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263 results for “Norte”
Effectiveness of COVID-19 vaccination on reduction of hospitalizations and deaths in elderly patients in Rio Grande do Norte, Brazil
<p><strong>Data Repository </strong></p> <p><strong>Dataset name:</strong> covid19_rn-br.csv </p> <p><strong>Version: </strong>1.0 </p> <p><strong>Data collection period:</strong> 04/2020 - 08/2021 </p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances:</strong> 12,635</p> <p><strong>Number of Attributes: </strong>16</p> <p><strong>Missing Values:</strong> Yes </p> <p><strong>Area(s): </strong>Health</p> <p><strong>Sources:</strong> </p> <p>- <em>Primary</em>: </p> <ul> <li>RegulaRN (<a href="https://regulacao.saude.rn.gov.br/sala-situacao/sala_publica/">https://regulacao.saude.rn.gov.br/sala-situacao/sala_publica/</a>).</li> </ul> <p>- <em>Secondary</em>: </p> <ul> <li>RN Mais Vacina (<a href="https://rnmaisvacina.lais.ufrn.br/cidadao/">https://rnmaisvacina.lais.ufrn.br/cidadao/</a>).</li> </ul> <p><strong>Description:</strong> The covid19_rn-br.csv dataset is composed of data from individuals who were hospitalized due to the Sars-CoV-2 virus. The data comes from the ecosystem of services that includes the regulatory system for clinical and critical beds related to Covid-19 (RegulaRN) and the vaccination system against Covid-19 that records the data of the general population (RN Mais Vacina) from Rio Grande do Norte state, Brazil. This dataset provides elementary data to analyze the impact of vaccination on patients hospitalized in the state. Table 1 presents the dictionary used during the data analysis.</p> <p> </p> <p><strong>Table 1: </strong>Description of Dataset Features. </p> <table> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>usp</strong></p> </td> <td> <p>Unified Score for Prioritization scale, which combines the parameters described in the quick Sequential Organ Failure Assessment (qSOFA), the Charlson Comorbidity Index (CCI), the Clinical Frailty Scale (CFS) and The Karnofsky Performance Status scores</p> </td> <td> <p>Numerical</p> </td> <td> <p>2.0. 3.0, 4.0, 5.0, 6.0+</p> </td> </tr> <tr> <td> <p><strong>age</strong></p> </td> <td> <p>Informs the patient's age</p> </td> <td> <p>Numerical.</p> </td> <td> <p>integer value for age</p> </td> </tr> <tr> <td> <p><strong>outcome</strong></p> </td> <td> <p>Informs the outcome of the hospitalized patient after leaving the hospital</p> </td> <td> <p>Categorical</p> </td> <td> <p>“Discharge” or “Death"</p> </td> </tr> <tr> <td> <p><strong>comorbidities</strong></p> </td> <td> <p>Informs if the patient has comorbidities</p> </td> <td> <p>Categorical.</p> </td> <td> <p>“Yes” or “No”</p> </td> </tr> <tr> <td> <p><strong>vaccine</strong></p> </td> <td> <p>Informs which type of vaccine was applied to the patient</p> </td> <td> <p>Categorical</p> </td> <td> <p>“Vaccine #1”, “Vaccine #2” or NaN</p> </td> </tr> <tr> <td> <p><strong>bed_date_admission</strong> </p> </td> <td> <p>Informs the date the patient was hospitalized</p> </td> <td> <p>Date</p> </td> <td> <p>Date</p> </td> </tr> <tr> <td> <p><strong>bed_date_outcome</strong></p> </td> <td> <p>Informs the date that the patient left the hospital bed</p> </td> <td> <p>Date</p> </td> <td> <p>Date</p> </td> </tr> <tr> <td> <p><strong>length_hospitalization</strong></p> </td> <td> <p>Informs the number of days that the patient was hospitalized</p> </td> <td> <p>Numerical</p> </td> <td> <p>An integer value for days</p> </td> </tr> <tr> <td> <p><strong>interval_d1_hospitalization</strong></p> </td> <td> <p>Informs the interval (in days) that the patient had between the first dose and admission</p> </td> <td> <p>Numerical</p> </td> <td> <p>An integer value for days or NaN</p> </td> </tr> <tr> <td> <p><strong>interval_d2_hospitalization</strong></p> </td> <td> <p>Informs the interval (in days) that the patient had between the second dose and admission</p> </td> <td> <p>Numerical</p> </td> <td> <p>An integer value for days or NaN</p> </td> </tr> <tr> <td> <p><strong>dt_d1</strong></p> </td> <td> <p>Informs the date of application of the patient's first dose</p> </td> <td> <p>Date</p> </td> <td> <p>Date or NaN</p> </td> </tr> <tr> <td> <p><strong>dt_d2</strong></p> </td> <td> <p>Informs the patient's second dose application date</p> </td> <td> <p>Date</p> </td> <td> <p>Date or NaN</p> </td> </tr> <tr> <td> <p><strong>comorbidities_txt</strong></p> </td> <td> <p>Informs patients' comorbidities</p> </td> <td> <p>Categorical</p> </td> <td> <p>Free text or NaN</p> </td> </tr> <tr> <td> <p><strong>immunization</strong></p> </td> <td> <p>It informs the patient's immunization level according to the number of doses received and the interval (in days) of application of these doses</p> </td> <td> <p>Categorical</p> </td> <td> <p>“Partially”, “Fully” or “Not vaccinated”</p> </td> </tr> <tr> <td> <p><strong>health_professionals</strong></p> </td> <td> <p>Informs if the patient is a health professional</p> </td> <td> <p>Boolean</p> </td> <td> <p>0 or 1</p> </td> </tr> <tr> <td> <p><strong>age_group</strong></p> </td> <td> <p>Informs the age group of the hospitalized patient according to their age</p> </td> <td> <p>Categorical</p> </td> <td> <p>0-19, 20-49, 50-59, 60-69, 70-79, 80-89, 90+</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Article</strong>: Effectiveness of COVID-19 vaccination on reduction of hospitalizations and deaths in elderly patients in Rio Grande do Norte, Brazil</p> <p><br> <strong>Authors</strong>: Ana Isabela L. Sales-Moioli, Leonardo J. Galvão-Lima, Talita K. B. Pinto, Pablo H. Cardoso, Rodrigo D. Silva; Felipe Fernandes, Ingridy M. P. Barbalho, Fernando L. O. Farias, Nicolas V. R. Veras, Daniele M. S. Barros; Agnaldo S. Cruz; Ion G. M. Andrade; Lúcio Gama and Ricardo A. M. Valentim</p>
HAQES 3-Hourly Ensemble mean surface PM2.5 Black Carbon concentration at county level, North America V1 (HAQES_NA_PM25_BC_COUNTY) at GES DISC
This product provides HAQES 3-hourly ensemble mean surface PM2.5 Black Carbon concentration at the county level over the continental United States (CONUS). The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST).
HAQES 3-Hourly Ensemble mean surface PM2.5 Black Carbon concentration at census level, North America V1 (HAQES_NA_PM25_BC_CENSUS) at GES DISC
This product provides HAQES 3-hourly ensemble mean surface PM2.5 Black Carbon concentration at the census level over the continental United States (CONUS). The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST).
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