The Health Statistics and Health Research Database is Estonian largest set of health-related statistics and survey results administrated by National Institute for Health Development. Use of the database is free of charge.

The database consists of eight main areas divided into sub-areas. The data tables included in the sub-areas are assigned unique codes. The data tables presented in the database can be both viewed in the Internet environment, and downloaded using different file formats (.px, .xlsx, .csv, .json). You can download the detailed database user manual here (.pdf).

The database is constantly updated with new data. Dates of updating the existing data tables and adding new data are provided in the release calendar. The date of the last update to each table is provided after the title of the table in the list of data tables.

A contact person for each sub-area is provided under the "Definitions and Methodology" link of each sub-area, so you can ask additional information about the data published in the database. Contact this person for any further questions and data requests.

Read more about publication of health statistics by National Institute for Health Development in Health Statistics Dissemination Principles.

A 2023 report on data breaches in the healthcare system in the United States revealed that in most incidents, the leaked data was located in the network server, with almost 70 percent of data breaches indicating this location. The second-most common location of breached data was e-mail, with over 18 percent of the cases, followed by paper or films, with nearly six percent of the cases.

Background: Digital data sources have become ubiquitous in modern culture in the era of digital technology but often tend to be under-researched because of restricted access to data sources due to fragmentation, privacy issues, or industry ownership, and the methodological complexity of demonstrating their measurable impact on human health. Even though new big data sources have shown unprecedented potential for disease diagnosis and outbreak detection, we need to investigate results in the existing literature to gain a comprehensive understanding of their impact on and benefits to human health.Objective: A systematic review of systematic reviews on identifying digital data sources and their impact area on people's health, including challenges, opportunities, and good practices.Methods: A multidatabase search was performed. Peer-reviewed papers published between January 2010 and November 2020 relevant to digital data sources on health were extracted, assessed, and reviewed.Results: The 64 reviews are covered by three domains, that is, universal health coverage (UHC), public health emergencies, and healthier populations, defined in WHO's General Programme of Work, 2019–2023, and the European Programme of Work, 2020–2025. In all three categories, social media platforms are the most popular digital data source, accounting for 47% (N = 8), 84% (N = 11), and 76% (N = 26) of studies, respectively. The second most utilized data source are electronic health records (EHRs) (N = 13), followed by websites (N = 7) and mass media (N = 5). In all three categories, the most studied impact of digital data sources is on prevention, management, and intervention of diseases (N = 40), and as a tool, there are also many studies (N = 10) on early warning systems for infectious diseases. However, they could also pose health hazards (N = 13), for instance, by exacerbating mental health issues and promoting smoking and drinking behavior among young people.Conclusions: The digital data sources presented are essential for collecting and mining information about human health. The key impact of social media, electronic health records, and websites is in the area of infectious diseases and early warning systems, and in the area of personal health, that is, on mental health and smoking and drinking prevention. However, further research is required to address privacy, trust, transparency, and interoperability to leverage the potential of data held in multiple datastores and systems. This study also identified the apparent gap in systematic reviews investigating the novel big data streams, Internet of Things (IoT) data streams, and sensor, mobile, and GPS data researched using artificial intelligence, complex network, and other computer science methods, as in this domain systematic reviews are not common.

At a time of digital transformation, correlating as much relevant data as possible can provide a powerful lever in the fight against fraud. Focusing on the issue of the sources of this data, it appears that 73 percent of the players in the French healthcare ecosystem who responded to the survey in 2017 placed their partners and peers as the primary source of data collection. It was also found that open data occupied an equivalent place to data obtained from patients, clients and insured persons.

The information flow of the Hospital Discharge database (SDO flow) is the tool for collecting information relating to all hospitalization episodes provided in public and private hospitals throughout the national territory.

Born for purely administrative purposes of the hospital setting, the SDO, thanks to the wealth of information contained, not only of an administrative but also of a clinical nature, has become an indispensable tool for a wide range of analyzes and elaborations, ranging from areas to support of health planning activities for monitoring the provision of hospital assistance and the Essential Levels of Assistance, for use for proxy analyzes of other levels of assistance as well as for more strictly clinical-epidemiological and outcome analyzes. In this regard, the SDO database is a fundamental element of the National Outcomes Program (PNE).

The information collected includes the patient's personal characteristics (including age, sex, residence, level of education), characteristics of the hospitalization (for example institution and discharge discipline, hospitalization regime, method of discharge, booking date, priority class of hospitalization) and clinical features (e.g. main diagnosis, concomitant diagnoses, diagnostic or therapeutic procedures)

Information relating to drugs administered during hospitalization or adverse reactions to them (subject to other specific information flows) is excluded from the discharge form.

The Open Database of Healthcare Facilities (ODHF) is a collection of open data containing the names, types, and locations of health facilities across Canada. It is released under the Open Government License - Canada. The ODHF compiles open, publicly available, and directly-provided data on health facilities across Canada. Data sources include regional health authorities, provincial, territorial and municipal governments, and public health and professional healthcare bodies. This database aims to provide enhanced access to a harmonized listing of health facilities across Canada by making them available as open data. This database is a component of the Linkable Open Data Environment (LODE).

