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Age UK’s Index of Wellbeing in Later Life Age UK’s Index of Wellbeing in Later Life

Age UK’s Index of Wellbeing in Later Life - PowerPoint Presentation

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Age UK’s Index of Wellbeing in Later Life - PPT Presentation

University of Exeter Medical School 19 January 2017 Prof James Goodwin Chief Scientist Age UK Prof José Iparraguirre Chief Economist Age UK Index Rationale Data sources Process Conceptual framework ID: 550855

wellbeing analysis people variables analysis wellbeing variables people age factor principal data factors structural equation indicators score social inequality

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Slide1

Age UK’s Index of Wellbeing in Later LifeUniversity of Exeter Medical School19 January 2017

Prof James Goodwin

Chief Scientist, Age UK

Prof José Iparraguirre

Chief Economist,

Age

UKSlide2

IndexRationaleData sources Process

Conceptual framework

Data

analysis

Factor Analysis

Structural Equation Model

Principal component analysis – Domains

Inequality in Wellbeing in later life

Possible usesSlide3

RationaleSlide4

RationaleUp to now, there has been no way to measure in the round:What

is important in later life

How older people are doing

How much we love later life

Whether this is a great place to grow old(

er

)

While researchers have developed important, valid, and reliable instruments to assess different aspects of well-being… we believe that the various models have not yet been integrated into a single and coherent scale covering well-being, overall, and in the most important domains of life

.” (

Prilleltensky

et al, 2015) Slide5

RationaleAn index should:Combine multiple indicators into one single measure, but also allow for dis-aggregation

Include strata such as dimensions or indicators

Help understand inter-relationships between indicators and their direct and indirect effects on wellbeingSlide6

RationaleAge UK aims to measure how older people in the UK are doing. We are using the term wellbeing as the outcome of interest.

We need to be able to understand where and why wellbeing is low to inform our influencing activity. And

to gain

an understanding of the policy and practical levers for improving wellbeing.

Local

Age UKs need data intelligence to target their support services. We hypothesise low wellbeing is a proxy for need. Providing intelligence on where people with low wellbeing live will

help inform local services

. Slide7

2. Data sources Slide8

Data sourcesSlide9

3. ProcessSlide10

What constitutes wellbeing?What does NOT affect your wellbeing?How have the factors changed through your lifetime?How much do the factors differ between people – in general?

Consultation with older people - objectivesSlide11

Consultation with older people

Themes

from workshops

Good physical and mental health

Cognitive ability

Coping with ill health

Coping with stress (in general and stress of ageing)

Mental resilience

Feeling respected

Peace of mind

Religious belief

Being independent

Mobility

Mutual support with other people

Healthcare

Social care

Good family relationships

Good friendships

Not being lonely

Living in own home

Feeling safe

Enough money

Having things to do

Leisure time

Healthy lifestyle

Freedom of expressionSlide12

4. Conceptual frameworkSlide13

Conceptual frameworkSlide14

5.

Data analysisSlide15

Review of existing approaches and creation of conceptual model

Exploration of existing datasets

. Choice

of datasets and variables

Factor analysis to boil down

variables (when necessary

eg

GHQ-12

, Big-5, Cognitive

Ability)

Structural Equation

Modelling

Multigroup

invariance analysis

(by gender, age groups, England/Rest of UK)

Process

Estimation of individual WB

scores

Principal component analysis

Domain selection and namingSlide16

35 factors identified from lit revTwo datasets explored: ELSA & UsocELSA: 50+ in EnglandUSoc: 16+ in UK

USoc

more comprehensive.

35 factors … over 200 variables!

Some variables were immediate (e.g. gender, age). Others resulted from adding different variables (weighted or not –it depends) (e.g. morbidity, benefit income)

And still some needed factor analysis (e.g. GHQ-12, cognitive capacity)

Dataset preparationSlide17

6. Factor AnalysisSlide18

Factor Analysis(P.

Tryfos

, Methods for Business Analysis and Forecasting: Text & Cases, Wiley,

1998)

Factor analysis is a method for investigating whether a number of variables of interest

are

linearly related to a

smaller number of unobservable

factors

Factor analysis consists of a number of statistical techniques the aim of which is to

simplify

complex sets of data.

(P. Kline, An Easy Guide to Factor Analysis, Routledge, 2014)Slide19

Factor Analysis - example

concentration

loss of sleep

playing a useful role

capable of making decisions

constantly under strain

problem overcoming difficulties

enjoy day-to-day activities

ability to face problems

unhappy or depressed

losing confidence

believe worthless

general happiness Slide20

7. Structural Equation ModelSlide21

Once we got the 35 factors, we wrote up a Structural Equation Model: a `comprehensible statistical approach to testing hypotheses about relationships among observed and unobserved variables’(R. Hoyle. Structural Equation Modeling: Concepts, Issues, and Applications, Sage, 1995

. p.1)

WB is also unobservable

It is defined by these 35 factors

(our hypothesis)

Many of these factors are unobserved and inter-related

Structural Equation ModellingSlide22

(Handbook of Structural Equation Modeling, R. Hoyle (ed.). Guilford Publ., 2014, p. 7

Structural Equation ModellingSlide23

SEM

ModelSlide24

SEM Results - Contribution

of individual

indicatorsSlide25

8. Principal component analysis – DomainsSlide26

Principal Components AnalysisThe central idea of principal component analysis is to reduce the

dimensionality of

a data set in which there are a large number of

interrelated variables

, while retaining as much as possible of the variation present

in the

data set. This reduction is achieved by transforming to a new set

of variables, the principal components, which are uncorrelated, and which

are ordered

so that the first

few

retain most of the variation present in

all

of the

original variables.

