Mapping Common Psychiatric Disorders Structure and Predictive Validity in the National Epidemiologic Survey on Alcohol and Related Conditions

被引:72
作者
Blanco, Carlos [1 ,3 ]
Krueger, Robert F. [4 ]
Hasin, Deborah S. [1 ,2 ,3 ]
Liu, Shang-Min [1 ]
Wang, Shuai [1 ]
Kerridge, Bradley T. [5 ]
Saha, Tulshi [6 ]
Olfson, Mark [1 ,3 ]
机构
[1] Columbia Univ Coll Phys & Surg, Dept Psychiat, New York, NY 10032 USA
[2] Columbia Univ Coll Phys & Surg, Mailman Sch Publ Hlth, Dept Epidemiol, New York, NY 10032 USA
[3] New York State Psychiat Inst & Hosp, New York, NY 10032 USA
[4] Univ Minnesota, Dept Psychol, Minneapolis, MN 55455 USA
[5] Univ Maryland, Dept Epidemiol & Biostat, Sch Publ Hlth, College Pk, MD 20742 USA
[6] NIAAA, Intramural Lab Epidemiol & Biometry, Rockville, MD 20852 USA
基金
美国国家卫生研究院;
关键词
INTERVIEW SCHEDULE AUDADIS; ENVIRONMENTAL RISK-FACTORS; DSM-IV DISORDERS; MENTAL-DISORDERS; DRUG MODULES; EXTERNALIZING PSYCHOPATHOLOGY; PERSONALITY-DISORDERS; META-STRUCTURE; UNITED-STATES; COMORBIDITY;
D O I
10.1001/jamapsychiatry.2013.281
中图分类号
R749 [精神病学];
学科分类号
100205 ;
摘要
Context: Clinical experience and factor analytic studies suggest that some psychiatric disorders may be more closely related to one another, as indicated by the frequency of their co-occurrence, which may have etiologic and treatment implications. Objective: To construct a virtual space of common psychiatric disorders, spanned by factors reflecting major psychopathologic dimensions, and locate psychiatric disorders in that space, as well as to examine whether the location of disorders at baseline predicts the prevalence and incidence of disorders at 3-year follow-up. Design, Setting, and Patients: A total of 34 653 individuals participated in waves 1 and 2 of the National Epidemiologic Survey on Alcohol and Related Conditions. Main Outcome Measures: The distance between disorders at wave 1, calculated using the loadings of the factors spanning the space of disorders as coordinates. This distance was correlated with the adjusted odds ratios for age, sex, and race/ethnicity of the prevalence and incidence of Axis 1 disorders in wave 2, with the aim of determining whether smaller distances between disorders at wave 1 predicts higher disorder prevalence and incidence at wave 2. Results: A model with 3 correlated factors provided an excellent fit (Comparative Fit Index = 0.99, Tucker-Lewis Index = 0.98, root mean square error of approximation = 0.008) for the structure of common psychiatric disorders and was used to span the space of disorders. Distances ranged from 0.070 (between drug abuse and dysthymia) to 1.032 (between drug abuse and avoidant personality disorder). The correlation of distance between disorders in wave 1 with adjusted odds ratios of prevalence in wave 2 was -0.56. The correlation of distance in wave 1 with adjusted odds ratios of incidence in wave 2 was -0.57. Conclusions: Mapping psychiatric disorders can be used to quantify the distances among disorders. Proximity in turn can be used to predict prospectively the incidence and prevalence of Axis I disorders. JAMA Psychiatry. 2013;70(2):199-208. Published online December 24, 2012. doi:10.1001/jamapsychiatry.2013.281
引用
收藏
页码:199 / 208
页数:10
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