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109 results for “Covid-19 mortality”
TomoBreast randomized clinical trial's lung-heart outcomes and mortality through the 2020 COVID-19 pandemic: data and software
<p>Dataset and R script to reproduce the analyses of the manuscript:</p> <p>Vinh-Hung V, Gorobets O, Adriaenssens N, Van Parijs H, Storme G, Verellen D, Nguyen NP, Magne N, De Ridder M.</p> <p><strong>Lung-heart outcomes and mortality through the 2020 COVID-19 pandemic in a prospective cohort of breast cancer radiotherapy patients.</strong></p> <p>Cancers 2022; 14(24):6241. https:// doi.org/10.3390/cancers14246241</p> <p>https://www.mdpi.com/2072-6694/14/24/6241</p> <p>PubMed: PMID: 36551726</p> <p>PMCID: PMC9777311</p> <p>Info on the variables in file "aelq6_public.R"</p> <p>reproduced in "aelq_2_3_readme.txt":</p> <p>"aelq2_base2.txt" = baseline characteristics.</p> <p>"aelq3.txt" = longitudinal maesurements.</p> <p>Variables in "aelq2_base2.txt":</p> <p>"<strong>aelq2_base2.txt</strong>" = baseline characteristics. <br># Age at randomization, years. <br># RTdose: cf TomoBreast papers. <br># 51 Gy = hypofractionated, simultaneous integrated boost<br># 42 Gy = hypofractionated, no boost, mastectomy cases only<br># 50 Gy = conventional, no boost, mastectomy cases only<br># 66 Gy = conventional, sequential boost<br># Weight kg, Height cm, <br># Detection 1=found by screening (senology follow-up/controle)<br># 2=found by symptoms (pain, palpable)<br># 9=unknown<br># Smoker 0= Not smoker<br># 1= Smoker<br># 2=ex-smoker<br># Mastectomy (and other binary coded) 1= yes<br># chemosched 0=none<br># 1= planned after RT (sequential)<br># 2= prior to RT and is finished (sequential)<br># 3= chemo is on-going or is planned to start with RT (concomitant)<br># hormonetherapy 0=no<br># 1=tamoxifen (nolvadex)<br># 2=Femara (Letrozole)<br># 3=zoladex<br># 4=tamoxifen + zoladex<br># Laterality 1,=Right, 2=Left, 3=Bilateral<br># LengthFU: length of follow-up, days from randomization</p> <p>"<strong>aelq3.txt</strong>" = longitudinal maesurements.<br># "Nr" = Case ID<br># "Time" in days from origin (origin =date of randomization), <br># if negative =before randomization<br># "KPS" "Weight" <br># "Died" "LocalRec" "Metast" "NewPrim" = binary code, 0=no, 1=yes<br># "fAEBreast" "fAEHeart" "fAELung" "fAEOther" <br># fAE = freedom from breast, heart, lung, other adverse event score<br># "LVEF2" = ejection fraction, %<br># "MacIver" = estimated cardiac strain</p> <p># the following are pulmonary function tests, untransformed units<br># "FVC", "FEV1", "PEF", "VC", "TLC", "RV", "FRC", "Raw", "sRaw", "DLCO",<br># "VA", "PF"</p> <p># "fDY", "fFA", "fPA" = freedom from dyspnea, from fatigue, from pain<br># range 0 to 100 (best)<br># see papers:</p> <p># Van Parijs, H.; Vinh-Hung, V.; Fontaine, C.; Storme, G.; Verschraegen, C.;<br># Nguyen, D.M.; Adriaenssens, N.; Nguyen, N.P.; Gorobets, O.; De Ridder, M.<br># Cardiopulmonary-related patient-reported outcomes in a randomized clinical<br># trial of radiation therapy for breast cancer. BMC Cancer 2021, 21, 1177,<br># doi:10.1186/s12885-021-08916-z.</p> <p># preprint:<br># Van Parijs, H.; Cecilia-Joseph, E.; Gorobets, O.; Storme, G.; <br># Adriaenssens, N.; Heyndrickx, B.; Verschraegen, C.; Nguyen, N.P.;<br># De Ridder, M.; Vinh-Hung, V. Lung-heart toxicity in a randomized <br># clinical trial of hypofractionated image guided radiation therapy for<br># breast cancer. Preprints 2022, 202212, 0214.<br># https://doi.org/10.20944/preprints202212.0214.v1</p> <p># <br># "Year" = year of the observation<br># example: randomized 1/1/2011, measurement done 1/31/2011, time = 30 days,<br># Year =2011<br>#<br> </p>
Coffee Consumption per Capita and Covid-19 Mortality Rate
<p>There is a correlation between average of "Coffee Consumption per capita" and average of "Covid-19 Mortality Rate" for countries with high coffee consumption per capita (the countries that has more than 5.4 kg per capita per year consumption).</p> <p>The Details of computations and data are provided in an attached supplementary file (Excel File Format).</p> <p>Data gathered on 10 Aug 2021</p> <p> </p>
