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

National Priorities List (NPL) Sites with Status Information CSV file for the EPA's Where You Live page under the Superfund web area.SourceHazard Ranking System (HRS) documentation record/NPL listing documentation. The latitude and longitude coordinates for the sites displayed in the map are also derived from HRS documentation records.

Point locations for sites in EPA Region 6 which are documented as being part of the National Priorities List as of 3/31/2023. The locations were determined by EPA Region 6 Superfund RPMs and/or site HRS Documentation reports. EPA Region 6 includes AR, LA, NM, OK, TX.

Polygenic scores (PGSs) are specific to each individual and represent an individual load for the common variants that are associated with a trait under study. They are increasingly used to predict disease risks.

PGSs have been constructed for a number of behavioural, emotional and health-related phenotypes in the English Longitudinal Study of Ageing (ELSA) study (see the main ELSA study under https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=5050"> SN 5050). ELSA is a large, multidisciplinary study of cohort of men and women living in England aged 50 or over and representative of the English population both in terms of socioeconomic profile and geographic region. The methods employed for creating PGSs are those outlined by the US https://hrs.isr.umich.edu/about"> Health and Retirement Study (HRS - not held at the UK Data Service). This was done in order to harmonise the research across age-related longitudinal studies by adopting a consistent methodology for creating PGSs. By making these PGSs publicly available, it is hoped that they will facilitate wide use among the ELSA data users.

Latest edition information:

For the second edition (November 2022), updated polygenic scores data and documentation were deposited.

BackgroundCycling is a recreational activity and mode of commuting with substantial potential to improve public health in many countries around the world. The aim of this study was to examine prospective associations between recreational and commuter cycling, changes in cycling habits, and risk of type 2 diabetes (T2D) in Danish adults from the Diet, Cancer and Health cohort study.Methods and FindingsAt baseline from 1993 to 1997, 24,623 men and 27,890 women from Denmark, 50–65 y of age and free of T2D and other chronic diseases, underwent a number of assessments, including completing a lifestyle questionnaire also addressing cycling habits. Approximately 5 y later, at a second examination, participants completed a new, updated lifestyle questionnaire. Cox regression was used to estimate hazard ratios (HRs) of incident T2D registered in the Danish National Diabetes Registry, according to recreational and commuter cycling and changes in cycling habits, with adjustment for a priori known T2D risk factors. During 743,245.4 person-years of follow-up (mean follow-up 14.2 y), 6,779 incident cases of T2D were documented. Multivariable adjusted HRs (95% confidence interval [CI]) were 1, 0.87 (0.82, 0.93), 0.83 (0.77, 0.89), 0.80 (0.74, 0.86) and 0.80 (0.74, 0.87) (p for trend = 300 min/wk of total cycling (recreational and commuter cycling), respectively. In analysis of seasonal cycling, multivariable adjusted HRs (95% CI) were 1, 0.88 (0.83, 0.94), and 0.80 (0.76, 0.85) for non-cyclists, seasonal cyclists (those cycling only in summer or winter), and those cycling during both summer and winter, respectively. How changes in total cycling from baseline to the second examination affected risk was also investigated, and multivariable adjusted HRs (95% CI) were 1, 0.88 (0.78, 1.01), 0.80 (0.69, 0.91), and 0.71 (0.65, 0.77) for non-cyclists and for those who ceased, initiated, or continued cycling between baseline and the second examination, respectively. Lastly, in the analysis of commuter cycling, multivariable HRs (95% CI) were 1, 0.72 (0.60, 0.87), 0.83 (0.69, 1.00), and 0.70 (0.57, 0.85) (p for trend = 150 min/wk to work, respectively. The main limitation of the current study is the use of self-reported physical activity.ConclusionsCommuter and recreational cycling was consistently associated with lower risk of T2D in Danish adults. Our results also provide evidence that late-in-life initiation of or continued engagement in cycling lowers risk of T2D.

Yearly citation counts for the publication titled "European Heart Rhythm Association (EHRA) consensus document on the management of supraventricular arrhythmias, endorsed by Heart Rhythm Society (HRS), Asia-Pacific Heart Rhythm Society (APHRS), and Sociedad Latinoamericana de Estimulación Cardiaca y Electrofisiologia (SOLAECE)".

The Harmonised Cognitive Assessment Protocol (HCAP) is part of the Healthy Cognitive Aging Project, a study examining how people's memory and thinking change as they get older. In England, HCAP is a sub-study of ELSA, the English Longitudinal Study of Ageing (ELSA), a longitudinal survey of ageing and quality of life among older people that explores the dynamic relationships between health and functioning, social networks and participation, and economic position as people plan for, move into and progress beyond retirement. (The main ELSA study is held under SN 5050.)

ELSA-HCAP1 took place in 2018 and interviewed ELSA core members aged 65 and over. It included a second, shorter interview with an informant, a family member or friend nominated by the ELSA core member to complete an interview on their behalf. ELSA-HCAP2 took place in 2023 and interviewed ELSA-HCAP1 sample members and additional ELSA core members aged 65 and over, and also included an informant interview.

