Comorbid Forms of Psychopathology: Key Patterns and Future Research Directions

Robert Wood Johnson Foundation Health and Society Scholars Program, Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109-2029, USA.
Epidemiologic Reviews (Impact Factor: 6.67). 08/2008; 30(1):155-77. DOI: 10.1093/epirev/mxn003
Source: PubMed


The purpose of this review is to systematically appraise the peer-reviewed literature about clustered forms of psychopathology and to present a framework that can be useful for studying comorbid psychiatric disorders. The review focuses on four of the most prevalent types of mental health problems: anxiety, depression, conduct disorder, and substance abuse. The authors summarize existing empirical research on the distribution of concurrent and sequential comorbidity in children and adolescents and in adults, and they review existing knowledge about exogenous risk factors that influence comorbidity. The authors include articles that used a longitudinal study design and used psychiatric definitions of the disorders. A total of 58 articles met the inclusion criteria and were assessed. Current evidence demonstrates a reciprocal, sequential relation between most comorbid pairs, although the mechanisms that mediate such links remain to be explained. Methodological concerns include the inconsistency of measurement of the disorders across studies, small sample sizes, and restricted follow-up times. Given the significant mental health burden placed by comorbid disorders, and their high prevalence across populations, research on the key risk factors for clustering of psychopathology is needed.

Download full-text


Available from: Magdalena Cerda, Feb 17, 2015
  • Source
    • "Furthermore, more than half of the interventions in the present analysis were developed to primarily target anxiety disorders. Despite the high prevalence of comorbidity in the young population, depression and anxiety disorders are two different disorders with their own behavioral symptoms [27]. CBT interventions for the two disorders often contain similar contents [28]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The majority of internet-based anxiety and depression intervention studies have targeted adults. An increasing number of studies of children, youth, and young adults have been conducted, but the evidence on effectiveness has not been synthesized. The objective of this research is to systematically review the most recent findings in this area and calculate overall (pooled) effect estimates of internet-based anxiety and/or depression interventions.
    BMC Health Services Research 07/2014; 14(1):313. DOI:10.1186/1472-6963-14-313 · 1.71 Impact Factor
  • Source
    • "The model explains disorder associations by using an IRT-type latent structure, with discrete time survival functions that model the age of onset of the disorders. We applied this model to data on mood and anxiety mental disorders from the ESEMED study (Alonso et al., 2002), implementing a conceptual internalizing model (Cerda et al., 2008; Clark, 2005). The proposed factor analytic model yields a realistic estimation of the lifetime prevalences and provides an estimate of the individuals' inherent vulnerability (diathesis) towards internalizing mental disorders, which summarizes the comorbidity pattern data, excluding the random comorbidity association, which is referred to as pseudocomorbidity (Kraemer et al., 2006). "
    [Show abstract] [Hide abstract]
    ABSTRACT: The assessment of the lifetime prevalence of mental disorders under comorbidity conditions is an important area in mental health research. Because information on lifetime disorders is usually gathered retrospectively within cross-sectional studies, the information is necessarily right censored and this should be taken into account when setting up models for the estimation of lifetime prevalences. We propose a factor analytic discrete time survival model combining mixture item response theory and discrete time hazard functions to describe disorder associations while accounting for censoring. This model is used for describing the lifetime prevalence and comorbidity of eight depression and anxiety disorders from the European Study of the Epidemiology of Mental Disorders.
    Journal of the Royal Statistical Society Series C Applied Statistics 01/2014; 63(1). DOI:10.1111/rssc.12026 · 1.49 Impact Factor
  • Source
    • "The total score is calculated by transforming the raw scores to a scale ranging from 0 (worst) to 100 (best). An individual with a score of 52 or less was considered as experiencing symptoms indicative of anxiety disorders and/or depression (Cerdà et al. 2008), although no specific diagnosis could be hypothesized. "
    [Show abstract] [Hide abstract]
    ABSTRACT: This study investigates the sociodemographic factors associated with self-report of common mental problems by the psychologically distressed in order to gain insight into the profile of the population subgroups least likely to receive mental health support whenever needed. Data from the 2006-2008 french National Survey on Health, Health Care and Insurance, were used, measuring psychological distress based on the Mental Health Inventory MHI-5. The patterns associated with education, employment situation and living arrangement were investigated in a sample of 11,543 subjects aged 30-54 years. Men with lower educational level were found to be doubly disadvantaged, as they were more subjected to distress than those with higher educational level and at the same time less likely to report common mental problems whenever distressed. While in both genders subjects not living with a spouse and non-employed subjects were also more subjected to distress, they were more likely than the others to report common mental problems in presence of distress. The findings were discussed in terms of living conditions, stigma, mental health literacy and help-seeking behaviour. Mental health promotion programmes should aim at educating the public, and particularly men and the lower educated public, on the signs of distress and their significance.
    Community Mental Health Journal 12/2013; 50(5). DOI:10.1007/s10597-013-9680-9 · 1.03 Impact Factor
Show more