Downloads of Add Health require submission of the following information, which is shared with the original producer of Add Health: supervisor name, supervisor email, and reason for download. A Data Guide for this study is available as a web page and for download. The National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994-2018 [Public Use] is a longitudinal study of a nationally representative sample of U.S. adolescents in grades 7 through 12 during the 1994-1995 school year. The Add Health cohort was followed into young adulthood with four in-home interviews, the most recent conducted 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. Add Health Wave I data collection took place between September 1994 and December 1995, and included both an in-school questionnaire and in-home interview. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12, and gathered information on social and demographic characteristics of adolescent respondents, education and occupation of parents, household structure, expectations for the future, self-esteem, health status, risk behaviors, friendships, and school-year extracurricular activities. All students listed on a sample school's roster were eligible for selection into the core in-home interview sample. In-home interviews included topics such as health status, health-facility utilization, nutrition, peer networks, decision-making processes, family composition and dynamics, educational aspirations and expectations, employment experience, romantic and sexual partnerships, substance use, and criminal activities. A parent, preferably the resident mother, of each adolescent respondent interviewed in Wave I was also asked to complete an interviewer-assisted questionnaire covering topics such as inheritable health conditions, marriages and marriage-like relationships, neighborhood characteristics, involvement in volunteer, civic, and school activities, health-affecting behaviors, education and employment, household income and economic assistance, parent-adolescent communication and interaction, parent's familiarity with the adolescent's friends and friends' parents. Add Health data collection recommenced for Wave II from April to August 1996, and included almost 15,000 follow-up in-home interviews with adolescents from Wave I. Interview questions were generally similar to Wave I, but also included questions about sun exposure and more detailed nutrition questions. Respondents were asked to report their height and weight during the course of the interview, and were also weighed and measured by the interviewer. From August 2001 to April 2002, Wave III data were collected through in-home interviews with 15,170 Wave I respondents (now 18 to 26 years old), as well as interviews with their partners. Respondents were administered survey questions designed to obtain information about family, relationships, sexual experiences, childbearing, and educational histories, labor force involvement, civic participation, religion and spirituality, mental health, health insurance, illness, delinquency and violence, gambling, substance abuse, and involvement with the criminal justice system. High School Transcript Release Forms were also collected at Wave III, and these data comprise the Education Data component of the Add Health study. Wave IV in-home interviews were conducted in 2008 and 2009 when the original Wave I respondents were 24 to 32 years old. Longitudinal survey data were collected on the social, economic, psychological, and health circumstances of respondents, as well as longitudinal geographic data. Survey questions were expanded on educational transitions, economic status and financial resources and strains, sleep patterns and sleep quality, eating habits and nutrition, illnesses and medications, physical activities, emotional content and quality of current or most recent romantic/cohabiting/marriage relationships, and maltreatment during childhood by caregivers. Dates and circumstances of key life events occurring in young adulthood were also recorded, including a complete marriage and cohabitation history, full

This item contains Population Health Messaging TV Ad Data (2012 & 2016). There is a Stata 13 file and an SPSS file. These data are at the ad level, meaning each row is a unique ad. If you want information on when and where the ads aired, or the ads' video files, you'll need to purchase the ad files for the 2012 & 2016 cycles separately.

Contractual obligations with our data provider, Kantar/CMAG, necessitate that we limit access to the data to members of academic institutions using the data for academic purposes. To learn more and purchase data visit https://mediaproject.wesleyan.edu/dataaccess/.

This item is part of the project Population Health Messaging TV Ad Data (2012 & 2016).

The expenditure on digital static display advertising of the healthcare category in the Netherlands increased by **** million U.S. dollars (***** percent) in 2021 in comparison to the previous year. With ***** million U.S. dollars, the advertising spending thereby reached its highest value in the observed period. For more insights about advertising in the Netherlands: In 2021, in comparison to the ad expenditure of the healthcare category on internet, the ad expenditure of the healthcare category on magazines as well as on outdoor was considerably lower.

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;

This item contains 2013-2018 Health Insurance TV Ad Airing Data v1.0. Includes SPSS and Stata 13 files.

Contractual obligations with our data provider, Kantar/CMAG, necessitate that we limit access to the data to members of academic institutions using the data for academic purposes. To learn more and purchase data visit https://mediaproject.wesleyan.edu/dataaccess/.

This item is part of the project Health Insurance TV Advertising 2013-2018.

