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Background Low back pain is a common disorder that has many consequences. This study is an attempt to meta-analyze the risk of depression symptoms in back pain. Method Four databases were selected for review, and this search was conducted using key words. Eleven eligible articles were selected for review and meta-analysis was conducted. Subgroup analyses were continued with study design and the method of measuring depression. Also, the heterogeneity and publication bias were examined. Results Eleven cohort and cross-sectional articles are used in the meta-analysis between back pain and depressive symptoms. The odds ratio 2.07 was calculated for this relationship. In prospective-cohort studies, 1.71 (95% confidence interval = 1.24–2.36) results indicated that back pain is a risk factor for depression symptoms and in cross-sectional studies, pooled odds ratio (2.33; 95% confidence interval = 1.29–4.21) showed that back pain is associated with depression symptoms. Some degree of publication bias was not found in the study. Conclusions Back pain is an effective factor in increasing the likelihood of depression. Adoption of effective prevention and treatment approaches can play an important role in reducing the psychological consequences in these individuals.

Introduction Mental disorders are among the most prevalent health problems of the adult population in the world. This study aimed to identify the subgroups of staff based on mental disorders and assess the independent role of metabolic syndrome (MetS) on the membership of participants in each latent class. Methods This cross-sectional study was conducted among 694 staff of a military unit in Tehran in 2017. All staff of this military unit was invited to participate in this study. The collected data included demographic characteristics, anthropometric measures, blood pressure, biochemical parameters, and mental disorders. We performed latent class analysis using a procedure for latent class analysis (PROC LCA) in SAS to identify class membership of mental disorders using Symptom Checklist-90. Results Three latent classes were identified as healthy (92.7%), mild (4.9%), and severe (2.4%) mental disorders. Having higher age significantly decreased the odds of belonging to the mild class (adjusted OR (aOR = 0.21; 95% confidence interval (CI): 0.05–0.83) compared to the healthy class. Also, obesity decreased the odds of membership in mild class (aOR = 0.10, 95% CI: 0.01–0.92) compared to healthy class. On the other hand, being female increased the odds of being in severe class (aOR = 9.76; 95% CI: 1.35–70.65) class in comparison to healthy class. Conclusion This study revealed that 7.3% of staff fell under mild and severe classes. Considering educational workshops in the workplace about mental disorders could be effective in enhancing staff’s knowledge of these disorders. Also, treatment of comorbid mental disorders may help reduce their prevalence and comorbidity.

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