Cancer Epidemiology and Prevention Research, Alberta Health Services, Calgary, Alberta, Canada
Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
BMJ Open. 2021 Jul 1;11(7):e045410. doi: 10.1136/bmjopen-2020-045410.
The population attributable fraction (PAF) is an important metric for estimating disease burden associated with causal risk factors. In an International Agency for Research on Cancer working group report, an approach was introduced to estimate the PAF using the average of a continuous exposure and the incremental relative risk (RR) per unit. This 'average risk' approach has been subsequently applied in several studies conducted worldwide. However, no investigation of the validity of this method has been done.
To examine the validity and the potential magnitude of bias of the average risk approach.
We established analytically that the direction of the bias is determined by the shape of the RR function. We then used simulation models based on a variety of risk exposure distributions and a range of RR per unit. We estimated the unbiased PAF from integrating the exposure distribution and RR, and the PAF using the average risk approach. We examined the absolute and relative bias as the direct and relative difference in PAF estimated from the two approaches. We also examined the bias of the average risk approach using real-world data from the Canadian Population Attributable Risk of Cancer study.
The average risk approach involves bias, which is underestimation or overestimation with a convex or concave RR function (a risk profile that increases more/less rapidly at higher levels of exposure). The magnitude of the bias is affected by the exposure distribution as well as the value of RR. This approach is approximately valid when the RR per unit is small or the RR function is approximately linear. The absolute and relative bias can both be large when RR is not small and the exposure distribution is skewed.
We recommend that caution be taken when using the average risk approach to estimate PAF.
人群归因分数（PAF）是一种用于评估与因果风险因素相关疾病负担的重要指标。在国际癌症研究机构工作组的一份报告中，引入了一种使用连续暴露量和单位增量相对风险（RR）的平均值来估计 PAF 的方法。这种“平均风险”方法随后已在全球范围内的多项研究中得到应用。然而，尚未对该方法的有效性进行调查。
检验平均风险方法的有效性和潜在偏倚程度。
我们从理论上证明了偏倚的方向取决于 RR 函数的形状。然后，我们使用基于各种风险暴露分布和单位 RR 的模拟模型。我们通过整合暴露分布和 RR 来估计无偏 PAF，并且使用平均风险方法来估计 PAF。我们检验了两种方法估计的 PAF 的绝对和相对偏倚，即直接和相对差异。我们还使用来自加拿大癌症人群归因风险研究的真实世界数据来检验平均风险方法的偏倚。
平均风险方法存在偏倚，即 RR 函数为凸函数或凹函数时会出现低估或高估（暴露水平越高，风险增加越快或越慢的风险分布）。偏倚的程度受暴露分布以及 RR 的值影响。当单位 RR 较小或 RR 函数近似线性时，该方法近似有效。当 RR 不小时且暴露分布偏斜时，绝对和相对偏倚都可能很大。
我们建议在使用平均风险方法估计 PAF 时要谨慎。
