The "https://addhealth.cpc.unc.edu/" Target="_blank">National Longitudinal Study of Adolescent to Adult Health (Add Health) is a longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32*. Add Health combines longitudinal survey data on respondents' social, economic, psychological and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. The fourth wave of interviews expanded the collection of biological data in Add Health to understand the social, behavioral, and biological linkages in health trajectories as the Add Health cohort ages through adulthood. The fifth wave of data collection is planned to begin in 2016.

Initiated in 1994 and supported by three program project grants from the "https://www.nichd.nih.gov/" Target="_blank">Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) with co-funding from 23 other federal agencies and foundations, Add Health is the largest, most comprehensive longitudinal survey of adolescents ever undertaken. Beginning with an in-school questionnaire administered to a nationally representative sample of students in grades 7-12, the study followed up with a series of in-home interviews conducted in 1995, 1996, 2001-02, and 2008. Other sources of data include questionnaires for parents, siblings, fellow students, and school administrators and interviews with romantic partners. Preexisting databases provide information about neighborhoods and communities.

Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health, and Waves I and II focus on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants have aged into adulthood, however, the scientific goals of the study have expanded and evolved. Wave III, conducted when respondents were between 18 and 26** years old, focuses on how adolescent experiences and behaviors are related to decisions, behavior, and health outcomes in the transition to adulthood. At Wave IV, respondents were ages 24-32* and assuming adult roles and responsibilities. Follow up at Wave IV has enabled researchers to study developmental and health trajectories across the life course of adolescence into adulthood using an integrative approach that combines the social, behavioral, and biomedical sciences in its research objectives, design, data collection, and analysis.

* 52 respondents were 33-34 years old at the time of the Wave IV interview.
** 24 respondents were 27-28 years old at the time of the Wave III interview.

The Wave III public-use data are helpful in analyzing the transition between adolescence and young adulthood. Included here are education data weights.

The National Longitudinal Study of Adolescent to Adult Health (Add Health) Parent Study Public Use collection includes data gathered as part of the Add Health longitudinal survey of adolescents. The original Add Health survey is a longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States during the 1994-1995 school year. In Wave 1 of the Add Health Study (1994-1995), a parent of each Add Health Sample Member (AHSM) was interviewed. The Add Health Parent Study gathered social, behavioral, and health survey data in 2015-2017 from the parents of Add Health Sample members who were originally interviewed at Wave 1 (1994-1995). Wave 1 Parents were asked about their adolescent children, their relationships with them, and their own health. The Add Health Parent Study interview is a comprehensive survey of Add Health parents' family relations, education, religious beliefs, physical and mental health, social support, and community involvement experiences. In addition, survey data contains cognitive assessments, a medications log linked to a medications database lookup table, and household financial information collection. The survey also includes permission for administrative data linkages and includes data from a Family Health History Leave-Behind questionnaire. Interviews were conducted with parents' spouse/partner when available. Research domains targeted in the survey and research questions that may be addressed using the Add Health Parent Study data include: Health Behaviors and Risks Many health conditions and behaviors run in families; for example, cardiovascular disease, obesity and substance abuse. How are health risks and behaviors transmitted across generations or clustered within families? How can we use information on the parents' health and health behavior to better understand the determinants of their (adult) children's health trajectories? Cognitive Functioning and Non-Cognitive Personality Traits What role does the intergenerational transmission of personality and locus of control play in generating intergenerational persistence in education, family status, income and health? How do the personality traits of parents and children, and how they interact, influence the extent and quality of intergenerational relationships and the prevalence of assistance across generations? Decision-Making, Expectations, and Risk Preferences Do intergenerational correlations in risk preferences represent intergenerational transmission of preferences? If so, are the transmission mechanisms a factor in biological and environmental vulnerabilities? Does the extent of genetic liability vary in response to both family-specific and generation-specific environmental pressures? Family Support, Relationship Quality and Ties of Obligation How does family complexity affect intergenerational obligations and the strength of relationship ties? As parents near retirement: What roles do they play in their children's lives and their children in their lives? What assistance are they providing to their adult children and grandchildren? What do they receive in return? And how do these ties vary with divorce, remarriage and familial estrangement? Economic Status and Capacities What are the economic capacities of the parents' generation as they reach their retirement years? How have fared through the wealth and employment shocks of the Great Recession? Are parents able to provide for their own financial need? And, do they have the time and financial resources to help support their children and grandchildren and are they prepared to do so?

Longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States during the 1994-95 school year. Public data on about 21,000 people first surveyed in 1994 are available on the first phases of the study, as well as study design specifications. It also includes some parent and biomarker data. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents social, economic, psychological and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. The fourth wave of interviews expanded the collection of biological data in Add Health to understand the social, behavioral, and biological linkages in health trajectories as the Add Health cohort ages through adulthood. The restricted-use contract includes four hours of free consultation with appropriate staff; after that, there''s a fee for help. Researchers can also share information through a listserv devoted to the database.

