Effort-reward imbalance and common mental disorders among public sector employees of Iran: a cross-sectional analysis.

BACKGROUND: The effort-reward imbalance (ERI) model is a widely used theoretical model to measure stress in the workplace. The objective of this study was to investigate the relationship between ERI and three common mental disorders: major depressive disorder (MDD), generalized anxiety disorder (GAD), and obsessive-compulsive disorder (OCD). METHODS: In this cross-sectional analysis, the study sample consisted of 4453 baseline participants of the Employees' Health Cohort Study of Iran (EHCSIR). Trained psychologists utilized the Persian version of the Composite International Diagnostic Interview (CIDI-2.1) during the baseline assessment to identify common mental disorders. Additionally, the validated Persian version of the 23-item ERI questionnaire was employed to assess effort, reward, overcommitment, and effort-reward ratio. To examine the association of ERI components with three common mental disorders (MDD, GAD, and OCD) over the past twelve months, multiple logistic regression analyses were conducted. RESULTS: The prevalence of effort-reward imbalance in the study sample was 47.1%. Higher ERI score was significantly associated with MDD (OR: 3.43, 95% CI: 2.30-5.13), GAD (OR: 2.42, 95% CI: 1.27-4.63), and OCD (OR: 2.23, 95% CI:1.19-4.19). The study participants who reported higher scores on work overcommitment had a higher likelihood of having MDD (OR: 1.16, 95% CI:1.10-1.23), GAD (OR: 1.07, 95% CI: 1.01-1.14), and OCD (OR: 1.19, 95% CI: 1.09-1.29). CONCLUSIONS: According to the study's findings, work-related stress, as determined by the ERI model, is a significant factor in the development of common mental disorders among employees in the public sector.

Preexisting depression and COVID-19: a cohort study on the risk of susceptibility and hospitalization.

BACKGROUND: Depression can have negative effects on a person's physical health. However, the available evidence on the risk of susceptibility to COVID-19 and its adverse outcomes in people with mental disorders, including depression, is limited and inconsistent. Therefore, we investigated the relationship between major depressive disorder (MDD) and the risk of susceptibility to COVID-19 infection and hospitalization. The data used in the study were obtained from the Employees' Health Cohort Study of Iran (EHCSIR). METHODS: We conducted a cohort study that included 3355 participants who had complete data on major depressive disorder at baseline assessment and two annual telephone follow-ups between January 2020 and March 2022. Trained psychologists used the Persian version of the Composite International Diagnostic Interview (CIDI-2.1) to identify major depressive disorders during the baseline assessment. We applied log binomial regression models to adjust for sociodemographic factors and background health conditions. RESULTS: We found that 11.4% of participants had lifetime MDD and 7.3% had MDD in the past 12 months. During the pandemic, 26.1% of participants were infected with COVID-19, and 14.4% of those who were infected were hospitalized. The risk of susceptibility to COVID-19 infection was significantly higher among participants with lifetime MDD than among those without MDD (adjusted risk ratio (ARR) = 1.24, 95% CI: 1.06-1.47). However, lifetime MDD or 12-month MDD was not independently associated with hospitalization among COVID-19 cases. CONCLUSIONS: Preexisting major depressive disorder may increase the risk of susceptibility to COVID-19.

Developing the Medication Reminder Mobile Application "Seeb".

INTRODUCTION: Today, the structure of comprehensive health care emphasizes self-care more than therapy. Medication therapy is a strong instrument for therapy received through the health setting, especially in medication area. Error in medication administration has produced different problems and they cost billions of dollars every year. Regarding mobile phone extensions, we developed a local medication reminder mobile application called "Seeb" as a suitable solution for decreasing medication errors for Iranians. METHODS: We conducted a mixed methods study in three Phases: 1) Comparative study of existing mobile applications; 2) developed its object-oriented model; 3) Developed the initial version of "Seeb" that was approved for production. RESULTS: This application was designed for the appropriate medication administration including time and dosages through: recording patient and medication data; scheduling patients' medication; and reporting medication administration on progress. CONCLUSION: "Seeb" has been designed in compliance with Iranian health information technologists and pharmacists requirements. It is expected to reduce medication error and improve patient adherence to medical prescriptions.