Open government data use: The Brazilian states and federal district cases
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ABSTRACT:
SUBMITTER: Kawashita I
PROVIDER: S-EPMC10914293 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:BackgroundPsychoactive substances (PASs) are an important risk factor for suicide. This study investigated the sociodemographic characteristics, data related to the suicidal behavior, the methods employed, the circumstances of the events, and the use of PASs before dying in all suicides that occurred between 2005-2014 in the Brazilian Federal District, comparing cases with positive and negative detection for PASs in the post-mortem analysis to identify groups at greatest risk.MethodsA population-based, observational, cross-sectional study with an analytical aspect was conducted with suicides cases collected from local police, which toxicological examination was performed (headspace gas chromatographic-mass spectrometry-HS-GC/MS) for detection of ethanol and methanol in blood samples; immunoassay for other substances (cocaine, marijuana, benzodiazepine).ResultsThe results showed that the increase in the suicide rate was 10 × greater than the population growth, and 44% of the individuals used PASs before suicide. Individuals are more likely to die by suicide at home, be male, have tried before, and change their behavior days before death; they choose to hang as the method and are influenced by alcohol.ConclusionIdentifying what sociodemographic characteristics are associated with a fatal suicide attempt among individuals who use PASs and those who do not use and those who have/do not have mental disorders and what methods are employed could be employed as a path to better interventions. Thus, prevention actions could be planned and directed to individuals with greater risk.
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Project description:BackgroundThe Brazilian Amazon is the world's largest rainforest regions and plays a key role in biodiversity conservation as well as climate adaptation and mitigation. The government has created a network of protected areas (PAs) to ensure long-term conservation of the region. However, despite the importance of and positive advances in the establishment of PAs, natural resource depletion in the Brazilian Amazon is pervasive.MethodsWe evaluated a total of 4,243 official law enforcement records generated between 2010 and 2015 to understand the geographical distribution of the illegal use of resources in federal PAs in the Brazilian Amazon. We classified illegal activities into ten categories and used generalized additive models (GAMs) to evaluate the relationship between illegal use of natural resources inside PAs with management type, age of PAs, population density, and accessibility.ResultsWe found 27 types of illegal use of natural resources that were grouped into 10 categories of illegal activities. Most infractions were related to suppression and degradation of vegetation (37.40%), followed by illegal fishing (27.30%) and hunting activities (18.20%). The explanatory power of the GAMs was low for all categories of illegal activity, with a maximum explained variation of 41.2% for illegal activities as a whole, and a minimum of 14.6% for hunting activities.DiscussionThese findings demonstrate that even though PAs are fundamental for nature conservation in the Brazilian Amazon, the pressures and threats posed by human activities include a broad range of illegal uses of natural resources. Population density up to 50 km from a PA is a key variable, influencing illegal activities. These threats endanger long-term conservation and many efforts are still needed to maintain PAs that are large enough and sufficiently intact to maintain ecosystem functions and protect biodiversity.
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Project description:This study identifies differences in the content of open public data managed by the central government, local governments, public institutions, and the office of education in Korea through keyword network analysis. Pathfinder network analysis was performed by extracting keywords assigned to 1,200 data cases, open to the Korean Public Data Portals. Subject clusters were derived for each type of government and their utility was compared using download statistics. Eleven clusters were formed for public institutions with specialized information on national issues such as Health care and Real estate, while 15 clusters were formed for the central government with national administrative information, including Crime and Safety policing. Local governments and offices of education were assigned 16 and 11 topic clusters respectively, with data focusing on regional life such as Local factories and manufacturing, Resident registration, and Lifelong education. Usability was higher in public and central governments that deal with national-level specialized information than for regional-level information. It was also confirmed that subject clusters such as Health care, Real estate, and Crime showed high usability. Furthermore, there was a large gap in data utilization because of the existence of popular data that showed extremely high usage.Supplementary informationThe online version contains supplementary material available at 10.1007/s11135-023-01630-x.
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Project description:Open Government Data (OGD), including statistical data, such as economic, environmental and social indicators, are data published by the public sector for free reuse. These data have a huge potential when exploited using Machine Learning methods. Linked Data technologies facilitate retrieving integrated statistical indicators by defining and executing SPARQL queries. However, statistical indicators are available in different temporal and spatial granularity levels as well using different units of measurement. This data article describes the integrated statistical indicators that were retrieved from the official Scottish data portal in order to facilitate the exploitation of Machine Learning methods in OGD. Multiple SPARQL queries as well as manual search in the data portal were employed towards this end. The resulted dataset comprises the maximum number of compatible datasets, i.e., datasets with matching temporal and spatial characteristics. In particular, the data include 60 statistical indicators from seven categories such as health and social care, housing, and crime and justice. The indicators refer to the 6,976 "2011 data zones" of Scotland, while the year of reference is 2015. Data are ready to be used by the research community, students, policy makers, and journalists and give rise to plenty of social, business, and research scenarios that can be solved using Machine Learning technologies and methods.