Age-adjusted Covid-19 mortality rates for Brazilian municipalities
<p>This dataset present Covid-19 crude and age-adjusted mortality rates for Brazilian municipalities from 2020 to 2022 per epidemiological week, on 100,000 inhabitants base. </p><p>The mortality data source is the "Sistema de Informações de Mortalidade -- SIM", available at https://opendatasus.saude.gov.br/dataset/sim .</p><p>The population reference is the Brazilian age-structure at 2020.</p><p>Notebook with method and code: https://rfsaldanha.github.io/posts/std_br_covid_rates.html</p>
COVID-19 mortality correlation with cloudiness, sunlight, latitude in European countries
<p>"COVID-19 mortality correlation with cloudiness, sunlight, latitude in European countries"</p> <p>Dataset for preprint titled <br> "COVID-19 mortality: positive correlation with cloudiness but no correlation with sunlight and latitude in Europe"<br> https://doi.org/10.1101/2021.01.27.21250658 </p> <p>by SECIL OMER, ADRIAN IFTIME, VICTOR BURCEA</p> <p>Corresponding author: A. Iftime, University of Medicine and Pharmacy "Carol Davila", Biophysics Department, 8 Blvd. Eroii Sanitari, 050474 Bucharest, Romania. Email address: adrian.iftime [at] umfcd.ro.</p> <p> </p> <p>===========<br> Dataset file: <br> 2.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_December_2020.csv</p> <p><br> Dataset graphical preview: <br> 2.0.0.INFOGRAPHIC_CloudFraction_vs_COVID-19_mortality_Europe_March-December_2020.png</p> <p>DATASET:<br> 444 rows (records), with the following fields:</p> <p>"Country" :<br> Country name; 37 European countries included.</p> <p>"Date": <br> Date stamp at the collection time.<br> Data collection was performed in the last day of every month. <br> Date format: YYYY-MM-DD</p> <p>"Month_Key" : <br> Date stamp at the collection time, formatted for easier monthly time series analysis.<br> Date format: YYYY-MM</p> <p>"Month_Fct2020"<br> Date stamp at the collection time,formatted for easier graphing, as a string with names of the months<br> (in English). </p> <p>"Deaths_per_1Mpop" :<br> Monthly mortality from COVID-19 raported in the country, <br> reported as number of COVID-19 deaths per 1 million population of the country, <br> in that particular month / country. <br> NB: it is reported as million population, not patients. </p> <p>"LogDeaths_per_1Mpop" :<br> Log10 transformation of "Deaths_per_1Mpop"</p> <p>"Insolation_Average" :<br> Insolation average (solar irradiance at ground level),<br> in that particular month / country. <br> It is expressed in Watt / square meter of the ground surface. <br> Data derived from data avaialble at NASA Langley Research Center, NASA’s Earth Observatory, <br> CERES / FLASHFlux team, 2020, <br> https://neo.gsfc.nasa.gov/view.php?datasetId=CERES_INSOL_M<br> (old link: https://neo.sci.gsfc.nasa.gov/view.php?datasetId=CERES_INSOL_M )</p> <p>"Cloud_Fraction" :<br> Cloudiness (also known as cloud fraction, cloud cover, cloud amount or sky cover),<br> as decimal fraction of the sky obscured by clouds, <br> in that particular month / country. <br> Data derived from NASA Goddard Space Flight Center, NASA’s Earth Observatory,<br> MODIS Atmosphere Science Team, 2020, <br> https://neo.gsfc.nasa.gov/view.php?datasetId=MODAL2_M_CLD_FR<br> (old link: https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MODAL2_M_CLD_FR )</p> <p>"CENTR_latitude" and<br> "CENTR_longitude" :<br> Latitude and Longitude of the country centroid, for each country. <br> Data derived from Google LLC, "Dataset publishing language: country centroids",<br> https://developers.google.com/public-data/docs/canonical/countries_csv <br> NOTE: This is identical in every month (obviuously); <br> it is redundantly included for easier monthly sectional analysis of the data. </p> <p>===========</p> <p>Versioning of the dataset: <br> MAJOR: changes yearly; 1 = 2020<br> MINOR: changes if new monthly data is added in that particular year. <br> PATCH: Changes only if errors or minor edits were performed. </p> <p><br> ===========<br> CHANGELOG: </p> <p>Version 2.