the uk data service: an introduction to data on ageing · james nazroo, kris mekli, neil pendleton...
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The UK Data Service: an introduction to data on ageing
Welcome!Some introductions……
Vanessa HigginsUK Data Service
Alan MarshallFrailty, Resilience And Inequality in Later Life (fRraill)University of Manchester
What we will cover today
• Introduction to the UK Data Service• Types of data held by the UK Data Service?• Useful resources• How to access the data• The research potential of the data - examples from fRail
• Questions
• Can be typed in, but we will wait until the end to answer them
What is the UK Data Service?
• a comprehensive resource funded
by the ESRC
• a single point of access to a wide range of secondary social science data
• support, training and guidance
ukdataservice.ac.uk
Who is it for?
• academic researchers and students
• government analysts
• charities and foundations
• business consultants
• independent research centres
• think tanks
ukdataservice.ac.uk
Our main data types• UK government surveys
• Longitudinal datasets
• Census data: census.ukdataservice.ac.uk
• Cross-national surveys
• Qualitative data
• Country level macro data from intergovernmental organisations
• Business micro data
ukdataservice.ac.uk/get-data/key-data.aspx
Examples of data on ageing…. • English Longitudinal Study of Ageing (2002)
• Other longitudinal/cohort studies e.g. National Child Development Study (1958), British Household Panel Study (1991); Understanding Society;CLOSER - www.closerprogramme.co.uk/
• Cross-sectional surveys e.g. Health Survey for England, Opinions & Lifestyle Survey
• Census data (aggregate data; flow data; Sample of Anonymised Records)
• International aggregate data e.g. World Bank data on life expectancy
• Qualitative data e.g. The Last Refuge, 1950s, Peter Townsend
• Other more specific studies e.g. Migration, nutrition and ageing across the lifecoursein Bangladeshi Families (2009-2012)
• Lots out there!!! To find data on ageing go to the Discover catalogue: discover.ukdataservice.ac.uk
Searching for data in Discover
ukdataservice.ac.uk
ukdataservice.ac.uk
Useful resources: Theme pages on Ageing
ukdataservice.ac.uk
Other useful resources - Video tutorials e.g. how to download survey data; how to
access Census aggregate data- Guides e.g. guide to weighting data; dataset guides- Case studies of research on ageing- Teaching resources- Advice for new users
• how to find data with Discover• how to register and access data• what kinds of data we hold• how you can get in touch?
- Have a query? See our help pages and FAQs
How to access the data: summary• Access to Discover does not require registration but downloading
data does
• Registration and authentication required for most data.
• However some data available without registration and authentication under Open Government Licence.
• Most data is available directly from website (more detailed data available under Special Licence and from Secure Lab).
• Data are free except for commercial usages
• Important web pages for access:• Access pages: ukdataservice.ac.uk/get-data/how-to-access.aspx• Discover catalogue: discover.ukdataservice.ac.uk/• Detailed info on Census data: census.ukdataservice.ac.uk/get-data.aspx
ukdataservice.ac.uk
Access pages on the website
ukdataservice.ac.uk
Ageing webinar
Health inequalities in later life
Frailty, resilience and inequality in later life project (fRaill)www.micra.manchester.ac.uk/research/fraill/
Dr Alan MarshallCathie Marsh Centre for Census and Survey Research
James Nazroo, Kris Mekli, Neil Pendleton Bram Vanhoutte, Gindo Tampubolon
AimsShow the research potential of data on the health and circumstances of older people across three research themes
1. Trends in the health (frailty) of older people2. Retirement and health3. National context and health care
Main data source is the English Longitudinal Study of Ageing (but I will also use the Census and the Health and Retirement Study (US))
Health inequalities in later life• Poor health is most common at the older age • Understanding health inequalities in later life crucial• Recent research suggests inequalities continue to grow
with age (Benzeval et al. 2011)• Absolute socio-economic inequalities in mortality rise
with age (Huisman 2000)• Accumulation of disadvantage over the life course• Stark inequalities at the older ages• Life expectancy at 65 is 21 years in Harrow and 14 years
in Glasgow• Ten year gap in the levels of frailty between the richest
and poorest older people in England
The English Longitudinal Study of Ageing
Panel study (5 waves of data) Sample at wave 1 (2002) was approximately 11,400 people born
before 1st March 1952 who were in the private household sector. Face to face interview every two years since 2002, with a
biomedical assessment carried out by a nurse every four years. Those incapable of doing the interview have a proxy interview. End of life interviews are carried out with the partners or carers of
people who died after wave 1. Detailed content on: demographics, health, performance,
biomarkers, wellbeing, economics, housing, employment, social relationships, social civic and cultural participation, life history.
Sister study to HRS, SHARE, KLOSA, CHARLS, etc.
