This database offers addresses, phone numbers, administrator names and state registration or licensure status for Minnesota health care providers. Federal certification classifications are also included. Provider types in the directory are boarding care homes, home health agencies, home care providers, hospices, hospitals, housing with services, nursing homes and supervised living facilities and other non-long term care providers. Providers can be identified by type, county, city or name. This page provides a link to download current data from the MDH database. The link works best in Internet Explorer and Firefox. This data is provided in tabular format. There is no assoicated geographic dataset; results require geocoding to be mapped. A link to the file with the field names and definitions is also provided below.

Abstract: Covid-19 has had a big impact on many aspects of our life, including mental health. Since the start of the pandemic, a whole body of Belgian research has been performed on the relation between covid-19 and mental health (care). The mental health & covid-19 working group of the superior health council lists these studies in order to provide advice to policy makers and the general public. The first advisory report focused on international literature on contagious outbreaks, since not many studies on covid-19 and, especially, not many Belgian studies were published yet. This advice can be found here: https://www.health.belgium.be/en/report-9589-mental-health-and-covid-19 As part of the work performed in the first advisory report, the Policy Coordination Working group has asked the Superior Health Council to list all Belgian studies investigating the relation between covid-19 and mental health and/or mental health care and to provide regular updates. The superior Health Council, therefore, started the project of the Belgian mental health data repository. This repository will consist of ongoing studies, preliminary results, accepted and published articles with a Belgian population. For each study, an overview will be given of the authors (including contact details), level of evidence and a short description of the study. The Belgian Mental Health Data Repository will allow for other researchers, policy makers, health care providers and the general public to have a better idea of and easier access to the mental health studies in Belgium. Additionally, more in-depth analyses across studies can be facilitated leading to better insights into the impact of covid-19 on mental health. An update of the living document will be published weekly.

These datasets are concordance files that link the Geographic Classification for Health (GCH) to statistical geographies and geographic units commonly used in health research and analysis in Aotearoa New Zealand (NZ). More information about the develppment of the GCH is available in our Open Access publication. Our long-term aim is the comprehensive and accurate understanding of urban-rural variation in health outcomes and healthcare utilization at both national and regional levels. This is best achieved by the widespread uptake of the GCH by health researchers and health policy makers. The GCH is straightforward to use and most users will only need the relevant concordance file.
Statistical Area 1s (SA1s, small statistical areas which are the output geography for population data) were used as the building blocks for the Geographic Classification for Health (GCH) and are the preferred small areas when undertaking the analysis of health data using the GCH. It is however appreciated that a lot of health data is not available at the SA1 level and GCH concordance files are also available for Domicile (Census Area Units, CAU) and Statistical Area 2s (SA2) and Meshblock. The following concordance files are available in excel format:

SA12018_to_GCH2018.csv This concordance file applies a GCH category to each SA1 in NZ SA22018_to_GCH2018.csv This concordance file applies a GCH category to each SA2 in NZ MoH_HDOM_to_GCH2018.csv This concordance file applies a GCH category to each Domicile in NZ. Please read the additional information below if you plan to use this concordance file. MoH_MB_to_GCH2018.csv This concordance file applies a GCH category to each Meshblock in NZ. Please read the additional information below if you plan to use this concordance file.

Additional information relating to geographic units used by the Ministry of Health:

