The architecture of co-morbidity networks of physical and mental health conditions in military veterans. (29th July 2020)

• Record Type: Journal Article
• Authors: Alexander-Bloch, Aaron F.; Raznahan, Armin; Shinohara, Russell T.; Mathias, Samuel R.; Bathulapalli, Harini; Bhalla, Ish P.; Goulet, Joseph L.; Satterthwaite, Theodore D.; Bassett, Danielle S.; Glahn, David C.; Brandt, Cynthia A.
• Is Part Of: Proceedings. Volume 476:Number 2239(2020)
• Publication Date: 2020-07-29
• Subjects: network science -- co-morbidity -- veterans -- psychiatry -- modularity; Physical sciences -- Periodicals; Engineering -- Periodicals; Mathematics -- Periodicals; 500
• DOI: 10.1098/rspa.2019.0790
• Languages: English
• ISSNs: 1364-5021

Abstract

Abstract : Co-morbidity between medical and psychiatric conditions is commonly considered between individual pairs of conditions. However, an important alternative is to consider all conditions as part of a co-morbidity network, which encompasses all interactions between patients and a healthcare system. Analysis of co-morbidity networks could detect and quantify general tendencies not observed by smaller-scale studies. Here, we investigate the co-morbidity network derived from longitudinal healthcare records from approximately 1 million United States military veterans, a population disproportionately impacted by psychiatric morbidity and psychological trauma. Network analyses revealed marked and heterogenous patterns of co-morbidity, including a multi-scale community structure composed of groups of commonly co-morbid conditions. Psychiatric conditions including posttraumatic stress disorder were strong predictors of future medical morbidity. Neurological conditions and conditions associated with chronic pain were particularly highly co-morbid with psychiatric conditions. Across conditions, the degree of co-morbidity was positively associated with mortality. Co-morbidity was modified by biological sex and could be used to predict future diagnostic status, with out-of-sample prediction accuracy of 90–92%. Understanding complex patterns of disease co-morbidity has the potential to lead to improved designs of systems of care and the development of targeted interventions that consider the broader context of mental and physical health.