title: Machine Learning Model for Predicting Outcomes of Biologic Therapy in Psoriasis

abstract:
BackgroundBiological agents used for the therapy of psoriasis lose efficacy over time, which leads to discontinuation of the drug. Optimization of long-term biologic treatment is an area of medical need but there are currently no prediction tools for biologic drug discontinuation.

ObjectiveTo compare the accuracy of the risk factor-based frequentist statistical model to machine learning to predict the 5-year probability of biologic drug discontinuation.

MethodsThe national Danish psoriasis biologic therapy registry, Dermbio, comprising 6,172 treatment series with anti-TNF (Etanercept, Infliximab, Adalimumab), Ustekinumab, Guselkumab and anti-IL17 (Secukinumab and Ixekizumab) in 3,388 unique patients was used as data source. Hazard ratios (HR) were computed for all available predictive factors using Cox regression analysis. Different machine learning (ML) models for the prediction of 5-year risk of drug discontinuation were trained using the 5-fold cross validation technique and using 10 clinical features routinely assessed in psoriasis patients as input variables. Model performance was assessed using the area under the receiver operating characteristic curve (AUC).

ResultsThe lowest 5-year risk of discontinuation was associated with therapy with ustekinumab or ixekizumab, male sex and no previous exposure to biologic therapy. The predictive model based on those risk factors had an AUC of 0.61. The best ML model (gradient boosted tree) had an AUC of 0.85.

ConclusionsA machine learning-based approach, more than a statistical model, accurately predicts the risk of discontinuation of biologic therapy based on simple patient variables available in clinical practice. ML might be incorporated into clinical decision making.

category: dermatology


title: External validation and updating of a prediction model for the diagnosis of gestational diabetes mellitus

abstract:
IntroductionThe Monash early pregnancy prediction model calculates risks of developing GDM and is internationally externally validated and implemented in practice, however some gaps remain.

ObjectiveTo validate and update Monash GDM model, revising ethnicity categorisation, updating to recent diagnostic criteria, to improve performance and generalisability.

MethodsRoutine health data for singleton pregnancies from 2016 to 2018 in Australia included updated GDM diagnostic criteria. The Original Model predictors were included (age, body mass index, ethnicity, diabetes family history, past-history of GDM, past-history of poor obstetric outcomes, ethnicity), with ethnicity revised. Updating model methods were: recalibration-in-the-large (Model A); re-estimation of intercept and slope (Model B), and; coefficients revision using logistic regression (Mode1 C1 with original eight ethnicity categories, and Mode1 C2 with updated 6 ethnicity categories). Analysis included ten-fold cross-validation, performance measures (c-statistic, calibration-in-the-large value, calibration slope and expected-observed (E:O) ratio) and closed testing examining log-likelihood scores and AIC compared models.

ResultsIn 26,474 singleton pregnancies (4,756, 18% with GDM), we showed that temporal validation of the original model was reasonable (c-statistic 0.698) but with suboptimal calibration (E:O of 0.485). Model C2 was preferred, because of the high c-statistic (0.732), and it performed significantly better in closed testing compared to other models.

ConclusionsUpdating of the original model sustains predictive performance in a contemporary population, including ethnicity data, recent diagnostic criteria, and universal screening context. This supports the value of risk prediction models to guide risk-stratified care to women at risk of GDM.

Trial registration detailsThis study was registered as part of the PeRSonal GDM study on the Australian and New Zealand Clinical Trials Registry (ACTRN12620000915954); Pre-results.

category: endocrinology


title: Understanding Patients Experiences Living with Diabetes Mellitus: A Qualitative Study, Gujarat, India

abstract:
IntroductionDiabetes mellitus is leading health problem in India. India is experiencing the burden of communicable disease as well as non-communicable disease. It is believed due to unhealthy life style and faulty food habits. The International Diabetes Federation estimated that 72.9 million adults in India were living with diabetes in last four years. The study overviews the different factors like personal, familial, social, and financial affecting the life style and management of the disease. Thus, diabetes mellitus affects all the dimensions of health of an individual. The aims of the study to assess quality of life and experiences faced by the patients who are suffering from diabetes mellitus and how the progressive stage of the diseases altered the all the dimensions.

ObjectivesO_LITo explore the lived in experiences of diabetes mellitus patients.
C_LIO_LITo explore the various dimensions of the lived in experiences of diabetes mellitus patients.
C_LI

Materials and MethodsA qualitative, exploratory and descriptive design was used to explore and describe the experiences lived by participants suffering from diabetes in the selected areas of Kheda district Gujarat. The investigator used the phenomenological approach of the qualitative paradigm. The study conducted among 10 samples who are suffering from Diabetes Mellitus more than 07 years, sampling technique used was non-probability. Semi structured interview questions were used to conduct the detail history from the participants, where investigator has attain at least 6-8 sitting, (duration 30-45 Min.) with each participants.The investigator used local language for the interview and recorded in mobile, later all the recorded session has been verbatimaccording to the themes and sub-themes.

ResultsThe study resulted in the extraction of six themes, which reflect the experiences of the study participants. The themes are: 1) lived time; 2) lived body; 3) lived relationship; 4) lived economical; 5) lived vocational and 6) lived spiritual. It was found that diabetes still causes participants to suffer from fear, depression and anxiety along with they hate to have modification in dietary pattern in which they cannot have sweets and sweet products. Although emotional support was provided by the family members and friends. Participants absence from work in their early diagnosis was due to both their health conditions and emotional embarrassment. There are evidences that diabetes has psychological damage to patients life and so they feel more anxious and fatigue.

ConclusionThe study attempted to explore the lived in experiences of Diabetes Mellitus Patients and it was found that DM has affected all the dimensions of health especially in physical, psychological and economical dimensions. 1) In Physical dimension the patients were having common complaints of giddiness, weakness and headache which was leading them to certain problems. 2) In psychological dimension the patients were having more anxiety, fear regarding the disease and long term treatment along with depression. 3) In economical dimension some patients were having good family income so there were no issues related to expenses on diet and medication 4) In social dimension all the participants were having good relation with their everyone. 5) In vocational dimension all were having difficulty at work place to concentrate in their work but after starting the treatment their health was improved so later they continued their work properly. 6) In spiritual dimension only two participants were not having belief in God rest all were having faith in God and prayed daily.


title: Genetic and environmental effects on the immune phenotypes in type 1 diabetes

abstract: 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.


title: Genetic factors affecting immune phenotypes in type 1 diabetes


title: SARS-CoV-2 attack rate and population immunity in southern New England, March 2020 - May 2021

abstract: Estimating an infectious disease attack rate requires inference on the number of reported symptomatic cases of a disease, the number of unreported symptomatic cases, and the number of asymptomatic infections. Population-level immunity can then be estimated as the attack rate plus the number of vaccine recipients who had not been previously infected; this requires an estimate of the fraction of vaccines that were distributed to seropositive individuals. To estimate attack rates and population immunity in southern New England, we fit a validated dynamic epidemiological model to case, clinical, and death data streams reported by Rhode Island, Massachusetts, and Connecticut for the first 15 months of the COVID-19 pandemic, from March 1 2020 to May 31 2021. This period includes the initial spring 2020 wave, the major winter wave of 2020-2021, and the lagging wave of lineage B.1.1.7(Alpha) infections during March-April 2021. In autumn 2020, SARS-CoV-2 population immunity (equal to the attack rate at that point) in southern New England was still below 15%, setting the stage for a large winter wave. After the roll-out of vaccines in early 2021, population immunity in many states was expected to approach 70% by spring 2021, with more than half of this immune population coming from vaccinations. Our population immunity estimates for May 31 2021 are 73.4% (95% CrI: 72.9% - 74.1%) for Rhode Island, 64.1% (95% CrI: 64.0% - 64.4%) for Connecticut, and 66.3% (95% CrI: 65.9% - 66.9%) for Massachusetts, indicating that >33% of southern Englanders were still susceptible to infection when the Delta variant began spreading in July 2021. Despite high vaccine coverage in these states, population immunity in summer 2021 was lower than planned due to 34% (Rhode Island), 25% (Connecticut), and 28% (Massachusetts) of vaccine distribution going to seropositive individuals. Future emergency-setting vaccination planning will likely have to consider over-vaccination as a strategy to ensure that high levels of population immunity are reached during the course of an ongoing epidemic.

category: epidemiology


title: Healthy lifestyle, genetic risk, and incidence of cancer: A prospective cohort study of 13 cancer types

abstract:
BackgroundGenetic and lifestyle factors are related to the risk of cancer, but it is unclear whether a healthy lifestyle can offset genetic risk. Our aim was to investigate this for 13 cancer types using data from the UK Biobank prospective cohort.

MethodsIn 2006-2010, participants aged 37-73 years were assessed and followed until 2015-2019. Analyses were restricted to those of European ancestries with no history of malignant cancer (n=195,822). Polygenic risk scores (PRSs) were computed for 13 cancer types and these cancers combined ( overall cancer), and a healthy lifestyle score was calculated from current recommendations. Relationships with cancer incidence were examined using Cox regression, adjusting for relevant confounders. Interactions between HLI and PRSs were assessed.

ResultsThere were 15,240 incident cancers during the 1,926,987 person-years of follow-up (median follow-up= 10.2 years). After adjusting for confounders, an unhealthy lifestyle was associated with a higher risk of overall cancer [lowest vs highest tertile hazard ratio (95% confidence interval) = 1.32(1.26, 1.37)] and eight cancer types. The greatest increased risks were seen for cancers of the lung [3.5(2.96,4.15)], bladder [2.03 (1.57, 2.64)], and pancreas [1.98 (1.54,2.55)]. Positive additive interactions were observed, suggesting a healthy lifestyle may partially offset genetic risk of colorectal, breast, and pancreatic cancers, and may completely offset genetic risk of lung and bladder cancers.

