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599 results for “health data”
Data on plant health stakeholder priorities for tests and general prioritisation framework
<p>Data collected in the framework of work package 4 of the Valitest project. They correspond to a qualitative assessment of plant health stakeholder requirements, have been collected using online surveys supplemented by desk-based research, as well as impact assessments</p>
A letter to the Lancet Planetary Health: Assessment from our world in data of overall gain in mortality after COVID pharmaceutical intervention
<p>Straightforward comparison of COVID mortality before and after the pharmaceutical intervention as recovered from the site https://ourworldindata.org/explorers/coronavirus-data-explorer</p> <p>Submitted as a letter to Lancet Planetary Health</p>
Data repository of Predictors of Social Response to COVID-19 among Health Care Workers Caring for Individuals with Confirmed COVID-19 in Jordan
<p>The outbreak of COVID-19 forced public health authorities around the world to call for national emergency plans. Public responses, in form of social discrimination and stigmatizing behaviors, are increasingly being observed against confirmed individuals with confirmed COVID-19 and healthcare workers (HCWs) caring for those individuals. Hence, this study aimed to investigate the perception of social discrimination and coping strategies, and explore predictors of social discrimination and coping toward COVID-19 among HCWs and individuals with confirmed COVID-19. This study used a cross-sectional descriptive-comparative design to collect data using a convenience sample of 105 individuals with confirmed COVID-19 and 109 HCWs using a web-based survey format. In this study, individuals confirmed with COVID-19 reported a high level of social discrimination compared with HCWs (t = 2.62, <em>p</em> < .01). While HCWs reported high level of coping with COVID-19 compared with individuals with COVID-19 (t = -3.91, <em>p</em> < .001). Educational level, age, monthly income, and taking over-the-counter medication were predictors of social discrimination and coping with COVID-19 among HCWs and individuals confirmed with COVID-19. In conclusion, the findings showed individuals with confirmed COVID-19 were more likely to face social discrimination and HCWs perform better coping with COVID-19 than individuals with confirmed COVID-19.</p>
Open data for spatial public health research
<p><strong>Background</strong></p> <p>Preventive and health-promoting policies can guide (place and space-specific) factors influencing human health, such as the physical and social environment. Required is data that can lead to a more nuanced decision-making process and identify both, existing and future challenges. Along with the rise of new technologies, and thus the multiple opportunities to use and process data, new options have emerged to measure and monitor factors that affect health. Thus, in recent years, several gateways for open data (including governmental and geospatial data) became available. At present, an increasing number of research institutions as well as (state and private) companies and citizens' initiatives provide data. However, there is a lack of overviews covering the range of such offerings regarding health. In particular, for geographically differentiated analyses, there are challenges related to data availability at different spatial levels and the growing number of data providers.</p> <p><strong>Objectives</strong></p> <p>To provide an overview of open data resources available in the context of space and health to date. It also describes the technical and legal conditions for using open data</p> <p><strong>Results</strong></p> <p>An up-to-date summary of results including information on relevant data access and terms of use is provided along with a web visualization. All data is available for further use under an open license.</p>
Data from: Mental health ecosystem of Gipuzkoa (2015) for Bayesian network modelling
<p>This dataset include data from Mental Health network of Gipuzkoa (Spain). It is included information on resources (inputs) and outcomes (outputs) of care, which are described in the manuscript: "Almeda, N., Garcia-Alonso, C. R., Gutierrez-Colosia, M. R., Salinas-Perez, J. A., Iruin-Sanz, A., & Salvador-Carulla, L. (2022). Modelling the balance of care: Impact of an evidence-informed policy on a mental health ecosystem. PLoS ONE, 17(1 January), 1–16. https://doi.org/10.1371/journal.pone.0261621". This manuscript has been published in Plos One journal.