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10.1101/2020.08.25.20181982 | Cost-effectiveness of sleeping sickness elimination campaigns in five settings of the Democratic Republic of Congo | Gambiense human African trypanosomiasis (gHAT) is marked for elimination of transmission (EOT) by 2030, but the disease persists in several low-income countries. We coupled transmission and health outcomes models to examine the cost-effectiveness of four gHAT elimination strategies in in five settings - spanning low- to high-risk - of the Democratic Republic of Congo. Alongside passive screening (PS) in fixed health facilities, the strategies included active screening (AS) at average or intensified coverage levels, alone or with vector control (VC) with a scale-back algorithm when no cases were reported for three consecutive years. In high or moderate-risk settings, costs of gHAT strategies are primarily driven by AS and, if used, VC. Due to the cessation of AS and VC, most investments (75-80%) will be made by 2030 and VC might be cost-saving while ensuring EOT. In low-risk settings, costs are driven by PS, and minimum-cost strategies consisting of AS and PS lead to EOT by 2030 with high probability. | public and global health |
10.1101/2020.08.26.20182279 | Genomic epidemiology of COVID-19 in care homes in the East of England | COVID-19 poses a major challenge to care homes, as SARS-CoV-2 is readily transmitted and causes disproportionately severe disease in older people. Here, 1,167 residents from 337 care homes were identified from a dataset of 6,600 COVID-19 cases from the East of England. Older age and being a care home resident were associated with increased mortality. SARS-CoV-2 genomes were available for 700 residents from 292 care homes. By integrating genomic and temporal data, 409 viral clusters within the 292 homes were identified, indicating two different patterns - outbreaks among care home residents and independent introductions with limited onward transmission. Approximately 70% of residents in the genomic analysis were admitted to hospital during the study, providing extensive opportunities for transmission between care homes and hospitals. Limiting viral transmission within care homes should be a key target for infection control to reduce COVID-19 mortality in this population.
Impact statementSARS-CoV-2 can spread efficiently within care homes causing COVID-19 outbreaks among residents, who are at increased risk of severe disease, emphasising the importance of stringent infection control in this population. | infectious diseases |
10.1101/2020.08.26.20182279 | Genomic epidemiology of COVID-19 in care homes in the East of England | COVID-19 poses a major challenge to care homes, as SARS-CoV-2 is readily transmitted and causes disproportionately severe disease in older people. Here, 1,167 residents from 337 care homes were identified from a dataset of 6,600 COVID-19 cases from the East of England. Older age and being a care home resident were associated with increased mortality. SARS-CoV-2 genomes were available for 700 residents from 292 care homes. By integrating genomic and temporal data, 409 viral clusters within the 292 homes were identified, indicating two different patterns - outbreaks among care home residents and independent introductions with limited onward transmission. Approximately 70% of residents in the genomic analysis were admitted to hospital during the study, providing extensive opportunities for transmission between care homes and hospitals. Limiting viral transmission within care homes should be a key target for infection control to reduce COVID-19 mortality in this population.
Impact statementSARS-CoV-2 can spread efficiently within care homes causing COVID-19 outbreaks among residents, who are at increased risk of severe disease, emphasising the importance of stringent infection control in this population. | infectious diseases |
10.1101/2020.08.26.20182824 | Beyond Six Feet: A Guideline to Limit Indoor Airborne Transmission of COVID-19 | The revival of the worlds economy is being predicated on the Six-Foot Rule, a guideline that offers little protection from pathogen-bearing droplets sufficiently small to be continuously mixed through an indoor space. The importance of indoor, airborne transmission of COVID-19 is now widely recognized; nevertheless, no quantitative measures have been proposed to protect against it. In this article, we build upon models of airborne disease transmission in order to derive a safety guideline that would impose a precise upper bound on the "cumulative exposure time", the product of the number of occupants and their time in an enclosed space. We demonstrate the manner in which this bound depends on the ventilation rate and dimensions of the room; the breathing rate, respiratory activity and face-mask use of its occupants; and the infectiousness of the respiratory aerosols, a disease-specific parameter that we estimate from available data. Case studies are presented, implications for contact tracing considered, and appropriate caveats enumerated. | public and global health |
10.1101/2020.08.26.20182824 | Beyond Six Feet: A Guideline to Limit Indoor Airborne Transmission of COVID-19 | The revival of the worlds economy is being predicated on the Six-Foot Rule, a guideline that offers little protection from pathogen-bearing droplets sufficiently small to be continuously mixed through an indoor space. The importance of indoor, airborne transmission of COVID-19 is now widely recognized; nevertheless, no quantitative measures have been proposed to protect against it. In this article, we build upon models of airborne disease transmission in order to derive a safety guideline that would impose a precise upper bound on the "cumulative exposure time", the product of the number of occupants and their time in an enclosed space. We demonstrate the manner in which this bound depends on the ventilation rate and dimensions of the room; the breathing rate, respiratory activity and face-mask use of its occupants; and the infectiousness of the respiratory aerosols, a disease-specific parameter that we estimate from available data. Case studies are presented, implications for contact tracing considered, and appropriate caveats enumerated. | public and global health |
10.1101/2020.08.28.20183699 | Do Lockdowns Bring about Additional Mortality Benefits or Costs? Evidence based on Death Records from 300 Million Chinese People | After the COVID-19 outbreak, China immediately adopted stringent lockdown policies to contain the virus. Using comprehensive death records covering around 300 million Chinese people, we estimate the impacts of city and community lockdowns on non-COVID-19 mortality outside of Wuhan. Employing a difference-in-differences method, we find that lockdowns reduced the number of non-COVID-19 deaths by 4.9% (cardiovascular deaths by 6.2%, injuries by 9.2%, and non-COVID-19 pneumonia deaths by 14.3%). The health benefits are likely driven by significant reductions in air pollution, traffic, and human interactions. A back-of-the-envelope calculation shows that more than 32,000 lives could have been saved from non-COVID-19 diseases/causes during the 40 days of the lockdown on which we focus. The results suggest that the rapid and strict virus countermeasures not only effectively controlled the spread of COVID-19 but also brought about massive unintended public health benefits. These findings can help better inform policymakers around the world about the benefits and costs of city and community lockdowns policies in dealing with the COVID-19 pandemic. | health economics |
10.1101/2020.08.27.20068346 | Potential reduction in transmission of COVID-19 by digital contact tracing systems | BackgroundDigital tools are being developed to support contact tracing as part of the global effort to control the spread of COVID-19. These include smartphone apps, Bluetooth-based proximity detection, location tracking, and automatic exposure notification features. Evidence on the effectiveness of alternative approaches to digital contact tracing is so far limited.
MethodsWe use an age-structured branching process model of the transmission of COVID-19 in different settings to estimate the potential of manual contact tracing and digital tracing systems to help control the epidemic. We investigate the effect of the uptake rate and proportion of contacts recorded by the digital system on key model outputs: the effective reproduction number, the mean outbreak size after 30 days, and the probability of elimination.
ResultsEffective manual contact tracing can reduce the effective reproduction number from 2.4 to around 1.5. The addition of a digital tracing system with a high uptake rate over 75% could further reduce the effective reproduction number to around 1.1. Fully automated digital tracing without manual contact tracing is predicted to be much less effective.
ConclusionsFor digital tracing systems to make a significant contribution to the control of COVID-19, they need be designed in close conjunction with public health agencies to support and complement manual contact tracing by trained professionals. | infectious diseases |
10.1101/2020.08.27.20183574 | Data-driven Optimized Control of the COVID-19 Epidemics | Optimizing the impact on the economy of control strategies aiming at containing the spread of COVID-19 is a critical challenge. We use daily new case counts of COVID-19 patients reported by local health administrations from different Metropolitan Statistical Areas (MSAs) within the US to parametrize a model that well describes the propagation of the disease in each area. We then introduce a time-varying control input that represents the level of social distancing imposed on the population of a given area and solve an optimal control problem with the goal of minimizing the impact of social distancing on the economy in the presence of relevant constraints, such as a desired level of suppression for the epidemics at a terminal time. We find that with the exception of the initial time and of the final time, the optimal control input is well approximated by a constant, specific to each area, which contrasts with the implemented system of reopening in phases. For all the areas considered, this optimal level corresponds to stricter social distancing than the level estimated from data. Proper selection of the time period for application of the control action optimally is important: depending on the particular MSA this period should be either short or long or intermediate. We also consider the case that the transmissibility increases in time (due e.g. to increasingly colder weather), for which we find that the optimal control solution yields progressively stricter measures of social distancing. We finally compute the optimal control solution for a model modified to incorporate the effects of vaccinations on the population and we see that depending on a number of factors, social distancing measures could be optimally reduced during the period over which vaccines are administered to the population. | epidemiology |
10.1101/2020.08.26.20181990 | Empowering the crowd: Feasible strategies to minimize the spread of COVID-19 in high-density informal settlements | More than 1 billion people live in informal settlements worldwide, where precarious living conditions pose unique challenges to managing a COVID-19 outbreak. Taking Northwest Syria as a case-study, we simulated an outbreak in high-density informal Internally Displaced Persons (IDP) camps using a stochastic Susceptible-Exposed-Infectious-Recovered model. Expanding on previous studies, taking social conditions and population health/structure into account, we modeled several interventions feasible in these settings: moderate self-distancing, self-isolation of symptomatic cases, and protection of the most vulnerable in "safety zones". We considered complementary measures to these interventions that can be implemented autonomously by these communities, such as buffer zones, health-checks, and carers for isolated individuals, quantifying their impact on the micro-dynamics of disease transmission. All interventions significantly reduce outbreak probability and some of them reduce mortality when an outbreak does occur. Self-distancing reduces mortality by up to 35% if contacts are reduced by 50%. A reduction in mortality by up to 18% can be achieved by providing 1 self-isolation tent per 8 people. Protecting the most vulnerable in a safety zone reduces the outbreak probability in the vulnerable population and has synergistic effects with the other interventions. Our model predicts that a combination of all simulated interventions may reduce mortality by more than 90% and delay an outbreaks peak by almost two months. Our results highlight the potential for non-medical interventions to mitigate the effects of the pandemic. Similar measures may be applicable to controlling COVID-19 in other informal settlements, particularly IDP camps in conflict regions, around the world.
Key questionsO_ST_ABSWhat is already known?C_ST_ABSO_LISince the onset of the COVID-19 pandemic, many studies have provided evidence for the effectiveness of strategies such as social distancing, testing, contact tracing, case isolation, use of personal protective equipment/facemasks and improved hygiene to reduce the spread of the disease. These studies underlie the recommendations of the World Health Organisation, but their implementation is contingent on local conditions and resources.
C_LIO_LIMathematical modelling is the basis of many epidemiological studies and has helped inform policymakers considering COVID-19 responses around the world. Nevertheless, only a limited number of studies have applied these models to informal settlements.
C_LI
What are the new findings?O_LIWe developed a mathematical model to study the dynamics of COVID-19 in Syrian IDP camps, elaborating on previous efforts done in similar settings by explicitly parameterizing the camps demographics, living conditions and micro-dynamics of interpersonal contacts in our modelization.
C_LIO_LIWe designed interventions such as self-distancing, self-isolation and the creation of safety zones to protect the most vulnerable members of the population, among others, through conversations with camp managers with on-the-ground knowledge of what interventions would be feasible and have community buy-in.
C_LIO_LIOur results show how low-cost, feasible, community-led non-medical interventions can significantly mitigate the impact of COVID-19 in Northwest Syrian IDP camps.
C_LI
What do the new findings imply?O_LIOur model represents a step forward in the much-needed search for epidemiological models that are sufficiently flexible to consider specific social questions. The model can also help inform similar interventions in refugee camps in conflict-torn regions, and potentially be adapted to other informal settlements and vulnerable communities around the world.
C_LI | epidemiology |
10.1101/2020.08.26.20181990 | Empowering the crowd: Feasible strategies for epidemic management in high-density informal settlements. The case of COVID-19 in Northwest Syria | More than 1 billion people live in informal settlements worldwide, where precarious living conditions pose unique challenges to managing a COVID-19 outbreak. Taking Northwest Syria as a case-study, we simulated an outbreak in high-density informal Internally Displaced Persons (IDP) camps using a stochastic Susceptible-Exposed-Infectious-Recovered model. Expanding on previous studies, taking social conditions and population health/structure into account, we modeled several interventions feasible in these settings: moderate self-distancing, self-isolation of symptomatic cases, and protection of the most vulnerable in "safety zones". We considered complementary measures to these interventions that can be implemented autonomously by these communities, such as buffer zones, health-checks, and carers for isolated individuals, quantifying their impact on the micro-dynamics of disease transmission. All interventions significantly reduce outbreak probability and some of them reduce mortality when an outbreak does occur. Self-distancing reduces mortality by up to 35% if contacts are reduced by 50%. A reduction in mortality by up to 18% can be achieved by providing 1 self-isolation tent per 8 people. Protecting the most vulnerable in a safety zone reduces the outbreak probability in the vulnerable population and has synergistic effects with the other interventions. Our model predicts that a combination of all simulated interventions may reduce mortality by more than 90% and delay an outbreaks peak by almost two months. Our results highlight the potential for non-medical interventions to mitigate the effects of the pandemic. Similar measures may be applicable to controlling COVID-19 in other informal settlements, particularly IDP camps in conflict regions, around the world.
Key questionsO_ST_ABSWhat is already known?C_ST_ABSO_LISince the onset of the COVID-19 pandemic, many studies have provided evidence for the effectiveness of strategies such as social distancing, testing, contact tracing, case isolation, use of personal protective equipment/facemasks and improved hygiene to reduce the spread of the disease. These studies underlie the recommendations of the World Health Organisation, but their implementation is contingent on local conditions and resources.
C_LIO_LIMathematical modelling is the basis of many epidemiological studies and has helped inform policymakers considering COVID-19 responses around the world. Nevertheless, only a limited number of studies have applied these models to informal settlements.
C_LI
What are the new findings?O_LIWe developed a mathematical model to study the dynamics of COVID-19 in Syrian IDP camps, elaborating on previous efforts done in similar settings by explicitly parameterizing the camps demographics, living conditions and micro-dynamics of interpersonal contacts in our modelization.
C_LIO_LIWe designed interventions such as self-distancing, self-isolation and the creation of safety zones to protect the most vulnerable members of the population, among others, through conversations with camp managers with on-the-ground knowledge of what interventions would be feasible and have community buy-in.
C_LIO_LIOur results show how low-cost, feasible, community-led non-medical interventions can significantly mitigate the impact of COVID-19 in Northwest Syrian IDP camps.
C_LI
What do the new findings imply?O_LIOur model represents a step forward in the much-needed search for epidemiological models that are sufficiently flexible to consider specific social questions. The model can also help inform similar interventions in refugee camps in conflict-torn regions, and potentially be adapted to other informal settlements and vulnerable communities around the world.
C_LI | epidemiology |
10.1101/2020.08.27.20182980 | The impact of the COVID-19 pandemic on mental health in the general population: a comparison between Germany and the UK | BackgroundThe COVID-19 pandemic has led to dramatic social and economic changes in daily life. First studies report an impact on mental health of the general population showing increased levels of anxiety, stress and depression. In this study, we compared the impact of the pandemic on two culturally and economically similar European countries: the UK and Germany.
MethodsParticipants (UK=241, German=541) completed an online-survey assessing COVID-19 exposure, impact on financial situation and work, substance and media consumption, mental health using the tSymptom-Check-List-27 (SCL-27) and the Schizotypal Personality Questionnaire.
ResultsWe found distinct differences between the two countries. UK responders reported a stronger direct impact on health, financial situation and families. UK responders had higher clinical scores on the SCL-27, and higher prevalence. Interestingly, German responders were less hopeful for an end of the pandemic and more concerned about their life-stability.
ConclusionAs 25% of both German and UK responders reported a subjective worsening of the general psychological symptoms and 20-50% of German and UK responders reached the clinical cut-off for depressive and dysthymic symptoms as well as anxieties, it specifically shows the need for tailored intervention systems to support large proportions of the general public. | psychiatry and clinical psychology |
10.1101/2020.08.28.20183863 | Unmasking the conversation on masks: Natural language processing for topical sentiment analysis of COVID-19 Twitter discourse | In this exploratory study, we scrutinize a database of over one million tweets collected from March to July 2020 to illustrate public attitudes towards mask usage during the COVID-19 pandemic. We employ natural language processing, clustering and sentiment analysis techniques to organize tweets relating to mask-wearing into high-level themes, then relay narratives for each theme using automatic text summarization. In recent months, a body of literature has highlighted the robustness of trends in online activity as proxies for the sociological impact of COVID-19. We find that topic clustering based on mask-related Twitter data offers revealing insights into societal perceptions of COVID-19 and techniques for its prevention. We observe that the volume and polarity of mask-related tweets has greatly increased. Importantly, the analysis pipeline presented may be leveraged by the health community for qualitative assessment of public response to health intervention techniques in real time. | health informatics |
10.1101/2020.08.27.20183004 | Inheritance of a common androgen synthesis variant allele is associated with female COVID susceptibility in UK Biobank | ContextA sex discordance in COVID exists, with males disproportionately affected. Although sex steroids may play a role in this discordance, no definitive genetic data exist to support androgen-mediated immune suppression for viral susceptibility, nor for adrenally produced androgens.
ObjectiveThe common adrenal-permissive missense-encoding variant HSD3B1(1245C) that enables androgen synthesis from adrenal precursors and that has been linked to suppression of inflammation in severe asthma was investigated in COVID susceptibility and outcomes reported in the UK Biobank.
MethodsThe UK Biobank is a long-term study with detailed medical information and health outcomes for over 500,000 genotyped individuals. We obtained COVID test results, inpatient hospital records, and death records and tested for associations between COVID susceptibility or outcomes and HSD3B1(1245A/C) genotype. The outcomes were identification as a COVID case among all subjects, COVID positivity among COVID-tested subjects, and mortality among subjects identified as COVID cases.
ResultsAdrenal-permissive HSD3B1(1245C) genotype was associated with identification as a COVID case (odds ratio 1.11 per C allele, p = 0.00054) and COVID test positivity (OR 1.10, p = 0.0036) in older ([≥] 70 years of age) women. In women identified as COVID cases, there was a positive linear relationship between age and 1245C allele frequency (p < 0.0001). No associations were found between genotype and mortality.