ConclusionsA healthy lifestyle is beneficial for most cancers and may offset genetic risk of some cancers. These findings have important implications for those genetically predisposed to these cancers and population strategies for cancer prevention.


title: Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada

abstract:
IntroductionThis study aimed to produce community-level geo-spatial mapping of confirmed COVID-19 cases in Ontario, Canada in near real-time to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals.

MethodsCOVID-19 cases and locations were curated for geostatistical analyses from March 2020 through June 2021, corresponding to the first, second, and third waves of infections. Daily cases were aggregated according to designated forward sortation area [FSA], and postal codes [PC] in municipal regions covering Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, and Windsor/Essex county. Hotspots were identified with area-to-area tests including Getis-Ord Gi*, Global Morans I spatial autocorrelation, and Local Morans I asymmetric clustering and outlier analyses. Case counts were also interpolated across geographic regions by Empirical Bayesian Kriging, which localizes high concentrations of COVID-19 positive tests, independent of FSA or PC boundaries. The Geostatistical Disease Epidemiology Toolbox, which is freely-available software, automates the identification of these regions and produces digital maps for public health professionals to assist in pandemic management of contact tracing and distribution of other resources.

Results/DiscussionThis study provided indicators in real-time of likely, community-level disease transmission through innovative geospatial analyses of COVID-19 incidence data. Municipal and provincial results were validated by comparisons with known outbreaks at long-term care and other high density residences and on farms. PC-level analyses revealed hotspots at higher geospatial resolution than public reports of FSAs, and often sooner. Results of different tests and kriging were compared to determine consistency among hotspot assignments. Concurrent or consecutive hotspots in close proximity suggested potential community transmission of COVID-19 from cluster and outlier analysis of neighboring PCs and by kriging. Results were also stratified by population based-categories (sex, age, and presence/absence of comorbidities). Earlier recognition of hotspots could reduce public health burdens of COVID-19 and expedite contact tracing.


title: Might first-hand experience of ill-health and economic hardship during the COVID-19 pandemic strengthen public support for vaccination and the reallocation of health sector funding towards health emergency preparedness in South Africa?

abstract:
AimsWe examined whether first-hand experience of ill-health and economic hardship during the COVID-19 pandemic might strengthen public support for vaccination, and for the reallocation of health sector funding towards health emergency preparedness in South Africa - a country in which high rates of vaccine hesitancy go hand in hand with widespread discontent regarding public service delivery.

MethodsUsing data from 1,600 South African respondents who were surveyed during 2021 for the Eighth Round of Afrobarometer (AB-R8), discrete measures of household- and individual-level sociodemographic and economic factors were generated to permit confounder-adjusted analyses of probabilistic causal relationships between self-reported measures of: personal/household COVID-19 illness and job/income/business loss as a result of COVID-19; and the likelihood that respondents would accept a (government-approved) COVID-19 vaccine, or support the reallocation of health sector funding towards health emergency preparedness.

FindingsThere was little evidence that personal/household experience of COVID-19 illness was associated with the likelihood that respondents would (or would not) accept a (government-approved) COVID-19 vaccine (OR: 0.96; 95%CI: 0.72,1.28); or that these respondents would (or would not) support the reallocation of health sector funding towards health emergency preparedness (OR: 0.95; 95%CI: 0.71,1.26), even after adjustment for individual- or household-level sociodemographic and economic covariates considered likely confounders. There was similarly little evidence that personal/household experience of job/income/business loss as a result of the COVID-19 pandemic was associated with support for the reallocation of health sector resources for emergency preparedness (OR: 1.02; 95%CI: 0.80,1.30); again, even after adjustment for potential confounders.

However, respondents who reported that they or someone in their household had lost their job/income/or business as a result of the COVID-19 pandemic had only around half the odds of reporting that they would accept a (government-approved) COVID-19 vaccine (OR: 0.60; 95%CI: 0.47,0.77) - and this finding, like the others in these analyses, was largely unaffected by the inclusion/exclusion of covariates considered susceptible to change following the onset of the COVID-19 pandemic (i.e. those covariates potentially operating as colliders rather than genuine confounders).

ConclusionsThese findings suggest that - despite the postulated  experiential dividend of COVID-19 illness (i.e. its expected impact on vaccine hesitancy and support for the reallocation of health sector resources for health emergency preparedness) - no such  dividend was observed in this broadly representative sample of South African adults. Indeed, job/income/business loss (and associated economic hardship) also had little effect on support for the reallocation of health sector resources for health emergency preparedness; yet this was somewhat paradoxically associated with a much lower odds of vaccine acceptance - paradoxically, since vaccination has been widely viewed as a pragmatic (if somewhat neoliberal) intervention to protect economic activity. However, these findings might simply reflect inadequate confounder adjustment for preceding and entrenched attitudes towards vaccination amongst those South Africans who are also most vulnerable to job/income/business loss as a result of the COVID-19 pandemic. Protecting the livelihood and health of such individuals and households is likely to remain a substantial challenge and key priority for future emergencies in which economic activity is compromised.

AimThe aim of the present study was to examine whether first-hand experience of ill-health and economic hardship during the COVID-19 pandemic might have strengthened public support for vaccination, and for the reallocation of health sector funding towards health emergency preparedness in South Africa - a country in which high rates of vaccine hesitancy go hand in hand with widespread discontent regarding public service delivery. To this end we drew on data generated by the eighth round of household surveys undertaken by Afrobarometer (AB-R8) which interviewed 1,600 respondents across South Africa during 2021 - more than a year after the countrys first confirmed case of COVID-19 on 5 March 2020.


title: Machine learning quantification of amyloid deposits in histological images of ligamentum flavum

abstract:
BackgroundWild-type transthyretin amyloidosis (ATTRwt) is an underdiagnosed and potentially fatal disease. Interestingly, ATTRwt deposits have been found to deposit in the ligamentum flavum (LF) of patients with lumbar spinal stenosis prior to the development of systemic and cardiac amyloidosis. In order to study this phenomenon and its possible relationship with LF thickening and systemic amyloidosis, a precise method of quantifying amyloid deposits in histological slides of LF is critical. However, such a method is currently unavailable. Here, we present a machine learning quantification method with Trainable Weka Segmentation (TWS) to assess amyloid deposition in histological slides of LF.

MethodsImages of ligamentum flavum specimens stained with Congo red are obtained from spinal stenosis patients undergoing laminectomies and confirmed to be positive for ATTRwt. Amyloid deposits in these specimens are classified and quantified by TWS through training the algorithm via user-directed annotations on images of LF. TWS can also be automated through exposure to a set of training images with user-directed annotations, and then application to a set of new images without additional annotations. Additional methods of color thresholding and manual segmentation are also used on these images for comparison to TWS.

ResultsWe develop the use of TWS in images of LF and demonstrate its potential for automated quantification. TWS is strongly correlated with manual segmentation in the training set of images with user-directed annotations (R = 0.98; p = 0.0033) as well as in the application set of images where TWS was automated (R = 0.94; p = 0.016). Color thresholding was weakly correlated with manual segmentation in the training set of images (R = 0.78; p = 0.12) and in the application set of images (R = 0.65; p = 0.23).

ConclusionTWS machine learning closely correlates with the gold standard comparator of manual segmentation and outperforms the color thresholding method. This novel machine learning method to quantify amyloid deposition in histological slides of ligamentum flavum is a precise, objective, accessible, high throughput, and powerful tool that will hopefully pave the way towards future research and clinical applications.

category: pathology


title: Is sobriety from alcohol necessary for evaluation of patients presenting with suicidal ideation?

abstract:
BackgroundIt is commonly assumed that patients intoxicated with alcohol are unreliable with respect to their statements of suicidal intent, however no prior literature evaluates the impact of sobriety on suicidal ideation (SI). In typical emergency department (ED) settings, a common practice is to wait until intoxicated suicidal individuals have reached a legally sober limit (ethanol level less than 80 mg/dL) to evaluate safety. We are not aware of any study that establishes the diagnostic reliability of the clinical suicidal ideation evaluation as a function of alcohol intoxication.

MethodsThis study is a retrospective review of medical records for patients evaluated in a pre-COVID Midwestern ED for one calendar year. Cases were generated for review based on criteria of having a Psychiatric SW (Social Work) consult and blood alcohol level drawn while in the ED on every Wednesday and Friday of 2017 which produced 1084 cases for review. Chi-square analysis was used for comparison of variables of suicidal ideation with or without alcohol intoxication as defined by blood alcohol level (BAL) [≥]80 mg/dL.

ResultsIn reference to our initial hypothesis, patients presenting with suicidal ideation and concurrent alcohol intoxication were no longer reporting suicidal ideation at sobriety in 69% of cases, compared to 38% for patients without alcohol levels on presentation. Chi-square analysis demonstrated p=0.000012.

ConclusionThe goal of the present study was to demonstrate, with empirical data, a relationship between alcohol intoxication and suicidal ideation. Our data suggests that patients presenting to the ED with complaints related to suicidal behavior who are found to have concurrent alcohol intoxication are more likely to deny suicidal ideation when sober than patients with similar presenting complaints and no alcohol intoxication.

category: psychiatry and clinical psychology


title: The DIAMONDS intervention to support self-management of type 2 diabetes in people with severe mental illness: study protocol for a single-group feasibility study

abstract:
IntroductionThe DIAMONDS programme aims to evaluate a novel supported diabetes self-management intervention for people with severe mental illness (the "DIAMONDS intervention"). The purpose of this study is to test the feasibility of intervention delivery and data collection procedures to inform a definitive randomised controlled trial (RCT).