MoH_HDOM_to_GCH2018.csv This file has been designed specifically to add GCH to the Ministry of Health (MoH) datasets containing Domicile codes. Use this file if your dataset contains only Domicile codes. If your dataset also contains Meshblock codes, then use the MoH Meshblock to GCH concordance file. This file includes 2006 and 2013 domicile codes. The 2013 domiciles are still current as of 2022, and this file will still work well with data outside those years. Domicile boundaries do not align well with SA1 boundaries, and longitudinal health data usually contains some older Domiciles which have been phased out and replaced with multiple smaller Domiciles. These deprecated Domiciles may overlap multiple SA1s. Usually, all such SA1s belong to the same GCH category. Occasionally, a Domicile will overlap more than one GCH category. When this happens, we have assigned the GCH category to which the majority of people living in that Domicile belong. By necessity, this will allocate a minority of people in those Domiciles to a GCH category to which they do not belong.
MoH_MB_to_GCH2018.csv This file has been designed specifically to add GCH to Ministry of Health (MoH) datasets containing Meshblock codes. This file includes 2018, 2013, 2006, and 2001 Meshblock codes, but will still work well with data outside those years. Meshblock boundaries from census 2018 fit perfectly and completely within the Statistics New Zealand Statistical Area 1s (SA1) boundaries on which GCH is based. However, longitudinal health data usually contains some older Meshblocks which have been phased out and replaced by multiple smaller Meshblocks. These deprecated Meshblocks may overlap multiple SA1s. Usually, all such SA1s belong to the same GCH category. Occasionally, a Meshblock will overlap more than one GCH category. When this happens, we have assigned the GCH category to which the majority of people living in that Meshblock belong. By necessity, this will allocate a minority of people in those Meshblocks to a GCH category to which they do not belong.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. OP&R organizes and prepares for public health and medical emergencies through statewide partnerships with public - private and government agencies to coordinate an effective emergency response for the health and safety of all residents of Maryland. Last Updated: 10/06/2014 Feature Service Layer Link: http://geodata.md.gov/imap/rest/services/Health/MD_LongTermCareAssistedLiving/FeatureServer/1 ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

The proportion of births attended by skilled healthcare personnel is sourced from the United Nations Statistics website under data related to Sustainable Development Goals. This data is related to Goal 3, “Ensure healthy lives and promote well-being for all at all ages”, and falls under target 3.1, “By 2030, reduce the global maternal mortality ratio to less than 70 per 100,000 live births.” Gathering data regarding the proportion of births attended by skilled healthcare personnel can help to achieve these goals by identifying where there are care gaps and what conditions are risk factors for an increased rate of maternal mortality. National-level household surveys are the main data sources used to collect data for skilled health personnel. These surveys include Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), Reproductive Health Surveys (RHS) and other national surveys based on similar methodologies. Surveys are undertaken every 3 to 5 years. Data sources also include routine service statistics Population-based surveys is the preferred data source in countries with a low utilization of childbirth services, where private sector data are excluded from routine data collection, and/or with weak health information systems. These surveys include Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), Reproductive Health Surveys (RHS) and other national surveys based on similar methodologies. In MICS, DHS and similar surveys, the respondent is asked about the last live birth and who helped during delivery for a period up to five years before the interview. The surveys are generally undertaken every 3 to 5 years. Routine service/facility records is a more common data source in countries where a high proportion of births occur in health facilities and are therefore recorded. These data can be used to track the indicator on an annual basis.This data set is just one of the many datasets on the Global Midwives Hub, a digital resource with open data, maps, and mapping applications (among other things), to support advocacy for improved maternal and newborn services, supported by the International Confederation of Midwives (ICM), UNFPA, WHO, and Direct Relief.

The National Child Development Study (NCDS) is a continuing longitudinal study that seeks to follow the lives of all those living in Great Britain who were born in one particular week in 1958. The aim of the study is to improve understanding of the factors affecting human development over the whole lifespan.

The NCDS has its origins in the Perinatal Mortality Survey (PMS) (the original PMS study is held at the UK Data Archive under SN 2137). This study was sponsored by the National Birthday Trust Fund and designed to examine the social and obstetric factors associated with stillbirth and death in early infancy among the 17,000 children born in England, Scotland and Wales in that one week. Selected data from the PMS form NCDS sweep 0, held alongside NCDS sweeps 1-3, under SN 5565.

Survey and Biomeasures Data (GN 33004):
To date there have been ten attempts to trace all members of the birth cohort in order to monitor their physical, educational and social development. The first three sweeps were carried out by the National Children's Bureau, in 1965, when respondents were aged 7, in 1969, aged 11, and in 1974, aged 16 (these sweeps form NCDS1-3, held together with NCDS0 under SN 5565). The fourth sweep, also carried out by the National Children's Bureau, was conducted in 1981, when respondents were aged 23 (held under SN 5566). In 1985 the NCDS moved to the Social Statistics Research Unit (SSRU) - now known as the Centre for Longitudinal Studies (CLS). The fifth sweep was carried out in 1991, when respondents were aged 33 (held under SN 5567). For the sixth sweep, conducted in 1999-2000, when respondents were aged 42 (NCDS6, held under SN 5578), fieldwork was combined with the 1999-2000 wave of the 1970 Birth Cohort Study (BCS70), which was also conducted by CLS (and held under GN 33229). The seventh sweep was conducted in 2004-2005 when the respondents were aged 46 (held under SN 5579), the eighth sweep was conducted in 2008-2009 when respondents were aged 50 (held under SN 6137), the ninth sweep was conducted in 2013 when respondents were aged 55 (held under SN 7669), and the tenth sweep was conducted in 2020-24 when the respondents were aged 60-64 (held under SN 9412).

