Abstract
We present a pilot data mining analysis on the subset of the Public Health Database (PHD) of Lombardia Region concerning hospital discharge data relative to Acute Myocardial Infarctions without ST segment elevation (NON-STEMI). The analysis is carried out using nonlinear semi-parametric and parametric mixed effects models, in order to detect different patterns of growth in the number of NON-STEMI diagnoses within the 30 largest clinical structures of Lombardia Region, along the time period 2000–2007. The analysis is a seminal example of statistical support to decision makers in clinical context, aimed at monitoring the diffusion of new procedures and the effects of health policy interventions.
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Acknowledgements
This work is a part of the Strategic Program “Exploitation, integration and study of current and future health databases in Lombardia for Acute Myocardial Infarction” supported by “Ministero del Lavoro, della Salute e delle Politiche Sociali” and by “Direzione Generale Sanità—Regione Lombardia”. The authors wish to tank the Working Group for Cardiac Emergency in Milano and the Cardiology Society.
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Ieva, F., Paganoni, A.M., Secchi, P. (2013). Mining Administrative Health Databases for Epidemiological Purposes: A Case Study on Acute Myocardial Infarctions Diagnoses. In: Torelli, N., Pesarin, F., Bar-Hen, A. (eds) Advances in Theoretical and Applied Statistics. Studies in Theoretical and Applied Statistics(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35588-2_38
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DOI: https://doi.org/10.1007/978-3-642-35588-2_38
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