Konno Ko, Gibbons James, Lewis Ruth, Pullin Andrew S
School of Natural Sciences, Bangor University, Bangor, UK.
School of Medical and Health Sciences, Bangor University, Bangor, UK.
Environ Evid. 2024 Feb 7;13(1):1. doi: 10.1186/s13750-024-00324-7.
To inform environmental policy and practice, researchers estimate effects of interventions/exposures by conducting primary research (e.g., impact evaluations) or secondary research (e.g., evidence reviews). If these estimates are derived from poorly conducted/reported research, then they could misinform policy and practice by providing biased estimates. Many types of bias have been described, especially in health and medical sciences. We aimed to map all types of bias from the literature that are relevant to estimating causal effects in the environmental sector. All the types of bias were initially identified by using the Catalogue of Bias (catalogofbias.org) and reviewing key publications (n = 11) that previously collated and described biases. We identified 121 (out of 206) types of bias that were relevant to estimating causal effects in the environmental sector. We provide a general interpretation of every relevant type of bias covered by seven risk-of-bias domains for primary research: risk of confounding biases; risk of post-intervention/exposure selection biases; risk of misclassified/mismeasured comparison biases; risk of performance biases; risk of detection biases; risk of outcome reporting biases; risk of outcome assessment biases, and four domains for secondary research: risk of searching biases; risk of screening biases; risk of study appraisal and data coding/extraction biases; risk of data synthesis biases. Our collation should help scientists and decision makers in the environmental sector be better aware of the nature of bias in estimation of causal effects. Future research is needed to formalise the definitions of the collated types of bias such as through decomposition using mathematical formulae.
为指导环境政策与实践，研究人员通过开展初级研究（如影响评估）或次级研究（如证据综述）来估计干预措施/暴露因素的影响。如果这些估计值源自开展得不好或报告不充分的研究，那么它们可能会因提供有偏差的估计值而误导政策与实践。已经描述了许多种偏差类型，尤其是在健康和医学领域。我们旨在梳理文献中与环境领域因果效应估计相关的所有偏差类型。所有偏差类型最初是通过使用偏差目录（catalogofbias.org）并查阅之前整理和描述偏差的关键出版物（n = 11）来确定的。我们确定了与环境领域因果效应估计相关的206种偏差类型中的121种。我们对初级研究的七个偏差风险领域所涵盖的每种相关偏差类型进行了一般性解释：混杂偏差风险；干预/暴露后选择偏差风险；错分/错测比较偏差风险；执行偏差风险；检测偏差风险；结果报告偏差风险；结果评估偏差风险，以及对次级研究的四个领域：检索偏差风险；筛选偏差风险；研究评估和数据编码/提取偏差风险；数据合成偏差风险。我们的整理应有助于环境领域的科学家和决策者更好地了解因果效应估计中偏差的本质。未来需要开展研究，通过使用数学公式进行分解等方式，将整理出的偏差类型的定义形式化。
