to an individual by using biomedical information. Blood small molecule profiling coupled with machine learning (ML) can facilitate the goals of precision diagnostics, including earlier diagnosis and individualized risk scoring.

MethodsUsing data in a cohort of 537 adults with type 1 diabetes (T1D) we predicted five-year progression to DCs. Prediction models were computed first with clinical risk factors at baseline and then with clinical risk factors and blood-derived molecular data at baseline. Progression of diabetic kidney disease and diabetic retinopathy were predicted in two complication-specific models.

FindingsThe model predicts the progression to diabetic kidney disease with accuracy: 0.96{+/-}0.25 and 0.96{+/-}0.06 area under curve, AUC, with clinical measurements and with small molecule predictors respectively and highlighted main predictors to be albuminuria, glomerular filtration rate, retinopathy status at baseline, sugar derivatives and ketones. For diabetic retinopathy, AUC 0.75{+/-}0.14 and 0.79{+/-}0.16 with clinical measurements and with small molecule predictors respectively and highlighted key predictors, albuminuria, glomerular filtration rate and retinopathy status at baseline. Individual risk scores were built to visualize results.

InterpretationWith further validation ML tools could facilitate the implementation of precision diagnosis in the clinic. It is envisaged that patients could be screened for complications, before these occur, thus preserving healthy life-years for persons with diabetes.

FundingThis study has been financially supported by Novo Nordisk Foundation grant NNF14OC0013659.

Research in context

Evidence before this studyMicrovascular diabetes complications (DCs), such as diabetic kidney disease (DKD) and diabetic retinopathy (DR), lead to increased mortality, blindness, kidney failure and overall decreased quality of life in individuals with diabetes.

Added value of this studyWe have developed four algorithms based on traditional risk factors alone and risk factors with metabolomic and lipidomic data. Two accurately predicted the future progression in individuals with type 1 diabetes. The top predictors chosen by the algorithm were hemoglobin A1c, albuminuria and retinopathy status at baseline for diabetic retinopathy. The most promising models predicted future diabetic kidney disease in which albuminuria, eGFR, retinopathy status at baseline were complemented with a number of ketones and sugar derivatives.

Implications of all the available evidenceWith further validation in several cohorts, the prediction models presented here have the potential for early diagnosis of CKD in persons with diabetes, thus enabling appropriate decisions on the available therapies. It is envisaged that these algorithms will be trialed and support clinicians with precision diagnosis and treatments. The incidence of delirium in an acute geriatric community hospital: an observational cohort feasibility study. ObjectivesDelirium in hospitalised older adults is associated with negative health outcomes. Admission to an alternative care setting may lower the incidence of delirium. The Acute Geriatric Community Hospital (AGCH) was recently opened in the Netherlands and uses a multi-component non-pharmacological intervention strategy to prevent delirium. The objective of this study was to describe the incidence of delirium at the AGCH and compare this incidence to existing rates from literature. If a possible effect on delirium is seen in this comparison this would support conducting a larger prospectively controlled study on delirium in this new care setting.

DesignProspective cohort feasibility study; exploratory meta-analysis of proportions.

Setting and ParticipantsThe AGCH is an acute geriatric unit in a skilled nursing facility for patients aged >65 years with acute medical conditions.

MethodsDelirium assessment using the Confusion Assessment Method (CAM) upon admission and on day one, two and three or until delirium had resolved. Patients charts were reviewed if CAM was missing. In an linear mixed-effects model, the delirium incidence rate in AGCH was compared to pooled delirium incidence rates from six studies found in a high-quality review.

Results214 patients from the AGCH (mean age 81.9 years, 47% male, 12% with a history of dementia) were included in the analysis. Delirium developed in 8% (18/214) (95% confidence interval [CI] 5-13%) of patients during AGCH admission compared to 16% (95% CI 12-21%) in hospitals. Admission to the AGCH was associated with a decreased delirium incidence rate compared to the hospital control group (OR[odds ratio]= 0.49, 95% CI 0.24-0.98, p-value=0.044).

Conclusions and implicationsThe delirium incidence in the AGCH was low compared to those incidences found in general hospitals in literature. Based on these findings a controlled observational or randomized study measuring delirium in this care setting is recommended. Inflammasome-related markers at the ICU admission are not associated with outcome in the critically ill COVID-19 patients. PurposeDevelopment of targeted biological therapies for COVID-19 requires reliable biomarkers that could help indicate the responding patients. Hyperactivation of the inflammasome by SARS-CoV2 virus is hypothesized to contribute to severe course of the COVID-19 disease. Therefore, we aimed to evaluate the prognostic value of several inflammasome-related cytokines and proteins at the admission to the intensive care unit (ICU).