ConclusionOur study suggests that a common androgen synthesis variant regulates immune susceptibility to COVID infection in women, with increasingly strong effects as women age. | infectious diseases |
10.1101/2020.08.28.20184200 | Severity Prediction for COVID-19 Patients via Recurrent Neural Networks | The novel coronavirus disease-2019 (COVID-19) pandemic has threatened the health of tens of millions of people worldwide and posed enormous burden on the global healthcare systems. Many prediction models have been proposed to fight against the pandemic. In this paper, we propose a model to predict whether a patient infected with COVID-19 will develop severe outcomes based only on the patients historical electronic health records (EHR) using recurrent neural networks (RNN). The predicted severity risk score represents the probability for a person to progress into severe status (mechanical ventilation, tracheostomy, or death) after being infected with COVID-19. While many of the existing models use features obtained after diagnosis of COVID-19, our proposed model only utilizes a patients historical EHR so that it can enable proactive risk management before or at the time of hospital admission. | health informatics |
10.1101/2020.08.31.20185108 | Could Deficiencies in South African Data Be the Explanation for Its Early SARS-CoV-2 Peak? | The SARS-CoV-2 pandemic peaked very early in comparison to the thresholds predicted by an analysis of prior lockdown regimes. The most convenient explanation is that some, external factor changed the value of the basic reproduction number, r0; and there certainly are arguments for this. Other factors could, nonetheless, have played a role. This research attempts to reconcile the observed peak with the thresholds predicted by lockdown regimes similar to the one in force at the time. It contemplates the effect of two, different, hypothetical errors in the data: The first is that the true level of infection has been underestimated by a multiplicative factor, while the second is that of an imperceptible, pre-existing, immune fraction of the population. While it is shown that it certainly is possible to manufacture the perception of an early peak as extreme as the one observed, solely by way of these two phenomena, the values need to be fairly high. The phenomena would not, by any measure, be insignificant. It also remains an inescapable fact that the early peak in infections coincided with a fairly profound change in r0; in all the contemplated scenarios of data-deficiency. | epidemiology |
10.1101/2020.08.31.20184945 | I want to move my body - right now! The CRAVE Scale to measure state motivation for physical activity and sedentary behavior | Physical activity, and likely the motivation for it, varies throughout the day. The aim of this investigation was to create a short assessment (CRAVE) to measure motivation states (wants, desires, urges) for physical activity and sedentary behaviors. Five studies were conducted to develop and evaluate the construct validity and reliability of the scale, with 1,035 participants completing the scale a total of 1,697 times. In Study 1, 402 university students completed a questionnaire inquiring about the want or desire to perform behaviors "at the present moment (right now)". Items related to physical activity (e.g., "move my body") and sedentary behaviors (e.g., "do nothing active"). An exploratory structural equation model (ESEM) revealed that 10 items should be retained, loading onto two factors (5 each for Move and Rest). In Study 2, an independent sample (n= 444) confirmed these results and found that Move and Rest desires were associated with stage-of-change for exercise behavior. In Study 3, 127 community-residing participants completed the CRAVE at 6-month intervals over two years-two times each session. Across-session interclass correlations (ICC) for Move (ICC = .72-.95) and Rest (ICC = .69-.88) were higher than when when they were measured across 24-months (Move: ICC = .53; Rest: ICC = .49), indicating wants/desires have state-like qualities. In Study 4, a maximal treadmill test was completed by 21 university students. The CRAVE was completed immediately pre and post. Move desires decreased 26% and Rest increased 74%. Changes in Move and Rest desires were moderately associated with changes in perceived physical fatigue and energy. In Study 5, 41 university students sat quietly during a 50-minute lecture. They completed the CRAVE at 3 time points. Move increased 19.6% and Rest decreased 16.7%. Small correlations were detected between Move with perceived energy and tiredness, but not calmness or tension. In conclusion, the CRAVE scale has good psychometric properties. Data also support tenets of the WANT model of motivation states for movement and rest (Stults-Kolehmainen et al., 2020). Future studies need to explore how desires to move/rest relate to dynamic changes in physical activity and sedentarism. | sports medicine |
10.1101/2020.08.31.20184945 | I want to move my body - right now! The CRAVE Scale to measure state motivation for physical activity and sedentary behavior | Physical activity, and likely the motivation for it, varies throughout the day. The aim of this investigation was to create a short assessment (CRAVE) to measure motivation states (wants, desires, urges) for physical activity and sedentary behaviors. Five studies were conducted to develop and evaluate the construct validity and reliability of the scale, with 1,035 participants completing the scale a total of 1,697 times. In Study 1, 402 university students completed a questionnaire inquiring about the want or desire to perform behaviors "at the present moment (right now)". Items related to physical activity (e.g., "move my body") and sedentary behaviors (e.g., "do nothing active"). An exploratory structural equation model (ESEM) revealed that 10 items should be retained, loading onto two factors (5 each for Move and Rest). In Study 2, an independent sample (n= 444) confirmed these results and found that Move and Rest desires were associated with stage-of-change for exercise behavior. In Study 3, 127 community-residing participants completed the CRAVE at 6-month intervals over two years-two times each session. Across-session interclass correlations (ICC) for Move (ICC = .72-.95) and Rest (ICC = .69-.88) were higher than when when they were measured across 24-months (Move: ICC = .53; Rest: ICC = .49), indicating wants/desires have state-like qualities. In Study 4, a maximal treadmill test was completed by 21 university students. The CRAVE was completed immediately pre and post. Move desires decreased 26% and Rest increased 74%. Changes in Move and Rest desires were moderately associated with changes in perceived physical fatigue and energy. In Study 5, 41 university students sat quietly during a 50-minute lecture. They completed the CRAVE at 3 time points. Move increased 19.6% and Rest decreased 16.7%. Small correlations were detected between Move with perceived energy and tiredness, but not calmness or tension. In conclusion, the CRAVE scale has good psychometric properties. Data also support tenets of the WANT model of motivation states for movement and rest (Stults-Kolehmainen et al., 2020). Future studies need to explore how desires to move/rest relate to dynamic changes in physical activity and sedentarism. | sports medicine |
10.1101/2020.09.01.20185595 | Tumour volume distribution can yield information on tumour growth and tumour control | BackgroundIt is shown that tumour volume distributions, can yield information on two aspects of cancer research: tumour induction and tumour control.
Materials and methodsFrom the hypothesis that the intrinsic distribution of breast cancer volumes follows an exponential distribution, firstly the probability density function of tumour growth time was deduced via a mathematical transformation of the probability density functions of tumour volumes. In a second step, the distribution of tumour volumes was used to model the variation of the clonogenic cell number between patients in order to determine tumour control probabilities for radiotherapy patients.
ResultsDistribution of lag times, i.e. the time from the appearance of the first fully malignant cell until a clinicaly observable cancer, can be used to deduce the probability of tumour induction as a function of patient age. The integration of the volume variation with a Poisson-TCP model results in a logistic function which explains population-averaged survival data of radiotherapy patients.
ConclusionsThe inclusion of tumour volume distributions into the TCP formalism enables a direct link to be deduced between a cohort TCP model (logistic) and a TCP model for individual patients (Poisson). The TCP model can be applied to non-uniform tumour dose distributions. | oncology |
10.1101/2020.08.31.20185249 | Take-Home Dosing Experiences among Persons Receiving Methadone Maintenance Treatment During COVID-19 | PurposeMethadone maintenance treatment is a life-saving treatment for people with opioid use disorders (OUD). The coronavirus pandemic (COVID-19) introduces many concerns surrounding access to opioid treatment. In March 2020, the Substance Abuse and Mental Health Services Administration (SAMHSA) issued guidance allowing the expansion of take-home methadone doses. We sought to describe changes to treatment experiences from the perspective of persons receiving methadone at outpatient treatment facilities for OUD.
MethodsWe conducted an in-person survey among 104 persons receiving methadone from three clinics in central North Carolina. Surveys collected information on demographic characteristics, methadone treatment history, and experiences with take-home methadone doses in the context of COVID-19 (i.e., before and since March 2020).
ResultsBefore COVID-19, the clinic-level percent of participants receiving any amount of days supply of take-home doses at each clinic varied ranged from 56% to 82%, while it ranged from 78% to 100% since COVID-19. The clinic-level percent of participants receiving a take-homes days supply of a week or longer (i.e., [≥]6 days) since COVID-19 ranged from 11% to 56%. Of the 87 participants who received take-homes since COVID-19 began, only four reported selling their take-home doses.
ConclusionsOur study found variation in experiences of take-home dosing by clinic and little diversion of take-home doses. While SAMSHA guidance should allow expanded access to take-home doses, adoption of these guidelines may vary at the clinic level. The adoption of these policies should be explored further, particularly in the context of benefits to patients seeking treatment for OUD. | addiction medicine |
10.1101/2020.08.31.20185348 | Correlation between continuous Positive end-expiratory pressure (PEEP) values and occurrence of Pneumothorax and Pneumomediastinum in SARS-CoV2 patients during non-invasive ventilation with Helmet. | BackgroundAcute Hypoxemic Respiratory Failure is a common complication of SARS-CoV2 related pneumonia, for which non-invasive ventilation (NIV) with Helmet Continuous Positive Airway Pressure (CPAP) is widely used. The frequency of pneumothorax in SARS-CoV2 was reported in 0.95% of hospitalized patients in 6% of mechanically ventilated patients, and in 1% of a post-mortem case series.
ObjectivesAim of our retrospective study was to investigate the incidence of pneumothorax and pneumomediastinum (PNX/PNM) in SARS-CoV2 pneumonia patients treated with Helmet CPAP. Moreover, we examined the correlation between PNX/PNM and Positive end-expiratory pressure (PEEP) values.
MethodsWe collected data from patients admitted to "Luigi Sacco" University Hospital of Milan from 2 February to 5 May 2020 with SARS-CoV2 pneumonia requiring CPAP. Patients, who need NIV with bi-level pressure or endotracheal intubation (ETI) for any reason except those who needed ETI after PNX/PNM, were excluded. Population was divided in two groups according to PEEP level used ([≤]10 cmH2O and >10 cmH20).
Results154 patients were enrolled. In the overall population, 42 patients (27%) were treated with High-PEEP (>10 cmH2O), and 112 with Low-PEEP ([≤]10 cmH2O). During hospitalization 3 PNX and 2 PNM occurred (3.2%). Out of these five patients, 2 needed invasive ventilation after PNX and died. All the PNX/PNM occurred in the High-PEEP group (5/37 vs 0/112, p<0,001).
ConclusionThe incidence of PNX appears to be lower in SARS-CoV2 than SARS and MERS. Considering the association of PNX/PNM with high PEEP we suggest using the lower PEEP as possible to prevent these complications. | emergency medicine |
10.1101/2020.08.31.20184036 | Assessing the Impact of the Covid-19 Pandemic on US Mortality: A County-Level Analysis | BackgroundCovid-19 excess deaths refer to increases in mortality over what would normally have been expected in the absence of the Covid-19 pandemic. Several prior studies have calculated excess deaths in the United States but were limited to the national or state level, precluding an examination of area-level variation in excess mortality and excess deaths not assigned to Covid-19. In this study, we take advantage of county-level variation in Covid-19 mortality to estimate excess deaths associated with the pandemic and examine how the extent of excess mortality not assigned to Covid-19 varies across subsets of counties defined by sociodemographic and health characteristics.
Methods and FindingsIn this ecological, cross-sectional study, we made use of provisional National Center for Health Statistics (NCHS) data on direct Covid-19 and all-cause mortality occurring in U.S. counties from January 1 to December 31, 2020 and reported before March 12, 2021. We used data with a ten week time lag between the final day that deaths occurred and the last day that deaths could be reported to improve the completeness of data. Our sample included 2,096 counties with 20 or more Covid-19 deaths. The total number of residents living in these counties was 319.1 million. On average, the counties were 18.7% Hispanic, 12.7% non-Hispanic Black and 59.6% non-Hispanic White. 15.9% of the population was older than 65 years. We first modeled the relationship between 2020 all-cause mortality and Covid-19 mortality across all counties and then produced fully stratified models to explore differences in this relationship among strata of sociodemographic and health factors. Overall, we found that for every 100 deaths assigned to Covid-19, 120 all-cause deaths occurred (95% CI, 116 to 124), implying that 17% (95% CI, 14% to 19%) of excess deaths were ascribed to causes of death other than Covid-19 itself. Our stratified models revealed that the percentage of excess deaths not assigned to Covid-19 was substantially higher among counties with lower median household incomes and less formal education, counties with poorer health and more diabetes, and counties in the South and West. Counties with more non-Hispanic Black residents, who were already at high risk of Covid-19 death based on direct counts, also reported higher percentages of excess deaths not assigned to Covid-19. Study limitations include the use of provisional data that may be incomplete and the lack of disaggregated data on county-level mortality by age, sex, race/ethnicity, and sociodemographic and health characteristics.
ConclusionsIn this study, we found that direct Covid-19 death counts in the United States in 2020 substantially underestimated total excess mortality attributable to Covid-19. Racial and socioeconomic inequities in Covid-19 mortality also increased when excess deaths not assigned to Covid-19 were considered. Our results highlight the importance of considering health equity in the policy response to the pandemic.
Authors SummaryO_ST_ABSWhy Was This Study Done?C_ST_ABSO_LIThe Covid-19 pandemic has resulted in excess mortality that would not have occurred in the absence of the pandemic.
C_LIO_LIExcess deaths include deaths assigned to Covid-19 in official statistics as well as deaths that are not assigned to Covid-19 but are attributable directly or indirectly to Covid-19.
C_LIO_LIWhile prior studies have identified significant racial and socioeconomic inequities in directly assigned Covid-19 deaths, few studies have documented how excess mortality in 2020 has differed across sociodemographic or health factors in the United States.
C_LI
What Did the Researchers Do and Find?O_LILeveraging data from 2,096 counties on Covid-19 and all-cause mortality, we assessed what percentage of excess deaths were not assigned to Covid-19 and examined variation in excess deaths by county characteristics.
C_LIO_LIIn these counties, we found that for every 100 deaths directly assigned to Covid-19 in official statistics, an additional 20 deaths occurred that were not counted as direct Covid-19 deaths.
C_LIO_LIThe proportion of excess deaths not counted as direct Covid-19 deaths was even higher in counties with lower average socioeconomic status, counties with more comorbidities, and counties in the South and West. Counties with more non-Hispanic Black residents who were already at high risk of Covid-19 death based on direct counts, also reported a higher proportion of excess deaths not assigned to Covid-19.
C_LI
What Do These Findings Mean?O_LIDirect Covid-19 death counts significantly underestimate excess mortality in 2020.
C_LIO_LIMonitoring excess mortality will be critical to gain a full picture of socioeconomic and racial inequities in mortality attributable to the Covid-19 pandemic.
C_LIO_LITo prevent inequities in mortality from growing even larger, health equity must be prioritized in the policy response to the Covid-19 pandemic.
C_LI | epidemiology |
10.1101/2020.08.31.20184036 | Assessing the Impact of the Covid-19 Pandemic on US Mortality: A County-Level Analysis | BackgroundCovid-19 excess deaths refer to increases in mortality over what would normally have been expected in the absence of the Covid-19 pandemic. Several prior studies have calculated excess deaths in the United States but were limited to the national or state level, precluding an examination of area-level variation in excess mortality and excess deaths not assigned to Covid-19. In this study, we take advantage of county-level variation in Covid-19 mortality to estimate excess deaths associated with the pandemic and examine how the extent of excess mortality not assigned to Covid-19 varies across subsets of counties defined by sociodemographic and health characteristics.
Methods and FindingsIn this ecological, cross-sectional study, we made use of provisional National Center for Health Statistics (NCHS) data on direct Covid-19 and all-cause mortality occurring in U.S. counties from January 1 to December 31, 2020 and reported before March 12, 2021. We used data with a ten week time lag between the final day that deaths occurred and the last day that deaths could be reported to improve the completeness of data. Our sample included 2,096 counties with 20 or more Covid-19 deaths. The total number of residents living in these counties was 319.1 million. On average, the counties were 18.7% Hispanic, 12.7% non-Hispanic Black and 59.6% non-Hispanic White. 15.9% of the population was older than 65 years. We first modeled the relationship between 2020 all-cause mortality and Covid-19 mortality across all counties and then produced fully stratified models to explore differences in this relationship among strata of sociodemographic and health factors. Overall, we found that for every 100 deaths assigned to Covid-19, 120 all-cause deaths occurred (95% CI, 116 to 124), implying that 17% (95% CI, 14% to 19%) of excess deaths were ascribed to causes of death other than Covid-19 itself. Our stratified models revealed that the percentage of excess deaths not assigned to Covid-19 was substantially higher among counties with lower median household incomes and less formal education, counties with poorer health and more diabetes, and counties in the South and West. Counties with more non-Hispanic Black residents, who were already at high risk of Covid-19 death based on direct counts, also reported higher percentages of excess deaths not assigned to Covid-19. Study limitations include the use of provisional data that may be incomplete and the lack of disaggregated data on county-level mortality by age, sex, race/ethnicity, and sociodemographic and health characteristics.
ConclusionsIn this study, we found that direct Covid-19 death counts in the United States in 2020 substantially underestimated total excess mortality attributable to Covid-19. Racial and socioeconomic inequities in Covid-19 mortality also increased when excess deaths not assigned to Covid-19 were considered. Our results highlight the importance of considering health equity in the policy response to the pandemic.
Authors SummaryO_ST_ABSWhy Was This Study Done?C_ST_ABSO_LIThe Covid-19 pandemic has resulted in excess mortality that would not have occurred in the absence of the pandemic.
C_LIO_LIExcess deaths include deaths assigned to Covid-19 in official statistics as well as deaths that are not assigned to Covid-19 but are attributable directly or indirectly to Covid-19.
C_LIO_LIWhile prior studies have identified significant racial and socioeconomic inequities in directly assigned Covid-19 deaths, few studies have documented how excess mortality in 2020 has differed across sociodemographic or health factors in the United States.
C_LI
What Did the Researchers Do and Find?O_LILeveraging data from 2,096 counties on Covid-19 and all-cause mortality, we assessed what percentage of excess deaths were not assigned to Covid-19 and examined variation in excess deaths by county characteristics.
C_LIO_LIIn these counties, we found that for every 100 deaths directly assigned to Covid-19 in official statistics, an additional 20 deaths occurred that were not counted as direct Covid-19 deaths.
C_LIO_LIThe proportion of excess deaths not counted as direct Covid-19 deaths was even higher in counties with lower average socioeconomic status, counties with more comorbidities, and counties in the South and West. Counties with more non-Hispanic Black residents who were already at high risk of Covid-19 death based on direct counts, also reported a higher proportion of excess deaths not assigned to Covid-19.
C_LI
What Do These Findings Mean?O_LIDirect Covid-19 death counts significantly underestimate excess mortality in 2020.
C_LIO_LIMonitoring excess mortality will be critical to gain a full picture of socioeconomic and racial inequities in mortality attributable to the Covid-19 pandemic.
C_LIO_LITo prevent inequities in mortality from growing even larger, health equity must be prioritized in the policy response to the Covid-19 pandemic.
C_LI | epidemiology |
10.1101/2020.08.31.20185371 | Governor partisanship explains the adoption of statewide mask mandates in response to COVID-19 | Public mask use has emerged as a key tool in response to COVID-19. We develop and document a classification of statewide mask mandates that reveals variation in their scope and timing. Some U.S. states quickly mandated the wearing of face coverings in most public spaces, whereas others issued narrow mandates or no man-date at all. We consider how differences in COVID-19 epidemiological indicators and partisan politics affect when states adopted broad mask mandates, starting with the earliest broad public mask mandates in April 2020 and continuing though the end of 2020. The most important predictor is whether a state is led by a Republican governor. These states adopt statewide indoor mask mandates an estimated 98.0 days slower (95% CI: 88.8 to 107.3), if they did so at all (hazard ratio=7.54, 95% CI: 2.87 to 16.19). COVID-19 indicators such as confirmed cases or deaths per million are much less important predictors of statewide mask mandates. This finding highlights a key challenge to public efforts to increase mask-wearing, one of the most effective tools for preventing the spread of SARS-CoV-2 while restoring economic activity. | health policy |
10.1101/2020.09.02.20186874 | Modelling the impact of travel restrictions on COVID-19 cases in Newfoundland and Labrador | In many jurisdictions, public health authorities have implemented travel restrictions to reduce coronavirus disease 2019 (COVID-19) spread. Policies that restrict travel within countries have been implemented, but the impact of these restrictions is not well known. On May 4th, 2020, Newfoundland and Labrador (NL) implemented travel restrictions such that non-residents required exemptions to enter the province. We fit a stochastic epidemic model to data describing the number of active COVID-19 cases in NL from March 14th to June 26th. We predicted possible outbreaks over 9 weeks, with and without the travel restrictions, and for contact rates 40% to 70% of pre-pandemic levels. Our results suggest that the travel restrictions reduced the mean number of clinical COVID-19 cases in NL by 92%. Furthermore, without the travel restrictions there is a substantial risk of very large outbreaks. Using epidemic modelling, we show how the NL COVID-19 outbreak could have unfolded had the travel restrictions not been implemented. Both physical distancing and travel restrictions affect the local dynamics of the epidemic. Our modelling shows that the travel restrictions are a plausible reason for the few reported COVID-19 cases in NL after May 4th. | epidemiology |
10.1101/2020.09.02.20186874 | Modelling the impact of travel restrictions on COVID-19 cases in Newfoundland and Labrador | In many jurisdictions, public health authorities have implemented travel restrictions to reduce coronavirus disease 2019 (COVID-19) spread. Policies that restrict travel within countries have been implemented, but the impact of these restrictions is not well known. On May 4th, 2020, Newfoundland and Labrador (NL) implemented travel restrictions such that non-residents required exemptions to enter the province. We fit a stochastic epidemic model to data describing the number of active COVID-19 cases in NL from March 14th to June 26th. We predicted possible outbreaks over 9 weeks, with and without the travel restrictions, and for contact rates 40% to 70% of pre-pandemic levels. Our results suggest that the travel restrictions reduced the mean number of clinical COVID-19 cases in NL by 92%. Furthermore, without the travel restrictions there is a substantial risk of very large outbreaks. Using epidemic modelling, we show how the NL COVID-19 outbreak could have unfolded had the travel restrictions not been implemented. Both physical distancing and travel restrictions affect the local dynamics of the epidemic. Our modelling shows that the travel restrictions are a plausible reason for the few reported COVID-19 cases in NL after May 4th. | epidemiology |
10.1101/2020.09.01.20135194 | Defining the role of asymptomatic and pre-symptomatic SARS-CoV-2 transmission: a living systematic review | BackgroundReports suggest that asymptomatic individuals (those with no symptoms at all throughout the infection) with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are infectious, but the extent of asymptomatic transmission requires further understanding.
