TY - JOUR
T1 - The architecture of co-morbidity networks of physical and mental health conditions in military veterans
AU - Alexander-Bloch, Aaron F.
AU - Raznahan, Armin
AU - Shinohara, Russell T.
AU - Mathias, Samuel R.
AU - Bathulapalli, Harini
AU - Bhalla, Ish P.
AU - Goulet, Joseph L.
AU - Satterthwaite, Theodore D.
AU - Bassett, Danielle S.
AU - Glahn, David C.
AU - Brandt, Cynthia A.
N1 - Funding Information:
Ethics. The study was approved by the Human Investigation Committees at VA Connecticut Healthcare System’ West Haven. Data accessibility. Data are available at https://github.com/aaronab/comorbidity_networks. Authors’ contributions. A.F.A.-B. designed the study, analysed data and wrote the paper; A.R., R.T.S., S.R.M., H.B., I.P.B., J.L.G., T.D.S., D.S.B. and D.C.G. provided critical feedback and wrote the paper; C.A.B. participated in the generation of the dataset, designed the study and wrote the paper. All authors gave final approval for publication and agree to be held accountable for the work performed therein. Competing interests. We declare we have no competing interests. Funding. The Veterans Women’s Cohort Study is supported by VA HSR&D grant no. DHI 07-065. Financial support for authors was provided by National Institutes of Health (NIH) grant nos. K08MH120564 (PI: A.F.A.-B.), R01MH078143 (PI: D.C.G.), R01MH112847 (PIs: R.T.S./T.D.S.) and R01HD086888 (PI: D.S.B.), as well as the NIH intramural program (ZIA MH002794: PI, A.R.). Acknowledgements. We are grateful to Bob Rosenheck and Joe Erdos for helpful feedback on earlier drafts of the manuscript. We are grateful to Rick Betzel for code to visualize community-labelled networks. Disclaimer. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the US Department of Veterans Affairs.
Publisher Copyright:
© 2020 The Author(s).
PY - 2020/7
Y1 - 2020/7
N2 - 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.
AB - 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.
KW - co-morbidity
KW - modularity
KW - network science
KW - psychiatry
KW - veterans
UR - http://www.scopus.com/inward/record.url?scp=85094661071&partnerID=8YFLogxK
U2 - 10.1098/rspa.2019.0790
DO - 10.1098/rspa.2019.0790
M3 - Article
AN - SCOPUS:85094661071
SN - 1364-5021
VL - 476
JO - Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
JF - Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
IS - 2239
M1 - 0790
ER -