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           central Pudong New Area of Shanghai from 2007 to 2020.Methods From January to December 2021,we retrospectively
           collected the death data during 2007 to 2020 of registered permanent residents living in Dongming Road Community of Pudong
           New Area from the all-cause death monitoring database,and population composition data from Pudong New Area Public
           Security Bureau. We used the crude mortality rate(CMR),standardized mortality rate(SMR,with reference to the SMR of
           the general Chinese population in 2010),and average annual percent change (AAPC) to analyze the status and trends of
           cancer mortality,and used the decomposition method to estimate the contribution rates of demographic and non-demographic
           factors to the change of cancer mortality. Results From 2007 to 2020,a total of 2 446 cancer-induced deaths were reported
           by the Dongming Road Community,accounting for 28.48% of the total reported deaths(8 589 cases). The CMR of cancers
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           was 268.56/10 ,in which the CMR due to lung cancer,colorectal cancer,and gastric cancer was 69.61/10 ,33.05/10  and
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           31.84/10 ,respectively. The SMR of cancers was 71.46/10  ,in which the SMR due to lung cancer,colorectal cancer,and
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           gastric cancer was 33.12/10 ,15.46/10 ,and 15.29/10 ,respectively. During this period,the CMR for cancers among men,
           women and the whole population remained stable (Z=1.82,-0.54,0.87,P>0.05). The CMR and SMR in men were higher
           than those in women (u=6.69,P<0.001; Z=-13.22,P<0.001). The SMR in men showed a downward trend (AAPC=-1.80%,
           Z=-2.19,P<0.05),while in women,it remained stable(Z=-1.63,P>0.05). As in the whole population,it showed a
           downward trend year by year (AAPC=-1.73%,Z=-3.09,P=0.010). Furthermore,the CMR for cancers in those aged 65-79
           showed a downward trend gradually (AAPC=-4.17%,Z=-1.80,P<0.01). The difference between the CMR of cancers during
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           2007—2013 and that during 2014—2020 was 3.80/10 . The added value of demographic factors to mortality was 28.97/10 ,
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           with a contribution rate of 53.51%. And the added value of non-demographic factors to mortality -25.17/10 ,with a contribution
           rate of 46.49%. Conclusion In Dongming Road Community from 2007 to 2020,both SMRs for cancers in the whole population
           and male residents demonstrated a downward trend,and the CMR for cancers in those aged 65-79 demonstrated a year-by-year
           downward trend. Older age may be the major demographic factor contributing to the stability of CMR of cancers.
               【Key words】 Neoplasms;Mortality;Demographic aging;Trend analysis;Vital statistics;Data collection

               恶性肿瘤是经济发达国家的主要死因,近十年来发                          1.2 研究方法 居民死因分类按 2010 年国际疾病分
           展中国家居民由于人口老龄化程度加深和人口增长速度                            类   第 10 版(The International Statistical Classification
           加快,以及越来越多地采用与恶性肿瘤发病相关的高危                            of Diseases and Related Health Problems 10th Revision,
           生活方式,包括吸烟、缺乏体力活动和“西方化”的饮                            ICD-10)进行编码及归类,其中恶性肿瘤编码范围为
           食方式,恶性肿瘤疾病负担在经济发展中国家日益加重,                           C00~C99。全死因数据均由经过严格培训的临床医生编
           已成为发展中国家公共卫生领域的重点问题                    [1] 。中国      码,每个记录由当地社区医生核实,并由市、区两级疾
           恶性肿瘤发病率和死亡率逐年上升,不断增加的疾病负                            病预防控制中心进一步验证。如果存在任何差异,则对
           担很大程度上归因于老龄化及社会人口结构的变化。已                            医疗记录、家属报告或警方记录进行审查                  [8] 。
           有的对全国发病率估计的报道大部分来自中国局部地                             1.3 统计学方法 应用 SPSS 16.0 统计学软件进行数
           区,或基于特定的年份,因此,可能会限制有效的癌症                            据分析。社区各年份平均人口数为相邻 2 年年末人
           控制策略的制订       [2-5] ,需要对时间跨度大、人口结构                  口数的平均值。死亡年龄 =(死亡日期 - 出生日期)
           变化有代表性的地区的肿瘤发病率和死亡率数据分析。                            /365.25,死亡率的标化以 2010 年中国标准人口为参
           随着改革开放的逐步深入,浦东新区中部社区居民饮食                            照。各性别死亡率的差异检验采用 Poisson 分布的 u 检
           方式、生活习惯和环境在经济发展过程中经历了巨大改                            验,标化率死亡率的差异比较采用 Mantel-Haenszel χ              2
           变 [6] 。此外,发展中国家的经济较发达地区人口结构                         检 验。 以 年 均 变 化 百 分 比(average annual percentage
           逐步老龄化,人口构成因素对恶性肿瘤死亡带来的影响                            change,AAPC)计算各相邻年份间死亡率的变化量,
           及贡献需要深入分析         [7] 。因此,本研究对 2007—2020            采用 Joinpoint 线性回归模型分析各年份粗死亡率(crude
           年浦东新区中部某社区的恶性肿瘤死亡资料进行分析,                            mortality rate,CMR) 和 标 化 死 亡 率(standardized
           结果报道如下。                                             mortality rate,SMR)的变化趋势,并采用死亡率差别
           1 资料与方法                                             分解法计算人口构成因素和非人口构成因素对 CMR 变
           1.1 资料来源 2021 年 1—12 月,以浦东新区东明路                     化的贡献率。死亡率差别分解采用一种无残差的方法:
                                                                                                       A
           街道社区户籍居民为研究对象,研究资料源于该社区                             假定需分解人口 A 和 B 死亡率差别,设 CMR 、CMR                 B
           2007—2020 年户籍居民全死因监测系统库。人口构成                        分别为人口 A 和 B 死亡率,diff 为人口 A 和 B 的死亡
           资料由上海市浦东新区公安局提供。                                    率差,C 为人口的年龄构成比,M 为年龄别死亡率,则
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