Patients and methodsPlasma samples were obtained from 45 critically ill COVID-19 patients and from 10 patients any without any signs of infection (TBI, traumatic brain injury) on admission to the ICU. The concentration of IL-1, IL-1{beta}, IL-18, IL-1RA, galectin-1, ASC, LDH, ferritin, and gasdermin D were analyzed. A novel cell-free caspase-1 plasma assay was developed by inhibitor-based immunoprecipitation followed by Western Blot. Demographic and clinical characteristics were recorded.

ResultsIn-hospital mortality in COVID-19 patients reached 62%. Galectin-1 was 1.8-fold lower in COVID-19 than in TBI patients (17101.84 vs. 30764.20 pg/ml, p=0.007), but other inflammasome-related biomarkers were at similar concentrations. Patients with SOFA score of >9 on admission who were at high risk of death had significantly higher galectin-1 but lower IL-1RA in comparison to low-risk patients (25551.3 pg/ml vs 16302.7 pg/ml, p=0.014; 14.5 pg/ml vs 39.4 pg/ml, p=0.04, respectively). Statistically significant correlations were observed between: IL-1 and platelets (r=-0.37), IL-1{beta} and platelets (r=-0.36), ferritin and INR (r=0.39). Activated caspase-1 p35 was detectable in 12/22 COVID-19 patients but in none of the TBI patients. Its presence was related with higher fibrinogen and lower D-dimers. Moreover, the densitometric analysis showed a significantly higher amount of p35 in patients with SOFA> 9.

ConclusionOur results indicate that the systemic markers of activation of the inflammasome in critically ill COVID-19 patients is not directly related with outcome. Therefore, potential interventions aimed at the inflammasome pathway in this group of patients may be of limited effectiveness and should be biomarker-guided. Background rates of five thrombosis with thrombocytopenia syndromes of special interest for COVID-19 vaccine safety surveillance: incidence between 2017 and 2019 and patient profiles from 25.4 million people in six European countries BackgroundThrombosis with thrombocytopenia syndrome (TTS) has been reported among individuals vaccinated with adenovirus-vectored COVID-19 vaccines. In this study we describe the background incidence of TTS in 6 European countries.

MethodsElectronic medical records from France, Netherlands, Italy, Germany, Spain, and the United Kingdom informed the study. Incidence rates of cerebral venous sinus thrombosis (CVST), splanchnic vein thrombosis (SVT), deep vein thrombosis (DVT), pulmonary embolism (PE), and stroke, all with concurrent thrombocytopenia, were estimated among the general population between 2017 to 2019. A range of additional adverse events of special interest for COVID-19 vaccinations were also studied in a similar manner.

FindingsA total of 25,432,658 individuals were included. Background rates ranged from 1.0 (0.7 to 1.4) to 8.5 (7.4 to 9.9) per 100,000 person-years for DVT with thrombocytopenia, from 0.5 (0.3 to 0.6) to 20.8 (18.9 to 22.8) for PE with thrombocytopenia, from 0.1 (0.0 to 0.1) to 2.5 (2.2 to 2.7) for SVT with thrombocytopenia, and from 0.2 (0.0 to 0.4) to 30.9 (28.6 to 33.3) for stroke with thrombocytopenia. CVST with thrombocytopenia was only identified in one database, with incidence rate of 0.1 (0.1 to 0.2) per 100,000 person-years. The incidence of TTS increased with age, with those affected typically having more comorbidities and greater medication use than the general population. TTS was also more often seen in men than women. A sizeable proportion of those affected were seen to have been taking antithrombotic and anticoagulant therapies prior to their TTS event.

InterpretationAlthough rates vary across databases, TTS has consistently been seen to be a very rare event among the general population. While still very rare, rates of TTS are typically higher among older individuals, and those affected were also seen to generally be male and have more comorbidities and greater medication use than the general population.

FundingThis study was funded by the European Medicines Agency (EMA/2017/09/PE Lot 3). "What do you mean I can't have a doctor? This is Canada!" - A Qualitative Study of the Myriad Consequences for Unattached Patients Awaiting Primary Care Attachment BackgroundPatient access to primary healthcare (PHC) is the foundation of a strong healthcare system and healthy populations. Attachment to a regular PHC provider, a key to healthcare access, has seen a decline in some jurisdictions. This study explored the consequences of unattachment from a patient perspective, an under-studied phenomenon to date.

MethodsA realist-informed qualitative study was conducted with unattached patients in Nova Scotia, Canada. Semi-structured interviews with nine participants were conducted and transcribed for analysis. The framework method was used to carry out analysis, which was guided by Donabedians model of assessing healthcare access and quality.

ResultsFive key findings were noted in this study: 1) Participants experienced a range of consequences from not having a regular PHC provider. Participants used creative strategies to 2) attempt to gain attachment to a regular PHC provider, and, to 3) address their health needs in the absence of a regular PHC provider. 4) Participants experienced negative feelings about themselves and the healthcare system, and 5) stress related to the consequences and added work of being unattached and lost care.