The "https://addhealth.cpc.unc.edu/" Target="_blank">National Longitudinal Study of Adolescent to Adult Health (Add Health) is a longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32*. Add Health combines longitudinal survey data on respondents' social, economic, psychological and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. The fourth wave of interviews expanded the collection of biological data in Add Health to understand the social, behavioral, and biological linkages in health trajectories as the Add Health cohort ages through adulthood. The fifth wave of data collection is planned to begin in 2016.

Initiated in 1994 and supported by three program project grants from the "https://www.nichd.nih.gov/" Target="_blank">Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) with co-funding from 23 other federal agencies and foundations, Add Health is the largest, most comprehensive longitudinal survey of adolescents ever undertaken. Beginning with an in-school questionnaire administered to a nationally representative sample of students in grades 7-12, the study followed up with a series of in-home interviews conducted in 1995, 1996, 2001-02, and 2008. Other sources of data include questionnaires for parents, siblings, fellow students, and school administrators and interviews with romantic partners. Preexisting databases provide information about neighborhoods and communities.

Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health, and Waves I and II focus on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants have aged into adulthood, however, the scientific goals of the study have expanded and evolved. Wave III, conducted when respondents were between 18 and 26** years old, focuses on how adolescent experiences and behaviors are related to decisions, behavior, and health outcomes in the transition to adulthood. At Wave IV, respondents were ages 24-32* and assuming adult roles and responsibilities. Follow up at Wave IV has enabled researchers to study developmental and health trajectories across the life course of adolescence into adulthood using an integrative approach that combines the social, behavioral, and biomedical sciences in its research objectives, design, data collection, and analysis.

* 52 respondents were 33-34 years old at the time of the Wave IV interview.
** 24 respondents were 27-28 years old at the time of the Wave III interview.

The Wave III public-use data are helpful in analyzing the transition between adolescence and young adulthood. Included in this dataset are data on current pregnancies.

The "https://addhealth.cpc.unc.edu/" Target="_blank">National Longitudinal Study of Adolescent to Adult Health (Add Health) is a longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32*. Add Health combines longitudinal survey data on respondents' social, economic, psychological and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. The fourth wave of interviews expanded the collection of biological data in Add Health to understand the social, behavioral, and biological linkages in health trajectories as the Add Health cohort ages through adulthood. The fifth wave of data collection is planned to begin in 2016.

Initiated in 1994 and supported by three program project grants from the "https://www.nichd.nih.gov/" Target="_blank">Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) with co-funding from 23 other federal agencies and foundations, Add Health is the largest, most comprehensive longitudinal survey of adolescents ever undertaken. Beginning with an in-school questionnaire administered to a nationally representative sample of students in grades 7-12, the study followed up with a series of in-home interviews conducted in 1995, 1996, 2001-02, and 2008. Other sources of data include questionnaires for parents, siblings, fellow students, and school administrators and interviews with romantic partners. Preexisting databases provide information about neighborhoods and communities.

Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health, and Waves I and II focus on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants have aged into adulthood, however, the scientific goals of the study have expanded and evolved. Wave III, conducted when respondents were between 18 and 26** years old, focuses on how adolescent experiences and behaviors are related to decisions, behavior, and health outcomes in the transition to adulthood. At Wave IV, respondents were ages 24-32* and assuming adult roles and responsibilities. Follow up at Wave IV has enabled researchers to study developmental and health trajectories across the life course of adolescence into adulthood using an integrative approach that combines the social, behavioral, and biomedical sciences in its research objectives, design, data collection, and analysis.

* 52 respondents were 33-34 years old at the time of the Wave IV interview.
** 24 respondents were 27-28 years old at the time of the Wave III interview.

The Wave III public-use data are helpful in analyzing the transition between adolescence and young adulthood. Included in this dataset is the in-home questionnaire data.

PATRON is a human ethics approved program of research incorporating an enduring de-identified repository of Primary Care data facilitating research and knowledge generation. PATRON is a part of the 'Data for Decisions' initiative of the Department of General Practice, University of Melbourne. 'Data for Decisions' is a research initiative in partnership with general practices. It is an exciting undertaking that makes possible primary care research projects to increase knowledge and improve healthcare practices and policy. Principal Researcher: Jon EmeryData Custodian: Lena SanciData Steward: Douglas BoyleManager: Rachel CanawayMore information about Data for Decisions and utilising PATRON data is available from the Data for Decisions website.

