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Data Report: "Health care of Persons Deprived of Liberty" Course from Brazil's Unified Health System Virtual Learning Environment
<p><strong>Dataset name: </strong>asppl-dataset.csv</p> <p><strong>Version: </strong>1.0</p> <p><strong>Dataset period: </strong>06/07/2018- 05/25/2021</p> <p><strong>Dataset Characteristics: </strong>Multivalued</p> <p><strong>Number of Instances: </strong>4861</p> <p><strong>Number of Attributes: </strong>33</p> <p><strong>Missing Values: </strong>Yes</p> <p><strong>Area(s): </strong>Health and education </p> <p><strong>Sources: </strong></p> <ul> <li> <p><strong>Primary</strong>: Unified Health System Virtual Learning Environment (AVASUS, in Portuguese: Ambiente Virtual de Aprendizagem do Sistema Único de Saúde) [1];</p> </li> <li> <p><strong>Secondary: </strong></p> <ol> <li> <p>Brazilian Classification of Occupations (CBO, in Portuguese: Classificação Brasileira de Ocupação) [2];</p> </li> <li> <p>National Registry of Health Establishments (CNES, in Portuguese: Cadastro Nacional de Estabelecimentos de Saúde) [3]; and </p> </li> <li> <p>Brazilian Institute of Geography and Statistics (IBGE, in Portuguese: Instituto Brasileiro de Geografia e Estatística) [4].</p> </li> </ol> </li> </ul> <p><strong>Description: </strong>The data contained on the asppl-dataset.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health care of Persons Deprived of Liberty”. The course is available on the Unified Health System Virtual Learning Environment [1]. This dataset provides elementary data for analyzing the course’s impact and reach, as well as the profile of its participants.</p> <p> </p>
GERONTE H2020 Project - GERDAT002 - Composition of the health care professional consortium
<p><strong>The present document is a dataset generated as part of Deliverable D1.1. of the GERONTE project, which has received funding from the European Union’s Horizon 2020 Programme under Grant Agreement N°945218. It aims to provide the geriatric oncology professional community</strong> <strong>with a dataset of core health care professionals that should be involvoed in the evaluation and treatment trajectories of older patients with cancer and multimorbidity.</strong></p> <p>GERONTE is a 5-year research and innovation project (April 2021 to Mars 2026) funded by the European Union within the framework of the H2020 Research and Innovation programme, in response to the health societal challenge topic SC1-BHC-24-2020 “Healthcare interventions for the management of the elderly multimorbid patient”. The overall aim of GERONTE is to improve quality of life - defined as well-being on three levels: global health status, physical functioning and social functioning- for older multimorbid patients, while reducing overall costs of care. To this end, GERONTE will co-design, test, and prepare for deployment an innovative cost-effective patient-centred holistic health management system, hereafter referred to as the GERONTE intervention. GERONTE intervention will rely on an ICT based application for real-time collection and integration of standardised clinical and home patient-reported data. GERONTE intervention will be demonstrated in the context of care of multimorbid patients having cancer as a dominant morbidity, and be adaptable to any other combination of morbidities.</p> <p>An important component of the GerOnTe care pathway was to determine which health care professionals should be included in the health care professional consortium (HPC) providing care for the patient. Beforehand, we had considered the option of four core members and at least eight other participants depending on the patient’s specificities or profile.</p> <p>Based on clinical experience, we developed a list of 15 potential participants, including general practitioner, one or more oncology specialists (such as surgeons, medical oncologists, radiotherapists), geriatrician, oncology nurse, social worker, clinical pharmacist, physiotherapist, anaesthesiologist, home care nurse, dietician, occupational therapist, spiritual helpers/clerics, psychologist/psychiatrist, palliative care specialist, organ-specific physician(s) such as cardiologist, pulmonologist, nephrologist, rheumatologist etc.</p> <p>This list was presented to the expert panel, and they were asked to determine whether or not these participants should be involved in decision-making and/or the subsequent oncologic care trajectory; experts could specify if these participants should be involved for all patients, only in specific situations/profiles, or did not need to be involved.</p> <p>The results of this expert panel survey and the subsequent composition of the health care professional consortium forms the basis of this dataset.</p>
THE RELEVANCY OF MASSIVE HEALTH EDUCATION IN THE BRAZILIAN PRISON SYSTEM: THE COURSE "HEALTH CARE FOR PEOPLE DEPRIVED OF FREEDOM" AND ITS IMPACTS
<p><strong>Dataset name:</strong><em> asppl_dataset_v2.csv </em></p> <p><strong>Version: </strong>2.0 </p> <p><strong>Dataset period: </strong>06/07/2018 - 01/14/2022</p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>8118</p> <p><strong>Number of Attributes: </strong>9</p> <p><strong>Missing Values: </strong>Yes</p> <p><strong>Area(s): </strong>Health and education<strong> </strong></p> <p><strong>Sources: </strong></p> <ul> <li> <p>Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a); </p> </li> <li> <p>Brazilian Occupational Classification (CBO) (Brasil, 2022b);</p> </li> <li> <p>National Registry of Health Establishments (CNES) (Brasil, 2022c); </p> </li> <li> <p>Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e). </p> </li> </ul> <p><strong>Description: </strong>The data contained in the <em>asppl_dataset_v2.csv</em> dataset (see Table 1) originates from participants of the technology-based educational course “Health Care for People Deprived of Freedom.” The course is available on the AVASUS (Brasil, 2022a). This dataset provides elementary data for analyzing the course’s impact and reach and the profile of its participants. In addition, it brings an update of the data presented in work by Valentim et al. (2021).