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Background Cancer is the second leading cause of death worldwide. In Peru, cancer represented 20% of deaths nationwide between 1986 and 2015, where the types of cancer with the highest incidence include prostate, breast, and stomach cancer. Cancer diagnosis is a complex communicative process that is associated with the idea of ​​death and suffering, especially those in advanced stages. Regarding the psychological factor, cancer is associated with an increase in depressive symptoms, anxiety, and stress. These represent an obstacle to recovery from the disease and can affect the quality of life of cancer patients. This study aims to determine the association between quality of life and depressive symptoms, anxiety, stress, and emotional distress in Peruvian cancer patients. Method A cross-sectional study was conducted on 500 literate National Institute of Neoplastic Diseases cancer patients over 18 years of age. Additionally, descriptive statistical analyses and Poisson association measures of mental health and quality of life factors were performed. Results A prevalence of anxious symptoms (27.5%), depressive symptoms (20.4%), symptoms of moderate-severe stress (83.2%), and emotional discomfort (57.4%) has been identified. Regarding the Poisson regression model, cancer patients with 12 or more years of education were less likely to have emotional distress symptoms. Cancer patients with a low or average mental quality of life are twice as likely to present signs of moderate or severe stress. Notably, neither the type of cancer or the clinical stage presented significant values ​​for any of the mental health problems, mental quality of life, and physical quality of life. The comorbidity sub-analysis has identified that as the number of comorbid mental health problems increases, the probability of having a low mental quality of life and physical quality of life increases. Conclusions Emotional distress and stress are factors associated with the quality of life of cancer patients. Other covariates such as high educational level are factors related to mental health problems. Likewise, the results directly affect the development of public policies and effective health strategies in this type of population.

Background: The COVID-19 pandemic has caused a bigger problem for mental health in the world. The aim of this study was to propose a model of mental health variables in hospitalized patients of COVID-19 and determine which model explained best the depression symptoms in both groups with and without biological response.Method: We conducted a cross sectional study, following a simple random sampling. Data from 277 hospitalized patients with COVID-19 in Lima-Peru, were collected to assess mental health variables (i.e., depression, anxiety, post-traumatic stress, somatic symptoms), self-perception of COVID-19 related symptoms, and neutrophil/lymphocyte ratio (NLR) such as inflammatory marker. By performing a structural equation modeling analysis as a technique to evaluate a predictive model of depression. Results: The results showed a prevalence of depressive symptoms (11.2%), anxious symptoms (7.9%), somatic symptoms (2.2%), and symptoms of post-traumatic stress (6.1%) in the overall sample. No association was found between the prevalence of these mental health problems among people with and without severe inflammatory response. The mental health indicators with the highest prevalence were sleep problems (48%), low energy (47.7%), nervousness (48.77%), worry (47.7%), irritability (43.7%) and back pain (52%) in the overall sample. The model proposed to explain depressive symptoms was able to explain more than 83.7% of the variance and presented good goodness-of-fit indices. Also, a different performance between the proposed model was found between people with and without severe inflammatory response, mainly in the relationship between anxiety and post-traumatic stress, and between the perception of COVID-19 related symptoms and somatic symptoms.Conclusions: Results demonstrated that our model of mental health variables may explain depression in hospitalized patients of COVID-19 from a third-level hospital in Peru. In the model, perception of symptoms influences somatic symptoms, which influence both anxiety symptoms and post-traumatic stress. Thus, anxious symptoms could directly influence depressive symptoms or through post-traumatic stress. Our findings could be useful to decision-makers for the prevention of depression, using screening tools (i.e. perception of symptoms, somatic symptoms, anxiety) to identify vulnerable patients early.

