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Objective: The presence of two or more chronic diseases results in worse clinical outcomes than expected by a simple combination of diseases. This synergistic e ect is expected to be higher when combined with some conditions, depending on the number and severity of diseases. Multimorbidity is a relatively new term, with the first fundamental definitions appearing in . Studies usually define it as the presence of at least two chronic medical illnesses. However, little is known regarding the relationship between mental disorders and other non-psychiatric chronic diseases. This review aims at investigating the association between some mental disorders and non-psychiatric diseases, and their pattern of association.Methods: We performed a systematic approach to selecting papers that studied relationships between chronic conditions that included one mental disorder from to . These were processed using Covidence, including quality assessment.Results: This resulted in the inclusion of papers in this study. It was found that there are strong associations between depression, psychosis, and multimorbidity, but recent studies that evaluated patterns of association of diseases (usually using clustering methods) had heterogeneous results. Quality assessment of the papers generally revealed low quality among the included studies.Conclusions: There is evidence of an association between depressive disorders, anxiety disorders, and psychosis with multimorbidity. Studies that tried to examine the patterns of association between diseases did not find stable results.Systematic review registration: https://www.crd.york.ac.uk/prospero/display _record.php?ID=CRD , identifier: CRD .

Objective: Multimorbidity, or the occurrence of two or more chronic conditions, is a global challenge, with implications for mortality, morbidity, disability, and life quality. Psychiatric disorders are common among the chronic diseases that affect patients with multimorbidity. It is still not well understood whether psychiatric symptoms, especially depressive symptoms, moderate the effect of multimorbidity on cognition. Methods: We used a large (n=2,681) dataset to assess whether depressive symptomatology moderates the effect of multimorbidity on cognition using structural equation modelling. Results: It was found that the more depressive symptoms and chronic conditions, the worse the cognitive performance, and the higher the educational level, the better the cognitive performance. We found a significant but weak (0.009; p = 0.04) moderating effect. Conclusion: We have provided the first estimate of the moderating effect of depression on the relation between multimorbidity and cognition, which was small. Although this moderation has been implied by many previous studies, it was never previously estimated.

Background Physical, emotional, and social changes, including exposure to poverty, abuse, or violence, increases youth vulnerability to mental illness. These factors interfere with development, limit opportunities, and hamper achievement of a fulfilling life as adults. Addressing these issues can lead to improved outcomes at the population level and better cost-effectiveness for health services. Cash transfer programs have been a promising way to address social drivers for poor mental health. However, it is still unclear which pathways and mechanisms explain the association between socioeconomic support and lower mental illness among youth. Therefore, we will evaluate the effect of social drivers on youth mental health-related hospitalizations and suicide, test mechanisms and pathways of a countrywide socioeconomic intervention, and examine the timing of the intervention during the life course. Methods We will combine individual-level data from youth national hospitalization, mental health disorders and attempted suicide, suicide registries and notifications of violence, with large-scale databases, including “The 100 Million Brazilian Cohort”, over an 18-year period (2001–2018). Several approaches will be used for the retrospective quasi-experimental impact evaluations, such as Regression Discontinuity Designs, Propensity Score Matching and difference-in-differences, combined with multivariable regressions for cohort analyses. We will run multivariate regressions based on hierarchical analysis approach to evaluate the association between important social drivers (mental health care, demographic and economic aspects) on mental health-related hospitalizations and suicide among youth. Furthermore, we will perform microsimulations to generate projections regarding how mental health-related hospitalizations and suicide trends will be in the future based on the current state, and how BFP implementation scenarios will affect these trends. Discussion The results of this project will be of vital importance to guide policies and programs to improve mental health and reduce mental health-related hospitalizations and suicide in youth. It will provide information to improve the effectiveness of these programs worldwide. If cash transfers can decrease mental health problems among youth and reduce suicide.

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