Simkin Jonathan, Dummer Trevor J B, Erickson Anders C, Otterstatter Michael C, Woods Ryan R, Ogilvie Gina
Cancer Control Research, BC Cancer, Provincial Health Services Authority, Vancouver, BC, Canada.
School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
Front Oncol. 2022 Oct 19;12:833265. doi: 10.3389/fonc.2022.833265. eCollection 2022.
There is an increasing interest in small area analyses in cancer surveillance; however, technical capacity is limited and accessible analytical approaches remain to be determined. This study demonstrates an accessible approach for small area cancer risk estimation using Bayesian hierarchical models and data visualization through the smallareamapp R package.
Incident lung (N = 26,448), female breast (N = 28,466), cervical (N = 1,478), and colorectal (N = 25,457) cancers diagnosed among British Columbia (BC) residents between 2011 and 2018 were obtained from the BC Cancer Registry. Indirect age-standardization was used to derive age-adjusted expected counts and standardized incidence ratios (SIRs) relative to provincial rates. Moran's was used to assess the strength and direction of spatial autocorrelation. A modified Besag, York and Mollie model (BYM2) was used for model incidence counts to calculate posterior median relative risks (RR) by Community Health Service Areas (CHSA; N = 218), adjusting for spatial dependencies. Integrated Nested Laplace Approximation (INLA) was used for Bayesian model implementation. Areas with exceedance probabilities (above a threshold RR = 1.1) greater or equal to 80% were considered to have an elevated risk. The posterior median and 95% credible intervals (CrI) for the spatially structured effect were reported. Predictive posterior checks were conducted through predictive integral transformation values and observed versus fitted values.
The proportion of variance in the RR explained by a spatial effect ranged from 4.4% (male colorectal) to 19.2% (female breast). Lung cancer showed the greatest number of CHSAs with elevated risk (N = 50/218, N = 44/218), representing 2357 total excess cases. The largest lung cancer RRs were 1.67 (95% CrI = 1.06-2.50; exceedance probability = 96%; cases = 13) among women and 2.49 (95% CrI = 2.14-2.88; exceedance probability = 100%; cases = 174) among men. Areas with small population sizes and extreme SIRs were generally smoothed towards the null (RR = 1.0).
We present a ready-to-use approach for small area cancer risk estimation and disease mapping using BYM2 and exceedance probabilities. We developed the smallareamapp R package, which provides a user-friendly interface through an R-Shiny application, for epidemiologists and surveillance experts to examine geographic variation in risk. These methods and tools can be used to estimate risk, generate hypotheses, and examine ecologic associations while adjusting for spatial dependency.
在癌症监测中，对小区域分析的兴趣日益增加；然而，技术能力有限，尚待确定可行的分析方法。本研究展示了一种使用贝叶斯分层模型和通过smallareamapp R包进行数据可视化的小区域癌症风险估计的可行方法。
从不列颠哥伦比亚省（BC）癌症登记处获取了2011年至2018年间在BC省居民中诊断出的肺癌（N = 26,448）、女性乳腺癌（N = 28,466）、宫颈癌（N = 1,478）和结直肠癌（N = 25,457）病例。采用间接年龄标准化方法得出年龄调整后的预期病例数和相对于省级发病率的标准化发病率（SIR）。使用莫兰指数评估空间自相关的强度和方向。采用改良的贝萨格、约克和莫利模型（BYM2）对发病病例数进行建模，以按社区卫生服务区（CHSA；N = 218）计算后验中位数相对风险（RR），并对空间依赖性进行调整。使用集成嵌套拉普拉斯近似法（INLA）进行贝叶斯模型实现。超过概率（高于阈值RR = 1.1）大于或等于80%的区域被认为风险升高。报告了空间结构效应的后验中位数和95%可信区间（CrI）。通过预测积分变换值以及观察值与拟合值进行预测后验检验。
由空间效应解释的RR方差比例范围为4.4%（男性结直肠癌）至19.2%（女性乳腺癌）。肺癌显示出风险升高的CHSA数量最多（N = 50/218，N = 44/218），总计有2357例额外病例。女性中最大的肺癌RR为1.67（95% CrI = 1.06 - 2.50；超过概率 = 96%；病例数 = 13），男性中为2.49（95% CrI = 2.14 - 2.88；超过概率 = 100%；病例数 = 174）。人口规模小且SIR极端的区域通常向无效值（RR = 1.0）平滑。
我们提出了一种使用BYM2和超过概率进行小区域癌症风险估计和疾病绘图的现成方法。我们开发了smallareamapp R包，它通过R-Shiny应用程序提供了一个用户友好的界面，供流行病学家和监测专家检查风险的地理变异。这些方法和工具可用于估计风险、生成假设并在调整空间依赖性的同时检查生态关联。
