Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA.
Nat Med. 2022 Oct;28(10):2038-2044. doi: 10.1038/s41591-022-01973-2. Epub 2022 Oct 10.
Exposure to risks throughout life results in a wide variety of outcomes. Objectively judging the relative impact of these risks on personal and population health is fundamental to individual survival and societal prosperity. Existing mechanisms to quantify and rank the magnitude of these myriad effects and the uncertainty in their estimation are largely subjective, leaving room for interpretation that can fuel academic controversy and add to confusion when communicating risk. We present a new suite of meta-analyses-termed the Burden of Proof studies-designed specifically to help evaluate these methodological issues objectively and quantitatively. Through this data-driven approach that complements existing systems, including GRADE and Cochrane Reviews, we aim to aggregate evidence across multiple studies and enable a quantitative comparison of risk-outcome pairs. We introduce the burden of proof risk function (BPRF), which estimates the level of risk closest to the null hypothesis that is consistent with available data. Here we illustrate the BPRF methodology for the evaluation of four exemplar risk-outcome pairs: smoking and lung cancer, systolic blood pressure and ischemic heart disease, vegetable consumption and ischemic heart disease, and unprocessed red meat consumption and ischemic heart disease. The strength of evidence for each relationship is assessed by computing and summarizing the BPRF, and then translating the summary to a simple star rating. The Burden of Proof methodology provides a consistent way to understand, evaluate and summarize evidence of risk across different risk-outcome pairs, and informs risk analysis conducted as part of the Global Burden of Diseases, Injuries, and Risk Factors Study.
一生中接触到的各种风险会导致各种各样的结果。客观地判断这些风险对个人和人口健康的相对影响，是个人生存和社会繁荣的基础。现有的量化和排名这些无数影响的大小及其估计不确定性的机制在很大程度上是主观的，这为解释留下了空间，而这些解释可能会引发学术争议，并在传达风险时增加混乱。我们提出了一系列新的荟萃分析——称为“证明负担研究”——旨在帮助客观和定量地评估这些方法问题。通过这种数据驱动的方法，补充现有的系统，包括 GRADE 和 Cochrane 综述，我们旨在汇总多个研究的证据，并能够对风险-结果对进行定量比较。我们引入了证明负担风险函数 (BPRF)，该函数估计与现有数据一致的最接近零假设的风险水平。在这里，我们通过评估四个范例风险-结果对（吸烟与肺癌、收缩压与缺血性心脏病、蔬菜消费与缺血性心脏病以及未加工的红肉消费与缺血性心脏病）来说明 BPRF 方法。通过计算和总结 BPRF 来评估每个关系的证据强度，然后将总结转换为简单的星级评分。证明负担方法为理解、评估和总结不同风险-结果对的风险证据提供了一种一致的方法，并为作为全球疾病、伤害和危险因素研究一部分进行的风险分析提供了信息。
