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重庆市江北区肺癌死亡状况分析 被引量:7

Analysis of the death situation and mortality prediction of lung cancer in Jiangbei district,Chongqing
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摘要 目的了解重庆市江北区肺癌死亡状况和死亡率的变化趋势,为相关部门和个人制定防癌策略和措施提供依据。方法根据2007-2014年江北区死因监测系统中的肺癌死亡数据,计算构成比、死亡率等指标,采用灰色模型预测近期肺癌死亡率。结果 2007-2014年,居民因肺癌死亡的中位年龄为68.18岁,其中死亡的男性占74.80%。从职业分布来看,企业生产、工程施工人员最高;从文化程度分布看,高中、初中、中专、技校文化程度的人最多。肺癌死亡率随年龄的增加而升高,且55岁以后快速上升,在80岁以上达到死亡高峰。灰色模型预测结果,2015-2017年重庆市江北区肺癌死亡率分别为69.11/10万、75.71/10万和82.93/10万,2015年江北区肺癌死亡率实际值为68.90/10万。结论江北区肺癌死亡率较高,且近年来呈缓慢上升的趋势。应针对高危职业人群采取危险因素的控制措施,对55岁以上年龄组人群及早开展肺癌筛查,以减缓死亡率上升趋势。 Objective To study the death situation and the trend of mortality of lung cancer in Jiangbei district,Chongqing,and provide evidence and reference for relevant departments and individuals to develop lung cancer prevention strategies.Methods Based on surveillance system,lung cancer death data from 2007 to 2014 in Jiangbei district were collected;proportion of death causes and mortality of lung cancer were calculated. The recent lung cancer mortality was predicted by gray model. Results The average age of residents died of lung cancer was 68.18 years old,males accounted for 74.80%from 2007 to 2014. Among different occupations,enterprise workers and project builders accounted for the highest proportion;among different education levels,high school,junior high school,polytechnic school and technical school accounted for the highest proportion. The mortality increased with age,and it increased rapidly over 55 years old,reached a peak after 80 years old. The GM(1,1)was applied the mortality rate of lung cancer from 2015-2017 in Jiangbei district of Chongqing and the results were 69.11/10^5,75.71/105 and 82.93/10^5,respectively. Conclusions A high mortality of lung cancer in Jiangbei district was observed and it would still have a slowly rising trend in recent years. We should develop lung cancer screening for the age groups over 55 as soon as possible to slow the trend of increased mortality.
出处 《热带医学杂志》 CAS 2017年第2期258-262,共5页 Journal of Tropical Medicine
关键词 肺癌 死亡率 灰色模型 预测 Lung cancer Mortality Gray model Prediction
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