A Comparative Analysis of the Temperature-Mortality Risks Using Different Weather Datasets Across Heterogeneous Regions.
A Comparative Analysis of the Temperature-Mortality Risks Using Different Weather Datasets Across Heterogeneous Regions.
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DOI：
10.1029/2020gh000363
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发表时间：
2021-05
期刊：
影响因子：
4.8
通讯作者：
Vicedo-Cabrera AM
中科院分区：
文献类型：
--
作者：
de Schrijver E;Folly CL;Schneider R;Royé D;Franco OH;Gasparrini A;Vicedo-Cabrera AM
最近发布了新的基于空间分辨率模拟天气数据的网格化气候数据集（GCD），以探索气候变化的影响。在对温度和气候变化对健康的影响进行流行病学评估时，已建议将全球气候变化数据作为气象站数据的潜在替代方法。对于那些由于气象站数据有限或质量低而研究不足的地区，这些评估特别有用。然而，到目前为止，还没有研究对可变空间分辨率的GCD在不同地形、气候和大小的地区的温度死亡率评估中的应用进行严格评估。在这里，我们探讨了来自英国10个地区和瑞士26个州的全球ERA 5再分析数据集的人口加权日平均温度数据的性能，结合两个当地高分辨率GCD（分别为HadUK网格UKPOC-9和MeteoSwiss网格产品），并将其与气象站数据和未加权同源序列进行比较。我们应用具有分布滞后非线性模型的准泊松时间序列回归来获得GCD和地区特定温度死亡率的关联，并计算相应的冷和热相关超额死亡率。尽管五个暴露数据集得出了不同的平均区域水平温度估计值，但这些偏差并未导致温度-死亡率关联或影响的实质性变化。此外，当地人口加权的GCD显示出更好的整体性能，这表明它们可能是帮助提高气候和人口分布变异性大的偏远地区对气候变化影响的认识的优秀替代方案，由于缺乏可靠的暴露数据，目前的文献在很大程度上尚未探索。关于空间分辨天气数据集的新产品已经问世，但对其在健康研究中的适用性知之甚少。不同的暴露数据集在不同地区的温度-死亡率影响中产生了相似的模式全球可用的模拟天气数据有助于在气象站数据有限的地区增进对健康影响的了解。
New gridded climate datasets (GCDs) on spatially resolved modeled weather data have recently been released to explore the impacts of climate change. GCDs have been suggested as potential alternatives to weather station data in epidemiological assessments on health impacts of temperature and climate change. These can be particularly useful for assessment in regions that have remained understudied due to limited or low quality weather station data. However to date, no study has critically evaluated the application of GCDs of variable spatial resolution in temperature‐mortality assessments across regions of different orography, climate, and size. Here we explored the performance of population‐weighted daily mean temperature data from the global ERA5 reanalysis dataset in the 10 regions in the United Kingdom and the 26 cantons in Switzerland, combined with two local high‐resolution GCDs (HadUK‐grid UKPOC‐9 and MeteoSwiss‐grid‐product, respectively) and compared these to weather station data and unweighted homologous series. We applied quasi‐Poisson time series regression with distributed lag nonlinear models to obtain the GCD‐ and region‐specific temperature‐mortality associations and calculated the corresponding cold‐ and heat‐related excess mortality. Although the five exposure datasets yielded different average area‐level temperature estimates, these deviations did not result in substantial variations in the temperature‐mortality association or impacts. Moreover, local population‐weighted GCDs showed better overall performance, suggesting that they could be excellent alternatives to help advance knowledge on climate change impacts in remote regions with large climate and population distribution variability, which has remained largely unexplored in present literature due to the lack of reliable exposure data. New products on spatially resolved weather datasets have become available but little is known on their suitability in health studies Here, different exposure datasets yielded similar patterns in temperature‐mortality impacts across heterogeneous areas Globally available modeled weather data could help advance knowledge on health impacts in areas with limited weather station data