The new edition of A Dictionary of Epidemiology
Is the IEA Dictionary achieving its objectives? Is it accurate, useful, stimulating, and inclusive enough? Particularly, in this era of unprecedented speed, scope and depth of access to cognitive devices and information technologies, in the nature and scale of the sources of information and knowledge. In the midst of such galaxy, the dictionary is more relevant and useful than ever. This session will propose a reflection on epidemiology, science, technology, language, communication, policy, and philosophy in the systemic societies of our global village. The new edition contains many changes that stem from the ongoing methodological ďrevolutionĒ, which is deeply changing how we conceive epidemiological and clinical research, and how we assess the validity and relevance of findings. The renewal is having an immense impact on the production of scientific evidence in the health sciences, and on most policies, programs, services and products in which such evidence is used. One way to understand what is happening is to read the new definitions of concepts as risk, rate, risk ratio, attributable fraction, bias, residual confounding, interaction, open population, test hypothesis, null hypothesis, causal null, causal inference, Berksonís bias, Simpsonís paradox, frequentist statistics, generalizability, representativeness, missing data, standardization or overadjustment. Changes are also reflected in terms as collider, M-bias, causal diagram, backdoor (biasing path), instrumental variable, negative controls, inverse probability weighting, identifiability, transportability, positivity, ignorability, collapsibility, exchangeable, g-estimation, marginal structural models, risk set, immortal time bias, Mendelian randomization, nonmonotonic, dysregulation, potential outcome, sample space, or false discovery rate.