A Secure Access version of the NCDS is available under SN 9413, containing detailed sensitive variables not available under Safeguarded access (currently only sweep 10 data). Variables include uncommon health conditions (including age at diagnosis), full employment codes and income/finance details, and specific life circumstances (e.g. pregnancy details, year/age of emigration from GB).

Four separate datasets covering responses to NCDS over all sweeps are available. National Child Development Deaths Dataset: Special Licence Access (SN 7717) covers deaths; National Child Development Study Response and Outcomes Dataset (SN 5560) covers all other responses and outcomes; National Child Development Study: Partnership Histories (SN 6940) includes data on live-in relationships; and National Child Development Study: Activity Histories (SN 6942) covers work and non-work activities. Users are advised to order these studies alongside the other waves of NCDS.

From 2002-2004, a Biomedical Survey was completed and is available under End User Licence (EUL) (SN 8731) and Special Licence (SL) (SN 5594). Proteomics analyses of blood samples are available under SL SN 9254.

Linked Geographical Data (GN 33497):
A number of geographical variables are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies.

Linked Administrative Data (GN 33396):
A number of linked administrative datasets are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies. These include a Deaths dataset (SN 7717) available under SL and the Linked Health Administrative Datasets (SN 8697) available under Secure Access.

Multi-omics Data and Risk Scores Data (GN 33592)
Proteomics analyses were run on the blood samples collected from NCDS participants in 2002-2004 and are available under SL SN 9254. Metabolomics analyses were conducted on respondents of sweep 10 and are available under SL SN 9411.

Additional Sub-Studies (GN 33562):
In addition to the main NCDS sweeps, further studies have also been conducted on a range of subjects such as parent migration, unemployment, behavioural studies and respondent essays. The full list of NCDS studies available from the UK Data Service can be found on the NCDS series access data webpage.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from NCDS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Further information about the full NCDS series can be found on the Centre for Longitudinal Studies website.

The National Child Development Study: Linked Health Administrative Datasets (Hospital Episode Statistics), England, 1997-2023: Secure Access includes data files from the NHS Digital HES database for those cohort members who provided consent to health data linkage in the Age 50 sweep. The HES database contains information about all hospital admissions in England. The following linked HES data are available:

1) Accident and Emergency (A&E)
The A&E dataset details each attendance to an Accident and Emergency care facility in England, between 01-04-2007 and 31-03-2020 (inclusive). It includes major A&E departments, single speciality A&E departments, minor injury units and walk-in centres in England.

2) Admitted Patient Care (APC)
The APC data summarises episodes of care for admitted patients, where the episode occurred between 01-04-1997 and 31-03-2023 (inclusive).

3) Critical Care (CC)
The CC dataset covers records of critical care activity between 01-04-2009 and 31-03-2023 (inclusive).

4) Out Patient (OP)
The OP dataset lists the outpatient appointments between 01-04-2003 and 31-03-2023 (inclusive).

5) Emergency Care Dataset (ECDS)
The ECDS lists the emergency care appointments between 01-04-2020 and 31-03-2023 (inclusive).

6) Consent data
The consents dataset describes consent to linkage, and is current at the time of deposit.

CLS/ NHS Digital Sub-licence agreement
NHS Digital has given CLS permission for onward sharing of the NCDS/HES dataset via the UKDS Secure Lab. In order to ensure data minimisation, NHS Digital requires that researchers only access the HES variables needed for their approved research project. Therefore, the HES linked data provided by the UKDS to approved researchers will be subject to sub-setting of variables. The researcher will need to request a specific sub-set of variables from the NCDS/HES data dictionary, which will subsequently be made available within their UKDS Secure Account. Once the researcher has finished their research, the UKDS will delete the tailored dataset for that specific project. Any party wishing to access the data deposited at the UK Data Service will be required to enter into a Licence agreement with CLS (UCL), in addition to the agreements signed with the UKDS, provided in the application pack.

