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‘Big data’ is on trend and the term is used in equal measure to reflect both one of the greatest challenges and likeliest solutions to future scientific advances from fundamental understanding in astrophysics, climate change, economics, health and disease.1 Like many trends, it means different things to different people. In medicine, it is used to describe the data derived from large populations in epidemiology studies, high fidelity multiscale ‘omic datasets across spatial scales within individuals or sometimes a combination of the two. Big data will often capture information at a single time point. Typically, it does not address temporal scales of chronic disease including day-to-day variability, response to perturbations such as intercurrent infection, decompensation of the disease or response to therapeutic interventions and is rarely obtained over a life course. Observations will therefore always be limited by what is measured, when and in whom and will only ever provide estimates of what is ‘real’ within the larger group from which the sample is taken. Big data that includes large populations makes interpretations more robust and generalisable. Indeed, as the population studied or sample size approaches a majority, or at least a sizeable minority, of the whole population then the observations begin to no longer be estimates, but simply a description of the population.
In this issue of Thorax, Bloom and colleagues sought to investigate the frequency of asthma exacerbations in a general population and their association with age and asthma severity.2 Asthma exacerbations are when individuals experience symptoms beyond usual day-to-day fluctuation leading to a change in treatment defined here as a requirement for a course of oral …
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