Quantifying age- and gender-related diabetes comorbidity risks using nation-wide big claims data

TL;DR

This study uses nationwide Austrian inpatient data to quantify age- and gender-specific risks of diabetes-related comorbidities, revealing known and novel associations through a new statistical network approach.

Contribution

It introduces a novel statistical-network methodology for analyzing large-scale claims data to identify and characterize diabetes comorbidities with respect to age, gender, and temporal order.

Findings

Identified 123 significant comorbidities for diabetes.

Recovered known diabetic comorbidities like retinopathies and hypertension.

Discovered lesser-known associations such as epilepsy and mental disorders.

Abstract

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 recovered, such as retinopathies, hypertension, chronic kidney diseases, etc. This validates the method. Additionally, a number of comorbidities are identified which have only been recognized to a lesser extent, for example epilepsy, sepsis, or mental disorders. The temporal evolution, age, and gender-dependence of these comorbidities are discussed. The new statistical-network methodology developed here can be readily applied to other chronic diseases.