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Dataset results
133 results for “Health Economics”
Data from: Economic costs and health-related quality of life for hand, foot and mouth disease (HFMD) patients in China
Background: Hand, foot and mouth disease (HFMD) is a common illness in China that mainly affects infants and children. The objective of this study is to assess the economic cost and health-related quality of life associated with HFMD in China. Method: A telephone survey of caregivers were conducted in 31 provinces across China. Caregivers of laboratory-confirmed HFMD patients who were registered in the national HFMD enhanced surveillance database during 2012-2013 were invited to participate in the survey. Total costs included direct medical costs (outpatient care, inpatient care and self-medication), direct non-medical costs (transportation, nutrition, accommodation and nursery), and indirect costs for lost income associated with caregiving. Health utility weights elicited using EuroQol EQ-5D-3L and EQ-Visual Analogue Scale (VAS) were used to calculate associated loss in quality adjusted life years (QALYs). Results: The subjects comprised 1136 mild outpatients, 1124 mild inpatients, 1170 severe cases and 61 fatal cases. The mean total costs for mild outpatients, mild inpatients, severe cases and fatal cases were $201 (95%CI $187, $215), $1072 (95%CI $999, $1144), $3051 (95%CI $2905, $3197) and $2819 (95%CI $2068, $3571) respectively. The mean QALY losses per HFMD episode for mild outpatients, mild inpatients and severe cases were 3.6 (95%CI 3.4, 3,9), 6.9 (95%CI 6.4, 7.4) and 13.7 (95%CI 12.9, 14.5) per 1000 persons. Cases who were diagnosed with EV-A71 infection and had longer duration of illness were associated with higher total cost and QALY loss. Conclusion: HFMD poses a high economic and health burden in China. Our results provide economic and health utility data for cost-effectiveness analysis for HFMD vaccination in China.
Data from: Screening primary-care patients forgoing health care for economic reasons
Background: Growing social inequities have made it important for general practitioners to verify if patients can afford treatment and procedures. Incorporating social conditions into clinical decision-making allows general practitioners to address mismatches between patients' health-care needs and financial resources. Objectives: Identify a screening question to, indirectly, rule out patients' social risk of forgoing health care for economic reasons, and estimate prevalence of forgoing health care and the influence of physicians' attitudes toward deprivation. Design: Multicenter cross-sectional survey. Participants: Forty-seven general practitioners working in the French–speaking part of Switzerland enrolled a random sample of patients attending their private practices. Main Measures: Patients who had forgone health care were defined as those reporting a household member (including themselves) having forgone treatment for economic reasons during the previous 12 months, through a self-administered questionnaire. Patients were also asked about education and income levels, self-perceived social position, and deprivation levels. Key Results: Overall, 2,026 patients were included in the analysis; 10.7% (CI95% 9.4-12.1) reported a member of their household to have forgone health care during the 12 previous months. The question "Did you have difficulties paying your household bills during the last 12 months" performed better in identifying patients at risk of forgoing health care than a combination of four objective measures of socio-economic status (gender, age, education level, and income) (R2=0.184 vs. 0.083). This question effectively ruled out that patients had forgone health care, with a negative predictive value of 96%. Furthermore, for physicians who felt powerless in the face of deprivation, we observed an increase in the odds of patients forgoing health care of 1.5 times. Conclusion: General practitioners should systematically evaluate the socio-economic status of their patients. Asking patients whether they experience any difficulties in paying their bills is an effective means of identifying patients who might forgo health care.