PurposeThis living review aims to critically appraise available data about secondary attack rates from people with asymptomatic and pre-symptomatic SARS-CoV-2 infection.
Data sourcesMedline, EMBASE, China Academic Journals full-text database (CNKI), and preprint servers were searched from 30 December 2019 to 3 July 2020 using relevant MESH terms.
Study selectionStudies that report on contact tracing of index cases with asymptomatic or pre-symptomatic SARS-CoV-2 infection, in either English or Chinese were included.
Data extractionTwo authors independently extracted data and assessed study quality and risk of bias. We calculated the secondary attack rate as the number of contacts with SARS-CoV-2, divided by the number of contacts tested.
Data synthesisOf 928 studies identified, 19 were included. Secondary attack rates from asymptomatic index cases ranged from 0% to 2.8% (9 studies). Pre-symptomatic secondary attack rates ranged from 0.7% to 31.8% (10 studies). The highest secondary attack rates were found in contacts who lived in the same household as the index case. Other activities associated with transmission were group activities such as sharing meals or playing board games with the index case.
LimitationsWe excluded some studies because the index case or number of contacts were unclear. Owing to the anticipated heterogeneity, we did not produce a summary estimate of the included studies.
ConclusionAsymptomatic patients can transmit SARS-CoV-2 to others, but our findings indicate that such individuals are responsible for fewer secondary infections than people with symptoms in the same studies.
Systematic review registrationPROSPERO CRD42020188168
FundingNo funding was received | epidemiology |
10.1101/2020.09.01.20135194 | The role of asymptomatic and presymptomatic infection in SARS-CoV-2 transmission: a living systematic review | BackgroundReports suggest that asymptomatic individuals (those with no symptoms at all throughout the infection) with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are infectious, but the extent of asymptomatic transmission requires further understanding.
PurposeThis living review aims to critically appraise available data about secondary attack rates from people with asymptomatic and pre-symptomatic SARS-CoV-2 infection.
Data sourcesMedline, EMBASE, China Academic Journals full-text database (CNKI), and preprint servers were searched from 30 December 2019 to 3 July 2020 using relevant MESH terms.
Study selectionStudies that report on contact tracing of index cases with asymptomatic or pre-symptomatic SARS-CoV-2 infection, in either English or Chinese were included.
Data extractionTwo authors independently extracted data and assessed study quality and risk of bias. We calculated the secondary attack rate as the number of contacts with SARS-CoV-2, divided by the number of contacts tested.
Data synthesisOf 928 studies identified, 19 were included. Secondary attack rates from asymptomatic index cases ranged from 0% to 2.8% (9 studies). Pre-symptomatic secondary attack rates ranged from 0.7% to 31.8% (10 studies). The highest secondary attack rates were found in contacts who lived in the same household as the index case. Other activities associated with transmission were group activities such as sharing meals or playing board games with the index case.
LimitationsWe excluded some studies because the index case or number of contacts were unclear. Owing to the anticipated heterogeneity, we did not produce a summary estimate of the included studies.
ConclusionAsymptomatic patients can transmit SARS-CoV-2 to others, but our findings indicate that such individuals are responsible for fewer secondary infections than people with symptoms in the same studies.
Systematic review registrationPROSPERO CRD42020188168
FundingNo funding was received | epidemiology |
10.1101/2020.09.01.20184713 | The impact of high frequency rapid viral antigen screening on COVID-19 spread and outcomes: a validation and modeling study | High frequency screening of populations has been proposed as a strategy in facilitating control of the COVID-19 pandemic. We use computational modeling, coupled with clinical data from rapid antigen tests, to predict the impact of frequent viral antigen rapid testing on COVID-19 spread and outcomes. Using patient nasal or nasopharyngeal swab specimens, we demonstrate that the sensitivity/specificity of two rapid antigen tests compared to quantitative real-time polymerase chain reaction (qRT-PCR) are 82.0%/100% and 84.7%/85.7%, respectively; moreover, sensitivity correlates directly with viral load. Based on COVID-19 data from three regions in the United States and Sao Jose do Rio Preto, Brazil, we show that high frequency, strategic population-wide rapid testing, even at varied accuracy levels, diminishes COVID-19 infections, hospitalizations, and deaths at a fraction of the cost of nucleic acid detection via qRT-PCR. We propose large-scale antigen-based surveillance as a viable strategy to control SARS-CoV-2 spread and to enable societal re-opening. | epidemiology |
10.1101/2020.09.01.20184713 | Validating and modeling the impact of high-frequency rapid antigen screening on COVID-19 spread and outcomes | High frequency screening of populations has been proposed as a strategy in facilitating control of the COVID-19 pandemic. We use computational modeling, coupled with clinical data from rapid antigen tests, to predict the impact of frequent viral antigen rapid testing on COVID-19 spread and outcomes. Using patient nasal or nasopharyngeal swab specimens, we demonstrate that the sensitivity/specificity of two rapid antigen tests compared to quantitative real-time polymerase chain reaction (qRT-PCR) are 82.0%/100% and 84.7%/85.7%, respectively; moreover, sensitivity correlates directly with viral load. Based on COVID-19 data from three regions in the United States and Sao Jose do Rio Preto, Brazil, we show that high frequency, strategic population-wide rapid testing, even at varied accuracy levels, diminishes COVID-19 infections, hospitalizations, and deaths at a fraction of the cost of nucleic acid detection via qRT-PCR. We propose large-scale antigen-based surveillance as a viable strategy to control SARS-CoV-2 spread and to enable societal re-opening. | epidemiology |
10.1101/2020.09.02.20186999 | Temporal stability of the ventral attention network and general cognition along the Alzheimer's disease spectrum | Understanding the interrelationships of clinical manifestations of Alzheimers disease (AD) and functional connectivity (FC) as the disease progresses is necessary for use of FC as a potential neuroimaging biomarker. Degradation of resting-state networks in AD has been observed when FC is estimated over the entire scan, however, the temporal dynamics of these networks are less studied. We implemented a novel approach to investigate the modular structure of static (sFC) and time-varying (tvFC) connectivity along the AD spectrum in a two-sample Discovery/Validation design (n=80 and 81, respectively). Cortical FC networks were estimated across 4 diagnostic groups (cognitively normal, subjective cognitive decline, mild cognitive impairment, and AD) for whole scan (sFC) and with sliding window correlation (tvFC). Modularity quality (across a range of spatial scales) did not differ in either sFC or tvFC. For tvFC, group differences in temporal stability within and between multiple resting state networks were observed; however, these differences were not consistent between samples. Correlation analyses identified a relationship between global cognition and temporal stability of the ventral attention network, which was reproduced in both samples. While the ventral attention system has been predominantly studied in task-evoked designs, the relationship between its intrinsic dynamics at-rest and general cognition along the AD spectrum highlights its relevance regarding clinical manifestation of the disease. | neurology |
10.1101/2020.09.02.20186999 | Temporal stability of the ventral attention network and general cognition along the Alzheimer's disease spectrum | Understanding the interrelationships of clinical manifestations of Alzheimers disease (AD) and functional connectivity (FC) as the disease progresses is necessary for use of FC as a potential neuroimaging biomarker. Degradation of resting-state networks in AD has been observed when FC is estimated over the entire scan, however, the temporal dynamics of these networks are less studied. We implemented a novel approach to investigate the modular structure of static (sFC) and time-varying (tvFC) connectivity along the AD spectrum in a two-sample Discovery/Validation design (n=80 and 81, respectively). Cortical FC networks were estimated across 4 diagnostic groups (cognitively normal, subjective cognitive decline, mild cognitive impairment, and AD) for whole scan (sFC) and with sliding window correlation (tvFC). Modularity quality (across a range of spatial scales) did not differ in either sFC or tvFC. For tvFC, group differences in temporal stability within and between multiple resting state networks were observed; however, these differences were not consistent between samples. Correlation analyses identified a relationship between global cognition and temporal stability of the ventral attention network, which was reproduced in both samples. While the ventral attention system has been predominantly studied in task-evoked designs, the relationship between its intrinsic dynamics at-rest and general cognition along the AD spectrum highlights its relevance regarding clinical manifestation of the disease. | neurology |
10.1101/2020.09.03.20184051 | Personalized Computational Modeling Identifies Embolic Stroke of Undetermined Source Patients with Potential Arrhythmic Substrate | Cardiac magnetic resonance imaging (MRI) has revealed fibrosis in embolic stroke of undetermined source (ESUS) patients comparable to levels seen in atrial fibrillation (AFib). We used computational modeling to understand the absence of arrhythmia in ESUS despite the presence of putatively pro-arrhythmic fibrosis. MRI-based atrial models were reconstructed for 45 ESUS and 45 AFib patients. The fibrotic substrates arrhythmogenic capacity in each patient was assessed computationally. Reentrant drivers were induced in 24/45 (53%) ESUS and 22/45 (49%) AFib models. Inducible models had more fibrosis (16.7{+/-}5.45%) than non-inducible models (11.07{+/-}3.61%; P<0.0001); however, inducible subsets of ESUS and AFib models had similar fibrosis levels (P=0.90), meaning the intrinsic pro-arrhythmic substrate properties of fibrosis in ESUS and AFib are indistinguishable. This suggests some ESUS patients have latent pre-clinical fibrotic substrate that could be a future source of arrhythmogenicity. Thus, our work prompts the hypothesis that ESUS patients with fibrotic atria are spared from AFib due to an absence of arrhythmia triggers. | cardiovascular medicine |
10.1101/2020.09.04.20187963 | Estimating lengths-of-stay of hospitalized COVID-19 patients using a non-parametric model: a case study in Galicia (Spain). | Understanding the demand for hospital beds for COVID-19 patients is key for decision-making and planning mitigation strategies, as overwhelming healthcare systems has critical consequences for disease mortality. However, accurately mapping the time-to-event of hospital outcomes, such as the length-of-stay in the ICU, requires understanding patient trajectories while adjusting for covariates and observation bias, such as incomplete data. Standard methods, like the Kaplan-Meier estimator, require prior assumptions that are untenable given current knowledge. Using real-time surveillance data from the first weeks of the COVID-19 epidemic in Galicia (Spain), we aimed to model the time-to-event and event probabilities of patients hospitalized, without parametric priors and adjusting for individual covariates. We applied a nonparametric Mixture Cure Model and compared its performance in estimating hospital ward/ICU lengths-of-stay to the performances of commonly used methods to estimate survival. We showed that the proposed model outperformed standard approaches, providing more accurate ICU and hospital ward length-of-stay estimates. Finally, we applied our model estimates to simulate COVID-19 hospital demand using a Monte Carlo algorithm. We provided evidence that adjusting for sex, generally overlooked in prediction models, together with age is key for accurately forecasting ICU occupancy, as well as discharge or death outcomes. | epidemiology |
10.1101/2020.09.03.20187567 | Composite trait Mendelian Randomization reveals distinct metabolic and lifestyle consequences of differences in body shape | Obesity is a major risk factor for a wide range of cardiometabolic diseases, however the impact of specific aspects of body morphology remains poorly understood. We combined the GWAS summary statistics of fourteen anthropometric traits from UK Biobank through principal component analysis to reveal four major independent axes summarizing 99% of genetically driven variation in body shape and size: overall body size, adiposity, predisposition to abdominal fat deposition, and lean mass. Enrichment analyses suggest that body size and adiposity are affected by genes involved in neuronal signaling, whereas body fat distribution and lean mass are dependent on genes involved in morphogenesis and energy homeostasis. Using Mendelian randomization, we found that although both body size and adiposity contribute to the consequences of BMI, many of their effects are distinct, such as body size increasing the risk of diseases of the veins (b [≥] 0.044, p [≤] 8.9*10-10) and cardiac arrhythmia (b = 0.06, p = 4.2*10-17) while adiposity instead increased the risk of ischemic heart disease (b = 0.079, p = 8.2*10-21). The body mass-neutral component predisposing to abdominal fat deposition, likely reflecting a shift from subcutaneous to visceral fat, exhibited health effects that were weaker but specifically linked to lipotoxicity, such as ischemic heart disease (b = 0.067, p = 9.4*10-14) and diabetes (b = 0.082, p = 5.9*10-19). Combining their predicted effects significantly improved the prediction of obesity-related diseases, even when applied out-of-population (p < 10-10). The presented decomposition approach sheds light on the biological mechanisms underlying the remarkably heterogeneous nature of body morphology as well as its consequences on health and lifestyle. | epidemiology |
10.1101/2020.09.03.20187807 | Assessment of COVID-19 Pandemic in Nepal: A Lockdown Scenario Analysis | The Government of Nepal issued a nationwide lockdown from 24 March to 21 July 2020, prohibiting domestic and international travels, closure of the border and non-essential services. There were only two confirmed cases from 610 Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests and no fatalities when the government introduced nationwide lockdown. This study aimed to explore the overall scenario of COVID-19 including spatial distribution of cases; government efforts, and impact on public health, socio-economy, and education during the lockdown in Nepal. We collated and analysed data using official figures from the Nepalese Ministry of Health and Population. Nepal had performed 7,791 RT-PCR tests for COVID-19, the highest number of tests during the lockdown. It has recorded its highest daily rise in coronavirus infections with a total of 740 new cases from the total of 4,483 RT-PCR tests performed on a single day. Nepal had reported a total of 17,994 positive cases and 40 deaths at the end of lockdown. The spatial distribution clearly shows that the cases were rapidly spreading from the southern part of the country where most points of entry and exit from India are located. To contain the spread of the virus, the government has also initiated various preventive measures and strategies during the lockdown. The Government of Nepal needs to allocate more resources, increase its capacity to test and trace, establish dedicated isolation and quarantine facility and impose local restrictions such as a local lockdown based on risk assessment rather than a nationwide lockdown. | public and global health |
10.1101/2020.09.04.20188045 | Computer-aided interpretation of chest radiography reveals the spectrum of tuberculosis in rural South Africa | Computer-aided digital chest radiograph interpretation (CAD) can facilitate high-throughput screening for tuberculosis (TB), but its use in population-based active case finding programs has been limited. In an HIV-endemic area in rural South Africa, we used a CAD-algorithm (CAD4TBv5) to interpret digital chest x-rays (CXR) as part of a mobile health screening effort. Participants with TB symptoms or CAD4TBv5 score above the triaging threshold were referred for microbiological sputum assessment. During an initial pilot phase, a low CAD4TBv5 triaging threshold of 25 was selected to maximize TB case finding. We report the performance of CAD4TBv5 in screening 9,914 participants, 99 (1.0%) of whom were found to have microbiologically proven TB. CAD4TBv5 was able to identify TB cases at the same sensitivity but lower specificity as a blinded radiologist, whereas the next generation of the algorithm (CAD4TBv6) achieved comparable sensitivity and specificity to the radiologist. The CXRs of people with microbiologically-confirmed TB spanned a range of lung field abnormality, including 19 (19.2%) cases deemed normal by the radiologist. HIV-serostatus did not impact CAD4TBs performance. Notably, 78.8% of the TB cases identified during this population-based survey were asymptomatic and therefore triaged for sputum collection on the basis of CAD4TBv5 score alone. While CAD4TBv6 has the potential to replace radiologists for triaging CXRs in TB prevalence surveys, population-specific piloting is necessary to set the appropriate triaging thresholds. Further work on image analysis strategies is needed to identify radiologically-subtle active TB. | infectious diseases |
10.1101/2020.09.04.20188441 | Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis | Here, we develop a Bayesian approach (BayesW) that provides probabilistic inference of the genetic architecture of age-at-diagnosis of disease and time-to-event phenotypes. We show in extensive simulation work that our method provides insight into genetic effects underlying disease progression, achieving higher statistical power and improved genomic prediction as compared to other approaches. We develop a hybrid-parallel sampling scheme facilitating age-at-onset analyses in large-scale biobank data. In the UK Biobank, we find evidence for an infinitesimal contribution of many thousands of common genomic regions to variation in the onset of common complex disorders of high blood pressure (HBP), cardiac disease (CAD), and type-2 diabetes (T2D), and for the genetic basis of age-at-onset reflecting the underlying genetic liability to disease. In contrast, while age-at-menopause and age-at-menarche are highly polygenic, we find higher variance contributed by low frequency variants. We find 360 independent 50kb regions for age-at-menarche with 95% posterior inclusion probability of contributing 0.001% to the genetic variance, 115 regions for age-at-menopause, 246 regions for age-at-diagnosis of HBP, 32 regions for CAD, and 56 regions for T2D. Genomic prediction into the Estonian Genome Centre data shows that BayesW gives higher prediction accuracy than other approaches. | genetic and genomic medicine |
10.1101/2020.09.04.20188433 | Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits | Due to the complexity of linkage disequilibrium (LD) and gene regulation, understanding the genetic basis of common complex traits remains a major challenge. We develop a Bayesian model (BayesRR-RC) implemented in a hybrid-parallel algorithm that scales to whole-genome sequence data on many hundreds of thousands of individuals, taking 22 seconds per iteration to estimate the inclusion probabilities and effect sizes of 8.4 million markers and 78 SNP-heritability parameters in the UK Biobank. We show in theory and simulation that BayesRR-RC provides robust variance component and enrichment estimates, improved marker discovery and effect estimates over mixed-linear model association approaches, and accurate genomic prediction. Of the genetic variation captured for height, body mass index, cardiovascular disease, and type-2 diabetes in the UK Biobank, only [≤] 10% is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32-44% to intronic regions, and 22-28% to distal 10-500kb upstream regions. [≥] 60% of the variance contributed by these exonic, intronic and distal 10-500kb regions is underlain by many thousands of common variants, which on average have larger effect sizes than for other annotation groups. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having [≥] 95% probability of contributing [≥] 0.001% to the genetic variance of these four traits. Thus, these quantitative and disease traits are truly complex. The BayesRR-RC prior gives robust model performance across the data analysed, providing an alternative to current approaches. | genetic and genomic medicine |
10.1101/2020.09.04.20188326 | 50 policies, 1 pandemic, 500,000 deaths: Associations between state-level COVID-19 testing recommendations, tests per capita, undercounted deaths, vaccination policies, and doses per capita in the United States | BackgroundState health departments have been responsible for prioritizing and allocating SARS-CoV-2 tests and vaccines. Testing and vaccination recommendations in the United States varied by state and over time, as did vaccine rollouts, COVID-19 cases, and estimates of excess mortality.
