Department of Human Geography and Spatial Planning, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, the Netherlands.
Department of Human Geography and Spatial Planning, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, the Netherlands; Department of Geography and Resource Management and Institute of Space and Earth Information Science, Chinese University of Hong Kong, Hong Kong, China.
Sci Total Environ. 2024 Jan 1;906:167637. doi: 10.1016/j.scitotenv.2023.167637. Epub 2023 Oct 9.
BACKGROUND: Many studies on environment-health associations have emphasized that the selected buffer size (i.e., the scale of the geographic context when exposures are assigned at people's address location) may affect estimated effect sizes. However, there is limited methodological progress in addressing these buffer size-related uncertainties. AIM: We aimed to 1) develop a statistical multi-scale approach to address buffer-related scale effects in cohort studies, and 2) investigate how environment-health associations differ between our multi-scale approach and ad hoc selected buffer sizes. METHODS: We used lacunarity analyses to determine the largest meaningful buffer size for multiple high-resolution exposure surfaces (i.e., fine particulate matter [PM], noise, and the normalized difference vegetation index [NDVI]). Exposures were linked to 7.7 million Dutch adults at their home addresses. We assigned exposure estimates based on buffers with fine-grained distance increments until the lacunarity-based upper limit was reached. Bayesian Cox model averaging addressed geographic uncertainties in the estimated exposure effect sizes within the exposure-specific upper buffer limits on mortality. Z-tests assessed statistical differences between averaged effect sizes and those obtained through pre-selected 100, 300, 1200, and 1500 m buffers. RESULTS: The estimated lacunarity curves suggested exposure-specific upper buffer size limits; the largest was for NDVI (960 m), followed by noise (910 m) and PM (450 m). We recorded 845,229 deaths over eight years of follow-up. Our multi-scale approach indicated that higher values of NDVI were health-protectively associated with mortality risk (hazard ratio [HR]: 0.917, 95 % confidence interval [CI]: 0.886-0.948). Increased noise exposure was associated with an increased risk of mortality (HR: 1.003, 95 % CI: 1.002-1.003), while PM showed null associations (HR:0.998, 95 % CI: 0.997-1.000). Effect sizes of NDVI and noise differed significantly across the averaged and prespecified buffers (p < 0.05). CONCLUSIONS: Geographic uncertainties in residential-based exposure assessments may obscure environment-health associations or risk spurious ones. Our multi-scale approach produced more consistent effect estimates and mitigated contextual uncertainties.
背景：许多关于环境-健康关联的研究强调，所选缓冲区大小（即，在将暴露分配到人们的地址位置时使用的地理范围的大小）可能会影响估计的效应大小。然而，在解决这些与缓冲区大小相关的不确定性方面，方法学进展有限。
目的：我们旨在 1）开发一种统计多尺度方法来解决队列研究中的缓冲区相关尺度效应，2）研究我们的多尺度方法和特定选择的缓冲区大小之间的环境-健康关联有何不同。
方法：我们使用空隙分析来确定多个高分辨率暴露面（即细颗粒物[PM]、噪声和归一化差异植被指数[NDVI]）的最大有意义的缓冲区大小。将暴露情况与 770 万荷兰成年人在其家庭住址相关联。我们根据精细距离增量的缓冲区来分配暴露估计值，直到基于空隙的上限达到为止。贝叶斯 Cox 模型平均解决了在特定暴露的最大缓冲区限制内估计暴露效应大小的地理不确定性。Z 检验评估了平均效应大小与通过预选择的 100、300、1200 和 1500 米缓冲区获得的效应大小之间的统计学差异。
结果：估计的空隙曲线表明了特定暴露的最大缓冲区大小限制；最大的是 NDVI（960 米），其次是噪声（910 米）和 PM（450 米）。我们在八年的随访中记录了 845229 例死亡。我们的多尺度方法表明，较高的 NDVI 值与死亡率风险呈保护相关（风险比[HR]：0.917，95%置信区间[CI]：0.886-0.948）。噪声暴露增加与死亡率风险增加相关（HR：1.003，95%CI：1.002-1.003），而 PM 则无关联（HR：0.998，95%CI：0.997-1.000）。NDVI 和噪声的效应大小在平均和预指定的缓冲区之间存在显著差异（p<0.05）。
结论：基于住所的暴露评估中的地理不确定性可能会掩盖环境-健康关联或产生虚假关联。我们的多尺度方法产生了更一致的效应估计值，并减轻了上下文不确定性。
Res Rep Health Eff Inst. 2022-7
J Expo Sci Environ Epidemiol. 2023-11
Int J Environ Res Public Health. 2019-3-8
Environ Int. 2022-1
J Xenobiot. 2025-5-14
Environ Health Perspect. 2024-9
Zotero 插件