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.
Abstract:Purpose: Accurate measurement of drug adherence is essential for valid risk-benefit assessments of pharmacologic interventions. To date, measures of drug adherence have almost exclusively been applied for a fixed-time interval, and without considering changes over time. However, patients with irregular dosing behavior commonly have a different prognosis than patients with stable dosing behavior. Methods: We propose a method, based on the Proportion of Days Covered (PDC) method, to measure time-varying drug adherence and drug potency using electronic records. We use an irregularly dosing patient and a patient with stable adherence as examples. For these patients, we compare both a static PDC method with the time varying PDC method. Results: We demonstrate that time varying PDC method better distinguishes an irregularly dosing patient from a stably dosing patient, and demonstrate how the static method can result in a biased estimate of drug adherence. Furthermore, the time varying PDC method may be better used to reduce certain types of confounding and misclassification of exposure. Conclusions: The time varying PDC method may improve longitudinal and time-to-event studies that associate adherence with a clinical outcome, or (intervention) studies that seek to describe changes in adherence over time.http://mc.manuscriptcentral.com/pds Pharmacoepidemiology and Drug Safety 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 4• We demonstrate a method to measure time varying drug adherence, which better 5 distinguishes an irregularly dosing patient from a stably dosing patient, and which is 6 less likely to produce biased estimates. 7• The time varying PDC method may improve longitudinal and time-to-event studies 8 that associate adherence with a clinical outcome, or (intervention) studies that seek to 9 describe changes in adherence over time. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 time interval has passed. In all, time-constant drug adherence measures are disadvantageous 22 both in studies assessing cumulative incidence ratios and incidence rate ratios. 23There is a wide variety of methods to estimate adherence, each with their specific 24 advantages and disadvantages [6,[9][10][11][12][13] The extended PDC method is intended to be applied to data from drug prescription or 3 dispensing records. Initially, data should be ordered such that each row represents a single 4 drug prescription (or dispensed prescription). The in...
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.
ObjectiveTo inform development of guidelines for hypertension management in Vietnam, we evaluated the cost-effectiveness of different strategies on screening for hypertension in preventing cardiovascular disease (CVD).MethodsA decision tree was combined with a Markov model to measure incremental cost-effectiveness of different approaches to hypertension screening. Values used as input parameters for the model were taken from different sources. Various screening intervals (one-off, annually, biannually) and starting ages to screen (35, 45 or 55 years) and coverage of treatment were analysed. We ran both a ten-year and a lifetime horizon. Input parameters for the models were extracted from local and regional data. Probabilistic sensitivity analysis was used to evaluate parameter uncertainty. A threshold of three times GDP per capita was applied.ResultsCost per quality adjusted life year (QALY) gained varied in different screening scenarios. In a ten-year horizon, the cost-effectiveness of screening for hypertension ranged from cost saving to Int$ 758,695 per QALY gained. For screening of men starting at 55 years, all screening scenarios gave a high probability of being cost-effective. For screening of females starting at 55 years, the probability of favourable cost-effectiveness was 90% with one-off screening. In a lifetime horizon, cost per QALY gained was lower than the threshold of Int$ 15,883 in all screening scenarios among males. Similar results were found in females when starting screening at 55 years. Starting screening in females at 45 years had a high probability of being cost-effective if screening biannually was combined with increasing coverage of treatment by 20% or even if sole biannual screening was considered.ConclusionFrom a health economic perspective, integrating screening for hypertension into routine medical examination and related coverage by health insurance could be recommended. Screening for hypertension has a high probability of being cost-effective in preventing CVD. An adequate screening strategy can best be selected based on age, sex and screening interval.
Background and aims: Understanding why inequalities in alcohol-related mortality trends by sex and
Background and objectives Albuminuria change is often used to assess drug efficacy in intervention trials in nephrology. The change is often calculated using a variable number of urine samples collected at baseline and end of treatment. Yet more albuminuria measurements usually occur. Because albuminuria shows a large day-to-day variability, this study assessed to what extent the average and the precision of the antialbuminuric drug effect varies with the number of urine collections at each visit and the number of follow-up visits.Design, setting, participants, & measurements This study used data from three randomized intervention trials (Aliskiren Combined with Losartan in Type 2 Diabetes and Nephropathy, Selective Vitamin D Receptor Activation for Albuminuria Lowering, and Residual Albuminuria Lowering with Endothelin Antagonist Atrasentan) including patients with type 2 diabetes and macroalbuminuria. Albuminuria-lowering drug effects were estimated from one, two, or three urine collections at consecutive days before each study visit and reported as albuminuria change from baseline to end of treatment or the change over time considering an average of all follow-up albuminuria measurements.Results Increasing the number of urine collections for an albuminuria measurement at baseline and end of treatment or using all study visits during follow-up did not alter the average drug effect. The precision of the drug effect increased (decreased SEM) when the number of study visits and the number of urine collections per visit were increased. Using all albuminuria measurements at all study visits led to a 4-to 6-fold reduction in sample size to detect a 30% albuminuria-lowering treatment effect with 80% power compared with using baseline and end-of-treatment albuminuria measurements alone.Conclusions Increasing the number of urine collections per study visit and the number of visits over time does not change the average drug effect estimate but markedly increases the precision, thereby enhancing statistical power. Thus, clinical trial designs in diabetic nephropathy using albuminuria as an end point can be significantly improved, leading to smaller sample sizes and less complex trials.
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 mitigation of 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.
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