Cancer is a significant health issue globally and it is well known that cancer risk varies geographically. However in many countries there are no small area-level data on cancer risk factors with high resolution and complete reach, which hinders the development of targeted prevention strategies. Using Australia as a case study, the 2017-2018 National Health Survey was used to generate prevalence estimates for 2221 small areas across Australia for eight cancer risk factor measures covering smoking, alcohol, physical activity, diet and weight. Utilising a recently developed Bayesian two-stage small area estimation methodology, the model incorporated survey-only covariates, spatial smoothing and hierarchical modelling techniques, along with a vast array of small area-level auxiliary data, including census, remoteness, and socioeconomic data. The models borrowed strength from previously published cancer risk estimates provided by the Social Health Atlases of Australia. Estimates were internally and externally validated. We illustrated that in 2017-18 health behaviours across Australia exhibited more spatial disparities than previously realised by improving the reach and resolution of formerly published cancer risk factors. The derived estimates reveal higher prevalence of unhealthy behaviours in more remote areas, and areas of lower socioeconomic status; a trend that aligns well with previous work. Our study addresses the gaps in small area level cancer risk factor estimates in Australia. The new estimates provide improved spatial resolution and reach and will enable more targeted cancer prevention strategies at the small area level, supporting policy makers, researchers, and the general public in understanding the spatial distribution of cancer risk factors in Australia. To help disseminate the results of this work, they will be made available in the Australian Cancer Atlas 2.0.

翻译：癌症是全球性的重大健康问题，且已知癌症风险存在地理差异。然而许多国家缺乏高分辨率、全覆盖的小区域癌症风险因素数据，这阻碍了针对性预防策略的制定。本研究以澳大利亚为案例，利用2017-2018年全国健康调查数据，对全澳2221个小区域的八项癌症风险因素（涵盖吸烟、饮酒、体力活动、饮食和体重）进行流行率估计。采用近期开发的贝叶斯两阶段小区域估计方法，模型整合了仅含调查协变量、空间平滑和分层建模技术，并结合海量小区域辅助数据（包括人口普查、偏远程度及社会经济数据）。模型从澳大利亚社会健康图谱先前发表的癌症风险估计中借用了统计强度。估计值通过了内部和外部验证。研究表明，通过提升先前发表癌症风险因素数据的分辨率和覆盖范围，2017-2018年澳大利亚健康行为呈现的空间差异较以往认知更为显著。推导的估计值显示，偏远地区和社会经济地位较低区域的不良行为流行率更高——这一趋势与既往研究高度吻合。本研究填补了澳大利亚小区域癌症风险因素估计的空白。新估计值提供了更优的空间分辨率和覆盖范围，将支持小区域层面制定更具针对性的癌症预防策略，助力政策制定者、研究人员及公众理解澳大利亚癌症风险因素的空间分布特征。为促进研究成果传播，相关数据将纳入澳大利亚癌症图谱2.0版本。