This data includes the name and location of food service establishments and the violations that were found at the time of their last inspection. This dataset excludes inspections conducted in New York City (see: https://nycopendata.socrata.com/), Suffolk County (http://apps.suffolkcountyny.gov/health/Restaurant/intro.html) and Erie County (http://www.healthspace.com/erieny). Inspections are a “snapshot” in time and are not always reflective of the day-to-day operations and overall condition of an establishment. Occasionally, remediation may not appear until the following month due to the timing of the updates. Some counties provide this information on their own websites and information found there may be updated more frequently. This dataset is refreshed on a monthly basis. The inspection data contained in this dataset was not collected in a manner intended for use as a restaurant grading system, and should not be construed or interpreted as such. Any use of this data to develop a restaurant grading system is not supported or endorsed by the New York State Department of Health.

Last inspection data is the most recently submitted and available data. Historical inspection data through 2005 is also available. Active establishments can be found at: https://health.data.ny.gov/Health/Food-Service-Establishment-Inspections-Beginning-2/2hcc-shji. Inactive (closed) establishments can be found at: https://health.data.ny.gov/Health/Food-Service-Establishment-Inspections-Beginning-2/aaxz-j6pj

For more information, check out http://www.health.ny.gov/regulations/nycrr/title_10/part_14/subpart_14-1.htm, or go to the "About" tab.

The online health assessment market is experiencing robust growth, driven by increasing smartphone penetration, rising healthcare costs, and a growing preference for convenient, accessible healthcare solutions. The market, valued at approximately $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant expansion is fueled by several key factors. Firstly, the increasing adoption of telehealth and remote patient monitoring technologies is creating a surge in demand for online health assessments as a cost-effective and efficient preliminary diagnostic tool. Secondly, the convenience and accessibility offered by online assessments are particularly appealing to younger demographics (teenagers and adults) and those in geographically remote areas with limited access to traditional healthcare facilities. Finally, the integration of artificial intelligence (AI) and machine learning (ML) is enhancing the accuracy and efficiency of these assessments, further propelling market growth. The market is segmented by assessment type (condition-specific questionnaires, symptom checkers, eligibility checkers) and target demographic (teenagers, adults, elderly), offering diverse opportunities for market players. While data privacy concerns and the need for regulatory compliance represent potential restraints, the overall market outlook remains highly positive. The competitive landscape is characterized by a mix of established healthcare providers (Inova, CHRISTUS, Cigna, OSF Healthcare, Northwell Health, HCA, Kaiser, Beaumont, MyMichigan Health), technology companies (WebMD), and specialized health data providers (Global Health Metrics). These companies are actively investing in developing sophisticated online assessment tools and integrating them into their existing healthcare platforms. Regional growth is expected to be geographically diverse, with North America and Europe currently leading the market due to high levels of technology adoption and healthcare infrastructure. However, Asia-Pacific is poised for rapid growth in the coming years driven by increasing internet penetration and a burgeoning middle class with rising disposable incomes. Strategic partnerships, technological advancements, and expanding regulatory approvals are key factors that will influence the competitive dynamics and overall growth trajectory of the online health assessment market over the forecast period.

Generic health data commonly collected during regular health checks. It provides a suitable and adjustable framework for extensive variety of uses, such as analysis, testing, simulation and algorithm development.

This is the de-identified data set used to conduct the analyses in the preprint submitted to JMIR Human Factors under the title "Determinants of Laypersons’ Trust in Medical Decision Aids: Randomized Controlled Trial" (https://doi.org/10.2196/35219).

This dataset contains 494 respondents' appraisals of a fictitious case vignette. They received support from a decision aid (that always disagreed with participants' first appraisal) showing a mock symptom checker logo, a decision aid framed as anthropomorphic or as an AI. Their second appraisal - taking into account the symptom checker advice - was collected again.

