Age-period-cohort methodology:Confounding by birth cohort in cardiovascular pharmacoepidemiology

Preventive cardiovascular drugs are of the utmost importance for population health, and hence their information on their utilization and exact effectiveness is crucial. Clinical trials are commonly used for this assessment, but observational studies are also needed as clinical trial populations can differ behaviorally and demographically from end-users. However, observational studies are difficult to correctly perform; unmeasured variables that affect both drug utilization and cardiovascular outcomes, known as confounders, can distort estimates of drug effectiveness. A potentially important confounder is birth cohort; other studies have shown that birth cohort, which represents in what time-period a person is born, affects both drug utilization and affects cardiovascular mortality. If it turns out that birth cohort is indeed a confounder, then it should be taken into account when performing drug effectiveness studies.It was investigated whether birth cohort was a confounder, both at the population-level and at the patient-level, of the relation between statins (cholesterol lowering drugs) and cardiovascular mortality. This is not easy, as adjusting for birth cohort imposes a statistical identification problem (cohort = period – age). Data that was representative for the Netherlands in the period 1994 – 2012 was used. It was determined that birth cohort can confound population-level estimates of interventions on statin utilization. However, birth cohort was not a confounder of the individual-level effect of statins on cardiovascular mortality. Furthermore, an innovative method determining birth cohort effects was extended and assessed, which can better identify the causal pathways by which age, period and cohort affect an outcome

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

Age-period-cohort methodology:Confounding by birth cohort in cardiovascular pharmacoepidemiology

Preventive cardiovascular drugs are of the utmost importance for population health, and hence their information on their utilization and exact effectiveness is crucial. Clinical trials are commonly used for this assessment, but observational studies are also needed as clinical trial populations can differ behaviorally and demographically from end-users. However, observational studies are difficult to correctly perform; unmeasured variables that affect both drug utilization and cardiovascular outcomes, known as confounders, can distort estimates of drug effectiveness. A potentially important confounder is birth cohort; other studies have shown that birth cohort, which represents in what time-period a person is born, affects both drug utilization and affects cardiovascular mortality. If it turns out that birth cohort is indeed a confounder, then it should be taken into account when performing drug effectiveness studies. It was investigated whether birth cohort was a confounder, both at the population-level and at the patient-level, of the relation between statins (cholesterol lowering drugs) and cardiovascular mortality. This is not easy, as adjusting for birth cohort imposes a statistical identification problem (cohort = period – age). Data that was representative for the Netherlands in the period 1994 – 2012 was used. It was determined that birth cohort can confound population-level estimates of interventions on statin utilization. However, birth cohort was not a confounder of the individual-level effect of statins on cardiovascular mortality. Furthermore, an innovative method determining birth cohort effects was extended and assessed, which can better identify the causal pathways by which age, period and cohort affect an outcome

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

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