MethodsAdults aged 18 years or over with a diagnosis of type 2 diabetes and severe mental illness (schizophrenia, schizoaffective disorder, or bipolar disorder) will be eligible for inclusion. Individuals with other types of diabetes or non-psychotic mental illness and those lacking capacity to consent will not be eligible. Participants will be recruited from NHS mental health trusts and general practices across the North of England. All participants will receive the DIAMONDS intervention: weekly one-to-one sessions with a trained facilitator ("DIAMONDS Coach") to support goal setting, action planning, and diabetes education; ongoing self-management supported by a paper-based workbook and optional digital application (app); and monthly peer-support group sessions with other participants. The primary outcomes are: 1. Recruitment rate, measured as proportion of the recruitment target (N=30) achieved at 5 months from start of recruitment, 2. Attrition measured as the proportion of missing outcomes data at the end of the recruitment period (5 months from start of recruitment) for physiological and self-reported data items, 3. Intervention delivery rate recorded as the proportion of planned sessions delivered (measured by the number of completed intervention session logs per participant within 15 weeks of the first intervention session). Secondary outcomes include completeness of data collection at baseline and of process evaluation data at follow-up as well as the feasibility and acceptability of the intervention and of wearing a blinded continuous glucose monitoring device. An intervention fidelity framework will also be developed. Recruitment started in July 2021. The study was prospectively registered: ISRCTN15328700 (12th March 2021).

DiscussionThe results of this feasibility study will inform the refinement of the content and delivery of the DIAMONDS intervention, as well as research procedures, including recruitment and data collection, in preparation for the main DIAMONDS RCT.


title: SEDRA: Selective Entry Dynamic Risk Assessment. A mathematical model to safely keep the borders open during Covid-19

abstract: 1.The Covid-19 pandemic has brought the World to a near standstill for most of 2020 and 2021, causing chaos in international travel, driving many economies into the ground, particularly those largely based on tourism. The lack of standard tools to assist decision makers in structuring a coherent policy to allow foreign passengers into their county and the resulting panic-mode opening/closing the borders on every "new case" outburst or new variant "of concern", have led several countries to costly and often meaningless decisions based on fear rather than science or logic. This study aims at providing a universal method to safely keep the borders open and allow conditional immigration to foreign passengers according to a "Risk Group" table that includes all the countries reporting data on their Covid-19 situation to the WHO and other organisms. The RG table is recalculated on a weekly basis according to a mathematical model described in this paper, dynamically assessing the status of the pandemic worldwide through the calculation of a "Safety Index" for each country. A prototype algorithm has been implemented in VBA/EXCEL and its results are published bi-weekly on a Github repository.

category: public and global health


title: Does the presence of health insurance and health facilities improve healthcare utilisation for major morbidities among Indigenous communities and older widows: Evidence from the India Human Development Surveys I and II

abstract: In this paper, we examine whether access to treatment for major morbidity conditions is determined by the social class of the person who needs treatment. Secondly, we assess whether health insurance coverage and the presence of a PHC have any significant impact on the utilisation of health services, either public or private, for treatment and, more importantly, whether the presence of health insurance and PHC modify the treatment use behaviour for the two excluded communities of interest namely Indigenous communities and older widows using data from two rounds (2005 and 2012) of the nationally representative India Human Development Survey (IHDS). We estimated a multilevel mixed effects model with treatment for major morbidity as the outcome variable and social groups, older widows, the presence of a PHC and the survey wave as the main explanatory variables. The results confirmed access to treatment for major morbidity was affected by social class with Indigenous communities and older widows less likely to access treatment. Health insurance coverage did not have an effect that was large enough to induce a positive change in the likelihood of accessing treatment. The presence of a functional PHC increased the likelihood of treatment for all social groups except Indigenous communities. This is not surprising as Indigenous communities generally live in locations where the terrain is more challenging and decentralised healthcare up to the PHC might not work as effectively as it does for others. The social class to which one belongs has a significant impact on the ability of a person to access healthcare. Efforts to address inequity needs to take this into account and design interventions that are decentralised and planned with the involvement of local communities to be effective. Merely addressing one or two barriers to access in an isolated fashion will not lead to equitable access.


title: A Review of Interventions for Non-Communicable Diseases in Humanitarian Emergencies in Low-and Middle-Income Countries

abstract:
BackgroundLow-and middle-income countries (LMICs) not only experience the largest burden of humanitarian emergencies but are also disproportionately affected by non-communicable diseases (NCDs). Interventions addressing NCDs require humanitarian entities to consider complex challenges such as continuity of care, diagnostics, logistics and cost of care for recurrent or expensive treatments, yet primary focus on the topic is lacking. We conducted a systematic review on the effects of humanitarian disasters on NCDs in LMICs with the primary aim of identifying studies on epidemiology, interventions, and treatment. Key interventions were identified and their effects on populations in disaster settings were reviewed.

MethodsA systematic search was conducted in MEDLINE, MEDLINE (PubMed, for in-process and non-indexed citations), Social Science Citation Index, and Global Health (EBSCO) for indexed articles published before December 11, 2017. Publications reporting on interventions targeting NCDs during disasters in LMICs were included if they incorporated core intervention components as defined by the United States Department of Health and Human Services. Two separate screeners independently evaluated the titles, abstracts and full text of the eligible articles, with vetting by a third reviewer. Key intervention components including target population, phase of crisis, and measured outcomes among others were extracted into a template and synthesized using a thematic analysis approach. The full systematic review is registered at PROSPERO(CRD42018088769).

ResultsOf 85 articles eligible for the full systematic review, only seven articles describing interventions met inclusion criteria. Studies focused reporting on the response (n=4) and recovery (n=3) phases of disaster, with no studies reporting on the mitigation or preparedness phases. Successful interventions conducted extensive pre-deployment risk assessments to assess the burden and distinct epidemiology of NCDs amongst affected populations, worked in close cooperation with local health services, assessed individual needs of sub-populations in disaster regions in the response phase, promoted task shifting between humanitarian and development actors, and adopted flexibility in guideline implementation. Training and capacity building of staff were found to be essential elements of successful interventions due to an assessed lack of experience of healthcare workers in disaster settings with NCDs and successfully allowed for incorporation of community health workers.

ConclusionsWe found only limited interventions designed to address NCDs in humanitarian emergencies, with a particular dearth of studies addressing the mitigation and preparedness phases of humanitarian response. Delivering interventions for NCDs in humanitarian emergencies requires improved collaboration between humanitarian and development actors in addition to improved NCD training and capacity building amongst healthcare workers in disasters settings.


title: Beyond well-mixed: a simple probabilistic model of airborne disease transmission in indoor spaces

abstract: We develop a simple model for assessing risk of airborne disease transmission that accounts for non-uniform mixing in indoor spaces and is compatible with existing epidemiological models. A database containing 174 high-resolution simulations of airflow in classrooms, lecture halls, and buses is generated and used to quantify the spatial distribution of expiratory droplet nuclei for a wide range of ventilation rates, exposure times, and room configurations. Imperfect mixing due to obstructions, buoyancy, and turbulent dispersion results in concentration fields with significant variance. The spatial non-uniformity is found to be accurately described by a shifted lognormal distribution. A well-mixed mass balance model is used to predict the mean, and the standard deviation is parameterized based on ventilation rate and room geometry. When employed in a dose-response function risk model, infection probability can be estimated considering spatial heterogeneity that contributes to both short- and long-range transmission.


title: A One-Step open RT-qPCR for SARS-CoV-2 detection

abstract:
The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), etiological agent of the coronavirus disease 2019 (COVID-19), is currently detected by reverse transcription followed by quantitative polymerase chain reaction (RT-qPCR) of its viral RNA genome. Within the available alternatives, One-Step procedures are preferred since they are fast and significantly decrease preanalytical errors, minimizing the risk of diagnostic errors.

Increasing the testing capacity and tracing contacts are essential steps to control the pandemic. However, high-cost commercial reagents subject to shortage and poor scalability have hindered the use of these technologies and their adoption for a wide population-scale testing, being even more critical in developing countries. In the current context, open-source initiatives have promoted global collaboration to promote accessible solutions for rapid local deployment. As a result, open protocols are being developed for the local production of SARS-CoV-2 diagnostics.

This work aimed to produce an open-source system for SARS-CoV-2 diagnostic tests in RNA clinical samples. We provide guidelines for standardizing an open One-Step RT-qPCR master mix using recombinant M-MLV reverse transcriptase together with either Pfu-Sso7d or Taq DNA polymerase. Both were tested on synthetic RNA and clinical samples, observing a good correlation when compared to commercial RT-qPCR kits. Nevertheless, the best results were obtained using M-MLV RT combined with Taq DNA polymerase in a probe-based RT-qPCR assay, allowing successful discrimination between positive and negative samples with accuracies comparable to a CDC-recommended commercial kit.

Here, we demonstrate that these open RT-qPCR systems can be successfully used to identify SARS-CoV-2 in clinical samples and potentially be implemented in any molecular diagnostic laboratory.


title: Testing the Validity of the Modified Vaccine Attitude Question Battery across 22 Languages with a Large-scale International Survey Dataset

abstract: In this study, we tested the validity of the modified version of the Vaccine Attitude Question Battery (VAQB) across 22 different languages. Validity test was conducted with a large-scale international survey dataset, COVIDiSTRESSII Global Survey, collected from 20,601 participants from 62 countries. We employed exploratory and confirmatory factor analysis, measurement invariance test, and measurement alignment for internal validity test. Moreover, we examined correlation between the VAQB score, vaccination intent, compliance with preventive measures, and trust in public health-related agents. The results reported that the modified VAQB, which included five items, showed good validity across 22 languages with measurement alignment. Furthermore, the VAQB score showed negative association with vaccination intent, compliance, and trust as expected. The findings from this study provide additional evidence supporting the validity of the modified VAQB in 22 languages for future large-scale international research on COVID-19 and vaccination.


title: Testing the Validity of the Modified Vaccine Attitude Question Battery across 22 Languages with a Large-scale International Survey Dataset: Within the Context of COVID-19 Vaccination


title: SARS-CoV-2 Distribution in Residential Housing Suggests Contact Deposition and Correlates with Rothia

abstract:
Monitoring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on surfaces is emerging as an important tool for identifying past exposure to individuals shedding viral RNA. Our past work has demonstrated that SARS-CoV-2 reverse transcription-quantitative PCR (RT-qPCR) signals from surfaces can identify when infected individuals have touched surfaces such as Halloween candy, and when they have been present in hospital rooms or schools. However, the sensitivity and specificity of surface sampling as a method for detecting the presence of a SARS-CoV-2 positive individual, as well as guidance about where to sample, has not been established. To address these questions, and to test whether our past observations linking SARS-CoV-2 abundance to Rothia spp. in hospitals also hold in a residential setting, we performed detailed spatial sampling of three isolation housing units, assessing each sample for SARS-CoV-2 abundance by RT-qPCR, linking the results to 16S rRNA gene amplicon sequences to assess the bacterial community at each location and to the Cq value of the contemporaneous clinical test. Our results show that the highest SARS-CoV-2 load in this setting is on touched surfaces such as light switches and faucets, but detectable signal is present in many non-touched surfaces that may be more relevant in settings such as schools where mask wearing is enforced. As in past studies, the bacterial community predicts which samples are positive for SARS-CoV-2, with Rothia sp. showing a positive association.

