Objective To study the comorbidity of common mental disorders (CMDs) and cancer, and the mental health treatment gap among community residents with active cancer, cancer survivors and cancer-free respondents in 13 high- and 11 low-middle income countries. Methods Data were derived from the World Mental Health Surveys (N=66,387; n=357 active cancer, n=1,373 cancer survivors, n=64,657 cancer free respondents). The WHO/Composite International Diagnostic Interview was used in all surveys to estimate CMDs prevalence rates. Respondents were also asked about mental health service utilization in the preceding 12 months. Cancer status was ascertained by self-report of physician’s diagnosis. Results Twelve month prevalence rates of CMDs were higher among active cancer (18.4% SE=2.1) than cancer free respondents (13.3%, SE=0.2) adjusted for socio-demographic confounders and other lifetime chronic conditions (Adjusted Odds Ratio (AOR)=1.44 95% CI 1.05–1.97). CMD rates among cancer survivors (14.6% SE=0.9) compared with cancer-free respondents did not differ significantly (AOR=0.95 95% CI 0.82–1.11). Similar patterns characterized high and low-middle income countries. Of respondents with active cancer who had CMD in the preceding 12 months 59% sought services for mental health problems (SE=5.3). The pattern of service utilization among people with CMDs by cancer status (highest among persons with active cancer, lower among survivors and lowest among cancer-free respondents) was similar in high- (64.0% SE=6.0, 41.2% SE=3.0, 35.6% SE=0.6) and low-middle income countries (46.4% SE=11.0, 22.5% SE=9.1, 17.4% SE=0.7). Conclusions Community respondents with active cancer have relatively higher CMD rates and relatively high treatment gap. Comprehensive cancer care should consider both factors.

Background Prior studies on the depression-heart disease association have not usually used diagnostic measures of depression, nor taken other mental disorders into consideration. As a result, it is not clear whether the association between depression and heart disease onset reflects a specific association, or the comorbidity between depression and other mental disorders. Additionally, the relative magnitude of associations of a range of mental disorders with heart disease onset is unknown. Methods Face-to-face household surveys were conducted in 19 countries (n=52,095; person years=2,141,194). The Composite International Diagnostic Interview retrospectively assessed lifetime prevalence and age at onset of 16 DSM-IV mental disorders. Heart disease was indicated by self-report of physician’s diagnosis, or self-report of heart attack, together with their timing (year). Survival analyses estimated associations between first onset of mental disorders and subsequent heart disease onset. Results After comorbidity adjustment, depression, panic disorder, specific phobia, post-traumatic stress disorder and alcohol use disorders were associated with heart disease onset (ORs 1.3–1.6). Increasing number of mental disorders was associated with heart disease in a dose-response fashion. Mood disorders and alcohol abuse were more strongly associated with earlier onset than later onset heart disease. Associations did not vary by gender. Conclusions Depression, anxiety and alcohol use disorders were significantly associated with heart disease onset; depression was the weakest predictor. If confirmed in future prospective studies, the breadth of psychopathology’s links with heart disease onset has substantial clinical and public health implications.

BackgroundWe examined the extent to which disability mediates the observed associations of common mental and physical conditions with perceived health.Methods and FindingsWHO World Mental Health (WMH) Surveys carried out in 22 countries worldwide (n = 51,344 respondents, 72.0% response rate). We assessed nine common mental conditions with the WHO Composite International Diagnostic Interview (CIDI), and ten chronic physical with a checklist. A visual analog scale (VAS) score (0, worst to 100, best) measured perceived health in the previous 30 days. Disability was assessed using a modified WHO Disability Assessment Schedule (WHODAS), including: cognition, mobility, self-care, getting along, role functioning (life activities), family burden, stigma, and discrimination. Path analysis was used to estimate total effects of conditions on perceived health VAS and their separate direct and indirect (through the WHODAS dimensions) effects.Twelve-month prevalence was 14.4% for any mental and 51.4% for any physical condition. 31.7% of respondents reported difficulties in role functioning, 11.4% in mobility, 8.3% in stigma, 8.1% in family burden and 6.9% in cognition. Other difficulties were much less common. Mean VAS score was 81.0 (SD = 0.1). Decrements in VAS scores were highest for neurological conditions (9.8), depression (8.2) and bipolar disorder (8.1). Across conditions, 36.8% (IQR: 31.2–51.5%) of the total decrement in perceived health associated with the condition were mediated by WHODAS disabilities (significant for 17 of 19 conditions). Role functioning was the dominant mediator for both mental and physical conditions. Stigma and family burden were also important mediators for mental conditions, and mobility for physical conditions.ConclusionsMore than a third of the decrement in perceived health associated with common conditions is mediated by disability. Although the decrement is similar for physical and mental conditions, the pattern of mediation is different. Research is needed on the benefits for perceived health of targeted interventions aimed at particular disability dimensions.

Background Although variation in long-term course of major depressive disorder (MDD) is not strongly predicted by existing symptom subtype distinctions, recent research suggests that prediction can be improved by using machine learning methods. However, it is not known whether these distinctions can be refined by added information about comorbid conditions. The current report presents results on this question. Methods Data come from 8,261 respondents with lifetime DSM-IV MDD in the WHO World Mental Health (WMH) Surveys. Outcomes include four retrospectively-reported measures of persistence-severity of course (years in episode; years in chronic episodes, hospitalization for MDD; disability due to MDD). Machine learning methods (regression tree analysis; lasso, ridge, and elastic net penalized regression) followed by k-means cluster analysis were used to augment previously-detected subtypes with information about prior comorbidity to predict these outcomes. Results Predicted values were strongly correlated across outcomes. Cluster analysis of predicted values found 3 clusters with consistently high, intermediate, or low values. The high-risk cluster (32.4% of cases) accounted for 56.6–72.9% of high persistence, high chronicity, hospitalization, and disability. This high-risk cluster had both higher sensitivity and likelihood-ratio positive (relative proportions of cases in the high-risk cluster versus other clusters having the adverse outcomes) than in a parallel analysis that excluded measures of comorbidity as predictors. Conclusions Although results using the retrospective data reported here suggest that useful MDD subtyping distinctions can be made with machine learning and clustering across multiple indicators of illness persistence-severity, replication is need with prospective data to confirm this preliminary conclusion.

BACKGROUNDDue, in part, to family constraints in dealing with the economical burden of raising a family, a wave of street children is sweeping the developing world. Such children are prone to both somatic and mental illnesses. This is the first ever study that has been conducted to explore the psychopathology among street children in the Duhok Governorate.METHODSThe study was conducted between March 2004 and May 2005 in Duhok City among street children who attended the Zewa Center—the only center for street children in the region at the time of the study. Among a total of 107 eligible children, 100 agreed to participate (93% response rate). A modified family map (genogram) was used to obtain demographic data from the children and their caregivers through semi-structured interviews. In addition, the Mini International Neuropsychiatric Interview for Children and Adolescents (MINI-KID) structured interviews were conducted with the children.RESULTSThe study found that 98% of children worked on the street because of the economic need and pressure on their families. There was high rate of parental illiteracy (90% of fathers and 95% of mothers), and 61% of respondents were shown to have at least one psychiatric disorder. A high percentage (57%) of these children suffered from anxiety disorders including posttraumatic stress disorders (29%). Ten percent had depression, and 5% had attention deficit hyperactivity disorder.CONCLUSIONStreet children in Duhok seem to be working children due to their families’ needs.