title: Metabolomics insights into osteoporosis through association with bone mineral density

abstract: Osteoporosis, a disease characterized by low bone mineral density (BMD), increases the risk for fractures. Conventional risk factors alone do not completely explain measured BMD or osteoporotic fracture risk. Metabolomics may provide additional information. We aim to identify BMD-associated metabolomic markers that are predictive of fracture risk. We assessed 209 plasma metabolites by LC-MS/MS in 1,552 Framingham Offspring Study participants, and measured femoral neck (FN) and lumbar spine (LS) BMD 2-10 years later using dual energy x-ray absorptiometry. We assessed osteoporotic fractures up to 27-year follow-up after metabolomic profiling. We identified twenty-seven metabolites associated with FN-BMD or LS-BMD by LASSO regression with internal validation. Incorporating selected metabolites significantly improved the prediction and the classification of osteoporotic fracture risk beyond conventional risk factors (AUC=0.74 for the model with identified metabolites and risk factors vs AUC=0.70 with risk factors alone, p=0.001; Net reclassification index=0.07, p=0.03). We replicated significant improvement in fracture prediction by incorporating selected metabolites in 634 participants from the Hong Kong Osteoporosis Study (HKOS). The glycine, serine, and threonine metabolism pathway (including four identified metabolites: creatine, dimethylglycine, glycine, and serine) was significantly enriched (FDR p-value=0.028). Furthermore, three causally related metabolites (glycine, Phosphatidylcholine [PC], and Triacylglycerol [TAG]) were negatively associated with FN-BMD while PC and TAG were negatively associated with LS-BMD through Mendelian randomization analysis. In summary, metabolites associated with BMD are helpful in osteoporotic fracture risk prediction. Potential causal mechanisms explaining the three metabolites on BMD are worthy of further experimental validation. Our findings may provide novel insights into the pathogenesis of osteoporosis.


title: Modelling the Long-Term Effects of Covid-19 Cancer Services Disruption on Patient Outcome in Scotland

abstract:
BackgroundModelling the long-term effects of disruption of cancer services and minimising any excess cancer mortality due to the Covid-19 pandemic is of great importance. Here we adapted a stage-shift model to inform service planning decisions within NHS Scotland for the   Detect Cancer Early tumours, breast, colorectal and lung cancer which represent 46% of all cancers diagnosed in Scotland.

Methods & DataLung, colorectal and breast cancer incidence data for years 2017-18 were obtained from Public Health Scotland Cancer Quality Performance Indicators (QPI), to define a baseline scenario. The most current stage-specific 5-year survival data came from 2009-2014 national cancer registry and South East Scotland Cancer Network (SCAN) QPI audit datasets. The Degeling et al., inverse stage-shift model was adapted to estimate changes in stage at diagnosis, excess mortality and life-years lost from delays to diagnosis and treatment due to Covid-19-related health services disruption. Three and 6-month periods of disruption were simulated to demonstrate the model predictions.

ResultsApproximately, 1-9% reductions in stage I/II presentations leading up to 2-10% increases in stage III/IV presentations are estimated across the three cancer types. A 6-month period of service disruption is predicted to lead to excess deaths at 5 years of 32.5 (31.1, 33.9) per 1000 cases for lung cancer, 16.5 (7.9, 24.3) for colorectal cancer and 31.6 (28.5, 34.4) for breast cancer.

ConclusionsDisruption of cancer diagnostic services can lead to significant excess deaths in following years. Increasing diagnostic and capacity for cancer services to deal with the backlog of care are needed. Real time monitoring of incidence and referral patterns over the disruption and post-disruption period to reduce excess deaths including more rapid incidence data by stage and other key tumour/clinical characteristics at presentation for key cancer cases (on a quarterly basis). Real time monitoring in cancer care and referral patterns should help inform what type of interventions are needed to reduce excess mortality and whether different population subgroups require public health messaging campaigns. Specific mitigation measures can be the subject of additional modelling analysis to assess the benefits and inform service planning decision making.

category: epidemiology


title: Population-based seroprevalence of SARS-CoV-2 antibodies in a high-altitude setting in Peru

abstract:
BackgroundLittle evidence exists about the prevalence of COVID-19 infection at high altitude. We aimed to estimate the population-based seroprevalence of COVID-19 in Cusco at the end of the first wave.

MethodsA population-based survey was conducted in September 2020 in three settings in Cusco: (1) Cusco city at 3300 meters above the sea level (m.a.s.l.), (2) the periphery of Cusco (Santiago, San Jeronimo, San Sebastian, and Wanchaq) at 3300 m.a.s.l., and (3) Quillabamba city, located at 1050 m.a.s.l. People aged [≥]18 years within a family unit were included. The diagnosis of COVID-19 infection was based on identifying total antibodies (IgM and IgG) anti-SARS-CoV-2 in serum using the Elecsys Anti-SARS-CoV-2 chemiluminescence test.

FindingsWe enrolled 1924 participants from 712 families. Of the total, 637 participants were COVID-19 seropositive. Seroprevalence was 38{middle dot}8% (95%CI: 33{middle dot}4%-44{middle dot}9%) in Cusco city, 34{middle dot}9% (95%CI: 30{middle dot}4%-40{middle dot}1%) in the periphery of Cusco, and 20{middle dot}3% (95%CI: 16{middle dot}2%-25{middle dot}6%) in Quillabamba. In 141 families (19{middle dot}8%; 95%CI: 17{middle dot}0%-22{middle dot}8%) the whole members were positive to the test. Living with more than three persons in the same house, a positive COVID-19 case at home, and a member who died in the last five months were factors associated with COVID-19 positivity. The smell/taste alteration was the symptom most associated with seropositivity (aOR= 14{middle dot}27, 95% CI: 8{middle dot}24-24{middle dot}70); whereas always wearing a face shield (aOR= 0{middle dot}62; 95% CI: 0{middle dot}46-0{middle dot}84) or a facial mask (aOR= 0{middle dot}65, 95% CI: 0{middle dot}47-0{middle dot}88) reduced that probability.

InterpretationSeroprevalence of COVID-19 in Cusco was high, with significant differences between settings. Wearing masks and face shields were associated with lower rate of infection; however, efforts must be made to sustain them over time since there is still a high proportion of susceptible people.

FundingFondo Nacional de Desarrollo Cientifico, Tecnologico y de Innovacion Tecnologica (FONDECYT - Peru) and Universidad Andina del Cusco.


title: Sword of Damocles or choosing well. Population genetics sheds light into the future of the COVID-19 pandemic and SARS-CoV-2 new mutant strains.

abstract: An immense scientific effort has been made worldwide due to Covid-19s pandemic magnitude. It has made possible to identify almost 300,000 SARS-CoV-2 different genetic variants, connecting them with clinical and epidemiological findings. Among this immense data collection, that constitutes the biggest evolutionary experiment in history, is buried the answer to what will happen in the future. Will new strains, more contagious than the current ones or resistant to the vaccines, arise by mutation? Although theoretic population genetics is, by far, the most powerful tool we have to do an accurate prediction, it has been barely used for the study of SARS-CoV-2 due to its conceptual difficulty. Having in mind that the size of the SARS-CoV-2 population is astronomical we can apply a discrete treatment, based on the branching process method, Fokker-Plank equations and Kolmogoroffs forward equations, to calculate the survival likelihood through time, to elucidate the likelihood to become dominant genotypes and how long will this take, for new SARS-CoV-2 mutants depending on their selective advantage. Results show that most of the new mutants that will arise in the SARS-CoV-2 meta-population will stay at very low frequencies. However, some few new mutants, significantly more infectious than current ones, will still emerge and become dominant in the population favoured by a great selective advantage. Far from showing a "mutational meltdown", SARS-CoV-2 meta-population will increase its fitness becoming more infective. There is a probability, small but finite, that new mutants arise resistant to some vaccines. High infected numbers and slow vaccination programs will significantly increase this likelihood.


title: Estimating Effect-sizes to Infer if COVID-19 transmission rates were low because of Masks, Heat or High because of Air-conditioners, Tests

abstract: How does one interpret the observed increase or decrease in COVID-19 case rates? Did the compliance to the non-pharmaceutical interventions, seasonal changes in the temperature influence the transmission rates or are they purely an artefact of the number of tests? To answer these questions, we estimate the effect-sizes from these different factors on the reproduction ratios (Rt) from the different states of the USA during March 9 to August 9. Ideally Rt should be less than 1 to keep the pandemic under control and our model predicts many of these factors contributed significantly to the Rts: Post-lockdown opening of the restaurants and nightclubs contributed 0.04 (CI 0.04-0.04) and 0.11 (CI. 0.11-0.11) to Rt. The mask mandates helped reduce Rt by 0.28 (CI 0.28-0.29)), whereas the testing rates which may have influenced the number of infections observed, did not influence Rt beyond 10,000 daily tests 0.07 (CI -0.57-0.42). In our attempt to understand the role of temperature, the contribution to the Rt was found to increase on both sides of 55 F, which we infer as a reflection of the climatization needs. A further analysis using the cooling and heating needs showed contributions of 0.24 (CI 0.18-0.31) and 0.31 (CI 0.28-0.33) respectively. The work thus illustrates a data-driven approach for estimating the effect-sizes on the graded policies, and the possibility of prioritizing the interventions, if necessary by weighing the economic costs and ease of acceptance with them.


title: Development and validation of multivariable prediction models for adverse COVID-19 outcomes in IBD patients

abstract:
Structured AbstractO_ST_ABSImportanceC_ST_ABSRisk calculators can facilitate shared medical decision-making1. Demographics, comorbidities, medication use, geographic region, and other factors may increase the risk for COVID-19-related complications among patients with IBD2,3.

ObjectivesDevelop an individualized prognostic risk prediction tool for predicting the probability of adverse COVID-19 outcomes in patients with IBD.

Design, Setting, and ParticipantsThis study developed and validated prognostic penalized logistic regression models4 using reports to Surveillance Epidemiology of Coronavirus Under Research Exclusion for Inflammatory Bowel Disease (SECURE-IBD) from March-October 2020. Model development was done using a training data set (85% of cases reported March 13 - September 15, 2020), and model validation was conducted using a test data set (the remaining 15% of cases plus all cases reported September 16-October 20, 2020).

