Showing papers in "Epidemiology in 2017"

Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants

Abstract: Mendelian randomization investigations are becoming more powerful and simpler to perform, due to the increasing size and coverage of genome-wide association studies and the increasing availability of summarized data on genetic associations with risk factors and disease outcomes. However, when using multiple genetic variants from different gene regions in a Mendelian randomization analysis, it is highly implausible that all the genetic variants satisfy the instrumental variable assumptions. This means that a simple instrumental variable analysis alone should not be relied on to give a causal conclusion. In this article, we discuss a range of sensitivity analyses that will either support or question the validity of causal inference from a Mendelian randomization analysis with multiple genetic variants. We focus on sensitivity analyses of greatest practical relevance for ensuring robust causal inferences, and those that can be undertaken using summarized data. Aside from cases in which the justification of the instrumental variable assumptions is supported by strong biological understanding, a Mendelian randomization analysis in which no assessment of the robustness of the findings to violations of the instrumental variable assumptions has been made should be viewed as speculative and incomplete. In particular, Mendelian randomization investigations with large numbers of genetic variants without such sensitivity analyses should be treated with skepticism.

Fifty Shades of Green: Pathway to Healthy Urban Living.

Abstract: Currently half the world population lives in cities, and this proportion is expected to increase rapidly to 70% over the next years. Over the years, we have created large, mostly grey cities with many high-rise buildings and little green space. Disease rates tend to be higher in urban areas than in rural areas. More green space in cities could reduce these rates. Here, we describe the importance of green space for health, and make recommendations for further research. Green space has been associated with many beneficial health effects, including reduced all-cause and cardiovascular mortality and improved mental health, possibly through mediators, such as reduced air pollution, temperature and stress, and increased physical activity, social contacts, and restoration. Additional studies are needed to strengthen the evidence base and provide further guidelines to transport planners, urban planners, and landscape architects. We need more longitudinal studies and intervention studies, further understanding of the contribution of various mechanisms toward health, and more information on susceptible populations and on where, when, how much, and what type of green space is needed. Also needed are standardized methods for green space quality assessments and evaluations of effectiveness of green prescriptions in clinical practice. Many questions are ideally suited for environmental epidemiologists, who should work with other stakeholders to address the right questions and translate knowledge into action. In conclusion, a growing evidence base supports the hypothesis that greener cities are healthier cities.

Effort-Reward Imbalance at Work and Incident Coronary Heart Disease: A Multicohort Study of 90,164 Individuals.

Abstract: Background: Epidemiologic evidence for work stress as a risk factor for coronary heart disease is mostly based on a single measure of stressful work known as job strain, a combination of high deman ...

Generalizing Study Results: A Potential Outcomes Perspective.

Abstract: Great care is taken in epidemiologic studies to ensure the internal validity of causal effect estimates; however, external validity has received considerably less attention. When the study sample is not a random sample of the target population, the sample average treatment effect, even if internally valid, cannot usually be expected to equal the average treatment effect in the target population. The utility of an effect estimate for planning purposes and decision making will depend on the degree of departure from the true causal effect in the target population due to problems with both internal and external validity. Herein, we review concepts from recent literature on generalizability, one facet of external validity, using the potential outcomes framework. Identification conditions sufficient for external validity closely parallel identification conditions for internal validity, namely conditional exchangeability; positivity; the same distributions of the versions of treatment; no interference; and no measurement error. We also require correct model specification. Under these conditions, we discuss how a version of direct standardization (the g-formula, adjustment formula, or transport formula) or inverse probability weighting can be used to generalize a causal effect from a study sample to a well-defined target population, and demonstrate their application in an illustrative example.

Biases in randomized trials: a conversation between trialists and epidemiologists

Abstract: Trialists and epidemiologists often employ different terminology to refer to biases in randomized trials and observational studies, even though many biases have a similar structure in both types of study. We use causal diagrams to represent the structure of biases, as described by Cochrane for randomized trials, and provide a translation to the usual epidemiologic terms of confounding, selection bias, and measurement bias. This structural approach clarifies that an explicit description of the inferential goal-the intention-to-treat effect or the per-protocol effect-is necessary to assess risk of bias in the estimates. Being aware of each other's terminologies will enhance communication between trialists and epidemiologists when considering key concepts and methods for causal inference.

