PDF(Pubmed)
Abstract：
UNASSIGNED: Daily time-series regression models are commonly used to estimate the lagged nonlinear relation between temperature and mortality. A major impediment to this type of analysis is the restricted access to daily health records. The use of weekly and monthly data represents a possible solution unexplored to date.
UNASSIGNED: We temporally aggregated daily temperatures and mortality records from 147 contiguous regions in 16 European countries, representing their entire population of over 400 million people. We estimated temperature-lag-mortality relationships by using standard time-series quasi-Poisson regression models applied to daily data, and compared the results with those obtained with different degrees of temporal aggregation.
UNASSIGNED: We observed progressively larger differences in the epidemiological estimates with the degree of temporal data aggregation. The daily data model estimated an annual cold and heat-related mortality of 290,104 (213,745-359,636) and 39,434 (30,782-47,084) deaths, respectively, and the weekly model underestimated these numbers by 8.56% and 21.56%. Importantly, differences were systematically smaller during extreme cold and heat periods, such as the summer of 2003, with an underestimation of only 4.62% in the weekly data model. We applied this framework to infer that the heat-related mortality burden during the year 2022 in Europe may have exceeded the 70,000 deaths.
UNASSIGNED: The present work represents a first reference study validating the use of weekly time series as an approximation to the short-term effects of cold and heat on human mortality. This approach can be adopted to complement access-restricted data networks, and facilitate data access for research, translation and policy-making.
UNASSIGNED: The study was supported by the ERC Consolidator Grant EARLY-ADAPT (https://www.early-adapt.eu/), and the ERC Proof-of-Concept Grants HHS-EWS and FORECAST-AIR.
UNASSIGNED: We temporally aggregated daily temperatures and mortality records from 147 contiguous regions in 16 European countries, representing their entire population of over 400 million people. We estimated temperature-lag-mortality relationships by using standard time-series quasi-Poisson regression models applied to daily data, and compared the results with those obtained with different degrees of temporal aggregation.
UNASSIGNED: We observed progressively larger differences in the epidemiological estimates with the degree of temporal data aggregation. The daily data model estimated an annual cold and heat-related mortality of 290,104 (213,745-359,636) and 39,434 (30,782-47,084) deaths, respectively, and the weekly model underestimated these numbers by 8.56% and 21.56%. Importantly, differences were systematically smaller during extreme cold and heat periods, such as the summer of 2003, with an underestimation of only 4.62% in the weekly data model. We applied this framework to infer that the heat-related mortality burden during the year 2022 in Europe may have exceeded the 70,000 deaths.
UNASSIGNED: The present work represents a first reference study validating the use of weekly time series as an approximation to the short-term effects of cold and heat on human mortality. This approach can be adopted to complement access-restricted data networks, and facilitate data access for research, translation and policy-making.
UNASSIGNED: The study was supported by the ERC Consolidator Grant EARLY-ADAPT (https://www.early-adapt.eu/), and the ERC Proof-of-Concept Grants HHS-EWS and FORECAST-AIR.
摘要：
■每日时间序列回归模型通常用于估计温度与死亡率之间的滞后非线性关系。这种类型的分析的主要障碍是对日常健康记录的访问受到限制。使用每周和每月数据是迄今为止尚未探索的可能解决方案。
■我们对来自16个欧洲国家的147个连续地区的每日气温和死亡率记录进行了时间汇总，代表他们超过4亿人口的全部人口。我们通过使用适用于每日数据的标准时间序列准泊松回归模型来估计温度-滞后-死亡率关系，并将结果与不同时间聚集程度的结果进行了比较。
■我们观察到，随着时间数据的聚集程度，流行病学估计的差异越来越大。每日数据模型估计每年与冷和热相关的死亡率为290,104（213,745-359,636）和39,434（30,782-47,084）。分别,每周模型低估了这些数字8.56%和21.56%。重要的是,在极端寒冷和炎热时期，差异系统地较小，如2003年夏季，周数据模型的低估率仅为4.62%。我们应用这个框架来推断，欧洲2022年与热相关的死亡负担可能已经超过了7万人。
■本工作代表了第一项参考研究，该研究验证了每周时间序列的使用，以近似于寒冷和高温对人类死亡率的短期影响。可以采用这种方法来补充访问受限的数据网络，并促进研究的数据访问，翻译和决策。
■该研究得到了ERC合并器GrantEarly-ADAPT的支持（https://www.早期适应。欧盟/)，和ERC概念验证授予HHS-EWS和预测空气。
■我们对来自16个欧洲国家的147个连续地区的每日气温和死亡率记录进行了时间汇总，代表他们超过4亿人口的全部人口。我们通过使用适用于每日数据的标准时间序列准泊松回归模型来估计温度-滞后-死亡率关系，并将结果与不同时间聚集程度的结果进行了比较。
■我们观察到，随着时间数据的聚集程度，流行病学估计的差异越来越大。每日数据模型估计每年与冷和热相关的死亡率为290,104（213,745-359,636）和39,434（30,782-47,084）。分别,每周模型低估了这些数字8.56%和21.56%。重要的是,在极端寒冷和炎热时期，差异系统地较小，如2003年夏季，周数据模型的低估率仅为4.62%。我们应用这个框架来推断，欧洲2022年与热相关的死亡负担可能已经超过了7万人。
■本工作代表了第一项参考研究，该研究验证了每周时间序列的使用，以近似于寒冷和高温对人类死亡率的短期影响。可以采用这种方法来补充访问受限的数据网络，并促进研究的数据访问，翻译和决策。
■该研究得到了ERC合并器GrantEarly-ADAPT的支持（https://www.早期适应。欧盟/)，和ERC概念验证授予HHS-EWS和预测空气。
