American journal of epidemiology
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Whether persons without prevalent cardiovascular disease (CVD) but elevated levels of high-sensitivity cardiac troponin T (hs-cTnT) or N-terminal pro-B-type natriuretic peptide (NT-proBNP) are at high risk of infection is unknown. Using 1996-2013 data from the Atherosclerosis Risk in Communities Study, we estimated hazard ratios for incident hospitalization with infection in relation to plasma hs-cTnT and NT-proBNP concentrations among participants without prevalent CVD and contrasted them with hazard ratios for persons with prevalent CVD (coronary heart disease, heart failure, or stroke). In a multivariable Cox model, prevalent CVD was significantly associated with risk of hospitalization with infection (hazard ratio (HR) = 1.31, 95% confidence interval (CI): 1.19, 1.45). ⋯ The 15-year cumulative incidences of hospitalization with infection were similar for participants with prevalent CVD and participants who did not have prevalent CVD but had hs-cTnT ≥14 ng/L or NT-proBNP ≥248.1 pg/mL. Thus, hs-cTnT and NT-proBNP were independently associated with infection risk. Persons without CVD but with elevated hs-cTnT or NT-proBNP levels should be recognized to have similar infection risks as persons with prevalent CVD.
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Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on "Big Data," it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. ⋯ We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.
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Early childhood antibiotic exposure induces changes in gut microbiota reportedly associated with the development of attention-deficit/hyperactivity disorder (ADHD). We conducted a population-based cohort study to examine the association between antibiotic use in the first year of life and ADHD risk. We included children born in Manitoba, Canada, between 1998 and 2017. ⋯ In secondary analyses of the matched cohort, ADHD risk increase was observed in those exposed to 4 or more antibiotic courses or a duration longer than 3 weeks. These associations were not observed in the sibling cohort. We concluded that antibiotic exposure in the first year of life does not pose an ADHD risk on a population level.
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The incidence of tuberculosis (TB) in the United States has stabilized, and additional interventions are needed to make progress toward TB elimination. However, the impact of such interventions depends on local demography and the heterogeneity of populations at risk. Using state-level individual-based TB transmission models calibrated to California, Florida, New York, and Texas, we modeled 2 TB interventions: 1) increased targeted testing and treatment (TTT) of high-risk populations, including people who are non-US-born, diabetic, human immunodeficiency virus (HIV)-positive, homeless, or incarcerated; and 2) enhanced contact investigation (ECI) for contacts of TB patients, including higher completion of preventive therapy. ⋯ TTT delivered to smaller populations with higher TB risk (e.g., HIV-positive persons, homeless persons) and ECI were generally more efficient but had less overall impact on incidence. TTT targeted to smaller, highest-risk populations and ECI can be highly efficient; however, major reductions in incidence will only be achieved by also targeting larger, moderate-risk populations. Ultimately, to eliminate TB in the United States, a combination of these approaches will be necessary.