Health, United States is the report on the health status of the country. Every year, the report presents an overview of national health trends organized around four subject areas: health status and determinants, utilization of health resources, health care resources, and health care expenditures and payers.

The SWAN Public Use Datasets provide access to longitudinal data describing the physical, biological, psychological, and social changes that occur during the menopausal transition. Data collected from 3,302 SWAN participants from Baseline through the 10th Annual Follow-Up visit are currently available to the public. Registered users are able to download datasets in a variety of formats, search variables and view recent publications.

marmikpandya/mental-health dataset hosted on Hugging Face and contributed by the HF Datasets community

This table provides an overview of the key figures on health and care available on StatLine. All figures are taken from other tables on StatLine, either directly or through a simple conversion. In the original tables, breakdowns by characteristics of individuals or other variables are possible. The period after the year of review before data become available differs between the data series. The number of exam passes/graduates in year t is the number of persons who obtained a diploma in school/study year starting in t-1 and ending in t.

Data available from: 2001

Status of the figures:

2024: Most available figures are definite. Figures are provisional for: - causes of death; - youth care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university).

2023: Most available figures are definite. Figures are provisional for: - perinatal mortality at pregnancy duration at least 24 weeks; - diagnoses known to the general practitioner; - hospital admissions by some diagnoses; - average period of hospitalisation; - supplied drugs; - AWBZ/Wlz-funded long term care; - physicians and nurses employed in care; - persons employed in health and welfare; - average distance to facilities; - profitability and operating results at institutions. Figures are revised provisional for: - expenditures on health and welfare.

2022: Most available figures are definite. Figures are revised provisional for: - expenditures on health and welfare.

2021: Most available figures are definite, Figures are revised provisional for: - expenditures on health and welfare.f

2020 and earlier: All available figures are definite.

Changes as of 4 July 2025: More recent figures have been added for: - causes of death; - life expectancy; - life expectancy in perceived good health; - self-perceived health; - hospital admissions by some diagnoses; - sickness absence; - average period of hospitalisation; - contacts with health professionals; - youth care; - smoking, heavy drinkers, physical activity; - overweight; - high blood pressure; - physicians and nurses employed in care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university); - expenditures on health and welfare; - profitability and operating results at institutions.

Changes as of 18 december 2024: - Distance to facilities: the figures withdrawn on 5 June have been replaced (unchanged). - Youth care: the previously published final results for 2021 and 2022 have been adjusted due to improvements in the processing. - Due to a revision of the statistics Expenditure on health and welfare 2021, figures for expenditure on health and welfare care have been replaced from 2021 onwards. - Due to the revision of the National Accounts, the figures on persons employed in health and welfare have been replaced for all years. - AWBZ/Wlz-funded long term care: from 2015, the series Wlz residential care including total package at home has been replaced by total Wlz care. This series fits better with the chosen demarcation of indications for Wlz care.

When will new figures be published? New figures will be published in December 2025.

The Washington State Department of Health presents this information as a service to the public. This includes information on the work status, practice characteristics, education, and demographics of healthcare providers, provided in response to the Washington Health Workforce Survey. This is a complete set of data across all of the responding professions. The data dictionary identifies questions that are specific to an individual profession and aren't common to all surveys. The dataset is provided without identifying information for the responding providers. More information on the Washington Health Workforce Survey can be found at www.doh.wa.gov/workforcesurvey This dataset has been federated from https://data.wa.gov/Health/Washington-Health-Workforce-Survey-Data/cvrw-ujje.

Data Source: Substance Abuse and Mental Health Services Administration (SAMHSA) 2020, U.S. Department of Health and Human Services (HHS).

This is the dataset used for my first project for mental health analysis with Ann Bertram and Tiffany McBride at Purdue Fort Wayne. It has been cleaned and divided into datasets based on the states. Each dataset will include demographic information such as age, education level, ethnicity, race, genders, mental illness flags, etc. For more information, please refer to the codebook.