CLS Hospital Episode Statistics data access update July 2025

From March 2027, HES data linked to all four CLS studies will no longer be available via the UK Data Service. For projects ending before March 2027, uses should continue to apply via UKDS. However, if access to a wider range of linked Longitudinal Population Studies data is needed, UKLLC might be more suitable. For projects ending after March 2027, users must apply via UKLLC.

Latest edition information
For the third edition (April 2025), the data have been updated to include linked data for the financial years 2017-2022. In addition, a new dataset for Emergency Care (ECDS) episodes has been added, along with a dataset detailing the consent for linkage. Furthermore, the study documentation has also been updated.

Note: This web page provides data on health facilities only. To file a complaint against a facility, please see: https://www.cdph.ca.gov/Programs/CHCQ/LCP/Pages/FileAComplaint.aspx

The California Department of Public Health (CDPH), Center for Health Care Quality, Licensing and Certification (L&C) Program licenses and certifies more than 30 types of healthcare facilities. The Electronic Licensing Management System (ELMS) is a CDPH data system created to manage state licensing-related data and enforcement actions. This file includes California healthcare facilities that are operational and have a current license issued by the CDPH and/or a current U.S. Department of Health and Human Services’ Centers for Medicare and Medicaid Services (CMS) certification.

To link the CDPH facility IDs with those from other Departments, like HCAI, please reference the "Licensed Facility Cross-Walk" Open Data table at https://data.chhs.ca.gov/dataset/licensed-facility-crosswalk. Facility geographic variables are updated monthly, if latitude/longitude information is missing at any point in time, it should be available when the next time the Open Data facility file is refreshed.

Please note that the file contains the data from ELMS as of the 11th business day of the month. See DATA_DATE variable for the specific date of when the data was extracted.

Map of all Health Care Facilities in California: https://go.cdii.ca.gov/cdph-facilities

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.

The data set includes the full lists of German and Australian Health Insurers investigated, including a link to their apps.

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:

1. Which health insurers offer options for client to self-track health and fitness data?
2. How do insurers communicate about the programs?
3. How do those insurers communicate about data security?

OP&R organizes and prepares for public health and medical emergencies through statewide partnerships with public, private and government agencies to coordinate an effective emergency response for the health and safety of all residents of Maryland.The Office of Preparedness and Response is responsible for staffing the Maryland Department of Health & Mental Hygiene (DHMH) Command Center during a significant public health event such as pandemic, natural disaster, act of terrorism or any incident that requires the coordination of state level health department resources. All staff are trained in the National Incident Management System (NIMS) and Incident Command System (ICS). The DHMH Command Center coordinates the state health department response to an incident in collaboration with the Maryland Department of Emergency Management (MDEM, previously MEMA) Emergency Operations Center and other state agencies.This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Feature Service Layer Link:https://mdgeodata.md.gov/imap/rest/services/Health/MD_LongTermCareAssistedLiving/FeatureServer/3

This database automatically captures metadata, the source of which is NACIONAL PUBLIC HEALTH INŠTIT and corresponding to the source collection entitled “Number of born, live births by statistical regions, Slovenia, annually”.

Actual data are available in Px-Axis format (.px). With additional links, you can access the source portal page for viewing and selecting data, as well as the PX-Win program, which can be downloaded free of charge. Both allow you to select data for display, change the format of the printout, and store it in different formats, as well as view and print tables of unlimited size, as well as some basic statistical analyses and graphics.

Data set on the prevalence of self-care behaviors by non-institutionalized older adults. Personal interviews were conducted with 3,485 individuals 65 years of age and older, with oversampling of the oldest old. Questions were asked about the type and extent of self-care behaviors for activities of daily living, management of chronic conditions (through self-care activities, equipment use, and environmental modifications), medical self-care for acute conditions, health promotion/disease preventions, social support, health service utilization, and socio-demographic/economic status. A follow-up study by telephone was conducted in 1994 to continue examination of subjects. Many of the same questions from the baseline were asked, along with questions regarding change in health status since baseline and nursing home visits. For subjects who had been institutionalized since baseline (Part 2), information was gathered (by proxy) regarding demographic status, living arrangements prior to institutionalization, and reasons for institutionalization. For subjects who had died since baseline (Part 3), information was again gathered through interviews with proxies. Questions covered nursing home admissions and date and place of death. In both waves, a proxy was substituted if the subject was hospitalized (or institutionalized since baseline), too ill, cognitively not able to respond, or deceased. Survey data were linked to Medicare/Medicaid health utilization records. The baseline data are archived at NACDA as ICPSR Study No. 6718, and the followup data are archived as ICPSR Study No. 2592 and linkable to the baseline data. * Dates of Study: 1990-1994 * Study Features: Longitudinal * Sample Size: ** 1990-1: 3,485 (Baseline) ** 1994: 2,601 (Followup) Links: * 1990-1991 Baseline ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06718 * 1994 Follow-up ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02592