Main Outcomes and MeasuresCOVID-19 related:

O_LIHospitalization+: composite outcome of hospitalization, ICU admission, mechanical ventilation, or death
C_LIO_LIICU+: composite outcome of ICU admission, mechanical ventilation, or death
C_LIO_LIDeath
C_LI

We assessed the resulting models discrimination using the area under the curve (AUC) of the receiver-operator characteristic (ROC) curves and reported the corresponding 95% confidence intervals (CIs).

ResultsWe included 2709 cases from 59 countries (mean age 41.2 years [s.d. 18], 50.2% male). A total of 633 (24%) were hospitalized, 137 (5%) were admitted to the ICU or intubated, and 69 (3%) died. 2009 patients comprised the training set and 700 the test set.

The models demonstrated excellent discrimination, with a test set AUC (95% CI) of 0.79 (0.75, 0.83) for Hospitalization+, 0.88 (0.82, 0.95) for ICU+, and 0.94 (0.89, 0.99) for Death. Age, comorbidities, corticosteroid use, and male gender were associated with higher risk of death, while use of biologic therapies was associated with a lower risk.

Conclusions and RelevancePrognostic models can effectively predict who is at higher risk for COVID-19-related adverse outcomes in a population of IBD patients. A free online risk calculator (https://covidibd.org/covid-19-risk-calculator/) is available for healthcare providers to facilitate discussion of risks due to COVID-19 with IBD patients. The tool numerically and visually summarizes the patients probabilities of adverse outcomes and associated CIs. Helping physicians identify their highest-risk patients will be important in the coming months as cases rise in the US and worldwide. This tool can also serve as a model for risk stratification in other chronic diseases.

Key PointsO_ST_ABSQuestionC_ST_ABSHow well can a multivariable risk model predict the risk of hospitalization, intensive care unit (ICU) stay, or death due to COVID-19 in patients with inflammatory bowel disease (IBD)?

FindingsMultivariable prediction models developed using data from an international voluntary registry of IBD patients and available for use online (https://covidibd.org/) have very good discrimination for predicting hospitalization (Test set AUC 0.79) and excellent discrimination for ICU admission (Test set AUC 0.88) and death (Test set AUC 0.94). The models were developed with a training sample of 2009 cases and validated in an independent test sample of 700 cases comprised of a random sub-sample of cases and all cases entered in the registry during a one-month period after model development.

MeaningThis risk prediction model may serve as an effective tool for healthcare providers to facilitate conversations about COVID-19-related risks with IBD patients.

category: gastroenterology


title: Off-label use in Palliative Care. More common than expected. A retrospective chart review

abstract:
ObjectiveOff-label drug use seems to be integral to palliative care pharmacotherapy. Balancing potential risks and benefits in the context of limited therapeutic options is challenging. To provide specific support for clinicians in dealing with off-label use, it is essential to understand off-label use in everyday clinical practice.

The aim of this pilot study was to quantify and describe off-label use in a palliative care unit.

MethodsRetrospective chart review of all adult patients treated on a palliative care unit in 10/2017. All data on drug use e.g. indication, dose, route of administration were extracted and matched with the prescribing information. Identified off-label use was subsequently compared with recommendations in the relevant literature. The main outcome measure was frequency and type of off-label drug use.

Results2,352 drug application days (d) and 93 drugs were identified for 28 patients. Of all drugs, 47 (51%) were used off-label at least once. Most off-label uses concerned indication (57%), followed by mode of administration. In drugs highly relevant to palliative care the rate of off-label use was as high as 67%. The extent to which off-label therapy was supported by literature was very variable and ranged from 0 to 88%.

ConclusionsThis single-unit data confirms the high prevalence of off-label use in palliative medicine and demonstrates that off-label use in palliative care is very multifaceted. The data presented allows for a more precise characterization of various aspects of off-label use in order to derive concrete further measures for research and clinical practice.

What is already known on this subjectO_LIOff-label drug use is likely to be common in palliative care, but detailed data is very limited
C_LIO_LIOff-label drug use is a potential threat for patient safety
C_LIO_LIPhysicians state to make therapeutic decisions based on their own experience, due to a lack of available evidence and lack of support in assessment
C_LI

What this study addsO_LIoff-label use in palliative care is multifaceted
C_LIO_LIthe mode of administration (e.g. combination with other drugs in a syringe driver) is beside indication a common reasons for off label use
C_LIO_LIthe proportion of off-label use without sound evidence is high.
C_LI

category: palliative medicine


title: Population-scale patient safety data reveal inequalities in adverse events before and during COVID-19 pandemic

abstract: Adverse patient safety events were associated with 110 thousand deaths in the U.S. alone in 2019. The COVID-19 pandemic has further challenged the ability of healthcare systems to ensure safe medication use, and its effects on patient safety remain unknown. Here, we investigate negative outcomes associated with medication use before and during the pandemic. Using a dataset of 10,443,476 reports involving 3,624 drugs and 19,193 adverse events, we develop an algorithmic approach to analyze the pandemics impact on the incidence of drug safety events by evaluating disproportional reporting relative to the pre-pandemic time, quantifying unexpected trends in clinical outcomes, and adjusting for drug interference. Among 64 adverse events identified by our analyses, we find 54 have increased incidence rates during the pandemic, even though adverse event reporting decreased by 4.4% overall. We find clinically relevant differences in drug safety outcomes between demographic groups. Compared to male patients, women report 47.0% more distinct adverse events whose occurrence significantly increased during the pandemic relative to pre-pandemic levels. Out of 53 adverse events with a pre-pandemic gender gap, 33 have an increased gender gap during the pandemic. While musculoskeletal and metabolic side effects are disproportionately enriched in women during the pandemic, immune-related adverse events are enriched only in men. We also find the number of adverse events with an increased reporting ratio is higher in adults (by 16.8%) than in older patients (adjusted for population size). Our findings have implications for safe medication use and tie the variation in adverse events to patients that may be disproportionately affected by preventable inequities during a public health emergency.

category: pharmacology and therapeutics


title: Effects of Last Dosing Time on Population Pharmacokinetic Analysis of Tacrolimus Performed using Extracted Data from Electronic Health Records

abstract: A population pharmacokinetic (PK) study with 363 subjects was performed using real-world data extracted from electronic heath records (EHRs) to estimate the tacrolimus population PK profile. As population PK studies for oral medications performed using EHR data often assume a regular dosing schedule as prescribed without incorporating exact dosing time, we assessed the sensitivity of the PK parameter estimates to assumptions about dose timing using last-dose times extracted by our own natural language processing system, medExtractR. We also investigated the sensitivity of estimations to absorption rate constants that are often fixed at a published value in tacrolimus population PK analysis. There was no appreciable difference in parameters estimates with vs. without last-dose time incorporated in the data and our sensitivity analysis revealed little difference between parameters estimated assuming a range of absorption rate constants. We also conducted simulation studies to investigate how drug PK profiles and experimental designs such as concentration measurements affects sensitivity to incorrect assumptions about dose timing and absorption rates. Our findings suggest that drugs with a slower elimination rate (or a longer half-life) are less sensitive to dose timing errors and that experimental designs which only allow for trough blood concentrations are usually insensitive to deviation in absorption rate.


title: Sensitivity of Estimated Tacrolimus Population Pharmacokinetic Profile to Inaccurate Assumptions about Dose Timing and Absorption: An Investigation in Real-World and Simulated Data


title: Sleeping under the waves: a longitudinal study across the contagion peaks of COVID-19 pandemic in Italy

abstract:
After the March-April 2020 COVID-19 outbreak, a second contagion wave afflicted Europe in autumn. This study aimed to evaluate sleep health/patterns of Italians during this further challenging situation.

A total of 2013 Italians longitudinally participated in a web-based survey during the two contagion peaks of the COVID-19 outbreak. We investigated the risk factors for sleep disturbances during the second wave, and we compared sleep quality and psychological well-being between the two assessments (March-April and November-December 2020). Female gender, low education, evening chronotype, being at high-risk for COVID-19 infection, reporting negative social or economic impact, and evening smartphone overuse predicted a higher risk of poor sleep and insomnia symptoms during the second wave. Advanced age, living with high-risk subjects for COVID-19 infection, and having a relative/friend infected with COVID-19 before the prior two weeks were risk categories for poor sleep quality. Living with children, having contracted COVID-19 before the prior two weeks, being pessimistic on the vaccine, and working in healthcare were risk factors for insomnia symptoms. The follow-up assessment highlighted reduced insomnia symptoms and anxiety. Nevertheless, we showed reduced sleep duration, higher daytime dysfunction and sleep medication use, and advanced sleep phase, confirming the alarming prevalence of poor sleepers ([~]60%) and severe depression ([~]20%) in a context of increased perceived stress.

This study demonstrated a persistent impact of the COVID-19 pandemic on sleep and mental health. Large-scale interventions to counteract the chronicity and exacerbation of sleep and psychological disturbances are necessary, especially for the risk categories.

category: psychiatry and clinical psychology


title: Improved screening of COVID-19 cases through a Bayesian network symptoms model and psychophysical olfactory test

abstract: The infectiousness and presymptomatic transmission of SARS-CoV-2 hinder pandemic control efforts worldwide. Therefore, the frequency of testing, accessibility, and immediate results are critical for reopening societies until an effective vaccine becomes available for a substantial proportion of the population. The loss of sense of smell is among the earliest, most discriminant, and prevalent symptoms of COVID-19, with 75-98% prevalence when clinical olfactory tests are used. Frequent screening for olfactory dysfunction could substantially reduce viral spread. However, olfactory dysfunction is generally self-reported, which is problematic as partial olfactory impairment is broadly unrecognized. To address this limitation, we developed a rapid psychophysical olfactory test (KOR) deployed on a web platform for automated reporting and traceability based on a low-cost (about USD 0.50/test), six-odor olfactory identification kit. Based on test results, we defined an anosmia score -a classifier for olfactory impairment-, and a Bayesian Network (BN) model that incorporates other symptoms for detecting COVID-19. We trained and validated the BN model on two samples: suspected COVID-19 cases in five healthcare centers (n = 926; 32% COVID-19 prevalence) and healthy (asymptomatic) mining workers (n = 1, 365; 1.1% COVID-19 prevalence). All participants had COVID-19 assessment by RT-PCR assay. Using the BN model, we predicted COVID-19 status with 76% accuracy (AUC=0.79 [0.75 - 0.82]) in the healthcare sample and 84% accuracy (AUC=0.71 [0.63 - 0.79]) among miners. The KOR test and BN model enabled the detection of COVID-19 cases that otherwise appeared asymptomatic. Our results confirmed that olfactory dysfunction is the most discriminant symptom to predict COVID-19 status when based on olfactory function measurements. Overall, this work highlights the potential for low-cost, frequent, accessible, routine testing for COVID-19 surveillance to aid societys reopening.