Interventional Effects for Mediation Analysis with Multiple Mediators.

Abstract: The mediation formula for the identification of natural (in)direct effects has facilitated mediation analyses that better respect the nature of the data, with greater consideration of the need for confounding control. The default assumptions on which it relies are strong, however. In particular, they are known to be violated when confounders of the mediator-outcome association are affected by the exposure. This complicates extensions of counterfactual-based mediation analysis to settings that involve repeatedly measured mediators, or multiple correlated mediators. VanderWeele, Vansteelandt, and Robins introduced so-called interventional (in)direct effects. These can be identified under much weaker conditions than natural (in)direct effects, but have the drawback of not adding up to the total effect. In this article, we adapt their proposal to achieve an exact decomposition of the total effect, and extend it to the multiple mediator setting. Interestingly, the proposed effects capture the path-specific effects of an exposure on an outcome that are mediated by distinct mediators, even when-as often-the structural dependence between the multiple mediators is unknown, for instance, when the direction of the causal effects between the mediators is unknown, or there may be unmeasured common causes of the mediators.

Wildfire-specific Fine Particulate Matter and Risk of Hospital Admissions in Urban and Rural Counties.

Abstract: Background:The health impacts of wildfire smoke, including fine particles (PM2.5), are not well understood and may differ from those of PM2.5 from other sources due to differences in concentrations and chemical composition.Methods:First, for the entire Western United States (561 counties) for 2004–2

A Propensity-score-based Fine Stratification Approach for Confounding Adjustment When Exposure Is Infrequent.

Abstract: Background:When exposure is infrequent, propensity-score matching results in reduced precision because it discards a large proportion of unexposed patients. To our knowledge, the relative performance of propensity-score stratification in these circumstances has not been examined.Methods:Using an emp

Long-term Exposure to PM2.5 and Mortality Among Older Adults in the Southeastern US.

Abstract: Background:Little is known about what factors modify the effect of long-term exposure to PM2.5 on mortality, in part because in most previous studies certain groups such as rural residents and individuals with lower socioeconomic status (SES) are under-represented.Methods:We studied 13.1 million Med

Trade-offs of Personal Versus More Proxy Exposure Measures in Environmental Epidemiology.

Abstract: The technological ability to make personal measurements of toxicant exposures is growing rapidly While this can decrease measurement error and therefore help reduce attenuation of effect estimates, we argue that as measures of exposure or dose become more personal, threats to validity of study findings can increase in ways that more proxy measures may avoid We use directed acyclic graphs (DAGs) to describe conditions where confounding is introduced by use of more personal measures of exposure and avoided via more proxy measures of personal exposure or target tissue dose As exposure or dose estimates are more removed from the individual, they become less susceptible to biases from confounding by personal factors that can often be hard to control, such as personal behaviors Similarly, more proxy exposure estimates are less susceptible to reverse causation We provide examples from the literature where adjustment for personal factors in analyses that use more proxy exposure estimates have little effect on study results In conclusion, increased personalized exposure assessment has important advantages for measurement accuracy, but it can increase the possibility of biases from personal factors and reverse causation compared with more proxy exposure estimates Understanding the relation between more and less proxy exposures, and variables that could introduce confounding are critical components to study design

Outcome-wide Epidemiology.

Abstract: The author proposes that epidemiologic studies should more often assess the associations of a single exposure with multiple outcomes simultaneously. Such “outcome-wide epidemiology” will be especially important for exposures that may be beneficial for some outcomes but harmful for others. Outcome-wi

Matching Weights to Simultaneously Compare Three Treatment Groups: Comparison to Three-way Matching.

Abstract: Background:Propensity score matching is a commonly used tool. However, its use in settings with more than two treatment groups has been less frequent. We examined the performance of a recently developed propensity score weighting method in the three-treatment group setting.Methods:The matching weigh

Nature as a Trialist?: Deconstructing the Analogy Between Mendelian Randomization and Randomized Trials.