MethodsWe compiled data about COVID-19 testing, cases, and deaths, and excess pneumonia + influenza + COVID-19 deaths to assess relationships between testing recommendations, per capita tests performed, epidemic intensity, and excess mortality during the early months of the COVID-19 pandemic in the United States. We compiled further data about state-level SARS-CoV-2 vaccination policies and doses administered during the early months of the vaccine rollout.
ResultsAs of July 2020, 16 states recommended testing asymptomatic members of the general public. The rate of COVID-19 tests reported in each state correlated with more inclusive testing recommendations and with higher epidemic intensity. Higher per capita testing was associated with more complete reporting of COVID-19 deaths, which is a fundamental requirement for analyzing the pandemic. Testing per capita during the first three months was associated with vaccination per capita in the first three months of rollout. Per capita vaccine doses in each state were not associated with adherence to national guidelines.
ConclusionsReported deaths due to COVID-19 likely represent an undercount of the true burden of the pandemic. States that struggled with testing rollout have also frequently struggled with vaccine rollout. Coordinated, consistent guidelines for COVID-19 testing and vaccine administration should be a high priority for state and national health systems. | epidemiology |
10.1101/2020.09.04.20188680 | SAFE REOPENING STRATEGIES FOR EDUCATIONAL INSTITUTIONS DURING COVID-19: A DATA-DRIVEN AGENT-BASED APPROACH | Can educational institutions open up safely amid COVID-19? We build an epidemiological model to investigate the strategies necessary for institutions to reopen. The four measures that are most relevant for in-person opening are: (i) wide-spread rapid testing, possibly saliva-based, (ii) enforcement of mask wearing, (iii) social distancing, and (iv) contact tracing. We demonstrate that institutions need to test at a relatively high level (e.g., at least once every week) in the initial phases of reopening. Contact tracing is relatively more important when the positivity rate from random testing is relatively low, which is likely during the initial phases. A Bayesian adaptive testing strategy based on positivity rates can help institutions optimally manage the costs and risks of reopening. This paper contributes to the nascent literature on combating the COVID-19 pandemic and is especially relevant for large-scale organizations. This work is motivated and guided by the SHIELD program of UIUC. | epidemiology |
10.1101/2020.09.05.20188367 | Detecting sleep in free-living conditions without sleep-diaries: a device-agnostic, wearable heart rate sensing approach | The rise of multisensor wearable devices offers a unique opportunity for the objective inference of sleep outside laboratories, enabling longitudinal monitoring in large populations. To enhance objectivity and facilitate cross-cohort comparisons, sleep detection algorithms in free-living conditions should rely on personalized but device-agnostic features, which can be applied without laborious human annotations or sleep diaries. We developed and tested a heart rate-based algorithm that captures inter- and intra-individual sleep differences, does not require human input and can be applied in free-living conditions. The algorithm was evaluated across four study cohorts using different research- and consumer-grade devices for over 2,000 nights. Recording periods included both 24-hour free-living and conventional lab-based night-only data. Our method was systematically optimized and evaluated against polysomnography (PSG) and sleep diaries and compared to sleep periods produced by accelerometry-based angular change algorithms. Against sleep diaries, the algorithm yielded a mean squared error (MSE) of 0.04 to 0.06 and a total sleep time deviation of -2.70 ({+/-}5.74) and 12.80 ({+/-}3.89) minutes, respectively. When evaluated with PSG lab studies, the MSE ranged between 0.06 and 0.11 yielding a time deviation between -29.07 and -55.04 minutes. Our findings suggest that the heart rate-based algorithm can reliably and objectively infer sleep under longitudinal, free-living conditions, independent of the wearable device used. This represents the first open-source algorithm that can infer sleep using heart rate signals without actigraphy or diary annotations. | health informatics |
10.1101/2020.09.06.20189241 | Explicit knowledge of task structure is the primary determinant of human model-based action | Explicit information obtained through instruction profoundly shapes human choice behaviour. However, this has been studied in computationally simple tasks, and it is unknown how model-based and model-free systems, respectively generating goal-directed and habitual actions, are affected by the absence or presence of instructions. We assessed behaviour in a novel variant of a computationally more complex decision-making task, before and after providing information about task structure, both in healthy volunteers and individuals suffering from obsessive-compulsive (OCD) or other disorders. Initial behaviour was model-free, with rewards directly reinforcing preceding actions. Model-based control, employing predictions of states resulting from each action, emerged with experience in a minority of subjects, and less in OCD. Providing task structure information strongly increased model-based control, similarly across all groups. Thus, explicit task structural knowledge determines human use of model-based reinforcement learning, and is most readily acquired from instruction rather than experience. | psychiatry and clinical psychology |
10.1101/2020.09.02.20187096 | On the Role of Artificial Intelligence in Medical Imaging of COVID-19 | The global COVID-19 pandemic has accelerated the development of numerous digital technologies in medicine from telemedicine to remote monitoring. Concurrently, the pandemic has resulted in huge pressures on healthcare systems. Medical imaging (MI) from chest radiographs to computed tomography and ultrasound of the thorax have played an important role in the diagnosis and management of the coronavirus infection.
We conducted the, to date, largest systematic review of the literature addressing the utility of Artificial Intelligence (AI) in MI for COVID-19 management. Through keyword matching on PubMed and preprint servers, including arXiv, bioRxiv and medRxiv, 463 papers were selected for a meta-analysis, with manual reviews to assess the clinical relevance of AI solutions. Further, we evaluated the maturity of the papers based on five criteria assessing the state of the field: peer-review, patient dataset size and origin, algorithmic complexity, experimental rigor and clinical deployment.
In 2020, we identified 4977 papers on MI in COVID-19, of which 872 mentioned the term AI. 2039 papers of the 4977 were specific to imaging modalities with a majority of 83.8% focusing on CT, while 10% involved CXR and 6.2% used LUS. Meanwhile, the AI literature predominantly analyzed CXR data (49.7%), with 38.7% using CT and 1.5% LUS. Only a small portion of the papers were judged as mature (2.7 %). 71.9% of AI papers centered on disease detection.
This review evidences a disparity between clinicians and the AI community, both in the focus on imaging modalities and performed tasks. Therefore, in order to develop clinically relevant AI solutions, rigorously validated on large-scale patient data, we foresee a need for improved collaboration between the two communities ensuring optimal outcomes and allocation of resources. AI may aid clinicians and radiologists by providing better tools for localization and quantification of disease features and changes thereof, and, with integration of clinical data, may provide better diagnostic performance and prognostic value. | health informatics |
10.1101/2020.09.07.20189837 | Maternal and child genetic liability for smoking and caffeine consumption and child mental health: An intergenerational genetic risk score analysis in the ALSPAC cohort | Background and aimsPrevious studies suggest an association between maternal tobacco and caffeine consumption during and outside of pregnancy and offspring mental health. We aimed to separate effects of the maternal environment (intrauterine or postnatal) from pleiotropic genetic effects.
DesignSecondary analysis of a longitudinal study. We 1) validated smoking and caffeine genetic risk scores (GRS) derived from published GWAS for use during pregnancy, 2) compared estimated effects of maternal and offspring GRS on childhood mental health outcomes, and 3) tested associations between maternal and offspring GRS on their respective outcomes.
SettingWe used data from a longitudinal birth cohort study from England, the Avon Longitudinal Study of Parents and Children (ALSPAC).
ParticipantsOur sample included 7921 mothers and 7964 offspring.
MeasurementsMental health and non-mental health phenotypes were derived from questionnaires and clinical assessments: 79 maternal phenotypes assessed during and outside of pregnancy, and 71 offspring phenotypes assessed in childhood (<10 years) and adolescence (11-18 years).
FindingsThe maternal smoking and caffeine GRS were associated with maternal smoking and caffeine consumption during pregnancy (2nd trimester: Psmoking = 3.0x10-7, Pcaffeine = 3.28x10-5). Both the maternal and offspring smoking GRS showed evidence of association with reduced childhood anxiety symptoms ({beta}maternal = -0.033; {beta}offspring= -0.031) and increased conduct disorder symptoms ({beta}maternal= 0.024; {beta}offspring= 0.030), after correcting for multiple testing. Finally, the maternal and offspring smoking GRS were associated with phenotypes related to sensation seeking behaviours in mothers and adolescence (e.g., increased symptoms of externalising disorders, extraversion, and monotony avoidance). The caffeine GRS showed weaker evidence for associations with mental health outcomes.
ConclusionsWe did not find strong evidence that maternal smoking and caffeine genetic risk scores (GRS) have a causal effect on offspring mental health outcomes. Our results confirm that the smoking GRS also captures liability for sensation seeking personality traits. | epidemiology |
10.1101/2020.09.07.20190066 | Understanding SARSCOV-2 propagation, impacting factors to derive possible scenarios and simulations | ObjectivesWe aimed to analyze factors impacting the Covid-19 epidemic on a macro level, comparing multiple countries across the world, and verifying the occurrence at a micro level through cluster analysis.
DesignStatistical analysis of large datasets.
MethodsWe used publicly available large world datasets (1-11). Data was transformed to fit parametric distributions prior to statistical analyses, which were performed with Students t-test, linear regression and post-hoc tests. Especially for ordinary least squares regression, natural logarithmic transformations were done to remediate normality violations in the standardized residuals.
ResultsThe severity of the epidemic was most strongly related to exposure to ultraviolet light and extrapolated levels of vitamin D and to the health of the population, especially with regards to obesity. We found no county with an obesity level < 8% with a severe epidemic. We also found that countries where the population benefited from sun exposure or vitamin D supplementation and spent time outside fared well. Factors related to increased propagation of the virus included the use of heating ventilation and air conditioning (HVAC), population density, poorly aerated gatherings, relative humidity, timely policies of closing clustering places until aeration was improved, and daily amount of ridership on public transportation, especially subways. Population lockdowns, masks, and blood type did not provide much explanatory power. The excess mortality observed is within the ranges of severe past influenza epidemics of 2016/2017 or 1999/2000.
ConclusionsOur study suggested that prevention measures should be directed to improving aeration systems, enhancing diets and exercise, and ensuring adequate levels of vitamin D. Further research on masking is indicated as our study could not separate policies from how well they were actually followed.
FundingThis research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors
Strengths and Limitations of the StudyO_LIThe Study examines large datasets across countries to look for macrotrends in management of the Covid-19 outbreak.
C_LIO_LIThe Study cannot necessarily establish causation but rather correlation.
C_LIO_LIThe Study raises some novel possibilities for further studies in relation to country-wide and individual-level susceptibility to Covid-19 and to other epidemics in general.
C_LIO_LIThe Study raises questions about some political policies based upon country-level comparisons and suggests some areas for exploration of prevention policies.
C_LI | infectious diseases |
10.1101/2020.09.09.20190389 | Passive surveillance assesses compliance with COVID-19 behavioral restrictions in a rural US county | Following the emergence of SARS-CoV-2, early outbreak response relied on behavioral interventions. In the United States, local governments implemented restrictions aimed at reducing movements and contacts to limit viral transmission. In Pennsylvania, restrictions closed schools and businesses in the spring of 2020 and interventions eased later through the summer. In a rural Pennsylvania county, we use passive monitoring of vehicular traffic volume and mobile device derived visits to points of interest as proxies for movements and contacts. Rural areas have limited health care resources, which magnifies the importance of disease prevention. These data show the lowest levels of movement occurred during the strictest phase of restrictions, indicating high levels of compliance with behavioral intervention. We find that increases in movement correlated with increases in SARS-CoV-2 cases 9-18 days later. The methodology used in this study can be adapted to inform outbreak management strategies for other locations and future outbreaks that use behavioral interventions to reduce pathogen transmission. | epidemiology |
10.1101/2020.09.07.20190108 | Covid-19 Belgium: Extended SEIR-QD model with nursing homes and long-term scenarios-based forecasts | Following the spread of the COVID-19 pandemic and pending the establishment of vaccination campaigns, several non pharmaceutical interventions such as partial and full lockdown, quarantine and measures of physical distancing have been imposed in order to reduce the spread of the disease and to lift the pressure on healthcare system. Mathematical models are important tools for estimating the impact of these interventions, for monitoring the current evolution of the epidemic at a national level and for estimating the potential long-term consequences of relaxation of measures. In this paper, we model the evolution of the COVID-19 epidemic in Belgium with a deterministic age-structured extended compartmental model. Our model takes special consideration for nursing homes which are modelled as separate entities from the general population in order to capture the specific delay and dynamics within these entities. The model integrates social contact data and is fitted on hospitalisations data (admission and discharge), on the daily number of COVID-19 deaths (with a distinction between general population and nursing home related deaths) and results from serological studies, with a sensitivity analysis based on a Bayesian approach. We present the situation as in November 2020 with the estimation of some characteristics of the COVID-19 deduced from the model. We also present several mid-term and long-term projections based on scenarios of reinforcement or relaxation of social contacts for different general sectors, with a lot of uncertainties remaining. | epidemiology |
10.1101/2020.09.07.20190108 | Covid-19 Belgium: Extended SEIR-QD model with nursing homes and long-term scenarios-based forecasts | Following the spread of the COVID-19 pandemic and pending the establishment of vaccination campaigns, several non pharmaceutical interventions such as partial and full lockdown, quarantine and measures of physical distancing have been imposed in order to reduce the spread of the disease and to lift the pressure on healthcare system. Mathematical models are important tools for estimating the impact of these interventions, for monitoring the current evolution of the epidemic at a national level and for estimating the potential long-term consequences of relaxation of measures. In this paper, we model the evolution of the COVID-19 epidemic in Belgium with a deterministic age-structured extended compartmental model. Our model takes special consideration for nursing homes which are modelled as separate entities from the general population in order to capture the specific delay and dynamics within these entities. The model integrates social contact data and is fitted on hospitalisations data (admission and discharge), on the daily number of COVID-19 deaths (with a distinction between general population and nursing home related deaths) and results from serological studies, with a sensitivity analysis based on a Bayesian approach. We present the situation as in November 2020 with the estimation of some characteristics of the COVID-19 deduced from the model. We also present several mid-term and long-term projections based on scenarios of reinforcement or relaxation of social contacts for different general sectors, with a lot of uncertainties remaining. | epidemiology |
10.1101/2020.09.07.20189621 | COVID-19 in patients with hepatobiliary and pancreatic diseases in East London: A single-centre cross-sectional study | ObjectiveTo explore risk factors associated with COVID-19 susceptibility and survival in patients with pre-existing hepato-pancreato-biliary (HPB) conditions.
DesignCross-sectional study.
SettingEast London Pancreatic Cancer Epidemiology (EL-PaC-Epidem) study at Barts Health NHS Trust, UK. Linked electronic health records were interrogated on a cohort of participants (age [≥] 18 years), reported with HPB conditions between 1 April 2008 and 6 March 2020.
ParticipantsEL-PaC-Epidem study participants, alive on 12 February 2020, and living in East London within the previous six months (n=15 440). The cohort represents a multi-ethnic population with 51.7% belonging to the non-White background.
Main outcome measureCOVID-19 incidence and mortality.
ResultsSome 226 (1.5%) participants had confirmed COVID-19 diagnosis between 12 February and 12 June 2020, with an increased odds for men (OR 1.56; 95% CI 1.2 to 2.04) and Black ethnicity (2.04; 1.39 to 2.95) as well as patients with moderate to severe liver disease (2.2; 1.35 to 3.59). Each additional comorbidity increased the odds of infection by 62%. Substance mis-users were at more risk of infection, so were patients on Vitamin D treatment. The higher odds ratios in patients with chronic pancreatic or mild liver conditions, age>70, and history of smoking or obesity were due to co-existing comorbidities. Increased odds of death were observed for men (3.54; 1.68 to 7.85) and Black ethnicity (3.77; 1.38 to 10.7). Patients having respiratory complications from COVID-19 without a history of chronic respiratory disease also had higher odds of death (5.77; 1.75 to 19).
ConclusionsIn this large population-based study of HPB patients, men, Black ethnicity, pre-existing moderate to severe liver conditions, six common medical multi-morbidities, substance mis-use, and a history of Vitamin D treatment independently posed higher odds of acquiring COVID-19 compared to their respective counterparts. The odds of death were significantly high for men and Black people.
STRENGTHS AND LIMITATIONS OF THIS STUDYO_LIFirst multi-ethnic population-based study on COVID-19 in patients with hepato-pancreato-biliary group of diseases.
C_LIO_LISystematic identification of the effect, or the lack of it, of individual demographic and clinical factors on the infection and mortality of COVID-19 in a large cohort of over 15 000 patients, robustly controlling for potential confounders in their evaluation.
C_LIO_LIAccess to longitudinal data from linked primary and secondary care electronic health records, and use of rule-based phenotyping algorithms allowed for improved completeness and accuracy of the explored variables.
C_LIO_LISome observed increased odds of SARS-CoV-2 infection and related death could be plausibly explained by unmeasured confounding.
C_LIO_LIThe effects reported in the study could be influenced by the relatively smaller size of COVID-19 cases within this cohort.
C_LI | gastroenterology |
10.1101/2020.09.08.20190470 | Modelling the epidemic growth of preprints on COVID-19 and SARS-CoV-2 | The response of the scientific community to the global health emergency caused by the COVID-19 pandemic has produced an unprecedented number of manuscripts in a short period of time, the vast majority of which have been shared in the form of preprints posted on online preprint repositories before peer review. This surge in preprint publications has in itself attracted considerable attention, although mostly in the bibliometrics literature. In the present study we apply a mathematical growth model, known as the generalized Richards model, to describe the time evolution of the cumulative number of COVID-19 related preprints. This mathematical approach allows us to infer several important aspects concerning the underlying growth dynamics, such as its current stage and its possible evolution in the near future. We also analyze the rank-frequency distribution of preprints servers, ordered by the number of COVID-19 preprints they host, and find that it follows a power law in the low rank (high frequency) region, with the high rank (low frequency) tail being better described by a q-exponential function. The Zipf-like law in the high frequency regime indicates the presence of a cumulative advantage effect, whereby servers that already have more preprints receive more submissions. | public and global health |
10.1101/2020.09.07.20183665 | Copy number variant detection with low-coverage whole-genome sequencing is a viable replacement for the traditional array-CGH | Copy number variations (CNVs) are a type of structural variants involving alterations in the number of copies of specific regions of DNA, which can either be deleted or duplicated. CNVs contribute substantially to normal population variability; however, abnormal CNVs cause numerous genetic disorders. Nowadays, several methods for CNV detection are used, from the conventional cytogenetic analysis through microarray-based methods (aCGH) to next-generation sequencing (NGS). We present GenomeScreen - NGS-based CNV detection method for lowcoverage whole-genome sequencing. We determined the theoretical limits of its accuracy and confirmed it with extensive in-silico study and real patient samples with known genotypes. Theoretically, at least 6M uniquely mapped reads are required to detect CNV with a length of 100 kilobases (kb) or more with high confidence (Z-score > 7). In practice, the in-silico analysis showed the requirement of at least 8M to obtain >99% accuracy (for 100 kb deviations). We compared GenomeScreen with one of the currently used aCGH methods in diagnostic laboratories, which has a 200 kb mean resolution. GenomeScreen and aCGH both detected 59 deviations, GenomeScreen furthermore detected 134 other (usually) smaller variations. The performance of the proposed GenemoScreen tool is comparable or superior to the aCGH regarding accuracy, turnaround time, and cost-effectiveness, presenting a reasonable benefit particularly in a prenatal diagnosis setting. | health informatics |
10.1101/2020.09.08.20190629 | Model-informed COVID-19 vaccine prioritization strategies by age and serostatus | When a vaccine for COVID-19 becomes available, limited initial supply will raise the question of how to prioritize the available doses and thus underscores the need for transparent, evidence-based strategies that relate knowledge of, and uncertainty in, disease transmission, risk, vaccine efficacy, and existing population immunity. Here, we employ a model-informed approach to vaccine prioritization that evaluates the impact of prioritization strategies on cumulative incidence and mortality and accounts for population factors such as age, contact structure, and seroprevalence, and vaccine factors including imperfect and age-varying efficacy. This framework can be used to evaluate and compare existing strategies, and it can also be used to derive an optimal prioritization strategy to minimize mortality or incidence. We find that a transmission-blocking vaccine should be prioritized to adults ages 20-49y to minimize cumulative incidence and to adults over 60y to minimize mortality. Direct vaccination of adults over 60y minimizes mortality for vaccines that do not block transmission. We also estimate the potential benefit of using individual-level serological tests to redirect doses to only seronegative individuals, improving the marginal impact of each dose. We argue that this serology-informed vaccination approach may improve the efficiency of vaccination efforts while partially addressing existing inequities in COVID-19 burden and impact. | infectious diseases |
10.1101/2020.09.08.20190975 | Vitamin D and COVID-19 Susceptibility and Severity in the COVID-19 Host Genetics Initiative: a Mendelian Randomization Study | BackgroundIncreased vitamin D levels, as reflected by 25OHD measurements, have been proposed to protect against COVID-19 disease based on in-vitro, observational, and ecological studies. However, vitamin D levels are associated with many confounding variables and thus associations described to date may not be causal. Vitamin D Mendelian randomization (MR) studies have provided results that are concordant with large-scale vitamin D randomized trials. Here, we used two-sample MR to assess evidence supporting a causal effect of circulating 25OHD levels on COVID-19 susceptibility and severity.