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Project description:Open government data (OGD) holds great potential for firms and the digital economy as a whole and has attracted increasing interest in research and practice in recent years. Governments and organizations worldwide are struggling in exploiting the full potential of OGD and require a comprehensive understanding of this phenomenon. Although scientific debates in OGD research are intense and heterogeneous, the field lacks theoretical integration of OGD topics and their systematic consideration in the context of the digital economy. In addition, OGD has been widely neglected by information systems (IS) research, which promises great potential for advancing our knowledge of the OGD concept and its role in the digital economy. To fill in this gap, this study conducts a systematic literature review of 169 empirical OGD studies. In doing so, we develop a theoretical review framework of Antecedents, Decisions, Outcomes (ADO) to unify and grasp the accumulating isolated evidence on OGD in context of the digital economy and provide a theory-informed research agenda to tap the potential of IS research for OGD. Our findings reveal six related key topic clusters of OGD research and substantial gaps, opening up prospective research avenues and particularly outlining how IS research can inform and advance OGD research.Supplementary informationThe online version contains supplementary material available at 10.1007/s12525-022-00582-8.
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Project description:This dataset presents a narrative record of all announced federal government spending changes in Canada between 1949q1 and 2012q1. We use the federal government's budget documents, mostly the budget speech, to document announced spending measures. Other budget documents that we use include the Economic Statements, Financial Statements, Mini Budgets, Interim Budgets, and Economic and Budget Updates. We document the motivation behind each announced measure. Based on these motivations, we classify spending changes as exogenous or endogenous. Exogenous changes are those that are not motivated by contemporaneous economic conditions of the country. Endogenous changes are those that are taken in response to current economic conditions. We also document the size of a change, whether it was intended to be temporary or permanent, the duration of the measure, and the size of the measure that was to be implemented in the same year as it is announced. This is the first dataset for any country that comprehensively presents all spending changes undertaken by a government.
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Project description:Aims:Accurate evaluation of health care quality requires high-quality data, and case ascertainment (confirming eligible cases and deaths) is a foundation for accurate data collection. This study examined the accuracy of case ascertainment from two Japanese data sources. Methods:Using hospital-level data, we investigated the concordance in ascertaining trauma cases between a nationwide trauma registry (the Japan Trauma Data Bank) and annual government evaluations of tertiary hospitals between April 2012 and March 2013. We compared the median values for trauma case volumes, numbers of deaths, and case fatality rates from both data sources, and also evaluated the variability in discrepancies for the intrahospital differences of these outcomes. Results:The analyses included 136 hospitals. In the registry and annual evaluation data, the median case volumes were 120.5 cases and 180.5 cases, respectively; the median numbers of deaths were 11 and 12, respectively; and the median case fatality rates were 8.1% and 6.4%, respectively. There was broad variability in the intrahospital differences in these outcomes. Conclusions:The observed discordance between the two data sources implies that these data sources may have inaccuracies in case ascertainment. Measures are needed to evaluate and improve the accuracy of data from these sources.
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Project description:ImportanceFederal data underestimate the impact of COVID-19 on US nursing homes because federal reporting guidelines did not require facilities to report case and death data until the week ending May 24, 2020.ObjectiveTo assess the magnitude of unreported cases and deaths in the National Healthcare Safety Network (NHSN) and provide national estimates of cases and deaths adjusted for nonreporting.Design, setting, and participantsThis is a cross-sectional study comparing COVID-19 cases and deaths reported by US nursing homes to the NHSN with those reported to state departments of health in late May 2020. The sample includes nursing homes from 20 states, with 4598 facilities in 12 states that required facilities to report cases and 7401 facilities in 19 states that required facilities to report deaths. Estimates of nonreporting were extrapolated to infer the national (15 397 facilities) unreported cases and deaths in both May and December 2020. Data were analyzed from December 2020 to May 2021.ExposuresNursing home ownership (for-profit or not-for-profit), chain affiliation, size, Centers for Medicare & Medicaid Services star rating, and state.Main outcomes and measuresThe main outcome was the difference between the COVID-19 cases and deaths reported by each facility to their state department of health vs those reported to the NHSN.ResultsAmong 15 415 US nursing homes, including 4599 with state case data and 7405 with state death data, a mean (SE) of 43.7% (1.4%) of COVID-19 cases and 40.0% (1.1%) of COVID-19 deaths prior to May 24 were not reported in the first NHSN submission in sample states, suggesting that 68 613 cases and 16 623 deaths were omitted nationwide, representing 11.6% of COVID-19 cases and 14.0% of COVID-19 deaths among nursing home residents in 2020.Conclusions and relevanceThese findings suggest that federal NHSN data understated total cases and deaths in nursing homes. Failure to account for this issue may lead to misleading conclusions about the role of different facility characteristics and state or federal policies in explaining COVID outbreaks.
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