Abstract: Mendelian randomization (MR) studies are often described as naturally occurring randomized trials in which genetic factors are randomly assigned by nature. Conceptualizing MR studies as randomized trials has profound implications for their design, conduct, reporting, and interpretation. For example, analytic practices that are discouraged in randomized trials should also be discouraged in MR studies. Here, we deconstruct the oft-made analogy between MR and randomized trials. We describe four key threats to the analogy between MR studies and randomized trials: (1) exchangeability is not guaranteed; (2) time zero (and therefore the time for setting eligibility criteria) is unclear; (3) the treatment assignment is often measured with error; and (4) adherence is poorly defined. By precisely defining the causal effects being estimated, we underscore that MR estimates are often vaguely analogous to per-protocol effects in randomized trials, and that current MR methods for estimating analogues of per-protocol effects could be biased in practice. We conclude that the analogy between randomized trials and MR studies provides further perspective on both the strengths and the limitations of MR studies as currently implemented, as well as future directions for MR methodology development and application. In particular, the analogy highlights potential future directions for some MR studies to produce more interpretable and informative numerical estimates.

Causal Mediation Analysis of Survival Outcome with Multiple Mediators.

Abstract: Background Mediation analyses have been a popular approach to investigate the effect of an exposure on an outcome through a mediator. Mediation models with multiple mediators have been proposed for continuous and dichotomous outcomes. However, development of multimediator models for survival outcomes is still limited. Methods We present methods for multimediator analyses using three survival models: Aalen additive hazard models, Cox proportional hazard models, and semiparametric probit models. Effects through mediators can be characterized by path-specific effects, for which definitions and identifiability assumptions are provided. We derive closed-form expressions for path-specific effects for the three models, which are intuitively interpreted using a causal diagram. Results Mediation analyses using Cox models under the rare-outcome assumption and Aalen additive hazard models consider effects on log hazard ratio and hazard difference, respectively; analyses using semiparametric probit models consider effects on difference in transformed survival time and survival probability. The three models were applied to a hepatitis study where we investigated effects of hepatitis C on liver cancer incidence mediated through baseline and/or follow-up hepatitis B viral load. The three methods show consistent results on respective effect scales, which suggest an adverse estimated effect of hepatitis C on liver cancer not mediated through hepatitis B, and a protective estimated effect mediated through the baseline (and possibly follow-up) of hepatitis B viral load. Conclusions Causal mediation analyses of survival outcome with multiple mediators are developed for additive hazard and proportional hazard and probit models with utility demonstrated in a hepatitis study.

Estimating the Causal Effect of Low Levels of Fine Particulate Matter on Hospitalization.

Abstract: Background:In 2012, the EPA enacted more stringent National Ambient Air Quality Standards (NAAQS) for fine particulate matter (PM25) Few studies have characterized the health effects of air pollution levels lower than the most recent NAAQS for long-term exposure to PM25 (now 12 μg/m3)Methods:We

Brief Report: Investigating Uncertainty in the Minimum Mortality Temperature Methods and Application to 52 Spanish Cities

Abstract: The minimum mortality temperature from J- or U- shaped curves varies across cities with different climates. This variation conveys information on adaptation, but ability to characterize it is limited by the absence of a method to describe uncertainty in estimated minimum mortality temperatures. We propose an approximate parametric bootstrap estimator of confidence interval (CI) and standard error (SE) for the minimum mortality temperature from a temperature-mortality shape estimated by splines. The coverage of the estimated CIs was close to nominal value (95%) in the datasets simulated, though SEs were slightly high. Applying the method to 52 Spanish provincial capital cities showed larger minimum mortality temperatures in hotter cities, rising almost exactly at the same rate as annual mean temperature. The method proposed for computing CIs and SEs for minimums from spline curves allows comparing minimum mortality temperatures in different cities and investigating their associations with climate properly, allowing for estimation uncertainty.

Association Between Short-term Exposure to Ultrafine Particles and Mortality in Eight European Urban Areas.

Abstract: Background:Epidemiologic evidence on the association between short-term exposure to ultrafine particles and mortality is weak, due to the lack of routine measurements of these particles and standardized multicenter studies. We investigated the relationship between ultrafine particles and particulate

Validity of Cardiovascular Disease Event Ascertainment Using Linkage to UK Hospital Records.