Methods and findingsGenetic variants strongly associated with 25OHD levels in a genome-wide association study (GWAS) of 443,734 participants of European ancestry (including 401,460 from the UK Biobank) were used as instrumental variables. GWASs of COVID-19 susceptibility, hospitalization, and severe disease from the COVID-19 Host Genetics Initiative were used as outcome GWASs. These included up to 14,134 individuals with COVID-19, and 1,284,876 without COVID-19, from 11 countries. SARS-CoV-2 positivity was determined by laboratory testing or medical chart review. Population controls without COVID-19 were also included in the control groups for all outcomes, including hospitalization and severe disease. Analyses were restricted to individuals of European descent when possible. Using inverse-weighted MR, genetically increased 25OHD levels by one standard deviation on the logarithmic scale had no clear association with COVID-19 susceptibility (OR = 0.97; 95% CI: 0.95, 1.10; P=0.61), hospitalization (OR = 1.11; 95% CI: 0.91, 1.35; P=0.30), and severe disease (OR = 0.93; 95% CI: 0.73, 1.17; P=0.53). We used an additional 6 meta-analytic methods, as well as sensitivity analyses after removal of variants at risk of horizontal pleiotropy and obtained similar results. These results may be limited by weak instrument bias in some analyses. Further, our results do not apply to individuals with vitamin D deficiency.
ConclusionIn this two-sample MR study, we did not observe evidence to support an association between 25OHD levels and COVID-19 susceptibility, severity, or hospitalization. Hence, vitamin D supplementation as a mean of protecting against worsened COVID-19 outcomes is not supported by genetic evidence. Other therapeutic or preventative avenues should be given higher priority for COVID-19 randomized controlled trials.
Author SummaryO_LIWhy was this study done?
- Vitamin D levels have been associated with COVID-19 outcomes in multiple observational studies, though confounders are likely to bias these associations.
- By using genetic instruments which limit such confounding, Mendelian randomization studies have consistently obtained results concordant with vitamin D supplementation randomized trials. This provides rationale to undertake vitamin D Mendelian randomization studies for COVID-19 outcomes.
C_LIO_LIWhat did the researchers do and find?
- We used the genetic variants obtained from the largest consortium of COVID-19 cases and controls, and the largest study on genetic determinants of vitamin D levels.
We used Mendelian randomization to estimate the effect of increased vitamin D on COVID-19 outcomes, while limiting confounding.
- In multiple analyses, our results consistently showed no evidence for an association between genetically predicted vitamin D levels and COVID-19 susceptibility, hospitalization, or severe disease.
C_LIO_LIWhat do these findings mean?
- Vitamin D is a highly confounded variable, and traditional observational studies are at high risk of biased estimates.
- We did not find evidence that vitamin D supplementation would improve COVID-19 outcomes.
- Given Mendelian randomizations past track-record of anticipating the results of vitamin D randomized controlled trials, other therapeutic and preventative avenues should be prioritized for COVID-19 trials.
C_LI | infectious diseases |
10.1101/2020.09.08.20190975 | Vitamin D and COVID-19 susceptibility and severity in the COVID-19 Host Genetics Initiative: A Mendelian randomization study | BackgroundIncreased vitamin D levels, as reflected by 25OHD measurements, have been proposed to protect against COVID-19 disease based on in-vitro, observational, and ecological studies. However, vitamin D levels are associated with many confounding variables and thus associations described to date may not be causal. Vitamin D Mendelian randomization (MR) studies have provided results that are concordant with large-scale vitamin D randomized trials. Here, we used two-sample MR to assess evidence supporting a causal effect of circulating 25OHD levels on COVID-19 susceptibility and severity.
Methods and findingsGenetic variants strongly associated with 25OHD levels in a genome-wide association study (GWAS) of 443,734 participants of European ancestry (including 401,460 from the UK Biobank) were used as instrumental variables. GWASs of COVID-19 susceptibility, hospitalization, and severe disease from the COVID-19 Host Genetics Initiative were used as outcome GWASs. These included up to 14,134 individuals with COVID-19, and 1,284,876 without COVID-19, from 11 countries. SARS-CoV-2 positivity was determined by laboratory testing or medical chart review. Population controls without COVID-19 were also included in the control groups for all outcomes, including hospitalization and severe disease. Analyses were restricted to individuals of European descent when possible. Using inverse-weighted MR, genetically increased 25OHD levels by one standard deviation on the logarithmic scale had no clear association with COVID-19 susceptibility (OR = 0.97; 95% CI: 0.95, 1.10; P=0.61), hospitalization (OR = 1.11; 95% CI: 0.91, 1.35; P=0.30), and severe disease (OR = 0.93; 95% CI: 0.73, 1.17; P=0.53). We used an additional 6 meta-analytic methods, as well as sensitivity analyses after removal of variants at risk of horizontal pleiotropy and obtained similar results. These results may be limited by weak instrument bias in some analyses. Further, our results do not apply to individuals with vitamin D deficiency.
ConclusionIn this two-sample MR study, we did not observe evidence to support an association between 25OHD levels and COVID-19 susceptibility, severity, or hospitalization. Hence, vitamin D supplementation as a mean of protecting against worsened COVID-19 outcomes is not supported by genetic evidence. Other therapeutic or preventative avenues should be given higher priority for COVID-19 randomized controlled trials.
Author SummaryO_LIWhy was this study done?
- Vitamin D levels have been associated with COVID-19 outcomes in multiple observational studies, though confounders are likely to bias these associations.
- By using genetic instruments which limit such confounding, Mendelian randomization studies have consistently obtained results concordant with vitamin D supplementation randomized trials. This provides rationale to undertake vitamin D Mendelian randomization studies for COVID-19 outcomes.
C_LIO_LIWhat did the researchers do and find?
- We used the genetic variants obtained from the largest consortium of COVID-19 cases and controls, and the largest study on genetic determinants of vitamin D levels.
We used Mendelian randomization to estimate the effect of increased vitamin D on COVID-19 outcomes, while limiting confounding.
- In multiple analyses, our results consistently showed no evidence for an association between genetically predicted vitamin D levels and COVID-19 susceptibility, hospitalization, or severe disease.
C_LIO_LIWhat do these findings mean?
- Vitamin D is a highly confounded variable, and traditional observational studies are at high risk of biased estimates.
- We did not find evidence that vitamin D supplementation would improve COVID-19 outcomes.
- Given Mendelian randomizations past track-record of anticipating the results of vitamin D randomized controlled trials, other therapeutic and preventative avenues should be prioritized for COVID-19 trials.
C_LI | infectious diseases |
10.1101/2020.09.08.20190611 | The relationship between cognitive function and sleep duration: a Mendelian randomisation study | Structured abstractO_ST_ABSImportanceC_ST_ABSSleep duration is associated with cognitive function, with Mendelian randomisation evidence supporting a relationship in this direction. However, whether cognitive function may also precede problematic sleep duration remains unclear.
ObjectiveTo assess whether reaction time and visual memory are causally associated with sleep duration.
DesignSummary-level Mendelian randomisation design between visual memory (30 SNPs), reaction time (44 SNPs), and self-reported and objective sleep duration Setting: Population-based study.
ParticipantsIndividuals from the UK Biobank, who were included in genome-wide association studies for our exposures and outcomes, aged 40-69y at baseline (mean 56y), 54% female and self-reported sleep was 7.2 hours.
ExposuresVisual memory, reactiontime
Main outcomesself-reported and objective sleep duration
ResultsMendelian randomisation results showed that worse performance on the visual memory task was associated with longer ({beta}=0.09, 95% CI=0.02;0.17), while slower reaction time was associated with shorter ({beta}=-0.15, 95% CI=-0.29;-0.01), objective sleep duration. Sensitivity analyses revealed no issues with horizontal pleiotropy (MR-Egger intercept p-value <0.05). No association was observed between either cognitive measure and self-reported sleep duration.
Conclusions and relevanceThese results suggest a potential causative relationship between reaction time and objective sleep duration, where worse visual memory is associated with longer, and worse reaction time with shorter objective sleep duration. This study furthers our understanding of the relationship between brain health and sleep duration and sheds light on the causal nature of these associations.
Key pointsO_ST_ABSQuestionC_ST_ABSIs genetically predicted sleep duration (accelerometer-derived and self-reported) associated with cognitive function outcomes?
FindingsIn this summary-level Mendelian randomisation study, worse visual memory was associated with longer, whereas worse reaction time was associated with shorter, objective sleep duration. No associations were observed between cognitive measures and self-reported sleep duration.
MeaningThese findings suggest a causal association between cognition and objective sleep duration measures, expanding our understanding of the relationship between cognition and sleep. | epidemiology |
10.1101/2020.09.08.20186908 | Unique prediction of developmental psychopathology from genetic and familial risk | BackgroundEarly detection is critical for easing the rising burden of psychiatric disorders. However, the specificity of psychopathological measurements and genetic predictors is unclear among youth.
MethodsWe measured associations between genetic risk for psychopathology (polygenic risk scores (PRS) and family history (FH) measures) and a wide range of behavioral measures in a large sample (n=5204) of early adolescent participants (9-11 years) from the Adolescent Brain and Cognitive Development (ABCD) StudySM. Associations were measured both with and without taking into consideration shared variance across measures of genetic risk.
ResultsPolygenic risk for Attention Deficit Hyperactivity Disorder (ADHD) and depression (DEP) shared many significant associations with externalizing, internalizing and psychosis-related behaviors. However, when accounting for all measures of genetic and familial risk these two PRS also showed clear, unique patterns of association: the DEP PRS showed significantly stronger associations with somatic complaints and depression symptoms; whereas the ADHD PRS showed stronger associations with ADHD symptoms, impulsivity and prodromal psychosis. The Schizophrenia PRS showed a unique negative association with performance on cognitive tasks measuring fluid abilities, such as working memory and executive function, that was not accounted for by other measures of genetic risk. FH accounted for unique variability in behavior above and beyond PRS and vice versa with FH measures explaining a greater proportion of unique variability compared to the PRS.
ConclusionOur results indicate that, among youth, many behaviors show shared genetic influences; however, there is also specificity in the profile of emerging psychopathologies for individuals with high genetic risk for particular disorders. This may be useful for quantifying early, differential risk for psychopathology in development.
FundingThe ABCD Study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, U24DA041147, U01DA041093, and U01DA041025. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. R.L was supported by Kavli Innovative Research Grant under award number 2019-1624. C.F. was supported by grant R01MH122688 and RF1MH120025 funded by the National Institute for Mental Health (NIMH). | genetic and genomic medicine |
10.1101/2020.09.03.20184226 | A New Screening Method for COVID-19 based on Ocular Feature Recognition by Machine Learning Tools | The Coronavirus disease 2019 (COVID-19) has affected several million people. With the outbreak of the epidemic, many researchers are devoting themselves to the COVID-19 screening system. The standard practices for rapid risk screening of COVID-19 are the CT imaging or RT-PCR (real-time polymerase chain reaction). However, these methods demand professional efforts of the acquisition of CT images and saliva samples, a certain amount of waiting time, and most importantly prohibitive examination fee in some countries. Recently, some literatures have shown that the COVID-19 patients usually accompanied by ocular manifestations consistent with the conjunctivitis, including conjunctival hyperemia, chemosis, epiphora, or increased secretions. After more than four months study, we found that the confirmed cases of COVID-19 present the consistent ocular pathological symbols; and we propose a new screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras, could reliably make a rapid risk screening of COVID-19 with very high accuracy. We believe a system implementing such an algorithm should assist the triage management or the clinical diagnosis. To further evaluate our algorithm and approved by the Ethics Committee of Shanghai public health clinic center of Fudan University, we conduct a study of analyzing the eye-region images of 303 patients (104 COVID-19, 131 pulmonary, and 68 ocular patients), as well as 136 healthy people. Remarkably, our results of COVID-19 patients in testing set consistently present similar ocular pathological symbols; and very high testing results have been achieved in terms of sensitivity and specificity. We hope this study can be inspiring and helpful for encouraging more researches in this topic. | infectious diseases |
10.1101/2020.09.03.20184226 | A New Screening Method for COVID-19 based on Ocular Feature Recognition by Machine Learning Tools | The Coronavirus disease 2019 (COVID-19) has affected several million people. With the outbreak of the epidemic, many researchers are devoting themselves to the COVID-19 screening system. The standard practices for rapid risk screening of COVID-19 are the CT imaging or RT-PCR (real-time polymerase chain reaction). However, these methods demand professional efforts of the acquisition of CT images and saliva samples, a certain amount of waiting time, and most importantly prohibitive examination fee in some countries. Recently, some literatures have shown that the COVID-19 patients usually accompanied by ocular manifestations consistent with the conjunctivitis, including conjunctival hyperemia, chemosis, epiphora, or increased secretions. After more than four months study, we found that the confirmed cases of COVID-19 present the consistent ocular pathological symbols; and we propose a new screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras, could reliably make a rapid risk screening of COVID-19 with very high accuracy. We believe a system implementing such an algorithm should assist the triage management or the clinical diagnosis. To further evaluate our algorithm and approved by the Ethics Committee of Shanghai public health clinic center of Fudan University, we conduct a study of analyzing the eye-region images of 303 patients (104 COVID-19, 131 pulmonary, and 68 ocular patients), as well as 136 healthy people. Remarkably, our results of COVID-19 patients in testing set consistently present similar ocular pathological symbols; and very high testing results have been achieved in terms of sensitivity and specificity. We hope this study can be inspiring and helpful for encouraging more researches in this topic. | infectious diseases |
10.1101/2020.09.03.20184226 | A New Screening Method for COVID-19 based on Ocular Feature Recognition by Machine Learning Tools | The Coronavirus disease 2019 (COVID-19) has affected several million people. With the outbreak of the epidemic, many researchers are devoting themselves to the COVID-19 screening system. The standard practices for rapid risk screening of COVID-19 are the CT imaging or RT-PCR (real-time polymerase chain reaction). However, these methods demand professional efforts of the acquisition of CT images and saliva samples, a certain amount of waiting time, and most importantly prohibitive examination fee in some countries. Recently, some literatures have shown that the COVID-19 patients usually accompanied by ocular manifestations consistent with the conjunctivitis, including conjunctival hyperemia, chemosis, epiphora, or increased secretions. After more than four months study, we found that the confirmed cases of COVID-19 present the consistent ocular pathological symbols; and we propose a new screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras, could reliably make a rapid risk screening of COVID-19 with very high accuracy. We believe a system implementing such an algorithm should assist the triage management or the clinical diagnosis. To further evaluate our algorithm and approved by the Ethics Committee of Shanghai public health clinic center of Fudan University, we conduct a study of analyzing the eye-region images of 303 patients (104 COVID-19, 131 pulmonary, and 68 ocular patients), as well as 136 healthy people. Remarkably, our results of COVID-19 patients in testing set consistently present similar ocular pathological symbols; and very high testing results have been achieved in terms of sensitivity and specificity. We hope this study can be inspiring and helpful for encouraging more researches in this topic. | infectious diseases |
10.1101/2020.09.09.20189886 | Nasal systems immunology identifies inflammatory and tolerogenic myeloid cells that determine allergic outcome following challenge | Innate mononuclear phagocytic system (MPS) cells preserve mucosal immune homeostasis. Here, we investigated their role at nasal mucosa following challenge with house dust mite. We combined single cell proteome and transcriptome profiling on immune cells from nasal biopsy cells of allergic rhinitis and non-allergic subjects, before and after repeated nasal allergen challenge. Nasal biopsies of patients showed infiltrating inflammatory HLA-DRhi CD14+ monocytes and CD16+ monocytes, and transcriptional changes in resident CD1C+ CD1A+ conventional dendritic cells (cDC)2 following challenge. Importantly, although clinically silent, non-allergic individuals displayed a distinct innate MPS response to allergen challenge: predominant infiltration of myeloid-derived suppressor cells (HLA-DRlow CD14+ monocytes), as well as cDC2 clusters expressing increased inhibitory/tolerogenic transcripts. Therefore, we identified not only clusters involved in airway inflammation but also a non-inflammatory, homeostatic blueprint of innate MPS responses to allergens in non-allergic individuals. Future therapies should target innate MPS for treatment of inflammatory airway diseases.
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10.1101/2020.09.09.20191676 | Exhaled CO2 as COVID-19 infection risk proxy for different indoor environments and activities | CO2 is co-exhaled with aerosols containing SARS-CoV-2 by COVID-19 infected people and can be used as a proxy of SARS-CoV-2 concentrations indoors. Indoor CO2 measurements by low-cost sensors hold promise for mass monitoring of indoor aerosol transmission risk for COVID-19 and other respiratory diseases. We derive analytical expressions of CO2-based risk proxies and apply them to various typical indoor environments. The relative infection risk in a given environment scales with excess CO2 level, and thus keeping CO2 as low as feasible in a space allows optimizing the protection provided by ventilation. We show that the CO2 level corresponding to a given absolute infection risk varies by over 2 orders of magnitude for different environments and activities. Although large uncertainties, mainly from virus exhalation rates, are still associated with infection risk estimates, our study provides more specific and practical recommendations for low-cost CO2-based indoor infection risk monitoring.
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10.1101/2020.09.10.20191841 | The King's College London Coronavirus Health and Experiences of Colleagues at King's Study: SARS-CoV-2 antibody response in an occupational sample | We report test results for SARS-CoV-2 antibodies in an occupational group of postgraduate research students and current members of staff at Kings College London. Between June and July 2020, antibody testing kits were sent to n=2296 participants; n=2004 (86.3%) responded, of whom n=1882 (93.9%) returned valid test results. Of those that returned valid results, n=124 (6.6%) tested positive for SARS-CoV-2 antibodies, with initial comparisons showing variation by age group and clinical exposure. | epidemiology |
10.1101/2020.09.10.20192310 | A comparison of ten polygenic score methods for psychiatric disorders applied across multiple cohorts | BackgroundPolygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies (GWASs). PGS methods differ in which DNA variants are included and the weights assigned to them; some require an independent tuning sample to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, e.g., phenotype definition or technical factors.
