School of the Environment, Yale University, New Haven, CT, USA.
J Expo Sci Environ Epidemiol. 2023 Jul;33(4):637-645. doi: 10.1038/s41370-023-00544-y. Epub 2023 Apr 7.
Many studies have explored the heat-mortality relationship; however, comparability of results is hindered by the studies' use of different exposure methods.
This study evaluated different methods for estimating exposure to temperature using individual-level data and examined the impacts on the heat-mortality relationship.
We calculated different temperature exposures for each individual death by using a modeled, gridded temperature dataset and a monitoring station dataset in North Carolina for 2000-2016. We considered individual-level vs. county-level averages and measured vs. modeled temperature data. A case-crossover analysis was conducted to examine the heat-mortality risk under different exposure methods.
The minimum mortality temperature (MMT) (i.e., the temperature with the lowest mortality rate) for the monitoring station dataset was 23.87 °C and 22.67 °C (individual monitor and county average, respectively), whereas for the modeled temperature dataset the MMT was 19.46 °C and 19.61 °C (individual and county, respectively). We found higher heat-mortality risk while using temperature exposure estimated from monitoring stations compared to risk based on exposure using the modeled temperature dataset. Individual-aggregated monitoring station temperature exposure resulted in higher heat mortality risk (odds ratio (95% CI): 2.24 (95% CI: 2.21, 2.27)) for a relative temperature change comparing the 99th and 90th temperature percentiles, while modeled temperature exposure resulted in lower odds ratio of 1.27 (95% CI: 1.25, 1.29).
Our findings indicate that using different temperature exposure methods can result in different temperature-mortality risk. The impact of using various exposure methods should be considered in planning health policies related to high temperatures, including under climate change. IMPACT STATEMENT: (1) We estimated the heat-mortality association using different methods to estimate exposure to temperature. (2) The mean temperature value among different exposure methods were similar although lower for the modeled data, however, use of the monitoring station temperature dataset resulted in higher heat-mortality risk than the modeled temperature dataset. (3) Differences in mortality risk from heat by urbanicity varies depending on the method used to estimate temperature exposure.
许多研究已经探讨了热死亡率之间的关系,但由于研究采用了不同的暴露方法,结果的可比性受到了阻碍。
本研究使用个体水平数据评估了不同的温度暴露估计方法,并考察了这些方法对热死亡率关系的影响。
我们通过使用 2000-2016 年北卡罗来纳州的网格化温度数据集和监测站数据集,为每个个体死亡计算了不同的温度暴露。我们考虑了个体水平与县水平平均值以及测量值与模型值温度数据。采用病例交叉分析方法,研究了不同暴露方法下的热死亡率风险。
监测站数据集的最低死亡率温度(MMT)(即死亡率最低的温度)为 23.87°C 和 22.67°C(个体监测站和县平均温度),而模型温度数据集的 MMT 为 19.46°C 和 19.61°C(个体和县平均温度)。我们发现,与基于模型温度数据集的暴露相比,使用监测站估计的温度暴露会导致更高的热死亡率风险。个体聚集的监测站温度暴露导致相对温度变化在第 99 百分位和第 90 百分位时,热死亡率风险更高(比值比(95%置信区间):2.24(95%置信区间:2.21,2.27)),而模型温度暴露导致比值比为 1.27(95%置信区间:1.25,1.29)。
我们的研究结果表明,使用不同的温度暴露方法会导致不同的温度死亡率风险。在制定与高温相关的卫生政策,包括在气候变化背景下,应考虑使用各种暴露方法的影响。
(1)我们使用不同的方法来估计温度暴露,来评估热死亡率的关联。(2)虽然模型数据的平均温度值较低,但不同暴露方法的平均温度值相似,然而,使用监测站温度数据集导致的热死亡率风险高于模型温度数据集。(3)根据用于估计温度暴露的方法,城市热死亡率风险的差异也有所不同。