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For all diseases, prevalence has been carefully studied. In the "classic"paradigm, the prevalence of different diseases has usually been studiedseparately. Accumulating evidences have shown that diseases can be"correlated". The joint analysis of prevalence of multiple diseases can provideimportant insights beyond individual-disease analysis, however, has not beenwell conducted. In this study, we take advantage of the uniquely valuableTaiwan National Health Insurance Research Database (NHIRD), and conduct apan-disease analysis of period prevalence trend. The goal is to identifyclusters within which diseases share similar period prevalence trends. For thispurpose, a novel penalization pursuit approach is developed, which has anintuitive formulation and satisfactory properties. In data analysis, the periodprevalence values are computed using records on close to 1 million subjects and14 years of observation. For 405 diseases, 35 nontrivial clusters (with sizeslarger than one) and 27 trivial clusters (with sizes one) are identified. Theresults differ significantly from those of the alternatives. A closerexamination suggests that the clustering results have sound interpretations.This study is the first to conduct a pan-disease clustering analysis ofprevalence trend using the uniquely valuable NHIRD data and can have importantvalue in multiple aspects.

Pan-disease clustering analysis of the trend of period prevalence