Prevalence of post-stroke depression in Iranian patients : a systematic review and meta-analysis

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Dalvand, Sahar and Gheshlagh, Reza and Kurdi, Amanj (2018) Prevalence of post-stroke depression in Iranian patients : a systematic review and meta-analysis. Neuropsychiatric Disease and Treatment, 14. pp. 3073-3080. ISSN 1176-6328 (https://doi.org/10.2147/NDT.S184905)

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Abstract

Background: Post-stroke depression (PSD) is the most common and important neuropsychiatric outcome of stroke, which can result in longer hospital stay, compromise the effectiveness of rehabilitation, and reduce the patients’ quality of life. However, Iranian studies have reported different and conflicting prevalence rates for PSD. Objective: The present systematic review and meta-analysis aimed to evaluate the overall prevalence of PSD in Iranian patients. Patients and methods: A systematic search was conducted in 2018 for Persian or English articles on PSD, published in the Scientific Information Database (SID), MagIran, PubMed, Scopus, and Web of Science, using the following keywords: Depression, Depressive Disorder, Stroke, Cerebrovascular Disorder, and Iran and all of their possible combinations. Heterogeneity across the studies was evaluated using the Cochran's Q test. According to the heterogeneity results, a random effects model was used to estimate the overall prevalence of PSD. The data were analyzed using the Stata 12 software. Results: In overall, 6 articles with a total sample size of 641 patients were included in the analysis. The overall prevalence of PSD in Iran was 46.9% (95%, CI: 30.1-63.7). In addition, the prevalence of PSD was higher in women (50.4%, 95% CI: 17.9-82.9) than in men (29.5%, 95% CI: 17.2-41.8). According to the results of univariate meta-regression, there was a significant relationship between PSD prevalence and sample size (p = .010). Conclusion: Around half of the stroke patients in Iran suffer from PSD. Given the overlap between neurological symptoms of stroke and depression, efforts should be made to quickly and accurately diagnose depression so that it can be effectively managed with minimum complications.

ORCID iDs

Dalvand, Sahar, Gheshlagh, Reza and Kurdi, Amanj ORCID logoORCID: https://orcid.org/0000-0001-5036-1988;
CORE (COnnecting REpositories)