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_December_2020.csv<br> - CERES/FLASHFLUX data for August-December 2020 became available at new links at nasa.gov<br> - These data were gathered, analyzed and introduced in this dataset (2.0.0). <br> - updated links for CERES/FLASHFLUX and MODIS dataset<br> - added DOI link for preprint<br> - minor edits on text. <br> -Dataset file source for this version (internal analysis source file):<br> db_covid_all-ANALYSIS.2020-all-year_versiunea18d.csv</p> <p><br> Version 1.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_August_2020.csv <br> First version<br> Dataset file source for this version (internal analysis source file):<br> db_covid_all-ANALYSIS.2020-09-22_r10.csv</p>
Ivercori Dataset and dictionary. Ivermectin impact in COVID-19 pneumonia mortality and need of respiratory support
<p>Dataset of IVERCORI and variables dictionary. IVERCORI is a propensity matched score retrospective study that analyses the impact of ivermectin in COVID-19 pneumonia in-hospital mortality and need of respiratory support:</p>
Data from: Spatial modeling of sociodemographic risk for COVID-19 mortality
Open the record for dataset details and reuse information.
History of daily forecast of cumulative COVID-19 mortality in multiple geographic entities across the world
<p>Forecasts are available from 2020-04-01 to 2021-10-20 for dozens to more than 200 geographic entities (GE) across the world (from 46 GE on 2020-04-01 to 246 GE on 2021-10-20). </p> <p>Each forecast of cumulative mortality is grounded on a probabilistic mixture of mortality trajectories of ahead-of-time geographic entities playing the role of real-life predictors eventually complemented by a parametric model based on a SIR representation. The methodology is presented in Soubeyrand, Ribaud et al. (2020, https://doi.org/10.1371/journal.pone.0238410) and Soubeyrand, Demongeot et al. (2020, https://doi.org/10.1016/j.onehlt.2020.100187). </p> <p>The forecast are daily implemented by a web application entitled "COVID-19 Visualization" available at https://shiny.biosp.inrae.fr/app_direct/mapCovid19/</p> <p>The original code is available here: https://gitlab.paca.inrae.fr/biosp/shinyMapCovid19</p> <p>Raw data for drawing the forecast are provided by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE; https://systems.jhu.edu/) available at https://github.com/CSSEGISandData/COVID-19/ (Dong et al., 2020, https://doi.org/10.1016/S1473-3099(20)30120-1). </p> <p>Information about the data set and the code are provided in the readme.txt file.</p> <p>Data are provided in the forecast_data.rds file produced originally with the saveRDS() function of the R Statistical Software (https://cran.r-project.org/). </p> <p>A code for loading the data set and extracting some data corresponding to specific dates and geographic entities with the R Statistical Software is provided in the read_data.R file.</p>
letter to the New England Journal of Medicine: COVID-19 mortality data perplexity
<p>Una explicación para todos los públicos:</p> <p>Lo que he hecho es ver los muertos por COVID-19 declarados por los organismos gubernamentales antes y después de que comience la intervención farmacológica con los productos que se han dado en llamar vacunas por los medios de comunicación y los gobiernos. Primero he considerado el periodo completo de 2020 hasta la "vacunación" y el periodo desde el comienzo de la vacunación hasta el final de los datos (7 de enero 2022). El comienzo es un poco distinto para cada país. Reino Unido y USA comenzaron a principios de diciembre 2020 con gran aparato mediático. Otros no empezaron hasta marzo 2021. Para cada país he calculado el incremento relativo de mortalidad.</p> <p>El resultado es que una gran mayoría tiene incremento positivo: más muertos COVID-19 durante la intervención farmacéutica que antes (2020). En particular USA tiene un incremento considerable. Un test estadístico estándar confirma que la diferencia es significativa, o sea que no podemos afirmar que "no hay diferencia en la mortalidad COVID-19 antes y después del comienzo de la intervención farmacéutica". Por otro lado podemos decir que hay "razones de sobra" para afirmar que hay más muertos COVID-19 durante la "vacunación" que antes de que se comenzara a inocular a las personas.