Di Gessa G, Glaser K and Tinker A

The health of grandparents caring for their grandchildren: The role of early and mid-life conditions Di 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 Conference Southampton, 1-3 September 2014

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 CalousteGulbenkian Foundation, Grandparents Plus and the Beth Johnson Foundation • Start April 2013 - October 2014 • Project Launch 15 March 2013 at Europe House, Westminster

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).

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.

Aim and objectives Examine the effects of caring for grandchildren on health among European grandparents using: • Longitudinal data • Life history data, and controlling for cumulative experiences across the life course (e.g. paid work histories; health and socio-economic position in childhood).

Data/ 1 3 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

Data /2 • Waves 1, 2 provide information on grandparents, includingdemographic and socio-economic characteristics, health, and household characteristics. • Wave 3 collects retrospective life history information about childhood conditions, and life events in adulthood.

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

Latent Class Childhood Disadvantage (w3) Overview of Analysis Number of unions; In paid work 1-75%; Never worked; Has suffered: i. Hunger; ii. ‘Adverse’ event; iii. Long periods of ill health (w3) Latent Health w2 Baseline 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;

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 Variableto 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]

Sample and Methods Sample: • ~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%) Analysis Linear regression of latent health at follow-up, controlling for baseline and life history socio-economic and demographic characteristics.

Results – descriptive /1 Distribution of grandparent childcare, by wave • Source: SHARE 2004/05, 2006 • Countries: France, Austria, Germany, Sweden, Denmark, Switzerland, The Netherlands, Italy, Spain, Greece, Belgium

Results – descriptive /2 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 /2

Conclusions Using waves 1, 2 and life history data • Grandchild care – both intensive and non-intensive – positively associated with good health over time; • Relationship remains even when taking into account childhood and adulthood disadvantage;

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, Sensitivityanalysis • “Selection effect” of grandparents who look after grandchildren. Unmeasured factor? • ELSA and quality of life.

Thanks for your attention! Questions, comments and feedback are welcome.

Di Gessa G, Glaser K and Tinker A