ImportanceSurface sampling for detecting SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), is increasingly being used to locate infected individuals. We tested which indoor surfaces had high versus low viral loads by collecting 381 samples from three residential units where infected individuals resided, and interpreted the results in terms of whether SARS-CoV-2 was likely transmitted directly (e.g. touching a light switch) or indirectly (e.g. by droplets or aerosols settling). We found highest loads where the subject touched the surface directly, although enough virus was detected on indirectly contacted surfaces to make such locations useful for sampling (e.g. in schools, where students do not touch the light switches and also wear masks so they have no opportunity to touch their face and then the object). We also documented links between the bacteria present in a sample and the SARS-CoV-2 virus, consistent with earlier studies.


title: COVID-19 Prognostic Modeling Using CT Radiomic Features and Machine Learning Algorithms: Analysis of a Multi-Institutional Dataset of 14,339 Patients

abstract:
ObjectiveIn this large multi-institutional study, we aimed to analyze the prognostic power of computed tomography (CT)-based radiomics models in COVID-19 patients.

MethodsCT images of 14,339 COVID-19 patients with overall survival outcome were collected from 19 medical centers. Whole lung segmentations were performed automatically using a previously validated deep learning-based model, and regions of interest were further evaluated and modified by a human observer. All images were resampled to an isotropic voxel size, intensities were discretized into 64-binning size, and 105 radiomics features, including shape, intensity, and texture features were extracted from the lung mask. Radiomics features were normalized using Z-score normalization. High-correlated features using Pearson (R2>0.99) were eliminated. We applied the Synthetic Minority Oversampling Technique (SMOT) algorithm in only the training set for different models to overcome unbalance classes. We used 4 feature selection algorithms, namely Analysis of Variance (ANOVA), Kruskal- Wallis (KW), Recursive Feature Elimination (RFE), and Relief. For the classification task, we used seven classifiers, including Logistic Regression (LR), Least Absolute Shrinkage and Selection Operator (LASSO), Linear Discriminant Analysis (LDA), Random Forest (RF), AdaBoost (AB), Naive Bayes (NB), and Multilayer Perceptron (MLP). The models were built and evaluated using training and testing sets, respectively. Specifically, we evaluated the models using 10 different splitting and cross-validation strategies, including different types of test datasets (e.g. non-harmonized vs. ComBat-harmonized datasets). The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were reported for models evaluation.

ResultsIn the test dataset (4301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83{+/-}0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + RF classifier. In RT-PCR-only positive test sets (3644), similar results were achieved, and there was no statistically significant difference. In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in highest performance of AUC, reaching 0.83{+/-}0.01 (CI95%: 0.81-0.85), with sensitivity and specificity of 0.77 and 0.74, respectively. At the same time, ComBat harmonization did not depict statistically significant improvement relevant to non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and LR classifier resulted in the highest performance of AUC (0.80{+/-}0.084) with sensitivity and specificity of 0.77 {+/-} 0.11 and 0.76 {+/-} 0.075, respectively.

ConclusionLung CT radiomics features can be used towards robust prognostic modeling of COVID-19 in large heterogeneous datasets gathered from multiple centers. As such, CT radiomics-based model has significant potential for use in prospective clinical settings towards improved management of COVID-19 patients.

category: radiology and imaging


title: Left Hemisphere Bias of NIH Stroke Scale is Most Severe for Middle Cerebral Artery Strokes and Influences Treatment

abstract:
Background and AimNIHSS score is higher for left versus right hemisphere strokes of equal volumes. However, differences in each vascular territory have not been evaluated yet. We hypothesized that left versus right differences are driven by the middle cerebral artery (MCA) territory, and there is no difference between hemispheres for other vascular territories.

MethodsThis study is based on data from 802 patients with evidence of acute or early subacute ischemic stroke. These patients had infarct restricted to one major arterial territory (MCA, n=437; PCA, n=209; ACA, n=21; vertebrobasilar, n=46) and received NIHSS and MRI at hospital admission. We examined differences in patients with left or right strokes regarding to lesion volume, NIHSS, and other covariates (age, sex, race). We used linear models to test the effects of these covariates on NIHSS. We looked at the whole sample as well as in the sample stratified by NIHSS (<=5 or >5) and by lesion location (MCA or PCA).

ResultsPatients with left MCA strokes had significantly higher NIHSS than those with right strokes. Only patients with MCA strokes showed NIHSS score affected by the hemisphere when controlling for stroke volume and patients age. This difference was driven by the more severe strokes (NIHSS>5). In addition, stroke volume and patients age significantly correlated with NIHSS.

ConclusionRight MCA infarcts are larger than left MCA infarcts associated with a given NIHSS score, after accounting for other significant associations, such as patients age. It is important to consider this systematic bias in the NIHSS when using the score for inclusion criteria for treatment or trials. Patients with right MCA stroke may be under-treated and left with disabling deficits that are not captured by the NIHSS.

category: neurology


title: Left Hemisphere Bias of NIH Stroke Scale is Most Severe for Middle Cerebral Artery Strokes


title: Impostor Phenomenon in the Nutrition and Dietetics Profession: An Online Cross-Sectional Survey

abstract:
BackgroundImpostor phenomenon (IP) (also known as impostor syndrome) describes high-achieving individuals who, despite their objective successes, fail to internalize their accomplishments and have persistent self-doubt and fear of being exposed as a fraud or impostor. Despite robust literature describing the effects of IP in other health care professions, there is an absence of research within the nutrition and dietetics profession.

ObjectiveTo assess the prevalence and predictors of IP within the nutrition and dietetics students and practitioners.

DesignAn online cross-sectional survey was conducted.

Participants/setting1,015 students, dietetic interns, and currently practicing and retired nutrition and dietetic technicians registered, and registered dietitian nutritionists provided complete responses.

Main outcome measuresImpostor phenomenon was assessed with the Clance Impostor Phenomenon Scale (CIPS). Self-reported Job satisfaction and well-being were assessed using validated scales.

Statistical analysesDescriptive statistics were summarized and reported using frequency counts and percentages. Unadjusted logistic regression models were used to assess the relationship between IP and sociodemographic outcomes, job satisfaction, and well-being.

ResultsRespondents were primarily female, non-Hispanic White, and practicing dietitians. The average CIPS score was 66.0 {+/-} 16.3 (range 22-99). 64% of survey respondents (n=655) experience intense or frequent IP and 62% (n=628) had a CIPS score [≥]62. Older age, educational attainment, professional level, and membership in Academy groups were associated with lower IP scores. Greater Social media use was associated with higher IP scores. Job satisfaction and overall well-being were inversely correlated with impostor phenomenon (p<0.001).

ConclusionsFindings from an online survey suggest that a majority of nutrition and dietetics students and practitioners experience IP. Our results reinforce the need to recognize and address this issue by raising awareness, using early prevention methods, and supporting individuals who are younger and/or new to the profession.

category: nutrition


title: Clinical features of UK Biobank subjects carrying protein truncating variants in genes implicated in schizophrenia pathogenesis

abstract:
The SCHEMA consortium has identified ten genes in which protein truncating variants (PTVs) confer substantial risk of schizophrenia. Exome-sequenced participants in the UK Biobank who carried PTVs in these genes were studied to determine to what extent they demonstrated features of schizophrenia or had neuropsychiatric impairment. Following automated quality control and visual inspection of reads, 251 subjects were identified as having well-supported PTVs in one of these genes. The frequency of PTVs in CACNA1G was higher than had been observed in SCHEMA cases, casting doubt on its role in schizophrenia pathogenesis, but otherwise rates were similar to those observed in SCHEMA controls. Numbers were too small to allow formal statistical analysis but in general carriers of PTVs did not appear to have high rates of psychiatric illness or reduced educational or occupational functioning. One subject with a PTV in SETD1A had a diagnosis of schizophrenia. one with a PTV in HERC1 had psychotic depression and two subjects seemed to have developmental disorders, one with a PTV in GRIN2A and one with a PTV in RBCC1. There seemed to be somewhat increased rates of affective disorders among carriers of PTVs in HERC1 and RB1CC1. Carriers of PTVs did not appear to have subclinical manifestations of schizophrenia. Although PTVs in these genes can substantially increase schizophrenia risk, their effect seems to be dichotomous and most carriers appear psychiatrically well.

This research has been conducted using the UK Biobank Resource.

category: genetic and genomic medicine


title: Evaluating and implementing block jackknife resampling Mendelian randomization to mitigate bias induced by overlapping samples

abstract:
Participant overlap has been thought to induce overfitting bias into Mendelian randomization (MR) and polygenic risk score (PRS) studies. This hinders the potential research into many unique traits and disease outcomes from large-scale biobanks. Here, we evaluated a block jackknife resampling framework for genome-wide association studies (GWAS) and PRS construction to mitigate the influence of overfitting bias on MR analyses compared to alternative approaches and implemented this study design in causal inference setting using data from the UK Biobank.