The Public Health Emergency (PHE) declaration for COVID-19 expired on May 11, 2023. As a result, the Aggregate Case and Death Surveillance System will be discontinued. Although these data will continue to be publicly available, this dataset will no longer be updated.

On October 20, 2022, CDC began retrieving aggregate case and death data from jurisdictional and state partners weekly instead of daily.

This dataset includes the URLs that were used by the aggregate county data collection process that compiled aggregate case and death counts by county. Within this file, each of the states (plus select jurisdictions and territories) are listed along with the county web sources which were used for pulling these numbers. Some states had a single statewide source for collecting the county data, while other states and local health jurisdictions may have had standalone sources for individual counties. In the cases where both local and state web sources were listed, a composite approach was taken so that the maximum value reported for a location from either source was used. The initial raw data were sourced from these links and ingested into the CDC aggregate county dataset before being published on the COVID Data Tracker.

The link provides statistical information on population health status, health care, health determinants, disease incidence and prevalence, disabilities and accidents at work.

This dataset contains model-based place (incorporated and census-designated places) estimates. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. The dataset includes estimates for 36 measures: 13 for health outcomes, 9 for preventive services use, 4 for chronic disease-related health risk behaviors, 7 for disabilities, and 3 for health status. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2021 or 2020 data, Census Bureau 2010 population data, and American Community Survey 2015–2019 estimates. The 2023 release uses 2021 BRFSS data for 29 measures and 2020 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours) that the survey collects data on every other year. More information about the methodology can be found at www.cdc.gov/places.

From the responding healthcare organizations, 93 percent reported using ambulatory care EHR and billing systems to collect data for their disease registry. Disease registries are a helpful tool to segment patients into actionable groups based on pre-defined parameters. This statistic shows the percentage of U.S. healthcare organizations who reported having connected select data sources to their disease registry as of 2019.

To monitor the population's knowledge, risk perceptions, preventive behaviors and confidence in the measures adopted during the COVID-19 epidemic in Spain.

Comparing the quality of ethnicity data recorded in health-related administrative data sources with Census 2021.

Agreement rates between ethnicity data recorded in health-related administrative data sources with Census 2021, by sociodemographic characteristics.

Organizations can license synthetic, structured data generated by Syntegra from electronic health record systems of community hospitals across the United States, reaching beyond just claims and Rx data.

The synthetic data provides a detailed picture of the patient's journey throughout their hospital stay, including patient demographic information and payer type, as well as rich data not found in any other sources. Examples of this data include: drugs given (timing and dosing), patient location (e.g., ICU, floor, ER), lab results (timing by day and hour), physician roles (e.g., surgeon, attending), medications given, and vital signs. The participating community hospitals with bed sizes ranging from 25 to 532 provide unique visibility and assessment of variation in care outside of large academic medical centers and healthcare networks.

Our synthetic data engine is trained on a broadly representative dataset made up of deep clinical information of approximately 6 million unique patient records and 18 million encounters over 5 years of history. Notably, synthetic data generation allows for the creation of any number of records needed to power your project.

EHR data is available in the following formats: — Cleaned, analytics-ready (a layer of clean and normalized concepts in Tuva Health’s standard relational data model format — FHIR USCDI (labs, medications, vitals, encounters, patients, etc.)

The synthetic data maintains full statistical accuracy, yet does not contain any actual patients, thus removing any patient privacy liability risk. Privacy is preserved in a way that goes beyond HIPAA or GDPR compliance. Our industry-leading metrics prove that both privacy and fidelity are fully maintained.

— Generate the data needed for product development, testing, demo, or other needs — Access data at a scalable price point — Build your desired population, both in size and demographics — Scale up and down to fit specific needs, increasing efficiency and affordability

Syntegra's synthetic data engine also has the ability to augment the original data: — Expand population sizes, rare cohorts, or outcomes of interest — Address algorithmic fairness by correcting bias or introducing intentional bias — Conditionally generate data to inform scenario planning — Impute missing value to minimize gaps in the data

This dataset contains model-based census tract-level estimates for the PLACES 2022 release. PLACES covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. The dataset includes estimates for 29 measures: 13 for health outcomes, 9 for preventive services use, 4 for chronic disease-related health risk behaviors, and 3 for health status. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2020 or 2019 data, Census Bureau 2010 population data, and American Community Survey 2015–2019 estimates. The 2022 release uses 2020 BRFSS data for 25 measures and 2019 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening) that the survey collects data on every other year. More information about the methodology can be found at www.cdc.gov/places.

The National Health Data System (SNDS) will make it possible to link:

• health insurance data (SNIIRAM database);
• hospital data (PMSI database);
• the medical causes of death (base of the CépiDC of Inserm);
• disability-related data (from MDPH - CNSA data);
• a sample of data from complementary health insurance organisations.