Data from: Geo-clustered chronic affinity: pathways from socio-economic disadvantages to health disparities
Objective: Our objective was to develop and test a new concept (affinity) analogous to multimorbidity of chronic conditions for individuals at census tract level in Memphis, TN. The use of affinity will improve the surveillance of multiple chronic conditions and facilitate the design of effective interventions. Methods: We used publicly available chronic condition data (Center for Disease Control and Prevention (CDC) 500 Cities project), socio-demographic data (U.S. Census Bureau) and demographics data (Environmental Systems Research Institute; ESRI). We examined the geographic pattern of the affinity of chronic conditions using global Moran's I and Getis-Ord Gi statistics and its association with socio-economic disadvantage (poverty, unemployment, and crime) using robust regression models. We also used the most common behavioral factor, smoking, and other demographic factors (percent of the male population, percent of the population 67 years and over and total population size) as control variables in the model. Results: A geo-distinctive pattern of clustered chronic affinity associated with socio-economic deprivation was observed. Statistical results confirmed that neighborhoods with higher rates of crime, poverty, and unemployment were associated with an increased likelihood of having a higher affinity among major chronic conditions. With the inclusion of smoking in the model, however, only the crime prevalence was statistically significantly associated with the chronic affinity. Conclusion: Chronic affinity disadvantages were disproportionately accumulated in socially disadvantaged areas. We showed links between commonly co-observed chronic diseases at the population level and systematically explored the complexity of affinity and socio-economic disparities. Our affinity score, based on publicly available datasets, served as a surrogate for multimorbidity at the population level, which may assist policymakers and public health planners to identify urgent hot spots for chronic disease and allocate clinical, medical and healthcare resources efficiently.
Economic and financial barriers to delivering mental health services in Georgia: Informing policy and acting on evidence
<p>Dataset includes qualitative data for the manuscript entitled "Economic and financial barriers to delivering mental health services in Georgia: Informing policy and acting on evidence".</p>
Replication package for: "Economic Distress and Children's Mental Health: Evidence from the Brazilian High Risk Cohort Study for Mental Conditions"
<p>This package replicates the results of "Economic distress and children's mental health: evidence from the Brazilian High Risk Cohort Study for Mental Conditions" (and its respective online appendix) using mostly Stata. The package does not include the paper's main dataset, which is confidential.</p>
Mapping the Landscape of Open Source Health Economic Models: A Systematic Database Review and Analysis
<p><span>Health economic models are crucial for health technology assessment (HTA) to evaluate the value of medical interventions. Open source models (OSMs), where source code and calculations are publicly accessible, enhance transparency, efficiency, credibility, and reproducibility. This study systematically reviews databases to map the landscape of available OSMs in health economics.</span></p>
Supplementary Materials for Potential Health and Economic Impacts of Shifting Manufacturing from China to Indonesia or India
<p>The Supplementary Materials for ariticle <em>Potential Health and Economic Impacts of Shifting Manufacturing from China to Indonesia or India. </em></p>
What is Health Economics
<p>This video was designed to raise awareness of Health Economic analysis and encourage Clinical Researchers to consider using these principles in their work, in alignment with the strategic aims of the Oxford BRC. The presentation and poster summarise the results of this exercise. <br> <br> We are grateful for viewers feedback to understand how successful this video has been in achieving these aims.You do not need to be an Oxford BRC funded researcher to provide this feedback. Please submit feedback here: <a href="https://forms.office.com/r/KNiECKN5fQ">https://forms.office.com/r/KNiECKN5fQ</a><br> <br> This resource was created and is being analysed as part of the Oxford BRC Next Generation Leaders Programme (<a href="https://oxfordbrc.nihr.ac.uk/brc-researchers-complete-next-generation-leaders-programme">https://oxfordbrc.nihr.ac.uk/brc-researchers-complete-next-generation-leaders-programme</a>).