Abstract
BACKGROUND: 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). METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.