Theme 1: Trends in frailty in older people
• Steady increase in life expectancy over the past century• Associated challenges such as costs of care provision• The extent of the future care challenge depends on the health
changes in the older population• Evidence on trends in healthy/disability free life expectancy is
mixed (methods, country, health measure, social class)
Research questions• Are there differences in levels and growth of frailty across
cohorts?• Do we see differences in these frailty cohort effects according
to wealth?
An average 80 year old in 2002 is more, less or equally frail compared to an average 80 year old in 2010?
Frailty
• Specific definitions and models of frailty are contested• Broad agreement that frailty is a non-specific state reflecting
age-related declines in multiple physiological systems which lead to adverse outcomes (mortality, hospitalisation)
Frailty index• Based on accumulation of ‘deficits’• Activities of Daily Living, cognitive function, chronic diseases,
CVD, depression/mental health, poor eyesight/hearing, Falls, fractures and joint replacements
• 0-1 scale for each component• Calculate the proportion of deficits held• At least 30 deficits with non-missing values
Modelling frailty trajectories by age cohort
Age
Frai
lty in
dex
70
70 year old in 2010
70 year old in 2002
Optimistic scenario:70 year olds in 2010 are less frail than 70 year olds in 2002 and are on a shallower trajectory
62
Most frail
Least frail
Level of frailty in the firstwave of ELSA (2002)
Frailty trajectories by cohort: All people
.1.2
.3.4
Mod
elle
d fra
ilty
scor
e
50 60 70 80 90Age
Frai
lty in
dex
Frail
Robust
Higher frailty in more recent cohorts (70-90)
No improvement in frailty (50-70)Frailty trajectories overlap
Age
Frailty trajectories by cohort: wealth.1
.2.3
.4P
redi
cted
frai
lty s
core
50 60 70 80 90Age
Poorest quintile Richest quintile
Frail
Frai
lty in
dex
Robust
Increase in frailty across cohorts: stronger for poorest
Poor
Rich
Summary
• Comparable levels of frailty across cohorts (ages 50-70)
• Higher levels of frailty in more recent cohorts (compared to later cohorts) over the age of 70
• Stronger increase in frailty across cohorts for poor compared to the rich
• Pessimistic outcome in the context of rising life expectancy
Interpretation• Similar findings in the US (Yang and Lee 2010)• Cohort differences may reflect improvements in medical
and care services across the life course that improve the survival probabilities for frail individuals.
• Or rises in unhealthy lifestyle choices (relating to exercise, diet)
• Social conditions appear to influence the rate of deficit accumulation in older populations.
• Stronger cohort differences for the poorest may reflect deterioration in their relative socio-economic circumstances
Theme 2: retirement and health• Does retirement have an effect on subsequent health?
• Does any ‘retirement effect’ on health vary according type of work?
• Might proposals to increase retirement age exacerbate health inequalities at older ages?
• ELSA well-suited to such questions. Detailed information on work characteristics and health and circumstances of older people
• Census enables us to look at subnational variation in patterns of self-reported illness at retirement
After retirement, do you think that an individual's self-reported health will increase, decrease or stay the same?
Does self-reported health improve after retirement?
0.2
.4.6
.8(p
p)
0 10 20 30 40 50 60 70 80 90Age
South Bucks BuryMerthyr Tydfil
Average Retirement age
Source: Census 2001
Age specific limiting long term illness rates (males)P
ropo
rtion
with
an
LLTI
Large post-retirement health improvement
No post-retirement health improvement
Modest post-retirement health improvement
Spatial inequalities in post-retirement health improvement
Source: Census(2001) and Edina
Does health improve after retirement?
0.2
.4.6
.8Ill
ness
rate
(pro
porti
on w
ith a
n ill
ness
)
0 10 20 30 40 50 60 70 80 90Age
South Bucks BuryMerthyr Tydfil
Source: Census 2001
Different populations
Post-retirement improvements in health? General population
.1.1
5.2
.25
.3Pr
opor
tion
with
LLT
I
-10 -5 0 5 10Time to retirement
RetirementObserved LLTI probabilities
Modelled LLTI probabilities
Post-retirement LLTI trajectories: NS-SECP
roba
bilit
y of
LLT
I
Time to retirement (years)
Managerial and professional Routine occupations0
.1.2
.3
-10 -5 0 5 10 -10 -5 0 5 10
Retirement and self-reported illness: summary
• Strong spatial distribution in patterns of LLTI rates at retirement (Marshall and Norman 2012)
• For individuals working in routine occupations we observe:• Faster increases in probabilities of having a limiting long term
illness in final years of employment• Levelling off in the probabilities of illness after retirement
• In line with other research Westerlund et al.(2009)
• Increasing retirement age may well exacerbate inequalities in self-assessed health at the older ages
Theme 3: Ageing in a national context
• Hypertension health care– US and England comparison– Do different health care systems lead to
different care outcomes for hypertension?