</p> <p>La revista ha comprobado los cálculos y no dicen en su respuesta que sean incorrectos. Ergo, no han falsificado lo que digo en la letter. </p> <p>En segundo lugar he considerado la pregunta ¿hay algún periodo de tiempo en el que la "vacunación" ha conseguido una disminución efectiva de la mortalidad COVID-19?</p> <p>Pudiera ser que al principio de la operación de intervención farmacéutica durante unos días se apreciara un efecto importante de la "vacunación" en forma de descenso de la mortalidad COVID-19, coincidiendo con la afirmación de las empresas de que los anticuerpos generados son fuertes al principio y se van debilitando, con la recomendación actual de "reforzar" con una nueva dosis al cabo de tres o cuatro meses. (curiosamente, el discurso oficial es que ahora han disminuido los casos graves gracias a unas inyecciones que se hicieron hace seis meses o más ¿no contradice lo anterior?)</p> <p>Para intentar responder a la pregunta, he considerado periodos de tiempo simétricos en torno al comienzo de la "vacunación" o sea, un día antes y un día después, dos días antes y después, y así hasta 300 días antes y después del comienzo de las inyecciones. Al hacer tests estadísticos, se encuentra que no hay efecto significativo desde el punto de vista estadístico (p-value < 0.01) hasta pasados más de 140 días, y para periodos mayores de 140 días la conclusión es que hay más muertos COVID-19 durante la "vacunación" que antes (estadísticamente significativo con p-value < 0.01). Hasta los 140 días, los resultados son que no se puede descartar que mortalidad COVID-19 antes y después tienen la misma mediana y que, por tanto, son indistinguibles estadísticamente.</p> <p>En palabras llanas, no se nota diferencia hasta más o menos 140 días y después la conclusión clara es que hay más muertos COVID-19 durante la "vacunación" que antes (en 2020). Otra vez, la revista no puede decir que los cálculos son incorrectos, por lo que simplemente indican que "no es de interés". No sé si esta calificación sería compartida por la población en general. Evidentemente, para los gobernantes estos datos y resultados son bastante "incómodos".</p> <p>La mayor limitación de este pequeño estudio es el conjunto de datos de base. Se puede argumentar sobre su inexactitud. De hecho una de las mayores inexactitudes es la declaración de comienzo de la "vacunación". Muchos países estaban vacunando antes de la fecha que aparece en OWID. De hecho, estaban vacunando a personas de riesgo o personal sanitario y auxiliar en sitios como residencias. Ajustando estas fechas, los resultados serían todavía más desfavorables para los productos sanitario. Otro ejemplo es China, cuya declaración de mortalidad es simplemente nula a partir de una fecha. No he intentado correcciones, simplemente he tomado los datos como lo he hecho con todos los demás países. Otra de las posibles inexactitudes son las primeras olas en países europeos y algunos estados de USA, en las que un estado de panico general y otras causas posibles pudieron influir en una fuerte sobre diagnosis de COVID-19, especialmente en las residencias de ancianos. Tampoco he hecho ningún intento de corrección, sino que he tomado los datos tal cual. Habrá otras limitaciones de las que no soy consciente.</p> <p>El test utilizado es el "Wilkinson rank sum" que no asume una distribución parametrizada de los datos, por lo que podemos estar bastante seguros de que no estoy introduciendo trucos estadísticos. </p> <p><strong>Importante</strong>: no estoy hablando de muertes producidas/declaradas por vacunas, aunque muchos muertos COVID-19 estaban "vacunados" durante 2021. No es hasta fechas recientes que los gobiernos han comenzado a precisar el estado "vacunal" de los muertos COVID-19, fundamentalmente como parte de la campaña de acoso a las personas "no vacunadas". Parece que las estadísticas recientes no son favorables a la estrategia gubernamental, por lo que los datos se van cubriendo de un velo de misterio.</p> <p><strong>En conclusión, la comparación de la mortalidad COVID-19 antes y después del comienzo de la "vacunación" más masiva de la historia de la humanidad (hasta ahora) no permite afirmar que las "vacunas" han disminuido la mortalidad COVID-19 hasta la fecha de los datos recogidos de OWID.</strong></p> <p> </p> <p>***** ******* **********</p> <p>Content:</p> <p>Data downloaded from Our World in Data at January 07, 2022</p> <p>Source code for the analysis and visualization </p> <p>submitted figure </p> <p>submitted letter</p> <p>response from the journal</p>