Abstract: Background:Use of electronic health records for ascertainment of disease outcomes in large population-based studies holds much promise due to low costs, diminished study participant burden, and reduced selection bias. However, the validity of cardiovascular disease endpoints derived from electronic

Traffic-related Air Pollution and Attention in Primary School Children: Short-term Association.

Abstract: Background Although air pollution's short-term effects are well understood to be marked and preventable, its acute neuropsychological effects have, to our knowledge, not yet been studied. We aim to examine the association between daily variation in traffic-related air pollution and attention. Methods We conducted a follow-up study from January 2012 to March 2013 in 2,687 school children from 265 classrooms in 39 schools in Barcelona (Catalonia, Spain). We assessed four domains of children's attention processes every 3 months over four repeated visits providing a total of 10,002 computerized tests on 177 different days using the child Attention Network test (ANT). Ambient daily levels of nitrogen dioxide (NO2) and elemental carbon (EC) in particulate matter Results Daily ambient levels of both NO2 and EC were negatively associated with all attention processes (e.g., children in the bottom quartile of daily exposure to ambient NO2 levels had a 14.8 msecond [95% confidence interval, 11.2, 18.4] faster response time than those in the top quartile, which was equivalent to a 1.1-month [0.84, 1.37] retardation in the natural developmental improvement in response speed with age). Similar findings were observed after adjusting for the average indoor (classroom) levels of pollutants. Associations for EC were similar to those for NO2 and robust to several sensitivity analyses. Conclusions The short-term association of traffic-related air pollutants with fluctuations in attention adds to the evidence that air pollution may have potential harmful effects on neurodevelopment. See video abstract at, http://links.lww.com/EDE/B158.

Impact of Road Traffic Pollution on Pre-eclampsia and Pregnancy-induced Hypertensive Disorders

Abstract: Background:Road traffic is a major source of air pollution and noise. Both exposures have been associated with hypertension in adults, but pregnant women have been less studied.Methods:We examined single and joint effects of ambient air pollution and road traffic noise on pre-eclampsia and pregnancy

Interrelationships Between Walkability, Air Pollution, Greenness, and Body Mass Index.

Abstract: Background:Recent studies have linked urban environmental factors and body mass index (BMI); however, such factors are often examined in isolation, ignoring correlations across exposures.Methods:Using data on Nurses’ Health Study participants living in the Northeastern United States in 2006, we esti

Biomass Burning as a Source of Ambient Fine Particulate Air Pollution and Acute Myocardial Infarction.

Abstract: Background:Biomass burning is an important source of ambient fine particulate air pollution (PM2.5) in many regions of the world.Methods:We conducted a time-stratified case-crossover study of ambient PM2.5 and hospital admissions for myocardial infarction (MI) in three regions of British Columbia, C

Exposure-Response Analyses of Asbestos and Lung Cancer Subtypes in a Pooled Analysis of Case-Control Studies.

Abstract: BACKGROUND Evidence is limited regarding risk and the shape of the exposure-response curve at low asbestos exposure levels. We estimated the exposure-response for occupational asbestos exposure and assessed the joint effect of asbestos exposure and smoking by sex and lung cancer subtype in general population studies. METHODS We pooled 14 case-control studies conducted in 1985-2010 in Europe and Canada, including 17,705 lung cancer cases and 21,813 controls with detailed information on tobacco habits and lifetime occupations. We developed a quantitative job-exposure-matrix to estimate job-, time period-, and region-specific exposure levels. Fiber-years (ff/ml-years) were calculated for each subject by linking the matrix with individual occupational histories. We fit unconditional logistic regression models to estimate odds ratios (ORs), 95% confidence intervals (CIs), and trends. RESULTS The fully adjusted OR for ever-exposure to asbestos was 1.24 (95% CI, 1.18, 1.31) in men and 1.12 (95% CI, 0.95, 1.31) in women. In men, increasing lung cancer risk was observed with increasing exposure in all smoking categories and for all three major lung cancer subtypes. In women, lung cancer risk for all subtypes was increased in current smokers (ORs ~two-fold). The joint effect of asbestos exposure and smoking did not deviate from multiplicativity among men, and was more than additive among women. CONCLUSIONS Our results in men showed an excess risk of lung cancer and its subtypes at low cumulative exposure levels, with a steeper exposure-response slope in this exposure range than at higher, previously studied levels. (See video abstract at, http://links.lww.com/EDE/B161.).