category: public and global health


title: The Socioeconomic Impact of COVID-19 in Urban Informal Settlements

abstract:
The COVID-19 pandemic has reached almost every corner of the world. Without a pharmaceutical solution, governments have been forced to implement regulations and public policies to control social behavior and prevent the spread of the virus. There is dramatic evidence of the social and economic effects of these measures and their disparate impact on vulnerable communities. Individuals living in urban informal settlements are in a structurally disadvantaged position to cope with a health crisis such as the COVID-19 pandemic. This paper examines the socioeconomic impact of the crisis brought by the pandemic in informal settlements in Chile. We use a three-wave panel study to compare the situation in informal settlements before and during the health crisis. We show that households living in informal settlements are paying a high toll. Their employment loss is dramatic, substantially larger than the loss reported in the general population, and has particularly affected the inmigrant population. We also find that the pandemic has triggered neighborhood cooperation within the settlements. Targeted government assistance programs have reached these communities; however, this groups coverage is not enough to counteract the magnitude of the crisis. Our results suggest that governments, the non-profit sector, and the community need to urgently provide economic support and protections to individuals living in informal settlements and consider this opportunity for long-term improvements in these marginalized communities.

HighlightsO_LIGovernments have implemented large-scale non-pharmaceutical interventions to control the spread of the COVID-19 pandemic
C_LIO_LIThese measures have had dramatic social and economic effects on the population, particularly affecting vulnerable communities
C_LIO_LIIndividuals living in urban informal settlements are in a structurally disadvantaged position to cope with this crisis
C_LIO_LIUsing panel data, we document a dramatic employment loss among informal settlements dwellers, substantially larger than the general population
C_LIO_LIThe pandemic has also triggered neighborhood cooperation within the settlements as well as targeted government assistance, but not enough to counteract the magnitude of the economic loss
C_LI


title: Gaps in maternal, newborn, and child health research: a scoping review of 72 years in Ethiopia

abstract:
ObjectivesDespite significant reductions in maternal and child mortality over the past few decades, a disproportionate number of global deaths occur in low and middle-income country settings, such as Ethiopia. To prioritize research questions that would generate policy recommendations for better outcomes, we conducted a scoping review that gathers the current knowledge of maternal, newborn, and child health (MNCH) and illustrates remaining gaps in Ethiopia.

DesignScoping review.

MethodsWe conducted a search strategy from 1946-2018 in PubMed/MEDLINE, EMBASE, and the WHO African Index Medicus. The study team of reviewers independently screened titles, abstracts, and full-texts; abstracted data; and reconciled differences in pairs. Descriptive analyses were conducted.

ResultsWe identified 7,829 unique articles of which 2,170 were included. Most MNCH publications in Ethiopia (70.0%) were published in the last decade, 2010-2018. Most studies included children aged one to less than 10 years old (30.5%), women of reproductive age (22.0%), and pregnant women (21.9%); fewer studies included newborns (7.0%), infants (6.6%), and postpartum women (2.9%). Research topics included demographics and social determinants of health (43.4%), nutrition (15.3%), and infectious diseases (13.0%). There were limited studies on violence (1.4%), preterm birth (0.8%), antenatal/postpartum depression (0.7%), stillbirths (0.1%), and accidents (0.1%). Most study designs were cross-sectional (53.6%). A few study designs included prospective cohort studies (5.5%) and randomized control trials (2.3%).

ConclusionsThis is the first scoping review to describe the landscape of MNCH research in Ethiopia. Understanding the depth of existing knowledge will support the prioritization and development of future research questions. Additional studies are needed to focus on the neonatal, infant, and postpartum populations as well as preterm and stillbirth outcomes.

STRENGTHS AND LIMITATIONS OF THIS STUDYO_LITo our knowledge, this is the first published review to have comprehensively described or summarized this body of literature in Ethiopia, allowing for a deeper understanding of the expanse of existing knowledge.
C_LIO_LIA quality assessment of each study was not feasible due to the broad range of included topics; however, one may be conducted in the future to further refine results.
C_LIO_LIAlthough conclusions may not be widely generalizable beyond Ethiopia, this review may inform the need to conduct similar reviews for all sub-Saharan countries to support effective prioritization of future research questions.
C_LI


title: EFFICIENT SURVEILLANCE AND TEMPORAL CALIBRATION OF DISEASE RESPONSE

abstract:
AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSDisease surveillance and response are critical components of epidemic preparedness. The disease response, in most cases, is a set of reactive measures that follow the outcomes of the disease surveillance. Hence, efficient surveillance is a prerequisite for an effective response.

Methodology/Principal FindingsWe introduce the principle of epidemiological soundness and utilize it to construct a disease spread network. The network implicitly quantifies the fertility (whether a case leads to secondary cases) and reproduction (number of secondary cases per infectious case) of the cases as well as the size and generations (of the infection chain) of the outbreaks. We empirically confirm that high morbidity relates positively with delay in disease response. Moreover, we identify what constitutes efficient surveillance by applying various thresholds of disease response delay to the network and report their impact on case fertility, reproduction, number of generations and ultimately, outbreak size. Lastly, we identify the components of the disease surveillance system that can be calibrated to achieve the identified efficiency thresholds.

ConclusionWe identify practically achievable, efficient surveillance thresholds (on temporal scale) that lead to an effective response and identify how they can be satisfied. Our approach can be utilized to provide guidelines on spatially and demographically targeted resource allocation for public awareness campaigns as well as to improve diagnostic abilities and turn-around times for the doctors and laboratories involved.

Author SummaryEfficient surveillance and effective response capabilities are pivotal to the prevention and control of the infectious diseases. The disease response is a set of reactive actions that follow the outcomes of the disease surveillance. Ergo, efficient surveillance is a perquisite for the deployment of an effective response. The quantification of the efficiency of a disease surveillance system largely depends on the epidemiological characteristics of the disease. In this paper, we introduce an approach that builds on these characteristics and measures the performance of a disease surveillance system through its impact on the incidence of the disease. Using this approach, we obtain quantitative (on a temporal scale) efficient surveillance thresholds, which if followed by a timely response, lead to a considerable reduction in the disease incidence. Furthermore, we show that these thresholds are practically achievable by identifying the obstacles that lead to less than efficient surveillance outcomes. Our approach can be applied to obtain guidelines on spatially, temporally and demographically targeted resource allocations for public awareness campaigns as well to improve diagnostic ability and turn-around times in treating doctors and pathology labs.


title: Adversarial Attack Vulnerability of Deep Learning Models for Oncologic Images

abstract:
PurposeDeep learning (DL) models have rapidly become a popular and cost-effective tool for image classification within oncology. A major limitation of DL models is output instability, as small perturbations in input data can dramatically alter model output. The purpose of the study is to investigate the robustness of DL models in the oncologic image domain through the application of adversarial images: manipulated images with small pixel-level perturbations designed to assess the stability of DL models.

Experimental DesignWe examined the impact of adversarial images on the classification accuracies of DL models trained to classify cancerous lesions across three common oncologic imaging modalities (CT, mammogram, and MRI). The CT model was trained to classify malignant lung nodules using the LIDC dataset. The mammogram model was trained to classify malignant breast lesions using the DDSM dataset. The MRI model was trained to classify brain metastases using an institutional dataset. We also explored the utility of an iterative adversarial training approach to improve the stability of DL models to small pixel-level changes.

ResultsOncologic images showed instability with small pixel-level changes. A pixel-level of perturbation of .004 resulted in a majority of oncologic images to be misclassified by their respective DL models (CT 25.64%, mammogram 23.93%, MRI 6.36%). Adversarial training mitigated improved the stability and robustness of DL models trained on oncologic images compared to naive models [(CT 67.72% vs 26.92%), mammogram (63.39% vs 27.68%), MRI (87.20% vs 24.32%)].

ConclusionsDL models naively trained on oncologic images exhibited dramatic instability to small pixel-level changes resulting in substantial decreases in accuracy. Adversarial training techniques improved the stability and robustness of DL models to such pixel-level changes. Prior to clinical implementation, adversarial training should be considered to proposed DL models to improve overall performance and safety.

category: radiology and imaging


title: Using Adversarial Images to Assess the Stability of Deep Learning Models Trained on Diagnostic Images in Oncology


title: COVID-19 in 823 Transplant patients: A Systematic Scoping Review

abstract:
BackgroundManagement of COVID-19 in transplant patients is a big challenge. Data on immunosuppression management, clinical picture, and outcomes are lacking.

ObjectivesTo summarize the current literature on COVID-19 in transplant patients especially the data regarding the immunosuppression protocols, clinical presentation, and outcomes.

Search strategyA systematic search of MEDLINE, EBSCO, CENTRAL, CINAHL, LitCovid, Web of Science, and Scopus electronic databases. The references of the relevant studies were also searched. The search was last updated on June 3, 2020.

Selection CriteriaPrimary reports of solid organ transplant patients who developed COVID-19. An overlap of cases in different reports was checked.

Data collection and analysisA descriptive summary of immunosuppression therapy (before and after COVID-19), clinical presentation (symptoms, imaging, laboratory, and disease severity), management (oxygen therapy, antiviral, and antibacterial), major outcomes (Intensive care admission, invasive mechanical ventilation, acute kidney injury), and mortality.

Main resultsWe identified 74 studies reporting 823 cases of solid organ transplantation with COVID-19. Among 372 patients, 114 (30.6%) were mild COVID-19, 101 (27.2%) moderate, and 157 (42.2%) severe or critical.

Major outcomes included intensive care unit admission, invasive ventilation, and acute kidney injury, which occurred in 121 (14.7%), 97 (11.8%), and 63 (7.7%) of patients, respectively. Mortality was reported in 160 (19.4%) patients. Missing individual data hindered making clinical correlations.

