Identifying comorbidity patterns of health conditions via cluster analysis of pairwise concordance statistics

Identification of comorbidity patterns of health conditions is critical for evidence-based practice to improve the prevention, treatment and health care of relevant diseases. Existing approaches focus mainly on either using descriptive measures of comorbidity in terms of the prevalence of coexisting conditions, or addressing the prevalence of comorbidity based on a particular disease (e.g. psychosis) or a specific population (e.g. hospital patients). As coincidental comorbidity by chance increases with the prevalence rates of the conditions, which in turn depend heavily on the population under study, research findings on comorbidity patterns using those approaches may provide unreliable results. In this paper, we propose an asymmetric version of Somers' D statistic to provide a quantitative measure of comorbidity that accounts for co-occurrence of conditions by chance, and develop a unified clustering algorithm to identify comorbidity patterns with adjustment for multiple testing and control for the false discovery rate. We assess the applicability of the proposed comorbidity measure and investigate the performance of the proposed procedure for the adjustment of multiple testing by conducting a comparative study and a sensitivity analysis, respectively. The proposed method is illustrated using a national survey data set of mental health and wellbeing and a national health survey data set in Australia.