Sociodemographic Factors and Depression in Patients With Breast Cancer: A Multicenter, Cross-sectional Study in Georgia.

IF 1.8 Q3 ONCOLOGY
Breast Cancer : Basic and Clinical Research Pub Date : 2025-03-12 eCollection Date: 2025-01-01 DOI:10.1177/11782234251323775
Tamar Kakhniashvili, Nino Okribelashvili, Ivane Kiladze
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引用次数: 0

Abstract

Background: Depression commonly occurs in patients with breast cancer (BC), affecting their quality of life.

Objectives: The relationships between depression and different sociodemographic characteristics in patients with BC are under-researched. We conducted a multicenter study to determine the magnitude of depression and its association with different sociodemographic characteristics.

Design: In this multi-institutional study, clinical data were collected, prospectively between October 2019 and January 2023 from 207 patients who were on active treatment for BC diagnosis in tertiary oncology hospitals in Georgia.

Methods: Patients' sociodemographic characteristics were analyzed and their association with depression was assessed, using Patient Health Questionnaire-9 (PHQ-9) for the identification of depressive symptoms. Patients were stratified using basic information.

Results: The median age of participants was 53 years (ranging from 31 to 77). Of the participants, 63.2% were married, 44.5% were employed, and only 16.4% reported having adequate financial status. Based on pro-rated PHQ-9 scores, 42% of patients reported some level of depressive symptoms, and 14.5% met the criteria for probable depressive disorder. Women with very inadequate financial status (10/21, 47.6%) reported significantly more depressive symptoms than those with adequate financial support (3/34, 8.8%) (P = .001). Unemployed women (12/42, 28.6%) were nearly 3 times more likely to experience moderate or severe depressive symptoms compared with employed patients (8/92, 8.7%) (P = .002). A significant difference in depressive symptoms was also observed based on education level, with individuals with higher education (12/119, 10%) reporting fewer depressive symptoms compared with those with middle education (18/88, 20.4%) (P = .036). No statistically significant difference in depressive symptoms was found based on marital status or social support.

Conclusions: Our study found a significant relationship between depression and factors such as financial status, education level, and employment. Lower household income and education level were identified as predictors of clinical depression among patients with BC. These findings can help oncologists in Georgia recognize the importance of providing psychological support to cancer patients. Early detection and prompt referral to mental health specialists can play a key role in effectively managing depression.

社会人口因素与乳腺癌患者抑郁:乔治亚州的一项多中心横断面研究
背景:抑郁症常见于乳腺癌(BC)患者,影响其生活质量。目的:抑郁症与BC患者不同社会人口学特征之间的关系尚不清楚。我们进行了一项多中心研究,以确定抑郁症的程度及其与不同社会人口统计学特征的关系。设计:在这项多机构研究中,前瞻性地收集了2019年10月至2023年1月期间在格鲁吉亚三级肿瘤医院接受BC诊断积极治疗的207例患者的临床数据。方法:采用患者健康问卷-9 (PHQ-9)进行抑郁症状的识别,分析患者的社会人口学特征并评估其与抑郁症的相关性。使用基本信息对患者进行分层。结果:参与者的中位年龄为53岁(范围从31岁到77岁)。在参与者中,63.2%已婚,44.5%有工作,只有16.4%的人有足够的经济状况。根据PHQ-9评分,42%的患者报告了一定程度的抑郁症状,14.5%的患者符合可能的抑郁障碍标准。经济状况非常不足的妇女(10/21,47.6%)报告的抑郁症状明显多于经济状况充足的妇女(3/34,8.8%)(P = .001)。失业妇女(12/42,28.6%)出现中度或重度抑郁症状的可能性是在职妇女(8/92,8.7%)的近3倍(P = 0.002)。在抑郁症状方面,受教育程度也存在显著差异,高等教育个体(12/119,10%)报告的抑郁症状少于中等教育个体(18/88,20.4%)(P = 0.036)。婚姻状况或社会支持在抑郁症状方面没有统计学上的显著差异。结论:我们的研究发现抑郁与经济状况、教育程度、就业等因素有显著的关系。较低的家庭收入和教育水平被确定为BC患者临床抑郁的预测因素。这些发现可以帮助乔治亚州的肿瘤学家认识到为癌症患者提供心理支持的重要性。早期发现和及时转诊到心理健康专家可以在有效管理抑郁症方面发挥关键作用。
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来源期刊
CiteScore
5.10
自引率
3.40%
发文量
22
审稿时长
8 weeks
期刊介绍: Breast Cancer: Basic and Clinical Research is an international, open access, peer-reviewed, journal which considers manuscripts on all areas of breast cancer research and treatment. We welcome original research, short notes, case studies and review articles related to breast cancer-related research. Specific areas of interest include, but are not limited to, breast cancer sub types, pathobiology, metastasis, genetics and epigenetics, mammary gland biology, breast cancer models, prevention, detection, therapy and clinical interventions, and epidemiology and population genetics.
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