The effect of adherence to statin therapy on cardiovascular mortality : quantification of unmeasured bias using falsification end-points

Background: To determine the clinical effectiveness of statins on cardiovascular mortality in practice, observational studies are needed. Control for confounding is essential in any observational study. Falsification end-points may be useful to determine if bias is present after adjustment has taken place. Methods: We followed starters on statin therapy in the Netherlands aged 46 to 100 years over the period 1996 to 2012, from initiation of statin therapy until cardiovascular mortality or censoring. Within this group (n = 49,688, up to 16 years of follow-up), we estimated the effect of adherence to statin therapy (0 = completely non-adherent, 1 = fully adherent) on ischemic heart diseases and cerebrovascular disease (ICD10-codes I20-I25 and I60-I69) as well as respiratory and endocrine disease mortality (ICD10-codes J00-J99 and E00-E90) as falsification end points, controlling for demographic factors, socio-economic factors, birth cohort, adherence to other cardiovascular medications, and diabetes using time-varying Cox regression models. Results: Falsification end-points indicated that a simpler model was less biased than a model with more controls. Adherence to statins appeared to be protective against cardiovascular mortality (HR: 0.70, 95 % CI 0.61 to 0.81). Conclusions: Falsification end-points helped detect overadjustment bias or bias due to competing risks, and thereby proved to be a useful technique in such a complex setting

Educational note:Causal decomposition of population health differences using Monte Carlo integration and the g-formula

One key objective of the population health sciences is to understand why one social group has different levels of health and well-being compared with another. Whereas several methods have been developed in economics, sociology, demography, and epidemiology to answer these types of questions, a recent method introduced by Jackson and VanderWeele (2018) provided an update to decompositions by anchoring them within causal inference theory. In this paper, we demonstrate how to implement the causal decomposition using Monte Carlo integration and the parametric g-formula. Causal decomposition can help to identify the sources of differences across populations and provide researchers with a way to move beyond estimating inequalities to explaining them and determining what can be done to reduce health disparities. Our implementation approach can easily and flexibly be applied for different types of outcome and explanatory variables without having to derive decomposition equations. We describe the concepts of the approach and the practical steps and considerations needed to implement it. We then walk through a worked example in which we investigate the contribution of smoking to sex differences in mortality in South Korea. For this example, we provide both pseudocode and R code using our package, cfdecomp. Ultimately, we outline how to implement a very general decomposition algorithm that is grounded in counterfactual theory but still easy to apply to a wide range of situations

Does postponing retirement affect cognitive function? A counterfactual experiment to disentangle life course risk factors

Evidence suggests that contemporaneous labor force participation affects cognitive function; however, it is unclear whether it is employment itself or endogenous factors related to individuals’ likelihood of employment that protects against cognitive decline. We exploit innovations in counterfactual causal inference to disentangle the effect of postponing retirement on later-life cognitive function from the effects of other life-course factors. With the U.S. Health and Retirement Study (1996–2014, n = 20,469), we use the parametric g-formula to estimate the effect of postponing retirement to age 67. We also study whether the benefit of postponing retirement is affected by gender, education, and/or occupation, and whether retirement affects cognitive function through depressive symptoms or comorbidities. We find that postponing retirement is protective against cognitive decline, accounting for other life-course factors (population: 0.34, 95% confidence interval (CI): 0.20,0.47; individual: 0.43, 95% CI: 0.26,0.60). The extent of the protective effect depends on subgroup, with the highest educated experiencing the greatest reduction in cognitive decline (individual: 50%, 95% CI: 32%,71%). By using innovative models that better reflect the empirical reality of interconnected life-course processes, this work makes progress in understanding how retirement affects cognitive function.Publisher PDFPeer reviewe

Modelling the socio-economic determinants of fertility: a mediation analysis using the parametric g-formula

Theories predict that the timing of childbearing and number of children born are determined by multiple socio-economic factors. Despite this, many methods cannot investigate the interrelationships between these determinants, including the direct and indirect influence that they have on fertility over the life course. Here we use the parametric g-formula to examine the interdependent influences of time-varying socio-economic processes, education, employment status and partnership status?on fertility. To demonstrate this approach, we study a cohort of women who were born in the UK in 1970. Our results show that socio-economic processes play an important role in determining fertility, not only directly but also indirectly. We show that increasing attendance in higher education has a largely direct effect on early childbearing up to age 25 years, resulting in a substantial increase in childlessness. However, childbearing at later ages is dominated by an indirect effect of education on fertility, via partnership status and employment status, that is twice as large as the direct effect. We also use the g-formula to examine bias due to unobserved heterogeneity, and we demonstrate that our results appear to be robust. We conclude that the method provides a valuable tool for mediation analysis in studies of interdependent life course processes