Conducted by the National Association of County and City Health Officials (NACCHO), the purpose of this survey of local health departments (LHDs) was to advance and support the development of a database for LHDs to describe and understand their structure, function, and capacities. A core set of questions was submitted to every LHD. In addition, some LHDs received one of two randomly assigned modules of supplemental questions. The core questions covered governance, funding, workforce (staffing levels, occupations employed, top executive education and licensure), LHD activities, community health assessment and health improvement planning, accreditation through the Public Health Accreditation Board, and policy-making and advocacy. The surveyed LHD activities include immunization, screening for diseases and conditions, treatment for communicable diseases, maternal and child health, epidemiology and surveillance activities, population-based primary prevention activities, and regulation, inspection and/or licensing activities. Topics covered by Module 1 included LHD interaction with academic institutions, Partnerships and collaboration, Cross-jurisdictional sharing of services, Emergency preparedness, and Access to healthcare services. Module 2 examined additional issues related to jurisdiction and governance, community health assessment and planning, human resources issues, quality improvement, public health informatics, and use of the Community Guide of Preventive Services.

The Dog Health Vitals Dataset is a collection of recordings and vital statistics related to the health of dogs. It includes data captured during various recording sessions, providing insights into the physiological characteristics of the dogs.

This data set shows locations at which Austin Public Health has a presence, those locations with office hours contain service providers. Some locations are owned by the Department, while other locations house Austin Public Health staff.

No warranty is made by the City of Austin or Austin Public Health regarding the specific accuracy, relevance, or completeness of this data set.

The global medical database software market is experiencing robust growth, driven by the increasing adoption of electronic health records (EHRs) and the rising need for efficient health information management (HIM) systems. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated $45 billion by 2033. This expansion is fueled by several key factors: the increasing digitization of healthcare, the growing demand for data-driven insights to improve patient care and operational efficiency, and the expanding adoption of cloud-based solutions offering scalability and accessibility. Pharmaceutical companies and academic/research institutions are significant drivers, leveraging these systems for drug discovery, clinical trials management, and advanced research initiatives. However, challenges such as data security concerns, high implementation costs, and the need for robust interoperability between different systems pose restraints to market growth. The market is segmented by software type (EHR, HIM) and application (pharmaceutical companies, academic institutions, others), providing diverse opportunities for specialized vendors. Geographic expansion continues, with North America and Europe currently holding significant market share, but growth is anticipated across Asia-Pacific and other regions as healthcare infrastructure modernizes. The competitive landscape is dynamic, with established players like NextGen Healthcare and emerging companies like Pabau and EHR Your Way vying for market share. The success of individual vendors depends on factors including the scalability of their solutions, the depth of their data analytics capabilities, and the strength of their customer support network. The market's trajectory is heavily influenced by government regulations regarding data privacy and interoperability, the ongoing evolution of healthcare technology, and the increasing focus on personalized medicine. Further growth is likely to be seen in areas such as AI-powered diagnostics, predictive analytics, and advanced data visualization tools integrated within medical databases.

A 10,000-patient database that contains in total 10,000 virtual patients, 36,143 admissions, and 10,726,505 lab observations.

My HealtheVet (www.myhealth.va.gov) is a Personal Health Record portal designed to improve the delivery of health care services to Veterans, to promote health and wellness, and to engage Veterans as more active participants in their health care. The My HealtheVet portal enables Veterans to create and maintain a web-based PHR that provides access to patient health education information and resources, a comprehensive personal health journal, and electronic services such as online VA prescription refill requests and Secure Messaging. Veterans can visit the My HealtheVet website and self-register to create an account, although registration is not required to view the professionally-sponsored health education resources, including topics of special interest to the Veteran population. Once registered, Veterans can create a customized PHR that is accessible from any computer with Internet access.

Graph and download economic data for Health Services Expenditures (HLTHSEEXPHCSA) from 2000 to 2021 about healthcare, health, expenditures, services, and USA.

Patient Health and Medication Data

Column Descriptions

• Age: The age of the patient in years.
• Medication_Duration: The duration for which the patient has been on the current medication, measured in days.
• Allergy: Indicates the type of allergy the patient has, if any (e.g., Pollen, Peanuts, Shellfish, or None).
• Cholesterol: The patient's cholesterol level, categorized as HIGH or NORMAL.
• Sex: The sex of the patient, categorized as Male (M) or Female (F).
• BP (Blood Pressure): The patient's blood pressure reading, categorized as HIGH, NORMAL, or LOW.
• Na_to_K: The ratio of sodium to potassium in the patient's blood, a numerical value.
• Drug: The specific drug or medication prescribed to the patient, which could be the generic name or brand name of the drug.

