摘要
目的采用年龄-时期-队列模型(age-period-cohort, APC)分析我国2004-2019年10~84岁农村人群自杀死亡率的变化趋势,为自杀的防治评价提供科学依据。方法收集2005-2020年《中国卫生统计年鉴》中10~84岁农村人群的自杀死亡监测资料,采用APC模型和内在估计法对年龄、时期和队列效应进行参数估计。结果中国农村居民自杀率随年份的增加总体呈下降趋势;APC模型分析结果显示自杀死亡风险的年龄效应总体上随年龄增长而增加,青少年时期增速最快,除中年时期,其余年龄段女性自杀死亡风险均高于男性;时期效应表明自杀死亡风险随年代迁移呈缓慢下降趋势,女性的自杀死亡风险降速高于男性;总人群及性别亚组队列效应波动均较大,自杀死亡风险在1979年以前呈现下降趋势,随后保持稳定,在1995年之后急速上升。结论自杀死亡风险的变化可能与重大历史事件密切相关;APC模型能够较好地描述死亡率的效应趋势。
Objective To analyze the trend of suicide mortality among rural population aged 10-84 years from 2004 to 2019, in order to provide scientific basis for the suicide prevention. Methods Data of suicide death among rural population aged 10-84 years were collected from China Health Statistics Yearbook from 2005 to 2020. The parameters of age, period and cohort effect were estimated by using age-period-cohort(APC) model and itrinsic estimator. Results The suicide rate of rural residents in China was gradually declined. APC model analysis results showed that the age effect of suicide death increased with age, especially in adolescents. The risk of suicide death was higher for females than males in all age except in middle age. The period effect showed that the risk of suicide death decreased slowly with period advances, and the decrease speed of the suicide death was significantly higher for female than for male. Cohort effect of suicide death decreased before 1979, followed by steady between 1980 and 1994, and increased sharply after 1995. Conclusions The changes of the risk of suicide death may be closely related to the major historical events. APC model can describe the trend of suicide mortality well.
作者
侯皓
裴一霏
俞斌
高修银
王威
HOU Hao;PEI Yi-fei;YU Bin;GAO Xiu-yin;WANG Wei(Faculty of Community and Health Education,School of Public Health,Xuzhou Medical University,Xuzhou 221000,China;Department of Preventive Medicine,School of Public Health,Wuhan University,Wuhan 430061,China)
出处
《中华疾病控制杂志》
CAS
CSCD
北大核心
2022年第1期34-39,111,共7页
Chinese Journal of Disease Control & Prevention
基金
国家自然科学基金(82003484)
江苏省高等学校自然科学研究(20KJB330005)
江苏省博士后科研资助(2020Z177)。