The Contribution of Health Behaviors to Depression Risk across Birth Cohorts

More recent birth cohorts are at a higher depression risk than cohorts born in the early 20th century. We aimed to investigate to what extent changes in alcohol consumption, smoking, physical activity, and obesity contribute to these birth cohort variations. METHODS: We analyzed panel data from US adults born 1916–1966 enrolled in the Health and Retirement Study (N = 163,760 person-years). We performed a counterfactual decomposition analysis by combining age-period-cohort models with g-computation. We thereby compared the predicted probability of elevated depressive symptoms (CES-D 8 score ≥3) in the natural course to a counterfactual scenario where all birth cohorts had the health behaviors of the 1945 birth cohort. We stratified analyses by sex and race-ethnicity. RESULTS: We estimated that depression risk of the 1916–1949 and 1950–1966 birth cohort would be on average 2.0% (–2.3 to –1.7) and 0.5% (–0.9 to –0.1) higher with the alcohol consumption levels of the 1945 cohort. In the counterfactual with the 1945 BMI distribution, depression risk is on average 2.1% (1.8 to 2.4) higher for the 1916–1940 cohorts and 1.8% (–2.2 to –1.5) lower for the 1950–1966 cohorts. We find no cohort variations in depression risk for smoking and physical activity. The contribution of alcohol is more pronounced for Whites than for other race-ethnicity groups, and the contribution of BMI more pronounced for women than for men. CONCLUSION: Increased obesity levels were associated with exacerbated depression risk in recent birth cohorts in the United States, while drinking patterns only played a minor role

The contribution of health behaviors to depression risk across birth cohorts

Background: More recent birth cohorts are at a higher depression risk than cohorts born in the early twentieth century. We aimed to investigate to what extent changes in alcohol consumption, smoking, physical activity and obesity, contribute to these birth cohort variations. Methods: We analyzed panel data from US adults born 1916-1966 enrolled in the Health and Retirement Study (N=163,760 person-years). We performed a counterfactual decomposition analysis by combining age-period-cohort models with g-computation. This allowed us to compare the predicted probability of elevated depressive symptoms (CES-D 8 score ≥3) in the natural course to a counterfactual scenario where all birth cohorts had the health behavior of the 1945 birth cohort. We stratified analyses by sex and race/ethnicity. Results: Depression risk of the 1916-1949 and 1950-1966 birth cohort would be on average 2% (-2.3 to -1.7) and 0.5% (-0.9 to -0.1) higher had they had the alcohol consumption levels of the 1945 cohort. In the counterfactual with the 1945 BMI distribution, depression risk is on average 2.1% (1.8 to 2.4) higher for the 1916-1940 cohorts and 1.8% (-2.2 to -1.5) lower for the 1950-1966 cohorts. We find no cohort variations in depression risk for smoking and physical activity. The contribution of alcohol is more pronounced for Whites than for other race/ethnicity groups, and the contribution of BMI more pronounced for women than for men. Conclusion: Increased obesity levels exacerbated depression risk in recent birth cohorts in the US, while drinking patterns only played a minor role

The role of labor market inequalities in explaining the gender gap in depression risk among older US adults

We aim to investigate to what extent gender inequality at the labor market explains higher depression risk for older US women compared to men. We analyze data from 35,699 US adults aged 50–80 years that participated in the Health and Retirement Study. The gender gap is calculated as the difference in prevalence in elevated depressive symptoms (score ≥ 3 on the 8-item Center for Epidemiological Studies Depression Scale) between women and men. We employ a dynamic causal decomposition and simulate the life course of a synthetic cohort from ages 50–80 with the longitudinal g-formula and introduce four nested interventions by assigning women the same probabilities of A) being in an employment category, B) occupation class, C) current income and D) prior income group as men, conditional on women's health and family status until age 70. The gender gap in depression risk is 2.9%-points at ages 50–51 which increases to 7.6%-points at ages 70–71. Intervention A decreases the gender gap over ages 50–71 by 1.2%-points (95%CI for change: 2.81 to 0.4), intervention D by 1.64%-points (95%CI for change: 3.28 to −0.15) or 32% (95%CI: 1.39 to 62.83), and the effects of interventions B and C are in between those of A and D. The impact is particularly large for Hispanics and low educated groups. Gender inequalities at the labor market substantially explain the gender gap in depression risk in older US adults. Reducing these inequalities has the potential to narrow the gender gap in depression.</p

