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PurposeIn young adults (18 to 49 years old), investigation of the acute respiratory distress syndrome (ARDS) after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been limited. We evaluated the risk factors and outcomes of ARDS following infection with SARS-CoV-2 in a young adult population.MethodsA retrospective cohort study was conducted between January 1st, 2020 and February 28th, 2021 using patient-level electronic health records (EHR), across 241 United States hospitals and 43 European hospitals participating in the Consortium for Clinical Characterization of COVID-19 by EHR (4CE). To identify the risk factors associated with ARDS, we compared young patients with and without ARDS through a federated analysis. We further compared the outcomes between young and old patients with ARDS.ResultsAmong the 75,377 hospitalized patients with positive SARS-CoV-2 PCR, 1001 young adults presented with ARDS (7.8% of young hospitalized adults). Their mortality rate at 90 days was 16.2% and they presented with a similar complication rate for infection than older adults with ARDS. Peptic ulcer disease, paralysis, obesity, congestive heart failure, valvular disease, diabetes, chronic pulmonary disease and liver disease were associated with a higher risk of ARDS. We described a high prevalence of obesity (53%), hypertension (38%- although not significantly associated with ARDS), and diabetes (32%).ConclusionTrough an innovative method, a large international cohort study of young adults developing ARDS after SARS-CoV-2 infection has been gather. It demonstrated the poor outcomes of this population and associated risk factor.

Multistate methodology proves effective in analyzing hospitalized coronavirus disease 2019 (COVID-19) patients with emerging variants in real time. An analysis of 2,548 admissions in Freiburg, Germany, showed reduced severity over time in terms of shorter hospital stays and higher discharge rates when comparing more recent phases with earlier phases of the pandemic.

While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking.A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1-365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021.Advanced age (HR 2.77, 95%CI 2.53-3.04, p 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03-4.17, p 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55-5.00, p 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14-1.39, p 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37-0.46, p 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17-1.54, p 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20-1.58, p 0.0001), male sex (HR 1.67, 95%CI 1.45-1.93, p 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80-13.91, p 0.0001), and hypertension (HR 1.22, 95%CI 1.10-1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32-1.67) and 365 days (RR 1.54, 95%CI 1.21-1.96) compared to COVID-19 patients with no AKI.COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery.Authors are supported by various funders, with full details stated in the acknowledgement section.

Purpose In young adults (18 to 49 years old), investigation of the acute respiratory distress syndrome (ARDS) after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been limited. We evaluated the risk factors and outcomes of ARDS following infection with SARS-CoV-2 in a young adult population. Methods A retrospective cohort study was conducted between January 1st, 2020 and February 28th, 2021 using patient-level electronic health records (EHR), across 241 United States hospitals and 43 European hospitals participating in the Consortium for Clinical Characterization of COVID-19 by EHR (4CE). To identify the risk factors associated with ARDS, we compared young patients with and without ARDS through a federated analysis. We further compared the outcomes between young and old patients with ARDS. Results Among the 75,377 hospitalized patients with positive SARS-CoV-2 PCR, 1001 young adults presented with ARDS (7.8% of young hospitalized adults). Their mortality rate at 90 days was 16.2% and they presented with a similar complication rate for infection than older adults with ARDS. Peptic ulcer disease, paralysis, obesity, congestive heart failure, valvular disease, diabetes, chronic pulmonary disease and liver disease were associated with a higher risk of ARDS. We described a high prevalence of obesity (53%), hypertension (38%- although not significantly associated with ARDS), and diabetes (32%). Conclusion Trough an innovative method, a large international cohort study of young adults developing ARDS after SARS-CoV-2 infection has been gather. It demonstrated the poor outcomes of this population and associated risk factor.

