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The human diet consists of a complex mixture of components. To realistically assess dietary impacts on health, new statistical tools that can better address nonlinear, collinear, and interactive relationships are necessary. Using data from 1,928 healthy participants in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort (1985-2006), we explored the association between 12 dietary factors and 10-year predicted risk of atherosclerotic cardiovascular disease (ASCVD) using an innovative approach, Bayesian kernel machine regression (BKMR). Employing BKMR, we found that among women, unprocessed red meat was most strongly related to the outcome: An interquartile range increase in unprocessed red meat consumption was associated with a 0.07-unit (95% credible interval: 0.01, 0.13) increase in ASCVD risk when intakes of other dietary components were fixed at their median values (similar results were obtained when other components were fixed at their 25th and 75th percentile values). Among men, fruits had the strongest association: An interquartile range increase in fruit consumption was associated with -0.09-unit (95% credible interval (CrI): -0.16, -0.02), -0.10-unit (95% CrI: -0.16, -0.03), and -0.11-unit (95% CrI: -0.18, -0.04) lower ASCVD risk when other dietary components were fixed at their 25th, 50th (median), and 75th percentile values, respectively. Using BKMR to explore the complex structure of the total diet, we found distinct sex-specific diet-ASCVD relationships and synergistic interaction between whole grain and fruit consumption., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Methodological advancements in epidemiology, biostatistics, and data science have strengthened the research world's ability to use data captured from electronic health records (EHRs) to address pressing medical questions, but gaps remain. We describe methods investments that are needed to curate EHR data toward research quality and to integrate complementary data sources when EHR data alone are insufficient for research goals. We highlight new methods and directions for improving the integrity of medical evidence generated from pragmatic trials, observational studies, and predictive modeling. We also discuss needed methods contributions to further ease data sharing across multisite EHR data networks. Throughout, we identify opportunities for training and for bolstering collaboration among subject matter experts, methodologists, practicing clinicians, and health system leaders to help ensure that methods problems are identified and resulting advances are translated into mainstream research practice more quickly., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

With global climate change, more frequent severe snowstorms are expected; however, evidence regarding their health effects is very limited. We gathered detailed medical records on hospital admissions (n = 433,037 admissions) from the 4 largest hospitals in Boston, Massachusetts, during the winters of 2010-2015. We estimated the percentage increase in hospitalizations for cardiovascular and cold-related diseases, falls, and injuries on the day of and for 6 days after a day with low (0.05-5.0 inches), moderate (5.1-10.0 inches), or high (>10.0 inches) snowfall using distributed lag regression models. We found that cardiovascular disease admissions decreased by 32% on high snowfall days (relative risk (RR) = 0.68, 95% confidence interval (CI): 0.54, 0.85) but increased by 23% 2 days after (RR = 1.23, 95% CI: 1.01, 1.49); cold-related admissions increased by 3.7% on high snowfall days (RR = 3.7, 95% CI: 1.6, 8.6) and remained high for 5 days after; and admissions for falls increased by 18% on average in the 6 days after a moderate snowfall day (RR = 1.18, 95% CI: 1.09, 1.27). We did not find a higher risk of hospitalizations for injuries. To our knowledge, this is the first study in which the time course of hospitalizations during and immediately after snowfall days has been examined. These findings can be translated into interventions that prevent hospitalizations and protect public health during harsh winter conditions., (© The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Building on a unique exposure assessment project in New York, New York, we examined the relationship of particulate matter with aerodynamic diameter less than 2.5 μm and nitrogen dioxide with birth weight, restricting the population to term births to nonsmokers, along with other restrictions, to isolate the potential impact of air pollution on growth. We included 252,967 births in 2008-2010 identified in vital records, and we assigned exposure at the residential location by using validated models that accounted for spatial and temporal factors. Estimates of association were adjusted for individual and contextual sociodemographic characteristics and season, using linear mixed models to quantify the predicted change in birth weight in grams related to increasing pollution levels. Adjusted estimates for particulate matter with aerodynamic diameter less than 2.5 μm indicated that for each 10-µg/m(3) increase in exposure, birth weights declined by 18.4, 10.5, 29.7, and 48.4 g for exposures in the first, second, and third trimesters and for the total pregnancy, respectively. Adjusted estimates for nitrogen dioxide indicated that for each 10-ppb increase in exposure, birth weights declined by 14.2, 15.9, 18.0, and 18.0 g for exposures in the first, second, and third trimesters and for the total pregnancy, respectively. These results strongly support the association of urban air pollution exposure with reduced fetal growth.