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：癌症是全球重大的健康问题，且已知其风险存在地理差异。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例，利用2017-2018年国家健康调查数据，对全澳2,221个小区域的八类癌症风险因素指标（涵盖吸烟、饮酒、身体活动、饮食及体重）进行流行率估算。通过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、空间平滑及分层建模技术，并引入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）。模型借鉴了澳大利亚社会健康地图集先前发表的癌症风险估计结果，并经内外效度验证。研究表明，相较于已发表的癌症风险因素数据，2017-2018年澳大利亚健康行为在空间分布上呈现出比以往认知更显著的差异性。估算结果显示，更偏远地区及社会经济地位较低区域的非健康行为流行率更高，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，新估计数据显著提升了空间分辨率与覆盖范围，可支撑小区域层面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地图集2.0版本。