Made available in DSpace on 2022-04-29T08:35:59Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-12-01 Division of Intramural Research, National Institute of Allergy and Infectious Diseases Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases National Institute of Allergy and Infectious Diseases National Center for Advancing Translational Sciences National Heart, Lung, and Blood Institute National Institute of Environmental Health Sciences U.S. National Library of Medicine National Institute of Neurological Disorders and Stroke Division of Cancer Prevention, National Cancer Institute Neurological complications worsen outcomes in COVID-19. To define the prevalence of neurological conditions among hospitalized patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test in geographically diverse multinational populations during early pandemic, we used electronic health records (EHR) from 338 participating hospitals across 6 countries and 3 continents (January–September 2020) for a cross-sectional analysis. We assessed the frequency of International Classification of Disease code of neurological conditions by countries, healthcare systems, time before and after admission for COVID-19 and COVID-19 severity. Among 35,177 hospitalized patients with SARS-CoV-2 infection, there was an increase in the proportion with disorders of consciousness (5.8%, 95% confidence interval [CI] 3.7–7.8%, pFDR < 0.001) and unspecified disorders of the brain (8.1%, 5.7–10.5%, pFDR < 0.001) when compared to the pre-admission proportion. During hospitalization, the relative risk of disorders of consciousness (22%, 19–25%), cerebrovascular diseases (24%, 13–35%), nontraumatic intracranial hemorrhage (34%, 20–50%), encephalitis and/or myelitis (37%, 17–60%) and myopathy (72%, 67–77%) were higher for patients with severe COVID-19 when compared to those who never experienced severe COVID-19. Leveraging a multinational network to capture standardized EHR data, we highlighted the increased prevalence of central and peripheral neurological phenotypes in patients hospitalized with COVID-19, particularly among those with severe disease. Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Department of Biomedical Informatics Harvard Medical School Department of Neurology University of Pittsburgh, Biomedical Science Tower 3, Suite 7014, 3501 5th Avenue Department of Pediatrics Wake Forest School of Medicine Department of Critical Care National University Health Systems Department of Preventive Medicine Northwestern University Department of Biomedical Informatics University of Pittsburgh Department of Surgery National University Health Systems Department of Internal Medicine University of Kansas Medical Center Data Analytics Center University of Pennsylvania Health System Department of Computational Medicine and Bioinformatics University of Michigan Department of Biomedical Informatics University of Kentucky Department of Legal Economic and Social Sciences University Magna Graecia of Catanzaro Health Catalyst INC. Department of Surgery ASST Pavia Lombardia Region Health System Clinical Research Unit of Botucatu Medical School São Paulo State University Pediatric Emergency Department Hôpital Necker-Enfants Malades Assistance Publique-Hôpitaux de Paris National Center for Infectious Diseases Tan Tock Seng Hospital Departments of Biomedical Informatics Pediatrics Cincinnati Children’s Hospital Medical Center University of Cincinnati Department of Pediatrics Medical University of South Carolina Department of Surgery St. Luke’s University Health Network Department of Medicine David Geffen School of Medicine at UCLA UOC Ricerca Innovazione e Brand Reputation ASST Papa Giovanni XXIII Department of Electrical Computer and Biomedical Engineering University of Pavia IT Department Innovation & Data APHP Greater Paris University Hospital I.T. Department ASST Pavia Health Informatics Hospital Universitario, 12 de Octubre Strategy and Transformation Department APHP Greater Paris University Hospital Faculty of Medicine and Medical Center University of Freiburg Digital Research Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children Scientific Direction IRCCS Ca’ Granda Ospedale Maggiore Policlinico di Milano North Carolina Translational and Clinical Sciences (NC TraCS) Institute University of North Carolina BIOMERIS (BIOMedical Research Informatics Solutions) Department of Biomedical Informatics HEGP APHP Greater Paris University Hospital Department of Medical and Surgical Sciences Data Analytics Research Center University Magna Graecia of Catanzaro Department of Anesthesia St. Luke’s University Health Network Université Paris-Saclay Inria