American journal of epidemiology
-
Women comprise about half of senior epidemiologists, but little is known about whether they are also viewed as leaders (i.e., authorities) in the field. We believe editorial roles are markers of leadership in a field. Our objective was to describe the distribution of gender across authorship of editorials published in 5 high-impact epidemiology journals over the past 8 years. ⋯ Only 31% (682/2,228) of all editorial authors and 36% (524/1,477) of unique editorial authors (i.e., counting each editorial author name only once) were women. We identified 1,180 editorials; 594 had sole authors, 24% (141/594) of whom were women, and 586 had 2 or more authors, 31% (184/586) of which had women as first authors. If women are underrepresented as editorial authors across epidemiology journals (e.g., as a marker of epidemiology leadership), the situation merits immediate correction.
-
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.
-
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.