ConclusionCOVID-19 in solid organ transplant patients probably has a more disease severity, worse major outcomes (Intensive care admission, invasive ventilation, acute kidney injury), and higher mortality than in non-transplant patients.

category: transplantation


title: Race and ethnicity-related differences in neuroimaging markers of neurodegeneration and cerebrovascular disease in middle and older age

abstract:
INTRODUCTIONNumerous neuroimaging studies demonstrated racial and ethnic disparities in brain health at older ages. It remains unclear whether racial and ethnic disparities increase with aging and whether they are already apparent in midlife.

METHODSWe investigated differences in MRI markers of aging and cerebrovascular disease in 969 participants from the Washington Heights-Inwood Columbia Aging Project (WHICAP; mean age: 75 years) and 496 participants from the Offspring study (mean age: 55 years) across race and ethnicity (white, Black, Latinx).

RESULTSOlder whites had greater cortical thickness compared with Latinxs, who also had greater thickness than Blacks. Cortical thickness was similar across race in the middle-aged cohort. Regarding white matter hyperintensity (WMH) volume, Blacks had disproportionately greater WMH volume compared to both whites and Latinxs at older ages. Racial disparities are already apparent in midlife, where Blacks have disproportionately greater WMH than whites.

DO_SCPLOWISCUSSIONC_SCPLOWThese findings suggest that racial disparities in WMH volume are already apparent in midlife.

category: neurology


title: Complement activity in myasthenia gravis is independent of autoantibody titer and disease severity.

abstract: Acetylcholine receptor (AChR) autoantibodies, found in patients with autoimmune myasthenia gravis (MG), can directly contribute to disease pathology through activation of the classical complement pathway. Accordingly, complement inhibitors are used as a therapeutic strategy, but the response can be heterogeneous even though AChR autoantibodies are present. The mechanisms underlying the variable response are not defined. Yet there is a need for further understanding so that responses can be better predicted. There is a broad spectrum of circulating complement activity levels activity among MG patients. It is not clear whether this activity associates with disease burden or the circulating levels of autoantibodies. We measured complement activity and investigated these associations in MG patients as a means to explore candidate biomarkers. Most study subjects had complement activity within the range defined by healthy controls and no association between this activity and disease burden or AChR autoantibody titer was observed. Assays measuring the complement activating properties of AChR autoantibodies are needed to identifying patients expected to respond to complement inhibitor-based treatments.


title: Cognitive endophenotypes in racially- and ethnically-diverse middle-aged adult offspring aggregate with parental magnetic resonance imaging biomarkers of Alzheimer's disease risk

abstract: A family history of Alzheimers disease (AD) increases risk for AD in an individual by 1.5-to 3-fold. Heritability of AD risk may be due in part to the aggregation of neurodegeneration and cerebrovascular changes with cognitive endophenotypes within families. The purpose of this study was to determine the extent to which cognitive functioning in middle-aged adults is associated with objectively-measured neurodegenerative and cerebrovascular neuroimaging markers linked to risk for clinical AD in their parents, and the extent to which these associations differ by race/ethnicity and language, as proxy variables for social advantage. Middle-aged children enrolled in the Offspring study (n=356; 53.1{+/-}10.1 years old; 13% Non-Hispanic White, 27% Non-Hispanic Black, 26% Latinx tested in English, 34% Latinx tested in Spanish; 65% women; 13.5{+/-}3.4 years education) were administered the NIH Toolbox, a computerized neuropsychological battery, in their preferred language. Older adults were a subset of the Washington-Heights Inwood Columbia Aging Project (77.3{+/-}6.6 years old; 75% women; 10.0{+/-}4.6 years education) who underwent T1w- and T2w-MRI and who had a child enrolled in the Offspring study. We tested the associations of parental MRI measures reflecting neurodegeneration (hippocampal volume, cortical thickness) and cerebrovascular disease (white matter hyperintensity (WMH) volume, presence of infarct) with cognitive tests scores in Offspring participants. We further stratified the models by race/ethnicity. Better offspring cognitive scores aggregated lower parental neurodegeneration and cerebrovascular disease among Non-Hispanic White and Latinx participants, and with lower parental cerebrovascular disease alone among Non-Hispanic Black participants. Associations were generally strongest in Non-Hispanic White participants compared to the other groups. These results suggest a more consistent link between offspring cognitive endophenotype and parental brain health in intergenerational AD transmission among Non-Hispanic White participants compared to racial/ethnic and minority groups in which other social factors may be adding variance.


title: Development and validation of a multiple-choice question-based delirium care knowledge quiz for critical care nurses

abstract:
AimsTo develop and psychometrically test a multiple choice questions (MCQs)-based quiz of delirium care knowledge for critical care nurses.

DesignInstrument development and psychometric evaluation study.

MethodsThe development and validation process including two phases. Phase I focused on the quiz development, conducted by the following steps: (1) generated initial 20-item pool; (2) examined content validity and (3) face validity; (4) conducted pilot testing, data were collected from 217 critical care nurses via online survey during 01 October to 07 November, 2020; (5) performed item analysis and eliminated items based on the item difficulty and discrimination indices. The MCQs quiz was finalised through the development process. Then, phases II emphasised the quiz validation, to estimate the internal consistency, split-half and test-retest reliability, and construct validity using parallel analysis with the exploratory factor analysis (EFA).

ResultsA final 16-item MCQs quiz was emerged from the item analysis. The Kuder- Richardson Formula 20 coefficient for the overall quiz showed good internal consistency (0.85), and the intraclass correlation coefficient with a 30-day interval also indicated that the questionnaire had satisfactory stability (0.96). The EFA confirmed appropriate construct validity for the quiz, four factors could explain the total variance of 60.87%.

ConclusionThis study developed the first MCQs quiz for delirium care knowledge and it is a reliable and valid tool that can be implemented to assess the level of delirium care knowledge.

ImpactThis study offers an evidence-based quiz designed for future research and education purposes in delirium care that has significant implications for knowledge test by using MCQs in clinical practice.

category: nursing


title: The management of Moderate Acute Malnutrition (MAM) in children aged 6-59 months: A systematic review and meta-analysis

abstract:
BackgroundMalnutrition is a leading cause of morbidity and mortality in children aged under five years, especially in low- and middle-income countries (LMICs). Although severe acute malnutrition (SAM) is considered the most serious form of malnutrition, moderate acute malnutrition (MAM) affects greater numbers globally and, unlike SAM, guidelines lack a robust evidence-base. This systematic review and meta-analysis assessed the evidence for lipid-based nutrient supplements (LNS), fortified-blended-flours (FBF) and nutrition counselling, in the treatment of MAM.

MethodsFive databases were systematically searched for studies conducted in LMICs that compared the effectiveness of food-based products versus any comparator group in promoting recovery from MAM in children aged 6-59 months. Where appropriate, pooled estimates of effect were estimated using random-effects meta-analyses.

ResultsA total of thirteen trials were identified for inclusion. All used active controls rather than  standard care, which is often minimal in most settings. There was evidence of increased probability of recovery (as assessed by gaining normal weight-for-height and/or MUAC) among children treated with LNS compared to children treated with FBF (RR 1{middle dot}05, 95%CI 1{middle dot}01-1{middle dot}09, p=0{middle dot}009). Treatment with an LNS was also associated with a lower risk of persistent MAM at the end of treatment compared with a FBF (RR 0{middle dot}82, 95%CI 0{middle dot}71-0{middle dot}95, p=0{middle dot}007).

ConclusionBased on a relatively small number of studies mainly from Africa, LNS are superior to FBF in improving anthropometric recovery from MAM. The true benefit of MAM treatment may be underestimated due to all studies using active controls rather than usual care which is minimal.

More high-quality evidence is needed to evaluate nutrition education/counselling alone as a MAM intervention. Studies should also assess a wider range of outcomes including body composition, morbidity and development - not weight-gain alone.

category: nutrition


title: Impact of Retinopathy and Systemic Vascular Comorbidities on All-Cause Mortality

abstract:
Aims/hypothesisTo investigate the joint effects of retinopathy and systemic vascular comorbidities on mortality.

MethodsThis study included 5703 participants ([≥]40 years old) from the 2005-2008 National Health and Nutrition Examination Survey. The Early Treatment Diabetic Retinopathy Study grading scale was used to evaluate the retinopathy status. Systemic vascular comorbidities included diabetes mellitus (DM), high blood pressure (HBP), chronic kidney disease (CKD) and cardiovascular disease (CVD). Time to death was calculated as the time from baseline to either the date of death or censoring (December 31st, 2015), whichever came first. Risks of mortality were estimated using Cox proportional hazards models.

ResultsAfter adjusting for confounders, the presence of retinopathy predicted higher all-cause mortality (hazard ratio [HR], 1.40; 95% confidence interval [CI], 1.09-1.81). The all-cause mortality among participants with both retinopathy and systemic vascular comorbidities including DM (HR, 1.63; 95% CI, 1.06-2.50), HBP (HR, 1.46; 95% CI, 1.03-2.08), CKD (HR, 1.71; 95% CI, 1.24-2.35) and CVD (HR, 1.88; 95% CI, 1.19-2.96) was significantly higher than that among those without either condition.

Conclusions/interpretationIn this prospective study, individuals with retinopathy had increased all-cause mortality. The joint effects of retinopathy and major systemic vascular comorbidities increased the all-cause mortality further, suggesting that more extensive vascular risk factor assessment and management are needed to detect the burden of vascular pathologies and improve long-term survival in individuals with retinopathy.

Research in contextO_ST_ABSWhat is already known about this subject?C_ST_ABSRetinopathy has been recognized as an independent risk factor for mortality.

What is the key question?What are the joint effects of retinopathy and systemic vascular comorbidities (including diabetes mellitus, hypertension and chronic kidney disease and cardiovascular disease) on mortality?

What are the new findings?Consistent evidence on the increased risk of mortality among individuals with retinopathy was noted in a large sample of middle-aged and older adults.

The co-occurrence of retinopathy and systemic vascular conditions (diabetes mellitus, hypertension and chronic kidney disease and cardiovascular disease) further increased all-cause mortality independent of other covariates.

How might this impact on clinical practice in the foreseeable future?Individuals with retinopathy may benefit from a comprehensive vascular assessment.

Intensive vascular risk reduction is needed in the management of patients with retinopathy and and micro- or macrovascular disorders.