MethodsThe Psychiatric Genomics Consortium working groups for schizophrenia (SCZ) and major depressive disorder (MDD) bring together many independently collected case- control cohorts. We used these resources (31K SCZ cases, 41K controls; 248K MDD cases, 563K controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and nine methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) are compared.
ResultsCompared to PC+T, the other nine methods give higher prediction statistics, MegaPRS, LDPred2 and SBayesR significantly so, up to 9.2% variance in liability for SCZ across 30 target cohorts, an increase of 44%. For MDD across 26 target cohorts these statistics were 3.5% and 59%, respectively.
ConclusionsAlthough the methods that more formally model genetic architecture have similar performance, MegaPRS, LDpred2, and SBayesR rank highest in most comparison and are recommended in applications to psychiatric disorders. | genetic and genomic medicine |
10.1101/2020.09.07.20189662 | Scarring and Selection in the Great Irish Famine | What is the health impact of famines on survivors? We use a population exposed to severe famine conditions during infancy to document two opposing effects. The first: exposure leads to poor health into adulthood, a scarring effect. The second: survivors do not themselves suffer health consequences, a selection effect. Anthropometric evidence on over 21,000 subjects born before, during and after the Great Irish Famine (1845-52), among modern historys most severe famines, suggests selection is strongest where mortality is highest. Individuals born in heavily-affected areas experienced no measurable stunted growth; scarring was found only where excess mortality was low. | health economics |
10.1101/2020.09.11.20191692 | Hyaluronan is abundant in COVID-19 respiratory secretions | Thick, viscous respiratory secretions are a major pathogenic feature of COVID-19 disease, but the composition and physical properties of these secretions are poorly understood. We characterized the composition and rheological properties (i.e. resistance to flow) of respiratory secretions collected from intubated COVID-19 patients. We found the percent solids and protein content are all greatly elevated in COVID-19 compared to heathy control samples and closely resemble levels seen in cystic fibrosis (CF), a genetic disease known for thick, tenacious respiratory secretions. DNA and hyaluronan are major components of respiratory secretions in COVID-19 and are likewise abundant in cadaveric lung tissues from these patients. COVID-19 secretions exhibited heterogeneous rheological behaviors with thicker samples showing increased sensitivity to DNase and hyaluronidase treatment. These results highlight the dramatic biophysical properties of COVID-19 respiratory secretions and suggest that DNA and hyaluronan may be viable therapeutic targets in COVID-19 infection. | infectious diseases |
10.1101/2020.09.10.20190348 | High efficacy of face masks explained by characteristic regimes of airborne SARS-CoV-2 virus abundance | Airborne transmission by droplets and aerosols is important for the spread of viruses and face masks are a well-established preventive measure, but their effectiveness for mitigating COVID-19 is still under debate. We show that variations in mask efficacy can be explained by different regimes of virus abundance. For SARS-CoV-2, the virus load of infectious individuals can vary by orders of magnitude, but we find that most environments and contacts are in a virus-limited regime where simple surgical masks are highly effective on individual and population-average levels, whereas more advanced masks and other protective equipment are required in potentially virus-rich indoor environments such as medical centers and hospitals. Due to synergistic effects, masks are particularly effective in combination with other preventive measures like ventilation and distancing.
One Sentence SummaryFace masks are highly effective due to prevailing virus-limited environments in airborne transmission of COVID-19. | infectious diseases |
10.1101/2020.09.11.20192690 | Characteristics of anti-SARS-CoV-2 antibodies in recovered COVID-19 subjects. | Coronavirus Disease 2019 (COVID-19) is a global pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While detection of SARS-CoV-2 by polymerase chain reaction with reverse transcription (RT-PCR) is currently used to diagnose acute COVID-19 infection, serological assays are needed to study the humoral immune response to SARS-CoV-2. SARS-CoV-2 IgG/A/M antibodies against SARS-CoV-2 spike (S) protein and its receptor-binding domain (RBD) were characterized using an enzyme-linked immunosorbent assay (ELISA) and assessed for their ability to neutralize live SARS-CoV-2 virus in recovered subjects who were RT-PCR-positive (n=153), RT-PCR-negative (n=55), and control samples collected pre-COVID-19 (n=520). Anti-SARS-CoV-2 antibodies were detected in 90.9% of resolved subjects up to 180 days post-symptom onset. Anti-S protein and anti-RBD IgG titers correlated (r= 0.5157 and r = 0.6010, respectively) with viral neutralization. Of the RT-PCR-positive subjects, 22 (14.3%) did not have anti-SARS-CoV-2 antibodies; and of those, 17 had RT-PCR cycle threshold (Ct) values >27, raising the possibility that these indeterminate results are from individuals who were not infected, or had mild infection that failed to elicit an antibody response. This study highlights the importance of serological surveys to determine population-level immunity based on infection numbers as determined by RT-PCR. | infectious diseases |
10.1101/2020.09.11.20192971 | The Impact of Mask-Wearing in Mitigating the Spread of COVID-19 During the Early Phases of the Pandemic | Masks have been widely recommended as a precaution against COVID-19 transmission. Several studies have shown the efficacy of masks at reducing droplet dispersion in lab settings. However, during the early phases of the pandemic, the usage of masks varied widely across countries. Using individual response data from the Imperial College London -- YouGov personal measures survey, this study investigates the effect of mask use within a country on the spread of COVID-19. The survey shows that mask-wearing exhibits substantial variations across countries and over time during the pandemics early phase. We use a reduced form econometric model to relate population-wide variation in mask-wearing to the growth rate of confirmed COVID-19 cases. The results indicate that mask-wearing plays an important role in mitigating the spread of COVID-19. Widespread mask-wearing within a country associates with an expected 7% (95% CI: 3.94% -- 9.99%) decline in the growth rate of daily active cases of COVID-19 in the country. This daily decline equates to an expected 88.5% drop in daily active cases over a 30-day period when compared to zero percent mask-wearing, all else held equal. The decline in daily growth rate due to the combined effect of mask-wearing, reduced outdoor mobility, and non-pharmaceutical interventions averages 28.1% (95% CI: 24.2%-32%). | epidemiology |
10.1101/2020.09.11.20175026 | Genome-wide association meta-analysis of childhood and adolescent internalising symptoms | Internalising symptoms in childhood and adolescence are as heritable as adult depression and anxiety, yet little is known of their molecular basis. This genome-wide association meta-analysis of internalising symptoms included repeated observations from 64,641 individuals, aged between 3 and 18. The N-weighted meta-analysis of overall internalising symptoms (INToverall) detected no genome-wide significant hits and showed low SNP heritability (1.66%, 95% confidence intervals 0.84-2.48%, Neffective=132,260). Stratified analyses indicated rater-based heterogeneity in genetic effects, with self-reported internalising symptoms showing the highest heritability (5.63%, 95% confidence intervals 3.08-8.18%). Additive genetic effects on internalising symptoms appeared stable over age, with overlapping estimates of SNP heritability from early-childhood to adolescence. Genetic correlations were observed with adult anxiety, depression, and the wellbeing spectrum (|rg|> 0.70), as well as with insomnia, loneliness, attention-deficit hyperactivity disorder, autism, and childhood aggression (range |rg|=0.42-0.60), whereas there were no robust associations with schizophrenia, bipolar disorder, obsessive-compulsive disorder, or anorexia nervosa. The pattern of genetic correlations suggests that childhood and adolescent internalising symptoms share substantial genetic vulnerabilities with adult internalising disorders and other childhood psychiatric traits, which could partially explain both the persistence of internalising symptoms over time and the high comorbidity amongst childhood psychiatric traits. Reducing phenotypic heterogeneity in childhood samples will be key in paving the way to future GWAS success. | psychiatry and clinical psychology |
10.1101/2020.09.11.20192997 | Ventilation and the SARS-CoV-2 Coronavirus Analysis of outbreaks in a restaurant and on a bus in China, and at a Call Center in South Korea | In a previous paper [10] a model of the distribution of respiratory droplets and aerosols by Lagrangian turbulent air-flow was developed. It is used to show how the SARS-CoV-2 Coronavirus can be spread by the breathing of single infected person. The model shows that the concentration of viruses in the cloud, exhaled by one person, can increase to infectious levels within a certain amount of time, in a confined space where the air re-circulates. In [10] the model was used to analyze the air-flow and SARS-CoV-2 Coronavirus build-up in a restaurant in Guangzhou, China [23, 22]. In this paper, we add the analysis of two more cases, an outbreak among lay-Buddhists, on a bus [30], traveling to a ceremony in Zhejiang province, China, and an outbreak in a Call Center in Seoul, Korea [24]. The analysis and comparison of these three cases, leads to the conclusion that the SARS-CoV-2 Coronavirus attacks in two steps: The first step is a linear spread between individuals with a couple of days delay. The second step is an polynomial spread effected by the air-conditioning system affecting a much larger number of people. Thus in the second step, the ventilation can become the super-spreader. | infectious diseases |
10.1101/2020.09.11.20192849 | Factors contributing to inadequate access to condoms and sources of condoms during novel coronavirus diseases 2019 in South Africa. | BackgroundEvidence has shown that the prescribed lockdown and physical distancing due to the novel coronavirus disease 2019 (COVID-19) have made accessing essential health care services much more difficult in low-and middle-income countries. Access to contraception is an essential service and should not be denied, even in a global crisis, because of its associated health benefits. Therefore, it is important to maintain timely access to contraception without unnecessary barriers. Hence, this study examines the factors contributing to limited access to condoms and preferred source of condoms during the COVID-19 pandemic in South Africa.
MethodsThis study used data from the National Income Dynamics Study-Coronavirus Rapid Mobile Survey (NIDS-CRAM) wave 1 survey. The NIDS-CRAM is a nationally representative survey of the National Income Dynamics Survey (NIDS), which involves a sample of South Africans from 2017 NIDS wave 5, who were then re{square}interviewed via telephone interview. This is the first secondary dataset on coronavirus from NIDS during the coronavirus pandemic. A total of 5,304 respondents were included in the study. Data were analysed using frequencies and percentages, chi-square test and binary logistic regression analysis.
ResultsAlmost one-quarter (22.40%) of South Africans could not access condoms, and every 7 in 10 South Africans preferred public source of condoms. Those who were other population groups [aOR=0.37; 95% CI=0.19-0.74] and those who were in the third wealth quintile [aOR=0.60; 95% CI=0.38-0.93] had lower odds of having access to condoms while those respondents who were aged 25-34 [aOR=0.48; 95% CI=0.27-0.83] and those with a secondary level of education and above [aOR=0.24; 95% CI=0.08-0.71] were less likely to prefer public source of condom.
ConclusionThis study concludes that there was limited access to condoms during the COVID-19 pandemic and that the preferred source of condoms was very skewed to public source in South Africa. Strategic interventions such as community distribution of free condoms to avert obstruction of condom access during the COVID-19 pandemic or any future pandemics should be adopted. | infectious diseases |
10.1101/2020.09.12.20193342 | Ancestry May Confound Genetic Machine Learning: Candidate-Gene Prediction of Opioid Use Disorder as an Example | BackgroundMachine learning (ML) models are beginning to proliferate in psychiatry, however machine learning models in psychiatric genetics have not always accounted for ancestry. Using an empirical example of a proposed genetic test for OUD, and exploring a similar test for tobacco dependence and a simulated binary phenotype, we show that genetic prediction using ML is vulnerable to ancestral confounding.
MethodsWe utilize five ML algorithms trained with 16 brain reward-derived "candidate" SNPs proposed for commercial use and examine their ability to predict OUD vs. ancestry in an out-of-sample test set (N=1000, stratified into equal groups of n=250 cases and controls each of European and African ancestry). We rerun analyses with 8 random sets of allele-frequency matched SNPs. We contrast findings with 11 genome-wide significant variants for tobacco smoking. To document generalizability, we generate and test a random phenotype.
ResultsNone of the 5 ML algorithms predict OUD better than chance when ancestry was balanced but were confounded with ancestry in an out-of-sample test. In addition, the algorithms preferentially predicted admixed subpopulations. Random sets of variants matched to the candidate SNPs by allele frequency produced similar bias. Genome-wide significant tobacco smoking variants were also confounded by ancestry. Finally, random SNPs predicting a random simulated phenotype show that the bias attributable to ancestral confounding could impact any ML-based genetic prediction.
ConclusionsResearchers and clinicians are encouraged to be skeptical of claims of high prediction accuracy from ML-derived genetic algorithms for polygenic traits like addiction, particularly when using candidate variants. | addiction medicine |
10.1101/2020.09.13.20193656 | Inter-population differences of allele frequency and regulome tagging are associated with the heterogeneity of loci identified by cross-ancestry genome-wide association studies | To provide novel insight regarding the inter-population diversity of loci associated with complex traits, we integrated genome-wide data from UK Biobank (UKB) and 1,000 Genomes Project (1KG) data representative of the genetic diversity among worldwide populations. We investigated genome-wide data of 4,359 traits from 361,194 UKB participants of European descent. Using 1KG data, we explored the allele frequency differences and linkage disequilibrium (LD) structure of UKB genome-wide significant (GWS) loci across worldwide populations. Functional annotation data were used to identify regulatory elements and evaluate the tagging properties of GWS variants. No significant difference was observed in allele frequency between UKB and 1KG GBR (British in England and Scotland). Considering other population groups, we identified genome-wide significant alleles with frequencies different from what expected by chance: UKB vs. 1KG Europeans without GBR (rs74945666; allele=T [0.908 vs. 0.03], standing height pGWAS=1.48x10-17), UKB vs. 1KG African (rs556562; allele=A [0.942 vs. 0.083], platelet count pGWAS=4.84x10-15), UKB vs. 1KG Admixed Americans (rs1812378; allele=T [0.931 vs. 0.089], standing height pGWAS=4.23x10-12), UKB vs. 1KG East Asian (rs55881864; allele=T [0.911 vs. 0.001], monocyte count pGWAS=7.29x10-13), and UKB vs. South Asian (rs74945666; allele=T [0.908 vs. 0.061], standing height pGWAS=1.48x10-17). LD-structure analysis and computational prediction showed differences in how these alleles tag functional elements across human populations. In conclusion, the human diversity of certain GWS loci appear to be affected by local adaptation while in other cases the associations may be biased by residual population stratification. | genetic and genomic medicine |
10.1101/2020.09.13.20193664 | Plasma Metabolomic Profiling in Patients with Rheumatoid Arthritis Identifies Biochemical Features Indicative of Quantitative Disease Activity | BackgroundRheumatoid arthritis (RA) is a chronic, autoimmune disorder characterized by joint inflammation and pain. In patients with RA, metabolomic approaches, i.e., high-throughput profiling of small-molecule metabolites, on plasma or serum has thus far enabled the discovery of biomarkers for clinical subgroups, risk factors, and predictors of treatment response. Despite these recent advancements, the identification of blood metabolites that reflect quantitative disease activity remains an important challenge in precision medicine for RA. Herein, we use global plasma metabolomic profiling analyses to detect metabolites associated with, and predictive of, quantitative disease activity in patients with RA.
MethodsUltra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was performed on a discovery cohort consisting of 128 plasma samples from 64 RA patients, and on a validation cohort of 12 samples from 12 patients. The resulting metabolomic profiles were analyzed with two different strategies to find metabolites associated with RA disease activity defined by the Disease Activity Score-28 using C-reactive protein (DAS28-CRP). More specifically, mixed-effects regression models were used to identify metabolites differentially abundant between two disease activity groups ( lower, DAS28-CRP [≤] 3.2; and higher, DAS28-CRP > 3.2); and to identify metabolites significantly associated with DAS28-CRP scores. A generalized linear model (GLM) was then constructed for estimating DAS28-CRP using plasma metabolite abundances. Finally, for associating metabolites with CRP (an indicator of inflammation), metabolites differentially abundant between two patient groups ( low-CRP, CRP [≤] 3.0 mg/L; high-CRP, CRP > 3.0 mg/L) were investigated.
ResultsWe identified 33 metabolites differentially abundant between lower and higher disease activity groups (P < 0.05). Additionally, we identified 51 metabolites associated with DAS28-CRP (P < 0.05). A GLM based upon these 51 metabolites resulted in higher prediction accuracy (mean absolute error [MAE]{+/-}SD: 1.51{+/-}1.77) compared to a GLM without feature selection (MAE{+/-}SD: 2.02{+/-}2.21). The predictive value of this feature set was further demonstrated on a validation cohort of twelve plasma samples, wherein we observed a stronger correlation between predicted vs. actual DAS28-CRP (with feature selection: Spearmans{rho} = 0.69, 95% CI: [0.18, 0.90]; without feature selection: Spearmans{rho} = 0.18, 95% CI: [-0.44, 0.68]). Lastly, among all identified metabolites, the abundances of eight were significantly associated with CRP patient groups while controlling for potential confounders (P < 0.05).
ConclusionsWe demonstrate for the first time the prediction of quantitative disease activity in RA using plasma metabolomes. The metabolites identified herein provide insight into circulating pro-/anti-inflammatory metabolic signatures that reflect disease activity and inflammatory status in RA patients. | rheumatology |
10.1101/2020.09.14.20194167 | Depression Stigma Scale in the Portuguese population: validation and psychometric properties | BackgroundStigma is one of the most significant constraints on people living with depression. There is a lack of validated scales in Portugal to measure depression stigma; therefore, validation of the Depression Stigma Scale (DSS) is an essential step to the depression stigma research in Portugal.
MethodsWe developed the adaptation process with the ITC Guidelines for Translation and Adapting Tests taken into consideration. We collected the sample as part of the OSPI program - Optimizing suicide prevention programs and their implementation in Europe, specifically within the application in Portugal, and included 1693 participants. Floor-ceiling effects and response ranges were analyzed, and we calculated Cronbach alphas, conducted a Principal Component Analysis and Confirmatory Analysis. Validity evidence was tested with two well-documented hypotheses, using data on gender and depression symptoms.
ResultsThe sample was well comparable with the general Portuguese population, indicating its representativeness. We identified a three-factor structure in each subscale (personal and perceived stigma): weak-not-sick, discrimination, and dangerous/unpredictable. The Cronbachs alphas were satisfactory, and validity was confirmed.
ConclusionsThis study established the validity and demonstrated good psychometric properties of the DSS in the Portuguese population. The validation of the DSS can be beneficial in exploring stigma predictors and evaluating the effectiveness of stigma reduction interventions. | psychiatry and clinical psychology |
10.1101/2020.09.14.20194167 | Depression Stigma Scale in the Portuguese population: validation and psychometric properties | BackgroundStigma is one of the most significant constraints on people living with depression. There is a lack of validated scales in Portugal to measure depression stigma; therefore, validation of the Depression Stigma Scale (DSS) is an essential step to the depression stigma research in Portugal.
MethodsWe developed the adaptation process with the ITC Guidelines for Translation and Adapting Tests taken into consideration. We collected the sample as part of the OSPI program - Optimizing suicide prevention programs and their implementation in Europe, specifically within the application in Portugal, and included 1693 participants. Floor-ceiling effects and response ranges were analyzed, and we calculated Cronbach alphas, conducted a Principal Component Analysis and Confirmatory Analysis. Validity evidence was tested with two well-documented hypotheses, using data on gender and depression symptoms.
ResultsThe sample was well comparable with the general Portuguese population, indicating its representativeness. We identified a three-factor structure in each subscale (personal and perceived stigma): weak-not-sick, discrimination, and dangerous/unpredictable. The Cronbachs alphas were satisfactory, and validity was confirmed.