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D

All credit for variables in AHRQ_included_variables.csv is attributed to

• AHRF: Area Health Resources Files (AHRF) 2009-2018. US Department of Health and
  Human Services, Health Resources and Services Administration, Bureau of Health Workforce,
  Rockville, MD.
• AHRQ SDOH Database (v1), from American Hospital Association Annual Survey Data,
  https://www.ahadata.com/,
• AHRQ SDOH Database (v1), from Health Resources and Services Administration. (2021, Nov 1).
  Area Health Resources Files [Database]. U.S. Department of Health and Human Services, Health
  Resources and Services Administration, Bureau of Health Workforce.
  https://data.hrsa.gov/data/download.
• AHRQ SDOH Database (v1), from the Foundation for AIDS Research. Opioid & Health Indicators
  Database. https://opioid.amfar.org/about/download
• AHRQ SDOH Database (v1), from United States Census Bureau. (n.d.). County Adjacency File
  [Data file]. https://www.census.gov/geographies/reference-files/2010/geo/countyadjacency.html.
• AHRQ SDOH Database (v1), from United States Census Bureau. County Business Patterns [Data
  series]. U.S. Department of Commerce, Bureau of the Census, Data User Services Division.
  https://www.census.gov/programs-surveys/cbp/data/tables.html.
• AHRQ SDOH Database (v1), from Local Education Agency (School District) Finance Survey (F-33)
  Data, v.1a—Provisional Common Core of Data (CCD) - CCD Data Files (ed.gov). Retrieved March
  2021 from https://nces.ed.gov/ccd/files.asp#Fiscal:1,LevelId:5,Page:1.
• AHRQ SDOH Database (v1), from Centers for Disease Control and Prevention, Division for Heart
  Disease and Stroke Prevention, Interactive Atlas of Heart Disease and Stroke,
  http://nccd.cdc.gov/DHDSPAtlas.
• AHRQ SDOH Database (v1), from Centers for Disease Control and Prevention. NCHHSTP
  AtlasPlus. Updated 2017. https://www.cdc.gov/nchhstp/atlas/index.htm.
• AHRQ SDOH Database (v1), from PLACES: Local Data for Better Health. Center for Disease
  Control and Prevention (2020 and 2021 Release). Retrieved January 2022 from:
  https://chronicdata.cdc.gov/browse?category=500+Cities+%26+Places&sortBy=newest&utf8.
• AHRQ SDOH Database (v1), from Centers for Disease Control and Prevention, National Center for
  Health Statistics. Multiple Cause of Death, 1999–2019, in CDC WONDER Online Database. (2021).
  Data are from the Multiple Cause of Death Files, 1999–2019, as compiled from data provided by
  the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program.
  http://wonder.cdc.gov/mcd-icd10.html.
• United States Census Bureau. Cartographic Boundary Files – Shapefile.
  https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html.
• AHRQ SDOH Database (v1), from Robert Wood Johnson Foundation, & University of Wisconsin
  Population Health Institute. (2020). County Health Rankings & Roadmaps.
  https://www.countyhealthrankings.org.
• AHRQ SDOH Database (v1), from U.S. Department of Education. (n.d.). Civil Rights Data
  Collection. Retrieved September 2021, from https://ocrdata.ed.gov.
• AHRQ SDOH Database (v1), from Community Resilience Estimates Social, Economic, and
  Housing Statistics Division U.S. Census Bureau. Retrieved September 2021 from
  https://www.census.gov/programs-surveys/community-resilience-estimates.html.
• AHRQ SDOH Database (v1), from United States Environmental Protection Agency. Outdoor Air
  Quality Data. Air Quality Statistics Report. Retrieved March 2022 from:
  https://www.epa.gov/outdoor-air-quality-data/air-quality-statistics-report
• AHRQ SDOH Database (v1), from United States Environmental Protection Agency (EPA) National
  Walkability Index (2021). Retrieved June 2021 from: https://catalog.data.gov/dataset/walkabilityindex.
• AHRQ SDOH Database (v1), from U.S. Department of Agriculture, Economic Research Service.
  2010 Rural-Urban Commuting Area codes, from https://www.ers.usda.gov/data-products/ruralurban-commuting-area-codes.
• AHRQ SDOH Database (v1), from Economic Research Service (ERS), U.S. Department of
  Agriculture (USDA). Food Access Research Atlas, Retrieved November 2021 from
  https://www.ers.usda.gov/data-products/food-access-research-atlas/
• AHRQ SDOH Database (v1), from U.S. Department of Agriculture (USDA), Economic Research
  Service (ERS) 2020. Food Environment Atlas. Retrieved September 2021 from:
  https://www.ers.usda.gov/data-products/food-environment-atlas/data-access-anddocumentation-downloads/.
• AHRQ SDOH Database (v1), from LTCFocus Public Use Data sponsored by the National Institute
  on Aging (P01 AG027296) through a cooperative agreement with the Brown University School of
  Public Health. Available at www.ltcfocus.org. https://doi.org/10.26300/h9a2-2c26.
