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 seven through 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 seven through 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.

Wave IV was designed to study the developmental and health trajectories across the life course of adolescence into young adulthood. Biological data was gathered in an attempt to acquire a greater understanding of pre-disease pathways, with a specific focus on obesity, stress, and health risk behavior. Included in this dataset are the Wave IV measures of inflammation and immune function.

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 partners and relationships.

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.

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

AD: Current Health Expenditure Per Capita: Current Price data was reported at 0.004 USD mn in 2023. This records an increase from the previous number of 0.003 USD mn for 2022. AD: Current Health Expenditure Per Capita: Current Price data is updated yearly, averaging 0.003 USD mn from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 0.004 USD mn in 2023 and a record low of 0.001 USD mn in 2000. AD: Current 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 expenditures on health per capita in current US dollars. Estimates of current health expenditures include healthcare goods and services consumed during each year.;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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AD: Domestic General Government Health Expenditure: % of GDP data was reported at 5.639 % in 2023. This records an increase from the previous number of 5.537 % for 2022. AD: Domestic General Government Health Expenditure: % of GDP data is updated yearly, averaging 4.790 % from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 6.377 % in 2020 and a record low of 2.957 % in 2007. AD: Domestic General Government 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. Public expenditure on health from domestic sources as a share of the economy as measured by GDP.;World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database). The data was retrieved on April 4, 2025.;Weighted average;

ObjectivesThe association between tinnitus and anxiety disorder remains debated. We used a retrospective cohort study to investigate the relationship between anxiety disorder and tinnitus, aiming to decipher possible risk factors for tinnitus in patients with anxiety disorder.MethodData on a total of 7,525 patients with anxiety disorder and 15,050 patients without (comparison cohort) were extracted from the Longitudinal Health Insurance Database 2005 in Taiwan. The Kaplan–Meier estimator with the log rank test and the Cox proportional-hazard regression model were used to compare the incidence of tinnitus in both groups and to identify risk factors that predicted tinnitus.ResultsAfter adjusting for related covariates, the hazard ratio for the development of tinnitus during the follow-up period was 3.54 (95% confidence interval: 3.11–4.02, P < .001) for anxiety disorder cohort relative to comparison cohort. Age ≧ 60 years, female sex, hypertension, and hyperlipidemia were statistically significant predictive risk factors of tinnitus in patients with anxiety disorder.ConclusionA significant increase in the lifetime incidence of tinnitus was exhibited in patients with anxiety disorder. Elderly subjects, female sex, hypertension, and hyperlipidemia were risk factors. Clinicians should be alert to the possibility of tinnitus in subjects with anxiety disorder.

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;

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.

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

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.

Scotland data added to the WHO database of 600 health/health-related indicators for over 50 countries in Europe (including UK), for 1970 to the present, where available. Data are presented in a user-friendly, graphical or tabular form, allowing time trend and international comparisons. Accompanying briefing notes provide a summary of the findings and some interpretation.

Source agency: ISD Scotland (part of NHS National Services Scotland)

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: Scotland and European HfA Database

Objective: Routinely collected health data, collected for administrative and clinical purposes, without specific a priori research questions, are increasingly used for observational, comparative effectiveness, health services research, and clinical trials. The rapid evolution and availability of routinely collected data for research has brought to light specific issues not addressed by existing reporting guidelines. The aim of the present project was to determine the priorities of stakeholders in order to guide the development of the REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. Methods: Two modified electronic Delphi surveys were sent to stakeholders. The first determined themes deemed important to include in the RECORD statement, and was analyzed using qualitative methods. The second determined quantitative prioritization of the themes based on categorization of manuscript headings. The surveys were followed by a meeting of RECO...

The Nigeria MDG (Millennium Development Goals) Information System – NMIS health facility data is collected by the Office of the Senior Special Assistant to the President on the Millennium Development Goals (OSSAP-MDGs) in partner with the Sustainable Engineering Lab at Columbia University. A rigorous, geo-referenced baseline facility inventory across Nigeria is created spanning from 2009 to 2011 with an additional survey effort to increase coverage in 2014, to build Nigeria’s first nation-wide inventory of health facility. The database includes 34,139 health facilities info in Nigeria. The goal of this database is to make the data collected available to planners, government officials, and the public, to be used to make strategic decisions for planning relevant interventions. For data inquiry, please contact Ms. Funlola Osinupebi, Performance Monitoring & Communications, Advisory Power Team, Office of the Vice President at funlola.osinupebi@aptovp.org To learn more, please visit http://csd.columbia.edu/2014/03/10/the-nigeria-mdg-information-system-nmis-takes-open-data-further/ Suggested citation: Nigeria NMIS facility database (2014), the Office of the Senior Special Assistant to the President on the Millennium Development Goals (OSSAP-MDGs) & Columbia University