</p> <p>This research focused on developing a formal causal model based on Bayesian network prototypes which were designed by formalizing expert knowledge (by using Expertbased Cooperative Analysis) and resulting in Direct Acyclic Graphs. The best Bayesian networks and their corresponding regression models were used to estimate the statistical ranges or confidence intervals for the dependent variable (potential effect, consequence, or output) given the independent variable values. These ranges, adjusted to delimited statistical distributions (triangular, trapezoidal and gamma), were managed by a Monte Carlo simulation engine for intervention assessment. A computer-based Decision Support System (DSS) was used to assess the status of ecosystem performance: RTE, statistical stability and entropy.</p> <p>Main results of the analyses pointed out that by combining causal reasoning and statistical methods, decision makers can obtain a deep view of both pre-implementing and post-implementing situations. Knowing the causal levers, it is possible to act directly to the causes in order to potentially produce de appropriate results considering the uncertainty: to provide a more balanced and integrated MH care provision in the community. In this particular case, an improvement in the outpatient workforce increases both ecosystem performance (RTE) and stability and slightly decreases entropy.</p>
Analytics, Visualisation and Machine Learning of General Practitioner Prescribing using Open Health Data
<p>Open Prescription data used in Postgraduate project into Northern Ireland General Practice prescribing.</p>
An ordinal severity scale for COVID-19 retrospective studies using electronic health record data
<p><span>Objectives: </span><span>Although the World Health Organization (WHO) Clinical Progression Scale for COVID-19 is useful in prospective clinical trials, it cannot be effectively used with retrospective Electronic Health Record (EHR) datasets. Modifying the existing WHO Clinical Progression Scale, we developed an ordinal severity scale (OS) and assessed its usefulness in the analyses of COVID-19 patient outcomes using retrospective EHR data.</span></p> <p><span>Results: </span><span>The data set used in this analysis consists of 2,880,456</span> <span>patients</span><span>. PCA of the day-to-day variation in OS levels over the totality of the 28-day</span> <span>period revealed contrasting patterns of variation in disease severity within the first and second 14 days and illustrated the importance of evaluation over the full 28-day period.</span></p> <p><span>Discussion:</span><span> An OS with well-defined, robust </span><span>features, based on discrete EHR data elements, is </span><span>useful for assessments of COVID-19 patient outcomes, providing insights on progression of COVID-19 disease severity over time.</span></p> <p><span>Conclusion</span><span>: The </span><span>OS </span><span>provides a framework which can facilitate better understanding of the course of acute COVID-19, informing clinical decision-making and resource allocation.</span></p>
Umbrella review data of colour-associated bioactive pigments found in fruit and vegetables, compared to placebo or low intakes, on human health outcomes relevant to public health
<p>This dataset comprises data extracted from 86 publications included in an umbrella review which compared the effect of colour-associated bioactive pigments found in fruit and vegetables (carotenoids, flavonoids, betalains and chlorophyll) on human health outcomes relevant to public health. Meta-analysed data from 83 systematic literature reviews were available for 17 different bioactive pigments spanning all colours of fruit and vegetables except green, with additional data from two randomised controlled trials and one cohort study for chlorophyll. This dataset represents 2,847 original research studies and data from over 37 million participants. There were 449 meta-analysed health outcomes extracted from the 83 systematic literature reviews. Extracted health outcomes were categorised according to pigment, comparator type, broad health outcome, study design, age group, source of pigment, risk of bias, and confidence in the estimated effect. Data extracted were dose, intervention duration, sample size, number of original studies, effect estimate, 95% confidence intervals, I2 statistics, publication bias and p-value. Rigorous analysis, including estimations of a common effect size, stratification of the evidence, study level sensitivity analyses and reporting on heterogeneity and potential biases, may be carried out using these data, to further evaluate the effect of colour-associated bioactive pigments in fruit and vegetables on human health.</p>
Vaccination data for 4, level-3 health centres in central Uganda
<p>This dataset describes the number of individuals who receiving vaccines at four level-3 health centers in four subcounties in central Uganda between 2017 and 2021. </p>
Data for "Health of neonates born in the maternity hospital in Bern, Switzerland, 1880-1900 and 1914-1922"