On a Square-Root Transformation of the Odds Ratio for a Common Outcome.

Abstract: Proposition 2. For causative exposures with p1 > p0, if 0:2 p0; p1 0:8, then 1 OR RR 4 and 4 5 sqrt(OR) RR 5 4 ; and if 0:1 p0; p1 0:9, then 1 OR RR 9 and 3 5 sqrt(OR) RR 5 3 . Proof. If 0:1 p0; p1 0:9, then OR RR = 1 p0 1 p1 1 0:1 1 0:9 = 9 and if 0:2 p0; p1 0:8 then OR RR = 1 p0 1 p1 1 0:2 1 0:8 = 4. We also have that sqrt(OR) RR = q p1(1 p0) (1 p1)p0 = p1 p0 = q p0(1 p0) (1 p1)p1 ; if 0:1 p0; p1 0:9

Collinearity and Causal Diagrams: A Lesson on the Importance of Model Specification.

Abstract: Background:Correlated data are ubiquitous in epidemiologic research, particularly in nutritional and environmental epidemiology where mixtures of factors are often studied. Our objectives are to demonstrate how highly correlated data arise in epidemiologic research and provide guidance, using a dire

Patterns of Gestational Weight Gain in Early Pregnancy and Risk of Gestational Diabetes Mellitus.

Abstract: Background Despite a call to study the effect of weight gain pattern on development of gestational diabetes mellitus, few studies have correctly adjusted for independent effects of gain after the first trimester. We used a conditional percentile approach to model the independent association between first and second trimester weight gain trajectories and development of gestational diabetes. Methods We sampled women delivering singleton infants from 1998 to 2010 at Magee-Womens Hospital in Pittsburgh, PA, (n = 124,590) using a case-cohort design. We modeled weight gain trajectories in the first and second trimesters of pregnancy using conditional weight gain percentiles, and used multivariable logistic regression to assess independent associations of the trajectory with gestational diabetes. We studied associations separately by prepregnancy body mass index category. Results The final cohort included 806 women with gestational diabetes and 4,819 randomly sampled women who delivered without gestational diabetes. In normal-weight women, every SD increase in weight gain in the first trimester above her predicted gain was associated with a 23% increased odds of gestational diabetes (95% confidence interval: 0.2%, 51%). Second trimester gain trajectory was not associated with gestational diabetes (odds ratio: 1.1, [95% confidence interval: 0.9, 1.3]) although the direction of effect was positive. This pattern was similar in obese class I and II but not in overweight and obese class III women. Conclusions An upward weight gain trajectory in the first trimester was positively associated with gestational diabetes for women of most prepregnancy BMI categories. Second trimester weight gain trajectory was not associated with gestational diabetes for any group.

Parametric Mediational g-Formula Approach to Mediation Analysis with Time-varying Exposures, Mediators, and Confounders.

Abstract: The assessment of direct and indirect effects with time-varying mediators and confounders is a common but challenging problem, and standard mediation analysis approaches are generally not applicable in this context. The mediational g-formula was recently proposed to address this problem, paired with

Education, Socioeconomic Status, and Intelligence in Childhood and Stroke Risk in Later Life: A Meta-analysis.

Abstract: Background:Stroke is the second most common cause of death, and a common cause of dependency and dementia. Adult vascular risk factors and socioeconomic status (SES) are associated with increased risk, but less is known about early life risk factors, such as education, childhood SES, or intelligence

The Changing Face of Epidemiology: Gender Disparities in Citations?

Abstract: Background:Female biomedical scientists tend to publish fewer articles as last author than their male colleagues and accrue fewer citations per publication. We seek to understand whether epidemiology follows this pattern.Methods:We gathered aggregate information on the current gender distribution of

Variable Selection for Confounding Adjustment in High-dimensional Covariate Spaces When Analyzing Healthcare Databases.

Abstract: Background:Data-adaptive approaches to confounding adjustment may improve performance beyond expert knowledge when analyzing electronic healthcare databases and have additional practical advantages for analyzing multiple databases in rapid cycles. Improvements seemed possible if outcome predictors w