Background: Peru has a deficit in mental health care with 10,051 mental health workers per 100,000 inhabitants. The aim of this study was to characterise the offer of undergraduate and second speciality mental health programmes in Peru.Method: We conducted a scoping review of mental health training offered by Peruvian universities. We searched for programmes in the University Information System database and on the universities' websites. We extracted the name of the university, the name of the programme, the target population, the duration, the cost, the learning environment, the region, and the type of institution where they are offered. We collated the information and used descriptive statistics to analyze the data.Results: We found a total of 252 undergraduate and 63 second speciality programmes, with 43% and 54% of the programmes respectively offered in the capital city. Undergraduate mental health programmes last between 3 and 7 years and cost between US$ 55.71 and US$ 6,952.03 in private universities for the first semester. As for second speciality programmes, the total cost of the programme varies from US$ 729.26 to US$ 12,140.17, where the latter offers more practical training for physicians and nurses. Conclusions: In Peru, there are few undergraduate programmes oriented towards social work and medical technology (focused on occupational therapy and speech therapy), as well as a few second speciality programmes for psychologists. This affects the possibility of having multidisciplinary teams trained in mental health. We believe that to improve the competencies of professionals to manage mental health problems, institutions involved in health and education should develop policies to decentralise programmes, make them more accessible to low-income people, respond to mental health issues, and ensure the quality of all programmes.

Background: The use of technologies has served to reduce gaps in access to treatment and digital health interventions show promise in the care of mental health problems. However, to understand what and how these interventions work, it's imperative to document the aspects related to their challenging implementation. Objective: To determine what evidence is available for synchronous digital mental health implementation and develop a framework, informed by a realist review, to explain what makes digital mental health interventions work for people with mental health problems.Methods: The SPIDER framework was used to develop the following review question: What makes digital mental health interventions with a synchronous component work on people with mental health problems, including depression, anxiety, or stress, based on implementation, economic, quantitative, qualitative, and mixed methods studies? MEDLINE, EBM Reviews, PsycINFO, EMBASE, SCOPUS, CINAHL Complete, and Web of Science databases from 1st January 2015 to September 2020 were searched. Realist synthesis analysis allowed for developing a framework on the implementation of synchronous digital mental health using a grounded theory approach with an emergent approach.Results: 21 systematic reviews were included in the study. The realist synthesis allowed the development of three hypotheses to identify the context and mechanisms in which these interventions achieve these outcomes: Hypothesis 1: These interventions reach populations otherwise unable to have access because they do not require the physical presence of the therapist nor the patient, thereby tackling geographic barriers posed by in-person therapy. Hypothesis 2: These interventions reach populations otherwise unable to have access because they can be successfully delivered by non-specialists, which makes them more cost-effective to implement in health services. Hypothesis 3: These interventions are acceptable and show good results in satisfaction, because they require less need of disclosure and provide more privacy, comfortability, and participation, enabling the establishment of rapport with the therapist.Conclusion: We developed a framework with three hypotheses that explain what makes digital mental health interventions with a synchronous component work on people with mental health problems. Each hypothesis represented essential outcomes in the implementation process.Systematic review registration: PROSPERO (CRD420203811).

We evaluated the relationship between job satisfaction, burnout syndrome, and depressive symptoms among Peruvian physicians using the job demand–control framework. We carried out a secondary data analysis of the National Survey of Satisfaction of Users in Health 2016 in Peru. The first part was based on the association among the variables based on the job demand–control framework. The second part focused on estimating the acceptability of the proposed model based on the study variables through the structural equation modeling technique. A total of 2,100 physicians were included, and the prevalence of depressive symptoms was 3.3%. Physicians who had a work-related illness were more than twice as likely to have depressive symptoms (PR=2.23) compared with those who did not. The first predictive model based on the variables, depressive symptoms, burnout syndrome, and job satisfaction had a low goodness-of-fit index. Therefore, for a second evaluation, models with correlated errors were considered, and optimal goodness-of-fit indices were found (CFI=0.974; RMSEA=0.060). Our study identified a stable model to explain the relationship between job satisfaction, burnout, and depressive symptoms among physicians. The results are consistent with the job demand–control framework and can be applied to decision making in occupational contexts in Peru.