<p>Datasets underlying the analysis of the paper: "Health of neonates born in the maternity hospital in Bern, Switzerland, 1880-1900 and 1914-1922"</p> <p>This upload includes the following two data sets:</p> <ul> <li><strong>Bern_birth.csv</strong> : data from the maternity hospital in Bern for the years 1880- 1900 and 1914-1922 <ul> <li>Year: Year of birth </li> <li>Insurance: 1=yes, 0=no</li> <li>matage: Age of mother</li> <li>married: woman is married yes or no</li> <li>parity: number of parities</li> <li>gest: gestational age</li> <li>birthday2: birthday of newborn</li> <li>Grippe: flu 1=yes</li> <li>weight: birth weight of newborn</li> <li>city: 1=urban, 0=rural</li> <li>Month: birth month of newborn</li> <li>boy: 1= male, 0=femal</li> <li>stillborn: 1=yes, 0=no</li> <li>multiple: multiple births 1=yes, 0=no</li> <li>matheight2: 1=small, 2=medium, 3=tall</li> <li>matbody2: 1=grazil, 2=medium, 3=strong</li> <li>malnutrition2: 1=yes, 0=no</li> <li>occupation: german description of women's occupations</li> <li>occupation2: groups of occupations, 1= farm worker, 2=Maid, 3= Worker, 4=Housewife, 5/6/7=other</li> <li>agemenarche: age of first menstruation</li> </ul> </li> <li><strong>Flu_Bern.csv</strong> : Weekly recorded flu numbers for the canton of Bern <ul> <li>Year: Year of influenca</li> <li>week: week of influenca</li> <li>KW: calender week</li> <li>Canton: Number of influenca cases in canton of Bern</li> </ul> </li> </ul>
Data reported in development and cross-validation of a veterans mental health risk factor screen
<p>Background. VA primary care patients are routinely screened for current symptoms of PTSD, depression, and alcohol disorders, but many who screen positive do not engage in care. In addition to stigma about mental disorders and a high value on autonomy, some veterans may not seek care because of uncertainty about whether they need treatment to recover. A screen for mental health risk could provide an alternative motivation for patients to engage in care.</p> <p>Results. Twelve items assessing dissociation, emotional lability, life stress, and moral injury correctly classified 86% of those who later had elevated PTSD and/or depression symptoms (sensitivity) and 75% of those whose later symptoms were not elevated (specificity). Performance was also very good for 110 veterans who identified as members of ethnic/racial minorities.</p> <p>Conclusions. Mental health status was prospectively predicted in VA primary care patients with high accuracy using a screen that is brief, easy to administer, score, and interpret, and fits well into VA's integrated primary care. When care is readily accessible, appealing to veterans, and not perceived as stigmatizing, information about mental health risk may result in higher rates of engagement than information about current mental disorder status.</p>
Occupational and environmental diseases by operating results report of 43 files from Health Data Center (HDC)
<p>Data used in this study were from the Health Data Center (HDC); permission to use these data can be requested from the Health Data Center (HDC), Ministry of Public Health Thailand. <br> This study got approval from the Health Data Center (HDC)(reference no.0212-78) to use the data on pesticide pollutions (insecticide and herbicide) in provinces years 2018-2020 excluding Bangkok because data from Bangkok were not reported on HDC. Included pesticide poisoning from DIAGNOSIS_OPD and DIAGNOSIS_IPD files which used to DIAGCODE were 'T600','T601','T602','T603','T604','T608','T609' without the X68 code. </p> <p><strong>File descriptions</strong>: The pesticide pollutions (pesticide-induced, insecticide, and herbicide) in provinces years 2018-2020:<br> cwt : post code <br> cwt_n : province names<br> 63_pop : population in 2020<br> N63_pesticide : pesticide-induced patients in 2020<br> P63_pesticide : prevalence of pesticide-induced patients in years 2020<br> N63_Insecticide : insecticide patients in 2020<br> P63_Insecticide : prevalence of insecticide patients in years 2020<br> N63_Herbicide : herbicide patients in 2020<br> P63_Herbicide : prevalence of herbicide patients in years 2020<br> N63_Other : other pesticide patients in 2020<br> P63_Other : prevalence of other pesticide patients in years 2020<br> 62_pop : population in 2019<br> N62_pesticide : pesticide-induced patients in 2019<br> P62_pesticide : prevalence of pesticide-induced patients in years 2019<br> N62_Insecticide : insecticide patients in 2019<br> P62_Insecticide : prevalence of insecticide patients in years 2019<br> N62_Herbicide : herbicide patients in 2019<br> P62_Herbicide : prevalence of herbicide patients in years 2019<br> N62_Other : other pesticide patients in 2019<br> P62_Other : prevalence of other pesticide patients in years 2019<br> 