The last 40 years of JAMA Psychiatry are reviewed as a celebration of its achievements. The focus of this article is on the evolution of big data as reflected in key journal articles. The review begins in 1984 with the introduction of the Epidemiology Catchment Area (ECA) study and Freedman’s editorial “Psychiatric Epidemiology Counts.” The ECA study (N = 17 000), for the first time in a survey, used clinical diagnosis in 5 urban communities, thus linking research and care to population rates of psychiatric diagnosis. The review then traces the subsequent evolution of big data to 5 overlapping phases, other population surveys in the US and globally, cohort studies, administrative claims, large genetic data sets, and electronic health records. Each of these topics are illustrated in articles in JAMA Psychiatry. The many caveats to these choices, the historical roots before 1984, as well as the controversy around the choice of topics and the term big data are acknowledged. The foundation for big data in psychiatry was built on the development of defined and reliable diagnosis, assessment tools that could be used in large samples, the computational evolution for handling large data sets, hypothesis generated by smaller studies of humans and animals with carefully crafted phenotypes, the welcoming of investigators from all over the world with calls for broader diversity, open access and the sharing of data, and introduction of electronic health records more recently. Future directions as well as the opportunities for the complementary roles of big and little data are described. JAMA Psychiatry will continue to be a rich resource of these publications.
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Weissman MM. Big Data Begin in Psychiatry. JAMA Psychiatry. 2020;77(9):967–973. doi:10.1001/jamapsychiatry.2020.0954
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