The National Prosthetics Patient Database (NPPD) established a central database of Prosthetics data recorded at each Veterans Health Administration facility. Its objective was to enable clinical reviews to increase quality, reduce costs, and improve efficiency of the Prosthetics program. Increase the quality of the services to our Veterans by providing a means to develop consistency in services, review prescription and management practices, develop training, monitor Home Medical Equipment, and measure performance improvements. Reduce costs by comparing costs system-wide, identifying common items for consolidated contracting, identifying costs for Medical Cost Care Funds (MCCF) purposes and improving contracting cost benefit. Improve efficiency by validating the data, improving budget management, determining where coding errors occur, providing training, and comparing unique social security numbers for multiple site usage and item issue. The NPPD Menu provides patient information, patient eligibility, Prosthetic treatment, date of provision, cost, vendor, and purchasing agent information. This system tracks average cost data and its usage and provides on both a monthly and quarterly basis detailed and summary reports by station, Veterans Integrated Service Network (VISN) and agency. The NPPD Menu resides in Veterans Health Information Systems and Technology Architecture (VistA) at the medical center level. This data is updated quarterly. Data is rolled up at each facility and transmitted to Hines. The data is then loaded into the Corporate Data Warehouse (CDW) from which data extracts are done. The data is also put into a ProClarity cube and is available to VA local, regional, and national managers online. National managers have the ability to properly monitor, oversee and manage the national program and regional managers are able to effectively manage their respective areas using this tool. The primary purpose of this database is to provide financial and clinical oversight of the Prosthetics program and is used primarily by the Prosthetics and Sensory Aids (PSA) including VISN staff, VISN Prosthetics Representatives, Prosthetics Program Managers and other Prosthetics staff.

A database providing detailed mortality and population data to those interested in the history of human longevity. For each country, the database includes calculated death rates and life tables by age, time, and sex, along with all of the raw data (vital statistics, census counts, population estimates) used in computing these quantities. Data are presented in a variety of formats with regard to age groups and time periods. The main goal of the database is to document the longevity revolution of the modern era and to facilitate research into its causes and consequences. New data series is continually added to this collection. However, the database is limited by design to populations where death registration and census data are virtually complete, since this type of information is required for the uniform method used to reconstruct historical data series. As a result, the countries and areas included are relatively wealthy and for the most part highly industrialized. The database replaces an earlier NIA-funded project, known as the Berkeley Mortality Database. * Dates of Study: 1751-present * Study Features: Longitudinal, International * Sample Size: 37 countries or areas

Andorra AD: Out-of-Pocket Health Expenditure: % of Current Health Expenditure data was reported at 10.981 % in 2023. This records an increase from the previous number of 8.845 % for 2022. Andorra AD: Out-of-Pocket Health Expenditure: % of Current Health Expenditure data is updated yearly, averaging 13.222 % from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 18.000 % in 2007 and a record low of 8.845 % in 2022. Andorra AD: Out-of-Pocket 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 out-of-pocket payments of total current health expenditures. Out-of-pocket payments are spending on health directly out-of-pocket by households.;World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database). The data was retrieved on April 4, 2025.;Weighted average;

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;

AD: Immunization: Measles: % of Children Aged 12-23 Months data was reported at 99.000 % in 2023. This records an increase from the previous number of 98.000 % for 2022. AD: Immunization: Measles: % of Children Aged 12-23 Months data is updated yearly, averaging 98.000 % from Dec 1997 (Median) to 2023, with 27 observations. The data reached an all-time high of 99.000 % in 2023 and a record low of 90.000 % in 1998. AD: Immunization: Measles: % of Children Aged 12-23 Months 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. Child immunization, measles, measures the percentage of children ages 12-23 months who received the measles vaccination before 12 months or at any time before the survey. A child is considered adequately immunized against measles after receiving one dose of vaccine.;WHO and UNICEF (http://www.who.int/immunization/monitoring_surveillance/en/).;Weighted average;