Public health-related decision-making on policies aimed at controlling the COVID-19 pandemic outbreak depends on complex epidemiological models that are compelled to be robust and use all relevant available data. This data article provides a new combined worldwide COVID-19 dataset obtained from official data sources with improved systematic measurement errors and a dedicated dashboard for online data visualization and summary. The dataset adds new measures and attributes to the normal attributes of official data sources, such as daily mortality, and fatality rates. We used comparative statistical analysis to evaluate the measurement errors of COVID-19 official data collections from the Chinese Center for Disease Control and Prevention (Chinese CDC), World Health Organization (WHO) and European Centre for Disease Prevention and Control (ECDC). The data is collected by using text mining techniques and reviewing pdf reports, metadata, and reference data. The combined dataset includes complete spatial data such as countries area, international number of countries, Alpha-2 code, Alpha-3 code, latitude, longitude, and some additional attributes such as population. The improved dataset benefits from major corrections on the referenced data sets and official reports such as adjustments in the reporting dates, which suffered from a one to two days lag, removing negative values, detecting unreasonable changes in historical data in new reports and corrections on systematic measurement errors, which have been increasing as the pandemic outbreak spreads and more countries contribute data for the official repositories. Additionally, the root mean square error of attributes in the paired comparison of datasets was used to identify the main data problems. The data for China is presented separately and in more detail, and it has been extracted from the attached reports available on the main page of the CCDC website. This dataset is a comprehensive and reliable source of worldwide COVID-19 data that can be used in epidemiological models assessing the magnitude and timeline for confirmed cases, long-term predictions of deaths or hospital utilization, the effects of quarantine, stay-at-home orders and other social distancing measures, the pandemic’s turning point or in economic and social impact analysis, helping to inform national and local authorities on how to implement an adaptive response approach to re-opening the economy, re-open schools, alleviate business and social distancing restrictions, design economic programs or allow sports events to resume.

The share of internet users seeking health information online in the United Kingdom declined to 63.29 percent in 2020. This means a decline of 3.6 percentage points in comparison to the previous year.The EU survey on the use of Information and Communication Technologies (ICT) in households and by individuals is an annual survey conducted since 2002 aiming at collecting and disseminating harmonised and comparable information on the use of ICT in households and by individuals. Data presented in this domain are collected on a yearly basis by the National Statistical Institutes and are based on Eurostat's annual model questionnaire. This questionnaire is updated each year to reflect the evolving situation of information and communication technologiesFind more statistics on other topics about the United Kingdom with key insights such as share of internet users looking for and applying for jobs online, share of internet users reading news online, and share of people that upload self-created content.

The Research and Development Survey (RANDS) is a platform designed for conducting survey question evaluation and statistical research. RANDS is an ongoing series of surveys from probability-sampled commercial survey panels used for methodological research at the National Center for Health Statistics (NCHS). RANDS estimates are generated using an experimental approach that differs from the survey design approaches generally used by NCHS, including possible biases from different response patterns and sampling frames as well as increased variability from lower sample sizes. Use of the RANDS platform allows NCHS to produce more timely data than would be possible using traditional data collection methods. RANDS is not designed to replace NCHS’ higher quality, core data collections. Below are experimental estimates of reduced access to healthcare for three rounds of RANDS during COVID-19. Data collection for the three rounds of RANDS during COVID-19 occurred between June 9, 2020 and July 6, 2020, August 3, 2020 and August 20, 2020, and May 17, 2021 and June 30, 2021. Information needed to interpret these estimates can be found in the Technical Notes. RANDS during COVID-19 included questions about unmet care in the last 2 months during the coronavirus pandemic. Unmet needs for health care are often the result of cost-related barriers. The National Health Interview Survey, conducted by NCHS, is the source for high-quality data to monitor cost-related health care access problems in the United States. For example, in 2018, 7.3% of persons of all ages reported delaying medical care due to cost and 4.8% reported needing medical care but not getting it due to cost in the past year. However, cost is not the only reason someone might delay or not receive needed medical care. As a result of the coronavirus pandemic, people also may not get needed medical care due to cancelled appointments, cutbacks in transportation options, fear of going to the emergency room, or an altruistic desire to not be a burden on the health care system, among other reasons. The Household Pulse Survey (https://www.cdc.gov/nchs/covid19/pulse/reduced-access-to-care.htm), an online survey conducted in response to the COVID-19 pandemic by the Census Bureau in partnership with other federal agencies including NCHS, also reports estimates of reduced access to care during the pandemic (beginning in Phase 1, which started on April 23, 2020). The Household Pulse Survey reports the percentage of adults who delayed medical care in the last 4 weeks or who needed medical care at any time in the last 4 weeks for something other than coronavirus but did not get it because of the pandemic. The experimental estimates on this page are derived from RANDS during COVID-19 and show the percentage of U.S. adults who were unable to receive medical care (including urgent care, surgery, screening tests, ongoing treatment, regular checkups, prescriptions, dental care, vision care, and hearing care) in the last 2 months. Technical Notes: https://www.cdc.gov/nchs/covid19/rands/reduced-access-to-care.htm#limitations

As of 2023, ** percent of adults in Finland agreed they had sufficient access to their digital healthcare data online. In contrast, ** percent of surveyed adults in Iceland said they were satisfied with their access to digital health data.