Highlighted the importance of retinopathy screening using retinal imaging for identifying individuals at high risk of mortality, particularly among individuals with systemic vascular comorbidities.

category: ophthalmology


title: Leveraging fine-mapping and non-European training data to improve trans-ethnic polygenic risk scores

abstract: Polygenic risk scores (PRS) based on European training data suffer reduced accuracy in non-European target populations, exacerbating health disparities. This loss of accuracy predominantly stems from LD differences, MAF differences (including population-specific SNPs), and/or causal effect size differences. PRS based on training data from the non-European target population do not suffer from these limitations, but are currently limited by much smaller training sample sizes. Here, we propose PolyPred, a method that improves cross-population polygenic prediction by combining two complementary predictors: a new predictor that leverages functionally informed fine-mapping to estimate causal effects (instead of tagging effects), addressing LD differences; and BOLT-LMM, a published predictor. In the special case where a large training sample is available in the non-European target population (or a closely related population), we propose PolyPred+, which further incorporates the non-European training data, addressing MAF differences and causal effect size differences. PolyPred and PolyPred+ require individual-level training data (for their BOLT-LMM component), but we also propose analogous methods that replace the BOLT-LMM component with summary statistic-based components if only summary statistics are available. We applied PolyPred to 49 diseases and complex traits in 4 UK Biobank populations using UK Biobank British training data (average N=325K), and observed statistically significant average relative improvements in prediction accuracy vs. BOLT-LMM ranging from +7% in South Asians to +32% in Africans (and vs. LD-pruning + P-value thresholding (P+T) ranging from +77% to +164%), consistent with simulations. We applied PolyPred+ to 23 diseases and complex traits in UK Biobank East Asians using both UK Biobank British (average N=325K) and Biobank Japan (average N=124K) training data, and observed statistically significant average relative improvements in prediction accuracy of +24% vs. BOLT-LMM and +12% vs. PolyPred. The summary statistic-based analogues of PolyPred and PolyPred+ attained similar improvements. In conclusion, PolyPred and PolyPred+ improve cross-population polygenic prediction accuracy, ameliorating health disparities.

category: genetic and genomic medicine


title: Leveraging fine-mapping and non-European training data to improve cross-population polygenic risk scores


title: RPE65-related retinal dystrophy: mutational and phenotypic spectrum in 44 affected patients.

abstract:
BackgroundBiallelic pathogenic RPE65 variants are related to a spectrum of clinically overlapping inherited retinal dystrophies (IRD). Most affected individuals show a severe progression, with 50% of patients legally blind by 20 years of age. A better knowledge of the mutational spectrum and the phenotype-genotype correlation in RPE65-related IRD is needed.

MethodsForty-five affected subjects from 27 unrelated families with a clinical diagnosis of RPE65-related IRD were included. Clinical evaluation consisted on self-reported ophthalmological history and objective ophthalmological examination. Patients genotype was classified accordingly to variant class (truncating or missense) or to variant location at different protein domains. Main phenotypic outcome was age at onset (AAO) of the symptomatic disease and a Kaplan-Meier analysis of disease symptom event-free survival was performed.

ResultsTwenty-nine different RPE65 variants were identified in our cohort, 7 of them novel. Most frequent variants were p.(Ile98Hisfs*26), p.(Pro111Ser) and p.(Gly187Glu) accounting for the 24% of the detected alleles. Patients carrying two missense alleles showed a later disease onset than those with 1 or 2 truncating variants (Log Rank test p<0.05). While the 60% of patients carrying a missense/missense genotype presented symptoms before or at the first year of life, almost all patients with at least 1 truncating allele (91%) had an AAO [≤]1 year (p<0.05).

ConclusionOur findings suggest an association between the type of the RPE65 carried variant and the AAO. Thus, our results provide useful data on RPE65-associated IRD phenotypes which may help to improve clinical and therapeutic management of these patients.


title: RPE65-related retinal dystrophy: mutational and phenotypic spectrum in 45 affected patients.


title: Validating quantitative PCR assays for cell-free DNA detection without DNA extraction: Exercise induced kinetics in systemic lupus erythematosus patients

abstract: Circulating cell-free DNA (cfDNA) has been investigated as a screening tool for many diseases. To avoid expensive and time-consuming DNA isolation, direct quantification PCR assays can be established. However, rigorous validation is required to provide reliable data in the clinical and non-clinical context. Considering International Organization for Standardization, as well as bioanalytical method validation guidelines we provide a comprehensive procedure to validate assays for cfDNA quantification from unpurified blood plasma. A 90 and 222 bp assay was validated to study the kinetics of cfDNA after exercise in patients with systemic lupus erythematosus. The assays showed ultra-low limit of quantification (LOQ) with 0.47 and 0.69 ng/ml, repeatability [≤] 11.6% (95% CI: 8.1-20.3), and intermediate precision [≤] 12.1% (95% CI: 9.2-17.7). Incurred sample reanalysis confirmed the precision of the procedure. The additional consideration of pre-analytical factors shows that centrifugation speed and temperature do not change cfDNA concentrations. In SLE patients cfDNA increases [~]2 fold after all out walking exercise, normalizing after 60 min of rest. The established assays allow reliable and cost-efficient quantification of cfDNA in minute amounts of plasma in the clinical setting and can be used as a standard to control pre-analytical factors including cfDNA losses during purification.


title: Analysis of communities of countries with similar dynamics of the COVID-19 pandemic evolution

abstract: This work addresses the spread of the coronavirus through a non-parametric approach, with the aim of identifying communities of countries based on how similar their evolution of the disease is. The analysis focuses on the number of daily new COVID-19 cases per ten thousand people during a period covering at least 250 days after the confirmation of the tenth case. Dynamic analysis is performed by constructing Minimal Spanning Trees (MST) and identifying groups of similarity in contagions evolution in 95 time windows of a 150-day amplitude that moves one day at a time. The number of times countries belonged to a similar performance group in constructed time windows was the intensity measure considered. Groups composition is not stable, indicating that the COVID-19 evolution needs to be treated as a dynamic problem in the context of complex systems. Three communities were identified by applying the Louvain algorithm. Identified communities analysis according to each countrys socioeconomic characteristics and variables related to the disease sheds light on whether there is any suggested course of action. Even when strong testing and tracing cases policies may be related with a more stable dynamic of the disease, results indicate that communities are conformed by countries with diverse characteristics. The best option to counteract the harmful effects of a pandemic may be having strong health systems in place,with contingent capacity to deal with unforeseen events and available resources capable of a rapid expansion of its capacity.

category: health economics


title: Enhancing the Human Health Status Prediction: the ATHLOS Project

abstract: Preventive healthcare is a crucial pillar of health as it contributes to staying healthy and having immediate treatment when needed. Mining knowledge from longitudinal studies has the potential to significantly contribute to the improvement of preventive healthcare. Unfortunately, data originated from such studies are characterized by high complexity, huge volume and a plethora of missing values. Machine Learning, Data Mining and Data Imputation models are utilized as part of solving the aforementioned challenges, respectively. Towards this direction, we focus on the development of a complete methodology for the ATHLOS (Ageing Trajectories of Health: Longitudinal Opportunities and Synergies) Project - funded by the European Unions Horizon 2020 Research and Innovation Program, which aims to achieve a better interpretation of the impact of aging on health. The inherent complexity of the provided dataset lie in the fact that the project includes 15 independent European and international longitudinal studies of aging. In this work, we particularly focus on the HealthStatus (HS) score, an index that estimates the human status of health, aiming to examine the effect of various data imputation models to the prediction power of classification and regression models. Our results are promising, indicating the critical importance of data imputation in enhancing preventive medicines crucial role.

category: health informatics


title: State-of-the-Art Risk Models for Diabetes, Hypertension, Visual Diminution, and COVID-19 Severity in Mexico

abstract:
BACKGROUNDDiabetes and hypertension are among top public health priorities, particularly in low and middle-income countries where their health and socioeconomic impact is exacerbated by the quality and accessibility of health care. Moreover, their connection with severe or deadly COVID-19 illness has further increased their societal relevance. Tools for early detection of these chronic diseases enable interventions to prevent high-impact complications, such as loss of sight and kidney failure. Similarly, prognostic tools for COVID-19 help stratify the population to prioritize protection and vaccination of high-risk groups, optimize medical resources and tests, and raise public awareness.

METHODSWe developed and validated state-of-the-art risk models for the presence of undiagnosed diabetes, hypertension, visual complications associated with diabetes and hypertension, and the risk of severe COVID-19 illness (if infected). The models were estimated using modern methods from the field of statistical learning (e.g., gradient boosting trees), and were trained on publicly available data containing health and socioeconomic information representative of the Mexican population. Lastly, we assembled a short integrated questionnaire and deployed a free online tool for massifying access to risk assessment.

RESULTSOur results show substantial improvements in accuracy and algorithmic equity (balance of accuracy across population subgroups), compared to established benchmarks. In particular, the models: i) reached state-of-the-art sensitivity and specificity rates of 90% and 56% (0.83 AUC) for diabetes, 80% and 64% (0.79 AUC) for hypertension, 90% and 56% (0.84 AUC) for visual diminution as a complication, and 90% and 60% (0.84 AUC) for development of severe COVID disease; and ii) achieved substantially higher equity in sensitivity across gender, indigenous/non-indigenous, and regional populations. In addition, the most relevant features used by the models were in line with risk factors commonly identified by previous studies. Finally, the online platform was deployed and made accessible to the public on a massive scale.

CONCLUSIONSThe use of large databases representative of the Mexican population, coupled with modern statistical learning methods, allowed the development of risk models with state-of-the-art accuracy and equity for two of the most relevant chronic diseases, their eye complications, and COVID-19 severity. These tools can have a meaningful impact on democratizing early detection, enabling large-scale preventive strategies in low-resource health systems, increasing public awareness, and ultimately raising social well-being.


title: Impact of Public Health Education Program on the Novel Coronavirus Outbreak in the United States

abstract: The coronavirus outbreak in the United States continues to pose a serious threat to human lives. Public health measures to slow down the spread of the virus involve using a face mask, social-distancing, and frequent hand washing. Since the beginning of the pandemic, there has been a global campaign on the use of non-pharmaceutical interventions (NPIs) to curtail the spread of the virus. However, the number of cases, mortality, and hospitalization continue to rise globally, including in the United States. We developed a mathematical model to assess the impact of a public health education program on the coronavirus outbreak in the US. Our simulation showed the prospect of an effective public health education program in reducing both the cumulative and daily mortality of the novel coronavirus. Finally, our result suggests the need to obey public health measures as loss of willingness would increase the cumulative and daily mortality in the US.

category: health policy


title: Hidden Parameters impacting resurgence of SARS-CoV-2 Pandemic

abstract:
The spread of the COVID-19 virus has had an enormous impact on the worlds health and socioeconomic system. While lockdowns, which severely limit the movement of the population, have been implemented in March 2020 and again recently, the psychological and economic cost are severe. Removal of these restrictions occurred with varying degrees of success.