We simulated PRS and MR under three scenarios: (1) using weighted SNP estimates from an external GWAS, (2) using weighted SNP estimates from an overlapping GWAS sample and (3) using a block jackknife resampling framework. Based on a conventional P-value threshold to derive genetic instruments for MR studies (P<5x10-8), our block-jackknifing PRS did not suffer from overfitting bias (mean R2=0.034) compared to the externally weighted PRS (mean R2=0.040). In contrast, genetic instruments derived from overlapping samples explained a higher proportion of variance (mean R2=0.048) compared to the externally derived score. The detrimental impact of overfitting bias became considerably larger when using a more liberal P-value threshold to construct PRS (e.g., P<0.05, mean R2=0.103), whereas estimates using jackknife score remained robust to overfitting (mean R2=0.084).

In an applied setting, we examined (A) the effects of body mass index on circulating biomarkers and (B) the effect of childhood body size on levels of testosterone in adulthood using methods described above. In the first applied analysis, overlapping sample PRS and block jackknife resampled PRS led to comparable effect sizes, whereas narrower confidence intervals were identified when using the overlapping sample instrument. In the second example, through sex-stratified multivariable and bi-directional MR, we demonstrate that childhood body size indirectly leads to lower testosterone levels in adulthood in males, an effect mediated through adult body size.

Author summaryUsing genetic variants as instrumental variables for risk factors, Mendelian randomization (MR) provides an approach to explore the genetically predicted effects of modifiable risk factors on disease which is robust to confounding and reverse causation. Genetic instrumental variables are conventionally selected from results of genome-wide association studies on an independent dataset whose sample does not overlap with the dataset being analysed using MR analysis, as this can lead to overfitting bias. This can often be challenging to entirely avoid however, as such association studies are increasingly being performed by meta-analysing several biobanks to achieve the maximum power to detect variants with smaller effect sizes. Moreover, when investigating exposures and outcomes which only a single biobank has measured in sufficiently large samples, avoiding participant overlap requires splitting the study population into subgroups which can limit statistical power. Block jackknife resampling MR provides a solution to conduct causal inference under these circumstances with the maximum statistical power while avoiding bias due to overlapping participants. In this study, we evaluated this study design with simulated dataset in comparison to MR using genetic variants discovered from an external dataset or one with overlapping samples. We applied this approach using UK Biobank to investigate the role of body mass index on circulating biomarkers, as well as the causal relationship between childhood adiposity and testosterone levels in adulthood.


title: SIR Modeling the Dual Disaster Impacts of Omicron B.1.1.529 and Natural Disaster Events on Simulated 6 Months (from December 2021 to May 2022) Healthcare System Resiliences in Fragile SE Asia Ring of Fire Ecosystems

abstract: For some countries that have experienced numerous natural disasters, including massive earthquakes and tsunamis, managing the COVID-19 pandemic can be very challenging. This situation arises considering that the disaster can directly and indirectly affect the healthcare system s capacity to serve the COVID-19 cases. With severely damaged healthcare facilities due to the disaster, there will be severely ill COVID-19 cases unmanaged. The coupling and interplay between these two phenomena can indeed be catastrophic. One of the regions where this issue becomes concerned is in Southeast Asia, where most of the Asian countries lie in the fragile ring of fire ecosystem, contributing to the high tsunami and earthquake disasters in the world. At the same time, Asia is one of the regions that have been severely impacted due to the current COVID-19 Delta Variant. Recently, a more contagious Omicron Variant has emerged and put a more massive burden on the healthcare facilities that are impacted by disasters. Then, in this situation, this paper aims to assess healthcare resilience in managing the Omicron pandemic amid disaster impacts. SIR simulation was used to determine whether severely ill Omicron cases were below or above healthcare and ICU capacity under different vaccination coverage. Our result confirms that vaccination coverage was the imminent factor in reducing the severely ill cases in every healthcare facility, whether the facilities were damaged or not. Increasing vaccination coverage from 30% to 60% will significantly reduce the number of severely ill cases that fall below the capacity of healthcare. Based on the current SIR model on the Omicron epidemic variables and Ro, it is estimated that the Omicron will reach its peak after 180 days in February 2022 and will totally disappear in May 2022 in this modeled area. When healthcare system facilities were fully operational and no disaster happened, combined with 60% vaccination rates, all Omicron case numbers were below and under the available hospital beds and even available ICU beds. While the situation is changed when a disaster occurs and causes 30% damage or reduction to healthcare facilities. In this situation, there are portions of Omicron cases that cannot be managed by the healthcare system since the cases have exceeded the available beds. The situations become more apparent where the healthcare facilities are severely damaged and lose 60% of their functionality. In this situation, all modeled Omicron cases and even the severe cases have exceeded the ICU capacity.

category: health informatics


title: Predicted CTL responses from pressured epitopes in SARS-CoV-2 correlate with COVID-19 severity

abstract: Heterogeneity in susceptibility among individuals to COVID-19 has been evident through the pandemic worldwide. Protective cytotoxic T lymphocyte (CTL) responses generated against pathogens in certain individuals are known to impose selection pressure on the pathogen, thus driving emergence of new variants. In this study, we focus on the role played by host genetic heterogeneity in terms of HLA-genotypes in determining differential COVID-19 severity in patients and dictating mechanisms of immune evasion adopted by SARS-CoV-2 due to the imposed immune pressure at global and cohort levels. We use bioinformatic tools for CTL epitope prediction to identify epitopes under immune pressure. Using HLA-genotype data of COVID-19 patients from a local cohort, we observe that asymptomatic individuals recognize a larger number of pressured epitopes which could facilitate emergence of mutations at these epitopic regions to overcome the protectivity they offer to the host. Based on the severity of COVID-19, we also identify HLA-alleles and epitopes that offer higher protectivity against severe disease in infected individuals. Finally, we shortlist a set of pressured and protective epitopes that represent regions in the viral proteome that are under higher immune pressure across SARS-CoV-2 variants due to the protectivity they offer. Identification of such epitopes could potentially aid in prediction of indigenous variants of SARS-CoV-2 and other pathogens, defined by the distribution of HLA-genotypes among members of a population.

category: infectious diseases


title: A prospective diagnostic evaluation of accuracy of self-taken and healthcare worker-taken swabs for rapid COVID-19 testing

abstract:
ObjectivesTo compare self-taken and healthcare worker (HCW)-taken throat/nasal swabs to perform rapid diagnostic tests (RDT) for SARS-CoV-2, and how these compare to RT-PCR. We hypothesised that self-taken samples are non-inferior for use with RDTs and in clinical and research settings could have substantial individual and public health benefit.

DesignA prospective diagnostic accuracy evaluation as part of the  Facilitating Accelerated Clinical Evaluation of Novel Diagnostic Tests for COVID -19 (FALCON C-19), workstream C (undifferentiated community testing).

SettingNHS Test and Trace drive-through community PCR testing site (Liverpool, UK). Participants

Eligible participants 18 years or older with symptoms of COVID-19. 250 participants recruited; one withdrew before analysis.

SamplingSelf-administered throat/nasal swab for the Covios(R) RDT, a trained HCW taken throat/nasal sample for PCR and HCW comparison throat/nasal swab for RDT.

Main outcome measuresSensitivity, specificity, and positive and negative predictive values (PPV, NPV) were calculated; comparisons between self-taken and HCW-taken samples used McNemars test.

ResultsSeventy-five participants (75/249, 30.1%) were positive by RT-PCR. RDTs with self-taken swabs had a sensitivity of 90.5% (67/74, 95% CI: 83.9-97.2), compared to 78.4% (58/74, 95% CI: 69.0-87.8) for HCW-taken swabs (absolute difference 12.2%, 95% CI: 4.7-19.6, p=0.003). Specificity for self-taken swabs was 99.4% (173/174, 95% CI: 98.3-100.0), versus 98.9% (172/174, 95% CI: 97.3-100.0) for HCW-taken swabs (absolute difference 0.6%, 95% CI: 0.5-1.7, p=0.317). The PPV of self-taken RDTs (98.5%, 67/68, 95% CI: 95.7-100.0) and HCW-taken RDTs (96.7%, 58/60, 95% CI 92.1-100.0) were not significantly different (p=0.262). However, the NPV of self-taken swab RDTs was significantly higher (96.1%, 173/180, 95% CI: 93.2-98.9) than HCW-taken RDTs (91.5%, 172/188, 95% CI 87.5-95.5, p=0.003).

ConclusionSelf-taken swabs for COVID-19 testing offer substantial individual benefits in terms of convenience, accuracy, and reduced risk of transmitting infection. Our results demonstrate that self-taken throat/nasal samples can be used by lay individuals as part of rapid testing programmes for symptomatic adults.

Trial RegistrationIRAS ID:28422, clinical trial ID: NCT04408170

SummaryO_ST_ABSWhat is already known on this topic?C_ST_ABSO_LIRapid diagnostic tests (RDTs)for SARS-CoV-2 Ag are a cheaper point-of-care alternative to RT-PCR for diagnosing COVID-19 disease.
C_LIO_LIThe accuracy of tests can vary dependent on sampling technique, test processing and reading of results.
C_LI

What this study adds?O_LISelf-taken throat-nasal swabs for RDTs can be used by symptomatic adults to give reliable results to diagnose SARS-CoV-2.
C_LIO_LISelf-sampling can be implemented with little training and no assistance.
C_LI


title: Individual vaccine efficacy variation with time since mRNA BNT162b2 vaccination estimated by rapid, quantitative antibody measurements from a finger-prick sample.

abstract: We show that an individuals immune status to Covid-19 can be monitored through quantitative antibody measurements using a method based on centrifugal microfluidics, specifically designed for speed to result (20 min), high throughput (8 samples simultaneously) and accuracy from a finger-prick blood sample. Anti-Receptor Binding Domain (RBD) IgG concentration showed a log-normal distribution with mean decreasing with time following the second vaccination with mRNA BNT162b2 (Pfizer). Using a model for an individuals antibody concentration-dependent vaccine efficacy allowed comparison with literature data on changing vaccine efficacy against symptomatic disease across a population. Even though the trial was small (n = 100) the computed population vaccine efficacy was in reasonable agreement with that obtained from a large population survey. The derived parameters for the vaccine efficacy model were in good agreement with those expected from previous studies and from a simple theoretical model. The results and modelling show that the major proportion of breakthrough infections are for people whose antibody concentration is in the tail of the distribution. The results provide strong support for personalized booster programmes that, by targeting people in the tail of the distribution, should be more effective at diminishing breakthrough infection and optimising booster dose supply than a program that simply mandates a booster at a specific post-vaccination time point.


title: Facilitating safe discharge through predicting disease progression in moderate COVID-19: a prospective cohort study to develop and validate a clinical prediction model in resource-limited settings

abstract:
BackgroundIn locations where few people have received COVID-19 vaccines, health systems remain vulnerable to surges in SARS-CoV-2 infections. Tools to identify patients suitable for community-based management are urgently needed.