IntroductionA required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data.MethodsThe system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application’s performance and functionality.ResultsThe system is implemented as an open source web application (available at https://odmanalysis.uni-muenster.de) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects.DiscussionMedical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.

The basic purpose of the Health Interview Survey is to obtain information about the amount and distribution of illness, its effects in terms of disability and chronic impairments, and the kinds of health services people receive. There are five types of records in this core survey, each in a separate data file. The variables in the Household File (Part 1) include type of living quarters, size of family, number of families in household, and geographic region. The variables in the Person File (Part 2) include sex, age, race, marital status, veteran status, education, income, industry and occupation codes, and limits on activity. These variables are found in the Condition, Doctor Visit, and Hospital Episode Files as well. The Person File also supplies data on height, weight, bed days, doctor visits, hospital stays, years at residence, and region variables. The Condition (Part 3), Doctor Visit (Part 4), and Hospital Episode (Part 5) Files contain information on each reported condition, two-week doctor visit, or hospitalization (twelve-month recall), respectively. A sixth, seventh, eighth, and ninth file have been added, along with the five core files. The Alcohol/Health Practices Supplement File (Part 6) includes information on diet, smoking and drinking habits, and health problems. The Bed Days and Dental Care Supplement File (Part 7) contains information on the number of bed days, the number of and reason for dental visits, treatment(s) received, type of dentist seen, and travel time for visit. The Doctor Services Supplement File (Part 8) supplies data on visits to doctors or other health professionals, reasons for visits, health conditions, and operations performed. The Health Insurance Supplement File (Part 9) documents basic demographic information along with medical coverage and health insurance plans, as well as differentiates between hospital, doctor visit, and surgical insurance coverage.

Verified dataset of healthcare marketing in 2025: U.S. digital ad spend, patient search behavior, online reviews, SEO & social trends, and email benchmarks.

Health-related statistics and survey results are published in the database on health statistics and surveys. The data sources are the Institute for Health Development, the medical registers located in the Institute, the Health Board, the State Agency of Medicines and other bodies. A reference to the data source can be found in the general information in the table.

The database consists of key areas, subdivided into sub-areas: population characteristics, morbidity, use of health services and causes of treatment, health resources and their use, health behaviour and health, use of medicinal products, basic data on health profiles, statistics on health information system data, nutrition — reference database for nutrition surveys.

Data tables in sub-areas have a unique code that facilitates data search. The data tables in the database can be viewed both online and downloaded in different file formats.

HCUPnet is an online data tool based on data from the Healthcare Cost and Utilization Project (HCUP). The data tool provides healthcare statistics and information for hospital inpatient and emergency department settings, as well as population-based healthcare data on counties. Users are able to query HCUP data to access detailed or summary statistics on inpatient stays and emergency department visits by patient, hospital, and encounter characteristics. Users are also able to generate tables and graphs on national and regional statistics and trends for community hospitals in the United States.

THIS RESOURCE IS NO LONGER IN SERVICE, documented July 15, 2016. Database containing location and descriptive information about a wide variety of information resources including organizations, research resources, projects, and databases concerned with health and biomedicine. This information may not be readily available in bibliographic databases. Each record may contain information on the publications, holdings, and services provided. These information resources fall into many categories including federal, state, and local government agencies; information and referral centers; professional societies; self-help groups and voluntary associations; academic and research institutions and their programs; information systems and research facilities. Topics include HIV/AIDS, maternal and child health, most diseases and conditions including genetic and other rare diseases, health services research and technology assessment. DIRLINE can be searched using subject words (such as disease or condition) including Medical Subject Headings (MeSH) or for the name or location of a resource. It now offers an A to Z list of over 8,500 organizations.

Introduction

Global Big Data in Healthcare Market size is expected to be worth around USD 145.8 Billion by 2033 from USD 42.2 Billion in 2023, growing at a CAGR of 13.2% during the forecast period from 2024 to 2033.

Big data in healthcare encompasses vast amounts of diverse, unstructured data sourced from medical journals, biometric sensors, electronic medical records (EMRs), Internet of Medical Things (IoMT), social media platforms, payer records, omics research, and data repositories. Integrating this unstructured data into traditional systems presents considerable challenges, primarily in data structuring and standardization. Effective data structuring is essential for ensuring compatibility across systems and enabling robust analytical processes.