The HCAP study originated with the Health and Retirement Study (HRS) in the United States, which is a sister study to ELSA, a longitudinal study of people aged 50 and over in the United States. Researchers on HRS developed the protocols for HCAP, in discussion with researchers from ELSA and other international studies, and fieldwork in the United States began while ELSA-HCAP in England was still in the planning stages.

The aim of ELSA-HCAP is to measure the prevalence of dementia and cognitive impairment among older people in the ELSA panel, in order to:

• Understand more about how widespread these conditions are in England and increase our understanding of dementia;
• Test how well the cognitive assessments used in this study can identify these conditions.
• Examine the 5-year change in cognitive function in the subset of respondents who take part across multiple waves of ELSA-HCAP.

HCAP scores developed by Alden Gross and colleagues - February 2024

For the third edition (February 2024), harmonised general and domain-specific cognitive scores were added from HCAP studies across six countries: China, England, India, Mexico, South Africa and the USA. The harmonised cognitive function scores have been developed by Alden Gross and colleagues. These scores empirically reflect comparable domains of cognitive function among older adults across the six countries, have high reliability and are useful for population-based research. The accompanying documentation includes a guidance file and the publication by Gross et al. (with supplement) that explains the scores and how they were derived. Each of the 1,273 participants in HCAP1 has a score on general cognitive function, executive function, language, orientation, and memory.

ELSA-HCAP2 and Family and Friends (Informant) data deposited - February 2025

For the fourth edition (February 2025), the ELSA-HCAP2 and Family and Friends (Informant) 2023 data and documentation were deposited. The ELSA-HCAP2 dataset contains 2,022 cases and the Family and Friends (Informant) dataset contains 1,807 cases. Data were collected between April to November 2023 for ELSA-HCAP2 and April to December 2023 for Family and Friends (Informants). ELSA-HCAP2 had an additional aim; to examine the 5-year change in cognitive function in the subset of respondents that took part in ELSA-HCAP1 in 2018.

RE-DEPOSIT of ELSA-HCAP2 and Family and Friends (Informant) data - August 2025

For the fifth edition (August 2025), updated versions of the HCAP2 respondent and informant datasets were deposited, with an updated version of the score variables data dictionary, and a new HCAP2 technical report. Information on the changes is provided below.

Changes in the HCAP2 respondent dataset version 2

The re-deposited version of the HCAP2 respondent dataset includes the cross-sectional and longitudinal weights. The ethnicity variable ‘fqethnmr’ has also been added along with recoded versions of the Logical Memory and Constructional Praxis test items that align with the ‘cleaned’ summary scores. Corrections have been made to the scores in ‘AT_delayed_exact’, the missing values for variables ‘TMT_A_secs’ and ‘TMT_B_secs’ have been corrected/recoded, and ‘indager’ has been recalculated to reflect respondent age at the point of sampling.

Changes in the HCAP2 informant dataset version 2

In re-deposited version of the HCAP2 informant dataset, variable ‘CSI_cogact1a_me’ has been added, corrections have been made to the derived variable ‘csi_cogact_attempt’, and a small number of cases in ‘I_educ_final’ and ‘I_freq_final’ have been recoded. Further minor changes have been made to variable and value labels and to the order of the variables in the dataset.

ChIP-Seq data for 3 mature neutrophil - G-CSF/Dex. Treatment (16-20 hrs) sample(s). 23 run(s), 23 experiment(s), 23 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

RNA-Seq data for 3 mature neutrophil - G-CSF/Dex. Treatment (16-20 hrs) sample(s). 3 run(s), 3 experiment(s), 3 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_rnaseq_analysis_crg_20150820

Abstract

The Longitudinal Aging Study in India (LASI) aims to understand the situation of India’s elderly population by collecting data on their health, social situations, and economic circumstances. It will provide a foundation for innovative, rigorous, and multidisciplinary studies of aging in India that will inform policy and advance scientific knowledge. Its goal is to provide data harmonized with the Health and Retirement Study (HRS) and its sister studies around the world. A pilot study has been conducted that includes household survey data, Computer-Assisted Personal Interviews (CAPI) and molecular biomarkers. The results of the pilot study will inform the design of a full-scale, nationally representative LASI, with a sample of roughly 30,000 to be followed longitudinally (with refresher populations added as needed). Due to its harmonized design with parallel international studies, LASI will contribute to scientific insights and policy development in other countries as well. LASI will ultimately be part of a worldwide effort aimed at understanding how different institutions, cultures, and policies can understand and prepare for population ageing.

Documentation

You can download the pilot data at the Harvard Program on the Global Demography of Aging website

Methodology

The LASI pilot survey targeted 1,600 individuals aged 45 and older and their spouses, and will inform the design and rollout of a full-scale, nationally representative LASI survey. The expectation is that LASI will be a biennial survey and will be representative of Indians aged 45 and older, with no upper age limit.

