Causal Modeling in Environmental Health

被引：49
作者：
Bind, Marie-Abele [1 ]
机构：
[1] Harvard Univ, Fac Arts & Sci, Dept Stat, Cambridge, MA 02138 USA
来源：
ANNUAL REVIEW OF PUBLIC HEALTH, VOL 40 | 2019年 / 40卷
关键词：
causal inference; public health; epidemiology; air pollution; statistical methods; FINE PARTICULATE MATTER; AIR-POLLUTION EPIDEMIOLOGY; GENE-SPECIFIC METHYLATION; CENTER NERC EXPERIMENT; HOSPITAL ADMISSIONS; HEART-DISEASE; PRINCIPAL STRATIFICATION; CONTROLLED EXPOSURE; SUSTAINED EXPOSURE; MEDIATION ANALYSIS;
D O I：
10.1146/annurev-publhealth-040218-044048
中图分类号：
R1 [预防医学、卫生学];
学科分类号：
1004 ; 120402 ;
摘要：
The field of environmental health has been dominated by modeling associations, especially by regressing an observed outcome on a linear or nonlinear function of observed covariates. Readers interested in advances in policies for improving environmental health are, however, expecting to be informed about health effects resulting from, or more explicitly caused by, environmental exposures. The quantification of health impacts resulting from the removal of environmental exposures involves causal statements. Therefore, when possible, causal inference frameworks should be considered for analyzing the effects of environmental exposures on health outcomes.
引用
收藏
页码：23 / 43
页数：21
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