Inspired from - https://www.kaggle.com/datasets/prathamtripathi/drug-classification

1) Data Introduction • The Mental Health Dataset is constructed from pre-analyzed text-based sources and consists of various quantitative features such as sentiment analysis indicators (e.g., sentiment compound, positive/negative/neutral scores), psychological attributes (e.g., isolation, economic stress, substance use), and readability indices (e.g., ARI, Flesch-Kincaid).

2) Data Utilization (1) Characteristics of the Mental Health Dataset: • The dataset converts qualitative psychological signals from text into numerical form, making it suitable for training AI models in tasks such as emotion analysis, risk factor prediction, and reading level assessment.

(2) Applications of the Mental Health Dataset: • Development of mental health risk prediction models: The dataset can be used to train classification or regression-based AI models that detect early signs of psychological distress using features such as sentiment scores, isolation, and stress indicators.

Electronic Health Records and clinical longitudinal data have been visualized in a wide range of applications to assist the understanding of the status and evolution of patients. Few studies have objectively assessed these applications. We utilized the insights-based method to objectively assess the effectiveness of an application that visualizes longitudinal data from the Australian national electronic health record. Five professional psychiatrists took part in the assessment study.

Health Service Research (HSR) PubMed Queries contains preformulated specialized PubMed searches on healthcare quality and costs.

The U.S. Census Bureau, in collaboration with five federal agencies, launched the Household Pulse Survey to produce data on the social and economic impacts of Covid-19 on American households. The Household Pulse Survey was designed to gauge the impact of the pandemic on employment status, consumer spending, food security, housing, education disruptions, and dimensions of physical and mental wellness.

The survey was designed to meet the goal of accurate and timely weekly estimates. It was conducted by an internet questionnaire, with invitations to participate sent by email and text message. The sample frame is the Census Bureau Master Address File Data. Housing units linked to one or more email addresses or cell phone numbers were randomly selected to participate, and one respondent from each housing unit was selected to respond for him or herself. Estimates are weighted to adjust for nonresponse and to match Census Bureau estimates of the population by age, gender, race and ethnicity, and educational attainment. All estimates shown meet the NCHS Data Presentation Standards for Proportions.

Summary of logistic regression (for the complete dataset refer to data in S1 Appendix).

This point datalayer contains the location of community health centers (CHCs) in Massachusetts. The layer was produced by the Massachusetts Department of Public Health (MA DPH) Center for Environmental Health (CEH) GIS program. The source material was provided by Tina Ford Wright, Publications and Marketing Assistant, Massachusetts League of Community Health Centers, a.k.a. "the League," (http://www.massleague.org). The League defines a community health center as a non-profit community-based organization that offers comprehensive primary and preventive health care, including medical, social and/or mental health services, to anyone in need regardless of their medical status, ability to pay, culture or ethnicity.CHCs are grouped into Main and Satellite locations. Main CHCs may have one or more satellite locations (also known as access points). The MCHC_CODE item defines the affiliation between main CHCs and their satellites.

CHCs vary by both the facility and/or building type in which they are located, scope of clinical services offered, and target patient population(s). The CEH GIS program used the MassGIS Hospitals, Schools, Colleges and Universities, and Prisons datalayers, and Internet Web sites in the case of homeless shelters, to derive the locations of health centers in these facilities. Health centers known to be administrative offices are attributed accordingly. With respect to clinical services, this GIS datalayer makes no distinction among CHCs. An exception is eye care and dental service providers that are indicated in the EYE and DENTAL fields. No information regarding target patient populations is explicitly defined, though assumptions may be based on health center name and/or location.

In all cases, patients seeking care should contact the CHCs directly to verify availability of clinical services, hours, etc., rather than rely on the information contained in this GIS datalayer, as such information is subject to change.