</p> <p><strong>Table 1: </strong>Description of AVASUS 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>gender </strong></p> </td> <td> <p>Gender of the course participant. </p> </td> <td> <p>Categorical. </p> </td> <td> <p>Feminino / Masculino / Não Informado. (In English, Female, Male or Uninformed)</p> </td> </tr> <tr> <td> <p><strong>course_progress</strong></p> </td> <td> <p>Percentage of completion of the course. </p> </td> <td> <p>Numerical. </p> </td> <td> <p>Range from 0 to 100.</p> </td> </tr> <tr> <td> <p><strong>course_evaluation</strong></p> </td> <td> <p>A score given to the course by the participant. </p> </td> <td> <p>Numerical. </p> </td> <td> <p>0, 1, 2, 3, 4, 5 or NaN.</p> </td> </tr> <tr> <td> <p><strong>evaluation_commentary</strong></p> </td> <td> <p>Comment made by the participant about the course.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Free text or NaN.</p> </td> </tr> <tr> <td> <p><strong>region</strong></p> </td> <td> <p>Brazilian region in which the participant resides.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Brazilian region according to IBGE: Norte, Nordeste, Centro-Oeste, Sudeste or Sul (In English North, Northeast, Midwest, Southeast or South). </p> </td> </tr> <tr> <td> <p><strong>CNES</strong></p> </td> <td> <p>The CNES code refers to the health establishment where the participant works.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>CNES Code or NaN.</p> </td> </tr> <tr> <td> <p><strong>health_care_level</strong></p> </td> <td> <p>Identification of the health care network level for which the course participant works.</p> </td> <td> <p>Categorical.</p> </td> <td> <p>“ATENCAO PRIMARIA”,</p> <p>“MEDIA COMPLEXIDADE”, </p> <p>“ALTA COMPLEXIDADE”, </p> <p>and their possible combinations.<br> <br> (In English "PRIMARY HEALTH CARE", "SECONDARY HEALTH CARE" AND "TERTIARY HEALTH CARE") </p> </td> </tr> <tr> <td> <p><strong>year_enrollment</strong></p> </td> <td> <p>Year in which the course participant registered.</p> </td> <td> <p>Numerical.</p> </td> <td> <p>Year (YYYY).</p> </td> </tr> <tr> <td> <p><strong>CBO</strong></p> </td> <td> <p>Participant occupation.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Text coded according to the Brazilian Classification of Occupations or “Indivíduo sem afiliação formal.” (In English “Individual without formal affiliation.”)</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Dataset name: </strong><em>prison_syphilis_and_population_brazil.csv</em></p> <p><strong>Dataset period: </strong>2017 - 2020</p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>6</p> <p><strong>Number of Attributes: </strong>13</p> <p><strong>Missing Values: </strong>No</p> <p><strong>Source: </strong></p> <ul> <li> <p>National Penitentiary Department (DEPEN) (Brasil, 2022d); </p> </li> </ul> <p><strong>Description: </strong>The data contained in the <em>prison_syphilis_and_population_brazil.csv</em> dataset (see Table 2) originate from the National Penitentiary Department Information System (SISDEPEN) (Brasil, 2022d). This dataset provides data on the population and prevalence of syphilis in the Brazilian prison system. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil.</p> <p><strong>Table 2:</strong> Description of DEPEN dataset Features. </p> <table align="center"> <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>Region</strong></p> </td> <td> <p>Brazilian region in which the participant resides. In addition, the sum of the regions, which refers to Brazil.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Brazil and Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South.</p> </td> </tr> <tr> <td> <p><strong>syphilis_2017</strong></p> </td> <td> <p>Number of syphilis cases in the prison system in 2017.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of syphilis cases.</p> </td> </tr> <tr> <td> <p><strong>syphilis_rate_2017</strong></p> </td> <td> <p>Normalized rate of syphilis cases in 2017.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Syphilis case rate.</p> </td> </tr> <tr> <td> <p><strong>syphilis_2018</strong></p> </td> <td> <p>Number of syphilis cases in the prison system in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of syphilis cases.</p> </td> </tr> <tr> <td> <p><strong>syphilis_rate_2018</strong></p> </td> <td> <p>Normalized rate of syphilis cases in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Syphilis case rate.</p> </td> </tr> <tr> <td> <p><strong>syphilis_2019</strong></p> </td> <td> <p>Number of syphilis cases in the prison system in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of syphilis cases.</p> </td> </tr> <tr> <td> <p><strong>syphilis_rate_2019</strong></p> </td> <td> <p>Normalized rate of syphilis cases in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Syphilis case rate.</p> </td> </tr> <tr> <td> <p><strong>syphilis_2020</strong></p> </td> <td> <p>Number of syphilis cases in the prison system in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of syphilis cases.</p> </td> </tr> <tr> <td> <p><strong>syphilis_rate_2020</strong></p> </td> <td> <p>Normalized rate of syphilis cases in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Syphilis case rate.</p> </td> </tr> <tr> <td> <p><strong>pop_2017</strong></p> </td> <td> <p>Prison population in 2017.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Population number.