CEA INRIA Sophia-Antipolis–ZENITH Team LIRMM Computational Health Informatics Program Boston Children’s Hospital Department of Internal Medicine University of Kentucky Unit of Internal Medicine and Endocrinology Istituti Clinici Scientifici Maugeri SpA SB IRCCS Department of Internal Medicine and Therapeutics University of Pavia Informatics Institute University of Alabama at Birmingham IAM Unit Bordeaux University Hospital/ERIAS-Inserm U1219 BPH Biomedical Informatics Center Medical University of South Carolina Clinical Research Informatics Boston Children’s Hospital Department of Biomedical and Health Informatics Children’s Hospital of Philadelphia SED/SIERRA Inria Centre de Paris Health Information Technology & Services University of Michigan Internal Medicine Department Botucatu Medical School São Paulo State University Heinrich-Lanz-Center for Digital Health University Medicine Mannheim Heidelberg University Department of Anesthesiology Critical Care and Pain Medicine Boston Children’s Hospital Department of Learning Health Sciences University of Michigan Medical School MSHI Medical University of South Carolina Department of Medicine Massachusetts General Hospital Division of Human Genetics Department of Pediatrics The Children’s Hospital of Philadelphia Center for Medical Information and Communication Technology University Hospital Renaissance Computing Institute/Department of Computer Science University of North Carolina Clinical Research Unit Saint Antoine Hospital APHP Greater Paris University Hospital Clevy.io Department of Biostatistics Harvard T.H. Chan School of Public Health VA Informatics and Computing Infrastructure VA Salt Lake City Health Care System MICHR Informatics University of Michigan Laboratory of Informatics and Systems Engineering for Clinical Research Istituti Clinici Scientifici Maugeri SpA SB IRCCS Harvard Catalyst Harvard Medical School Clinical Research Unit Paris Saclay APHP Greater Paris University Hospital Department of Anesthesiology and Critical Care Children’s Hospital of Philadelphia VA Informatics and Computing Infrastructure Tennessee Valley Healthcare System Veterans Affairs Medical Center École Normale Supérieure PSL Université Paris BIG-ARC The University of Texas Health Science Center at Houston School of Biomedical Informatics Pediatric Infectious Disease Department Hospital Universitario, 12 de Octubre Department of Infectious Diseases Great Ormond Street Hospital for Children Department of Neurology Massachusetts General Hospital Internal Medicine Department of Botucatu Medical School São Paulo State University Department of Pediatrics Boston Children’s Hospital Center for Biomedical Informatics Wake Forest School of Medicine Department of Medical Informatics University of Erlangen-Nürnberg Department of Veterans Affairs Office of Research and Development Biomedical Data Science Lab ITACA Institute Universitat Politècnica de València Nurse Department of FMB-Medicine School of Botucatu Management Engineering ASST Pavia Lombardia Region Health System Department of Anesthesiology University Hospital Erlangen FAU Erlangen-Nürnberg Critical Care Medicine Department of Medicine St. Luke’s University Health Network Department of Medicine National University Hospital Department of Information Management Chang Gung University Clinical & Translational Science Institute Medical College of Wisconsin Montréal Neurological Institute McGill University SequeL Inria Lille Respiratory Department ICS S Maugeri IRCCS Department of Health Management and Informatics University of Missouri Department of Oncology ASST Papa Giovanni XXIII Clinical Research Unit of Botucatu Medical School São Paulo State University Internal Medicine Department Botucatu Medical School São Paulo State University Internal Medicine Department of Botucatu Medical School São Paulo State University Division of Intramural Research, National Institute of Allergy and Infectious Diseases: AI11679 Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases: AI11679 National Institute of Allergy and Infectious Diseases: AI11679 National Center for Advancing Translational Sciences: CTSA Award #UL1TR001878 National Center for Advancing Translational Sciences: CTSA Award #UL1TR002366 National Heart, Lung, and Blood Institute: K23HL148394 National Institute of Environmental Health Sciences: P30ES017885 U.S. National Library of Medicine: R01LM012095 National Institute of Neurological Disorders and Stroke: R01NS098023 Division of Cancer Prevention, National Cancer Institute: U24CA210967 National Center for Advancing Translational Sciences: UL1TR001420 National Center for Advancing Translational Sciences: UL1TR001857