• AHRQ SDOH Database (v1), from Centers for Medicare & Medicaid Services. (n.d.). Home Health
  Compare. https://www.medicare.gov/nursinghomecompare/search.html.
• AHRQ SDOH Database (v1), from the Department of Homeland Security. Homeland Infrastructure
  Foundation-Level Data (HIFLD)—Urgent Care Facilities. 2018. Available: https://hifldgeoplatform.opendata.arcgis.com/datasets/urgent-care-facilities.
• AHRQ SDOH Database (v1), from the Indian Health Service. IHS facilities. 2021. Available:
  https://www.ihs.gov/locations/.
• AHRQ SDOH Database (v1), from the Centers for Medicare & Medicaid Services. (2020, June). MA
  State/County Penetration, from https://www.cms.gov/Research-Statistics-DataandSystems/Statistics-Trends-and-Reports/MCRAdvPartDEnrolData/MA-State-CountyPenetration.
• AHRQ SDOH Database (v1), from the Centers for Medicare & Medicaid Services. (2020, June).
  Medicare Geographic Variation Public Use File, from https://www.cms.gov/Research-StatisticsData-and-Systems/Statistics-Trends-and-Reports/Medicare-Geographic-Variation/GV_PUF.
• AHRQ SDOH Database (v1), from the Centers for Disease Control and Prevention; U.S.
  Department of Health and Human Services; Agency for Toxic Substances and Disease Registry.
  Minority Health Social Vulnerability Index 2018. Retrieved July 2021 from
  https://www.minorityhealth.hhs.gov/minority-health-svi/.
• AHRQ SDOH Database (v1), from the Centers for Medicare & Medicaid Services, Office of Minority
  Health. (2020). Medicare Mapping Disparities Tool, from https://data.cms.gov/tools/mappingmedicare-disparities-by-population.
• AHRQ SDOH Database (v1), from the Health Resources and Services Administration. (2021).
  Medically Underserved Areas, from https://data.hrsa.gov/data/download.
• AHRQ SDOH Database (v1), from Ingram, D. D., & Franco, S. J. (2014, April). 2013 NCHS urban–
  rural classification scheme for counties. Vital and Health Statistics. Series 2, Data Evaluation and
  Methods Research. Number 166, 1–73. https://pubmed.ncbi.nlm.nih.gov/24776070.
• AHRQ SDOH Database (v1), from the Centers for Disease Control and Prevention. (2020).
  National Environmental Public Health Tracking Network from https://www.cdc.gov/ephtracking.
• AHRQ SDOH Database (v1), from the Centers for Medicare & Medicaid Services. (n.d.). Nursing
  Home Compare. https://www.medicare.gov/nursinghomecompare/search.html.
• Vose, R. S., Applequist, S., Squires, M., Durre, I., Menne, M. J., Williams, C. N., Jr., Fenimore, C.,
  Gleason, K., & Arndt, D. (2014). Improved Historical Temperature and Precipitation Time Series for
  U.S. Climate Divisions, Journal of Applied Meteorology and Climatology, 53(5), 1232-1251.
  Retrieved Oct 29, 2021, from https://journals.ametsoc.org/view/journals/apme/53/5/jamc-d-13-
  0248.1.xml.
  AHRQ SDOH Database (v1), from Vose, Russell S.; Applequist, Scott; Squires, Mike; Durre, Imke;
  Menne, Matthew J.; Williams, Claude N., Jr.; Fenimore, Chris; Gleason, Karin; Arndt, Derek (2014):
  NOAA Monthly U.S. Climate Divisional Database (NClimDiv). NOAA National Climatic Data Center.
  doi:10.7289/V5M32STR. Retrieved from: https://www.ncei.noaa.gov/data/climdiv/access/.
• AHRQ SDOH Database (v1), from National Center for Environmental Information (NCEI), National
  Environmental Satellite, Data and Information Service (NESDIS) and National Oceanic and
  Atmospheric Administration (NOAA), Storm Events Database 2009-2020 Retrieved August 2021
  from https://www.ncei.noaa.gov/pub/data/swdi/stormevents/csvfiles/.
• AHRQ SDOH Database (v1), from U.S. Census Bureau, Opportunity Insights. Opportunity Atlas [All
  Outcomes by Census Tract Race, Gender, and Parental Income Percentile, All Outcomes by
  County Race, Gender, and Parental Income Percentile] Retrieved October 2021 from:
  https://www.c
• AHRQ SDOH Database (v1), from National Center for Education Statistics. U.S. Skills Map: State
  and County Indicators of Adult Literacy and Numeracy. Retrieved March 2021 from:
  https://nces.ed.gov/surveys/piaac/skillsmap/.
• AHRQ SDOH Database (v1), from Centers for Medicare & Medicaid Services. (n.d.). Physician
  Compare. https://data.cms.gov/provider-data/topics/doctors-clinicians.
• AHRQ SDOH Database (v1), from Centers for Medicare & Medicaid Services. (n.d.). Provider of
  Services File. https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-PublicUse-Files/Provider-of-Services.
• AHRQ SDOH Database (v1), from Small Area Health Insurance Estimates. (n.d.). U.S. Census
  Bureau. https://www.census.gov/data/datasets/time-series/demo/sahie/estimates-acs.html
• AHRQ SDOH Database (v1), from Reardon, S. F., Ho, A. D., Shear, B. R., Fahle, E. M., Kalogrides, D.,
  Jang, H., &