MethodsWe prospectively recruited adults presenting to two hospitals in India with moderate symptoms of laboratory-confirmed COVID-19 in order to develop and validate a clinical prediction model to rule-out progression to supplemental oxygen requirement. The primary outcome was defined as any of the following: SpO2 < 94%; respiratory rate > 30 bpm; SpO2/FiO2 < 400; or death. We specified a priori that each model would contain three clinical parameters (age, sex and SpO2) and one of seven shortlisted biochemical biomarkers measurable using near-patient tests (CRP, D-dimer, IL-6, NLR, PCT, sTREM-1 or suPAR), to ensure the models would be suitable for resource-limited settings. We evaluated discrimination, calibration and clinical utility of the models in a temporal external validation cohort.

Findings426 participants were recruited, of whom 89 (21{middle dot}0%) met the primary outcome. 257 participants comprised the development cohort and 166 comprised the validation cohort. The three models containing NLR, suPAR or IL-6 demonstrated promising discrimination (c-statistics: 0{middle dot}72 to 0{middle dot}74) and calibration (calibration slopes: 1{middle dot}01 to 1{middle dot}05) in the validation cohort, and provided greater utility than a model containing the clinical parameters alone.

InterpretationWe present three clinical prediction models that could help clinicians identify patients with moderate COVID-19 suitable for community-based management. The models are readily implementable and of particular relevance for locations with limited resources.

FundingMedecins Sans Frontieres, India.

RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSA living systematic review by Wynants et al. identified 137 COVID-19 prediction models, 47 of which were derived to predict whether patients with COVID-19 will have an adverse outcome. Most lacked external validation, relied on retrospective data, did not focus on patients with moderate disease, were at high risk of bias, and were not practical for use in resource-limited settings. To identify promising biochemical biomarkers which may have been evaluated independently of a prediction model and therefore not captured by this review, we searched PubMed on 1 June 2020 using synonyms of "SARS-CoV-2" AND ["biomarker" OR "prognosis"]. We identified 1,214 studies evaluating biochemical biomarkers of potential value in the prognostication of COVID-19 illness. In consultation with FIND (Geneva, Switzerland) we shortlisted seven candidates for evaluation in this study, all of which are measurable using near-patient tests which are either currently available or in late-stage development.

Added value of this studyWe followed the TRIPOD guidelines to develop and validate three promising clinical prediction models to help clinicians identify which patients presenting with moderate COVID-19 can be safely managed in the community. Each model contains three easily ascertained clinical parameters (age, sex, and SpO2) and one biochemical biomarker (NLR, suPAR or IL-6), and would be practical for implementation in high-patient-throughput low resource settings. The models showed promising discrimination and calibration in the validation cohort. The inclusion of a biomarker test improved prognostication compared to a model containing the clinical parameters alone, and extended the range of contexts in which such a tool might provide utility to include situations when bed pressures are less critical, for example at earlier points in a COVID-19 surge.

Implications of all the available evidencePrognostic models should be developed for clearly-defined clinical use-cases. We report the development and temporal validation of three clinical prediction models to rule-out progression to supplemental oxygen requirement amongst patients presenting with moderate COVID-19. The models are readily implementable and should prove useful in triage and resource allocation. We provide our full models to enable independent validation.


title: Personalised Cardiac Rehabilitation Outreach Service Reduces Mortality and Hospitalisations in Heart Failure with Reduced Ejection Fraction

abstract:
BackgroundHeart failure (HF) has become a major cause of morbidity and mortality worldwide. Despite significant improvements in the management of HF, the overall outcomes remain poor. In addition to pharmacotherapy and device therapy, non-pharmacological interventions are needed to mitigate the effects of this illness. The aim of this study was to evaluate the impact of the heart failure outreach program on the rate of mortality, HF hospitalisations and guideline directed medical therapy (GDMT) for HF in South Western Sydney Local Health District (SWSLHD).

MethodsIn this observational, registry based study, adult patients diagnosed with Heart failure with reduced ejection fraction (HFrEF) within the South Western Sydney Local Health District (SWSLHD) and invited to participate in the heart failure outreach service between March 2011 and January 2016 were included in the study. The primary outcome was all-cause mortality. In addition, we examined the rate of optimal medical therapy, HF hospitalisations and the total lengths of stay.

ResultsA total of 818 patients were included in the study; 470 (57.5%) patients were enrolled and 348 (42.5 %) not enrolled into the program. At the end of the follow up period (median 978 days, interquartile range (IQR) 720-1237), the primary outcome of mortality was observed significantly less in the enrolled group (122 (26%) vs. 133 (38.2%), p<0.001) independently of other variables. In addition, significantly fewer enrolled patients had >3 hospital admissions for HF (16.2% vs. 35.6%, p<0.001) and reduced median admission days (14.5 days [IQR 8-25] vs 22 [IQR 12-37], p <0.001). Patients enrolled into the program were much more likely to be on GDMT (76.6% vs 56.6%, p<0.001).

ConclusionsEnrolment in the heart failure outreach program was associated with a significant reduction in mortality as well as a reduction in the frequency and length of hospital admissions. In addition, the rate of GDMT was significantly higher in the enrolled group. With the high prevalence of heart failure, these programs should be considered in the routine management of patients with HFrEF.

category: cardiovascular medicine


title: Assessing the causal role of epigenetic clocks in the development of multiple cancers: a Mendelian randomization study

abstract:
BackgroundEpigenetic clocks have been associated with cancer risk in several observational studies. Nevertheless, it is unclear whether they play a causal role in cancer risk or if they act as a non-causal biomarker.

MethodsWe conducted a two-sample Mendelian randomization (MR) study to examine the genetically predicted effects of epigenetic age acceleration as measured by HannumAge (9 single-nucleotide polymorphisms (SNPs)), Horvath Intrinsic Age (24 SNPs), PhenoAge (11 SNPs) and GrimAge (4 SNPs) on multiple cancers (i.e., breast, prostate, colorectal, ovarian and lung cancer). We obtained genome-wide association data for biological ageing from a meta-analysis (N=34,710), and for cancer from the UK Biobank (N cases=2,671-13,879; N controls=173,493-372,016), FinnGen (N cases=719-8,401; N controls=74,685-174,006) and several international cancer genetic consortia (N cases=11,348-122,977; N controls=15,861-105,974). Main analyses were performed using multiplicative random effects inverse variance weighted (IVW) MR. Individual study estimates were pooled using fixed effect meta-analysis. Sensitivity analyses included MR-Egger, weighted median, weighted mode and Causal Analysis using Summary Effect Estimates (CAUSE) methods, which are robust to some of the assumptions of the IVW approach.

ResultsMeta-analysed IVW MR findings suggested that higher GrimAge acceleration increased the risk of colorectal cancer (OR=1.12 per year increase in GrimAge acceleration, 95%CI 1.04-1.20, p=0.002). The direction of the genetically predicted effects was consistent across main and sensitivity MR analyses. Among subtypes, the genetically predicted effect of GrimAge acceleration was greater for colon cancer (IVW OR=1.15, 95%CI 1.09-1.21, p=0.006), than rectal cancer (IVW OR=1.05, 95%CI 0.97-1.13, p=0.24). We also found evidence that higher GrimAge acceleration decreased the risk of prostate cancer (pooled IVW OR=0.93 per year increase in GrimAge acceleration, 95%CI 0.87-0.99, p=0.02). This was supported by MR sensitivity analyses, but did not replicate in MR analyses using data on parental history of prostate cancer in UK Biobank (IVW OR=1.00, 95%CI 0.96-1.04, p=1.00). Results were less consistent for associations between other epigenetic clocks and cancers.

ConclusionsGrimAge acceleration may increase the risk of colorectal cancer. Additionally, there is more limited evidence that it may be protective against prostate cancer. Findings for other clocks and cancers were inconsistent. Further work is required to investigate the potential mechanisms underlying the results.

FundingFMB was supported by a Wellcome Trust PhD studentship in Molecular, Genetic and Lifecourse Epidemiology (218495/Z/19/Z). KKT was supported by a Cancer Research UK (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme) and by the Hellenic Republics Operational Programme "Competitiveness, Entrepreneurship & Innovation" (O{Pi}{Sigma} 5047228). PH was supported by Cancer Research UK (C18281/A29019).