Pyrethroid resistance is now expanding in the two main arboviruses vectors Aedes aegypti and Aedes albopictus from Cameroon. Although recent studies suggested the implication of target site (kdr) resistance and overexpression of detoxification enzymes as key mechanisms, the implication of additional mechanisms such as cuticular resistance has not been investigated. The present study assesses the possible implication of cuticle in Aedes species resistance to pyrethroids. High cuticular hydrocarbon (CHC) content was detected in Ae. aegypti populations from Douala and Yaoundé. The difference (38% increase) between the Douala and susceptible populations was found to be statistically significant.The study confirms the implication of cuticular-based mechanisms in resistant to pyrethroids and DDT Aedes aegypti mosquitoes from Cameroon.

International audience; ObjectiveFor multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information.Materials and Methods For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or can be a single center, corresponding to transfer learning.Results Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations.Conclusions The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.

Arbovirus diseases represent a significant public health problem in Cameroon and vector surveillance is a key component of prevention strategies. However, there is still not enough evidence of the efficacy of different sampling methods used to monitor Aedes mosquito population dynamic in different epidemiological settings. The present study provides data on the evaluation of ovitraps and different adult sampling methods in the city of Yaoundé and its close vicinity. Entomological surveys were carried out from February 2020 to March 2021 in two urban (Obili, Mvan), two peri-urban (Simbock, Ahala), and two rural (Lendom, Elig-essomballa) sites in the city of Yaoundé. The efficacy of three sampling methods, namely ovitraps, Biogent Sentinel trap, and sweep nets, was evaluated. Different ovitrap indices were used to assess the infestation levels across study sites; a general linear model was used to determine if there are statistical differences between positive ovitraps across ecological zones. A total of 16,264 Aedes mosquitoes were collected during entomological surveys. Ovitraps provided the highest mosquito abundance (15,323; 91.14%) and the highest species diversity. Of the five Aedes species collected, Aedes albopictus (59.74%) was the most commonly recorded in both urban and rural settings. Different Aedes species were collected in the same ovitrap. The ovitrap positivity index was high in all sites and varied from 58.3% in Obili in the urban area to 86.08% in Lendom in the rural area. The egg density index varied from 6.42 in Mvan (urban site) to 13.70 in Lendom (rural area). Adult sampling methods recorded mostly Aedes albopictus. The present study supports high infestation of Aedes species in the city of Yaoundé. Ovitraps were highly efficient in detecting Aedes distribution across study sites. The situation calls for regular surveillance and control of Aedes population to prevent sudden occurrence of outbreaks.

ObjectiveTo assess changes in international mortality rates and laboratory recovery rates during hospitalisation for patients hospitalised with SARS-CoV-2 between the first wave (1 March to 30 June 2020) and the second wave (1 July 2020 to 31 January 2021) of the COVID-19 pandemic.Design, setting and participantsThis is a retrospective cohort study of 83 178 hospitalised patients admitted between 7 days before or 14 days after PCR-confirmed SARS-CoV-2 infection within the Consortium for Clinical Characterization of COVID-19 by Electronic Health Record, an international multihealthcare system collaborative of 288 hospitals in the USA and Europe. The laboratory recovery rates and mortality rates over time were compared between the two waves of the pandemic.Primary and secondary outcome measuresThe primary outcome was all-cause mortality rate within 28 days after hospitalisation stratified by predicted low, medium and high mortality risk at baseline. The secondary outcome was the average rate of change in laboratory values during the first week of hospitalisation.ResultsBaseline Charlson Comorbidity Index and laboratory values at admission were not significantly different between the first and second waves. The improvement in laboratory values over time was faster in the second wave compared with the first. The average C reactive protein rate of change was –4.72 mg/dL vs –4.14 mg/dL per day (p=0.05). The mortality rates within each risk category significantly decreased over time, with the most substantial decrease in the high-risk group (47.1% in March–April 2020 vs 30.8% in November 2020 to January 2021, pConclusionsAdmission profiles of patients hospitalised with SARS-CoV-2 infection did not differ greatly between the first and second waves of the pandemic, but there were notable differences in laboratory improvement rates during hospitalisation. Mortality risks among patients with similar risk profiles decreased over the course of the pandemic. The improvement in laboratory values and mortality risk was consistent across multiple countries.

