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 

Alan.Marshall@manchester.ac.uk

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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