61_pop : population in 2018<br> N61_pesticide : pesticide-induced patients in 2018<br> P61_pesticide : prevalence of pesticide-induced patients in years 2018<br> N61_Insecticide : insecticide patients in 2018<br> P61_Insecticide : prevalence of insecticide patients in years 2018<br> N61_Herbicide : herbicide patients in 2018<br> P61_Herbicide : prevalence of herbicide patients in years 2018<br> N61_Other : other pesticide patients in 2018<br> P61_Other : prevalence of other pesticide patients in years 2018</p> <p>P3y_pesticide : average prevalence of pesticide-induced patients from 2018 to 2020<br> P3y_Insecticide : average prevalence of insecticide patients from 2018 to 2020<br> P3y_Herbicide : average prevalence of herbicide patients from 2018 to 2020<br> P3y_Other : average prevalence of other pesticide patients from 2018 to 2020</p>
Patient-reported outcomes via electronic health record portal vs. telephone: process and retention data in a pilot trial of anxiety or depression symptoms in epilepsy
<p>Objective: To close gaps between research and clinical practice, tools are needed for efficient pragmatic trial recruitment and patient-reported outcome(PROM) collection. The objective was to assess feasibility and process measures for patient-reported outcome collection in a randomized trial comparing electronic health record(EHR) patient portal questionnaires to telephone interview among adults with epilepsy and anxiety or depression symptoms.</p> <p>Results: Participants were 60% women, 77% White/non-Hispanic, with mean age 42.5 years. Among 15 individuals randomized to EHR portal, 10(67%, CI 41.7-84.8%) met the 6-month retention endpoint, versus 100%(CI 79.6-100%) in the telephone group(p=0.04). EHR outcome collection at 6 months required 11.8 minutes less research staff time per participant than telephone (5.9, CI 3.3-7.7 vs. 17.7, CI 14.1-20.2). Subsequent telephone contact after unsuccessful EHR attempts enabled near complete data collection and still saved staff time.</p> <p>Discussion: Data from this randomized pilot study of pragmatic outcome collection methods for patients with anxiety or depression symptoms in epilepsy includes baseline participant characteristics, recruitment flow resulting from a novel EHR-based, care-embedded recruitment process, and data on retention along with various process measures at 6-months.</p>
The workload of manual data entry for integration between mobile health applications and eHealth infrastructure
<div> <p>In this study, we conducted a time-motion study observing healthcare workers (HCWs) completing data management activities including monitoring and evaluation (M&E) and manual data linkage of individual-level app data to electronic medical records (EMRS). This study served as a baseline study for an open-source app to mirror EMRS and reduce HCW workload while improving care in the Nurse-led Community-based Antiretroviral therapy Program (NCAP) in Lilongwe, Malawi.</p> </div>
Data from: Determinants of appropriate health-seeking behavior for febrile illness among caregivers of children under 5 years in Busia county, Kenya
<p>Almost 10 million children under the age of five in underdeveloped countries each year die mostly because of fever-related diseases, including pneumonia and malaria. In Kenya, about ten million children under the age of five require treatment for severe febrile infections. Busia County has a much higher incidence of febrile diseases than other Kenyan counties, with malaria accounting for 13% of all child fatalities under the age of five. Unfortunately, a considerable percentage of caregivers of children under five (15%) in Busia County engage in poor health-seeking behaviors by failing to seek early medical help for these disorders. The purpose of this study was to determine the prevalence of febrile illnesses among children under five and the health-related factors of health-seeking behaviors among their caregivers in Butula Sub-County, Busia County, Kenya. This cross-sectional mixed-method study included 271 caregivers, 11 community health volunteers, and health facility workers in Butula Sub-County. Homes with children under five were sampled using systematic random sampling, and key informants were using purposive sampling. A survey collected data on the demographic characteristics of children and caregivers, the prevalence of febrile, HSBs for febrile illness, and health-system factors influencing HSBs. The prevalence of febrile illness in under-five children was 64.5%. Health system factors strongly linked with caregivers' health-seeking behaviors were feeling a facility was too far (adjusted odds ratio = 0.86, 95% CI: 0.526 - 0.914, p = 0.027), getting health education (adjusted odds ratio = 1.8, 95% CI: 1.201-4.122, p=0.015), and facility level (adjusted odds ratio = 4.4, 95% CI: 2.015 - 9.750, p < 0.001). According to the qualitative findings, health system-related factors impacting health-seeking behaviors include stockouts, facility distance, and staff workload. Conclusion: The study revealed important health system determinants for health-seeking behaviors among caregivers of children under five with febrile illness.</p>
Research data on health facility-level factors that contribute to delayed diagnosis of cervical cancer
<p>In Kenya, cervical cancer is the 2nd commonly diagnosed type of cancer and the top cause of cancer-related deaths among women. Globally, over 50% of cervical cancer diagnoses are made late, with this proportion rising to 80% in developing countries. Poor Health systems can cause delays in diagnosis, thus, this study focused on determining the health facility-level factors that contribute to delayed diagnosis among cervical cancer patients at the Kenyatta National Hospital (KNH). An analytical cross-sectional mixed method study was adopted to collect data on hospital and referral experiences from 139 cervical cancer patients systematically sampled at KNH, using a semi-structured questionnaire. Associations between the stage at diagnosis and hospital and referral experiences were tested using a logistic regression model at 95% Confidence Interval. 86 (61.9%) were diagnosed at advanced stages III and IV. The potential predictors for delayed diagnosis were; More number of hospital referral times (p-value=0.000), Facing referral challenges (p-value=0.041), Longer time taken for diagnosis appointment (p-value=0.059), and Longer time taken for diagnostic results (p-value=0.007) in the bivariate analysis. More number of hospital referral times (p-value=0.001) and longer time taken for diagnostic results (p-value=0.025), were significantly associated with delayed diagnosis of cervical cancer in the multivariate logistic regression test model. Referral challenges included misdiagnosis, cost of diagnosis, and prolonged diagnosis appointments. The study concluded that the cause of delays in diagnosis for most patients is due to poor health and referral systems and inadequate medical personnel and diagnosis equipment. This study recommends improving referral systems and encouraging partnerships to decentralize diagnostic centers and equipment and train more expertise on cervical cancer.</p>
Rewarding Fitness Tracking – The Communication and Promotion of Health Insurers' Bonus Programs and the Use of Self-Tracking Data
<p>The data set offers additional information for the study on "Rewarding Fitness Tracking – The Communication and Promotion of Health Insurers’ Bonus Programs and the Usage of Self-Monitored Data", to be submitted at HCII 2018.</p> <p>The data set includes the full lists of German and Australian Health Insurers investigated, including a link to their apps.</p> <p>This study aims at giving an overview on the current status quo of health insurances that investigate self-tracking opportunities and possible rewards for customers that share their fitness and health activities. We are interested in how insurers promote their health and well-being programs (intended program goals) and motivate customers to live healthier (incentives). We introduce research in progress while firstly focusing on the countries Germany and Australia. We discuss the current situation of health insurance clients’ data use, data security issues as well as long-term health benefits regarding those programs based on recent research on self-tracking activities. The research questions are: </p> <ol> <li>Which health insurers offer options for client to self-track health and fitness data?</li> <li>How do insurers communicate about the programs?</li> <li>How do those insurers communicate about data security?</li> </ol>
Survey data on the demographics, motivations, mental-health issues and regrets of r/RoastMe posters
<p>Dataset including the data analysed in the "r/RoastMe: Characterising Self-Requested Online Mocking" paper describing the demographics, motivations, mental-health issues and consequences of posting on the r/RoastMe subreddit.</p>
Supporting data for "Taking connected mobile-health diagnostics of infectious diseases to the field"
<p>Raw and intermediate data used to create figures 1 and 4 of Wood, C.,<em> et al., "</em>Taking connected mobile-health diagnostics of infectious diseases to the field", <strong>Nature</strong> (2019).</p>
Fully synthetic longitudinal real-world data from hearing aid wearers for public health policy modeling
<p>Real-world data from hearing aids and Bluetooth connected smartphones. The associated data report can be found here: <a href="https://doi.org/10.3389/fnins.2019.00850">https://doi.org/10.3389/fnins.2019.00850</a></p>
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.