The emergence of insecticide resistance in Aedes mosquitoes could pose major challenges for arboviral-borne disease control. In this paper, insecticide susceptibility level and resistance mechanisms were assessed in Aedes aegypti (Linnaeus, 1762) and Aedes albopictus (Skuse, 1894) from urban settings of Cameroon. The F1 progeny of Aedes aegypti and Aedes albopictus collected in Douala, Yaoundé and Dschang from August to December 2020 was tested using WHO tube assays with four insecticides: deltamethrin 0.05%, permethrin 0.75%, DDT 4% and bendiocarb 0.1%. TaqMan, qPCR and RT-qPCR assays were used to detect kdr mutations and the expression profiles of eight detoxification genes. Aedes aegypti mosquitoes from Douala were found to be resistant to DDT, permethrin and deltamethrin. Three kdr mutations, F1534C, V1016G and V1016I were detected in Aedes aegypti populations from Douala and Dschang. The kdr allele F1534C was predominant (90%) in Aedes aegypti and was detected for the first time in Aedes albopictus (2.08%). P450s genes, Cyp9J28 (2.23–7.03 folds), Cyp9M6 (1.49–2.59 folds), Cyp9J32 (1.29–3.75 folds) and GSTD4 (1.34–55.3 folds) were found overexpressed in the Douala and Yaoundé Aedes aegypti populations. The emergence of insecticide resistance in Aedes aegypti and Aedes albopictus calls for alternative strategies towards the control and prevention of arboviral vector-borne diseases in Cameroon.

The emergence of insecticide resistance in Aedes mosquitoes could pose major challenges for arboviral-borne disease control. In this paper, insecticide susceptibility level and resistance mechanisms were assessed in Aedes aegypti (Linnaeus, 1762) and Aedes albopictus (Skuse, 1894) from urban settings of Cameroon. The F1 progeny of Aedes aegypti and Aedes albopictus collected in Douala, Yaoundé and Dschang from August to December 2020 was tested using WHO tube assays with four insecticides: deltamethrin 0.05%, permethrin 0.75%, DDT 4% and bendiocarb 0.1%. TaqMan, qPCR and RT-qPCR assays were used to detect kdr mutations and the expression profiles of eight detoxification genes. Aedes aegypti mosquitoes from Douala were found to be resistant to DDT, permethrin and deltamethrin. Three kdr mutations, F1534C, V1016G and V1016I were detected in Aedes aegypti populations from Douala and Dschang. The kdr allele F1534C was predominant (90%) in Aedes aegypti and was detected for the first time in Aedes albopictus (2.08%). P450s genes, Cyp9J28 (2.23–7.03 folds), Cyp9M6 (1.49–2.59 folds), Cyp9J32 (1.29–3.75 folds) and GSTD4 (1.34–55.3 folds) were found overexpressed in the Douala and Yaoundé Aedes aegypti populations. The emergence of insecticide resistance in Aedes aegypti and Aedes albopictus calls for alternative strategies towards the control and prevention of arboviral vector-borne diseases in Cameroon.

