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2005-2018年中国分省死亡率及期望寿命分析 被引量:34

Analysis on all-cause mortality rate and life expectancy in China,2005-2018
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摘要 目的掌握2005-2018年全国及分省死亡率及期望寿命现状分布及变化情况。方法利用全国人口死亡信息登记管理系统死因监测数据、全国妇幼卫生监测数据、全国死因监测漏报调查数据及社会决定因素相关协变量数据,对2005-2018年全国及分省居民死亡率及期望寿命进行估计,描述全国及分省死亡率和期望寿命差异及变化趋势。采用死亡数变化分解方法,分析年龄别死亡率、人口老龄化及人口增长原因对全国及分省总死亡数变化的贡献程度。结果2018年,我国居民估计死亡数为10482297(95%CI:9723233-11466875)人,男性6113926(95%CI:5773158-6572407)人,女性4368241(95%CI:3950075-4894468)人;死亡率为755.54/10万(95%CI:701.49/10万-825.78/10万),男性861.78/10万(95%CI:813.75/10万-926.40/10万),女性642.73/10万(95%CI:581.20/10万-720.15/10万);标化死亡率为652.27/10万(95%CI:599.22/10万-721.71/10万),男性806.38/10万(95%CI:755.10/10万-874.31/10万),女性503.37/10万(95%CI:450.50/10万-572.01/10万)。2018年,全国人均期望寿命为77.15(95%CI:75.92-78.11)岁,男性74.81(95%CI:73.57-75.76)岁,女性79.87(95%CI:78.61-80.91)岁。期望寿命较高的省份有上海市、北京市、江苏省、浙江省等发达省份,较低的有西藏自治区、贵州省、新疆维吾尔自治区和青海省等欠发达省份。2005-2018年,全国死亡数共增长29.87%,男性27.74%,女性31.29%;其中,年龄别死亡率原因占比-35.74%,人口增长原因占比7.34%,人口老龄化原因占比58.28%。结论2005-2018年,我国居民估计死亡率呈上升趋势,标化死亡率呈下降趋势,人口老龄化为其主要原因。期望寿命涨幅较明显,但省际间差异大。 Objective To understand the geographical variations and temporal trends of all-cause mortality rate and life expectancy in China at national and subnational levels during 2005-2018.Methods Using data from National Cause-of-death Reporting System,China National Maternal and Child Health Surveillance System,Under-reporting Surveys,and related social determinants covariates,we estimated all-cause mortality rate and life expectancy at national and subnational levels in China during 2005-2018.We depicted the geographical variations and temporal trends between provinces on mortality rate and life expectancy.We then decomposed changes in national and subnational deaths into three explanatory components:change due to age-specific mortality rate,change due to the population structure by age,and change due to growth of the total population.Results In 2018,it was estimated that there were 10482297 total deaths(95%CI:9723233-11466875 deaths)in China,with 6113926 men(95%CI:5773158-6572407 men)and 4368241 women(95%CI:3950075-4894468 women).The all-cause mortality rate was 755.54 per 100000(95%CI:701.49 per 100000-825.78 per 100000),with 861.78 per 100000(95%CI:813.75 per 100000-926.40 per 100000)in men and 642.73 per 100000(95%CI:581.20 per 100000-720.15 per 100000)in women,while age-standardized all-cause mortality rate was 652.27 per 100000(95%CI:599.22 per 100000-721.71 per 100000),with 806.38 per 100000(95%CI:755.10 per 100000-874.31 per 100000)in men and 503.37 per 100000(95%CI:450.50 per 100000-572.01 per 100000)in women.In 2018,it was estimated that the life expectancy in the whole country was 77.15 years old(95%CI:75.92-78.11 years old),with 74.81(95%CI:73.57-75.76)in men and 79.87(95%CI:78.61-80.91)in women.Developed areas as Shanghai,Beijing,Jiangsu,and Zhejiang owned comparatively higher life expectancy,while undeveloped areas like Tibet,Guizhou,Xinjiang,and Qinghai showed lower levels.During 2005-2018,there was a 29.87%increase in total deaths at the national level,with 27.74%in men and 31.29%in women.Changes due to age-specific mortality rate,the population structure by age,and the growth of the total population constituted-35.74%,7.34%,and 58.28%of the total increase,respectively.Conclusions From 2005 to 2018,the all-cause mortality rate increased while the age-standardized mortality rate decreased substantially among Chinese residents.Change due to population structure by age was the dominant driver.An upward trend of life expectancy was observed in all provinces,with marked differences between the provinces.
作者 王薇 殷鹏 王黎君 刘韫宁 刘江美 齐金蕾 由金玲 林琳 周脉耕 Wang Wei;Yin Peng;Wang Lijun;Liu Yunning;Liu Jiangmei;Qi Jinlei;You Jinling;Lin Lin;Zhou Maigeng(National Center for Chronic and Non-communicable Disease Control and Prevention,Chinese Center for Disease Control and Prevention,Beijing 100050,China)
出处 《中华流行病学杂志》 CSCD 北大核心 2021年第8期1420-1428,共9页 Chinese Journal of Epidemiology
基金 国家重点研发计划(2018YFC1315301)。
关键词 死亡率 期望寿命 死亡数变化分解 地区差异 变化趋势 All-cause mortality rate Life expectancy Decomposition of changes in deaths Geographical variations Temporal trends
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