Background The presence of psychosocial risks at work are associated with mental and physical health issues in workers. The study aim was to adapt the COPSOQ-ISTAS21 (Spanish version of the Copenhagen Psychosocial Questionnaire and Union Institute of Work, Environment and Health) Medium-Version to the Peruvian context and to develop a Short-Version of the instrument. Method Cross-sectional design study. The COPSOQ-ISTAS21 Medium Version was used. A confirmatory factor analysis was performed to determine the internal structure of each subdimension (first-order) and dimension (second-order) using the Robust Maximum Likelihood estimation method, and classic fit indices in the literature (CFI, SRMR, RMSEA). Internal consistency was evaluated using the alpha and omega coefficients. A short version was developed based on the items with the highest factorial load and that reduce the factorial complexity. Results A total of 1707 participants were evaluated. In the confirmatory factor analysis, the goodness-of-fit indices for seventeen of the 20 one-dimensional models (subdimensions) were identified; two subdimensions could not be evaluated because they presented only two items. When conducting a multidimensional analysis, we identified that all second-order models presented optimal goodness-of-fit indices, except “psychological demands at work”. Finally, a short version of only 31 items was designed from the items with optimal fit indices. Conclusions The new adapted versions of COPSOQ-ISTAS21 were renamed CENSOPAS-COPSOQ (National center of occupational health and environment protection for health -in Spanish- and Copenhagen Psychosocial Questionnaire). The CENSOPAS-COPSOQ is an instrument with sufficient evidence of validity and reliability in its medium and short version, which is why its use is recommended in Peruvian work centers to identify the evaluation and prevention of psychosocial risks at work in Peru.

The Hospital Anxiety and Depression Scale (HADS) despite being widely studied in various populations, there is still no consensus on its factor structure. Our study aims to evaluate the psychometric properties of HADS in people with cancer. It involved 467 patients-diagnosed with cancer, who could read and write and were treated in a public institution specialized in cancer. It was found that HADS is best suited to a bifactorial structure where there is one general factor (emotional distress) and two specific factors (anxiety and depression). HADS proves to be invariant according to sex and years of education. It is moderately related to Beck's anxiety and depression inventory. Also, it presents acceptable levels of reliability and relationship with instruments used in the diagnosis of anxiety and depression. Its brevity, versatility, hospital-focused design, and extensive study make HADS a very important instrument in the detection of anxiety and depression in cancer patients.

Background This study has two aims. First, determine the fit of the fear model to COVID-19, anxiety, and post-traumatic stress in the general population and health-care workers. Second, determine which model best explains the relationship between depression and the triad of fear, anxiety, and post-traumatic stress in both groups. Method A cross-sectional study was conducted using self-reported questionnaires for anxiety, fear of COVID-19, depression, and post-traumatic stress. Information was collected from adults living in Lima, the capital and the most populous city in Peru. The explanatory models were evaluated using a structural equation model. Results A total of 830 participants were included, including general population (n = 640) and health-care workers (n = 190). A high overall prevalence of depressive symptoms (16%), anxiety (11.7%), and post-traumatic stress (14.9%) were identified. A higher prevalence of depressive, anxious, or stress symptoms was identified in the general population (28.6%) compared to health-care workers (17.9%). The triad model of fear of COVID-19, anxiety, and stress presented adequate goodness-of-fit indices for both groups. A model was identified that manages to explain depressive symptoms in more than 70% of the general population and health-care workers, based on the variables of the triad (CFI = 0.94; TLI = 0.94; RMSEA = 0.06; SRMR = 0.06). In the general population post-traumatic stress mediated the relationship between anxiety and depression (β = 0.12; 95%CI = 0.06 to 0.18) which was significant, but the indirect effect of post-traumatic stress was not significant in health care workers (β = 0.03; 95%CI = − 0.11 to 0.19). Limitations The prevalence estimates relied on self-reported information. Other variables of interest, such as intolerance to uncertainty or income level, could not be evaluated. Conclusions Our study proposes and tests one model that explains more than 70% of depressive symptoms. This explanatory model can be used in health contexts and populations to determine how emotional factors can affect depressive symptoms.