The first two categories of data are already available and constitute the first version of the SNDS. The medical causes of death should feed the SNDS from the second half of 2017. The first data from the CNSA will arrive from 2018 and the sample of complementary organizations in 2019.

The purpose of the SNDS is to make these data available in order to promote studies, research or evaluations of a nature in the public interest and contributing to one of the following purposes:

• health information;
• the implementation of health policies;
• knowledge of health expenditure;
• informing professionals and establishments about their activities;
• innovation in the fields of health and medico-social care;
• monitoring, surveillance and health security.

This dataset contains model-based place (incorporated and census-designated places) level estimates for the PLACES 2022 release. PLACES covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. The dataset includes estimates for 29 measures: 13 for health outcomes, 9 for preventive services use, 4 for chronic disease-related health risk behaviors, and 3 for health status. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2020 or 2019 data, Census Bureau 2010 population data, and American Community Survey 2015–2019 estimates. The 2022 release uses 2020 BRFSS data for 25 measures and 2019 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening) that the survey collects data on every other year. More information about the methodology can be found at www.cdc.gov/places.

Health Nutrition and Population Statistics database provides key health, nutrition and population statistics gathered from a variety of international and national sources. Themes include global surgery, health financing, HIV/AIDS, immunization, infectious diseases, medical resources and usage, noncommunicable diseases, nutrition, population dynamics, reproductive health, universal health coverage, and water and sanitation.

Background: Digital data sources have become ubiquitous in modern culture in the era of digital technology but often tend to be under-researched because of restricted access to data sources due to fragmentation, privacy issues, or industry ownership, and the methodological complexity of demonstrating their measurable impact on human health. Even though new big data sources have shown unprecedented potential for disease diagnosis and outbreak detection, we need to investigate results in the existing literature to gain a comprehensive understanding of their impact on and benefits to human health.Objective: A systematic review of systematic reviews on identifying digital data sources and their impact area on people's health, including challenges, opportunities, and good practices.Methods: A multidatabase search was performed. Peer-reviewed papers published between January 2010 and November 2020 relevant to digital data sources on health were extracted, assessed, and reviewed.Results: The 64 reviews are covered by three domains, that is, universal health coverage (UHC), public health emergencies, and healthier populations, defined in WHO's General Programme of Work, 2019–2023, and the European Programme of Work, 2020–2025. In all three categories, social media platforms are the most popular digital data source, accounting for 47% (N = 8), 84% (N = 11), and 76% (N = 26) of studies, respectively. The second most utilized data source are electronic health records (EHRs) (N = 13), followed by websites (N = 7) and mass media (N = 5). In all three categories, the most studied impact of digital data sources is on prevention, management, and intervention of diseases (N = 40), and as a tool, there are also many studies (N = 10) on early warning systems for infectious diseases. However, they could also pose health hazards (N = 13), for instance, by exacerbating mental health issues and promoting smoking and drinking behavior among young people.Conclusions: The digital data sources presented are essential for collecting and mining information about human health. The key impact of social media, electronic health records, and websites is in the area of infectious diseases and early warning systems, and in the area of personal health, that is, on mental health and smoking and drinking prevention. However, further research is required to address privacy, trust, transparency, and interoperability to leverage the potential of data held in multiple datastores and systems. This study also identified the apparent gap in systematic reviews investigating the novel big data streams, Internet of Things (IoT) data streams, and sensor, mobile, and GPS data researched using artificial intelligence, complex network, and other computer science methods, as in this domain systematic reviews are not common.

The PLACES (Population Level Analysis and Community Estimates) is an expansion of the original 500 Cities project and is a collaboration between the CDC, the Robert Wood Johnson Foundation (RWJF), and the CDC Foundation (CDCF). The original 500 Cities Project provided city- and census tract-level estimates for chronic disease risk factors (5), health outcomes (13), and clinical preventive services use (9) for the 500 largest US cities. The PLACES Project extends these estimates to all counties, places (incorporated and census designated places), census tracts and ZIP Code Tabulation Areas (ZCTA) across the United States. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. Data sources used to generate these measures include BRFSS data (2018 or 2017), Census Bureau 2010 census population data or annual population estimates for county vintage 2018 or 2017, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates.The health outcomes include arthritis, current asthma, high blood pressure, cancer (excluding skin cancer), high cholesterol, chronic kidney disease, chronic obstructive pulmonary disease (COPD), coronary heart disease, diagnosed diabetes, mental health not good for >=14 days, physical health not good for >=14 days, all teeth lost and stroke.The preventive services uses include lack of health insurance, visits to doctor for routine checkup, visits to dentist, taking medicine for high blood pressure control, cholesterol screening, mammography use for women, cervical cancer screening for women, colon cancer screening, and core preventive services use for older adults (men and women).The unhealthy behaviors include binge drinking, current smoking, obesity, physical inactivity, and sleeping less than 7 hours.For more information about the methodology, visit https://www.cdc.gov/places or contact places@cdc.gov.CDC's source webpage.CDC's feature service.