Additionally, the data contains participants'

• age
• gender
• education
• medical training
• propensity to trust
• eHealth Literacy
• certainty in their appraisals
• trust in the decision aid

This dataset is historical only and ends at 5/7/2021. For more information, please see http://dev.cityofchicago.org/open%20data/data%20portal/2021/05/04/covid-19-testing-by-person.html. The recommended alternative dataset for similar data beyond that date is https://data.cityofchicago.org/Health-Human-Services/COVID-19-Daily-Testing-By-Test/gkdw-2tgv.

This is the source data for some of the metrics available at https://www.chicago.gov/city/en/sites/covid-19/home/latest-data.html.

For all datasets related to COVID-19, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19.

This dataset contains counts of people tested for COVID-19 and their results. This dataset differs from https://data.cityofchicago.org/d/gkdw-2tgv in that each person is in this dataset only once, even if tested multiple times. In the other dataset, each test is counted, even if multiple tests are performed on the same person, although a person should not appear in that dataset more than once on the same day unless he/she had both a positive and not-positive test.

Only Chicago residents are included based on the home address as provided by the medical provider.

Molecular (PCR) and antigen tests are included, and only one test is counted for each individual. Tests are counted on the day the specimen was collected. A small number of tests collected prior to 3/1/2020 are not included in the table.

Not-positive lab results include negative results, invalid results, and tests not performed due to improper collection. Chicago Department of Public Health (CDPH) does not receive all not-positive results.

Demographic data are more complete for those who test positive; care should be taken when calculating percentage positivity among demographic groups.

All data are provisional and subject to change. Information is updated as additional details are received.

Data Source: Illinois National Electronic Disease Surveillance System

We discuss a statistical framework that underlies envelope detection schemes as well as dynamical models based on Hidden Markov Models (HMM) that can encompass both discrete and continuous sensor measurements for use in Integrated System Health Management (ISHM) applications. The HMM allows for the rapid assimilation, analysis, and discovery of system anomalies. We motivate our work with a discussion of an aviation problem where the identification of anomalous sequences is essential for safety reasons. The data in this application are discrete and continuous sensor measurements and can be dealt with seamlessly using the methods described here to discover anomalous flights. We specifically treat the problem of discovering anomalous features in the time series that may be hidden from the sensor suite and compare those methods to standard envelope detection methods on test data designed to accentuate the differences between the two methods. Identification of these hidden anomalies is crucial to building stable, reusable, and cost-efficient systems. We also discuss a data mining framework for the analysis and discovery of anomalies in high-dimensional time series of sensor measurements that would be found in an ISHM system. We conclude with recommendations that describe the tradeoffs in building an integrated scalable platform for robust anomaly detection in ISHM applications.

This web map contains fictitious neighborhood health data to be used for software testing and demonstration purposes only.The purpose of this web map is to show how predominant conditions can be mapped using the new Predominant Smart Symbology styling setting in ArcGIS Online.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. Count of COVID-19-associated deaths by date of death. Deaths reported to either the OCME or DPH are included in the COVID-19 data. COVID-19-associated deaths include persons who tested positive for COVID-19 around the time of death and persons who were not tested for COVID-19 whose death certificate lists COVID-19 disease as a cause of death or a significant condition contributing to death. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics Note the counts in this dataset may vary from the death counts in the other COVID-19-related datasets published on data.ct.gov, where deaths are counted on the date reported rather than the date of death

