Constructing and Validating Iranians' COVID-19-related Stressors (ICRS) and Its Relation with Mental Health

Background: The outbreak of coronavirus disease 2019 (COVID-19) has caused stress and anxiety for various parts of society all over the world. This concern and stress are not limited to being affected by COVID-19 because this condition has also caused changes in people's lifestyles. Objectives: The purpose of this study was to design and evaluate the validity and reliability of the COVID-19-related stressors questionnaire and determine the contribution of each of the stressors to the prediction of mental health in the Iranian population. Methods: This descriptive-analytical and correlational study was performed on 301 volunteers living in Tehran, Iran. Due to the prevalence of COVID-19, questionnaires were distributed by convenience sampling method and online conduction. The instruments used in the present study were the researcher-made questionnaire on Iranians' COVID-19-related stressors (ICRS) and the general health questionnaire (GHQ-12). Content validity (qualitative and quantitative), face validity, structural validity (exploratory factor analysis), Cronbach's alpha, and logistic regression were used to analyze the data. Statistical analysis was performed by SPSS version 24 and AMOS24 software. Results: In exploratory factor analysis, the final 25 items were obtained in seven factors with an explanation of 66.25% variance. The results of logistic regression analysis showed that worrying about changing economic status, getting COVID-19 and change in lifestyle increased the chance of developing mental health disorder up to 1.29 (OR = 1.29), 1.28 (OR = 1.28), and 1.18 times (OR = 1.18), respectively. The variable of changing family interactions reduces the chance of developing a mental health disorder by 0.85 times (OR = 0.85). Conclusions: The COVID-19-related stressors questionnaire is valid and can be used in future research. Identifying the most important stressors associated with the COVID-19 pandemic and determining each contribution to mental health prediction will help health policymakers to make better decisions.