DATASUS provides information that can serve to support objective analyses of the health situation, evidence-based decision-making, and the development of health action programs. Measuring the health status of the population is a tradition in public health. It began with the systematic recording of mortality and survival data (Vital Statistics – Mortality and Live Births). With advances in the control of infectious diseases (Epidemiological and Morbidity Information) and with a better understanding of the concept of health and its population determinants, the analysis of the health situation began to incorporate other dimensions of health status. Data on morbidity, disability, access to services, quality of care, living conditions, and environmental factors became metrics used in the construction of Health Indicators, which translate into relevant information for the quantification and evaluation of health information. In this section, information is also found on Healthcare Assistance for the population, registries (Assistance Network) of hospital and outpatient networks, the registry of health establishments, as well as information on financial resources and Demographic and Socioeconomic information. Furthermore, in Supplementary Health, links are provided to the information pages of the National Supplementary Health Agency – ANS. Translated from Portuguese Original Text: O DATASUS disponibiliza informações que podem servir para subsidiar análises objetivas da situação sanitária, tomadas de decisão baseadas em evidências e elaboração de programas de ações de saúde. A mensuração do estado de saúde da população é uma tradição em saúde pública. Teve seu início com o registro sistemático de dados de mortalidade e de sobrevivência (Estatísticas Vitais – Mortalidade e Nascidos Vivos). Com os avanços no controle das doenças infecciosas (informações Epidemiológicas e Morbidade) e com a melhor compreensão do conceito de saúde e de seus determinantes populacionais, a análise da situação sanitária passou a incorporar outras dimensões do estado de saúde. Dados de morbidade, incapacidade, acesso a serviços, qualidade da atenção, condições de vida e fatores ambientais passaram a ser métricas utilizadas na construção de Indicadores de Saúde, que se traduzem em informação relevante para a quantificação e a avaliação das informações em saúde. Nesta seção também são encontradas informações sobre Assistência à Saúde da população, os cadastros (Rede Assistencial), das redes hospitalares e ambulatoriais, o cadastro dos estabelecimentos de saúde, além de informações sobre recursos financeiros e informações Demográficas e Socioeconômicas. Além disso, em Saúde Suplementar, são apresentados links para as páginas de informações da Agência Nacional de Saúde Suplementar – ANS.

This data is an accurate summary of the validated data extracted from the NHS's HR and Payroll system. It has a provisional status as the data may change slightly over time where trusts make updates to their live operational systems. In addition to the regular monthly reports there are a series of quarterly reports (first published on 26 July 2016 looking at the data for March 2016) which include statistics on staff in Trusts and CCGs and information for NHS Support Organisations and Central Bodies. The quarterly analysis will be published each; September (showing June statistics) December (showing September statistics) March (showing December statistics) June (showing March statistics). Note: From March 2017 these quarterly reports will also include statistics on; i) Bank staff employed directly by trusts and paid through the Electronic Staff Record (ESR) pay and human resources system (as covered by our consultation on NHS workforce statistics). They are exploratory and experimental statistics. ii) The nationality of staff (previously published every six months) showing quarterly figures from September 2015 onwards. CSV data is available for every month back to September 2009 within the March 2016 report. Due to their size they are broken down into several files. A link to the March 2016 data is given in the 'Related Links' section below. Additional healthcare workforce data relating to GPs and Independent Sector workforce are also available (for March 2017 and June 2017 for high level GP numbers), links to this data are available below. We welcome feedback on the methodology and tables within this publication. Please email us with your comments and suggestions, clearly stating Monthly HCHS Workforce as the subject heading, via enquiries@nhsdigital.nhs.uk or 0300 303 5678

