INTRODUCTION Road traffic noise increases the risk of mortality from ischemic heart disease (IHD). Because noise is highly localized, high resolution maps of exposures and health outcomes are key to urban planning interventions that are informed by health risks. In Australia, publicly accessible IHD deaths data are only available at the coarse spatial aggregation level of local government area (LGA), in which about 130,000 people reside. Herein, we addressed this limitation of health data using statistical downscaling and generated environmental health risk maps for noise at the meshblock level (MB; ~ 90 people). METHODS We estimated noise exposures at the MB level using a model of road traffic noise in Melbourne, Australia, from 2011. As recommended by the World Health Organization, a non-linear exposure-response function for traffic noise and IHD was used to calculate odds ratios for noise related IHD in all MBs. Noise attributable risks of IHD death were then estimated by statistically downscaling LGA-level IHD rates to the MB level. RESULTS Noise levels of 80 dB were recorded in some MBs. From the given noise maps, approximately 5% of the population was exposed to traffic noise above the risk threshold of 55 dB. Maps of excess risk at the MB level identified areas in which noise levels and exposed populations are large. Attributable rates of IHD deaths due to noise were generally very low, but some were as high as 5-10 per 100,000, and in extremely noisy and populated MBs represented more than 8% excess risk of IHD death. We presented results as interactive maps of excess risk due to noise at the small neighbourhood scale. CONCLUSION Our method accommodates low-resolution health data and could be used to inform urban planning and public health decision making for various environmental health concerns. Estimated noise related IHD deaths were relatively few in Melbourne in 2011, likely because road traffic is one of many noise sources and the current noise model underestimates exposures. Nonetheless, this novel computational framework could be used globally to generate maps of noise related health risks using scant health outcomes data.

中文翻译：

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用于生成高空间分辨率健康风险图的统计缩减方法：以澳大利亚墨尔本的道路噪声和缺血性心脏病死亡率为例。

引言道路交通噪声会增加缺血性心脏病（IHD）致死的风险。由于噪声高度局部化，因此暴露和健康状况的高分辨率地图对于以健康风险为依据的城市规划干预至关重要。在澳大利亚，只有在大约13万人居住的地方政府区域（LGA）的粗略空间聚集级别上，才可以公开获取IHD死亡数据。本文中，我们使用统计缩小比例解决了健康数据的局限性，并生成了网格块级别（MB；约90人）的噪声环境健康风险图。方法我们从2011年开始使用澳大利亚墨尔本的道路交通噪声模型估算了MB级的噪声暴露。根据世界卫生组织的建议，使用交通噪声和IHD的非线性曝光响应函数来计算所有MB中与噪声相关的IHD的比值比。然后，通过统计将LGA级IHD比例降低到MB级，估算出IHD死亡的噪声归因风险。结果在某些MB中记录了80 dB的噪声水平。根据给定的噪声图，大约5％的人口暴露于高于55 dB风险阈值的交通噪声。在MB级别的超额风险地图确定了噪声水平和暴露人群较大的区域。由于噪声引起的IHD死亡的可归因率通常很低，但有些则高达每10万人5-10，而且在嘈杂且人口众多的MB中，IHD死亡的额外风险超过8％。我们将结果表示为在小邻域范围内因噪声而产生的额外风险的交互式地图。结论我们的方法可容纳低分辨率的健康数据，并可用于为各种环境健康问题提供城市规划和公共卫生决策依据。在墨尔本，2011年与噪声有关的IHD估计死亡人数相对较少，这很可能是因为道路交通是许多噪声源之一，而当前的噪声模型低估了暴露程度。但是，这种新颖的计算框架可以在全球范围内使用很少的健康结果数据生成与噪声相关的健康风险图。在墨尔本，2011年与噪声有关的IHD估计死亡人数相对较少，这很可能是因为道路交通是许多噪声源之一，而当前的噪声模型低估了暴露程度。但是，这种新颖的计算框架可以在全球范围内使用很少的健康结果数据生成与噪声相关的健康风险图。在墨尔本，2011年与噪声有关的IHD估计死亡人数相对较少，这很可能是因为道路交通是许多噪声源之一，而当前的噪声模型低估了暴露程度。但是，这种新颖的计算框架可以在全球范围内使用很少的健康结果数据生成与噪声相关的健康风险图。