ConclusionsThis study established the validity and demonstrated good psychometric properties of the DSS in the Portuguese population. The validation of the DSS can be beneficial in exploring stigma predictors and evaluating the effectiveness of stigma reduction interventions. | psychiatry and clinical psychology |
10.1101/2020.09.14.20194167 | Psychometric properties of the Depression Stigma Scale in the Portuguese population and its association with gender and depressive symptomatology | BackgroundStigma is one of the most significant constraints on people living with depression. There is a lack of validated scales in Portugal to measure depression stigma; therefore, validation of the Depression Stigma Scale (DSS) is an essential step to the depression stigma research in Portugal.
MethodsWe developed the adaptation process with the ITC Guidelines for Translation and Adapting Tests taken into consideration. We collected the sample as part of the OSPI program - Optimizing suicide prevention programs and their implementation in Europe, specifically within the application in Portugal, and included 1693 participants. Floor-ceiling effects and response ranges were analyzed, and we calculated Cronbach alphas, conducted a Principal Component Analysis and Confirmatory Analysis. Validity evidence was tested with two well-documented hypotheses, using data on gender and depression symptoms.
ResultsThe sample was well comparable with the general Portuguese population, indicating its representativeness. We identified a three-factor structure in each subscale (personal and perceived stigma): weak-not-sick, discrimination, and dangerous/unpredictable. The Cronbachs alphas were satisfactory, and validity was confirmed.
ConclusionsThis study established the validity and demonstrated good psychometric properties of the DSS in the Portuguese population. The validation of the DSS can be beneficial in exploring stigma predictors and evaluating the effectiveness of stigma reduction interventions. | psychiatry and clinical psychology |
10.1101/2020.09.14.20194662 | Communicating the health risks of wildfire smoke exposure: Health literacy considerations of public health campaigns | Effective communication about the health effects of wildfire smoke is important to protect the public, especially those most vulnerable to the effects of exposure: people with chronic respiratory conditions, children, and older adults. The objective of this paper is to examine the clarity and accessibility of materials intended to provide education about the health effects of wildfire. The Centers for Disease Controls Clear Communications Index (CCI) is used to evaluate whether materials adhere to the main principles of health literacy: clarity and accessibility. This analysis found that only 32% of the materials received a passing score on the Clear Communications Index. Most materials were successful at clearly presenting specific behavioral recommendations, particularly that people should avoid exposure to air polluted by wildfire smoke by staying indoors, reducing activity levels, and using air purifiers or approved dust masks. However, materials often failed to acknowledge any uncertainty around these recommendations. Creators of these materials may want to incorporate more relevant illustrations to support the main message, and consider how information about the risks and benefits of the recommended behaviors can most clearly be presented. | public and global health |
10.1101/2020.09.15.20194811 | Disease control as an optimization problem | Traditionally, expert epidemiologists devise policies for disease control through a mixture of intuition and brute force. Namely, they use their know-how to narrow down the set of logically conceivable policies to a small family described by a few parameters, following which they conduct a grid search to identify the optimal policy within the set. This scheme is not scalable, in the sense that, when used to optimize over policies which depend on many parameters, it will likely fail to output an optimal disease policy in time for its implementation. In this article, we use techniques from convex optimization theory and machine learning to conduct optimizations over disease policies described by hundreds of parameters. In contrast to past approaches for policy optimization based on control theory, our framework can deal with arbitrary uncertainties on the initial conditions and model parameters controlling the spread of the disease. In addition, our methods allow for optimization over weekly-constant policies, specified by either continuous or discrete government measures (e.g.: lockdown on/off). We illustrate our approach by minimizing the total time required to eradicate COVID-19 within the Susceptible-Exposed-Infected-Recovered (SEIR) model proposed by Kissler et al. (March, 2020). | epidemiology |
10.1101/2020.09.14.20194589 | Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves | We construct a recursive Bayesian smoother, termed EpiFilter, for estimating the effective reproduction number, R, from the incidence of an infectious disease in real time and retrospectively. Our approach borrows from Kalman filtering theory, is quick and easy to compute, generalisable, deterministic and unlike many current methods, requires no change-point or window size assumptions. We model R as a flexible, hidden Markov state process and exactly solve forward-backward algorithms, to derive R estimates that incorporate all available incidence information. This unifies and extends two popular methods, EpiEstim, which considers past incidence, and the Wallinga-Teunis method, which looks forward in time. We find that this combination of maximising information and minimising assumptions significantly reduces the bias and variance of R estimates. Moreover, these properties make EpiFilter more statistically robust in periods of low incidence, where existing methods can become destabilised. As a result, EpiFilter offers improved inference of time-varying transmission patterns that are especially advantageous for assessing the risk of upcoming waves of infection in real time and at various spatial scales.
Author SummaryInferring changes in the transmissibility of an infectious disease is crucial for understanding and controlling epidemic spread. The effective reproduction number, R, is widely used to assess transmissibility. R measures the average number of secondary cases caused by a primary case and has provided insight into many diseases including COVID-19. An upsurge in R can forewarn of upcoming infections, while suppression of R can indicate if public health interventions are working. Reliable estimates of temporal changes in R can contribute important evidence to policymaking. Popular R-inference methods, while powerful, can struggle when cases are few because data are noisy. This can limit detection of crucial variations in transmissibility that may occur, for example, when infections are waning or when analysing transmissibility over fine geographic scales. In this paper we improve the general reliability of R-estimates and specifically increase robustness when cases are few. By adapting principles from control engineering, we formulate EpiFilter, a novel method for inferring R in real time and retrospectively. EpiFilter can potentially double the information extracted from epidemic time-series (when compared to popular approaches), significantly filtering the noise within data to minimise both bias and uncertainty of R-estimates and enhance the detection of salient changepoints in transmissibility. | epidemiology |
10.1101/2020.09.14.20194589 | Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves | We construct a recursive Bayesian smoother, termed EpiFilter, for estimating the effective reproduction number, R, from the incidence of an infectious disease in real time and retrospectively. Our approach borrows from Kalman filtering theory, is quick and easy to compute, generalisable, deterministic and unlike many current methods, requires no change-point or window size assumptions. We model R as a flexible, hidden Markov state process and exactly solve forward-backward algorithms, to derive R estimates that incorporate all available incidence information. This unifies and extends two popular methods, EpiEstim, which considers past incidence, and the Wallinga-Teunis method, which looks forward in time. We find that this combination of maximising information and minimising assumptions significantly reduces the bias and variance of R estimates. Moreover, these properties make EpiFilter more statistically robust in periods of low incidence, where existing methods can become destabilised. As a result, EpiFilter offers improved inference of time-varying transmission patterns that are especially advantageous for assessing the risk of upcoming waves of infection in real time and at various spatial scales.
Author SummaryInferring changes in the transmissibility of an infectious disease is crucial for understanding and controlling epidemic spread. The effective reproduction number, R, is widely used to assess transmissibility. R measures the average number of secondary cases caused by a primary case and has provided insight into many diseases including COVID-19. An upsurge in R can forewarn of upcoming infections, while suppression of R can indicate if public health interventions are working. Reliable estimates of temporal changes in R can contribute important evidence to policymaking. Popular R-inference methods, while powerful, can struggle when cases are few because data are noisy. This can limit detection of crucial variations in transmissibility that may occur, for example, when infections are waning or when analysing transmissibility over fine geographic scales. In this paper we improve the general reliability of R-estimates and specifically increase robustness when cases are few. By adapting principles from control engineering, we formulate EpiFilter, a novel method for inferring R in real time and retrospectively. EpiFilter can potentially double the information extracted from epidemic time-series (when compared to popular approaches), significantly filtering the noise within data to minimise both bias and uncertainty of R-estimates and enhance the detection of salient changepoints in transmissibility. | epidemiology |
10.1101/2020.09.13.20192856 | Early stopping in clinical PET studies: how to reduce expense and exposure | Clinical positron emission tomography (PET) research is costly and entails exposing participants to radioactivity. Researchers should therefore aim to include just the number of subjects needed to fulfill the purpose of the study. In this tutorial we show how to apply sequential Bayes Factor testing in order to stop the recruitment of subjects in a clinical PET study as soon as enough data have been collected to make a conclusion. By using simulations, we demonstrate that it is possible to stop a study early, while keeping the number of erroneous conclusions low. We then apply sequential Bayes Factor testing to a real PET data set and show that it is possible to obtain support in favor of an effect while simultaneously reducing the sample size with 30%. Using this procedure allows researchers to reduce expense and radioactivity exposure for a range of effect sizes relevant for PET research. | psychiatry and clinical psychology |
10.1101/2020.09.13.20192856 | Early stopping in clinical PET studies: how to reduce expense and exposure | Clinical positron emission tomography (PET) research is costly and entails exposing participants to radioactivity. Researchers should therefore aim to include just the number of subjects needed to fulfill the purpose of the study. In this tutorial we show how to apply sequential Bayes Factor testing in order to stop the recruitment of subjects in a clinical PET study as soon as enough data have been collected to make a conclusion. By using simulations, we demonstrate that it is possible to stop a study early, while keeping the number of erroneous conclusions low. We then apply sequential Bayes Factor testing to a real PET data set and show that it is possible to obtain support in favor of an effect while simultaneously reducing the sample size with 30%. Using this procedure allows researchers to reduce expense and radioactivity exposure for a range of effect sizes relevant for PET research. | psychiatry and clinical psychology |
10.1101/2020.09.15.20195263 | Clinical Thrombosis Rate was not Increased in a Cohort of Cancer Patients with COVID-19 | Increased rates of thromboembolic events (TE) have been reported in patients with coronavirus disease (COVID-19), even without prior predisposition to thrombosis. D-dimer levels have been shown to positively correlate with disease severity and mortality, leading to adoption of new empiric anticoagulation protocols by many centers.
We aimed to assess whether COVID-19 further increased the risk of TE events in a cancer population who tested positive for COVID-19 at Montefiore Medical Center, Bronx, NY.
The electronic medical records of 218 cancer patients were retrospectively reviewed up to April 10th, 2020. Work-up of thrombosis was done by the primary team upon clinical or laboratory suspicion. All imaging studies reports, within 20 days of COVID-19 positive test, were reviewed for presence of new arterial or venous thrombosis. Mortality was assessed up to one month since positive COVID-19 test result.
Twelve patients (5.5%) were found to have new arterial (N=6, 50%) or venous (N=6, 50%) thrombosis. Five patients (41.7%) had history of prior TE events. Incidence of deep venous thrombosis and pulmonary embolism was 1.8% and 0.5%, respectively. Arterial events occurred in the brain (66.7%), aorta (16.7%) and coronary arteries (16.7%). Median time from COVID test was 8 days (IQR, 1.5 - 11.3). Five patients (41.7%) had received either prophylactic or therapeutic anticoagulation for a median 2 days (IQR, 1 - 5). Median peak D-dimer within 36 hours of the TE event was 9.8 mcg/mL (N=4 patients, IQR, 1.7 - 18.3). Mortality did not differ significantly between the patients with new TE events vs those without; mortality 41.7% vs 37.4%, respectively, p=0.77. Empiric anticoagulation did not improve mortality.
Fifty percent of all TE events were arterial. The overall TE rate of 5.5% in the cancer population was not higher than the risk of general population. Our findings support the need for larger studies in the COVID-19+ cancer population. | infectious diseases |
10.1101/2020.09.15.20195263 | Increased incidence of thrombosis in a cohort of cancer patients with COVID-19 | Increased rates of thromboembolic events (TE) have been reported in patients with coronavirus disease (COVID-19), even without prior predisposition to thrombosis. D-dimer levels have been shown to positively correlate with disease severity and mortality, leading to adoption of new empiric anticoagulation protocols by many centers.
We aimed to assess whether COVID-19 further increased the risk of TE events in a cancer population who tested positive for COVID-19 at Montefiore Medical Center, Bronx, NY.
The electronic medical records of 218 cancer patients were retrospectively reviewed up to April 10th, 2020. Work-up of thrombosis was done by the primary team upon clinical or laboratory suspicion. All imaging studies reports, within 20 days of COVID-19 positive test, were reviewed for presence of new arterial or venous thrombosis. Mortality was assessed up to one month since positive COVID-19 test result.
Twelve patients (5.5%) were found to have new arterial (N=6, 50%) or venous (N=6, 50%) thrombosis. Five patients (41.7%) had history of prior TE events. Incidence of deep venous thrombosis and pulmonary embolism was 1.8% and 0.5%, respectively. Arterial events occurred in the brain (66.7%), aorta (16.7%) and coronary arteries (16.7%). Median time from COVID test was 8 days (IQR, 1.5 - 11.3). Five patients (41.7%) had received either prophylactic or therapeutic anticoagulation for a median 2 days (IQR, 1 - 5). Median peak D-dimer within 36 hours of the TE event was 9.8 mcg/mL (N=4 patients, IQR, 1.7 - 18.3). Mortality did not differ significantly between the patients with new TE events vs those without; mortality 41.7% vs 37.4%, respectively, p=0.77. Empiric anticoagulation did not improve mortality.
Fifty percent of all TE events were arterial. The overall TE rate of 5.5% in the cancer population was not higher than the risk of general population. Our findings support the need for larger studies in the COVID-19+ cancer population. | infectious diseases |
10.1101/2020.09.15.20195305 | Proteomics identifies a type I IFN, prothrombotic hyperinflammatory circulating COVID-19 neutrophil signature distinct from non-COVID-19 ARDS | Understanding the mechanisms by which infection with SARS-CoV-2 leads to acute respiratory distress syndrome (ARDS) is of significant clinical interest given the mortality associated with severe and critical coronavirus induced disease 2019 (COVID-19). Neutrophils play a key role in the lung injury characteristic of non-COVID-19 ARDS, but a relative paucity of these cells is observed at post-mortem in lung tissue of patients who succumb to infection with SARS-CoV-2. With emerging evidence of a dysregulated innate immune response in COVID-19, we undertook a functional proteomic survey of circulating neutrophil populations, comparing patients with COVID-19 ARDS, non-COVID-19 ARDS, moderate COVID-19, and healthy controls. We observe that expansion of the circulating neutrophil compartment and the presence of activated low and normal density mature and immature neutrophil populations occurs in both COVID-19 and non-COVID-19 ARDS. In contrast, release of neutrophil granule proteins, neutrophil activation of the clotting cascade and formation of neutrophil platelet aggregates is significantly increased in COVID-19 ARDS. Importantly, activation of components of the neutrophil type I IFN responses is specific to infection with SARS-CoV-2 and linked to metabolic rewiring. Together this work highlights how differential activation of circulating neutrophil populations may contribute to the pathogenesis of ARDS, identifying processes that are specific to COVID-19 ARDS. | intensive care and critical care medicine |
10.1101/2020.09.16.20194639 | Dual methylation and hydroxymethylation study of alcohol use disorder | Using an integrative, multi-tissue design we sought to characterize methylation and hydroxymethylation changes in blood and brain associated with alcohol use disorder (AUD). First, we used epigenomic deconvolution to perform cell-type specific methylome-wide association studies within subpopulations of granulocytes/T-cells/B-cells/monocytes in 1,132 blood samples. Blood findings were then examined for overlap with AUD-related associations in methylation and hydroxymethylation in 50 human post-mortem brain samples. Follow-up analyses investigated if overlapping findings mediated AUD-associated transcription changes in the same brain samples. Lastly, we replicated our blood findings in an independent sample of 412 individuals and aimed to replicate published alcohol methylation findings using our results.
Cell-type specific analyses in blood identified methylome-wide significant associations in monocytes and T-cells. The monocyte findings were significantly enriched for AUD-related methylation and hydroxymethylation in brain. Hydroxymethylation in specific sites mediated AUD-associated transcription in the same brain samples. As part of the most comprehensive methylation study of AUD to date, this work involved the first cell-type specific methylation study of AUD conducted in blood, identifying and replicating a finding in DLGAP1 that may be involved in AUD-related brain impairment. In this first study to consider the role of hydroxymethylation in AUD, we found evidence for a novel mechanism for cognitive deficits associated with AUD. Our results suggest promising new avenues for AUD research. | psychiatry and clinical psychology |
10.1101/2020.09.17.20190595 | The "Great Lockdown": Inactive Workers and Mortality by Covid-19 | In response to the Covid-19 outbreak the Italian Government imposed an economic lockdown on March 22, 2020 and ordered the closing of all non-essential economic activities. This paper estimates the causal effects of this measure on mortality by Covid-19 and on mobility patterns. The identification of the causal effects exploits the variation in the active population across municipalities induced by the economic lockdown. The difference-in-differences empirical design compares outcomes in municipalities above and below the median variation in the share of active population before and after the lockdown within a province, also controlling for municipality-specific dynamics, daily-shocks at the provincial level and municipal unobserved characteristics. Our results show that the intensity of the economic lockdown is associated with a statistically significant reduction in mortality by Covid-19 and, in particular, for age groups between 40-64 and older (with larger and more significant effects for individuals above 50). Back of the envelope calculations indicate that 4,793 deaths were avoided, in the 26 days between April 5 to April 30, in the 3,518 municipalities which experienced a more intense lockdown. Several robustness checks corroborate our empirical findings. | health economics |
10.1101/2020.09.17.20190595 | The "Great Lockdown": Inactive Workers and Mortality by Covid-19 | In response to the Covid-19 outbreak the Italian Government imposed an economic lockdown on March 22, 2020 and ordered the closing of all non-essential economic activities. This paper estimates the causal effects of this measure on mortality by Covid-19 and on mobility patterns. The identification of the causal effects exploits the variation in the active population across municipalities induced by the economic lockdown. The difference-in-differences empirical design compares outcomes in municipalities above and below the median variation in the share of active population before and after the lockdown within a province, also controlling for municipality-specific dynamics, daily-shocks at the provincial level and municipal unobserved characteristics. Our results show that the intensity of the economic lockdown is associated with a statistically significant reduction in mortality by Covid-19 and, in particular, for age groups between 40-64 and older (with larger and more significant effects for individuals above 50). Back of the envelope calculations indicate that 4,793 deaths were avoided, in the 26 days between April 5 to April 30, in the 3,518 municipalities which experienced a more intense lockdown. Several robustness checks corroborate our empirical findings. | health economics |
10.1101/2020.09.16.20190694 | KIM-1/TIM-1 is a Receptor for SARS-CoV-2 in Lung and Kidney | SARS-CoV-2 precipitates respiratory distress by infection of airway epithelial cells and is often accompanied by acute kidney injury. We report that Kidney Injury Molecule-1/T cell immunoglobulin mucin domain 1 (KIM-1/TIM-1) is expressed in lung and kidney epithelial cells in COVID-19 patients and is a receptor for SARS-CoV-2. Human and mouse lung and kidney epithelial cells express KIM-1 and endocytose nanoparticles displaying the SARS-CoV-2 spike protein (virosomes). Uptake was inhibited by anti-KIM-1 antibodies and TW-37, a newly discovered inhibitor of KIM-1-mediated endocytosis. Enhanced KIM-1 expression by human kidney tubuloids increased uptake of virosomes. KIM-1 binds to the SARS-CoV-2 Spike protein in vitro. KIM-1 expressing cells, not expressing angiotensin-converting enzyme 2 (ACE2), are permissive to SARS-CoV-2 infection. Thus, KIM-1 is an alternative receptor to ACE2 for SARS-CoV-2. KIM-1 targeted therapeutics may prevent and/or treat COVID-19. | infectious diseases |
10.1101/2020.09.17.20187054 | Genome-wide association study of over 40,000 bipolar disorder cases provides new insights into the underlying biology | Bipolar disorder (BD) is a heritable mental illness with complex etiology. We performed a genome-wide association study (GWAS) of 41,917 BD cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. BD risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating eQTL data implicated 15 genes robustly linked to BD via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of BD subtypes indicated high but imperfect genetic correlation between BD type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of BD, identify novel therapeutic leads and prioritize genes for functional follow-up studies. | genetic and genomic medicine |
10.1101/2020.09.18.20186866 | Clinical characteristics of children with COVID-19 admitted in a tertiary referral center in Peru | IntroductionCOVID-19 pandemic represents a big impact on childrens health, this study describes the behavior of the disease in hospitalized pediatric patients in the Instituto Nacional de Salud del Nino San Borja (INSN-SB).