1,600 age-qualifying individuals were drawn from a stratified, multistage area probability sampling design. After a series of pre-pilot studies designed to test the instrument and the key ideas behind it, pilot data were collected through face-to-face interviews over three month time periods. Descriptive analyses of the data will be performed and lessons will be drawn to inform the launching of a full-scale LASI survey.

The LASI pilot survey was conducted in four states: Karnataka, Kerala, Punjab, and Rajasthan. To capture regional variation we have included two northern states (Punjab and Rajasthan) and two southern states (Karnataka and Kerala). Karnataka and Rajasthan were included in the Study on Global AGEing and Adult Health (SAGE), which will enable us to compare our findings with the SAGE data. The inclusion of Kerala and Punjab demonstrates our aim to obtain a broader representation of India, where geographic variations accompanied by socioeconomic and cultural differences call for careful study and deliberation. Punjab is an example of an economically developed state, while Rajasthan is relatively poor, with very low female literacy, high fertility, and persisting gender disparities. Kerala, which is known for its relatively efficient health care system, has undergone rapid social development and is included as a potential harbinger of how other Indian states might evolve.

This descriptive study identified US Medicare beneficiaries who would face financial precarity if exposed to the Medicare Part A hospital deductible ($1,600). Nationally representative estimates of financial precarity, defined as having insufficient funds to pay the Part A deductible, were examined across four scenarios that considered checking/savings account balances, total liquid assets (with a reserve for future living costs), and supplemental insurance. Disparities in financial precarity were examined by Medicare beneficiaries' race, ethnicity, and different dimensions of health status. Primary analyses were conducted using the 2018 wave of the Health and Retirement Study.This file directory provides analytic code for setting up our HRS analytic file and our statistical analyses. Please see the enclosed Readme file for documentation about the sequencing and setup of the data analyses. Data file setup and statistical analyses were performed in SAS statistical software. Readers with questions should contact Paula Chatterjee, MD, MPH (pchat@pennmedicine.upenn.edu) or Eric Roberts, PhD (eric.roberts@pennmedicine.upenn.edu).

This is a map of Superfund National Priorities List (NPL) Sites. This application allows for querying of the sites based on a site name, site EPA ID, Superfund Enterprise Management System (SEMS) ID, site status or EPA region number. EPA’s Superfund program is responsible for cleaning up some of the nation’s most contaminated land and responding to environmental emergencies, oil spills and natural disasters. For more information:Superfund: https://www.epa.gov/superfundSuperfund National Priorities List: https://www.epa.gov/superfund/superfund-national-priorities-list-nplSearch for Sites in the Superfund Enterprise Management Systems (SEMS): https://www.epa.gov/superfund/search-superfund-documentsSourceHazard Ranking System (HRS) documentation record/NPL listing documentationLayersSuperfund National Priorities List (NPL) Sites with Status InformationU.S. EPA - Regions

The English Longitudinal Study of Ageing (see the main study under SN 5050) is a longitudinal household survey for the study of health, economic position, and quality of life among the elderly.

The ELSA COVID-19 Study, Waves 1-2, 2020 (SN 8688) is a follow-up substudy based on the sample of the main ELSA study. Within the context of the Coronavirus Disease 2019 (COVID-19) outbreak, all participants for the COVID-19 substudy were selected from the existing ELSA sample to measure the socio-economic effects/psychological impact of the lockdown on the 50+ population of England. The ELSA COVID-19 substudy allows a cross-sectional analysis of the dynamics of the lockdown, enabling too the possibility to link the data collected with previous and future waves of ELSA for longitudinal analysis.

Subsequent to that, the University of Southern California (USC) "https://g2aging.org/home"> Gateway to Global Aging Data team has created the Harmonized ELSA COVID data file, along with a codebook, to facilitate cross-country comparisons across international harmonized COVID studies, including the harmonized Health and Retirement Study (HRS) COVID study and the harmonized Survey of Health, Ageing and Retirement in Europe (SHARE) COVID study. This is a separate data product which is a user-friendly version of a subset of the ELSA COVID-19 substudy. The Harmonized ELSA COVID dataset uses data from the third edition of the ELSA COVID-19 substudy, released in February 2022. It follows the conventions of variable naming and data structure first developed by the RAND Center for the Study of Aging.

The harmonized ELSA COVID data file is built using variables from the harmonized ELSA data file (SN 5050) and the ELSA COVID-19 substudy data files (SN 8688). It does not include any data which is not released under UKDS End User Licence access conditions.

November 2025 Update

For the second edition (November 2025), Version A.2 of the Gateway Harmonized ELSA COVID was deposited, which incorporates the latest released version of ELSA data. The new file contains 7,362 observations. New variables have been added, and adjustments and corrections made. The documentation has also been updated accordingly. For a full list of these please see the accompanying Gateway Harmonized ELSA COVID Documentation (pp.4-5).

RNA-Seq data for 3 mature neutrophil - G-CSF/Dex. Treatment (16-20 hrs) sample(s). 3 run(s), 3 experiment(s), 3 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_rnaseq_analysis_crg_20160816