ConclusionsUnattached patients experienced a burden of care related to lost care and managing their own health and related information, due to the download of medical record management and system navigation to them. These findings may underestimate the consequences for further at-risk populations who would not have been included in our recruitment. This may result in poorer health outcomes, which could be mitigated by interventions at the structural level, such as enhanced centralized waitlists to promote attachment. Such waitlists may benefit from a triage approach to appropriately attach patients based on need. Signals of significantly increased vaccine breakthrough, decreased hospitalization rates, and less severe disease in patients with COVID-19 caused by the Omicron variant of SARS-CoV-2 in Houston, Texas Genetic variants of SARS-CoV-2 continue to dramatically alter the landscape of the COVID-19 pandemic. The recently described variant of concern designated Omicron (B.1.1.529) has rapidly spread worldwide and is now responsible for the majority of COVID-19 cases in many countries. Because Omicron was recognized very recently, many knowledge gaps exist about its epidemiology and clinical severity and disease course. A comprehensive genome sequencing study of SARS-CoV-2 in the Houston Methodist healthcare system identified 862 symptomatic patients with infections caused by Omicron from late November 2021 through December 18, 2021. Omicron very rapidly increased in only three weeks to cause 90% of all new COVID-19 cases. Compared to patients infected with either Alpha or Delta variants in our healthcare system, Omicron patients were significantly younger, had significantly increased vaccine breakthrough rates, and were significantly less likely to be hospitalized. Omicron patients required less intense respiratory support and had a shorter length of hospital stay, consistent with decreased disease severity. Although the number of Omicron patients we studied is relatively small, in the aggregate the data document the unusually rapid spread and increased occurrence of COVID-19 caused by the Omicron variant in metropolitan Houston, and provide information about disease character. Grey and white matter micro-structure is associated with polygenic risk for schizophrenia BackgroundRecent discovery of approximately 270 common genetic variants associated with schizophrenia has enabled polygenic risk scores (PRS) to be measured in the population. We hypothesized that normal variation in PRS would be associated with magnetic resonance imaging (MRI) phenotypes of brain morphometry and tissue composition.

MethodsWe used the largest extant genome-wide association dataset (N = 69,369 cases and N = 236,642 healthy controls) to measure PRS for schizophrenia in a large sample of adults from the UK Biobank (Nmax = 29,878) who had multiple micro- and macro-structural MRI metrics measured at each of 180 cortical areas, seven subcortical structures, and 15 major white matter tracts. Linear mixed effect models were used to investigate associations between PRS and brain structure at global and regional scales, controlled for multiple comparisons.

ResultsPolygenic risk was significantly associated with reduced neurite density index (NDI) at global brain scale, at 149 cortical regions, five subcortical structures and 14 white matter tracts. Other micro-structural parameters, e.g., fractional anisotropy, that were correlated with NDI were also significantly associated with PRS. Genetic effects on multiple MRI phenotypes were co-located in temporal, cingulate and prefrontal cortical areas, insula, and hippocampus. Post-hoc bidirectional Mendelian randomization analyses provided preliminary evidence in support of a causal relationship between (reduced) thalamic NDI and (increased) risk of schizophrenia.

ConclusionsRisk-related reduction in NDI is plausibly indicative of reduced density of myelinated axons and dendritic arborization in large-scale cortico-subcortical networks. Cortical, subcortical and white matter micro-structure may be linked to the genetic mechanisms of schizophrenia. A Randomised Trial of a Wearable UV dosimeter for Skin Cancer Prevention BackgroundNon-melanoma skin cancer (NMSC) is the most prevalent cancer in the United States, affecting 5 million people and costing $8.1 billion per year. Despite well-defined guidelines on ultraviolet radiation (UVR) avoidance, it remains difficult for people to assess their individual exposure, as UVR is invisible and the onset of symptoms due to UVR damage are delayed.

MethodsIn a prospective, randomized-controlled trial, 97 patients with a history of actinic keratoses were enrolled from March 2018 to July 2018 and followed over 6 months. 50 patients were given a wearable device that measured UV exposure and a smartphone application that provided both real-time and cumulative UV exposure information, and 47 patients were provided with UV protection counseling by a dermatologist.

ResultsWe observed a significant decrease in the incidence rate of NMSC in the intervention group compared to the control group over 3 months (p = 0.02). We did not observe a significant decrease in the incidence rate of actinic keratoses (AK) in the intervention group compared to the control group. The observed clinical benefit was not accompanied by psychological side effects such as anxiety and depression.

ConclusionsThis study suggests that providing real-time UV exposure data using a wearable UV dosimeter is a safe and effective behavioral change strategy to prevent NMSC. (Funded by the National Cancer Institute, contract HHSN261201700005C; ClinicalTrials.gov number NCT03315286.)