| Literature DB >> 32831602 |
Aaron F Alexander-Bloch1,2, Armin Raznahan3, Russell T Shinohara4, Samuel R Mathias5, Harini Bathulapalli6,7, Ish P Bhalla8, Joseph L Goulet6,7, Theodore D Satterthwaite1, Danielle S Bassett1,9,10,11,12,13, David C Glahn5, Cynthia A Brandt6,7.
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.Entities:
Keywords: co-morbidity; modularity; network science; psychiatry; veterans
Year: 2020 PMID: 32831602 PMCID: PMC7426059 DOI: 10.1098/rspa.2019.0790
Source DB: PubMed Journal: Proc Math Phys Eng Sci ISSN: 1364-5021 Impact factor: 2.704