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社会经济因素对印度南部一家大型癌症医院宫颈癌患者延迟报告和晚期就诊情况的影响。

Impact of socio-economic factors in delayed reporting and late-stage presentation among patients with cervix cancer in a major cancer hospital in South India.

作者信息

Kaku Michelle, Mathew Aleyamma, Rajan B

机构信息

Stony Brook University, School of Medicine, NY, USA.

出版信息

Asian Pac J Cancer Prev. 2008 Oct-Dec;9(4):589-94.

Abstract

The impact of socio- economic and demographic status (SEDS) factors on the stage of cervical cancer rat diagnosis, symptom duration and delay-time from diagnosis to registration was determined by analysing data for the year 2006 from the Regional Cancer Centre (RCC), Trivandrum, Kerala, India. Patients (n=349) were included if they were from the states of Kerala or Tamil Nadu. SEDS factors included age, residing district, religion, marital status, income, education and occupation. Associations between SEDS factors by stage at diagnosis and symptom duration were tested using chi-square statistics with odds ratios (OR) estimated through logistic regression modeling. Elevated risks for late stage reporting among cervical cancer patients were observed for women who were widowed/divorced (OR=2.08; 95%CI: 1.24-3.50) and had a lower education (OR=2.62; 95%CI:1.29-5.31 for women with primary school education only). Patients who had symptoms of bleeding/bleeding with other symptoms (77%) were more likely to seek treatment within one month, compared to patients with other symptoms only (23%) (p=0.016). This analysis helped to identify populations at increased risk of diagnosis at later stages of cancer with the ultimate intent of providing health education and detecting cancer at earlier stages.

摘要

通过分析印度喀拉拉邦特里凡得琅地区癌症中心(RCC)2006年的数据,确定了社会经济和人口状况(SEDS)因素对宫颈癌确诊分期、症状持续时间以及从诊断到登记的延迟时间的影响。如果患者来自喀拉拉邦或泰米尔纳德邦,则纳入研究(n = 349)。SEDS因素包括年龄、居住地区、宗教、婚姻状况、收入、教育程度和职业。使用卡方统计检验确诊分期时SEDS因素与症状持续时间之间的关联,并通过逻辑回归模型估计比值比(OR)。观察到宫颈癌患者中,丧偶/离婚女性(OR = 2.08;95%CI:1.24 - 3.50)和教育程度较低的女性(仅接受小学教育的女性OR = 2.62;95%CI:1.29 - 5.31)晚期报告风险升高。有出血/伴有其他症状的出血症状的患者(77%)比仅有其他症状的患者(23%)更有可能在一个月内寻求治疗(p = 0.016)。该分析有助于识别癌症晚期诊断风险增加的人群,并最终旨在提供健康教育并在早期阶段检测癌症。

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