Data set from a long-term population-based prospective study of non-institutionalized residents (aged 21 or older, or aged 16-21 and older if married) in Alameda County, California investigating social and behavioral risk factors for morbidity, mortality, functioning and health. Questions were asked on marital and life satisfaction, parenting, physical activities, employment, health status, and childhood experiences. Demographic information on age, race, height, weight, education, income, and religion was also collected. Included with this dataset is a separate file (part 2) containing mortality data. With the aging of this cohort, data are becoming increasingly valuable for examining the life-long cumulative effects of social and behavioral factors on a well-characterized population. The first wave collected information for 6,928 respondents (including approximately 500 women aged 65 years and older) on chronic health conditions, health behaviors, social involvements, and psychological characteristics. The 1974 questionnaire was sent to 6,246 living subjects who had responded in 1965, and were able to be located. The third wave provides a follow-up of 2,729 original 1965 and 1974 respondents and examines health behaviors such as alcohol consumption and smoking habits, along with social activities. Also included is information on health conditions such as diabetes, osteoporosis, hormone replacement, and mental illness. Another central topic investigated is activities of daily living (including self-care such as dressing, eating, and shopping), along with use of free time and level of involvement in social, recreational, religious, and environmental groups. The fourth wave is a follow-up to the 1994 panel and examines changes in functional abilities such as self-care activities, employment, involvement in community activities, visiting friends/family, and use of free time since 1994. * Dates of Study: 1965-1999 * Sample Size: 1965: 6,928; 1974: 4,864; 1994: 2,729; 1995: 2,569, 1999: 2,123 * Study Features: Longitudinal Links: * 1965 ICPSR, http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06688 * 1974 ICPSR, http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06838 * 1994 and 1995 ICPSR, http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03083 * 1999 ICPSR, http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/04432#summary

Please cite the following paper when using this dataset: N. Thakur, “Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions,” Preprints, 2022, DOI: 10.20944/preprints202206.0383.v1 Abstract The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and use cases in assisted living, military, healthcare, firefighting, and industries. With the projected increase in the diverse uses of exoskeletons in the next few years in these application domains and beyond, it is crucial to study, interpret, and analyze user perspectives, public opinion, reviews, and feedback related to exoskeletons, for which a dataset is necessary. The Internet of Everything era of today's living, characterized by people spending more time on the Internet than ever before, holds the potential for developing such a dataset by mining relevant web behavior data from social media communications, which have increased exponentially in the last few years. Twitter, one such social media platform, is highly popular amongst all age groups, who communicate on diverse topics including but not limited to news, current events, politics, emerging technologies, family, relationships, and career opportunities, via tweets, while sharing their views, opinions, perspectives, and feedback towards the same. Therefore, this work presents a dataset of about 140,000 Tweets related to exoskeletons. that were mined for a period of 5-years from May 21, 2017, to May 21, 2022. The tweets contain diverse forms of communications and conversations which communicate user interests, user perspectives, public opinion, reviews, feedback, suggestions, etc., related to exoskeletons. Instructions: This dataset contains about 140,000 Tweets related to exoskeletons. that were mined for a period of 5-years from May 21, 2017, to May 21, 2022. The tweets contain diverse forms of communications and conversations which communicate user interests, user perspectives, public opinion, reviews, feedback, suggestions, etc., related to exoskeletons. The dataset contains only tweet identifiers (Tweet IDs) due to the terms and conditions of Twitter to re-distribute Twitter data only for research purposes. They need to be hydrated to be used. The process of retrieving a tweet's complete information (such as the text of the tweet, username, user ID, date and time, etc.) using its ID is known as the hydration of a tweet ID. The Hydrator application (link to download the application: https://github.com/DocNow/hydrator/releases and link to a step-by-step tutorial: https://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweets) or any similar application may be used for hydrating this dataset. Data Description This dataset consists of 7 .txt files. The following shows the number of Tweet IDs and the date range (of the associated tweets) in each of these files. Filename: Exoskeleton_TweetIDs_Set1.txt (Number of Tweet IDs – 22945, Date Range of Tweets - July 20, 2021 – May 21, 2022) Filename: Exoskeleton_TweetIDs_Set2.txt (Number of Tweet IDs – 19416, Date Range of Tweets - Dec 1, 2020 – July 19, 2021) Filename: Exoskeleton_TweetIDs_Set3.txt (Number of Tweet IDs – 16673, Date Range of Tweets - April 29, 2020 - Nov 30, 2020) Filename: Exoskeleton_TweetIDs_Set4.txt (Number of Tweet IDs – 16208, Date Range of Tweets - Oct 5, 2019 - Apr 28, 2020) Filename: Exoskeleton_TweetIDs_Set5.txt (Number of Tweet IDs – 17983, Date Range of Tweets - Feb 13, 2019 - Oct 4, 2019) Filename: Exoskeleton_TweetIDs_Set6.txt (Number of Tweet IDs – 34009, Date Range of Tweets - Nov 9, 2017 - Feb 12, 2019) Filename: Exoskeleton_TweetIDs_Set7.txt (Number of Tweet IDs – 11351, Date Range of Tweets - May 21, 2017 - Nov 8, 2017) Here, the last date for May is May 21 as it was the most recent date at the time of data collection. The dataset would be updated soon to incorporate more recent tweets.