Andorra AD: Current Health Expenditure: % of GDP data was reported at 7.928 % in 2023. This records an increase from the previous number of 7.537 % for 2022. Andorra AD: Current Health Expenditure: % of GDP data is updated yearly, averaging 6.786 % from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 8.787 % in 2020 and a record low of 4.923 % in 2007. Andorra AD: Current Health Expenditure: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Andorra – Table AD.World Bank.WDI: Social: Health Statistics. Level of current health expenditure expressed as a percentage of GDP. Estimates of current health expenditures include healthcare goods and services consumed during each year. This indicator does not include capital health expenditures such as buildings, machinery, IT and stocks of vaccines for emergency or outbreaks.;World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database). The data was retrieved on April 4, 2025.;Weighted average;

The Health Insurance Marketplace Public Use Files contain data on health and dental plans offered to individuals and small businesses through the US Health Insurance Marketplace.

Exploration Ideas

To help get you started, here are some data exploration ideas:

• How do plan rates and benefits vary across states?
• How do plan benefits relate to plan rates?
• How do plan rates vary by age?
• How do plans vary across insurance network providers?

See this forum thread for more ideas, and post there if you want to add your own ideas or answer some of the open questions!

Data Description

This data was originally prepared and released by the Centers for Medicare & Medicaid Services (CMS). Please read the CMS Disclaimer-User Agreement before using this data.

Here, we've processed the data to facilitate analytics. This processed version has three components:

1. Original versions of the data

The original versions of the 2014, 2015, 2016 data are available in the "raw" directory of the download and "../input/raw" on Kaggle Scripts. Search for "dictionaries" on this page to find the data dictionaries describing the individual raw files.

2. Combined CSV files that contain

In the top level directory of the download ("../input" on Kaggle Scripts), there are six CSV files that contain the combined at across all years:

• BenefitsCostSharing.csv
• BusinessRules.csv
• Network.csv
• PlanAttributes.csv
• Rate.csv
• ServiceArea.csv

Additionally, there are two CSV files that facilitate joining data across years:

• Crosswalk2015.csv - joining 2014 and 2015 data
• Crosswalk2016.csv - joining 2015 and 2016 data

3. SQLite database

The "database.sqlite" file contains tables corresponding to each of the processed CSV files.

The code to create the processed version of this data is available on GitHub.

Andorra AD: Domestic Private Health Expenditure Per Capita: Current Price data was reported at 0.001 USD mn in 2023. This records an increase from the previous number of 0.001 USD mn for 2022. Andorra AD: Domestic Private Health Expenditure Per Capita: Current Price data is updated yearly, averaging 0.001 USD mn from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 0.001 USD mn in 2023 and a record low of 0.000 USD mn in 2000. Andorra AD: Domestic Private Health Expenditure Per Capita: Current Price data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Andorra – Table AD.World Bank.WDI: Social: Health Statistics. Current private expenditures on health per capita expressed in current US dollars. Domestic private sources include funds from households, corporations and non-profit organizations. Such expenditures can be either prepaid to voluntary health insurance or paid directly to healthcare providers.;World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database). The data was retrieved on April 4, 2025.;Weighted average;

