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Quantifying Age- and Gender-related Diabetes Comorbidity Risks Using Nation-wide Big Claims Data by Klimek, Peter; Kautzky-Willer, Alexandra; Chmiel, Anna; Schiller-Früwirth, Irmgard; Thurner, Stefan is a scholarly article available to read on EtoBox.

What is Quantifying Age- and Gender-related Diabetes Comorbidity Risks Using Nation-wide Big Claims Data about?

Currently emerging "big data" techniques are reshaping medical science into a data science. Medical claims data allow assessing an entire nation's health state in a quantitative way, in particular with regard to the occurrences and consequences of chronic and pandemic diseases like diabetes. We develop a quantitative, statistical approach to test for associations between the incidence of type 1 or type 2 diabetes and any possible other disease as provided by the ICD10 diagnosis codes using a complete set of Austrian inpatient data. With a new co-occurrence analysis the relative risks for each possible comorbidity are studied as a function of patient age and gender, a temporal analysis investigates whether the onset of diabetes typically precedes or follows the onset of the other disease. The samples is always of maximal size, i.e. contains all patients with that comorbidity within the country. The present study is an equivalent of almost 40,000 studies, all with maximum patient number available. Out of more than thousand possible associations, 123 comorbid diseases for type 1 or type 2 diabetes are identified at high significance levels. Well known diabetic comorbidities are recove

Author

Klimek, Peter; Kautzky-Willer, Alexandra; Chmiel, Anna; Schiller-Früwirth, Irmgard; Thurner, Stefan

Published

2013

Language

EN