Natural Experiments: An Overview of Methods, Approaches, and Contributions to Public Health Intervention Research

被引:316
作者
Craig, Peter [1 ]
Katikireddi, Srinivasa Vittal [1 ]
Leyland, Alastair [1 ]
Popham, Frank [1 ]
机构
[1] Univ Glasgow, MRC CSO Social & Publ Hlth Sci Unit, Glasgow G2 3QB, Lanark, Scotland
来源
ANNUAL REVIEW OF PUBLIC HEALTH, VOL 38 | 2017年 / 38卷
关键词
population health interventions; evaluation methods; causal inference; INTERRUPTED TIME-SERIES; REGRESSION DISCONTINUITY DESIGNS; PROPENSITY SCORE METHODS; SYNTHETIC CONTROL METHODS; COMPARATIVE-ANALYSIS QCA; SMOKE-FREE LEGISLATION; CAUSAL-INFERENCE; POPULATION HEALTH; IMPACT; POLICY;
D O I
10.1146/annurev-publhealth-031816-044327
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Population health interventions are essential to reduce health inequalities and tackle other public health priorities, but they are not always amenable to experimental manipulation. Natural experiment (NE) approaches are attracting growing interest as a way of providing evidence in such circumstances. One key challenge in evaluating NEs is selective exposure to the intervention. Studies should be based on a clear theoretical understanding of the processes that determine exposure. Even if the observed effects are large and rapidly follow implementation, confidence in attributing these effects to the intervention can be improved by carefully considering alternative explanations. Causal inference can be strengthened by including additional design features alongside the principal method of effect estimation. NE studies often rely on existing (including routinely collected) data. Investment in such data sources and the infrastructure for linking exposure and outcome data is essential if the potential for such studies to inform decision making is to be realized.
引用
收藏
页码:39 / 56
页数:18
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