(I.T. Jolliffe. Principal Components Analysis, Sringer, 2002. p.1) Slide27

Principal Components AnalysisSlide28

Principal component analysis

to group the indicators and results into

broad

areas (domains

).

Nine identified;

grouped into Five Slide29

29

Age UK’s Wellbeing in Later Life Index

1

PERSONAL

2.1 Social participation

2.2 Civic participation

2.3 Cultural participation

2.4

Neighbourliness

2.5 Friends

2.6 Big5 Personality

2

SOCIAL

1.1 Living arrangement

1.2 Marital status

1.3 Children

1.4 Education

1.5

Carer

1.6 Intergenerational

1.7 Cognitive ability

2.1 Social participation

2.2 Civic participation

2.3 Cultural participation

2.4

Neighbourliness

2.5 Friends

2.6 Big5 Personality

2

SOCIAL

3.1 Longstanding illness

3.2 Co-morbidity

3.3 Mental health

3.4 Mental wellbeing

3.5Sports activity

3

HEALTH

4.1 Employment

4.2 Income support

4.3 Pension

4.4

Housing wealth

4.5 Financial wealth

4

RESOURCES

4.6 Home ownership

4.7 material resources

5.1 Health services

5.2 Leisure services

5.3 Public transport

5.4 Shopping

5

LOCAL

5 domains listed across the top; the indicators are listed below within eachSlide30

Results: score for each domain as a percentage of the highest score attained Slide31

9. Inequality in Wellbeing in later lifeSlide32

Comparing the top and bottom

of

the

WB score distributionSlide33

Inequality – bottom 20 percent compared to top 20 percentSlide34

Inequality in overall wellbeing: some early findingsPeople aged 60+ in the UK who are in the bottom fifth of the wellbeing scale are:

More likely to be female and widowed

Half as likely to be married

More than twice as likely to be living alone

Much less likely to take part in cultural, social or civic events

Between three and four times as likely to have a longstanding illness and fourteen times as likely to have three or more diagnosed health conditions

Between four and five times more likely to have no educational qualifications at GCSE or above compared to those in the top fifth for wellbeing

On average, those people with the highest level of wellbeing (the top 20 percent) have:

More than 13 times as much financial wealth and about 14 times the income of those in the bottom twenty percent

They are also seven times more likely to participate in sport and have on average 50% more friends.Slide35

Subgroups

Overall

PERSONAL

SOCIAL

HEALTH

RESOURCES

LOCAL

 

 

 

 

 

 

 

Total

53.0

59.7

55.0

45.4

49.8

55.0

Sex

 

 

 

 

 

 

Men

54.0

61.5

55.0

46.8

51.6

55.6

Women

52.1

58.2

55.1

44.3

48.3

54.4

 

 

 

 

 

 

 

Age groups

 

 

 

 

 

 

age 60-64

55.1

67.1

55.1

48.0

49.6

54.4

age 65-69

55.8

65.6

56.4

49.2

51.4

53.7

age 70-79

53.4

59.6

55.9

45.5

50.4

55.8

age 80+

47.3

48.4

51.6

38.1

47.1

55.7

 

 

 

 

 

 

 

Limiting Long term illness

 

 

 

 

 

 

No

60.0

62.8

57.5

66.5

54.5

56.0

Yes

48.7

57.9

53.5

32.5

46.9

54.3

 

 

 

 

 

 

 

Group-specific

and

domain-specific wellbeing scoreSlide36

Subgroups

Overall

PERSONAL

SOCIAL

HEALTH

RESOURCES

LOCAL

 

 

 

 

 

 

 

Tenure

 

 

 

 

 

 

Home owned outright

57.1

62.8

57.0

48.8

60.7

55.2

Outstanding mortgage

54.1

66.4

56.9

46.1

44.4

53.6

Rented

43.7

51.5

49.9

37.8

28.2

54.8

 

 

 

 

 

 

 

Education

 

 

 

 

 

 

Higher

60.6

71.0

61.2

51.6

58.7

55.7

Not higher

49.9

55.3

52.3

42.8

46.3

54.6

 

 

 

 

 

 

 

Legal marital status

 

 

 

 

 

 

single, nvr marr/civ p

50.3

53.3

53.7

45.5

45.4

54.8

married

56.9

67.9

56.5

48.6

54.6

55.4

divorced

49.0

54.6

54.0

42.3

40.6

53.5

widowed

48.1

48.1

53.0

40.8

46.2

54.9

 

 

 

 

 

 

 

Group-specific

and

domain-specific wellbeing scoreSlide37

Subgroups

Overall

PERSONAL

SOCIAL

HEALTH

RESOURCES

LOCAL

 

 

 

 

 

 

 

Co-morbidity

 

 

 

 

 

 

0 = no morbidity

59.4

64.1

57.2

62.5

54.8

56.5

1

54.9

60.7

56.7

49.7

51.0

55.5

2

51.0

58.3

54.9

39.0

48.9

54.1

3

47.0

56.2

51.8

30.2

45.3

54.1

4

42.7

53.7

47.9

21.5

42.5

52.9

5+ ill-heath conditions

39.9

52.9

47.4

14.5

38.2

50.4

 

 

 

 

 

 

 

Group-specific

and

domain-specific wellbeing scoreSlide38

Inequality in WB and some indicatorsSlide39

Inequality in WB and some indicatorsSlide40

10. Possible usesSlide41

Possible uses

Record changes in WB of all older people or sub-groups

Identify relative importance of characteristics and variables

Investigate what determines low WB scores in later life

Inform interventions and policy and their evaluations -

Microsimulation

Develop local WB indicesSlide42

Thank you