This is the current Medical Service Study Area. California Medical Service Study Areas are created by the California Department of Health Care Access and Information (HCAI).Check the Data Dictionary for field descriptions.Search for the Medical Service Study Area data on the CHHS Open Data Portal.Checkout the California Healthcare Atlas for more Medical Service Study Area information.This is an update to the MSSA geometries and demographics to reflect the new 2020 Census tract data. The Medical Service Study Area (MSSA) polygon layer represents the best fit mapping of all new 2020 California census tract boundaries to the original 2010 census tract boundaries used in the construction of the original 2010 MSSA file. Each of the state's new 9,129 census tracts was assigned to one of the previously established medical service study areas (excluding tracts with no land area), as identified in this data layer. The MSSA Census tract data is aggregated by HCAI, to create this MSSA data layer. This represents the final re-mapping of 2020 Census tracts to the original 2010 MSSA geometries. The 2010 MSSA were based on U.S. Census 2010 data and public meetings held throughout California.Source of update: American Community Survey 5-year 2006-2010 data for poverty. For source tables refer to InfoUSA update procedural documentation. The 2010 MSSA Detail layer was developed to update fields affected by population change. The American Community Survey 5-year 2006-2010 population data pertaining to total, in households, race, ethnicity, age, and poverty was used in the update. The 2010 MSSA Census Tract Detail map layer was developed to support geographic information systems (GIS) applications, representing 2010 census tract geography that is the foundation of 2010 medical service study area (MSSA) boundaries. ***This version is the finalized MSSA reconfiguration boundaries based on the US Census Bureau 2010 Census. In 1976 Garamendi Rural Health Services Act, required the development of a geographic framework for determining which parts of the state were rural and which were urban, and for determining which parts of counties and cities had adequate health care resources and which were "medically underserved". Thus, sub-city and sub-county geographic units called "medical service study areas [MSSAs]" were developed, using combinations of census-defined geographic units, established following General Rules promulgated by a statutory commission. After each subsequent census the MSSAs were revised. In the scheduled revisions that followed the 1990 census, community meetings of stakeholders (including county officials, and representatives of hospitals and community health centers) were held in larger metropolitan areas. The meetings were designed to develop consensus as how to draw the sub-city units so as to best display health care disparities. The importance of involving stakeholders was heightened in 1992 when the United States Department of Health and Human Services' Health and Resources Administration entered a formal agreement to recognize the state-determined MSSAs as "rational service areas" for federal recognition of "health professional shortage areas" and "medically underserved areas". After the 2000 census, two innovations transformed the process, and set the stage for GIS to emerge as a major factor in health care resource planning in California. First, the Office of Statewide Health Planning and Development [OSHPD], which organizes the community stakeholder meetings and provides the staff to administer the MSSAs, entered into an Enterprise GIS contract. Second, OSHPD authorized at least one community meeting to be held in each of the 58 counties, a significant number of which were wholly rural or frontier counties. For populous Los Angeles County, 11 community meetings were held. As a result, health resource data in California are collected and organized by 541 geographic units. The boundaries of these units were established by community healthcare experts, with the objective of maximizing their usefulness for needs assessment purposes. The most dramatic consequence was introducing a data simultaneously displayed in a GIS format. A two-person team, incorporating healthcare policy and GIS expertise, conducted the series of meetings, and supervised the development of the 2000-census configuration of the MSSAs.MSSA Configuration Guidelines (General Rules):- Each MSSA is composed of one or more complete census tracts.- As a general rule, MSSAs are deemed to be "rational service areas [RSAs]" for purposes of designating health professional shortage areas [HPSAs], medically underserved areas [MUAs] or medically underserved populations [MUPs].- MSSAs will not cross county lines.- To the extent practicable, all census-defined places within the MSSA are within 30 minutes travel time to the largest population center within the MSSA, except in those circumstances where meeting this criterion would require splitting a census tract.- To the extent practicable, areas that, standing alone, would meet both the definition of an MSSA and a Rural MSSA, should not be a part of an Urban MSSA.- Any Urban MSSA whose population exceeds 200,000 shall be divided into two or more Urban MSSA Subdivisions.- Urban MSSA Subdivisions should be within a population range of 75,000 to 125,000, but may not be smaller than five square miles in area. If removing any census tract on the perimeter of the Urban MSSA Subdivision would cause the area to fall below five square miles in area, then the population of the Urban MSSA may exceed 125,000. - To the extent practicable, Urban MSSA Subdivisions should reflect recognized community and neighborhood boundaries and take into account such demographic information as income level and ethnicity. Rural Definitions: A rural MSSA is an MSSA adopted by the Commission, which has a population density of less than 250 persons per square mile, and which has no census defined place within the area with a population in excess of 50,000. Only the population that is located within the MSSA is counted in determining the population of the census defined place. A frontier MSSA is a rural MSSA adopted by the Commission which has a population density of less than 11 persons per square mile. Any MSSA which is not a rural or frontier MSSA is an urban MSSA. Last updated December 6th 2024.

