The Link Between Childhood Adversity and Adult Health Risk Trajectories

1. The Link Between Childhood Adversity and Adult Health Risk Trajectories Andrea Willson Kim Shuey The University of Western Ontario

2. Understanding Health from a Life Course Perspective • Health is positively associated with socioeconomic status (SES). • We know less about how health disparities are the outcome of long-term, accumulative processes. • Individual patterning of trajectories according to social statuses.

3. Cumulative Dis/Advantage Theory • Process through which • a favorable relative position generates further gains across the life course • Initial disadvantage accumulates over time, generating further disadvantage • Results in growth of the advantage of one individual or group relative to another over time (Eg., Dannefer 1987; 2003; Merton 1968; O’Rand 1996)

4. Cumulative Dis/Advantage & Health • Health inequalities follow a process of cumulative dis/advantage • Early life advantage/disadvantage generates diverging trajectories and widening health disparities over time • (e.g., Willson et al. 2007; Shuey & Willson 2008; MacLean 2010)

5. Cumulative Inequality • “Life course trajectories are influenced by early and accumulated inequalities but can be modified by available resources, perceived trajectories, and human agency.” (Ferraro and Shippee 2009)

6. Cumulative Inequality • Social systems generate inequality • Importance of childhood conditions • Influenced by genes and environment • Trajectories may be modified by resources and human agency (Ferraro and Shippee 2009)

7. The Long-term Effects of Childhood Economic Disadvantage • Poor children have: • Higher infant mortality rates • More asthma • More physical and mental health problems • Lower self-esteem • Lower grades • Lower high school grad rates • Higher unemployment • Lower wages • Higher rates of poverty

8. Cumulative Inequality & the Role of Childhood • Research has focused on role of childhood circumstances and cumulative processes in adulthood (Eg. Hayward & Gorman 2004; Hamil-Luker & O’Rand 2007)

9. Measuring Childhood Economic Disadvantage • Perhaps more important than overall level of deprivation: • Persistence/duration • Timing (early or late in childhood) • Trajectory (improving or deteriorating) (Wagmiller et al. 2006)

10. Research Questions • What is the effect of childhood disadvantage on health risk trajectories in middle age? • To what extent do adult resources and health behaviours alter the pathway between childhood disadvantage and health?

11. Data Source • Panel Study of Income Dynamics (PSID) • 1968-present • Representative sample of U.S. households • Followed split-offs from original sample households

12. Sample • Adult children of original PSID households • Baby Boom cohorts (born 1950-1964 in this analysis) • 2007: Ages 43-56 • N=4,241

13. Analysis • Latent Class Analysis • Collins & Lanza 2010; Vermunt & Magidson 2005 • SAS Proc LCA (The Methodology Center, Penn State U) • Person-centered approach. • Sorts individuals into mutually exclusive groups based on responses to a set of indicators. • Detects associations among variables due to an unmeasured, latent source of variation.

14. Analysis • Latent Class Analysis • Steps: • Estimate LCA for childhood disadvantage • Estimate baseline LCA of health risk trajectories • Use childhood disadvantage classes and other covariates to estimate multinomial logistic regression LCA of health risk trajectories

15. Variables • DV: • Latent Class Analysis • Health risk trajectories (Hamil-Luker & O’Rand 2005 ) • 5 Physician-diagnosed health conditions, 1999-2007 • Chronic condition=1 if any diagnosis in a survey wave • Respondents ages 43-56 • Age and gender controlled

16. Health Risk Trajectories

17. Variables • IVs: • Latent Class Analysis: Childhood Disadvantage • Indicators • Low income (averaged income <= 150% of U.S. poverty line) • Receipt of public assistance • Unemployed father • Single parent household • Ages 13-17

18. Childhood Disadvantage LCA

19. Variables • IVs: • Region of childhood (ever in the South) • Childhood health fair/poor (retrospective) • Adult SES • Education (in 1999) • Below-median average income (1992-1999) • Below-median average wealth (1992-1999) • Race (Non-Hispanic White vs. Non-white) • Sex • Age in 1999 • Adult health risk behaviors (1999-2007) • Smoking, physical activity, obesity

20. Estimated Odds Ratios for Multinomial Logistic Regression Model Predicting LCA Membership in Health Risk Trajectories (Ref=Low Risk)Model Includes: Childhood Disadv + Controls

21. Estimated Odds Ratios for Multinomial Logistic Regression Model Predicting LCA Membership in Health Risk Trajectories (Ref=Low Health Risk)Model Includes: Childhood Disadv + Adult SES + Controls

22. Estimated Odds Ratios for Multinomial Logistic Regression Model Predicting LCA Membership in Health Risk Trajectories (Ref=Low Health Risk)Model Includes: Childhood Disadv + Adult SES + Adult Health Behaviours + Controls

23. Conclusions • Childhood disadvantage has long-term, negative consequences for health. • But, pathways from childhood socioeconomic conditions to adult health may be mediated by resources and health behaviours in adulthood.

24. Conclusions • Methodological challenges: • Missing data, attrition and selection • Measurement

25. Conclusions • Broader goals: • Further our understanding of the mechanisms through which inequalities in health are perpetuated or alleviated across the life course and across generations. • Inform policies targeting early life inputs.