Unemployment and subsequent depression : A mediation analysis using the parametric G-formula

The effects of unemployment on depression are difficult to establish because of confounding and limited understanding of the mechanisms at the population level. In particular, due to longitudinal interdependencies between exposures, mediators and outcomes, intermediate confounding is an obstacle for mediation analyses. Using longitudinal Finnish register data on socio-economic characteristics and medication purchases, we extracted individuals who entered the labor market between ages 16 and 25 in the period 1996 to 2001 and followed them until the year 2007 (n = 42,172). With the parametric G-formula we estimated the population-averaged effect on first antidepressant purchase of a simulated intervention which set all unemployed person-years to employed. In the data, 74% of person-years were employed and 8% unemployed, the rest belonging to studying or other status. In the intervention scenario, employment rose to 85% and the hazard of first antidepressant purchase decreased by 7.6%. Of this reduction 61% was mediated, operating primarily through changes in income and household status, while mediation through other health conditions was negligible. These effects were negligible for women and particularly prominent among less educated men. By taking complex interdependencies into account in a framework of observed repeated measures data, we found that eradicating unemployment raises income levels, promotes family formation, and thereby reduces antidepressant consumption at the population-level.Peer reviewe

The gendered impacts of delayed parenthood on educational and labor market outcomes : a dynamic analysis of population-level effects over young adulthood

Later parenthood is often beneficial for women, but less is known about its impact on men. As first births continue to occur later in life, it is important to understand whether this delay influences the educational and labor market outcomes of women and men differently, and how it changes the socioeconomic characteristics of children’s parents at birth. However, education, employment, and fertility are linked, implying that complex models are required in order to analyze the time-varying impacts of delayed parenthood. We use dynamic longitudinal models and Finnish data to analyze how, and through which socioeconomic mechanisms, a material delay in parenthood is likely to influence educational and labor market outcomes over young adulthood. A three-year delay in young-adult parenthood for all women increases educational enrollment in their early 20s, employment in their late 20s, and partly due to higher education income in their 30s. The impact of the same delay for men is more modest, and almost negligible for their employment, suggesting that later parenthood exacerbates the educational advantage of women and attenuates the income advantage of men. However, it strengthens the socioeconomic standing of both men and women when they become parents, essentially due to the accumulation of effects

The effect of low childhood income on self-harm in young adulthood : Mediation by adolescent mental health, behavioural factors and school performance

Low childhood income is an established risk factor of self-harm in adolescence and young adulthood, and childhood income is additionally associated with various correlates of self-harm. How these correlates, such as psychiatric disorders, substance abuse, violent behaviour and school problems, mediate the effect of childhood income on self-harm, is less understood. The purpose of the current paper is to examine this mediation. The study is based on administrative register data on all Finnish children born in 1990-1995. An analytical sample of 384,121 children is followed from age 8 to 22. We apply the parametric g-formula to study the effect of childhood income on the risk of self-harm in young adulthood. Adolescent psychiatric disorders, substance abuse, prior self-harm, violent criminality and victimization, out-of-home placements, not being in education, employment or training and school performance are considered as potential mediators. We control for confounding factors related to childhood family characteristics. As a hypothetical intervention, we moved those in the lowest childhood income quintile to the second-lowest quintile, which resulted in a 7% reduction in hospital-presenting self-harm in young adulthood among those targeted by the intervention (2% reduction in the total population). 67% of the effect was mediated through the chosen mediators. The results indicate that increases in childhood material resources could protect from self-harm in young adulthood. Moreover, the large proportion of mediation suggests that targeted interventions for high-risk adolescents may be beneficial. To our knowledge, this is the first paper to use the parametric g-formula to study youth self-harm. Future applications are encouraged as the method offers several further opportunities for analysing the complex life course pathways to self-harm.Peer reviewe