Cette étude analyse les déterminants de la demande d’épargne à l’IMF par les femmes rurales camerounaises. L’enquête a été effectuée auprès de 613 femmes rurales membres et non membres des IMF dans les régions du Centre, Littoral et Ouest Cameroun. Les résultats obtenus à l’aide du modèle logit montrent que, le besoin de microcrédit, le service d’assurance, la formation offerte par les IMF et la sécurité sont les principaux déterminants structurels de l’épargne des femmes rurales camerounaises. Il apparaît également que, les facteurs liés à la femme qui stimulent fortement sa volonté d’épargne à l’IMF sont : l’âge, la taille du ménage. Le fait d’avoir des actifs, le fait que c’est la femme qui gère les fonds dans son ménage et la durée de l’activité qu’elle mène. Par ailleurs, la distance IMF-domiciles des clientes et l’épargne mobile sont les deux principaux facteurs environnementaux, qui réduisent fortement la volonté des femmes rurales camerounaises à garder leurs argents dans l’IMF. Enfin, l’appartenance à une tontine accroit significativement l’auto-exclusion des femmes rurales camerounaises des IMF tandis que l’utilisation du compte d’un membre de la famille, la réduit.

Made available in DSpace on 2022-04-29T08:35:26Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-10-01 National Human Genome Research Institute National Center for Advancing Translational Sciences National Heart, Lung, and Blood Institute National Institutes of Health U.S. National Library of Medicine National Institute of Neurological Disorders and Stroke Canadian Thoracic Society Background: Many countries have experienced 2 predominant waves of COVID-19–related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic. Objective: In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic. Methods: Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19. Results: Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain. Conclusions: Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve. Department of Biomedical Informatics Harvard Medical School BIOMERIS (BIOMedical Research Informatics Solutions) Clinical Research Unit Botucatu Medical School São Paulo State University Division of Nephrology Department of Medicine Ente Ospedaliero Cantonale Department of Electrical Computer and Biomedical Engineering University of Pavia Information Technology Department Azienda Socio-Sanitaria Territoriale di Pavia Unit of Internal Medicine and Endocrinology Istituti Clinici Scientifici Maugeri SpA SB IRCCS Massachusetts Veterans Epidemiology Research and Information Center Veterans Affairs Boston Healthcare System Department of Medicine Massachusetts General Hospital Department of Medicine David Geffen School of Medicine University of California Los Angeles Health Informatics Hospital Universitario 12 de Octubre Department of Learning Health Sciences University of Michigan Medical School Department of Biomedical Informatics University of Kentucky Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Institute for Biomedical Informatics University of Pennsylvania Perelman School of Medicine Department of Preventive Medicine Northwestern University Institute for Biomedical Informatics University of Kentucky Medical University of South Carolina Department of Computer Science Renaissance Computing Institute University of North Carolina at Chapel Hill Department of Biostatistics Harvard T.H. Chan School of Public Health Department of Anaesthesia National University Health System Institute of Medical Biometry and Statistics Faculty of Medicine and Medical Center University of Freiburg Michigan Institute for Clinical & Health Research Informatics University of Michigan Laboratory of Informatics and Systems Engineering for Clinical Research Istituti Clinici Scientifici Maugeri SpA SB IRCCS Clinical Hospital of Botucatu Medical School São Paulo State University Informatique et Archivistique Médicales Unit Bordeaux University Hospital Department of Biomedical Informatics University of Pittsburgh Department of Neurology Massachusetts General Hospital Department of Biomedical Informatics Hôpital Necker-Enfants Malade Assistance Publique Hôpitaux de Paris University of Paris Department of Biomedical Informatics Institute for Digital Medicine National University Health System Internal Medicine Department Botucatu Medical School São Paulo State University Department of Computational Medicine & Bioinformatics Internal Medicine Human Genetics and Public Health University of Michigan Division of Medical Informatics Department of Internal Medicine University of Kansas Medical Center Data Analytics Center University of Pennsylvania Health System Department of Medicine National University Health System Department of Neurology University of Pittsburgh Section of Nephrology Department of Pediatrics Brenner Children's Hospital Wake Forest School of Medicine Clinical Research Unit Botucatu Medical School São Paulo State University Clinical Hospital of Botucatu Medical School São Paulo State University Internal Medicine Department Botucatu Medical School São Paulo State University National Human Genome Research Institute: 3U01HG008685-05S2 National Human Genome Research Institute: 5R01HG009174-04 National Center for Advancing Translational Sciences: 5UL1TR001857-05 National Heart, Lung, and Blood Institute: K23HL148394 National Heart, Lung, and Blood Institute: L40HL148910 National Institutes of Health: P30ES017885 U.S. National Library of Medicine: R01LM012095 U.S. National Library of Medicine: R01LM013345 National Institute of Neurological Disorders and Stroke: R01NS098023 U.S. National Library of Medicine: T15LM007092 National Institutes of Health: U24CA210967 National Center for Advancing Translational Sciences: UL1TR000005 National Center for Advancing Translational Sciences: UL1TR001420 National Center for Advancing Translational Sciences: UL1TR001450 National Center for Advancing Translational Sciences: UL1TR001857 National Center for Advancing Translational Sciences: UL1TR001878 National Center for Advancing Translational Sciences: UL1TR001881 Canadian Thoracic Society: UL1TR001998 National Center for Advancing Translational Sciences: UL1TR002240 Canadian Thoracic Society: UL1TR002366 National Center for Advancing Translational Sciences: UL1TR002541

