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Dataset: English Longitudinal Study of Ageing (ELSA)

Basic Information
Dataset Full Name English Longitudinal Study of Ageing
Dataset Acronym ELSA
Summary

ELSA is a longitudinal study of a nationally representative sample of the English population over age 50. There are currently 7 waves of data spanning over 14 years. The primary purpose of the ELSA is to collect multidisciplinary, longitudinal data to enable the investigation into the causal processes and health trajectories associated with aging. The sample was drawn from respondents to the Health Survey for England (HSE). The HSE provides baseline data prior to the first wave of the ELSA study on respondents' health including details of morbidity, lifestyle, diets and blood samples. ELSA included a life history interview designed to document major life events from childhood to current time including retrospective self reported health and disability.

Key Terms

Aging, Longitudinal, Health, Disability, Health risk behaviors, Life history

Study Design Longitudinal
Data Type(s) Administrative
Clinical
Survey
Sponsoring Agency/Entity

4 independent agencies fund different parts of the study:

  • Department of Health, UK
  • Department for Work and Pensions, UK
  • Department for Transport, UK
  • National Institute on Aging, US
Health Conditions/Disability Measures
Health Condition(s)

Allergies, Alzheimer's/dementia, Anxiety disorders, Arthritis, Bipolar disorder, Blood disorder, Body mass index (BMI)/obesity, Cancer, Cardiovascular conditions, Chronic pain, Depression (CES-D), Diabetes, Epilepsy or seizure disorder, Eye diseases, Heart attack, Migraine or frequent headaches, Orthopedic conditions, Osteoporosis, Parkinson's disease, Pulmonary disorders, Stroke

Disability Measures

Ambulatory disability, Cognitive disability, Functional limitations (ADLs and/or IADLs), Hearing disability, Mental health disability, Physical disability, Special equipment use/assistive technology, Visual disability, Work limitation

Other measures/tests: Balance tests, Chair stands, 10-word list for memory evaluation, Grip strength, Chair rise, Pain, Rose Angina Scale, Timed walking speed

Measures/Outcomes of Interest
Topics
  • Health risks, Life expectancy, Health trajectories, Chronic diseases, Health care
  • Social support/participation, Social networks, Household demographics, Family structure, Expectations, Life satisfaction, Housing
  • Work, Job characteristics, Economic well-being, Income, Assets, Pensions, Retirement
Sample
Sample Population

Household population (Adults over 50 years)

Sample was drawn from households that responded to the Health Survey for England (HSE) between 1998 and 2011.

Sample Size/Notes
  • Wave 1 2002/03 Main interview (12,099)
  • Wave 2 2004/05 Main interview (9,432) Nurse visit (7,666)
  • Wave 3*  2006/07 Main interview (9,771) Life history (7,855)
  • Wave 4*  2008/09 Main interview (11,050) Nurse visit (8,643)
  • Wave 5 2010/11 Main interview (10,275) Risk module (1060 approx)
  • Wave 6* 2012/13 Main interview (10,601) Nurse visit (7,731)
  • Wave 7*  2014/15 Main interview (9,666)

* Note the sample was refreshed at waves 3, 4, 6 and 7 - not all respondents have participated since 2002.

http://www.elsa-project.ac.uk/timetable

Unit of Observation

Individual

Continent(s)

Europe

Countries

England

Geographic Coverage

National

Geographic specificity

Government Office Region

(More detailed variables can be requested directly from NatCen)

Special Population(s)

Aging/Older people

Data Collection
Data Collection Mode
  •  Computer assisted personal interviews (CAPI)
  •  Self-completed questionnaires
  •  Nurse visits at home
Years Collected

2002-present (ongoing)

Data Collection Frequency

Every 2 years

Strengths and Limitations
Strengths
  • Representative sample of the aging population in England. 
  • Can be used for longitudinal analyses on health risk factors and on the interactions of health and disability with social and economic aspects of life.
  • Objective markers of health are also included (biomarkers) 
  • Genetic data available for 7,400 participants (ELSA GWAS)
  • Some ELSA study outcome measures have been harmonized with other similar studies, such as the European SHARE and US HRS study, allowing for comparisons of outcomes and trends between different countries.
Limitations
  • Ethnic minorities are underrepresented.
  • Attrition and loss to follow up.
  • Information on earlier life medical history is self-reported and collected retrospectively.
Data Details
Primary Website

http://www.elsa-project.ac.uk/

Data Access

https://discover.ukdataservice.ac.uk/series/?sn=200011

Data Access Requirements
  • Data Use agreement, $ Cost for genetic data
  • Data Use agreement, no cost for non–genetic data
Summary Tables/reports

http://www.elsa-project.ac.uk/data_elsa

Data Components

Main components (not all were collected/available for each wave)

  • Core data
  • Nurse data
  • Index file
  • Life history data
  • Ryff self completion data
  • End of life interview data
  • Derived variables
  • Financial derived variables
  • Pension wealth derived variables

Listing of available Data by Wave:

Similar/Related Dataset(s)

Other longitudinal ageing studies:

Selected papers
Other Papers

Main Reports:

http://www.elsa-project.ac.uk/publications

Other publications:

https://discover.ukdataservice.ac.uk/catalogue/?sn=5050&type=Data%20catalogue#publications

Technical

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The Rehabilitation Research Cross-dataset Variable Catalog has been developed through the Center for Large Data Research & Data Sharing in Rehabilitation (CLDR). The Center for Large Data Research and Data Sharing in Rehabilitation involves a consortium of investigators from the University of Texas Medical Branch, Cornell University's Yang Tan Institute (YTI), and the University of Michigan. The CLDR is funded by NIH - National Institute of Child Health and Human Development, through the National Center for Medical Rehabilitation Research, the National Institute for Neurological Disorders and Stroke, and the National Institute of Biomedical Imaging and Bioengineering. (P2CHD065702).

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Acknowledgements: This tool was developed through the efforts of William Erickson and Arun Karpur, and web designers Jason Criss and Jeff Trondsen at Cornell University. Many thanks to graduate students Kyoung Jo Oh and Yeong Joon Yoon who developed much of the content used in this tool.

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