Adverse childhood experiences and incident coronary heart disease: a counterfactual analysis in the Whitehall II prospective cohort study.

OBJECTIVES: Adverse childhood experience is thought to be associated with risk of coronary heart disease, but it is not clear which experiences are cardiotoxic, and whether risk increases with the accumulation of adverse childhood experiences. METHODS: Participants were 5149 adults (72.6% men) in the Whitehall II cohort study. Parental death was recorded at phase 1 (median age in years 44.3), and 13 other adverse childhood experiences at phase 5 (55.3). We applied Cox proportional hazards regression with person-time from phase 5 to examine associations of adverse childhood experiences with incident coronary heart disease. We predicted hazard ratios according to count of the experiences, and examined dose-response effect. We finally estimated reduction of coronary heart disease in a hypothetical scenario, the absence of adverse childhood experiences. RESULTS: Among study participants, 62.9% had at least one adversity, with "financial problems" having the highest prevalence (26.1%). There were 509 first episodes of coronary heart disease during an average 12.9 years follow-up. Among 14 adverse childhood experiences in a multiply adjusted model, "parental unemployment" showed the highest hazard of coronary heart disease incidence (hazard ratio; 95% confidence interval: 1.53; 1.16 to 2.02). No dose-response effect was observed (constant for proportionality in hazard ratio: 1.05, 0.99 to 1.11). Based on the estimates of final model, in the absence of childhood adversities, we estimated a 6.0% reduction in coronary heart disease (0.94; 0.87 to 1.01), but the confidence interval includes one. CONCLUSION: Although individual adverse childhood experiences show some association with coronary heart disease, there is no clear relationship with the number of adverse experiences. Further research is required to quantify effects of multiple and combinations of adverse childhood experiences considering timing, duration, and severity.

Association of attrition with mortality: findings from 11 waves over three decades of the Whitehall II study.

BACKGROUND: Attrition, the loss of participants as a study progresses, is a considerable challenge in longitudinal studies. This study examined whether two forms of attrition, 'withdrawal' (formal discontinued participation) and 'non-response' (non-response among participants continuing in the study), have different associations with mortality and whether these associations differed across time in a multi-wave longitudinal study. METHODS: Participants were 10 012 civil servants who participated at the baseline of the Whitehall II cohort study with 11 data waves over an average follow-up of 28 years. We performed competing-risks analyses to estimate sub-distribution HRs and 95% CIs, and likelihood ratio tests to examine whether hazards differed between the two forms of attrition. We then applied linear regression to examine any trend of hazards against time. RESULTS: Attrition rate at data collections ranged between 13% and 34%. There were 495 deaths recorded from cardiovascular disease and 1367 deaths from other causes. Study participants lost due to attrition had 1.55 (95% CI 1.26 to 1.89) and 1.56 (1.39 to 1.76) times higher hazard of cardiovascular and non-cardiovascular mortality than responders, respectively. Hazards for withdrawal and non-response did not differ for either cardiovascular (p value =0.28) or non-cardiovascular mortality (p value =0.38). There was no linear trend in hazards over the 11 waves (cardiovascular mortality p value =0.11, non-cardiovascular mortality p value =0.61). CONCLUSION: Attrition can be a problem in longitudinal studies resulting in selection bias. Researchers should examine the possibility of selection bias and consider applying statistical approaches that minimise this bias.

The clustering of risk behaviours in adolescence and health consequences in middle age.

INTRODUCTION: There is increasing interest in the clustering of risk behaviours in adolescence. However, few studies have examined what clusters of risk behaviours exist among adolescents, their early-life predictors, and their associations with later health. METHODS: We analysed data derived from 8754 participants (women 53.3%) in the 1970 British Cohort Study. Latent class analysis was used to identify clusters of risk behaviours at age 16. Regression modelling was then used to examine predictors of clusters and their consequences of risk behaviours and health outcomes at age 42. RESULTS: We identified two latent classes: a risky-behaviour (men: 20.0%, women: 23.6%) and less-risky-behaviour class. Among men, those in the risky-behaviour class were more likely to report smoking, multiple binge drinking, sexual debut before 16, involvement in fights and delinquency than were women. Membership in risky-behaviour class was mainly predicted by sociodemographic and parental risk behaviours and monitoring. The risky-behaviour class at age 16 was associated with the following outcome age 42: smoking status (more strongly among women), excessive alcohol consumption (more strongly among men), worse self-rated health (more strongly among men), and psychological distress (only among women). CONCLUSIONS: Engagement in multiple risk behaviours in adolescence is an important driver of health inequalities later in life. Early life intervention, for example via school-based interventions, may be warranted for favourable lifelong health.