Sex-disaggregated Analysis of Risk Factors of COVID-19 Mortality Rates in India
<p>This Zenodo resource contains the data used to perform analysis in the article "Sex-disaggregated Analysis of Risk Factors of COVID-19 Mortality Rates in India".</p> <p>Data</p> <p>The data is organized in the form of tables.</p> <p>hypothesis-test-data</p> <p>This table contains data used to perform the two tailed hypothesis test on gender mortality in different regions.</p> <pre><code>* Region * Male_Deaths - Number of male COVID-19 deaths in region. * Female_Deaths - Number of female COVID-19 deaths in region. * Male_cases - Number of male COVID-19 positive in region. * Female_cases - Number of female COVID-19 positive in region. </code></pre> <p>lasso-covid19India</p> <p>This table contains data used for analysis on cases throughout India.</p> <p>Columns from COVID-19 India data</p> <pre><code>* State_Code * State * District * Confirmed * Active * Recovered * Deceased </code></pre> <p>Columns taken from NFHS data</p> <pre><code>* Sex_ratio_of_the_total_population_females_per_1000_males * Women_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm214_ * Men_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm2_ * Women_who_are_overweight_or_obese_BMI__250_kgm214_ * Men_who_are_overweight_or_obese_BMI__250_kgm2_ * All_women_age_1549_years_who_are_anaemic_ * Men_age_1549_years_who_are_anaemic_130_gdl_ * Women_Blood_sugar_level__high_140_mgdl_ * Men_Blood_sugar_level__high_140_mgdl_ * Women_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ * Men_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ </code></pre> <p>lasso-KA+TN-bulletin</p> <p>This table contains data used for analysis on the sub-cohort of Karnataka and Tamil Nadu.</p> <p>Data from Media Bulletin</p> <pre><code>* District * Total_Positives * total_deaths * male_deaths * female_deaths * Male_cases_in_data * Female_cases_in_data </code></pre> <p>Calculated Data</p> <pre><code>* Estimated_Male_cases - Estimated male cases using total positives column and existing case data * Estimated_Female_Cases - Estimated female cases using total positives column and existing case data * Male_Mortality - Estimated Male Cases / male_deaths * Female_Mortality - Estimated Female Cases / female_deaths </code></pre> <p>Columns taken from NFHS data</p> <pre><code>* Sex_Ratio_females_every_1000_males * State Women_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm214_ * Men_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm2_ * Women_who_are_overweight_or_obese_BMI__250_kgm214_ * Men_who_are_overweight_or_obese_BMI__250_kgm2_ * All_women_age_1549_years_who_are_anaemic_ * Men_age_1549_years_who_are_anaemic_130_gdl_ * Women_Blood_sugar_level__high_140_mgdl_ * Men_Blood_sugar_level__high_140_mgdl_ * Women_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ * Men_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ </code></pre> <p>Code</p> <p>The code is available at this <a href="https://github.com/harishpb26/Sex-disaggregated-Analysis-of-Risk-Factors-of-COVID-19-Mortality-Rates-in-India">Github Repository</a>.</p>
Development of a Predictive Model for In-Hospital Mortality in COVID-19 Patients Using CAR, IL-6, IL-6/LY, and NLR: A Single-Center Study in Indonesia
<p>Figure 1. ROC Curve of CAR, IL-6, IL-6/LY, and NLR</p> <p> </p> <p>Figure 2. Kaplan Meier curve of (a) CAR (b) IL-6 (c) IL-6/LY (d) NLR blue line represents group above cut off and green one represents group below cut-off</p> <p> </p>
International COVID-19 mortality forecast visualization: covidcompare.io
Open the record for dataset details and reuse information.
