1. 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

2. Outline Partnerships and timescale Background Aim and objectives Data and methods Results Conclusion 2

3. 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, Westminster 3

4. 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). 4

5. 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. 5

6. Aim and objectives Examine the effects of caring for grandchildren on health among European grandparents using: i)Longitudinal data ii)Life history data, and controlling for cumulative experiences across the life course (e.g. paid work histories; health and socio- economic position in childhood). 6

7. 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 7

8. 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. 8

9. 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 9

10. Overview of Analysis 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; 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) 10

11. 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] 11

12. 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. 12

13. Results – descriptive /1 Grandparental childcareWave 1Wave 2 Not looking after50.2 Not intensive36.136.8 Intensive13.713.0 Total15,8879,644 Distribution of grandparent childcare, by wave Source: SHARE 2004/05, 2006 Countries: France, Austria, Germany, Sweden, Denmark, Switzerland, The Netherlands, Italy, Spain, Greece, Belgium 13

14. Results – descriptive /2 Not looking after Not intensive Intensive SRH fair/poor 46.930.536.7 ADL limitations 16.96.97.4 Depressive symptoms 30.520.727.2 In couple >80% 71.078.983.2 Never-worked (W) 27.914.429.1 Suffered hunger 13.68.99.5 Advantaged & good health at 10 19.233.517.3 Disadvantaged & good health at age 10 73.558.775.8 Distribution of selected grandparent characteristics, by childcare 14

15. Results – linear regression /1 Beta 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). 15

16. Results – linear regression /2 Latent health0.558< 0.001 In lowest cognitive quintile– 0.0490.005 Depressive symptoms– 0.094< 0.001 Obese– 0.077< 0.001 Smoking– 0.0090.543 2 or more marital unions– 0.0180.352 In paid work for 1-75% of working life– 0.0220.114 Has never worked– 0.0460.019 Has suffered long periods of ill health– 0.154<0.001 Has suffered hunger– 0.0220.228 Has suffered any ‘adverse’ event– 0.0190.298 Disadvantaged & good health at age 100.0010.932 Disadvantaged & poor health at age 10 – 0.0390.054 Not intensive0.0330.010 Intensive0.0330.019 16

17. Conclusions Using 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; 17

18. 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. 18

19. Thanks for your attention! Questions, comments and feedback are welcome. 19

20. Childhood /1 3 classes Class proportion: 68%; 24%; 8% Classification accuracy: 0.84 Average Latent Class probability 123 10.940.040.02 2 0.960.02 30.090.060.85 20

21. Childhood /2 Figure 1. Conditional Response Probabilities 21

22. Somatic Health We used: Self rated health Self report of long-term health problems Self report of heart failure, chronic lung disease, stroke, diabetes, and cancer Activities of daily living Instrumental activities of daily living CFITLIRMSEA Uni-dimensional model0.9770.9690.037 22