</p> </td> </tr> <tr> <td> <p><strong>pop_2018</strong></p> </td> <td> <p>Prison population in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Population number.</p> </td> </tr> <tr> <td> <p><strong>pop_2019</strong></p> </td> <td> <p>Prison population in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Population number.</p> </td> </tr> <tr> <td> <p><strong>pop_2020</strong></p> </td> <td> <p>Prison population in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Population number.</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Dataset name: </strong><em>students_cumulative_sum.csv</em></p> <p><strong>Dataset period: </strong>2018 - 2020</p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>6</p> <p><strong>Number of Attributes: 7</strong></p> <p><strong>Missing Values: </strong>No</p> <p><strong>Source: </strong></p> <ul> <li> <p>Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a);</p> </li> <li> <p>Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e). </p> </li> </ul> <p><strong>Description: </strong>The data contained in the <em>students_cumulative_sum.csv</em> dataset (see Table 3) originate mainly from AVASUS (Brasil, 2022a). This dataset provides data on the number of students by region and year. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil. We used population data estimated by the IBGE (Brasil, 2022e) to calculate the rate.</p> <p><strong>Table 3:</strong> Description of Students dataset Features. </p> <table align="center"> <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>Region</strong></p> </td> <td> <p>Brazilian region of the course participant. In addition, the sum of the regions, which refers to Brazil.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Brazil and the Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South. </p> </td> </tr> <tr> <td> <p><strong>2018</strong></p> </td> <td> <p>Number of students enrolled in the course in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of students.</p> </td> </tr> <tr> <td> <p><strong>rate_2018</strong></p> </td> <td> <p>Standardized rate of students in the course in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> <tr> <td> <p><strong>2019</strong></p> </td> <td> <p>Sum of students enrolled in the course in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of students.</p> </td> </tr> <tr> <td> <p><strong>rate_2019</strong></p> </td> <td> <p>Standardized rate of students in the course in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> <tr> <td> <p><strong>2020</strong></p> </td> <td> <p>Sum of students enrolled in the course in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Number of students.</p> </td> </tr> <tr> <td> <p><strong>rate_2020</strong></p> </td> <td> <p>Standardized rate of students in the course in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Dataset name: </strong><em>syphilis_tests_brazil.csv</em></p> <p><strong>Dataset period: </strong>2017 - 2020</p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>6</p> <p><strong>Number of Attributes: </strong>9</p> <p><strong>Missing Values: </strong>No</p> <p><strong>Source: </strong></p> <ul> <li> <p>Brazilian Ministry of Health, through the Outpatient Information System of the Brazilian Health System (SIA/SUS) (Brasil, 2022f);</p> </li> <li> <p>Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e). </p> </li> </ul> <p><strong>Description: </strong>The data contained in the <em>syphilis_tests_brazil.csv</em> dataset (see Table 4) originate mainly from the Outpatient Information System of the Brazilian Health System (SIA/SUS). This dataset provides data on the number of tests for syphilis detection by region and year. In addition, it brings a rate that represents the normalized data to compare the populations of each region and Brazil. We used population data estimated by the IBGE (Brasil, 2022e) to calculate the rate.</p> <p><strong>Table 4:</strong> Description of Syphilis Testes dataset Features. </p> <table align="center"> <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>Region</strong></p> </td> <td> <p>Brazilian region where tests for syphilis were performed. In addition, the sum of the regions, which refers to Brazil.</p> </td> <td> <p>Categorical. </p> </td> <td> <p>Brazil and the Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South.</p> </td> </tr> <tr> <td> <p><strong>2017</strong></p> </td> <td> <p>The number of tests for syphilis performed in 2017.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>The number of tests.</p> </td> </tr> <tr> <td> <p><strong>rate_2017</strong></p> </td> <td> <p>Syphilis testing rate in 2017.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> <tr> <td> <p><strong>2018</strong></p> </td> <td> <p>The number of tests for syphilis performed in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>The number of tests.</p> </td> </tr> <tr> <td> <p><strong>rate_2018</strong></p> </td> <td> <p>Syphilis testing rate in 2018.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> <tr> <td> <p><strong>2019</strong></p> </td> <td> <p>The number of tests for syphilis performed in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>The number of tests.</p> </td> </tr> <tr> <td> <p><strong>rate_2019</strong></p> </td> <td> <p>Syphilis testing rate in 2019.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> <tr> <td> <p><strong>2020</strong></p> </td> <td> <p>The number of tests for syphilis performed in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>The number of tests.</p> </td> </tr> <tr> <td> <p><strong>rate_2020</strong></p> </td> <td> <p>Syphilis testing rate in 2020.</p> </td> <td> <p>Numerical. </p> </td> <td> <p>Normalized value.</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>REFERENCES</strong></p> <p>Brasil (2022a). Ambiente virtual de aprendizagem do sus - avasus. atenção à saúde da pessoa privada de liberdade Available from: <a href="https://avasus.ufrn.br/local/avasplugin/cursos/curso.php?id=114">https://avasus.ufrn.br/local/avasplugin/cursos/curso.php?id=114</a> .</p> <p>Brasil (2022b). Cbo - classificação brasileira de ocupações. Available from: <a href="http://www.mtecbo.gov.br/cbosite/pages/home.jsf">http://www.mtecbo.gov.br/cbosite/pages/home.jsf</a> .</p> <p>Brasil (2022c). Cnes - cadastro nacional de estabelecimentos de saúde. Available from: <a href="http://cnes.datasus.gov.br/">http://cnes.datasus.gov.br/</a> .</p> <p>Brasil (2022d). Departamento penitenciário nacional. levantamento nacional de informações penitenciárias. Available from: <a href="https://www.gov.br/depen/pt-br/servicos/sisdepen">https://www.gov.br/depen/pt-br/servicos/sisdepen</a> .</p> <p>Brasil (2022e). IBGE - Instituto Brasileiro de Geografia e Estatística. Estimativas da População. Available from: <a href="https://www.ibge.gov.br/estatisticas/sociais/populacao/9103-estimativas-de-populacao.html?edicao=31451&t=resultados">https://www.ibge.gov.br/estatisticas/sociais/populacao/9103-estimativas-de-populacao.html?edicao=31451&t=resultados</a> .</p> <p>Brasil (2022f). Ministério da saúde - sistema de informações ambulatoriais do sus (sia/sus). Available from: <a href="https://datasus.saude.gov.br/acesso-a-informacao/producao-ambulatorial-sia-sus/">https://datasus.saude.gov.br/acesso-a-informacao/producao-ambulatorial-sia-sus/</a> .</p> <p>Valentim, J., Oliveira, E. d. S. G., Valentim, R. A. d. M., Dias-Trindade, S., Dias, A. d. P., Cunha-Oliveira, A., et al. (2021). Data report: “health care of persons deprived of liberty” course from brazil’s unified health system virtual learning environment. Frontiers in Medicine 8. doi:10.3389/fmed.2021.742071.</p> <p> </p> <p><strong>ARTICLE:</strong></p> <p>THE RELEVANCY OF MASSIVE HEALTH EDUCATION IN THE BRAZILIAN PRISON SYSTEM: THE COURSE “HEALTH CARE FOR PEOPLE DEPRIVED OF FREEDOM” AND ITS IMPACTS <br> </p> <p><strong>AUTHORS:</strong></p> <p>Janaína L. R. S. Valentim<sup>1,2</sup>, Sara Dias-Trindade<sup>2,3</sup>, Eloiza da S. G. Oliveira<sup>1,4</sup>, José A. M. Moreira<sup>2,5</sup>, Felipe Fernandes<sup>1</sup>, Manoel Honório Romão<sup>1</sup>, Philippi S. G. de Morais<sup>1</sup>, Alexandre R. Caitano<sup>1</sup>, Aline P. Dias<sup>1</sup>, Carlos A. P. Oliveira<sup>1,4,6</sup>, Karilany D. Coutinho<sup>1</sup>, Ricardo B. Ceccim<sup>7</sup>, Ricardo A. M. Valentim<sup>1</sup></p> <p> </p> <p><sup>1</sup>Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil </p> <p><sup>2</sup>Univ Coimbra, Centre for Interdisciplinary Studies, Coimbra, Portugal</p> <p><sup>3</sup>Univ Coimbra, Centre for Interdisciplinary Studies, Faculty of Arts and Humanities, Coimbra, Portugal</p> <p><sup>4</sup>Multidisciplinary Institute for Human Development with Technologies, State University of Rio de Janeiro (UERJ), Rio de Janeiro, RJ, Brazil</p> <p><sup>5</sup>Open University (Universidade Aberta), Department of Education and Distance Learning (DEED), Lisbon, Portugal</p> <p><sup>6</sup>International Council for Open and Distance Education, Oslo, Norway</p> <p><sup>7</sup>Postgraduate Program in Education, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Rio Grande do Sul, Brazil</p> <p> </p> <p> </p>
Quantitative account of social interactions in a mental health care ecosystem: cooperation, trust and collective action
<p>Mental disorders have an enormous impact in our society, both in personal terms and in the economic costs associated with their treatment. In order to scale up services and bring down costs, administrations are starting to promote social interactions as key to care provision. We analyze quantitatively the importance of communities for effective mental health care, considering all community members involved. By means of citizen science practices, we have designed a suite of games that allow to probe into different behavioral traits of the role groups of the ecosystem. The evidence reinforces the idea of community social capital, with caregivers and professionals playing a leading role. Yet, the cost of collective action is mainly supported by individuals with a mental condition - which unveils their vulnerability. The results are in general agreement with previous findings but, since we broaden the perspective of previous studies, we are also able to find marked differences in the social behavior of certain groups of mental disorders. We finally point to the conditions under which cooperation among members of the ecosystem is better sustained, suggesting how virtuous cycles of inclusion and participation can be promoted in a ’care in the community’ framework.</p>
OpenChart-SE: A corpus of artificial Swedish electronic health records for imagined emergency care patients written by physicians in a crowd-sourcing project
<p>Electronic health records (EHRs) are a rich source of information for medical research and public health monitoring. Information systems based on EHR data could also assist in patient care and hospital management. However, much of the data in EHRs is in the form of unstructured text, which is difficult to process for analysis. Natural language processing (NLP), a form of artificial intelligence, has the potential to enable automatic extraction of information from EHRs and several NLP tools adapted to the style of clinical writing have been developed for English and other major languages. In contrast, the development of NLP tools for less widely spoken languages such as Swedish has lagged behind. A major bottleneck in the development of NLP tools is the restricted access to EHRs due to legitimate patient privacy concerns. To overcome this issue we have generated a citizen science platform for collecting artificial Swedish EHRs with the help of Swedish physicians and medical students. These artificial EHRs describe imagined but plausible emergency care patients in a style that closely resembles EHRs used in emergency departments in Sweden. In the pilot phase, we collected a first batch of 50 artificial EHRs, which has passed review by an experienced Swedish emergency care physician. We make this dataset publicly available as OpenChart-SE corpus (version 1) under an open-source license for the NLP research community. The project is now open for general participation and Swedish physicians and medical students are invited to submit EHRs on the project website (<a href="https://github.com/Aitslab/openchart-se">https://github.com/Aitslab/openchart-se</a>), where additional batches of quality-controlled EHRs will be released periodically. </p> <p> </p> <p><strong>Dataset content</strong></p> <p><em>OpenChart-SE, version 1 corpus (txt files and and dataset.csv)</em></p> <p>The OpenChart-SE corpus, version 1, contains 50 artificial EHRs (note that the numbering starts with 5 as 1-4 were test cases that were not suitable for publication). The EHRs are available in two formats, structured as a .csv file and as separate textfiles for annotation. Note that flaws in the data were not cleaned up so that it simulates what could be encountered when working with data from different EHR systems. All charts have been checked for medical validity by a resident in Emergency Medicine at a Swedish hospital before publication.</p> <p> </p> <p><em>Codebook.xlsx</em></p> <p>The codebook contain information about each variable used. It is in XLSForm-format, which can be re-used in several different applications for data collection.</p> <p> </p> <p><em>suppl_data_1_openchart-se_form.pdf</em></p> <p>OpenChart-SE mock emergency care EHR form.</p> <p> </p> <p><em>suppl_data_3_openchart-se_dataexploration.ipynb</em></p> <p>This jupyter notebook contains the code and results from the analysis of the OpenChart-SE corpus.</p> <p> </p> <p>More details about the project and information on the upcoming preprint accompanying the dataset can be found on the project website (<a href="https://github.com/Aitslab/openchart-se">https://github.com/Aitslab/openchart-se</a>).</p>
Symptoms in health care workers during the COVID-19 epidemic. A cross-sectional survey.
<p>data collected during the COVID-19 epidemics on workers of the Health Care Unit Roma4, Civitavecchia. Paper submitted.</p>
Data Visualization - Individual Project - G20 Countries - Military, Health Care and Educational Spendings
<p>This Project is part of the course work for Data visualization DATS 6401. In this project, I have created webpage to show data analysis on G20 countries - military, health care and educational spending from 2011 - 2017. Google Visualization API is used for all visualization graphs in the webpage.</p>
Assessment of Potential Risk Factors for COVID-19 among Health Care Workers in a Health Care Setting in Delhi, India
<p><strong><em>Executive summary</em></strong></p> <p>The novel coronavirus SARS-CoV2 (COVID-19), first detected by Wuhan Municipal Health Commission, China, in Wuhan, Hubei Province in December 2020 and eventually the disease became pandemic. It was declared as Public Health Emergency of International Concern (PHEIC) by WHO in January 2020. The COVID-19 disease primarily spreads through droplets of saliva or discharge from the nose when an infected person coughs or sneezes. People infected with the COVID-19 virus experiences mild, moderate or serious respiratory illness. </p> <p>Health workers play a critical role in the clinical management of patients with COVID-19 and hence are likely to be the most vulnerable for contracting the disease. Therefore, investigating the extent of infection in health care settings and identifying risk factors for infection among health workers along with follow-up within a facility in which a confirmed case of COVID-19 infection is receiving care can provide useful information on virus transmissibility and routes of transmission, and will bear important step in limiting amplification events in health care facilities. </p> <p> </p> <p><strong>Objectives:</strong></p> <p>1. To find out the extent of human-to-human transmission of the SARS-CoV-2 infection among health workers<br> 2. To study the clinical presentations of COVID-19 infection and the risk factors for infection among health workers.<br> 3 To evaluate the effectiveness of infection prevention and control measures among health workers in protecting against COVID-19.<br> 4. To evaluate the effectiveness of infection prevention and control programmes at health facility level <br> 5. To determine the serological response of health workers with symptomatic and possibly asymptomatic COVID-19 infection.</p> <p> </p> <p><strong>Materials and Methods:</strong></p> <p>This was a prospective cohort study conducted over a period of seven months, from December 2020 to June 2021, the period covering India’s deadly second wave of COVID-19 pandemic. This was done among the health care workers working in HIMSR & HAHC hospital, a tertiary health care setting (Dedicated COVID-19 Hospital) providing care to patients with a laboratory-confirmed COVID-19 infection. This hospital located in South East Delhi has 200 bedded COVID-19 Care Hospital and 1050 registered healthcare workers who come in contact with COVID-19-infected persons. The study population (sampling frame) included all the health personnel like doctors, nurses, paramedical staff, housekeeping staff, security staff, students of medical, nursing and paramedical sciences and other front office staff who come in contact with the patients. In this study, the first visit / interview (Baseline) was done when the staff came in contact with a confirmed COVID-19 case. The second visit / interview (Endline) was done between 22-28 days. During each of these two visits, biological sample in the form of serum was collected to check the presence of anti-COVID-19 antibodies</p> <p> </p> <p><strong>Results:</strong></p> <p>A total of 192 HCW were recruited in this study. All of them were interviewed and blood was collected for serology at the baseline visit as well as at endline. Out of 192 participants, 119 (61.97%) were detected with SARS-CoV2 antibodies at baseline whereas 73 (38.02%) were seronegative. Again, on22-28 days of follow-up, the seropositivity was 77.7% at the endline. We found that seropositivity was significantly and negatively associated with doctor as profession [OR:0.353, CI:0.176-0.710], COVID-19 symptoms [OR:0.210, CI:0.054-0.820], comorbidities [OR:0.139 , CI: 0.029 - 0.674], recent IPC Training [OR:0.250, CI:0.072 -0.864] , while positively associated with Partially [OR:3.303,CI: 1.256-8.685], as well as fully Vaccinated for COVID-19 [OR:2.428, CI:1.118-5.271]. We also observed seroconversion among 36.7% while 64.0% had increase in titre of antibodies during our follow-up period. The seroconversion was 63.2% in doctors, 42.9% in nurses and 13.0% in paramedics staff. Seroconversion was positively associated with doctor as profession [OR:11.43, CI:2.47 - 52.79] and with partially, as well as fully vaccinated for COVID-19 [OR: 32.63, CI: 5.11 - 208.49]. None of the HCW who were smokers and with any comorbidity did not found to have been seroconversion. We observe a negative and significant relationship of increase in titre of antibodies with recent any ILI symptoms [OR:0.17, 0.13 - 0.94], smokers[OR: 0.35, 95%CI: 0.13 - 0.94], HCW with comorbidities [OR:0.08,95CI: 0.01 - 0.71],, recent full IPC Training [OR:0.07, CI:0.01 -0.63] , while positively associated with partially [OR: 7.87, 95CI: 2.18 - 28.40)], as well as fully Vaccinated for COVID-19 [OR: 3.59, 95CI: 1.46 - 8.87]. Majority of the health care worker enrolled in our study had close contact exposure with COVID-19 patients while 5 had indirect exposure. It was observed that almost all (100% in both) doctors and nurses as well as almost all paramedical staff (99%) were wearing some kind of personal protective equipment (PPE) when they were exposed to a COVID-19 patient. We did not found adherences to any of the infection prevention measure adopted by the enrolled HCW during the recent contact with COVID-19 patients to be significantly associated with seroconversion. </p> <p><strong>Conclusion:</strong></p> <p>Majority of the health care worker (67% doctor, 80% nurses & 55% paramedics) enrolled in our study had close contact exposure with COVID-19 patient. The results show that among 192 HCW enrolled, 62% were seropositive at the baseline. At end line the seropositivity was increased to 77.7%. The seroconversion rate was also studied. It was found to be 36.7% in our study population (63.2% in doctors, 42.9% in nurses and 13.0% in paramedic’s staff.). Adherence to the recommended IPC measures was reported by most participants. About two third (63%) of the HCW in our study were not vaccinated against COVID-19; nurses and paramedics were higher in proportion among those who were unvaccinated. Fifteen percentage were partially vaccinated and 22% were fully vaccinated against COVID-19, with doctors comprising majority among them. We also found that vaccination had the strongest association with seropositivity, seroconversion as well as serial rise of titre.</p>
Response to COVID-19: Clients' perspectives on the utilization of reproductive, maternal and child health care services in Nigeria
<p>This dataset was a part of a cross-sectional descriptive study in 320 rural communities in 32 Local Government Areas (LGAs) across the Federal Capital Territory (FCT), Abuja, and 9 out of the 36 Nigerian States in 2020. The dataset contains views of women who used primary health care centres in the selected LGAs for maternal and child health care, and family planning services before, during, and after the COVID-19 pandemic lockdown in Nigeria. The sample size was determined using the Yamane sample size formula. The estimated sample size per State was 384 (3,840 for nine States and FCT), with a 10% adjustment for non-response, the total sample size was 422 per State or location (4220 for the nine States and FCT). The data are stored in SPSS format. <br> The study was an initiative of UNFPA Nigeria. They coordinated it under the One UN Basket fund to respond to COVID-19 and implemented it under the supervision of three national NGOs: The Women’s Health and Action Research Centre (WHARC), Education as a Vaccine (EVA), and the Planned Parenthood Federation of Nigeria (PPFN). </p> <p> </p>
Literaturrecherche: Blockchain & Health Care (Anlage 2 zur MHBA-Masterthesis)
<p>1. quantitative semantische Analysedaten (in ZIP-Datei) : Blockchain[title] AND health care in https://pubmed.ncbi.nlm.nih.gov/</p> <p>2. Exegese von „cases“ (Fallstudien) und „proof of concepts“ (Funktionsnachweise)</p> <p>mit den Suchstrings (TXT-Datei) nach</p> <p>a) Ansätzen zur Patienten- resp. Behandlungsdokumentation (EHRs)</p> <p>b) Ansätzen zur Prozessoptimierung (supply chain)</p> <p>c) Ansätzen in der Medizintechnik und Digital Health (Internet of Medical Things)</p> <p>sowie finale Liste (CSV-Datei) der eingeschlossenen und ausgeschlossenen Arbeiten.</p> <p>3. Ergebnisse der Autoren-/Länderanalyse der Publikationen (Formate: CSV, TXT)</p>
Use of health care services in community-dwelling older adults in two regions of Spain
<p>This dataset contains data on use of health care resources of community-dwelling older adults aged 70 or over, who were functionally independent. Data of health resources use included contacts along two consecutive years with: the general practitioner, primary care nurse, the specialists, visits to emergency rooms, and hospital admissions and length of stay. The data included also information about sex, region, polipharmacy, age-adjusted Charlson Comorbidity Index and funcionality, measured by Timed Up and Go test. The data collection was performed in two Spanish regions. Baseline assessment was done between 2015 and 2016, and patients were followed for 2 years. There were in total 1488 registries considering both years.</p>
The Application of Machine Learning for Classification on Blood Pressure Variability. A New Approach for an Old Idea - Professor Kelvin Tsoi (The Chinese University of Hong Kong, School of Public Health and Primary Care)
<p>This video is the eighth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>The Application of Machine Learning for Classification on Blood Pressure Variability. A New Approach for an Old Idea - Professor Kelvin Tsoi (The Chinese University of Hong Kong, School of Public Health and Primary Care)</p> <p>Bio: Professor Kelvin Tsoi is an Epidemiologist specialized in Digital Health. His research interests focus on digital innovation in chronic disease management, including mobile and telecare application for hypertension management, technological implementation and social engagement for cognitive screening, artificial intelligent application on electronic health records. He also works as the traditional epidemiologist on evidence-based medicine and population cohort studies. He obtained his Bachler Degree from Department of Statistics and Doctor of Philosophy from School of Public Health in the Chinese University of Hong Kong. He further received post-doctoral training in the Division of Gastroenterology and Hepatology, Department of Medicine and Therapeutics. He was also appointed as a Director of CUHK JC Bowel Cancer Education Centre to promote colorectal cancer screening. In 2011, he worked as a research scientist in Hospital Authority. He led projects covering a wide range of service areas on chronic diseases, such as service demand projection for schizophrenia and dementia. The experience of database management enhanced his understanding of the HA database structures. In 2013, he was invited to join the interdisciplinary team for Big Data research and worked closely with a team of engineers and data scientists. Currently, Professor Tsoi is an Associate Professor in JC School of Public Health and Primary Care, SH big Data Decision Analytics Research Centre and JC Institute of Ageing.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/liLVKA-JHiI</p>
Dataset: Sabra Health Care REIT, Inc. (SBRA) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Invesco S&P SmallCap Health Care ETF (PSCH) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Option Care Health, Inc. (OPCH) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Resilience of primary health care facilities - experiences from 16 European countries during the Covid-19 pandemic. A mixed-methods study conducted by EURIPA.
<p><strong><span>Resilience of primary health care facilities – experiences from 16 European countries during the Covid-19 pandemic. A mixed-methods study conducted by EURIPA.</span></strong></p> <p><strong><span>Abstract</span></strong></p> <p><strong><span>Background</span></strong></p> <p><span>The role of primary care during a pandemic varies across European countries. The COVID-19 pandemic has altered the working practices of family medicine doctors. It has also impacted the resilience of health systems.</span></p> <p><strong><span>Objectives</span></strong><span> </span></p> <p><span>This study aimed to examine European health system responses to the pandemic, focusing on rural and urban differences.</span></p> <p><strong><span>Material and methods</span></strong></p> <p><span>This cross-sectional, mixed-methods study used a semi-structured online questionnaire with 68 questions, including 21 free-text comments. Data were collected from May 2020 to January 2021. Key informants from sixteen EURIPA member countries distributed questionnaires to 406 primary care doctors. Data were analyzed using descriptive statistics and non-parametric tests (Chi-square, Kruskal-Wallis, Mann-Whitney U) with a significance threshold of 0.05. </span></p> <p><strong><span>Results</span></strong></p> <p><span>A statistically significant difference was found between rural (36.4%, 55/151), semirural (19.4%, 24/124), and urban populations (29.8%, 39/131) regarding medicine shortages (χ² = 9.91, df = 4, p = 0.042). <span> </span>The semirural one proved to have a statistically significant difference with the other settings(chi-square post-hoc test, p = 0.004 in post-hoc chi-square test). Significant differences were found between countries in resilience features including, Effectiveness of triage, Adapting to the rapidly changing requirements, Government help, Existence of a community resilience group, Improved interprofessional collaboration, Medicine Shortage and GPs involved in palliative care.</span></p> <p><strong><span>Conclusions</span></strong></p> <p><span>Medicine shortage was more prevalent in rural and urban areas compared to semirural ones. Differences were observed between countries in their responses to the pandemic, particularly in Adapting to the rapidly changing requirements, Effectiveness of triage, Government help, Existence of a Community resilience group. Qualitative analysis confirmed these disparities.</span></p> <p><span>The results emphasize the need for tailored approaches considering diverse contexts in shaping effective health system resilience.</span></p>
Linked collectors and determiners for: ABC_Useful plants of Malawi; medicinal plants used in maternal and child health care.
Natural history specimen data linked to collectors and determiners held within, "ABC_Useful plants of Malawi; medicinal plants used in maternal and child health care". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/789e1248-c737-4d6a-bcec-e4b92775becc">https://bionomia.net/dataset/789e1248-c737-4d6a-bcec-e4b92775becc</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/789e1248-c737-4d6a-bcec-e4b92775becc">https://gbif.org/dataset/789e1248-c737-4d6a-bcec-e4b92775becc</a>. Formatted as a Frictionless Data package.
Evaluation of Massive Education in Prison Health: a perspective of health care for the person deprived of freedom in Brazil
<p><strong>Dataset name</strong>: data_survey.xlsx <br> <strong>Version</strong>: 1.0 <br> <strong>Dataset period</strong>: 03/23/2022 - 06/30/2022<br> <strong>Dataset Characteristics</strong>: Multivalued <br> <strong>Number of Instances</strong>: 270<br> <strong>Number of Attributes</strong>: 88<br> <strong>Missing Values</strong>: Yes<br> <strong>Area(s)</strong>: Health and education </p> <p><br> <strong>Description</strong>: The data contained in the "data_survey.xlsx" dataset (Table 1, in the README) originate from a questionnaire (available in the download files section) composed of 37 questions administered to students who completed the course "Health Care for People Deprived of Freedom" from the learning path "Prison System." This course is a strategy for mass education in prison health in Brazil and is available in the Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2023). This dataset provides information about the course from the perspective of health professionals and other staff who work or wish to work in the Brazilian prison system.<br> <strong>Note</strong>: The dataset's content is provided in Brazilian Portuguese (pt-br) from native speakers.</p>
Can we collect health-related quality of life information from anticoagulated atrial fibrillation participants who have recently experienced a bleed? An observational feasibility study in primary, and secondary care and through an online forum
<p>The purpose of the study was to evaluate the feasibility of recruiting participants diagnosed with atrial fibrillation (AF) taking oral anticoagulation therapies (OACs) and recently experiencing a bleed to collect health-related quality of life (HRQoL) information.</p> <p><strong>Design</strong></p> <p>Observational feasibility study. The study aimed to determine the feasibility of recruiting participants with minor and major bleeds, the most appropriate route for recruitment and the appropriateness of the Patient Reported Outcome Measures (PROMs) selected for collecting HRQoL information in AF patients, and the preferred format of the surveys.</p> <p><strong>Setting</strong></p> <p>Primary care, secondary care, and via an online patient forum.</p> <p><strong>Participants</strong></p> <p>The study population was adult patients (≥ 18) with Atrial Fibrillation (AF) taking oral anticoagulation therapies (OATs) who had experienced a recent major or minor bleed within the last four weeks.</p> <p><strong>Primary and Secondary outcome measures</strong></p> <p>Primary outcome:</p> <p>Patient reported outcome measures (PROMs): EuroQol 5 dimensions-5 levels (EQ-5D-5L); Perception of anticoagulant treatment questionnaire, part 2 only (PACT-Q, part 2); Atrial fibrillation effect on quality of life (AFEQT)</p> <p>Secondary outcomes:</p> <p>Location of bleed; bleed severity; current treatment; patient perceptions of HRQoLin relation to bleeding events.</p> <p><strong>Results</strong></p> <p>We received initial expressions of interest from 103 participants. We subsequently recruited 32 participants to the study- 14 from primary care and 18 through the AF forum. No participants were recruited through secondary care. Despite 32 participants consenting, only 26 initial surveys were completed. We received follow-up surveys from 11 participants (8 primary care and 3 AF forum). COVID-19 had a major impact on the study. </p> <p><strong>Conclusions</strong></p> <p>Primary care was the most successful route for recruitment. Most participants recruited to the study experienced a minor bleed. Further ways to recruit in secondary care should be explored, especially to capture more serious bleeds. </p> <p><strong>Registration</strong></p> <p>The study was adopted onto the NIHR Portfolio (I.D. #47771) and registered with www.ClinicalTrials.gov (#NCT04921176) in February 2021.</p> <p>Dataset contains all anonymised data for the participants who completed the survey including demographics, details of bleeds, co-morbidities and completed patient reported outcomes</p>
Collaborating to Heal Addiction and Mental Health in Primary Care
ClinicalTrials.gov study NCT04600414. IPD Sharing: YES. Countries: 1. Publications: 1.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.