Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts 02115, USA.
Depress Anxiety. 2012 Sep;29(9):797-806. doi: 10.1002/da.21924. Epub 2012 May 14.
Disease burden estimates rarely consider comorbidity. Using a recently developed methodology for integrating information about comorbidity into disease burden estimates, we examined the comparative burdens of nine mental and 10 chronic physical disorders in the National Comorbidity Survey Replication (NCS-R).
Face-to-face interviews in a national household sample (n = 5,692) assessed associations of disorders with scores on a visual analog scale (VAS) of perceived health. Multiple regression analysis with interactions for comorbidity was used to estimate these associations. Simulation was used to estimate incremental disorder-specific effects adjusting for comorbidity.
The majority of respondents (74.9%) reported one or more disorders. Of respondents with disorders, 73.8-98.2% reported having at least one other disorder. The best-fitting model to predict VAS scores included disorder main effects and interactions for number of disorders. Adjustment for comorbidity reduced individual-level disorder-specific burden estimates substantially, but with considerable between-disorder variation (0.07-0.69 ratios of disorder-specific estimates with and without adjustment for comorbidity). Four of the five most burdensome disorders at the individual level were mental disorders based on bivariate analyses (panic/agoraphobia, bipolar disorder, posttraumatic stress disorder, major depression) but only two based on multivariate analyses, adjusting for comorbidity (panic/agoraphobia, major depression). Neurological disorders, chronic pain conditions, and diabetes were the other most burdensome individual-level disorders. Chronic pain conditions, cardiovascular disorders, arthritis, insomnia, and major depression were the most burdensome societal-level disorders.
Adjustments for comorbidity substantially influence estimates of disease burden, especially those of mental disorders, underlining the importance of including information about comorbidity in studies of mental disorders.
疾病负担的评估很少考虑合并症。本研究使用一种新开发的方法，将合并症的相关信息纳入疾病负担的评估中，以评估 9 种精神疾病和 10 种慢性躯体疾病在国家共病调查复制（NCS-R）中的相对负担。
对全国家庭样本（n=5692）进行面对面访谈，评估疾病与视觉模拟量表（VAS）上的健康感知评分之间的关联。采用多元回归分析方法，并对合并症进行交互作用分析，以估计这些关联。采用模拟方法，在调整合并症的基础上，估计疾病特异性的增量效应。
大多数受访者（74.9%）报告了一种或多种疾病。在有疾病的受访者中，73.8%-98.2%报告至少有另一种疾病。预测 VAS 评分的最佳拟合模型包括疾病的主要效应和合并症的交互作用。调整合并症会大大降低个体疾病特异性负担的估计值，但存在相当大的疾病间差异（不调整合并症与调整合并症时疾病特异性估计值的比值为 0.07-0.69）。基于双变量分析，在个体层面上，五种最具负担的疾病中有四种是精神疾病（惊恐/广场恐惧症、双相情感障碍、创伤后应激障碍、重度抑郁症），但基于多元分析，调整合并症后只有两种是精神疾病（惊恐/广场恐惧症、重度抑郁症）。在个体层面上，神经障碍、慢性疼痛状况和糖尿病是其他最具负担的疾病。在社会层面上，慢性疼痛状况、心血管疾病、关节炎、失眠和重度抑郁症是最具负担的疾病。
调整合并症会对疾病负担的评估产生重大影响，尤其是对精神疾病的评估，这强调了在精神疾病研究中纳入合并症相关信息的重要性。