• US system is dominated by private health care provision especially under the age of 65
• England has universal health cover through the NHS
• Combine data sources (ELSA and HRS) to investigate these issues
Hypertension - background
• 1 billion people worldwide have hypertension• The condition is usually asymptomatic• Unhealthy lifestyles, increasing longevity and population
growth are linked to rises in hypertensive population• Hypertension is a key risk factor for cardiovascular
disease• Hypertension is controllable (and cheaper than
interventions to deal with subsequent health problems)
Do you think levels of uncontrolled hypertension are higher or lower in England compared to the US?
Data
• Health and Retirement Survey (US) – wave 9 (2008-9) • English Longitudinal Study of Ageing (England) – wave 4
(2008)• Representative samples of the population (aged 50+)• Data includes a nurse visit with a blood pressure
measurement • Measured blood pressure • Diagnosis of hypertension• Total hypertensive population – anyone diagnosed
with hypertension or measured with high blood pressure
Hypertension care outcomes
• Hypertensive controlled – normal measured blood pressure but either diagnosed with or being treated for hbp.
• Hypertensive uncontrolled – measured hbp and have been diagnosed with or received treated for hbp
• Hypertensive undiagnosed - measured hbp but have never been diagnosed with or received treatment for for hbp.
Model probabilities of controlled, uncontrolled and undiagnosed hypertension (US and England)
Multinomial logistic regression model probabilities. Model controls for age, gender, ethnicity BMI and wealth.
Mod
el p
roba
bilit
y
Under 65 Over 65
Model probabilities of controlled, uncontrolled and undiagnosed hypertension (US insurance group and England)
Under 65 Over 65
Multinomial logistic regression model probabilities. Model controls for age, gender, ethnicity BMI and wealth.
Mod
el p
roba
bilit
y
Model probabilities of controlled, uncontrolled and undiagnosed hypertension by wealth quintiles (Under 65s)
Mo
de
l pro
bab
ility
0.1
.2.3
.4.5
.6
1 2 3 4 5 1 2 3 4 5
England US
Controlled UncontrolledUndiagnosed
Mod
el p
roba
bilit
y
Wealth quintiles
Graphs by Country
Note : 1= least affluent wealth quintile, 5=most affluent wealth quintile
England US
Conclusions
Lower risks of undiagnosed hypertension in US compared to England? • Differences in guidelines around diagnosis and treatment
No clear advantage to private health care systems?• US private insurance group do not have better
hypertension health care relative to Government insured
Hypertension care more equitable under Governmentfunded systems
Higher levels of undiagnosed hypertension for most affluent
What about local context (neighbourhood)?
Life expectancy 69.5 (Males)
Life expectancy =85.1 (Males)
ELSA has measures of neighbourhood perception and deprivation (IMD)Marshall et al. (2014) Does the level of wealth inequality within an area influence the prevalence of depression among older people. Health and Place. 27: p194-204.
Conclusions
• Complex set of factors contribute to the health inequalities in later life
• Socio-economic circumstances, events (retirement, death of spouse, national and local contexts, earlier lifecourse circumstances)
• Mediated by genetic and metabolomic factors • Longitudinal data sources such as the English
Longitudinal Study for Ageing enable us to model health trajectories at the older ages and test causal hypothesis
• Combining sources (census, administrative statistics, harmonised longitudinal data sources in other countries) to develop deeper understandings
• Exciting time for research on ageing!
References
Benzeval, M., Green, M., Leyland, A. (2011) Do social inequalities in health widen or converge with age? Longitudinal evidence from three cohorts in the West of Scotland. BMC Public Health. 11(947): p1-11.Huisman, M., Kunst, A.E., Andersen, O., Bopp, M., Borgan, J-K., Borrell, C., Costa, G., Deboosere, P., Desplanques, G., Donkin, A., Gadeyne, S., Minder, C., Regidor, E., Spadea, T., Valkonen, T. & Mackenbach J. P. (2004). Socioeconomic inequalities in mortality among elderly people in 11 European populations. Journal of Epidemiology and Community Health, 58: 468-475.Marshall, A., Norman, P. (2013) ‘Geographies of the impact of retirement on health in the United Kingdom’. Health and Place. 20: p1-12. Marshall A., Jivraj, S., Nazroo, J., Tampubolon, G., Vanhoutte, B. (2014) Does the level of wealth inequality within an area influence the prevalence of depression amongst older people? Health and Place. 27: 194-204.Westerlund, H., Kiyimaki, M., Singh-Manoux, A., Melcior, M., Ferrie, J., Pentti, J., Jokela, J., Leineweber, C., Goldberg, M., Zins, M., Vahtera, J, (2009). Self-rated health before and after retirement in France (GAZEL): a cohort study. The Lancet 374.Yang, Y., Lee, L. (2010) Dynamics and heterogeneity in the process of human frailty and aging: evidence from the U.S. older adult population. Journal of Gerontology: Social Sciences. 65B(2): p246-255.
Questions
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