Results from logistic regression models assessing associations between depression treatments and characteristics, and experiencing violence.

Results from logistic regression models assessing associations between depression treatments and characteristics, and experiencing violence.</p

Prevalence of depression treatments and characteristics by types of violence experienced, in those with previous clinician-diagnosed depression (n = 8,242).

Prevalence of depression treatments and characteristics by types of violence experienced, in those with previous clinician-diagnosed depression (n = 8,242).</p

A list of questions, translated into English, which were used in the National Health Survey 2019 to assess violence and depression outcomes.

PHQ-9: Patient Health Questionnaire. (DOCX)</p

Prevalence of different types of depression across populations experiencing violence.

Error bars represent 95% confidence intervals. PHQ-9: Patient Health Questionnaire.</p

Characteristics of survey respondents by experience of violence.

Characteristics of survey respondents by experience of violence.</p

Results from logistic regression models assessing associations between depression measures and types of violence experienced.

Results from logistic regression models assessing associations between depression measures and types of violence experienced.</p

Logistic regression models investigating the association between experience of violence and various depression variables in whole survey population.

Other variables included in the model are: gender; age; race; region; urban or rural locality; household income group; highest educational attainment; and marital status. Statistical significance of the adjusted Odd’s Ratio (aOR) is indicated by * for p (DOCX)</p

Characteristics of survey respondents by types of violence experienced.

Characteristics of survey respondents by types of violence experienced.</p

Full results of Fig 1: Prevalence of different types of depression across populations experiencing violence.

95% CI: 95% confidence intervals. PHQ-9: Patient Health Questionnaire. (DOCX)</p

Table_1_Patterns of multimorbidity and some psychiatric disorders: A systematic review of the literature.docx

ObjectiveThe presence of two or more chronic diseases results in worse clinical outcomes than expected by a simple combination of diseases. This synergistic effect 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 2015. 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.MethodsWe performed a systematic approach to selecting papers that studied relationships between chronic conditions that included one mental disorder from 2015 to 2021. These were processed using Covidence, including quality assessment.ResultsThis resulted in the inclusion of 26 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.ConclusionsThere 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 registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021216101, identifier: CRD42021216101.</p