The present study focuses on forest incomes of households around forest concessions in Cameroon. The contributions of forest income to the economy and well-being of households were measured and the explanatory factors for heterogeneity determined. We used the Gini index to evaluate the distribution of household forest incomes and their influence on well-being and income inequality amongst forest-dependent households. Three TOBIT econometric models with sample selection were estimated to identify factors that influence the level of each source of forest income. Results from our analysis show that forest contributes on average 38% of total annual household income with 19, 13 and 6% from illegal logging, hunting and non-timber forest products (NTFPs) of vegetal origin, respectively. Forest income overall contributes in increasing disparities among people by 3%. Income from illegal logging was found to be a major source of income inequality while other forest income sources such as NTFPs and hunting s...

As the market share of passivated emitter rear cell (PERC) technology in the PV industry continues to grow, the solar community has already begun looking for further improvements to increase cell and module power while lowering the levelized cost of electricity (LCOE). Standard PERC and passivated emitter rear totally diffused (PERT) technologies suffer from high recombination losses at the front- and rear-side metal contacts. Passivated contact technology has attracted considerable interest as a solution to this problem in order to reach efficiencies above 24.0% while retaining the use of existing production equipment. In order to achieve competitive and good surface passivation, it is crucial to focus on high quality ultra-thin tunnel oxide layers processed in line with high-throughput capable, stable and reliable production equipment. Furthermore, Meyer Burger is also working on similar methods for polycrystalline silicon processes. As of today, low-absorbing single-side poly-Si layers can be deposited by inline PECVD as doped (electron-selective or hole-selective) hydrogenated amorphous layers followed by subsequent high temperature crystallization. Here we present the latest results from Meyer Burger’s research facility in Germany which has achieved >22.5% on a passivated contact solar cell with a rear-side poly-Si/interfacial-oxide stack deposited using industrial production equipment.

Etymologically, words beginning with topo, usually are related with places, while words ending in nym are types of names. A toponym, therefore, is a name for a place or the name by which a geographical place is known. Shupamem is a language spoken by the Bamuns who are located in the Noun Division of the West region of Cameroon. This chapter aims at analysing toponyms as noun classes in Shupamem using the framework of cultural categories and cultural schemas. Given that the study deals with noun classes in relation to place names and cultural knowledge captured in cultural schemas, it falls within the field of Cultural Linguistics. In order to attain the above objective, data consisting of maps and names of places in the Noun area were collected. Our findings reveal that there are four main locative morphemes that always precede the stem of toponyms in Shupamem. Unlike in Proto-bantu where classes for locatives mostly refer to proximity, in Shupamem, the emphasis is laid on altitude. A keen observation of toponyms in the language is thus of a great relevance at the linguistic, the geographic as well as the ethnographic level. Considering classes 16, 17 and 18 of the Proto-bantu classification, we suggest that classes for locatives should be recognised as part of the classifier system of this language. This study will also be a contribution to the translation, documentation, preservation and teaching of Shupamem.