To study the resurgence of the virus in some communities we consider an epidemiological model, SIR-SD-L, that incorporates introduction of new population due to the removal of lockdown and identify parameters that impact the spread of the virus. This compartmental model of the epidemic incorporates a social distance metric based on progression of the infections; it models the dynamic propensity of infection spread based on the current infections relative to the susceptible population. The model is validated using data on growth of infections, hospitalizations and death, considering 24 counties in multiple US states and a categorization of the lockdown removal policies after the first lockdown. Model parameters, which include a compartment for the isolated population, are used to determine the rate at which the susceptible population increases to fit the rate of infections. Along with social distancing mandates, we identify active infections and the susceptible population as important factors in the resurgence of infections. We measure the efficacy of the lockdown removal policy via a ratio, PIR, which evaluates to less than 1 for counties where social distancing measures were mandated and which delayed complete re-opening of closed spaces like bars and restaurants. For other counties this ratio is greater than 1.

We also studied infection growth in the 24 US counties with respect to a release policy derived from CDC guidelines and compared against strategies that delay the removal of lockdown.

Our results illustrate that guidelines for deciding when lockdown rules are to be relaxed should consider the current state of the infectious population and the remaining susceptible population, hidden parameters that are deducible from models such as SIR-SD-L, and not limit policy considerations to the rate of new infections alone. This is especially true for counties where the growth rate of the virus is initially slow and misleading. Emphasis on social distancing is critical.


title: Optimizing SARS-CoV-2 vaccination strategies in France: Results from a stochastic agent-based model

abstract: The COVID-19 pandemic is a major global societal, economic and health threat. The availability of COVID-19 vaccines has raised hopes for a decline in the pandemic. We built upon a stochastic agent-based microsimulation model of the COVID-19 epidemic in France. We examined the potential impact of different vaccination strategies, defined according to the age, medical conditions, and expected vaccination acceptance of the target non-immunized adult population, on disease cumulative incidence, mortality, and number of hospital admissions. Specifically, we examined whether these vaccination strategies would allow to lift all non-pharmacological interventions (NPIs), based on a sufficiently low cumulative mortality and number of hospital admissions. While vaccinating the full adult non-immunized population, if performed immediately, would be highly effective in reducing incidence, mortality and hospital-bed occupancy, and would allow discontinuing all NPIs, this strategy would require a large number of vaccine doses. Vaccinating only adults at higher risk for severe SARS-CoV-2 infection, i.e. those aged over 65 years or with medical conditions, would be insufficient to lift NPIs. Immediately vaccinating only adults aged over 45 years, or only adults aged over 55 years with mandatory vaccination of those aged over 65 years, would enable lifting all NPIs with a substantially lower number of vaccine doses, particularly with the latter vaccination strategy. Benefits of these strategies would be markedly reduced if the vaccination was delayed, was less effective than expected on virus transmission or in preventing COVID-19 among older adults, or was not widely accepted.


title: An Evaluation of Methods for Monitoring Annual Quality Measures by Month to Predict Year-End Values

abstract:
BackgroundAn increasing number of healthcare quality measures are designed for annual reporting. These measures require an entire year of data to accurately report the percentage of patients who met the measure. Annual measures give providers latitude to prioritize clinical workload and patient needs; however, they do not provide a direct means to monitor performance throughout the reporting year. Although there are many possible methods for measuring annual measures at finer-grained timescales, our applied work showed that the most obvious methods could give a misleading and inaccurate view of progress throughout the year. Neither the definitions of the annual measures, nor the research literature, provided any guidance on the best methods for interim monitoring of annual measures.

ObjectiveOur objective was to evaluate four different methods for monitoring annually reported quality measures monthly to best predict year-end performance throughout the reporting year.

MethodsWe developed four methods for monitoring annual measures by month: 1) Monthly Proportion: The proportion of patients with one or more encounters in the month who still needed to meet the measure at their first encounter of the month and met the measure by the end of the month; (2) Monthly Lookback Proportion: The proportion of patients seen in the month who met the measure by the end of the month, regardless of whether it was met in that month or previously in the reporting year; (3) Rolling 12 Month: The annual measure reported as if each month was the twelfth month of a twelve-month reporting period; and (4) YTD (Year-to-Date) Cumulative: The proportion of patients with one or more visits from the start of the reporting year through the month who satisfy the measure. We applied each method to two annual dental quality measures using data from two reporting years, and four different dental sites. We used mean squared error (MSE) to evaluate year-end predictive performance.

ResultsMethod 3 (Rolling 12 Month) had the lowest MSE in 11 out of 16 cases (2 measures X 2 years X 4 sites) and lowest total MSE (262.39) across all 16 cases. In 5 of the 16 cases, YTD Cumulative had the lowest MSE.

ConclusionsThe Rolling 12 Month method was best for predicting the year-end value across both measures and all four sites.

category: health systems and quality improvement


title: Issues of Random Sampling with Rapid Antigen Tests for COVID-19 Diagnosis: A Special Reference to Kalmunai RDHS Division

abstract: Random sampling from the community and performing the rapid antigen test has become a debatable issue during this COVID-19 pandemic. This writing analyzes the concerns using the data from Kalmunai RDHS Division, Sri Lanka, related to COVID-19 and shows the issues of random sampling in a community wherein the incident rate is small. Therefore, suitable sampling protocol is to be developed for performing rapid antigen test for COVID-19 diagnosis.


title: City Reduced Probability of Infection (CityRPI) for Indoor Airborne Transmission of SARS-CoV-2 and Urban Building Energy Impacts

abstract: Airborne transmission of aerosols produced by asymptomatic individuals is a large portion of the SARS-CoV-2 spread indoors. Outdoor air ventilation rate, air filtration, room occupancy, exposure time, and mask-wearing are among the key parameters that affect its airborne transmission in indoor spaces. In this work, we developed a new web-based platform, City Reduced Probability of Infection - CityRPI, to calculate the indoor airborne transmission of COVID-19 in various buildings of a city scale. An archetype library of twenty-nine building types is developed based on several standards and references. Among the mitigation strategies recommended to reduce infection risk, some could result in significant energy impacts on buildings. To study the combined effects of energy consumption and reduced infection probability, we integrated CityRPI with City Building Energy Model. We applied the integrated model to Montreal City and studied the impact of six mitigation measures on the infection risk and peak energy demand in winter. It shows that the same strategy could perform quite differently, depending on building types and properties. In the winter season, increasing the outdoor air ventilation rate may cause massive building energy consumption. All strategies are shown to reduce the infection risk but wearing a mask and reducing exposure time are the most effective strategies in many buildings, with around 60% reduction. Doubling the outdoor air ventilation rate is not as effective as other strategies to reduce the risk with less than 35% reduction. It also significantly increases building peak heating demand with 10-60%.

category: infectious diseases


title: Emergence of a novel SARS-CoV-2 strain in Southern California, USA

abstract: Since October 2020, novel strains of SARS-CoV-2 including B.1.1.7, have been identified to be of global significance from an infection and surveillance perspective. While this strain (B.1.1.7) may play an important role in increased COVID rates in the UK, there are still no reported strains to account for the spike of cases in Los Angeles (LA) and California as a whole, which currently has some of the highest absolute and per-capita COVID transmission rates in the country. From the early days of the pandemic when LA only had a single viral genome uploaded onto GISAID we have been at the forefront of generating and analyzing the SARS-CoV-2 sequencing data from the LA region. We report a novel strain emerging in Southern California. Most current cases in the catchment population in LA fall into two distinct subclades: 1) 20G (24% of total) is the predominant subclade currently in the United States 2) a relatively novel strain in clade 20C, CAL.20C strain ([~]36% of total) is defined by five concurrent mutations. After an analysis of all of the publicly available data and a comparison to our recent sequences, we see a dramatic growth in the relative percentage of the CAL.20C strain beginning in November of 2020. The predominance of this strain coincides with the increased positivity rate seen in this region. Unlike 20G, this novel strain CAL.20C is defined by multiple mutations in the S protein, a characteristic it shares with both the UK and South African strains, both of which are of significant clinical and scientific interest


title: BAYESIAN GROUP TESTING WITH DILUTION EFFECTS

abstract: SO_SCPLOWUMMARYC_SCPLOWA Bayesian framework for group testing under dilution effects has been developed, using latticebased models. This work has particular relevance given the pressing public health need to enhance testing capacity for COVID-19 and future pandemics, and the need for wide-scale and repeated testing for surveillance under constantly varying conditions. The proposed Bayesian approach allows for dilution effects in group testing and for general test response distributions beyond just binary outcomes. It is shown that even under strong dilution effects, an intuitive group testing selection rule that relies on the model order structure, referred to as the Bayesian halving algorithm, has attractive optimal convergence properties. Analogous look-ahead rules that can reduce the number of stages in classification by selecting several pooled tests at a time are proposed and evaluated as well. Group testing is demonstrated to provide great savings over individual testing in the number of tests needed, even for moderately high prevalence levels. However, there is a trade-off with higher number of testing stages, and increased variability. A web-based calculator is introduced to assist in weighing these factors and to guide decisions on when and how to pool under various conditions. High performance distributed computing methods have also been implemented for considering larger pool sizes, when savings from group testing can be even more dramatic.


