Background-Although previous studies have suggested that competitive athletes have a higher risk of atrial fibrillation than the general population, limited and inconsistent data are available on the association between regular physical activity and the risk of atrial fibrillation. Methods and Results-A systematic, comprehensive literature search was performed using MEDLINE, EMBASE, and COCHRANE until 2011. Extracted data from the eligible studies were meta-analyzed using fixed effects model. Four studies, which included 95 526 subjects, were eligible for meta-analysis. For all of the studies included, the extreme groups (ie, maximum versus minimal amount of physical activity) were used for the current analyses. The total number of participants belonging to the extreme groups was 43 672. The pooled odds ratio (95% confidence interval) for atrial fibrillation among regular exercisers was 1.08 (0.97-1.21). Conclusions-Our data do not support a statistically significant association between regular physical activity and increased incidence of atrial fibrillation. (Circ Arrhythm Electrophysiol. 2013;6:252-256.)

Background: Exposure measurement error is a central concern in air pollution epidemiology. Given that studies have been using ambient air pollution predictions as proxy exposure measures, the potential impact of exposure error on health effect estimates needs to be comprehensively assessed. Objectives: We aimed to generate wide-ranging scenarios to assess direction and magnitude of bias caused by exposure errors under plausible concentration–response relationships between annual exposure to fine particulate matter [PM in aerodynamic diameter ( )] and all-cause mortality. Methods: In this simulation study, we use daily predictions at spatial resolution to estimate annual exposures and their uncertainties for ZIP Codes of residence across the contiguous United States between 2000 and 2016. We consider scenarios in which we vary the error type (classical or Berkson) and the true concentration–response relationship between exposure and mortality (linear, quadratic, or soft-threshold—i.e., a smooth approximation to the hard-threshold model). In each scenario, we generate numbers of deaths using error-free exposures and confounders of concurrent air pollutants and neighborhood-level covariates and perform epidemiological analyses using error-prone exposures under correct specification or misspecification of the concentration–response relationship between exposure and mortality, adjusting for the confounders. Results: We simulate 1,000 replicates of each of 162 scenarios investigated. In general, both classical and Berkson errors can bias the concentration–response curve toward the null. The biases remain small even when using three times the predicted uncertainty to generate errors and are relatively larger at higher exposure levels. Discussion: Our findings suggest that the causal determination for long-term exposure and mortality is unlikely to be undermined when using high-resolution ambient predictions given that the estimated effect is generally smaller than the truth. The small magnitude of bias suggests that epidemiological findings are relatively robust against the exposure error. In practice, the use of ambient predictions with a finer spatial resolution will result in smaller bias. https://doi.org/10.1289/EHP10389