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公共卫生领域中自然实验和准实验的概念化。

Conceptualising natural and quasi experiments in public health.

机构信息

Population Health Sciences, Bristol Medical School, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol, BS8 2PS, UK.

NIHR School for Public Health Research, Newcastle, UK.

出版信息

BMC Med Res Methodol. 2021 Feb 11;21(1):32. doi: 10.1186/s12874-021-01224-x.


DOI:10.1186/s12874-021-01224-x
PMID:33573595
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7879679/
Abstract

BACKGROUND: Natural or quasi experiments are appealing for public health research because they enable the evaluation of events or interventions that are difficult or impossible to manipulate experimentally, such as many policy and health system reforms. However, there remains ambiguity in the literature about their definition and how they differ from randomized controlled experiments and from other observational designs. We conceptualise natural experiments in the context of public health evaluations and align the study design to the Target Trial Framework. METHODS: A literature search was conducted, and key methodological papers were used to develop this work. Peer-reviewed papers were supplemented by grey literature. RESULTS: Natural experiment studies (NES) combine features of experiments and non-experiments. They differ from planned experiments, such as randomized controlled trials, in that exposure allocation is not controlled by researchers. They differ from other observational designs in that they evaluate the impact of events or process that leads to differences in exposure. As a result they are, in theory, less susceptible to bias than other observational study designs. Importantly, causal inference relies heavily on the assumption that exposure allocation can be considered 'as-if randomized'. The target trial framework provides a systematic basis for evaluating this assumption and the other design elements that underpin the causal claims that can be made from NES. CONCLUSIONS: NES should be considered a type of study design rather than a set of tools for analyses of non-randomized interventions. Alignment of NES to the Target Trial framework will clarify the strength of evidence underpinning claims about the effectiveness of public health interventions.

摘要

背景:自然或准实验对公共卫生研究很有吸引力,因为它们能够评估难以或不可能通过实验操纵的事件或干预措施,如许多政策和卫生系统改革。然而,文献中对于它们的定义以及它们与随机对照实验和其他观察性设计的区别仍然存在模糊性。我们在公共卫生评估的背景下对自然实验进行概念化,并将研究设计与目标试验框架对齐。

方法:进行了文献检索,并使用关键方法学论文来开展这项工作。同行评议的论文补充了灰色文献。

结果:自然实验研究(NES)结合了实验和非实验的特点。它们与计划性实验(如随机对照试验)不同,因为暴露分配不受研究人员控制。它们与其他观察性设计不同,因为它们评估导致暴露差异的事件或过程的影响。因此,从理论上讲,它们比其他观察性研究设计更不易受到偏差的影响。重要的是,因果推断很大程度上依赖于暴露分配可以被视为“假设随机”的假设。目标试验框架为评估这一假设以及支撑从 NES 中得出的因果推断的其他设计要素提供了一个系统的基础。

结论:NES 应被视为一种研究设计类型,而不是一组用于分析非随机干预措施的工具。将 NES 与目标试验框架对齐将澄清支持公共卫生干预有效性主张的证据强度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db17/7879679/19538a8dde7d/12874_2021_1224_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db17/7879679/19538a8dde7d/12874_2021_1224_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db17/7879679/19538a8dde7d/12874_2021_1224_Fig1_HTML.jpg

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本文引用的文献

[1]
Evidence of detrimental effects of prenatal alcohol exposure on offspring birthweight and neurodevelopment from a systematic review of quasi-experimental studies.

Int J Epidemiol. 2021-1-23

[2]
Alternative causal inference methods in population health research: Evaluating tradeoffs and triangulating evidence.

SSM Popul Health. 2019-12-9

[3]
Using natural experimental studies to guide public health action: turning the evidence-based medicine paradigm on its head.

J Epidemiol Community Health. 2019-11-19

[4]
Evaluation of the impact of calorie labeling on McDonald's restaurant menus: a natural experiment.

Int J Behav Nutr Phys Act. 2019-11-4

[5]
The Economics of Taxes on Sugar-Sweetened Beverages: A Review of the Effects on Prices, Sales, Cross-Border Shopping, and Consumption.

Annu Rev Nutr. 2019-5-22

[6]
Evaluating the impact of health policies: using a difference-in-differences approach.

Int J Public Health. 2019-5

[7]
TIDieR-PHP: a reporting guideline for population health and policy interventions.

BMJ. 2018-5-16

[8]
Communities in charge of alcohol (CICA): a protocol for a stepped-wedge randomised control trial of an alcohol health champions programme.

BMC Public Health. 2018-4-19

[9]
The C-Word: Scientific Euphemisms Do Not Improve Causal Inference From Observational Data.

Am J Public Health. 2018-3-22

[10]
GRADE guidelines: 18. How ROBINS-I and other tools to assess risk of bias in nonrandomized studies should be used to rate the certainty of a body of evidence.

J Clin Epidemiol. 2018-2-9

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