title: Clinical utility of Corona Virus Disease-19 serum IgG, IgM, and neutralizing antibodies and inflammatory markers

abstract: Most deaths from severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection occur in older subjects. We assessed age effects and clinical utility of serum SARS-CoV-2 immunoglobulin G (IgG), immunoglobulin M (IgM), and neutralizing antibodies and serum inflammatory markers. Serum IgG, IgM, and neutralizing antibody levels were measured using chemiluminescence assays from Diazyme (Poway, CA), while serum interleukin-6 (IL-6), C reactive protein (CRP), and ferritin were measured with immunoassays obtained from Roche (Indianapolis, IN). In 79,005 subjects, IgG and IgM levels were positive ([≥]1.0 arbitrary units [AU]/mL) in 5.29% and 3.25% of subjects, respectively. In antibody positive subjects, median IgG levels were 3.93 AU/mL if <45 years of age, 10.18 AU/mL if 45-64 years of age, and 10.85 AU/mL if [≥]65 years of age (p<0.0001). In SARS-CoV-2 RNA positive cases, family members and exposed subjects (n=1,111), antibody testing was found to be valuable for case finding, and persistent IgM levels were associated with chronic symptoms. In non-hospitalized and hospitalized subjects assessed for SARS-CoV-2 RNA (n=278), median IgG levels in AU/mL were 0.05 in negative subjects (n=100), 14.83 in positive outpatients (n=129), and 30.61 in positive hospitalized patients (n=49, p<0.0001). Neutralizing antibody levels correlated significantly with IgG (r=0.875; p<0.0001). Two or more of the criteria of IL-6 [≥]10 pg/mL, CRP [≥]10 mg/L, and/or IgM >1.0 AU/mL occurred in 97.7% of inpatients versus 1.8% of outpatients (>50-fold relative risk, C statistic 0.986, p<0.0001). Our data indicate that: 1) IgG levels are significantly higher in positive older subjects, possibly to compensate for decreased cellular immunity with aging; 2) IgG levels are important for case finding in family clusters; 3) IgG levels are significantly correlated with neutralizing antibody levels; 4) persistently elevated IgM levels are associated with chronic disease; and 5) markedly elevated IL-6, hs-CRP, and/or positive IgM accurately identify SARS-CoV-2 RNA positive subjects requiring hospitalization.


title: The Role of Disease Severity and Demographics in the Clinical Course of COVID-19 Patients Treated with Convalescent Plasma

abstract: Treatment of patients with COVID-19 using convalescent plasma from recently recovered patients has been shown to be safe, but the time course of change in clinical status following plasma transfusion in relation to baseline disease severity has not yet been described. We analyzed short, descriptive daily reports of patient status in 7,180 hospitalized recipients of COVID-19 convalescent plasma in the Mayo Clinic Expanded Access Program. We assessed, from the day following transfusion, whether the patient was categorized by his or her physician as better, worse or unchanged compared to the day before, and whether, on the reporting day, the patient received mechanical ventilation, was in the ICU, had died or had been discharged. Most patients improved following transfusion, but clinical improvement was most notable in mild to moderately ill patients. Patients classified as severely ill upon enrollment improved, but not as rapidly, while patients classified as critically ill/end-stage and patients on ventilators showed worsening of disease status even after treatment with convalescent plasma. Patients age 80 and over showed little or no clinical improvement following transfusion. Clinical status at enrollment and age appear to be the primary factors in determining the therapeutic effectiveness of COVID-19 convalescent plasma among hospitalized patients.


title: Learning from pandemics: using extraordinary events can improve disease now-casting models

abstract: Online searches have been used to study different health-related behaviours, including monitoring disease outbreaks. An obvious caveat is that several reasons can motivate individuals to seek online information and models that are blind to peoples motivations are of limited use and can even mislead. This is particularly true during extraordinary public health crisis, such as the ongoing pandemic, when fear, curiosity and many other reasons can lead individuals to search for health-related information, masking the disease-driven searches. However, health crisis can also offer an opportunity to disentangle between different drivers and learn about human behavior. Here, we focus on the two pandemics of the 21st century (2009-H1N1 flu and Covid-19) and propose a methodology to discriminate between search patterns linked to general information seeking (media driven) and search patterns possibly more associated with actual infection (disease driven). We show that by learning from such pandemic periods, with high anxiety and media hype, it is possible to select online searches and improve model performance both in pandemic and seasonal settings. Moreover, and despite the common claim that more data is always better, our results indicate that lower volume of the right data can be better than including large volumes of apparently similar data, especially in the long run. Our work provides a general framework that can be applied beyond specific events and diseases, and argues that algorithms can be improved simply by using less (better) data. This has important consequences, for example, to solve the accuracy-explainability trade-off in machine-learning.


title: NEWS2 and laboratory predictors correlated with clinical deterioration in hospitalised patients with COVID-19

abstract:
BackgroundWe aimed to determine prognostic values of NEWS2 and laboratory parameters during the first week of COVID-19.

MethodsAll adult patients who were hospitalized for a confirmed COVID-19 between the 11th of March and the 11th of May 2020 were retrospectively included. To evaluate the factors in prognosis which are admission to intensive care unit (ICU) and in-hospital death, univariate logistic regression analysis was performed at admission (D0), at day-3 (D3), day-5 (D5), and day-7 (D7). Additionally, receiver operating characteristic (ROC) analyses were performed.

ResultsOverall, 611 patients were included. Clinical deterioration was observed in 79 (12.9%) patients during hospitalisation, 36 (5.9%) during the first three days, 54 (8.8%) during the first five days, and 62 (10.1%) during the first week of hospitalisation. Our results showed that NEWS2, procalcitonin, neutrophil/lymphocyte ratio (NLR), and albumin were the best predictors for clinical deterioration at D0, D3, D5, and D7. Procalcitonin had the highest odds ratio for clinical deterioration on all days in univariate analysis. ROC analyses showed that NEWS2 at D7, procalcitonin at D5, albumin at D7, and NLR at D5 had highest AUC values. Additionally, we detected a strong correlation between NEWS2 and laboratory parameters including neutrophil, lymphocyte, NLR, platelet/lymphocyte ratio, CRP, procalcitonin, ferritin, and urea on all days.

ConclusionThis study provides a list of several laboratory parameters correlated with NEWS2 and potential predictors for ICU admission or in-hospital death during the clinical course of COVID-19. Dynamic monitoring of NEWS2 and laboratory parameters is vital for improving clinical outcomes.


title: Endothelial cell-activating antibodies in COVID-19

abstract:
ObjectiveWhile endothelial dysfunction has been implicated in the widespread thrombo-inflammatory complications of coronavirus disease-19 (COVID-19), the upstream mediators of endotheliopathy remain for the most part cryptic. Our aim was to identify circulating factors contributing to endothelial cell activation and dysfunction in COVID-19.

MethodsHuman endothelial cells were cultured in the presence of serum or plasma from 244 patients hospitalized with COVID-19 and plasma from 100 patients with non-COVID sepsis. Cell adhesion molecules (E-selectin, VCAM-1, and ICAM-1) were quantified by in-cell ELISA.

ResultsSerum and plasma from patients with COVID-19 increased surface expression of cell adhesion molecules. Furthermore, levels of soluble ICAM-1 and E-selectin were elevated in patient serum and tracked with disease severity. The presence of circulating antiphospholipid antibodies was a strong marker of the ability of COVID-19 serum to activate endothelium. Depletion of total IgG from antiphospholipid antibody-positive serum markedly restrained upregulation of cell adhesion molecules. Conversely, supplementation of control serum with patient IgG was sufficient to trigger endothelial activation.

ConclusionThese data are the first to suggest that some patients with COVID-19 have potentially diverse antibodies that drive endotheliopathy, adding important context regarding thrombo-inflammatory effects of autoantibodies in severe COVID-19.


title: Genomics of Aminoglycoside Resistance in Pseudomonas aeruginosa Bloodstream Infections at a United States Academic Hospital

abstract: Pseudomonas aeruginosa is a frequent cause of antibiotic-resistant infections. Although P. aeruginosa is intrinsically resistant to many antimicrobial agents, aminoglycosides are active against this organism in the absence of acquired resistance determinants and mutations. However, genes encoding aminoglycoside modifying enzymes (AMEs) are found in many strains that are resistant to these agents. We examined the prevalence of phenotypic resistance to the commonly used aminoglycosides gentamicin, tobramycin, and amikacin in a collection of 227 P. aeruginosa bloodstream isolates collected over two decades from a single U.S. academic medical institution. Resistance to these antibiotics was relatively stable over this time period. High-risk clones ST111 and ST298 were initially common but decreased in frequency over time. Whole genome sequencing identified relatively few AME genes in this collection compared to the published literature; only 14% of isolates contained an AME gene other than the ubiquitous aph(3)-IIb. Of those present, only ant(2")-Ia was associated with phenotypic resistance to gentamicin or to tobramycin. One extensively drug-resistant strain, PS1871, contained 5 AME genes, most of which were part of clusters of antibiotic resistance genes embedded within transposable elements. These findings suggest that AME genes play a relatively minor role in aminoglycoside resistance at our institution but that multidrug-resistant strains remain a problem.


title: Blood transcriptional biomarkers of acute viral infection for detection of pre-symptomatic SARS-CoV 2 infection

abstract: We hypothesised that host-response biomarkers of viral infections may contribute to early identification of SARS-CoV-2 infected individuals, critical to breaking chains of transmission. We identified 20 candidate blood transcriptomic signatures of viral infection by systematic review and evaluated their ability to detect SARS-CoV-2 infection, compared to the gold-standard of virus PCR tests, among a prospective cohort of 400 hospital staff subjected to weekly testing when fit to attend work. The transcriptional signatures had limited overlap, but were mostly co-correlated as components of type 1 interferon responses. We reconstructed each signature score in blood RNA sequencing data from 41 individuals over sequential weeks spanning a first positive SARS-CoV-2 PCR, and after 6-month convalescence. A single blood transcript for IFI27 provided the highest accuracy for discriminating individuals at the time of their first positive viral PCR result from uninfected controls, with area under the receiver operating characteristic curve (AUROC) of 0.95 (95% confidence interval 0.91-0.99), sensitivity 0.84 (0.7-0.93) and specificity 0.95 (0.85-0.98) at a predefined test threshold. The test performed equally well in individuals with and without symptoms, correlated with viral load, and identified incident infections one week before the first positive viral PCR with sensitivity 0.4 (0.17-0.69) and specificity 0.95 (0.85-0.98). Our findings strongly support further urgent evaluation and development of blood IFI27 transcripts as a biomarker for early phase SARS-CoV-2 infection, for screening individuals such as contacts of index cases, in order to facilitate early case isolation and early antiviral treatments as they emerge.


title: Factors associated with deaths due to COVID-19 versus other causes: population-based cohort analysis of UK primary care data and linked national death registrations within the OpenSAFELY platform

abstract:
BackgroundMortality from COVID-19 shows a strong relationship with age and pre-existing medical conditions, as does mortality from other causes. However it is unclear how specific factors are differentially associated with COVID-19 mortality as compared to mortality from other causes.