Data from: Clinical management and mortality among COVID-19 cases in sub-Saharan Africa: a retrospective study from Burkina Faso and simulated case analysis.
<p>Absolute numbers of COVID-19 cases and deaths reported to date in the sub-Saharan Africa (SSA) region have been relatively low. As a result, there has been limited investigation into deceased cases in the region, as well as the impacts of different case management strategies. We detail demographic, epidemiological, and clinical information derived from publicly available information on deceased cases in SSA and, for cases in Burkina Faso, from aggregate records at the Center Hospitalier Universitaire de Tengandogo. Logistic regression was conducted on a synthetic case population to evaluate the adjusted odds of survival for patients receiving oxygen therapy or convalescent plasma, based on therapeutic effectiveness observed for other respiratory illnesses. Across SSA, deceased cases have been predominantly male and over 50 years of age. After adjustment for sex, age, and underlying conditions, the odds of mortality among cases in the synthetic population not receiving oxygen therapy was significantly higher than those receiving oxygen (OR: 2.07; 95%CI: 1.56-2.75). Cases receiving convalescent plasma had 50% reduced odds of mortality (95%CI: 0.24-0.93).<b> </b>Investment in sustainable oxygen therapy could reduce COVID-19 deaths in SSA. Ongoing investigation into convalescent plasma is warranted, as data on its effectiveness specifically in treating COVID-19 becomes available.</p>
Neutrophil-mediated oxidative stress and albumin structural damage predict COVID-19-associated mortality
<p>This work reports that COVID-19-induced oxidative stress inflicts structural damages to human serum albumin (HSA) and is linked with mortality outcome in critically ill patients. Analyzing blood samples from patients and healthy individuals, the paper provides evidence that neutrophils are major sources of oxidative stress in blood and that hydrogen peroxide is highly accumulated in plasmas of non-survivors. The electron paramagnetic resonance spectra of spin-labeled fatty acids (SLFAs) bound with HSA in whole blood of control, survivor, and non-survivor subjects (n=10–11) were analyzed to probe structural damages to the protein. Non-survivors' HSA showed dramatically altered biophysical parameters that reflect remarkably fluid protein microenvironments. Following loading/unloading of 16-DSA, the results show that the transport function of HSA may be impaired in severe patients. Stratified at the means, Kaplan–Meier survival analysis indicated that lower values of S/W ratio and accumulated H<sub>2</sub>O<sub>2</sub> in plasma significantly predicted in-hospital mortality.</p>
Laboratory risk factors for mortality in severe and critical COVID-19 patients admitted to the ICU
<p>raw data sheet</p>
Laboratory risk factors for mortality in severe and critical COVID-19 patients admitted to the ICU
<p>Data legends</p>
Data and code underpinning: "The association of smoking status with SARS-CoV-2 infection, hospitalisation and mortality from COVID-19: A living rapid evidence review with Bayesian meta-analyses (version 12)"
<p>No description provided.</p>
Development of a Predictive Model for In-Hospital Mortality in COVID-19 Patients Using CAR, IL-6, IL-6/LY, and NLR: A Single-Center Study in Indonesia
<p>Table 1. Demographic data of the subject</p> <p><sup>*</sup>mean (+SD); <sup>#</sup>median (Q1-Q3)</p> <p> </p> <p>Table 2. CAR, IL-6, IL-6/LY, NLR cut-off values and performance in determining mortality</p>
Dataset and code for "Magnitude and determinants of excess total, age- and sex-specific all-cause mortality in 24 countries worldwide during 2020 and 2021: results on the impact of the COVID-19 pandemic from the C-MOR project"
<p>Data and statistical codes used for the results presented in the paper "Magnitude and determinants of excess total, age- and sex-specific all-cause mortality in 24 countries worldwide during 2020 and 2021: results on the impact of the COVID-19 pandemic from the C-MOR project"</p>
Analysis of Mortality of Critically Ill Patients With COVID-19
ClinicalTrials.gov study NCT04379258. IPD Sharing: YES. Countries: 1. Publications: 6.
External Validation of the 4C Mortality Score for Hospitalised Patients With COVID-19 in a Tunisian Cohort
ClinicalTrials.gov study NCT05498324. IPD Sharing: NO. Countries: 1. Publications: 2.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.