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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
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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
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Rationale
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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)
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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
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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.
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2. Data sources
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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
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3. Process
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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?
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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
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4. Conceptual framework
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Conceptual framework
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5. Data analysis
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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
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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)
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6. Factor Analysis
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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)
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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
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7. Structural Equation Model
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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
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Structural Equation Modelling
(Handbook of Structural Equation Modeling, R. Hoyle (ed.). Guilford Publ., 2014, p. 7
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SEM Model
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SEM Results - Contribution of individual indicators
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8. Principal component analysis – Domains
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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)
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Principal Components Analysis
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Principal component analysis
to group the indicators and results into broad areas (domains). Nine identified; grouped into Five
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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 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
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Results: score for each domain as a percentage of the highest score attained
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9. Inequality in Wellbeing in later life
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Comparing the top and bottom of the WB score distribution
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Inequality – bottom 20 percent compared to top 20 percent
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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.
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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
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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
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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
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Inequality in WB and some indicators
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Inequality in WB and some indicators
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10. Possible uses
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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
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Thank you
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