Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Environ Health Perspect. 2023 Jul;131(7):77002. doi: 10.1289/EHP11588. Epub 2023 Jul 5.
Seasonal temperature variability remains understudied and may be modified by climate change. Most temperature-mortality studies examine short-term exposures using time-series data. These studies are limited by regional adaptation, short-term mortality displacement, and an inability to observe longer-term relationships in temperature and mortality. Seasonal temperature and cohort analyses allow the long-term effects of regional climatic change on mortality to be analyzed.
We aimed to carry out one of the first investigations of seasonal temperature variability and mortality across the contiguous United States. We also investigated factors that modify this association. Using adapted quasi-experimental methods, we hoped to account for unobserved confounding and to investigate regional adaptation and acclimatization at the ZIP code level.
We examined the mean and standard deviation (SD) of daily temperature in the warm (April-September) and cold (October-March) season in the Medicare cohort from 2000 to 2016. This cohort comprised 622,427,230 y of person-time in all adults over the age of 65 y from 2000 to 2016. We used daily mean temperature obtained from gridMET to develop yearly seasonal temperature variables for each ZIP code. We used an adapted difference-in-difference approach model with a three-tiered clustering approach and meta-analysis to observe the relationship between temperature variability and mortality within ZIP codes. Effect modification was assessed with stratified analyses by race and population density.
For every 1°C increase in the SD of warm and cold season temperature, the mortality rate increased by 1.54% [95% confidence interval (CI): 0.73%, 2.15%] and 0.69% (95% CI: 0.22%, 1.15%) respectively. We did not see significant effects for seasonal mean temperatures. Participants who were classified by Medicare into an "other" race group had smaller effects than those classified as White for Cold and Cold SD and areas with lower population density had larger effects for Warm SD.
Warm and cold season temperature variability were significantly associated with increased mortality rates in U.S. individuals over the age of 65 y, even after controlling for seasonal temperature averages. Warm and cold season mean temperatures showed null effects on mortality. Cold SD had a larger effect size for those who were in the racial subgroup other, whereas Warm SD was more harmful for those living in lower population density areas. This study adds to the growing calls for urgent climate mitigation and environmental health adaptation and resiliency. https://doi.org/10.1289/EHP11588.
季节性温度变化仍然研究不足,并且可能会受到气候变化的影响。大多数温度-死亡率研究使用时间序列数据来检验短期暴露。这些研究受到区域适应、短期死亡率转移以及无法观察温度和死亡率之间长期关系的限制。季节性温度和队列分析可以分析区域气候变化对死亡率的长期影响。
我们旨在对美国大陆各地季节性温度变化与死亡率进行首次调查之一。我们还研究了影响这种关联的因素。使用适应性准实验方法,我们希望能够解释未观察到的混杂因素,并在邮政编码层面上研究区域适应和适应。
我们检查了 Medicare 队列中 2000 年至 2016 年暖季(4 月至 9 月)和冷季(10 月至 3 月)的日平均温度的平均值和标准差(SD)。该队列包括 2000 年至 2016 年所有 65 岁以上成年人的 622,427,230 人年。我们使用来自 gridMET 的日平均温度来为每个邮政编码开发年度季节性温度变量。我们使用了一种适应性差分法模型,采用三层聚类方法和荟萃分析来观察邮政编码内温度变化与死亡率之间的关系。通过按种族和人口密度进行分层分析来评估效应修饰。
温暖季节和寒冷季节温度 SD 每增加 1°C,死亡率分别增加 1.54%(95%置信区间:0.73%,2.15%)和 0.69%(95%置信区间:0.22%,1.15%)。我们没有发现季节性平均温度的显著影响。被 Medicare 归类为“其他”种族的参与者比被归类为白人的参与者对冷季和冷季 SD 的影响更小,而人口密度较低的地区对暖季 SD 的影响更大。
即使在控制季节性温度平均值后,美国 65 岁以上人群的温暖和寒冷季节温度变化与死亡率的增加显著相关。温暖和寒冷季节的平均温度对死亡率没有影响。冷季 SD 对属于其他种族亚组的人影响更大,而对于生活在人口密度较低地区的人,暖季 SD 的危害更大。本研究增加了对紧急气候缓解和环境卫生适应和恢复力的强烈呼吁。https://doi.org/10.1289/EHP11588。