Environmental epidemiology - Basics and proof of cause-effect

被引:21
|
作者
Weed, DL [1 ]
机构
[1] NCI, Div Canc Prevent, Off Prevent Oncol, EPS T41, Bethesda, MD 20892 USA
关键词
causal inference; cause; epidemiology; toxicology;
D O I
10.1016/S0300-483X(02)00476-6
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Bringing epidemiology and toxicology together to better understand cause and effect relationships requires attention to several interconnected problems: problems of commitment, complexity, and of communication. The most fundamental of these is commitment as it is reflected in the basic purpose of environmental epidemiology. The purpose of epidemiology is not to prove cause-effect relationships, and not only because scientific proof is elusive. The purpose of epidemiology is to acquire knowledge about the determinants and distributions of disease and to apply that knowledge to improve public health. A key problem, therefore, is how much and what kinds of evidence are sufficient to warrant public health (typically preventive) actions? The assessment of available evidence lays the foundation for the problem of complexity: relevant evidence arrives from toxicologic and epidemiological investigations, and reflects the acquisition of knowledge from many levels of scientific understanding: molecular, cellular, tissue, organ systems, complete organisms (man and mouse), relationships between individuals, and on to social and political processes that may impact human health. How to combine evidence from several levels of understanding will require the effective communication of current methodological practices. The practice of causal inference in contemporary environmental epidemiology, for example, relies upon three largely qualitative methods: systematic narrative reviews, criteria-based inference methods, and (increasingly) meta-analysis. These methods are described as they are currently used in practice and several key problems in that practice are highlighted including the relevance to public health practice of toxicological evidence. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.
引用
收藏
页码:399 / 403
页数:5
相关论文
共 50 条
  • [31] ESTIMATION OF CAUSE-EFFECT RELATIONSHIP UNDER NOISE
    PREKOPA, A
    JOURNAL OF APPLIED PROBABILITY, 1994, 31A : 343 - 350
  • [32] Cause-effect analysis for sustainable development policy
    Cucurachi, Stefano
    Suh, Sangwon
    ENVIRONMENTAL REVIEWS, 2017, 25 (03): : 358 - 379
  • [33] A MODEL OF CAUSE-EFFECT RELATIONS IN THE STUDY OF BEHAVIOR
    CHISHOLM, DC
    COOK, DA
    BEHAVIOR ANALYST, 1995, 18 (01): : 99 - 111
  • [34] CAUSE-EFFECT RELATIONSHIPS IN INFECTIOUS-DISEASES
    MAYR, A
    BIBRACK, B
    ZENTRALBLATT FUR BAKTERIOLOGIE MIKROBIOLOGIE UND HYGIENE SERIES A-MEDICAL MICROBIOLOGY INFECTIOUS DISEASES VIROLOGY PARASITOLOGY, 1974, 226 (02): : 168 - 183
  • [35] Comparing Communication Paradigms in Cause-Effect Chains
    Tang, Yue
    Jiang, Xu
    Guan, Nan
    Ji, Dong
    Luo, Xiantong
    Yi, Wang
    IEEE TRANSACTIONS ON COMPUTERS, 2023, 72 (01) : 82 - 96
  • [36] Cause-Effect Inference by Comparing Regression Errors
    Blobaum, Patrick
    Janzing, Dominik
    Washio, Takashi
    Shimizu, Shohei
    Scholkopf, Bernhard
    INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 84, 2018, 84
  • [37] There is no cause-effect relationship between the neural and the mental
    Fischer, R
    CYBERNETICA, 1995, 38 (02): : 141 - 151
  • [39] Formalizing Arguments From Cause-Effect Rules
    Sedki, Karima
    RECENT TRENDS AND FUTURE TECHNOLOGY IN APPLIED INTELLIGENCE, IEA/AIE 2018, 2018, 10868 : 279 - 285
  • [40] Multi-Level Cause-Effect Systems
    Chalupka, Krzysztof
    Perona, Pietro
    Eberhardt, Frederick
    ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 51, 2016, 51 : 361 - 369