The health of grandparents caring for their grandchildren: The role of early and mid-life
conditionsDi Gessa G, Glaser K and Tinker A
Institute of Gerontology, Department of Social Science, Health & Medicine, King’s College London, United Kingdom
ESRC ES/K003348/1
Symposium, Harnessing the power of secondary data analysis: insights from the “Ageing Cluster” of ESRC’s Secondary Data Analysis Initiative
British Society of Gerontology Annual ConferenceSouthampton, 1-3 September 2014
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Outline• Partnerships and timescale• Background• Aim and objectives• Data and methods• Results• Conclusion
The research study – partnerships and timescale
• Funded by ESRC, and in partnership with Calouste Gulbenkian Foundation, Grandparents Plus and the Beth Johnson Foundation
• Start April 2013 - October 2014• Project Launch 15 March 2013 at Europe
House, Westminster3
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Background /1• Grandparents play crucial role in family life• Evidence of the impact of childcare on
grandparents’ health is mixed: Custodial/primary grandchild carers experience
poorer health and wellbeing; Higher quality of life, fewer depressive symptoms
among grandparents providing grandchild care (vs no care).
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Background /2• Most studies are cross-sectional and samples
consist mostly of US grandparents;• Focus on primary and custodial care;• Few studies have studied the link between
grandchild care and grandparents’ health using a cumulative advantage/disadvantage framework.
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Aim and objectivesExamine the effects of caring for grandchildren on health among European grandparents using:i) Longitudinal dataii) Life history data, and controlling for
cumulative experiences across the life course (e.g. paid work histories; health and socio-economic position in childhood).
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Data/ 13 waves of multidisciplinary comparable surveys, representative of individuals 50+– Survey of Health, Ageing and Retirement in Europe
(SHARE) (N~27,000);France, Austria, Germany, Sweden, Denmark, Switzerland,The Netherlands, Italy, Spain, Greece, Belgium
– Household response rate: 62%, with individual response rates higher than 85%;
– First wave collected in 2004/05.
Focus on grandparents
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Data /2 Waves 1, 2 provide information on
grandparents, including demographic and socio-economic characteristics, health, and household characteristics.
Wave 3 collects retrospective life history information about childhood conditions, and life events in adulthood.
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Data /3«During the last 12 months,
have you looked after your grandchild[ren] without the presence of the parents?»
If so i) «how often?» [daily, weekly, monthly, less often]
ii) «about how many hours?»
Intensive grandparental childcare if grandchildren were looked after by grandparents on a daily basis or at least 15 hours per week
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Overview of Analysis
Latent Health w2Baseline Characteristics (w1)
Age; Gender; Education;Household type, Country;Wealth quintiles;Number & Age of grandchildren;Grandchild care;Paid work and social engagement;
Latent Health;Health behaviour (BMI, smoking);Depression; Cognitive function;
Latent Class Childhood
Disadvantage (w3)
Number of unions;In paid work 1-75%; Never worked;Has suffered: i. Hunger; ii. ‘Adverse’ event; iii. Long periods of ill health(w3)
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Measures Used Latent Class Analysis to classify
respondents by childhood conditions into advantaged/ disadvantaged subgroups;
[By age 10: Experienced parental difficulties; at least one parent died; Occupation of breadwinner; Books in HH; Toilet; Hot water; Bath; Heating; Poor/fair health; In hospital or bed for one month or more; With severe illness]
Used Latent Variable to represent ‘somatic’ health;
[Self-rated health, Self-report of conditions - cancer, lung, heart, stroke, diabetes, Self-report of limiting disability, Activities of Daily Living, Instrumental Activities of Daily Living]
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Sample and MethodsSample:• ~16,000 grandparents aged 50+ at baseline;• ~ 9,700 grandparents at 24-month follow-up;• ~ 7,200 with history data.• ~ 6,500 complete cases (~41%)
AnalysisLinear regression of latent health at follow-up, controlling for baseline and life history socio-economic and demographic characteristics.
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Results – descriptive /1
Grandparental childcare Wave 1 Wave 2
Not looking after 50.2 50.2
Not intensive 36.1 36.8
Intensive 13.7 13.0
Total 15,887 9,644
Distribution of grandparent childcare, by wave
Source: SHARE 2004/05, 2006Countries: France, Austria, Germany, Sweden, Denmark, Switzerland, The Netherlands, Italy, Spain, Greece, Belgium
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Results – descriptive /2Not looking
afterNot
intensive Intensive
SRH fair/poor 46.9 30.5 36.7ADL limitations 16.9 6.9 7.4Depressive symptoms 30.5 20.7 27.2
In couple >80% 71.0 78.9 83.2Never-worked (W) 27.9 14.4 29.1Suffered hunger 13.6 8.9 9.5
Advantaged & good health at 10 19.2 33.5 17.3Disadvantaged & good health at age 10 73.5 58.7 75.8
Distribution of selected grandparent characteristics, by childcare
Results – linear regression /1Beta coefficients from models of ‘good’ health at wave 2
• Younger grandparents, with higher educational levels, and in higher wealth quintiles at baseline more likely to report good health at wave 2;
• No gender differences;• No differences by household composition; age and
number of grandchildren not significant;• Social engagement at baseline not significant.• Positive effect of grandchild care (not intensive &
intensive on health).
Results – linear regression /2Latent health 0.558 < 0.001In lowest cognitive quintile – 0.049 0.005Depressive symptoms – 0.094 < 0.001Obese – 0.077 < 0.001Smoking – 0.009 0.543
2 or more marital unions – 0.018 0.352In paid work for 1-75% of working life – 0.022 0.114 Has never worked – 0.046 0.019Has suffered long periods of ill health – 0.154 <0.001Has suffered hunger – 0.022 0.228Has suffered any ‘adverse’ event – 0.019 0.298
Disadvantaged & good health at age 10 0.001 0.932Disadvantaged & poor health at age 10 – 0.039 0.054
Not intensive 0.033 0.010Intensive 0.033 0.019
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ConclusionsUsing waves 1, 2 and life history data i) Grandchild care – both intensive and non-
intensive – positively associated with good health over time;
ii) Relationship remains even when taking into account childhood and adulthood disadvantage;
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Limitations & Future work• Separate models by gender to account for differences in life
histories• Attrition can bias results, especially in the older population
where the most ‘disadvantaged’ have a higher probability of dropping out of the study;
Multiple Imputations, Sensitivity analysis• “Selection effect” of grandparents who look after
grandchildren. Unmeasured factor?• ELSA and quality of life.
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Thanks for your attention!
Questions, comments and feedback are welcome.