MethodsWorking on behalf of NHS England, we carried out a cohort study within the OpenSAFELY platform. Primary care data from England were linked to national death registrations. We included all adults (aged [≥]18 years) in the database on 1st February 2020 and with >1 year of continuous prior registration, the cut-off date for deaths was 9th November 2020. Associations between individual-level characteristics and COVID-19 and non-COVID deaths were estimated by fitting age- and sex-adjusted logistic models for these two outcomes.

Results17,456,515 individuals were included. 17,063 died from COVID-19 and 134,316 from other causes. Most factors associated with COVID-19 death were similarly associated with non-COVID death, but the magnitudes of association differed. Older age was more strongly associated with COVID-19 death than non-COVID death (e.g. ORs 40.7 [95% CI 37.7-43.8] and 29.6 [28.9-30.3] respectively for [≥]80 vs 50-59 years), as was male sex, deprivation, obesity, and some comorbidities. Smoking, history of cancer and chronic liver disease had stronger associations with non-COVID than COVID-19 death. All non-white ethnic groups had higher odds than white of COVID-19 death (OR for Black: 2.20 [1.96-2.47], South Asian: 2.33 [2.16-2.52]), but lower odds than white of non-COVID death (Black: 0.88 [0.83-0.94], South Asian: 0.78 [0.75-0.81]).

InterpretationSimilar associations of most individual-level factors with COVID-19 and non-COVID death suggest that COVID-19 largely multiplies existing risks faced by patients, with some notable exceptions. Identifying the unique factors contributing to the excess COVID-19 mortality risk among non-white groups is a priority to inform efforts to reduce deaths from COVID-19.

FundingWellcome, Royal Society, National Institute for Health Research, National Institute for Health Research Oxford Biomedical Research Centre, UK Medical Research Council, Health Data Research UK.


title: A rapid pharmacogenomic assay to detect NAT2 polymorphisms and guide isoniazid dosingfor tuberculosis treatment

abstract:
RationaleStandardized weight-based dose of anti-tubercular drugs contributes to a substantial incidence of toxicities, inadequate treatment response, and relapse, in part due to variable drug levels achieved. Single nucleotide polymorphisms (SNPs) in the N-acetyltransferase-2 (NAT2) gene explain the majority of interindividual pharmacokinetic variability of isoniazid (INH). However, an obstacle to implementing pharmacogenomic-guided dosing is the lack of a point-of-care assay.

ObjectivesTo develop and test a NAT2 classification algorithm, validate its performance in predicting isoniazid clearance, and develop a prototype pharmacogenomic assay.

MethodsWe trained random forest models to predict NAT2 acetylation genotype from unphased SNP data using a global collection of 8,561 phased genomes. We enrolled 48 pulmonary TB patients, performed sparse pharmacokinetic sampling, and tested the acetylator prediction algorithm accuracy against estimated INH clearance. We then developed a cartridge-based multiplex qPCR assay on the GeneXpert platform and assessed its analytical sensitivity on whole blood samples from healthy individuals.

Measurements and Main ResultsWith a 5-SNP model trained on two-thirds of the data (n=5,738), out-of-sample acetylation genotype prediction accuracy on the remaining third (n=2,823) was 100%. Among the 48 TB patients, predicted acetylator types were: 27 (56.2%) slow, 16 (33.3%) intermediate and 5 (10.4%) rapid. INH clearance rates were lowest in predicted slow acetylators (median 19.3 L/hr), moderate in intermediate acetylators (median 41.0 L/hr) and highest in fast acetylators (median 46.7 L/hr). The cartridge-based assay accurately detected all allele patterns directly from 25ul of whole blood.

ConclusionsAn automated pharmacogenomic assay on a platform widely used globally for tuberculosis diagnosis could enable personalized dosing of isoniazid.

SummaryThis manuscript describes the development and validation of point-of-care multiplex pharmacogenomic assay to guide personalized dosing of isoniazid for treatment or prevention of tuberculosis.


title: A rapid pharmacogenomic assay to detect NAT2 polymorphisms and guide isoniazid dosing for tuberculosis treatment


title: Viral dispersion in open air

abstract: The SARS-CoV-2 pandemic has revived the debate about the routes of virus transmission and their likelihoods. It is of utmost importance to assess the risks of contamination of susceptible people by infectious individuals and to evaluate the level of viral transmission in the community. Most countries have imposed non-pharmaceutical measures to contain SARS-CoV-2 transmission, including social distancing and mask wearing. Here we evaluated the spreading of viruses in open air using harmless Escherichia coli bacteriophages as a surrogate. Phages were sprayed towards Petri dishes seeded with bacteria at different lengths and angles. Median droplets size was 127 {micro}m, similar to those produced by sneeze. Our results showed that the transmission rate decreased exponentially with distance. The highest recorded transmission rate was 9 x 10-6 PFU/plate when phages were sprayed from a 1 m distance, suggesting that the probability of transmission of a single virus at a 1 m distance is 1:100,000. These results agree with the WHO recommendation that face mask protection in an uncrowded well-ventilated space is not required.


title: Meta-Analysis of Robustness of COVID-19 Diagnostic Kits During Early Pandemic

abstract:
BackgroundAccurate detection of severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) is necessary to mitigate the coronavirus disease-19 (COVID-19) pandemic. However, the test reagents and assay platforms are varied and may not be sufficiently robust to diagnose COVID-19.

MethodsWe reviewed 85 studies (21,530 patients), published from five regions of the world, to highlight issues involved in the diagnosis of COVID-19 in the early phase of the pandemic, following the standards outlined in the PRISMA statement. All relevant articles, published up to May 31, 2020, in PubMed, BioRiXv, MedRiXv, and Google Scholar, were included. We evaluated the qualitative (9749 patients) and quantitative (10,355 patients) performance of RT-PCR and serologic diagnostic tests for real-world samples, and assessed the concordance (5,538 patients) between methods in meta-analyses.

ResultsThe RT-PCR tests exhibited heterogeneity in the primers and reagents used. Of 1,957 positive RT-PCR COVID-19 participants, 1,585 had positive serum antibody (IgM +/- IgG) tests (sensitivity 0.81, 95%CI 0.66-.90). While 3,509 of 3581 participants RT-PCR negative for COVID-19 were found negative by serology testing (specificity 0.98, 95%CI 0.94-0.99). The chemiluminescent immunoassay exhibited the highest sensitivity, followed by ELISA and lateral flow immunoassays. Serology tests had higher sensitivity and specificity for laboratory-approval than for real-world reporting data.

ConclusionsThe robustness of the assays/platforms is influenced by variability in sampling and reagents. Serological testing complements and may minimize false negative RT-PCR results. Lack of standardized assay protocols in the early phase of pandemic might have contributed to the spread of COVID-19.


title: Inactivation of SARS-CoV-2 virus in saliva using a guanidium based transport medium suitable for RT-PCR diagnostic assays

abstract:
BackgroundUpper respiratory samples used to test for SARS-CoV-2 virus may be infectious and present a hazard during transport and testing. A buffer with the ability to inactivate SARS-CoV-2 at the time of sample collection could simplify and expand testing for COVID-19 to non-conventional settings.

MethodsWe evaluated a guanidium thiocyanate-based buffer, eNAT (Copan) as a possible transport and inactivation medium for downstream RT-PCR testing to detect SARS-CoV-2. Inactivation of SARS-CoV-2 USA-WA1/2020 in eNAT and in diluted saliva was studied at different incubation times. The stability of viral RNA in eNAT was also evaluated for up to 7 days at room temperature (28{degrees}C), refrigerated conditions (4{degrees}C) and at 35{degrees}C.

ResultsSARS-COV-2 virus spiked directly in eNAT could be inactivated at >5.6 log10 PFU/ml within a minute of incubation. When saliva was diluted 1:1 in eNAT, no cytopathic effect (CPE) on vero-E6 cell lines was observed, although SARS-CoV-2 RNA could be detected even after 30 min incubation and after two cell culture passages. A 1:2 (saliva:eNAT) dilution abrogated both CPE and detectable viral RNA after as little as 5 min incubation in eNAT. SARS-CoV-2 RNA from virus spiked at 5X the limit of detection remained positive up to 7 days of incubation in all tested conditions.

ConclusioneNAT and similar guanidinium thiocyanate-based media may be of value for transport, preservation, and processing of clinical samples for RT-PCR based SARS-CoV-2 detection.


title: Performance of intensive care unit severity scoring systems across different ethnicities.

abstract:
BackgroundDespite wide utilisation of severity scoring systems for case-mix determination and benchmarking in the intensive care unit, the possibility of scoring bias across ethnicities has not been examined. Recent guidelines on the use of illness severity scores to inform triage decisions for allocation of scarce resources such as mechanical ventilation during the current COVID-19 pandemic warrant examination for possible bias in these models. We investigated the performance of three severity scoring systems (APACHE IVa, OASIS, SOFA) across ethnic groups in two large ICU databases in order to identify possible ethnicity-based bias.

MethodData from the eICU Collaborative Research Database and the Medical Information Mart for Intensive Care were analysed for score performance in Asians, African Americans, Hispanics and Whites after appropriate exclusions. Discrimination and calibration were determined for all three scoring systems in all four groups.

FindingsWhile measurements of discrimination -area under the receiver operating characteristic curve (AUROC) -were significantly different among the groups, they did not display any discernible systematic patterns of bias. In contrast, measurements of calibration -standardised mortality ratio (SMR) -indicated persistent, and in some cases significant, patterns of difference between Hispanics and African Americans versus Asians and Whites. The differences between African Americans and Whites were consistently statistically significant. While calibrations were imperfect for all groups, the scores consistently demonstrated a pattern of over-predicting mortality for African Americans and Hispanics.

InterpretationThe systematic differences in calibration across ethnic groups suggest that illness severity scores reflect bias in their predictions of mortality.

FundingLAC is funded by the National Institute of Health through NIBIB R01 EB017205. There was no specific funding for this study.

category: intensive care and critical care medicine