埃塞俄比亚阿姆哈拉地区癫痫患者抑郁症状患病率及其相关因素:横断面研究

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES BMC Research Notes Pub Date : 2025-01-12 DOI:10.1186/s13104-025-07080-y
Sintayehu Simie Tsega, Yilkal Abebaw Wassie, Alebachew Ferede Zegeye, Mekdes Kiflu, Sisay Maru Wubante, Kennean Mekonnen, Birhaneslasie Gebeyehu Yazew, Birye Dessalegn Mekonnen, Yeshambel Andargie Tarekegn
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引用次数: 0

摘要

背景:抑郁症状是癫痫患者中最常见的精神合并症。由于精神卫生保健系统差和经济负担,低收入和中等收入国家的癫痫患者明确被确定为患抑郁症状的风险较高。癫痫患者同时出现抑郁症状会恶化疾病的预后,降低患者及其家属的生活质量。然而,关于埃塞俄比亚抑郁症状的患病率和相关因素的证据有限。因此,本研究旨在评估埃塞俄比亚阿姆哈拉地区癫痫患者抑郁症状的患病率及其相关因素。方法:对在埃塞俄比亚阿姆哈拉地区就诊的癫痫患者进行多中心机构横断面研究。使用医院焦虑抑郁工具评估抑郁症状。为了确定与抑郁症状相关的因素,采用二元logistic回归模型。在多变量二元logistic回归分析中报告了校正优势比(AOR)和95%置信区间(CI)。结果:共纳入406人,应答率为97.6%。癫痫患者抑郁症状患病率为53.9% [95%CI: 49.1%, 58.8%]。在多变量logistic回归分析中,接受综合治疗[AOR = 1.87, 95%CI: 1.04, 3.36]、感觉耻感[AOR = 5.73, 95%CI: 3.11, 10.55]、抗癫痫药物依从性差[AOR = 3.33, 95%CI: 1.30, 8.54]、社会支持差[AOR = 5.83, 95%CI: 2.44, 13.90]和社会支持不高[AOR = 3.08, 95%CI: 1.34, 7.09]与抑郁症状显著相关。结论:本研究揭示埃塞俄比亚癫痫患者抑郁症状的程度较高,多种因素决定了抑郁症状的可能性。因此,医疗保健提供者和相关利益相关者应加强全面的健康教育,以减少这部分人群中抑郁症状的严重程度和后果。此外,应给予癫痫患者特别关注的强有力的社会支持。
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Prevalence of depressive symptom and its associated factors among epilepsy patients in Amhara region, Ethiopia: cross-sectional study.

Background: Depressive symptom is the most common type of psychiatric co-morbidity among persons with epilepsy. Epilepsy patients are identified as at higher risk of suffering depressive symptom explicitly in low- and middle-income countries due to poor mental health care systems and financial burdens. The co-occurrence of depressive symptom among epilepsy patients deteriorates the prognosis of the disease and diminishes the quality of life of both the patients and their families. However, there is limited evidence on the prevalence of depressive symptom and associated factors in Ethiopia. Therefore, this study is intended to assess the prevalence of depressive symptom and associated factors among epilepsy patients attending in Amhara region, Ethiopia.

Method: A multi-center institution-based cross-sectional study was done among epilepsy patients attending at Amhara region, Ethiopia. The Hospital Anxiety Depression tool was used to assess depressive symptom. To determine the factors associated with depressive symptom, a binary logistic regression model was used. Adjusted Odds Ratio (AOR) with the 95% Confidence Interval (CI) was reported in the multivariable binary logistic regression analysis.

Results: About 406 participants were registered in the study with a response rate of 97.6%. The prevalence of depressive symptom among epilepsy patients was 53.9% [95%CI: 49.1%, 58.8%]. In the multivariable binary logistic regression analysis, taking polytherapy treatment [AOR = 1.87, 95% CI: 1.04, 3.36], perceived stigma [AOR = 5.73, 95%CI: 3.11, 10.55], poor antiepileptic medication adherence [AOR = 3.33, 95%CI: 1.30, 8.54] and having poor [AOR = 5.83, 95%CI: 2.44, 13.90] and moderate social support [AOR = 3.08, 95%CI: 1.34, 7.09] were significantly associated with depressive symptom.

Conclusions: This study revealed that the magnitude of depressive symptom among epilepsy patients in Ethiopia was relatively high and multiple factors determined the likelihood of depressive symptom. Thus, healthcare providers and concerned stakeholders should strengthen comprehensive health education to reduce the magnitude and consequences of depressive symptom among this segment of the population. Moreover, strong social support with special attention should be given to epilepsy patients.

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BMC Research Notes
BMC Research Notes Biochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (all)
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3.60
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期刊介绍: BMC Research Notes publishes scientifically valid research outputs that cannot be considered as full research or methodology articles. We support the research community across all scientific and clinical disciplines by providing an open access forum for sharing data and useful information; this includes, but is not limited to, updates to previous work, additions to established methods, short publications, null results, research proposals and data management plans.
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