PM2.5对人群发病和死亡影响研究中统计学模型的应用

doi:10.3969/j.issn.1006-7795.2015.01.029

摘要/Abstract

摘要： 目前国内外有许多采用统计学模型研究PM_2.5对人群发病和死亡的影响.本文针对时间序列模型、Logistic回归模型、Cox风险比例模型、Poisson回归模型等的特点以及在这些研究中的应用现状进行了综述,并分析了不同统计学模型在PM_2.5研究中存在的局限性.

关键词: 统计学模型, PM_2.5, 疾病, 影响

Abstract: There are a lot of studies regarding the impact of PM_2.5 on population morbidity and mortality by using the statistical model at home and abroad. Based on the characteristics of time series model, the Logistic regression model, Cox proportional hazards regression model and poisson regression model, the application of these models were reviewed in the paper, and the limitation of different statistical models in the study of PM_2.5 were analyzed.

Key words: statistical model, PM_2.5, disease, impact

中图分类号:

R191.1

胥芹, 刘龙, 王超, 曹凯, 高琦, 郭秀花. PM_2.5对人群发病和死亡影响研究中统计学模型的应用[J]. 首都医科大学学报, 2015, 36(1): 151-154.

Xu Qin, Liu Long, Wang Chao, Cao Kai, Gao Qi, Guo Xiuhua. Application of statistical model to study of the impact of PM_2.5 on population morbidity and mortality[J]. Journal of Capital Medical University, 2015, 36(1): 151-154.

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