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Summary: Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098, Introduction The past decade has witnessed a paradigm shift in the environmental sciences as studies have shifted from examining specific exposures to attempting to comprehensively measure and characterize the broad [...]

Background: The differential impact of various demographic characteristics and comorbid conditions on development of heart failure (HF) with preserved (pEF) and reduced ejection fraction (rEF) is not well studied among the elderly. Methods: Using Medicare claims data linked to electronic health records, we conducted an observational cohort study of individuals ≥ 65 years of age without HF. A Cox proportional hazards model accounting for competing risk of HFrEF and HFpEF incidence was constructed. A gradient-boosted model (GBM) assessed the relative influence (RI) of each predictor in the development of HFrEF and HFpEF. Results: Among 138,388 included individuals, 9701 developed HF (incidence rate = 20.9 per 1000 person-years). Males were more likely to develop HFrEF than HFpEF (HR = 2.07, 95% CI: 1.81– 2.37 vs. 1.11, 95% CI: 1.02– 1.20, P for heterogeneity < 0.01). Atrial fibrillation and pulmonary hypertension had stronger associations with the risk of HFpEF (HR = 2.02, 95% CI: 1.80– 2.26 and 1.66, 95% CI: 1.23– 2.22) while cardiomyopathy and myocardial infarction were more strongly associated with HFrEF (HR = 4.37, 95% CI: 3.21– 5.97 and 1.94, 95% CI: 1.23– 3.07). Age was the strongest predictor across all HF subtypes with RI from GBM > 35%. Atrial fibrillation was the most influential comorbidity for the development of HFpEF (RI = 8.4%) while cardiomyopathy was the most influential comorbidity for the development of HFrEF (RI = 20.7%). Conclusion: These findings of heterogeneous relationships between several important risk factors and heart failure types underline the potential differences in the etiology of HFpEF and HFrEF. [ABSTRACT FROM AUTHOR]

Small changes in bioavailability of narrow therapeutic index (NTI) drugs can alter clinical outcomes, raising concern over generic NTI substitution. We surveyed pharmacists to identify their perceptions of generic NTI drugs, their frequency of performing generic NTI substitution, and predictors of this behavior. Of 710 respondents (33% response rate), 87% perceived generic NTI drugs as effective as their brand‐name versions and 94% as safe. Whereas 82% almost always performed generic NTI substitution for initial prescriptions, only 60% did for refills. Pharmacists in non‐chain settings (odds ratio (OR) = 2.37; 95% confidence interval (CI) = 1.40–4.02), in practice longer (per year OR = 1.04; 95% CI = 1.02–1.06), in states with affirmative patient consent laws (OR = 1.88; 95% CI = 1.06–3.32), and in states with NTI‐specific substitution requirements (OR = 1.95; 95% CI = 1.16–3.26) were more likely not to substitute initial prescriptions. Education of non‐chain and veteran pharmacists and elimination of affirmative patient consent and NTI‐specific substitution requirements could increase generic NTI substitution. [ABSTRACT FROM AUTHOR]

Background: Ejection fraction (EF) class is an important predictor of treatment response in heart failure (HF); however, administrative claims databases lack information on EF, limiting their usefulness in clinical and health services research of HF.Methods and Results: We linked Medicare claims data to electronic medical records containing EF measurements for a cohort of 11 073 patients with HF from 2 academic medical centers. A a claims-based model predicting EF class was constructed using data from center 1 ("training sample") and validated using data from center 2 ("testing sample). Linear and logistic regression models with least absolute square shrinkage operator and Bayesian information criteria were developed to select the relevant predictor variables out of the total 57 candidate variables in the training sample. Higher accuracy was noted in the testing sample with models classifying patients into 2 EF classes (reduced EF <0.45) versus preserved EF (≥0.45) when compared with classifying patients into 3 EF classes (reduced, <0.40, moderately reduced, 0.40-0.49, or preserved, ≥0.50). In the testing sample, the most efficient model had 35 predictors and resulted in 83% of patients being correctly classified (95% CI, 82%-84%). The model had positive predictive value of 0.73 (95% CI, 0.68-0.78) and 0.84 (95% CI, 0.83-0.86) and sensitivity of 0.29 (95% CI, 0.25-0.32) and 0.97 (95% CI, 0.97-0.98) for reduced and preserved EF, respectively. In addition to HF-specific diagnosis codes, other factors including age, sex, medication use, and comorbidities, such as myocardial infarction and valve disorders, were important discriminators between EF classes.Conclusions: The claims-based model developed in this study may be used to identify patient subgroups with specific EF class in studies evaluating the health outcomes, utilization patterns, and cost, of HF patients in routine care when EF measurements are not available. [ABSTRACT FROM AUTHOR]