The data is presented by the ACT Government for the purpose of disseminating information for the benefit of the public. The ACT Government has taken great care to ensure the information in this report is as correct and accurate as possible. While the information is considered to be true and correct at the date of publication, changes in circumstances after the time of publication may impact on the accuracy of the information. Differences in statistical methods and calculations, data updates and guidelines may result in the information contained in this report varying from previously published information.

Context

The National Health and Nutrition Examination Survey (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the United States. The survey is unique in that it combines interviews and physical examinations. NHANES is a major program of the National Center for Health Statistics (NCHS). NCHS is part of the Centers for Disease Control and Prevention (CDC) and has the responsibility for producing vital and health statistics for the Nation.

The NHANES program began in the early 1960s and has been conducted as a series of surveys focusing on different population groups or health topics. In 1999, the survey became a continuous program that has a changing focus on a variety of health and nutrition measurements to meet emerging needs. The survey examines a nationally representative sample of about 5,000 persons each year. These persons are located in counties across the country, 15 of which are visited each year.

The NHANES interview includes demographic, socioeconomic, dietary, and health-related questions. The examination component consists of medical, dental, and physiological measurements, as well as laboratory tests administered by highly trained medical personnel.

To date, thousands of research findings have been published using the NHANES data.

Content

The 2013-2014 NHANES datasets include the following components:

1. Demographics dataset:

• A complete variable dictionary can be found here

1. Examinations dataset, which contains:

• Blood pressure

• Body measures

• Muscle strength - grip test

• Oral health - dentition

• Taste & smell

• A complete variable dictionary can be found here

1. Dietary data - total nutrient intake, first day:

• A complete variable dictionary can be found here

1. Laboratory dataset, which includes:

• Albumin & Creatinine - Urine

• Apolipoprotein B

• Blood Lead, Cadmium, Total Mercury, Selenium, and Manganese

• Blood mercury: inorganic, ethyl and methyl

• Cholesterol - HDL

• Cholesterol - LDL & Triglycerides

• Cholesterol - Total

• Complete Blood Count with 5-part Differential - Whole Blood

• Copper, Selenium & Zinc - Serum

• Fasting Questionnaire

• Fluoride - Plasma

• Fluoride - Water

• Glycohemoglobin

• Hepatitis A

• Hepatitis B Surface Antibody

• Hepatitis B: core antibody, surface antigen, and Hepatitis D antibody

• Hepatitis C RNA (HCV-RNA) and Hepatitis C Genotype

• Hepatitis E: IgG & IgM Antibodies

• Herpes Simplex Virus Type-1 & Type-2

• HIV Antibody Test

• Human Papillomavirus (HPV) - Oral Rinse

• Human Papillomavirus (HPV) DNA - Vaginal Swab: Roche Cobas & Roche Linear Array

• Human Papillomavirus (HPV) DNA Results from Penile Swab Samples: Roche Linear Array

• Insulin

• Iodine - Urine

• Perchlorate, Nitrate & Thiocyanate - Urine

• Perfluoroalkyl and Polyfluoroalkyl Substances (formerly Polyfluoroalkyl Chemicals - PFC)

• Personal Care and Consumer Product Chemicals and Metabolites

• Phthalates and Plasticizers Metabolites - Urine

• Plasma Fasting Glucose

• Polycyclic Aromatic Hydrocarbons (PAH) - Urine

• Standard Biochemistry Profile

• Tissue Transglutaminase Assay (IgA-TTG) & IgA Endomyseal Antibody Assay (IgA EMA)