However, advancements in big data analytics, artificial intelligence, and machine learning have significantly enhanced the ability to convert complex healthcare data into actionable insights. These advancements have transformed healthcare, driving informed decision-making, enabling early and accurate diagnostics, facilitating precision medicine, and enhancing patient engagement through digital self-service platforms, including online portals, mobile applications, and wearable health devices.

The role of big data in pharmaceutical R&D has become increasingly central, as analytics tools streamline drug discovery, accelerate clinical trial processes, and identify potential therapeutic targets more efficiently. The demand for business intelligence solutions within healthcare is rising, fueled by the surge of unstructured data and the focus on developing tailored treatment protocols. As a result, the global market for big data in healthcare is projected to grow steadily during the forecast period.

In 2023, ** percent of adults in Finland strongly agreed with the statement that they knew where they could access their digital healthcare data online. In contrast, only ** percent of the population in both Iceland and Sweden responded with a strong agreement to the statement.

Interactive Summary Health Statistics for Adults provide annual estimates of selected health topics for adults aged 18 years and over based on final data from the National Health Interview Survey.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This page leads to several types of statistics relating to health care and indemnity insurance, grouped by theme. Select the theme that interests you by clicking on it:

• Health care: Statistics on health care providers, cost for health care insurance, patient co-payment, prescriptions, etc.

• Allowances: Statistics on primary work incapacity, invalidity, maternity, birth leave, etc.

• Medical assessment and control: Statistics on the monitoring of health care providers and evaluation of medical practice.

• Administrative control: Statistics on the control of mutual funds and the fight against social fraud.

• Medications: Statistics and specific analyzes on drugs dispensed in public pharmacies: prescription, volume, cost for insurance, etc.

• Frontier workers: Statistics on frontier workers entering and leaving Belgium.

• People affiliated with a health insurance fund: Statistics on the number of people affiliated to a health insurance fund to benefit from healthcare and indemnity insurance in Belgium. Customize your search with our web program.

The European questionnaire on Information and Communication Technologies Data reveals that there exists a disparity between the internet usage of people of different age groups. This disparity although present in most countries, differs widely in its severity.

In 2020, 61 percent of internet users in the United Kingdom between 16 and 24 responded that they used the internet to search for health information online, 18 percent more then five years earlier. In 2020, 70 percent among the 25 to 24-year-olds also informed themselves about health issues online. Least likely to research for health information online were people aged between 55 and 64 years old, with 57 percent responding that they used the internet as for this purpose. Overall, the share of people searching for health information online increased since 2015 across all age groups.

BackgroundThe psychometric properties of an online test are not necessarily identical to its paper and pencil original. The aim of this study is to test whether the factor structure of the Community Assessment of Psychic Experiences (CAPE) is measurement invariant with respect to online vs. paper and pencil assessment.MethodThe factor structure of CAPE items assessed by paper and pencil (N = 796) was compared with the factor structure of CAPE items assessed by the Internet (N = 21,590) using formal tests for Measurement Invariance (MI). The effect size was calculated by estimating the Signed Item Difference in the Sample (SIDS) index and the Signed Test Difference in the Sample (STDS) for a hypothetical subject who scores 2 standard deviations above average on the latent dimensions.ResultsThe more restricted Metric Invariance model showed a significantly worse fit compared to the less restricted Configural Invariance model (χ2(23) = 152.75, p

Interactive Summary Health Statistics for Adults, by Detailed Race and Ethnicity provide estimates as three-year averages of selected health topics for adults aged 18 years and over based on final data from the National Health Interview Survey.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Our highly-targeted consumer healthcare database includes:

🗸 Name 🗸 Postal Address, Email Address, Telephone Number 🗸 Age, Gender 🗸 Most likely to ask a Doctor About an Advertised Prescription Medicine 🗸 Most likely looked for Medical Information on the Web 🗸 Most Likely to Prefer Brand Name Medicines 🗸 Most Likely to Buy Prescriptions through the Mail

The dataset is available for purchase by US region: 🗸 New England (Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont) 🗸 Middle Atlantic (New Jersey, New York, and Pennsylvania) 🗸 East North Central (Illinois, Indiana, Michigan, Ohio, and Wisconsin) 🗸 West North Central (Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota) 🗸 South Atlantic (Delaware; Florida; Georgia; Maryland; North Carolina; South Carolina; Virginia; Washington, D.C. and West Virginia) 🗸 East South Central (Alabama, Kentucky, Mississippi, and Tennessee) 🗸 West South Central (Arkansas, Louisiana, Oklahoma, and Texas) 🗸 Mountain (Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, and Wyoming) 🗸 Pacific (Alaska, California, Hawaii, Oregon, and Washington)

Underlying quantitative and anonymised qualitative data for the study, "A Pragmatic Trial of Interactive Online Statistical Tools for Teaching Biostatistics to First Year Medical Students: A Constructivism-Informed Approach"

Note: SD = Standard Deviation.*Value differs between Internet and Paper samples at significance level P