<br> <br> <strong>CRediT Authorship statement:</strong></p> <ul> <li>Conceptualization: Ying Cui, Rajna Golubic, Cassandra D. Gould van Praag, and Emmanuel Selvaraj.</li> <li>Data curation: Cassandra D. Gould van Praag.</li> <li>Formal analysis: Cassandra D. Gould van Praag.</li> <li>Investigation: Ying Cui, Rajna Golubic, Cassandra D. Gould van Praag, Laurence S. Roope, and Emmanuel Selvaraj.</li> <li>Methodology: Ying Cui, Rajna Golubic, Cassandra D. Gould van Praag, Laurence S. Roope, and Emmanuel Selvaraj.</li> <li>Project administration: Ying Cui, Rajna Golubic, Cassandra D. Gould van Praag, Laurence S. Roope, and Emmanuel Selvaraj.</li> <li>Resources: Ying Cui, Rajna Golubic, Cassandra D. Gould van Praag, and Emmanuel Selvaraj.</li> <li>Software: Ying Cui and Cassandra D. Gould van Praag.</li> <li>Supervision: Laurence S. Roope.</li> <li>Visualization: Ying Cui and Cassandra D. Gould van Praag, Rajna Golubic</li> <li>Writing - original draft: Ying Cui, Rajna Golubic, and Cassandra D. Gould van Praag.</li> <li>Writing - review & editing: Ying Cui, Rajna Golubic, Cassandra D. Gould van Praag, Laurence S. Roope, and Emmanuel Selvaraj.</li> </ul> <p> </p> <p><strong>Acknowledgements</strong></p> <ul> <li>Video audio: Feel So Lucky (Instrumental Version) by Cody Francis (https://www.epidemicsound.com)</li> <li>Video graphics: DrawKit “Health and Medical” and “Medical” collections (https://drawkit.com)</li> <li>Video hosting platform https://www.vidyard.com, using a 3 month free trial. </li> <li>Icons from Noun Project, created by Made x Made, Dewanata Designs, and ainul muttaqin (https://thenounproject.com) </li> <li>Viewer location map created using https://app.datawrapper.de</li> <li>Word clouds created with https://wordart.com/ </li> <li>We are grateful to our participant interviewees for volunteering their time in contributing to this resource.</li> <li>Thank you to the faculty of the Next Generation Leaders Programme (<a href="https://www.thrumleadership.com/ngl-faculty">https://www.thrumleadership.com/ngl-faculty</a>) for their guidance during this project.</li> </ul>
Patient-reported, Health Economic and Psychosocial Outcomes in Friedreich Ataxia
ClinicalTrials.gov study NCT05943002. IPD Sharing: Not stated. Countries: 3. Publications: 1.
PRE-EMPTIVE PHARMACOGENOMICS IN ACUTE CARE SETTINGS WITH HEALTH ECONOMIC EVALUATIONS (PHOENIX TRIAL)
ClinicalTrials.gov study NCT06907784. IPD Sharing: NO. Countries: 1. Publications: 4.
Health and Economic Outcomes of Two Different Follow up Strategies in Effectively Cured Advanced Head and Neck Cancer
ClinicalTrials.gov study NCT02262221. IPD Sharing: YES. Countries: 2. Publications: 1.
Innovative Behavioral Economics Incentives Strategies for Health
ClinicalTrials.gov study NCT02890459. IPD Sharing: NO. Countries: 1. Publications: 11.
Health, Economic Analysis and Clinical Aspects of Patients With Neurological Disabilities in Enteral Nutrition With Dedicated Formula. The Role of Nissen's Fundoplication in the Management of Gastroes
ClinicalTrials.gov study NCT05068089. IPD Sharing: NO. Countries: 1. Publications: 6.
Aha BOOST Arm-hand BOOST Therapy to Enhance Recovery After Stroke: Clinical, Health Economic and Process Evaluation
ClinicalTrials.gov study NCT06517251. IPD Sharing: YES. Countries: 1. Publications: 1.
CardioBBEAT - Randomized Controled Trial to Evaluate the Health Economic Impact of Remote Patient Monitoring
ClinicalTrials.gov study NCT02293252. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Supporting Health Behavior Among Persons Living With HIV Using Tech, MOST, and Behavioral Economics
ClinicalTrials.gov study NCT04518241. IPD Sharing: NO. Countries: 1. Publications: 1.
A Pragmatic Real-world Multicentre Observational Research Study to Explore the Clinical and Health Economic Impact of myCOPD
ClinicalTrials.gov study NCT05835492. IPD Sharing: UNDECIDED. Countries: 1. Publications: 11.
Tandem Control-IQ Evaluation Regarding Glucose Metrics, Sleep, and Health Economics
ClinicalTrials.gov study NCT05969106. IPD Sharing: NO. Countries: 1. Publications: 0.
The Cancer Home Life Intervention Study. A Randomised, Controlled Multicentre Trial and a Health Economic Evaluation
ClinicalTrials.gov study NCT02356627. IPD Sharing: NO. Countries: 1. Publications: 8.
Trial-based Economic Evaluation of a Mobile Health Intervention for Individuals With Chronic Non-specific Low Back Pain
ClinicalTrials.gov study NCT06651099. IPD Sharing: NO. 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.
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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.