Earlier this year, Dr. Hoffman and Dr. Fafard published a book chapter on the efficacy and legality of border closures enacted by governments in response to changing COVID-19 conditions. The authors concluded border closures are at best, regarded as powerful symbolic acts taken by governments to show they are acting forcefully, even if the actions lack an epidemiological impact and breach international law. This COVID-19 travel restriction project was developed out of a necessity and desire to further examine the empirical implications of border closures. The current dataset contains bilateral travel restriction information on the status of 179 countries between 1 January 2020 and 8 June 2020. The data was extracted from the ‘international controls’ column from the Oxford COVID-19 Government Response Tracker (OxCGRT). The data in the ‘international controls’ column outlined a country’s change in border control status, as a response to COVID-19 conditions. Accompanying source links were further verified through random selection and comparison with external news sources. Greater weight is given to official national government sources, then to provincial and municipal news-affiliated agencies. The database is presented in matrix form for each country-pair and date. Subsequently, each cell is represented by datum Xdmn and indicates the border closure status on date d by country m on country n. The coding is as follows: no border closure (code = 0), targeted border closure (= 1), and a total border closure (= 99). The dataset provides further details in the ‘notes’ column if the type of closure is a modified form of a targeted closure, either as a land or port closure, flight or visa suspension, or a re-opening of borders to select countries. Visa suspensions and closure of land borders were coded separately as de facto border closures and analyzed as targeted border closures in quantitative analyses. The file titled ‘BTR Supplementary Information’ covers a multitude of supplemental details to the database. The various tabs cover the following: 1) Codebook: variable name, format, source links, and description; 2) Sources, Access dates: dates of access for the individual source links with additional notes; 3) Country groups: breakdown of EEA, EU, SADC, Schengen groups with source links; 4) Newly added sources: for missing countries with a population greater than 1 million (meeting the inclusion criteria), relevant news sources were added for analysis; 5) Corrections: external news sources correcting for errors in the coding of international controls retrieved from the OxCGRT dataset. At the time of our study inception, there was no existing dataset which recorded the bilateral decisions of travel restrictions between countries. We hope this dataset will be useful in the study of the impact of border closures in the COVID-19 pandemic and widen the capabilities of studying border closures on a global scale, due to its interconnected nature and impact, rather than being limited in analysis to a single country or region only. Statement of contributions: Data entry and verification was performed mainly by GL, with assistance from MJP and RN. MP and IW provided further data verification on the nine countries purposively selected for the exploratory analysis of political decision-making.

MIMIC-III is a large, freely-available database comprising deidentified health-related data associated with over forty thousand patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. The database includes information such as demographics, vital sign measurements made at the bedside (~1 data point per hour), laboratory test results, procedures, medications, caregiver notes, imaging reports, and mortality (including post-hospital discharge).MIMIC supports a diverse range of analytic studies spanning epidemiology, clinical decision-rule improvement, and electronic tool development. It is notable for three factors: it is freely available to researchers worldwide; it encompasses a diverse and very large population of ICU patients; and it contains highly granular data, including vital signs, laboratory results, and medications.

The dataset, Survey-SR, provides the nutrient data for assessing dietary intakes from the national survey What We Eat In America, National Health and Nutrition Examination Survey (WWEIA, NHANES). Historically, USDA databases have been used for national nutrition monitoring (1). Currently, the Food and Nutrient Database for Dietary Studies (FNDDS) (2), is used by Food Surveys Research Group, ARS, to process dietary intake data from WWEIA, NHANES. Nutrient values for FNDDS are based on Survey-SR. Survey-SR was referred to as the "Primary Data Set" in older publications. Early versions of the dataset were composed mainly of commodity-type items such as wheat flour, sugar, milk, etc. However, with increased consumption of commercial processed and restaurant foods and changes in how national nutrition monitoring data are used (1), many commercial processed and restaurant items have been added to Survey-SR. The current version, Survey-SR 2013-2014, is mainly based on the USDA National Nutrient Database for Standard Reference (SR) 28 (2) and contains sixty-six nutrientseach for 3,404 foods. These nutrient data will be used for assessing intake data from WWEIA, NHANES 2013-2014. Nutrient profiles were added for 265 new foods and updated for about 500 foods from the version used for the previous survey (WWEIA, NHANES 2011-12). New foods added include mainly commercially processed foods such as several gluten-free products, milk substitutes, sauces and condiments such as sriracha, pesto and wasabi, Greek yogurt, breakfast cereals, low-sodium meat products, whole grain pastas and baked products, and several beverages including bottled tea and coffee, coconut water, malt beverages, hard cider, fruit-flavored drinks, fortified fruit juices and fruit and/or vegetable smoothies. Several school lunch pizzas and chicken products, fast-food sandwiches, and new beef cuts were also added, as they are now reported more frequently by survey respondents. Nutrient profiles were updated for several commonly consumed foods such as cheddar, mozzarella and American cheese, ground beef, butter, and catsup. The changes in nutrient values may be due to reformulations in products, changes in the market shares of brands, or more accurate data. Examples of more accurate data include analytical data, market share data, and data from a nationally representative sample. Resources in this dataset:Resource Title: USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES 2013-14 (Survey SR 2013-14). File Name: SurveySR_2013_14 (1).zipResource Description: Access database downloaded on November 16, 2017. US Department of Agriculture, Agricultural Research Service, Nutrient Data Laboratory. USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES (Survey-SR), October 2015. Resource Title: Data Dictionary. File Name: SurveySR_DD.pdf

Abstract

The 2015-16 Armenia Demographic and Health Survey (2015-16 ADHS) is the fourth in a series of nationally representative sample surveys designed to provide information on population and health issues. It is conducted in Armenia under the worldwide Demographic and Health Surveys program. Specifically, the objective of the 2015-16 ADHS is to provide current and reliable information on fertility and abortion levels, marriage, sexual activity, fertility preferences, awareness and use of family planning methods, breastfeeding practices, nutritional status of young children, childhood mortality, maternal and child health, domestic violence against women, child discipline, awareness and behavior regarding AIDS and other sexually transmitted infections (STIs), and other health-related issues such as smoking, tuberculosis, and anemia. The survey obtained detailed information on these issues from women of reproductive age and, for certain topics, from men as well.