Longitudinal study of pre-symptomatic Alzheimer's Disease. Longitudinal data from 348 participants are available. This includes multi-modal MRI images, neuropsychological tests, neurosensory assessments, general medical history, genetics and cerebrospinal fluid proteins levels.

Andorra 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. Andorra 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. Andorra 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;

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;

Andorra AD: External Health Expenditure: % of Current Health Expenditure data was reported at 0.000 % in 2018. This stayed constant from the previous number of 0.000 % for 2017. Andorra AD: External Health Expenditure: % of Current Health Expenditure data is updated yearly, averaging 0.000 % from Dec 2000 (Median) to 2018, with 19 observations. The data reached an all-time high of 0.000 % in 2018 and a record low of 0.000 % in 2018. Andorra AD: External 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 external sources. External sources compose of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country. External sources either flow through the government scheme or are channeled through non-governmental organizations or other schemes.;World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database). The data was retrieved on April 4, 2025.;Weighted average;

In the first half of 2023, Dr. Theiss Gruppe increased its advertising spending in the health and pharmaceuticals sector in Germany by ** million euros. On the other hand, Betterlife Healthcare decreased its ad expenditure on the health and pharma industry in the country by ** million.

In 2023, the ad spending of United Healthcare Svcs in the United States was the highest on television media, reaching a value of ****** million U.S. dollars. In contrast, newspaper ranked last across all considered media, only amounting to **** million U.S. dollars. Find further statistics regarding the U.S. advertising market like ad spending of Humana Medical Plan and ad spending of the Colonial Penn Life Insurance Company.

This dataset consists of structural brain MRI scans collected to support binary classification between Alzheimer's Disease (AD) and Cognitively Normal (CN) individuals. Each subject’s brain is represented using three orthogonal 2D views derived from 3D MRI scans:

Axial View (horizontal slice)

Sagittal View (side slice)

Coronal View (front slice)

The dataset is organized into two main class folders:

AD/ – Images from subjects diagnosed with Alzheimer’s Disease

CN/ – Images from cognitively normal subjects (healthy controls)

AD/ ├── axial/ ├── sagittal/ ├── coronal/

CN/ ├── axial/ ├── sagittal/ ├── coronal/

RNA-seq sequencing data. Visit https://dataone.org/datasets/sha256%3A3beb7e1f5dde850aaf613589a726ce782a8f1369b3d6b14b768682744a4f0c9c for complete metadata about this dataset.

Dataset

This dataset was created by samik_3301

Released under MIT

Contents

Alzheimer’s disease (AD) is the most common form of dementia and a growing public health burden in the United States. Significant progress has been made in identifying genetic risk for AD, but limited studies have investigated how AD genetic risk may be associated with other disease conditions in an unbiased fashion. In this study, we conducted a phenome-wide association study (PheWAS) by genetic ancestry groups within a large academic health system using the polygenic risk score (PRS) for AD. PRS was calculated using LDpred2 with genome-wide association study (GWAS) summary statistics. Phenotypes were extracted from electronic health record (EHR) diagnosis codes and mapped to more clinically meaningful phecodes. Logistic regression with Firth’s bias correction was used for PRS phenotype analyses. Mendelian randomization was used to examine causality in significant PheWAS associations. Our results showed a strong association between AD PRS and AD phenotype in European ancestry (OR = 1.26, 95% CI: 1.13, 1.40). Among a total of 1,515 PheWAS tests within the European sample, we observed strong associations of AD PRS with AD and related phenotypes, which include mild cognitive impairment (MCI), memory loss, and dementias. We observed a phenome-wide significant association between AD PRS and gouty arthropathy (OR = 0.90, adjusted p = 0.05). Further causal inference tests with Mendelian randomization showed that gout was not causally associated with AD. We concluded that genetic predisposition of AD was negatively associated with gout, but gout was not a causal risk factor for AD. Our study evaluated AD PRS in a real-world EHR setting and provided evidence that AD PRS may help to identify individuals who are genetically at risk of AD and other related phenotypes. We identified non-neurodegenerative diseases associated with AD PRS, which is essential to understand the genetic architecture of AD and potential side effects of drugs targeting genetic risk factors of AD. Together, these findings expand our understanding of AD genetic and clinical risk factors, which provide a framework for continued research in aging with the growing number of real-world EHR linked with genetic data.

