ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; CIBER Epidemiology and Public Health, Barcelona, Spain.
ISGlobal, Barcelona, Spain.
Lancet Planet Health. 2024 Oct;8(10):e766-e777. doi: 10.1016/S2542-5196(24)00212-2.
Exposure to environmental factors has a high burden on human health, with millions of premature annual deaths associated with the short-term health effects of ambient temperatures and air pollution. However, direct estimations of exposure-related mortality from real data are still not available in most parts of the world, especially in low-resource settings, due to the unavailability of daily health records to calibrate epidemiological models.
In this study, we have filled the crucial gap in available direct estimations by developing a method to make valid inference for the relationship between exposure and response data that uses only exposure and temporally aggregated response data. We provided the mathematical derivation of the method, and compared the results by using simulations applied to daily temperature and daily, weekly, and monthly mortality data. The method was then applied to the newly created database of the EARLY-ADAPT project.
The daily and weekly models produced similar and unbiased estimates of the temperature-related relative risks and attributable mortality, with only slightly more imprecision in the weekly model. Even the estimates of the monthly model were unbiased when using enough data, although at the expense of a substantial increase in variability. The real data analysis showed that the similarity between the regional values of two aggregation models increased with the number of years and regions of the dataset, and decreased with the difference in their degree of temporal aggregation.
Our method opens the door to conducting epidemiological studies in low-resource settings, where access to daily health data is not possible. Moreover, it allows accurate estimation of the short-term health effects of environmental exposures in near-real time, when daily health data are still not available, such as in the estimation of the mortality burden of recent record-breaking heat episodes. Overall, our method represents an important new approach to how the public health community can use data to create new evidence for research, translation and policy making.
European Research Council (ERC).
环境因素对人类健康造成了沉重负担，全球每年有数百万人因环境温度和空气污染的短期健康影响而过早死亡。然而，由于缺乏日常健康记录来校准流行病学模型，在世界大部分地区，特别是在资源匮乏的环境中，仍然无法直接从实际数据中估算与暴露相关的死亡率。
在这项研究中，我们通过开发一种仅使用暴露数据和时间聚合的响应数据来进行暴露与响应数据之间关系的有效推断的方法，填补了现有直接估算方法中的关键空白。我们提供了该方法的数学推导，并通过将模拟应用于每日温度和每日、每周和每月死亡率数据来比较结果。然后将该方法应用于新创建的 EARLY-ADAPT 项目数据库。
每日和每周模型产生了相似且无偏的与温度相关的相对风险和归因死亡率的估计值，而每周模型仅略微更不精确。即使在使用足够的数据时，每月模型的估计值也是无偏的，尽管这是以大大增加变异性为代价的。真实数据分析表明，两个聚合模型的区域值之间的相似性随着数据集的年份和区域数量的增加而增加，随着其时间聚合程度的差异的增加而降低。
我们的方法为在无法获取日常健康数据的资源匮乏环境中进行流行病学研究开辟了道路。此外，它允许在无法获得日常健康数据的情况下，例如在估算最近破纪录的热浪事件的死亡率负担时，实时准确地估算环境暴露的短期健康影响。总的来说，我们的方法代表了公共卫生界如何利用数据为研究、转化和政策制定创造新证据的一种重要新方法。
欧洲研究理事会（ERC）。