MethodsRetrospective study of patients with confirmed COVID-19 diagnostic between March and July 2020. Demographic, clinic, laboratory, radiology and treatment data were collected and for the analysis descriptive statistics were included.
ResultsFrom a total of 91 patients. 36.3% (33) were female. The age group who was affected the most were school children with a median age of 4 years old (IQR 1-8). Patients who came from Lima represented 61.5%. Previous contact was determined in 30.8% of the cases. PCR results for SARS CoV-2 were positive in 50.6% of the cases and 49.4% in the quick tests. Comorbidity was present in 53.8% of the cases. Most frequent symptoms were fever (39.6%), general discomfort (23.1%), cough (19.8%) and shortness of breath (14.3%). Presence of MIS-C was confirmed in 6 patients. Use of antibiotics represented 76.9% of the cases. The most frequent radiology pattern was bilateral interstitial (57.7%). Comorbidities were present in 68.2% (15/22) of patients in PICU. From a total of 9 deceased patients, 6 were admitted in PICU and 8 presented associated comorbidities.
ConclusionsCOVID-19 in children displays mild and moderate clinical manifestations. A great proportion of patients exhibited comorbidities, especially PICU patients and the ones that died.
What is known about the subjectIn pediatric patients, the prevalence and severity of COVID-19 are usually low, however, in the presence of MIS-C, greater severity and probability of admission to the PICU is observed.
What this study adds- This study describes the results of complex pediatric patients and the associated comorbidity in LMIC setting that showed greater severity and admission to the ICU.
- Microbiological isolates in cultures were low, therefore the initiation of empirical antibiotic therapy is not justified in most cases. | pediatrics |
10.1101/2020.09.19.20196915 | The Effects of Indias COVID-19 Lockdown on Critical Non-COVID Health Care and Outcomes: Evidence from a Retrospective Cohort Analysis of Dialysis Patients | Indias COVID-19 lockdown, one of the most severe in the world, is widely believed to have disrupted critical non-COVID health services. However, linking these disruptions to effects on health outcomes has been difficult due to the lack of reliable, up-to-date health outcomes data. We identified all dialysis patients under a statewide health insurance program in Rajasthan, India, and conducted surveys to examine the effects of the lockdown on care access, morbidity, and mortality. 63% of patients experienced a disruption to their care. Transport barriers, hospital service disruptions, and difficulty obtaining medicines were the most common causes. We compared monthly mortality in the four months after the lockdown with pre-lockdown mortality trends, as well as with mortality trends for a similar cohort in the previous year. Mortality in May 2020, after a month of exposure to the lockdown, was 1.70 percentage points or 64% (p=0.01) higher than in March 2020 and total excess mortality between April and July was estimated to be 22%. Morbidity, hospitalization, and mortality between May and July were strongly positively associated with lockdown-related disruptions to care, providing further evidence that the uptick in mortality was driven by the lockdown. Females, socioeconomically disadvantaged groups, and patients living far from the health system faced worse outcomes. The results highlight the unintended consequences of the lockdown on critical, life-saving non-COVID health services that must be taken into account in the implementation of future policy efforts to control the spread of pandemics. | public and global health |
10.1101/2020.09.19.20196915 | The Effects of Indias COVID-19 Lockdown on Critical Non-COVID Health Care and Outcomes: Evidence from a Retrospective Cohort Analysis of Dialysis Patients | Indias COVID-19 lockdown, one of the most severe in the world, is widely believed to have disrupted critical non-COVID health services. However, linking these disruptions to effects on health outcomes has been difficult due to the lack of reliable, up-to-date health outcomes data. We identified all dialysis patients under a statewide health insurance program in Rajasthan, India, and conducted surveys to examine the effects of the lockdown on care access, morbidity, and mortality. 63% of patients experienced a disruption to their care. Transport barriers, hospital service disruptions, and difficulty obtaining medicines were the most common causes. We compared monthly mortality in the four months after the lockdown with pre-lockdown mortality trends, as well as with mortality trends for a similar cohort in the previous year. Mortality in May 2020, after a month of exposure to the lockdown, was 1.70 percentage points or 64% (p=0.01) higher than in March 2020 and total excess mortality between April and July was estimated to be 22%. Morbidity, hospitalization, and mortality between May and July were strongly positively associated with lockdown-related disruptions to care, providing further evidence that the uptick in mortality was driven by the lockdown. Females, socioeconomically disadvantaged groups, and patients living far from the health system faced worse outcomes. The results highlight the unintended consequences of the lockdown on critical, life-saving non-COVID health services that must be taken into account in the implementation of future policy efforts to control the spread of pandemics. | public and global health |
10.1101/2020.09.18.20197319 | Uncovering clinical risk factors and prediction of severe COVID-19: A machine learning approach based on UK Biobank data | BackgroundCOVID-19 is a major public health concern. Given the extent of the pandemic, it is urgent to identify risk factors associated with disease severity. Accurate prediction of those at risk of developing severe infections is also of high clinical importance.
MethodsBased on the UK Biobank(UKBB data), we built machine learning(ML) models to predict the risk of developing severe or fatal infections, and to evaluate major risk factors involved. We first restricted the analysis to infected subjects(N=7846), then performed analysis at a population level, considering those with no known infection as controls(N for controls=465,728). Hospitalization was used as a proxy for severity. Totally 97 clinical variables(collected prior to COVID-19 outbreak) covering demographic variables, comorbidities, blood measurements(e.g. hematological/liver/renal function/metabolic parameters etc.), anthropometric measures and other risk factors (e.g. smoking/drinking habits) were included as predictors. We also constructed a simplified ( lite) prediction model using 27 covariates that can be more easily obtained (demographic and comorbidity data). XGboost (gradient boosted trees) was used for prediction and predictive performance was assessed by cross-validation. Variable importance was quantified by Shapley values and accuracy gain. Shapley dependency and interaction plots were used to evaluate the pattern of relationship between risk factors and outcomes.
ResultsA total of 2386 severe and 477 fatal cases were identified. For the analysis among infected individuals (N=7846),our prediction model achieved AUCs of 0.723(95% CI:0.711-0.736) and 0.814(CI: 0.791-0.838) for severe and fatal infections respectively. The top five contributing factors for severity were age, number of drugs taken(cnt_tx), cystatin C(reflecting renal function), wait-hip ratio (WHR) and Townsend Deprivation index (TDI). For prediction of mortality, the top features were age, testosterone, cnt_tx, waist circumference(WC) and red cell distribution width (RDW).
In analyses involving the whole UKBB population, the corresponding AUCs for severity and fatality were 0.696(CI:0.684-0.708) and 0.802(CI:0.778-0.826) respectively. The same top five risk factors were identified for both outcomes, namely age, cnt_tx, WC, WHR and TDI. Apart from the above features, Type 2 diabetes(T2DM), HbA1c and apolipoprotein A were ranked among the top 10 in at least two (out of four) analyses. Age, cystatin C, TDI and cnt_tx were among the top 10 across all four analyses.
As for the lite models, the predictive performances in terms of AUC are broadly similar, with estimated AUCs of 0.716, 0.818, 0.696 and 0.811 respectively. The top-ranked variables were similar to above, including for example age, cnt_tx, WC, male and T2DM.
ConclusionsWe identified a number of baseline clinical risk factors for severe/fatal infection by an ML approach. For example, age, central obesity, impaired renal function, multi-comorbidities and cardiometabolic abnormalities may predispose to poorer outcomes. The presented prediction models may be useful at a population level to help identify those susceptible to developing severe/fatal infections, hence facilitating targeted prevention strategies. Further replications in independent cohorts are required to verify our findings. | infectious diseases |
10.1101/2020.09.18.20197319 | Uncovering clinical risk factors and prediction of severe COVID-19: A machine learning approach based on UK Biobank data | BackgroundCOVID-19 is a major public health concern. Given the extent of the pandemic, it is urgent to identify risk factors associated with disease severity. Accurate prediction of those at risk of developing severe infections is also of high clinical importance.
MethodsBased on the UK Biobank(UKBB data), we built machine learning(ML) models to predict the risk of developing severe or fatal infections, and to evaluate major risk factors involved. We first restricted the analysis to infected subjects(N=7846), then performed analysis at a population level, considering those with no known infection as controls(N for controls=465,728). Hospitalization was used as a proxy for severity. Totally 97 clinical variables(collected prior to COVID-19 outbreak) covering demographic variables, comorbidities, blood measurements(e.g. hematological/liver/renal function/metabolic parameters etc.), anthropometric measures and other risk factors (e.g. smoking/drinking habits) were included as predictors. We also constructed a simplified ( lite) prediction model using 27 covariates that can be more easily obtained (demographic and comorbidity data). XGboost (gradient boosted trees) was used for prediction and predictive performance was assessed by cross-validation. Variable importance was quantified by Shapley values and accuracy gain. Shapley dependency and interaction plots were used to evaluate the pattern of relationship between risk factors and outcomes.
ResultsA total of 2386 severe and 477 fatal cases were identified. For the analysis among infected individuals (N=7846),our prediction model achieved AUCs of 0.723(95% CI:0.711-0.736) and 0.814(CI: 0.791-0.838) for severe and fatal infections respectively. The top five contributing factors for severity were age, number of drugs taken(cnt_tx), cystatin C(reflecting renal function), wait-hip ratio (WHR) and Townsend Deprivation index (TDI). For prediction of mortality, the top features were age, testosterone, cnt_tx, waist circumference(WC) and red cell distribution width (RDW).
In analyses involving the whole UKBB population, the corresponding AUCs for severity and fatality were 0.696(CI:0.684-0.708) and 0.802(CI:0.778-0.826) respectively. The same top five risk factors were identified for both outcomes, namely age, cnt_tx, WC, WHR and TDI. Apart from the above features, Type 2 diabetes(T2DM), HbA1c and apolipoprotein A were ranked among the top 10 in at least two (out of four) analyses. Age, cystatin C, TDI and cnt_tx were among the top 10 across all four analyses.
As for the lite models, the predictive performances in terms of AUC are broadly similar, with estimated AUCs of 0.716, 0.818, 0.696 and 0.811 respectively. The top-ranked variables were similar to above, including for example age, cnt_tx, WC, male and T2DM.
ConclusionsWe identified a number of baseline clinical risk factors for severe/fatal infection by an ML approach. For example, age, central obesity, impaired renal function, multi-comorbidities and cardiometabolic abnormalities may predispose to poorer outcomes. The presented prediction models may be useful at a population level to help identify those susceptible to developing severe/fatal infections, hence facilitating targeted prevention strategies. Further replications in independent cohorts are required to verify our findings. | infectious diseases |
10.1101/2020.09.21.20196428 | Living with Children and Adults' Risk of COVID-19: Observational Study | ObjectiveChildren are relatively protected from COVID-19, possibly due to cross-protective immunity. We investigated if contact with children also affords adults a degree of protection from COVID-19.
DesignCohort study based on linked administrative data.
SettingScotland
Study populationAll NHS Scotland healthcare workers and their household contacts as of March 2020.
Main exposureNumber of young children (0-11 years) living in the participants household.
Main outcomesCOVID-19 requiring hospitalisation, and any COVID-19 (any positive test for SARS-CoV-2) in adults aged [≥]18 years between 1 March and 12 October 2020.
Results241,266, 41,198, 23,783 and 3,850 adults shared a household with 0, 1, 2, and 3 or more young children respectively. Over the study period, the risk of COVID-19 requiring hospitalisation was reduced progressively with increasing numbers of household children - fully adjusted hazard ratio (aHR) 0.93 per child (95% CI 0.79-1.10). The risk of any COVID-19 was similarly reduced, with the association being statistically significant (aHR per child 0.93; 95% CI 0.88-0.98). After schools reopened to all children in August 2020, no association was seen between exposure to young children and risk of any COVID-19 (aHR per child 1.03; 95% CI 0.92-1.14).
ConclusionBetween March and October 2020, living with young children was associated with an attenuated risk of any COVID-19 and COVID-19 requiring hospitalisation among adults living in healthcare worker households. There was no evidence that living with young children increased adults risk of COVID-19, including during the period after schools re-opened. | epidemiology |
10.1101/2020.09.21.20198622 | Linkage to primary care public health facilities for cardiovascular disease prevention: A community-based cohort study from urban slums in India | ObjectivesHypertension and diabetes mellitus are key risk factors for Cardiovascular diseases. Pharmacotherapy and life-style modifications are necessary. Once screened, individuals need to be linked to primary health-care system for initiation and maintenance of therapies, to achieve optimal blood pressure and glycemic control. In the current study we evaluate predictors and barriers for non-linkage to primary-care public health facilities for CVD risk reduction.
MethodsWe conducted a community-based longitudinal study in 16 urban slum clusters in central India. Community health workers (CHWs) in each urban slum cluster screened all adults aged 30 years or more for hypertension and diabetes, and those positively screened were sought to be linked to Urban Primary Health Centres (UPHCs). We performed univariate and multivariate analysis to identify independent predictors for non-linkage to primary-care providers. We conducted in-depth assessment in 10% of all positively screened, to identify key barriers that potentially prevented linkages to primary-care facilities.
ResultsOf 6174 individuals screened 1451(23.5%; 95%-CI 22.5-24.6) were identified as high-risk, and required linkage to primary-care facilities for pharmacotherapy. Out of these, 544(37.5%) were linked to public primary-care facilities, 259(17.9%) to private providers, 142(9.8%) were treatment interrupters, and 506(34.9%) didnt get linked to any provider. On multivariate analysis, as compared to those linked to public primary care facilities, those who were not linked had age less than 45 years (OR 2.2 (95%CI 1.3-3.5)); were in lowest wealth quintile (OR 1.8 (95%CI 1.1-2.9); resided beyond a kilometre from UPHC (OR 1.7 (95%CI 1.2-2.4); and were engaged late by CHWs (OR 2.6 95%CI (1.8-3.7)). Despite having comparable knowledge level, denial about their risk-status and lack of family support were key barriers in this group.
ConclusionsThis study highlights importance of early engagement through CHWs after positive screening, strategies to engage with younger individuals who may be in denial about their risk-status.
Article summary - Strengths and limitations of this studyO_LIThis is community based longitudinal study implemented through community health workers (CHWs).
C_LIO_LIIt is "real-world" implementation as per national non-communicable disease control program in India (known as NPCDCS), which envisages population based screening through community health workers (CHWs), and linkages to public health facilities.
C_LIO_LIThis study highlights that within urban slum, being young, in a low socioeconomic position, distance from health facility are important determinants of linkage to public health facility.
C_LIO_LIEarly engagement by CHWs enhances likelihood of linkage.
C_LIO_LIThis study was limited to urban slum clusters from a single city, however we believe that health-infrastructure is broadly similar in such settings elsewhere.
C_LI | public and global health |
10.1101/2020.09.21.20198911 | Environmental impact of Personal Protective Equipment distributed for use by health and social care services in England in the first six months of the COVID-19 pandemic | ObjectivesUse of Personal Protective Equipment (PPE) has been central to controlling spread of SARS-CoV2. This study aims to quantify the environmental impact of this, and to model strategies for its reduction.
MethodsLife cycle assessment was used to determine environmental impacts of PPE supplied to health and social care in England during the first six months of the COVID-19 pandemic. The base scenario assumed all products were single-use, air freighted, and disposed via clinical waste. Scenario modelling was used to determine the effect of 1) switching mode of, or eliminating, international travel during supply, 2) reducing glove use 3) using reusable alternatives, 4) maximal recycling.
ResultsThe carbon footprint of PPE supplied during the study period totalled 158,838 tonnes CO2e, with greatest contributions from gloves, aprons, face shields, and Type IIR surgical masks. The estimated damage to human health was 314 DALYs (disability adjusted life years), impact on ecosystems was 0.67 species.year (loss of local species per year), and impact on resource depletion costing US $ 20.4 million.
Scenario modelling indicated one-third of the carbon footprint could be avoided through switching to shipping, and by 41% through manufacturing PPE in the UK. The carbon footprint was reduced by 83% compared with the base scenario through a combination of UK manufacturing, reducing glove use, using reusable gowns and reuse of face shields, and maximal recycling, estimated to save 259 DALYS, 0.54 species.year, and US $ 15 million due to resource depletion.
ConclusionsThe environmental impact of PPE could be reduced through shipping supplies or domestic manufacture, rationalising glove use, using reusables where possible, and optimising waste management.
SUMMARY BOXO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIThe current COVID-19 pandemic has seen a massive global increase in the use and manufacture of PPE which has contributed, with other measures, to the reduction in transmission of the virus in many countries.
C_LI
What this study addsO_LIThe carbon footprint of PPE supplied to health and social care in England in the first six months of the COVD-19 pandemic was 158,848 tonnes CO2e, equivalent to around 65,500 return flights from London to New York.
C_LIO_LIThe environmental impact of PPE could be reduced through shipping supplies or domestic manufacture, rationalising glove use, using reusables where possible, and optimising waste processing.
C_LI | health policy |
10.1101/2020.09.21.20198796 | COVID-19 seropositivity changes in asymptomatic individuals during the second and third waves of COVID-19 in Tokyo. | ImportanceFatality rates related to COVID-19 in Japan have been low compared to Western Countries and have decreased despite the absence of lockdown. Serological tests monitored across the course of the second wave can provide insights into the population-level prevalence and dynamic patterns of COVID-19 infection.
ObjectiveTo assess changes in COVID-19 seroprevalence among asymptomatic employees working in Tokyo during the second wave.
DesignWe conducted an observational cohort study. Healthy volunteers working for a Japanese company in Tokyo were enrolled from disparate locations to determine seropositivity against COVID19 from May 26 to August 25, 2020. COVID-19 IgM and IgG antibodies were determined by a rapid COVID19 IgM/IgG test kit using fingertip blood. Across the company, tests were performed and acquired weekly. For each participant, serology tests were offered twice, separated by approximately a month, to provide self-reference of test results and to assess for seroconversion and seroreversion.
SettingWorkplace setting within a large company.
ParticipantsHealthy volunteers from 1877 employees of a large Japanese company were recruited to the study from 11 disparate locations across Tokyo. Participants having fever, cough, or shortness of breath at the time of testing were excluded.
Main Outcome(s) and Measure(s)Seropositivity rate (SPR) was calculated by pooled data from each two-weeks window across the cohort. Either IgM or IgG positivity was defined as seropositive. Changes in immunological status against SARS-CoV-2 were determined by comparing results between two tests obtained from the same individual.
ResultsSix hundred fifteen healthy volunteers (mean + SD 40.8 + 10.0; range 19 - 69; 45.7 % female) received at least one test. Seroprevalence increased from 5.8 % to 46.8 % over the course of the summer. The most dramatic increase in SPR occurred in late June and early July, paralleling the rise in daily confirmed cases within Tokyo, which peaked on August 4. Out of the 350 individuals (mean + SD 42.5 + 10.0; range 19 - 69; 46.0 % female) who completed both offered tests, 21.4 % of those individuals who tested seronegative became seropositive and seroreversion was found in 12.2 % of initially seropositive participants. 81.1% of IgM positive cases at first testing became IgM negative in approximately one month.
Conclusions and RelevanceCOVID-19 infection may have spread widely across the general population of Tokyo despite the very low fatality rate. Given the temporal correlation between the rise in seropositivity and the decrease in reported COVID-19 cases that occurred without a shut-down, herd immunity may be implicated. Sequential testing for serological response against COVID-19 is useful for understanding the dynamics of COVID-19 infection at the population-level. | infectious diseases |