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

Ng, Shu Kay, Holden, Libby and Sun, Jing (2012) Identifying comorbidity patterns of health conditions via cluster analysis of pairwise concordance statistics. Statistics in Medicine, 31 27: 3393-3405. doi:10.1002/sim.5426


Author Ng, Shu Kay
Holden, Libby
Sun, Jing
Title Identifying comorbidity patterns of health conditions via cluster analysis of pairwise concordance statistics
Journal name Statistics in Medicine   Check publisher's open access policy
ISSN 0277-6715
1097-0258
Publication date 2012-11-01
Sub-type Article (original research)
DOI 10.1002/sim.5426
Volume 31
Issue 27
Start page 3393
End page 3405
Total pages 13
Place of publication West Sussex, United Kingdom
Publisher John Wiley and Sons
Collection year 2013
Language eng
Formatted abstract
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.
Keyword Asymmetric Somers' D statistic
Comorbidity
Concordance statistic
Multiplicity problem
National survey data
Overlapping clusters
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Non HERDC
School of Public Health Publications
 
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Created: Wed, 16 Jan 2013, 23:54:53 EST by Geraldine Fitzgerald on behalf of School of Public Health