The 2015-16 ADHS results are intended to provide information needed to evaluate existing social programs and to design new strategies to improve the health of and health services for the people of Armenia. Data are presented by region (marz) wherever sample size permits. The information collected in the 2015-16 ADHS will provide updated estimates of basic demographic and health indicators covered in the 2000, 2005, and 2010 surveys.

The long-term objective of the survey includes strengthening the technical capacity of major government institutions, including the NSS. The 2015-16 ADHS also provides comparable data for longterm trend analysis because the 2000, 2005, 2010, and 2015-16 surveys were implemented by the same organization and used similar data collection procedures. It also adds to the international database of demographic and health–related information for research purposes.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-49

Universe

The survey covered all de jure household members (usual residents), children age 0-4 years, women age 15-49 years and men age 15-49 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was designed to produce representative estimates of key indicators at the national level, for Yerevan, and for total urban and total rural areas separately. Many indicators can also be estimated at the regional (marz) level.

The sampling frame used for the 2015-16 ADHS is the Armenia Population and Housing Census, which was conducted in Armenia in 2011 (APHC 2011). The sampling frame is a complete list of enumeration areas (EAs) covering the whole country, a total number of 11,571 EAs, provided by the National Statistical Service (NSS) of Armenia, the implementing agency for the 2015-16 ADHS. This EA frame was created from the census data base by summarizing the households down to EA level. A representative probability sample of 8,749 households was selected for the 2015-16 ADHS sample. The sample was selected in two stages. In the first stage, 313 clusters (192 in urban areas and 121 in rural areas) were selected from a list of EAs in the sampling frame. In the second stage, a complete listing of households was carried out in each selected cluster. Households were then systematically selected for participation in the survey. Appendix A provides additional information on the sample design of the 2015-16 Armenia DHS. Because of the approximately equal sample size in each marz, the sample is not self-weighting at the national level, and weighting factors have been calculated, added to the data file, and applied so that results are representative at the national level.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Five questionnaires were used for the 2015-16 ADHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, the Biomarker Questionnaire, and the Fieldworker Questionnaire. These questionnaires, based on The DHS Program’s standard Demographic and Health Survey questionnaires, were adapted to reflect the population and health issues relevant to Armenia. Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organizations, and international donors. After all questionnaires were finalized in English, they were translated into Armenian. They were pretested in September-October 2015.

Cleaning operations

The processing of the 2015-16 ADHS data began shortly after fieldwork commenced. All completed questionnaires were edited immediately by field editors while still in the field and checked by the supervisors before being dispatched to the data processing center at the NSS central office in Yerevan. These completed questionnaires were edited and entered by 15 data processing personnel specially trained for this task. All data were entered twice for 100 percent verification. Data were entered using the CSPro computer package. The concurrent processing of the data was an advantage because the senior ADHS technical staff were able to advise field teams of problems detected during the data entry. In particular, tables were generated to check various data quality parameters. Moreover, the double entry of data enabled easy comparison and identification of errors and inconsistencies. As a result, specific feedback was given to the teams to improve performance. The data entry and editing phase of the survey was completed in June 2016.

Response rate

A total of 8,749 households were selected in the sample, of which 8,205 were occupied at the time of the fieldwork. The main reason for the difference is that some of the dwelling units that were occupied during the household listing operation were either vacant or the household was away for an extended period at the time of interviewing. The number of occupied households successfully interviewed was 7,893, yielding a household response rate of 96 percent. The household response rate in urban areas (96 percent) was nearly the same as in rural areas (97 percent).

In these households, a total of 6,251 eligible women were identified; interviews were completed with 6,116 of these women, yielding a response rate of 98 percent. In one-half of the households, a total of 2,856 eligible men were identified, and interviews were completed with 2,755 of these men, yielding a response rate of 97 percent. Among men, response rates are slightly lower in urban areas (96 percent) than in rural areas (97 percent), whereas rates for women are the same in urban and in rural areas (98 percent).

The 2015-16 ADHS achieved a slightly higher response rate for households than the 2010 ADHS (NSS 2012). The increase is only notable for urban households (96 percent in 2015-16 compared with 94 percent in 2010). Response rates in all other categories are very close to what they were in 2010.

