1. Age UK’s Index of Wellbeing in Later Life University of Exeter Medical School 19 January 2017
Prof James Goodwin
Chief Scientist, Age UK
Prof José Iparraguirre
Chief Economist, Age UK

2. Index Rationale Data sources Process Conceptual framework
Data analysis
Factor Analysis
Structural Equation Model
Principal component analysis – Domains
Inequality in Wellbeing in later life
Possible uses

3. Rationale

4. Rationale
“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)
Up 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)

5. Rationale An 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 wellbeing

6. Rationale
Age 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.

7. 2. Data sources

8. Data sources Consultations with Experts Age UK’s expertise Literature
Domains, indicators of Wellbeing in Later Life index
Consultations with Experts
Literature
review
Empirical work on Understanding Society
Views of older people
Age UK’s expertise

9. 3. Process

10. Consultation with older people - objectives
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?

11. 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 expression

12. 4. Conceptual framework

13. Conceptual framework

14. 5. Data analysis

15. Process 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)
Estimation of individual WB scores
Principal component analysis
Domain selection and naming

16. Dataset preparation 35 factors identified from lit rev
Two datasets explored: ELSA & Usoc
ELSA: 50+ in England
USoc: 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)

17. 6. Factor Analysis

18. Factor Analysis
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)
Factor analysis is a method for investigating whether a number of variables of interest are linearly related to a smaller number of unobservable factors
(P. Tryfos, Methods for Business Analysis and Forecasting: Text & Cases, Wiley, 1998)

19. Factor Analysis - example
unhappy or depressed
losing confidence
problem overcoming difficulties
believe worthless
constantly under strain
general happiness
enjoy day-to-day activities
ability to face problems
concentration
loss of sleep
capable of making decisions
playing a useful role

20. 7. Structural Equation Model

21. Structural Equation Modelling
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, p.1)
WB is also unobservable
It is defined by these 35 factors (our hypothesis)
Many of these factors are unobserved and inter-related

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

23. SEM Model

24. SEM Results - Contribution of individual indicators

25. 8. Principal component analysis – Domains

26. Principal Components Analysis
The 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, p.1)

27. Principal Components Analysis

28. Principal component analysis
to group the indicators and results into broad areas (domains).
Nine identified;
grouped into Five

29. Age UK’s Wellbeing in Later Life Index1
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
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
5.1 Health services
5.2 Leisure services
5.3 Public transport
5.4 Shopping 5
LOCAL
1.1 Living arrangement
1.2 Marital status
1.3 Children
1.4 Education
1.5 Carer
1.6 Intergenerational
1.7 Cognitive ability
4.6 Home ownership
4.7 material resources
5 domains listed across the top; the indicators are listed below within each

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

31. 9. Inequality in Wellbeing in later life

32. Comparing the top and bottom of the WB score distribution

33. Inequality – bottom 20 percent compared to top 20 percent

34. Inequality in overall wellbeing: some early findings
People 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.

35. Group-specific and domain-specific wellbeing score
Subgroups
Overall
PERSONAL
SOCIAL
HEALTH
RESOURCES
LOCAL
Total
53.0
59.7
55.0
45.4
49.8
Sex
Men
54.0
61.5
46.8
51.6
55.6
Women
52.1
58.2
55.1
44.3
48.3
54.4
Age groups
age 60-64
67.1
48.0
49.6
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
age 80+
47.3
48.4
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

36. Group-specific and domain-specific wellbeing score
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
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
married
67.9
56.5
48.6
55.4
divorced
49.0
54.0
42.3
40.6
53.5
widowed
48.1
53.0
40.8
46.2
54.9

37. Group-specific and domain-specific wellbeing score
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
58.3
39.0
48.9
54.1 3
47.0
56.2
51.8
30.2
45.3 4
42.7
53.7
47.9
21.5
42.5
52.9
5+ ill-heath conditions
39.9
47.4
14.5
38.2
50.4

38. Inequality in WB and some indicators

39. Inequality in WB and some indicators

40. 10. Possible uses

41. 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 indices

42. Thank you