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.

中文翻译：

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退伍军人身心健康状况的共病网络结构

医疗和精神疾病之间的共病通常被认为是在单独的一对疾病之间。然而，一个重要的替代方案是将所有条件视为共病网络的一部分，其中包含患者与医疗保健系统之间的所有相互作用。对共病网络的分析可以检测和量化小规模研究未观察到的一般趋势。在这里，我们调查了从大约 100 万美国退伍军人的纵向医疗保健记录中得出的共病网络，这些人受到精神疾病和心理创伤的影响不成比例。网络分析揭示了共病的显着和异质模式，包括由常见共病条件组组成的多尺度社区结构。包括创伤后应激障碍在内的精神疾病是未来医疗发病率的有力预测因素。神经系统疾病和与慢性疼痛相关的疾病与精神疾病的共病性特别高。在不同条件下，合并症的程度与死亡率呈正相关。合并症受生物性别影响，可用于预测未来的诊断状态，样本外预测准确率为 90-92%。了解疾病共病的复杂模式有可能改进护理系统的设计，并制定考虑到心理和身体健康更广泛背景的有针对性的干预措施。神经系统疾病和与慢性疼痛相关的疾病与精神疾病的共病性特别高。在不同条件下，合并症的程度与死亡率呈正相关。合并症受生物性别影响，可用于预测未来的诊断状态，样本外预测准确率为 90-92%。了解疾病合并症的复杂模式有可能改进护理系统的设计，并制定考虑到心理和身体健康更广泛背景的有针对性的干预措施。神经系统疾病和与慢性疼痛相关的疾病与精神疾病的共病性特别高。在不同条件下，合并症的程度与死亡率呈正相关。合并症受生物性别影响，可用于预测未来的诊断状态，样本外预测准确率为 90-92%。了解疾病合并症的复杂模式有可能改进护理系统的设计，并制定考虑到心理和身体健康更广泛背景的有针对性的干预措施。样本外预测准确率为 90-92%。了解疾病共病的复杂模式有可能改进护理系统的设计，并制定考虑到心理和身体健康更广泛背景的有针对性的干预措施。样本外预测准确率为 90-92%。了解疾病合并症的复杂模式有可能改进护理系统的设计，并制定考虑到心理和身体健康更广泛背景的有针对性的干预措施。