Finding Associations among Chronic Conditions by Bootstrap and Multiple Correspondence Analysis.

Contemporary societies are suffering from negative population growth, with the consequent population aging. The prevalence of some chronic diseases, of slow progress and long duration, have become one of the main problems for healthcare systems. In particular, high blood pressure, diabetes mellitus, chronic obstructive pulmonary disease, and depression are health-status with a high economic and social burden. In collaboration with University Hospital of Fuenlabrada (Spain), we analyze in this work data (mainly diagnoses and drugs, both coded) from patients suffering from these chronic conditions. Given the high dimensionality of the data, we performed a hypothesis test with bootstrapping in order to select discriminative features that we subsequently analyzed using Multiple Correspondence Analysis (MCA). MCA allowed us to find associations among features and health-statuses, which may reveal not evident relationships. From the analysis carried out, on the one hand, some evidences are concluded, which can be used to validate the methodology followed in this work. On the other hand, we have drawn some conclusions that could assist in clinical decision-making, such as for example, offering more specialized care to patients stratified in the same health-status.