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：癌症是全球范围内的重要健康问题，已知癌症风险存在地理差异。然而，许多国家缺乏高分辨率且全域覆盖的小区域层面癌症风险因素数据，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，为全国2221个小区域生成了涵盖吸烟、饮酒、体力活动、饮食与体重等八项癌症风险因素的流行率估计。我们采用近期开发的贝叶斯两阶段小区域估计方法，该模型融合了仅基于调查的协变量、空间平滑与分层建模技术，并结合了包括人口普查、偏远程度和社会经济数据在内的大量小区域辅助数据。模型还借鉴了澳大利亚社会健康地图集先前发布的癌症风险估计值。我们对估计结果进行了内部和外部验证。研究表明，通过提升此前已发表癌症风险因素数据的覆盖范围与分辨率，2017-2018年澳大利亚各地健康行为表现出的空间差异比以往认知更为显著。生成的估计值揭示出偏远地区和社会经济地位较低区域的健康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层面癌症风险因素估计的空白。新的估计值提供了更优的空间分辨率与覆盖范围，将有助于在小区域层面制定更精准的癌症预防策略，为政策制定者、研究人员及公众理解澳大利亚癌症风险因素的空间分布提供支持。为促进研究成果传播，相关数据将纳入《澳大利亚癌症地图集2.0》中发布。