Longitudinal data set of a nationally representative sample of the population aged 65 and over in Japan, comparable to that collected in the US and other countries. The first two waves of data are now available to the international research community. The sample is refreshed with younger members at each wave so it remains representative of the population at each wave. The study was designed primarily to investigate health status of the Japanese elderly and changes in health status over time. An additional aim is to investigate the impact of long-term care insurance system on the use of services by the Japanese elderly and to investigate the relationship between co-residence and the use of long term care. While the focus of the survey is health and health service utilization, other topics relevant to the aging experience are included such as intergenerational exchange, living arrangements, caregiving, and labor force participation. The initial questionnaire was designed to be comparable to the (US) Longitudinal Study of Aging II (LSOAII), and to the Asset and Health Dynamics Among the Oldest Old (AHEAD, a pre-1924 birth cohort) sample of the Health and Retirement Study (HRS), which has now been merged with the HRS. The sample was selected using a multistage stratified sampling method to generate 340 primary sampling units (PSUs). The sample of individuals was selected for the most part by using the National Residents Registry System, considered to be universal and accurate because it is a legal requirement to report any move to local authorities within two weeks. From each of the 340 PSUs, 6-11 persons aged 65-74 were selected and 8-12 persons aged 75+ were sampled. The population 75+ was oversampled by a factor of 2. Weights have been developed for respondents to the first wave of the survey to reflect sampling probabilities. Weights for the second wave are under development. With these weights, the sample should be representative of the 65+ Japanese population. In fall 1999, 4,997 respondents aged 65+ were interviewed, 74.6 percent of the initial target. Twelve percent of responses were provided by proxies, because of physical or mental health problems. The second wave of data was collected in November 2001. The third wave was collected in November 2003. Questionnaire topics include family structure, and living arrangements; subjects'''' parents/spouse''''s parents/children; socioeconomic status; intergenerational exchange; health behaviors, chronic conditions, physical functioning; activities of daily living and instrumental activities of daily living; functioning in the community; mental health depression measures; vision and hearing; dental health; health care and other service utilization. A CD is available which include the codebook and data files for the first and second waves of the national sample. The third wave of data will be released at a later date. * Dates of Study: 1999-2003 * Study Features: Longitudinal, International * Sample Size: ** 4,997 Nov/Dec 1999 Wave 1 ** 3,992 Nov 2001 Wave 2 ** Nov 2003 Wave 3 Link: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00156

After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

The following dataset provides state-aggregated data for hospital utilization. These are derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities.

The file will be updated regularly and provides the latest values reported by each facility within the last four days for all time. This allows for a more comprehensive picture of the hospital utilization within a state by ensuring a hospital is represented, even if they miss a single day of reporting.

No statistical analysis is applied to account for non-response and/or to account for missing data.

The below table displays one value for each field (i.e., column). Sometimes, reports for a given facility will be provided to more than one reporting source: HHS TeleTracking, NHSN, and HHS Protect. When this occurs, to ensure that there are not duplicate reports, prioritization is applied to the numbers for each facility.

On June 26, 2023 the field "reporting_cutoff_start" was replaced by the field "date".

On April 27, 2022 the following pediatric fields were added: