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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>
Mapping environmental injustices within the U.S. prison system: a nationwide dataset
<p>This open-access geospatial dataset (downloadable in csv or shapefile format) contains a total of 11 environmental indicators calculated for 1865 U.S. prisons. This consists of all active state- and federally-operated prisons according to the Homeland Infrastructure Foundation-Level Data (HIFLD), last updated June 2022. This dataset includes both raw values and percentiles for each indicator. Percentiles denote a way to rank prisons among each other, where the number represents the percentage of prisons that are equal to or have a lower ranking than that prison. Higher percentile values indicate higher vulnerability to that specific environmental burden compared to all the other prisons. Full descriptions of how each indicator was calculated and the datasets used can be found here: <a href="http://https://github.com/GeospatialCentroid/NASA-prison-EJ/blob/main/doc/indicator_metadata.md.">https://github.com/GeospatialCentroid/NASA-prison-EJ/blob/main/doc/indicator_metadata.md.</a></p> <p>From these raw indicator values and percentiles, we also developed three individual component scores to summarize similar indicators, and to then create a single vulnerability index (methods based on other EJ screening tools such as Colorado Enviroscreen, CalEnviroScreen and EPA’s EJ Screen). The three component scores include climate vulnerability, environmental exposures and environmental effects. Climate vulnerability factors reflect climate change risks that have been associated with health impacts and includes flood risk, wildfire risk, heat exposure and canopy cover indicators. Environmental exposures reflect variables of different types of pollution people may come into contact with (but not a real-time exposure to pollution) and includes ozone, particulate matter (PM 2.5), traffic proximity and pesticide use. Environmental effects indicators are based on the proximity of toxic chemical facilities and includes proximity to risk management plan (RMP) facilities, National Priority List (NPL)/Superfund facilities, and hazardous waste facilities. Component scores were calculated by taking the geometric mean of the indicator percentiles. Using the geometric mean was most appropriate for our dataset since many values may be related (e.g., canopy cover and temperature are known to be correlated).</p> <p>To calculate a final, standardized vulnerability score to compare overall environmental burdens at prisons across the U.S., we took the average of each component score and then converted those values to a percentile rank. While this index only compares environmental burdens among prisons and is not comparable to non-prison sites/communities, it will be able to heighten awareness of prisons most vulnerable to negative environmental impacts at county, state and national scales. As an open-access dataset it also provides new opportunities for other researchers, journalists, activists, government officials and others to further analyze the data for their needs and make comparisons between prisons and other communities. This is made even easier as we produced the methodology for this project as an open-source code base so that others can apply the code to calculate individual indicators for any spatial boundaries of interest. The codebase can be found on GitHub (<a href="https://github.com/GeospatialCentroid/NASA-prison-EJ">https://github.com/GeospatialCentroid/NASA-prison-EJ</a>) and is also published via Zenodo (<a href="https://zenodo.org/record/8306856">https://zenodo.org/record/8306856</a>).</p> <p> </p>
Perspectives and Challenges in the Analysis of Prison Systems Data: A Systematic Mapping
<p>This is a list of 207 selected papers from a systematic mapping performed to analyze existing evidence on prison systems original data from peer-reviewed studies published between 2000 and 2019.</p>
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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.