陈亿雄, 张文倩, 任萌, 李静, 李苑. 2012-2020年广东省深圳市某区非正常死亡监测分析[J]. 疾病监测. DOI: 10.3784/jbjc.2021.000
引用本文: 陈亿雄, 张文倩, 任萌, 李静, 李苑. 2012-2020年广东省深圳市某区非正常死亡监测分析[J]. 疾病监测. DOI: 10.3784/jbjc.2021.000
Chen Yixiong, Zhang Wenqian, Ren Meng, Li Jing, Li Yuan. Surveillance for abnormal death in a district in Shenzhen, 2012−2020[J]. Disease Surveillance. DOI: 10.3784/jbjc.2021.000
Citation: Chen Yixiong, Zhang Wenqian, Ren Meng, Li Jing, Li Yuan. Surveillance for abnormal death in a district in Shenzhen, 2012−2020[J]. Disease Surveillance. DOI: 10.3784/jbjc.2021.000

2012-2020年广东省深圳市某区非正常死亡监测分析

Surveillance for abnormal death in a district in Shenzhen, 2012−2020

  • 摘要:
    目的 了解2012—2020年广东省深圳市某区居民非正常死亡的分布特征,为制定相关防控措施提供参考依据。
    方法 死亡资料来源于深圳市死因监测系统,常住人口数据来自于深圳市某区统计年鉴。死亡数据参照疾病和有关健康问题的国际统计分类第10次修订本(ICD-10),编码V00~Y34被认定为非正常死亡,选取非正常死亡人口数计算非正常死亡率。应用Joinpoint回归分析经年龄调整的标化非正常死亡率的年度变化趋势,采用χ2检验比较非正常死亡率在不同性别和年龄段间的差异。统计非正常死亡的死因构成及顺位,并采用潜在减寿年数(PYLL)和平均潜在减寿年数(APYLL)衡量非正常死亡带来的寿命损失人年数。
    结果 2012—2020年深圳市某区居民非正常死亡累计2 806人,标化非正常死亡率为9.81/10万人; 2012—2020年深圳市某区居民标化非正常死亡率呈现缓慢上升趋势,年度变化百分比与平均年度变化百分比均为5.6% (95%置信区间: −5.9%~18.6%)。男性非正常死亡率为12.84/10万人,大于女性的6.10/10万,差异有统计学意义(χ2=321.392,P<0.001)。65岁及以上年龄组的非正常死亡率最高,达10.13/10万人,其次是15~64岁年龄组(9.95/10万),0~14岁年龄组非正常死亡率最低(8.95/10万),各年龄组的非正常死亡率比较差异无统计学意义(χ2=2.616,P=0.270)。深圳市某区居民5种主要死因中非正常死亡的PYLL和APYLL均为最高,恶性肿瘤次之。意外损伤的其他外因排在非正常死亡死因顺位第一位。非正常死亡PYLL最高为故意自害,其次为意外损伤的其他外因;而非正常死亡APYLL前3位分别是加害、意图不确定的事件造成的损伤、故意自害。
    结论 应进一步构建安全的作业环境,加强对男性、老年人以及工人重点人群的安全保护,提高个人防范意识,大力建设健康养老体系和非正常死亡的危机干预机制。

     

    Abstract:
    Objective To understand the distribution characteristics of abnormal death in residents in a district in Shenzhen from 2012 to 2020, and provide reference for the formulation of relevant prevention and control measures.
    Methods The death data were obtained from the Death Cause Surveillance Information System of Shenzhen, and the permanent population data were collected from local statistical yearbook. The death data were classified according to the International Classification of Diseases (ICD-10). The code V00-Y34 was recognized as abnormal death, and the abnormal mortality rate was calculated. Joinpoint regression was used to analyze the annual trend of age-adjusted standardized abnormal mortality rate, and χ2 test was used to compare the gender and age specific differences in abnormal mortality rate. The composition and rank of causes of abnormal death were analyzed, and potential years of life lost (PYLL) and average potential years of life lost (APYLL) were used to evaluate years of life lost due to abnormal death.
    Results From 2012 to 2020, a total of 2,806 abnormal deaths in residents were reported in this district, and the standardized abnormal mortality rate was 9.81/100,000, showing a gradual upward trend, the annual percent change and the average annual percent change were all 5.6% (95% CI: -5.9%~ 18.6%). The abnormal mortality rate was 12.84/100,000 in men, which was higher than that in women (6.10/100,000), the difference was significant (χ2=321.392, P<0.001). The abnormal mortality rate in age group ≥65 years was highest (10.13/100,000), followed by age group 15-64 years (9.95/100,000), and the lowest mortality rate was in children aged 0-14 years (8.95/100,000). There were no significant differences in abnormal mortality rate among age groups (χ2=2.616, P=0.270). Among the five main causes of death, the PYLL and APYLL of abnormal death were highest, followed by malignant tumor. Other causes of accidental injury ranked first in the cause of abnormal death. The PYLL of intentional self-injury was highest, followed by other causes of accidental injury. The top three abnormal death causes with high APYLL were injury, injury with uncertain intention and intentional self-injury.
    Conclusion It is necessary to establish a safe working environment, strengthen the protection for men, the elderly and workers, improve the awareness of self-protection and establish healthy elderly care system and intervention mechanism for abnormal death.

     

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