RMM was supported by the NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol and by a Cancer Research UK (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. GDS and CLR were supported by the Medical Research Council (MC_UU_00011/1 and MC_UU_00011/5) and by a Cancer Research UK (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme). REM was supported by an Alzheimers Society project grant (AS-PG-19b-010) and NIH grant (U01 AG-18-018, PI: Steve Horvath). RCR is a de Pass Vice Chancellors Research Fellow at the University of Bristol.


title: Post-Vaccination Symptoms after A Third Dose of mRNA SARS-CoV-2 Vaccination in Patients with Inflammatory Bowel Disease

abstract: Symptoms after SARS-CoV-2 primary vaccination among patients with inflammatory bowel disease (IBD) are generally of similar frequency, severity, and duration to those reported in the general population. The symptom profile after a 3rd mRNA vaccine dose in the predominantly immune-compromised IBD population is unknown. We aimed to assess symptomology after a 3rd or booster dose of mRNA vaccination in adults with IBD. We surveyed participants of the Coronavirus Risk Associations and Longitudinal Evaluation in IBD (CORALE-IBD) post-vaccination registry for symptom frequency and severity after a 3rd mRNA vaccine dose in an observational cohort study. In total, 524 participants (70% female, mean age 45 years) reported a third dose of mRNA vaccination through October 11, 2021. Overall, 41% reported symptoms after a third dose, with symptoms generally more frequent and more severe among participants younger than 55 years. The most frequent postvaccination symptoms were injection site pain (39%), fatigue or malaise (34%), and headache (23%). These symptoms were all less frequently reported after dose 3 than after dose 2. Gastrointestinal symptoms were reported by 8.8%, which was slightly more frequent than after dose 2 (7.8%). Those with severe symptoms after dose 2 were more likely to have severe symptoms after dose 3. These findings can reassure the IBD patient and provider communities that the likelihood and distribution of symptoms after a third mRNA vaccine dose are generally similar to those after a second dose, and that the frequency of postvaccination symptoms after dose 3 are generally lower than after dose 2.

category: gastroenterology


title: Systematic review of cardiac adverse effects in children and young people un-der 18 years of age after SARS-CoV-2 vaccination

abstract:
BackgroundReports of myocarditis and pericarditis following vaccination with mRNA vaccines for SARS-CoV-2 have occurred after countries began vaccinating adolescents. We undertook a systematic review of cardiac adverse effects associated with SARS-CoV-2 vaccine in children and young people (CYP)< 18 years.

MethodsSystematic review with protocol prospectively registered with PROSPERO (CRD42021275380).

Six electronic databases were searched from 1 December 2019 to 14 September 2021. Eligible studies were those reporting on CYP with reported or proven myocarditis, pericarditis and/or myopericarditis associated with vaccination against SARS-CoV-2. We summarized findings across all clinical cases reported in case report / case series studies. As a number of studies reported data from two publicly available vaccine surveillance systems, we updated estimates of reporting rates for cardiac adverse events up to 31 October for the US Vaccine Adverse Event Reporting System (VAERS) and 13 November for EudraVigilance covering European Union and European Economic Area (EUEA) countries.

ResultsTwenty-one studies were included from 338 identified records. Seventeen were case reports/series describing a total of 127 CYP. Three studies described reporting rates from passive surveillance databases (VAERS, EudraVigilance, and the WHO VigiBase) and one described 22 cases from the US Vaccine Safety Datalink (VSD).

Clinical series reported that 99.2% presented with chest pain, 100% had raised troponin and 73.8% had an abnormal ECG. Cardiovascular magnetic resonance (CMR) in 91 cases identified myocardial injury in 61.5%, with 90.1% showing late gadolinium enhancement. NSAIDs were the most common treatment (76.0%).

One US dataset (VSD) estimated a significant excess of 29.6 events per million vaccine doses across both sexes and doses. There were 1129 reports of myocarditis and 358 reports of pericarditis from across the USA and EU/EEA. The VAERS reporting rate per million for myocarditis was 12.4 for boys and 1.4 for girls after the first dose, and 49.6 for boys and 6.1 for girls after the second dose. There was a marked trend for VAERS reporting to be highest soon after initiation of the vaccine schedule, suggesting reporting bias.

ConclusionsCardiac adverse effects are very rare after mRNA vaccination for COVID-19 in CYP <18 years. The great majority of cases are mild and self-limiting without significant treatment. No data are yet available on children under 12 years. Larger detailed longitudinal studies are urgently needed from active surveillance sources.

category: pediatrics


title: Association of inferior division MCA stroke location with populations with atrial fibrillation incidence

abstract:
Background and AimAnatomical features of Middle Cerebral Artery (MCA) bifurcation favors larger emboli entering the inferior rather than superior division. As cardiac source emboli are on average larger than arterial source emboli, we hypothesize that patients with atrial fibrillation have infarcts that involve more temporal and parietal lobes than frontal lobes.

MethodsFrom 1,849 patients admitted with evidence of acute or early subacute ischemic stroke on diffusion-weighted MRI (DWI), 482 affected exclusively the "lobar" MCA (sparing the lenticulostriates). They were classified as "frontal predominant" (n=105) or "temporoparietal predominant" (n=197) if at least 75% of the infarct affected the frontal lobe or the temporal and parietal lobes, respectively. Differences between stroke location (frontal or temporoparietal), sex, and race were analyzed by Chi-square test. Results: Men were more likely than women, and white people were more likely than black people to have temporoparietal strokes versus frontal strokes. Patients with confirmed diagnosis of atrial fibrillation have more temporoparietal strokes, compared to frontal strokes.

ConclusionMCA ischemic strokes occur more often in temporoparietal areas in men and in white patients, populations with known elevated incidence of atrial fibrillation. Patients with confirmed diagnosis of atrial fibrillation have more temporoparietal strokes, compared to frontal strokes. Results align with the hypothesis that large emboli (mostly from cardiac source) are more likely to cause temporoparietal strokes in the MCA territory, compared to frontal strokes. This association can help guide search for the most likely etiology of infarcts.


title: The Impact of Mass Exodus on the Resurgence of COVID19 Cases: Study Case of Regions in Indonesia

abstract: The inclusion of the human mobility aspect is essential for understanding the behavior of COVID-19 spread, especially when millions of people travel across borders near Eid Al-Fitr. This study aims at grasping the effect of mass exodus among regions on the active cases of COVID-19 in a mathematical perspective. We construct a multi-region SIQRD (Susceptible-Infected-Quarantined-Recovered-Death) model that accommodates the direct transfer of people from one region to others. The mobility rate is estimated using the proposed Dawson-like function, which requires the Origin-Destination Matrix data. Assuming only susceptible, unapparent infected, and recovered individuals travel near Eid Al-Fitr, the rendered model is well-depicting the actual data at that time, giving either a significant spike or decline in the number of active cases due to the mass exodus. Most agglomerated regions like Jakarta and Depok City experienced the fall of active cases number, both in actual data and the simulated model. However, most rural areas experienced the opposite, like Bandung District and Cimahi City. This study should confirm that most travelers originated from big cities to the rural regions and scientifically justifies that massive mobility affects the COVID-19 transmission among areas.


title: Stroke prevalence among isolated systolic hypertension subjects in Indonesia

abstract:
IntroductionHypertension has been known to be a decisive factor for stroke in the elderly; however, limited studies reported stroke risk factors in subjects with isolated systolic hypertension (ISH).

MethodsThis cross-sectional survey was conducted using Riskesdas 2018 secondary data. Subjects aged 55 years who had a systolic blood pressure [≥]140 mmHg and diastolic < 90 mmHg were included as the study sample. According to the National Institute of Health Research and Development, the Ministry of Health, Republic of Indonesia, all study variables were measured using household and individual questionnaires. The data were analyzed using the chi-square test and Fishers test.

ResultsOf 3159 subjects with ISH 8.3% had a stroke, the risk factors that had a significant relationship with the prevalence of stroke (p<0.005) were gender (1,790; 1,420-2,256), smoking habits (1,645; 1,291-2,096), physical and mental stress (2,080; 1,618-2,673), area of residence (1,720; 1,331-2222), and education level (0.656; 0.515-0.835).

ConclusionPrevalence of stroke among ISH in Indonesia was 8.3%. Female with smoking habits, had mental and physical stress, liviing in urban area, and had low level eduatcion were associated with the status of stroke in ISH subjects in Indonesia.


title: Using patient biomarker time series to determine mortality risk in hospitalised COVID-19 patients: a comparative analysis across two New York hospitals

abstract: A large range of prognostic models for determining the risk of COVID-19 patient mortality exist, but these typically restrict the set of biomarkers considered to measurements available at patient admission. Additionally, many of these models are trained and tested on patient cohorts from a single hospital, raising questions about the generalisability of results. We used a Bayesian Markov model to analyse time series data of biomarker measurements taken throughout the duration of a COVID-19 patients hospitalisation for n = 1540 patients from two hospitals in New York: State University of New York (SUNY) Downstate Health Sciences University and Maimonides Medical Center. In doing so, we quantified the mortality risk associated with both static (e.g. demographic and patient history variables) and dynamic factors (e.g. changes in biomarkers) throughout hospitalisation. By using our model to make predictions across the hospitals, we assessed how predictive factors generalised between the two cohorts. The individual dynamics of the measurements and their associated mortality risk were remarkably consistent across the hospitals. The model accuracy in predicting patient outcome (death or discharge) was 72.3% (predicting SUNY; posterior median accuracy) and 71.4% (predicting Maimonides) respectively. Model sensitivity was higher for detecting patients who would go on to be discharged (79.2%) versus those who died (61.0%). Our results indicate the utility of including dynamic clinical measurements when assessing patient mortality risk but also highlight the difficulty of identifying high risk patients.


title: Reduced Neutralization of SARS-CoV-2 Omicron Variant by Vaccine Sera and monoclonal antibodies

abstract: Due to numerous mutations in the spike protein, the SARS-CoV-2 variant of concern Omicron (B.1.1.529) raises serious concerns since it may significantly limit the antibody-mediated neutralization and increase the risk of reinfections. While a rapid increase in the number of cases is being reported worldwide, until now there has been uncertainty about the efficacy of vaccinations and monoclonal antibodies. Our in vitro findings using authentic SARS-CoV-2 variants indicate that in contrast to the currently circulating Delta variant, the neutralization efficacy of vaccine-elicited sera against Omicron was severely reduced highlighting T-cell mediated immunity as essential barrier to prevent severe COVID-19. Since SARS-CoV-2 Omicron was resistant to casirivimab and imdevimab, genotyping of SARS-CoV-2 may be needed before initiating mAb treatment. Variant-specific vaccines and mAb agents may be required to treat COVID-19 due to Omicron and other emerging variants of concern.


title: Reduced Neutralization of SARS-CoV-2 Omicron Variant by Vaccine Sera and Monoclonal Antibodies


title: COVID-19 Vaccine Hesitancy Among Patients with Inflammatory Bowel Disease on Biologic Therapies; A cross-sectional study.

abstract:
BackgroundCOVID-19 Vaccinations have been shown to be effective in reducing risk of severe infection, hospitalization, and death. They also have been shown to be safe and effective in patients with inflammatory bowel disease (IBD) on biologic therapies. In this study, we aimed to evaluate the prevalence of vaccination among patients with IBD on biologic therapies.