• Trichomonas - Urine

• Two-hour Oral Glucose Tolerance Test

• Urinary Chlamydia

• Urinary Mercury

• Urinary Speciated Arsenics

• Urinary Total Arsenic

• Urine Flow Rate

• Urine Metals

• Urine Pregnancy Test

• Vitamin B12

• A complete data dictionary can be found here

1. Questionnaire dataset, which includes information on:

• Acculturation

• Alcohol Use

• Blood Pressure & Cholesterol

• Cardiovascular Health

• Consumer Behavior

• Current Health Status

• Dermatology

• Diabetes

• Diet Behavior & Nutrition

• Disability

• Drug Use

• Early Childhood

• Food Security

• Health Insurance

• Hepatitis

• Hospital Utilization & Access to Care

• Housing Characteristics

• Immunization

• Income

• Medical Conditions

• Mental Health - Depression Screener

• Occupation

• Oral Health

• Osteoporosis

• Pesticide Use

• Physical Activity

• Physical Functioning

• Preventive Aspirin Use

• Reproductive Health

• Sexual Behavior

• Sleep Disorders

• Smoking - Cigarette Use

• Smoking - Household Smokers

• Smoking - Recent Tobacco Use

• Smoking - Secondhand Smoke Exposure

• Taste & Smell

• Weight History

• Weight History - Youth

• A complete variable dictionary can be found here

1. Medication dataset, which includes prescription medications:

• A complete variable dictionary can be found here

Acknowledgements

Original data and additional documents related to the datasets or NHANES can be found here.

The learning disabilities health check scheme is designed to encourage practices to identify all patients aged 14 and over with learning disabilities, to maintain a learning disabilities 'health check' register and offer them an annual health check, which will include producing a health action plan. The learning disabilities health check scheme operates on a quarterly basis. This release contains data for the fourth quarter of 2020-21. The learning disabilities health check scheme is one of a number of GP enhanced services. Enhanced services are voluntary reward programmes that cover primary medical services; one of their main aims is to reduce the burden on secondary care services. Data for other enhanced services are published annually, the latest release of these data is available under related links below. Please note that field LDHC001 (register) is manually entered by GP practices and not extracted from GP systems. The contents of this field should be interpreted with caution as NHS Digital are unable to verify the data quality of these data. It should also be noted that where this field is blank it indicates that the practice did not manually enter this figure.

This is the de-identified data set used to conduct the analyses in the preprint submitted to JMIR Public Health and Surveillance under the title "Interactive versus static decision support tools for COVID-19: An experimental comparison" (https://doi.org/10.2196/preprints.33733).

This data set contains the appraisal of 196 respondents (without decision support, with static or with interactive decision support) to appropriate social and care-seeking behavior for seven fictitious descriptions of patients. Additionally, this data contains participants'

• gender
• educational background
• previous medical training
• affinity for technology
• experience with COVID-19 related medical questions
• perceived threat from COVID-19
• answers to a COVID-19 knowledge test
• accuracy
• decision certainty (after deciding)
• mental effort
• ratings of
  • the decision support tool's usefulness
  • ease of use
  • trust
  • future intention to use the tool

Determinants of individuals’ intention to undergo health checks without adjusted for sociodemographic data.

ORCHARDS is a longitudinal prospective study of K-12 student absenteeism and acute respiratory illness (ARI) in a family and community setting. This dataset was used to assess the performance of a rapid influenza diagnostic test.

This is an official DOHMH dataset. Creating derivative/community datasets based on this dataset is allowed so long as it is done in a manner that is not misleading and does not imply endorsement of such datasets by DOHMH.

NYC DOHMH tests consumer products collected during investigations of lead poisonings and store surveys, for lead. Certain consumer products are also tested for other metals such as arsenic, cadmium and mercury. This dataset contains the laboratory results for the consumer products that are tested. For more information on hazardous consumer products, visit nyc.gov/hazardous products.

Dataset test

The online health community has the functions of online consultation, health record management and disease information interaction as an online medical platform. In the context of the pandemic, the existence of online health communities has provided a favorable environment for information acquisition and knowledge sharing among different roles, effectively improving the health of human, and popularizing health knowledge. This paper analyzes the development and importance of domestic online health communities, and sorts out users’ participation behaviors, types of behaviors, and continuous participation behaviors, influence motives, and motivational patterns in online health communities. Taking the operation status of the online health community during the pandemic period as an example, the computer sentiment analysis method was used to obtain seven categories of participation behaviors and the proportion of various behaviors of online health community users, and the conclusion is: the emergence of the pandemic, making the online health community a platform where people are more inclined to choose to consult health issues, and user interaction behaviors have become more active on the platform.