Sampling error estimates

SAS computer software were used to calculate sampling errors for the 2015-16 ADHS. The programs used the Taylor linearization method of variance estimation for means or proportions and the Jackknife repeated replication method for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Nutritional status of children based on the NCHS/CDC/WHO International Reference Population - Vaccinations by background characteristics for children age 18-29 months

See details of the data quality tables in Appendix C of the survey final report.

This noise database was developed to provide researchers and other interested stakeholders with noise measurement results that the National Institute for Occupational Safety and Health (NIOSH) has collected during health hazard evaluation (HHE) surveys from 1996 through 2013. HHEs are requested by employees or their representatives, or employers, to help learn whether health hazards are present at their workplace. The scope of HHEs varies based on the requestors’ concerns and the NIOSH project officers’ professional judgment. Only noise measurement results are included in this database; however, many HHEs include evaluation of exposures other than noise. Individual HHE reports are published on the NIOSH website. When available, the database provides a direct link to the HHE report for each of the noise measurement results.

The noise database contains workplace noise measurement results from 77 HHE reports, including over 808 personal noise exposure measurements and 582 area noise measurements. It also includes the following information: U.S. state or territory; Occupational Safety and Health Administration (OSHA) region; National Occupational Research Agenda (NORA) sector; North American Industry Classification System (NAICS) code; facility description; type of dosimeter or sound level meter used; whether a hearing conservation program was in place; whether a hearing protection was used; whether octave band data was collected; job title; noise-generating activities; location of noise measurements; start and end date for site visit; type (full-shift, partial-shift, or task-based) and duration of noise measurement; type of noise (continuous, impulsive, or intermittent); exposure to ototoxic chemicals; and results in decibels A-weighted (dBA) and percent dose according to OSHA and NIOSH noise measurement criteria. This database is an ongoing project and will be updated at least yearly to add the most recent HHE noise measurement data.

Andorra AD: Domestic Private Health Expenditure: % of Current Health Expenditure data was reported at 28.879 % in 2023. This records an increase from the previous number of 26.534 % for 2022. Andorra AD: Domestic Private Health Expenditure: % of Current Health Expenditure data is updated yearly, averaging 29.563 % from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 40.461 % in 2006 and a record low of 25.840 % in 2010. Andorra AD: Domestic Private Health Expenditure: % of Current Health Expenditure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Andorra – Table AD.World Bank.WDI: Social: Health Statistics. Share of current health expenditures funded from domestic private sources. Domestic private sources include funds from households, corporations and non-profit organizations. Such expenditures can be either prepaid to voluntary health insurance or paid directly to healthcare providers.;World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database). The data was retrieved on April 4, 2025.;Weighted average;

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Since 2003, this office has commissioned the Institute of Food Science and Technology to carry out the promotion and guidance plan for health functional foods. Through activities such as establishing the "Health Food Industry Service Network", setting up a health food consultation window, holding practical technology seminars, and issuing newsletters, the office aims to establish a health food industry service platform. It also combines the research and development capabilities of domestic research institutions to develop health food materials, establish health food specifications and testing methods, conduct expert interviews and technical guidance for health food business operators, and hopes to promote the advancement of the health food industry, increase the competitiveness of domestic health food exports, and effectively increase the output value of the health food industry. In line with the government's promotion of open data measures, this office is now opening the "Health Food Industry Service Network" dataset (http://www.functionalfood.org.tw/), providing basic information of domestic health food manufacturers, including company names, addresses, contact information, and website, for everyone to use.

AimTo compare the risks of hospitalization for heart failure (HHF) associated with sulfonylurea (SU), dipeptidyl peptidase-4 inhibitor (DPP-4i), and thiazolidinedione (TZD) as add-on medications to metformin (MET) therapy using the data of Korean adults with type-2 diabetes from the Korean National Health Insurance database.MethodsWe identified 98,383 people who received SU (n = 42,683), DPP-4i (n = 50,310), or TZD (n = 5,390) added to initial treatment of MET monotherapy in patients with type-2 diabetes. The main outcome was the hospitalization for HHF. Hazard ratios for HHF by type of second-line glucose-lowering medication were estimated by Cox-proportional hazard models. Sex, age, duration of MET monotherapy, Charlson Comorbidity Index and additional comorbidities, and calendar year were controlled as potential confounders.ResultsThe observed numbers (rate per 100,000 person-years) of HHF events were 1,129 (658) for MET+SU users, 710 (455) for MET+DPP-4i users, and 110 (570) for MET+TZD users. Compared to that for MET+SU users (reference group), the adjusted hazard ratios for HHF events were 0.76 (95% confidence interval 0.69–0.84) for MET+DPP-4i users and 0.96 (95% confidence interval 0.79–1.17) for MET+TZD users.ConclusionDPP-4i as an add-on therapy to MET may lower the risks of HHF compared with SU.