MethodsA single-center prospective cross-sectional study conducted at a tertiary care inflammatory bowel disease center. Data from patients with inflammatory bowel disease (IBD) who attended the gastroenterology infusion clinic from June 1st, 2021 until October 31st, 2021 were retrieved. Patients received infliximab or vedolizumab at least 6 weeks before recruitment were included. The primary outcome was prevalence of COVID-19 vaccination. The secondary outcome was to assess whether prevalence of COVID-19 vaccination differed based sex, age, type of biologic therapy and citizenship status.

ResultsThe total number of inflammatory bowel disease (IBD) patients enrolled in the study was 280 (56.0% male and 44.0% female). The median age was 33.2 years and BMI was 24.8 kg/m2. 112 patients with ulcerative colitis (40.0%) and 168 (60.0%) with Crohns disease. 117 (41.8%) were vaccinated with either BNT162b2 or ChAdOx1 nCoV-19 and 163 (58.2%) were not vaccinated. Female patients were more likely to receive the vaccine compared to male patients (83.0% vs. 63.8%, p < 0.001). In addition, older patients (above the age 50) were also more likely to receive the vaccine than younger patients, below the age of 50 (95.6% vs 31.2% p< 0.001). Expatriates were more likely to receive the vaccine than citizens (84.8% vs 25.0%, p < 0.001). There was no statistical difference between patients on Infliximab and vedolizumab in terms of prevalence of vaccination (40.0% vs 48.0%, p= 0.34).

ConclusionThe overall prevalence of COVID-19 vaccination among patients with inflammatory bowel disease (IBD) on biologic therapies was lower than the general population and world health organization (WHO) recommendation. Female patients, patients above the age of 50, and expatriates were more likely to be vaccinated. On the other hand, male patients, patients below the age of 50, and citizens were less likely to be vaccinated.


title: PROBABILITY OF HOSPITALIZATION AND DEATH AMONG COVID-19 PATIENTS WITH COMORBIDITY DURING OUTBREAKS OCCURRING IN MEXICO CITY.

abstract:
BackgroundWorldwide, it has been observed that there is a strong association between the severity of COVID-19 and with being over 40 years of age, having diabetes mellitus (DM), hypertension and/or obesity.

ObjectiveTo compare the probability of death caused by COVID-19 in patients with comorbidities during three periods defined for this study as follows: first wave (March 23 to July 12, 2020), interwave period (July 13 to October 25, 2020), and the second wave (October 26, 2020, to March 29, 2021) using the different fatality rates observed in Mexico City.

MethodsThe cohort studied included individuals over 20 years of age. During the first wave (symptomatic), the interwave period, and the second wave (symptomatic and asymptomatic), participants were diagnosed using nasopharyngeal swabs taken in kiosks. Symptomatic individuals with risk factors for serious disease or death were referred to hospital. SARS-CoV-2 infection was defined by real time polymerase chain reaction in all hospitalized patients. All data from hospitalized patients and outpatients were added to the SISVER database.

ResultsThe total cohort size for this study was 2,260,156 persons (having a mean age of 43.1 years). Of these, 8.6% suffered from DM, 11.6% from hypertension, and 9.7% from obesity. Of the total of 2,260,156 persons, 666,694 tested positive (29.5%) to SARS CoV-2, (with a mean age of 45). During the first wave, 82,489 tested positive; in the interwave period, 112,115; and during the second wave, 472,090. That is, a considerable increase in the number of cases of infection was observed in all age groups between the first and second waves (an increase of +472% on the first wave).

Of the infected persons, a total of 85,587 (12.8%) were hospitalized: 24,023 in the first wave (29.1% of those who tested positive in this period); 16,935 (15.1%) during the interwave period, and 44,629 (9.5%) in the second wave, which represents an increase of 85.77% on the first wave.

Of the hospitalized patients, there were 42,979 deaths (50.2% of those hospitalized), in the first wave, 11,964 (49.8% of those hospitalized in this period), during the interwave period, 6,794 (40.1%), and in the second wave 24,221 (54.3%), an increase of +102.4% between the first wave and the second.

While within the general population, the probability of a patient dying having both COVID-19 and one of the specified comorbidities (DM, obesity, or arterial hypertension) showed a systematic reduction across all age groups, the probability of death for a hospitalized patient with comorbidities increased across all age groups during the second wave. When comparing the fatality rate of hospitalized COVID-19 patients in the second wave with those of the first wave and the interwave period, a significant increase was observed across all age groups, even in individuals without comorbidities.

ConclusionThe data from this study show a considerable increase in the number of detected cases of infection in all age groups between the first and second waves. In addition, 12.8% of those infected were hospitalized for severe COVID-19, representing an increase of +85.9% from the first wave to the second. A high mortality rate was observed among hospitalized patients (>50%), as was a higher probability of death in hospitalized COVID-19 patients with comorbidities for all age groups during the second wave, although there had been a slight decrease during the interwave period.

SUMMARY BOXO_ST_ABSWhat is already known?C_ST_ABSWorldwide the resurging of COVID-19 cases in waves has been observed. In Mexico, like in the rest of the world, we have observed surges of SARS CoV-2 infections, COVID-19 hospitalizations and fatal outcomes followed by decreases leading to local minima. Pre-existing health conditions such as being older, having diabetes mellitus (DM), hypertension and/or obesity has been observed to be associated with an increase in the severity of COVID-19.

What are the new findings?O_LIBetween the first and second waves, considerable increases were observed in the number of detected cases of infection (+472%), in the number of hospitalized subjects (+85.9%), and the number of hospitalized subjects and deaths (+102.4%) in all age groups.
C_LIO_LIWhen analysing only hospitalized individuals, with or without comorbidities, the Case Fatality Rate was high (50.2%), the probability of death increased considerably in all age groups between the first and second waves. This increase was more noticeable in those individuals with previously identified comorbidities (DM, hypertension, or obesity).
C_LIO_LIAn increased probability of death among individuals without comorbidities was observed between the first and second waves.
C_LI

What do the new findings imply?During the second wave, demand for hospitalization increased, magnifying the impact of age and comorbidities as risk factors. This situation highlights the importance of decreasing the prevalence of comorbidities among the population.


title: A novel approach to concentrate human and animal viruses from wastewater using receptors-conjugated magnetic beads

abstract:
Viruses are present at low concentrations in wastewater, and therefore an effective concentration of virus particles is necessary for accurate wastewater-based epidemiology (WBE). We designed a novel approach to concentrate human and animal viruses from wastewater using porcine gastric mucin-conjugated magnetic beads (PGM-MBs). We systematically evaluated the performances of the PGM-MBs method (sensitivity, specificity, and robustness to environmental inhibitors) with six viral species including Tulane virus (a surrogate for human norovirus), rotavirus, adenovirus, porcine coronavirus (transmissible gastroenteritis virus or TGEV), and two human coronaviruses (NL63 and SARS-CoV-2) in influent wastewater and raw sewage samples. We determined the multiplication factor (the ratio of genome concentration of the concentrated over that of the initial solution) for the PGM-MBs method, which ranged from 1.3 to 64.0 depending on the viral species. Because the recovery efficiency became significantly higher when calculated based on virus titers than genome concentration, the PGM-MBs method could be an appropriate tool for assessing the risk due to wastewater contaminated with infectious enteric viruses. PCR inhibitors were not concentrated by PGM-MBs, suggesting this tool will be successful for use with environmental samples. The PGM-MBs method is cost-effective (0.43 USD/sample) and fast turnaround (3 hours from virus concentration to genome quantification), and thus this method can be implemented for high throughput facilities. Based on good performance, intrinsic characteristics of targeting the infectious virus, robustness to wastewater, and adaptability to high throughput systems, we are confident that the PGM-MBs method can be applied for successful WBE and ultimately provides valuable public health information.

Graphical abstract

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title: Heart age estimated using explainable advanced electrocardiography

abstract:
BackgroundElectrocardiographic (ECG) Heart Age conveying cardiovascular risk has been estimated by both Bayesian and artificial intelligence approaches. We hypothesised that explainable measures from the 10-second 12-lead ECG could successfully predict Bayesian 5-min ECG Heart Age.

MethodsAdvanced analysis was performed on ECGs from healthy subjects and patients with cardiovascular risk or proven heart disease. Regression models were used to predict patients Bayesian 5-minute ECG Heart Ages from their standard, resting 10-second 12-lead ECGs. The difference between 5-min and 10-second ECG Heart Ages were analyzed, as were the differences between 10-second ECG Heart Age and the chronological age (the Heart Age Gap).

ResultsIn total, 2,771 subjects were included (n=1682 healthy volunteers, n=305 with cardiovascular risk factors, n=784 with cardiovascular disease). Overall, 10-second Heart Age showed strong agreement with the 5-minute Heart Age (R2=0.94, p<0.001, mean{+/-}SD bias 0.0{+/-}5.1 years). The Heart Age Gap was 0.0{+/-}5.7 years in healthy individuals, 7.4{+/-}7.3 years in subjects with cardiovascular risk factors (p<0.001), and 14.3{+/-}9.2 years in patients with cardiovascular disease (p<0.001).

ConclusionsHeart Age can be accurately estimated from a 10-second 12-lead ECG in a transparent and explainable fashion based on known ECG measures, without artificial intelligence techniques. The Heart Age Gap increases markedly with cardiovascular risk and disease.