Age of onset, sociodemographic, and clinical predictors of depression: a population-based study in Rural Southern Iran

BMC Public Health. 2025 May 17;25(1):1825. doi: 10.1186/s12889-025-22993-w.

ABSTRACT

BACKGROUND: Depression is the leading cause of disability worldwide and a growing public health concern. In Iran, the prevalence of depression has shown an increasing trend, with rural populations facing unique challenges in access to mental health care. This study aimed to determine sociodemographic and clinical predictors of depression and explore how these factors influence age at onset in a rural population, providing valuable insights for preventive strategies.

METHODS: The present cross-sectional investigation utilized baseline data of the Fasa PERSIAN Cohort, comprising 10,133 adults aged 35 and older from a rural region in southern Iran. Depression diagnoses were based on Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria. Logistic regression analyses were conducted to identify predictors of depression, while linear regression models examined associations between baseline characteristics and age at depression onset.

RESULTS: Among participants, 6.7% met the criteria for depression, with a higher prevalence among females (78.7%) and the unemployed (70.9%). Independent predictors included female sex, unemployed status, literacy, diabetes, fatty liver disease, and psychiatric comorbidities, which emerged as the strongest predictor (odds ratio = 6.605, p < 0.001). The average age at depression onset was 39.5 years, with men experiencing onset earlier than women. Earlier onset was also associated with higher education levels, opioid use, psychiatric comorbidities, and higher energy intake, whereas later onset was linked to medical conditions, including hypertension, cardiovascular disease, and stroke.

CONCLUSION: This study highlights important demographic and clinical factors linked to depression and its age of onset, underscoring the complex interplay between sociodemographic characteristics, lifestyle factors, and comorbidities. These findings can guide targeted mental health interventions and support tailored prevention strategies in similar rural populations.

PMID:40382587 | DOI:10.1186/s12889-025-22993-w