Mouse pupil videos acquired from AD mice for the paper "Identifying the bioimaging features of Alzheimer's Disease based on pupillary light response-driven brain-wide fMRI in awake mice" Use of this data should cite: ####

The EU has a longstanding commitment to support the principles on secure and adaptable employment, work-life balance and well adapted work environment. As highlighted in the Commission Communication on an EU Strategic Framework on Health and Safety at Work 2014-2020, it is necessary to improve the quality of statistical data collection on work-related accidents and diseases, occupational exposures and work-related ill-health. A repetition of the ad hoc module on accidents at work and work-related health problems in 2020, also conducted in 1999, 2007 and 2013, should make it possible to complement data transmitted by Member States on Community statistics on public health and health and safety at work, as regards statistics on accidents at work. Moreover, the repetition of this module should provide information on occupational exposure to risk factors for physical health and mental well-being.

The module is split in three submodules and includes 11 variables.

Submodule 1: Accidents at work

The first submodule has a target population of all persons aged 15 – 74 years old that are currently working or were working during the last 12 months before the reference week of the survey. It aims to provide an understanding of workplace safety and the results to enable decision makers in government, industry, business and other organisations to further reduce risks for workers' health and safety.

This submodule includes 4 variables:

• ACCIDNUM: Number of accidents at work during the last 12 months;
• ACCIDTYP: Type of accident at work;
• ACCIDJOB: Job linked to the accident;
• ACCIDBRK: Duration of absence from work because of the accident at work.

Submodule 2: Work-related health problems

The aim of the second submodule is to give another understanding of workplace health and safety on how many different health problems other than accidents (physical or mental health problems, illnesses, disabilities) persons aged 15 – 74 years old suffered from during the year before the end of the reference week, which were caused by or worsened for work.

This submodule includes 5 variables:

• HPROBNUM: Number of work-related health problems during the last 12 months;
• HPROBTYP: Type of work-related health problem;
• HPROBLIM: Health problem limiting daily activities;
• HPROBJOB: Job linked to the health problem;
• HPROBBRK: Duration of absence from work because of the work-related health problem.

Submodule 3: Risk factors for physical health and/or mental well-being

The third submodule aims to understand whether the respondent is exposed to work-related risk factors as listed in the answer categories which could affect his/her physical or mental well-being. The listed answer categories are used in the European Survey of Enterprises on New and Emerging Risks (ESENER) which looks at how European workplaces manage safety and health risks in practice.

This submodule includes 2 variables:

• PHYSRISK: Exposure to physical health risk factors;
• MENTRISK: Exposure to mental well-being risk factors.

Compared with the administrative data collection ESAW (European Statistics of Accidents at Work), the LFS AHMs 2007, 2013 and 2020 give the following additional value:

• providing information about accidents with less than four days of absence from work, as well as more information about the occurrence of road traffic accidents;
• including information about work-related health problems and risk factors for physical health and mental well-being;
• enabling the analysis of accidents and work-related health problems by LFS core variables;
• enabling a comparison of reporting levels between Member States, economic sectors and other variables.

Detailed information on the relevant methodology of the ad hoc module (including the Commission regulation and explanatory notes) as well as documentation from each participating country (national questionnaires and interviewers instructions) can be found on EU-LFS (Statistics Explained) – module.

Compared with the administrative data collection ESAW (European Statistics of Accidents at Work), the LFS AHMs 2007, 2013 and 2020 give the following additional value:

• providing information about accidents with less than four days of absence from work, as well as more information about the occurrence of road traffic accidents;
• including information about work-related health problems and risk factors for physical health and mental well-being;
• enabling the analysis of accidents and work-related health problems by LFS core variables;
• enabling a comparison of reporting levels between Member States, economic sectors and other variables.

Andorra AD: Domestic General Government Health Expenditure: % of Current Health Expenditure data was reported at 71.121 % in 2023. This records a decrease from the previous number of 73.466 % for 2022. Andorra AD: Domestic General Government Health Expenditure: % of Current Health Expenditure data is updated yearly, averaging 70.447 % from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 74.005 % in 2021 and a record low of 59.832 % in 2006. Andorra AD: Domestic General Government 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 public sources for health. Domestic public sources include domestic revenue as internal transfers and grants, transfers, subsidies to voluntary health insurance beneficiaries, non-profit institutions serving households (NPISH) or enterprise financing schemes as well as compulsory prepayment and social health insurance contributions. They do not include external resources spent by governments on health.;World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database). The data was retrieved on April 4, 2025.;Weighted average;

Comprehensive dataset containing 16 verified Medical Center businesses in AD with complete contact information, ratings, reviews, and location data.

A survey of representatives of the accident and health insurance industry in the United States found that in 2023 the sector spent *** million U.S. dollars on advertising. The industry's ad expenses stood at *** million dollars a year earlier.