Pengelly L David, Campbell Monica E, Cheng Chad S, Fu Chao, Gingrich Sarah E, Macfarlane Ronald
Department of Medicine, McMaster University, Hamilton, ON.
Can J Public Health. 2007 Sep-Oct;98(5):364-8. doi: 10.1007/BF03405420.
Periods of unusually hot weather, especially in temperate climates, carry with them a burden of morbidity and mortality, particularly in urban areas. With lessening debate on its origins, and signs of global warming already apparent, it is becoming imperative for public health practitioners to recognize and predict the risks of "heat waves", and to develop protective community responses to them. This study makes use of historical data and a methodology developed previously to examine the pattern of hot weather experienced over the last five decades in the City of Toronto, and to assess the associated burden of mortality.
Synoptic classification of air masses based on meteorological data for Toronto was used, to assign the annual mean burden of illness (in terms of elevated mortality) associated with hot weather and air pollution. Then, coefficients relating daily mortality risk to historical daily weather and air quality data were determined with a model system that (for each air mass) assessed the factors that contributed to day-to-day variability in mortality.
Over the period of study, there were 120 (95% CI: 105-135) heat-related deaths on average per year, with great variability from year to year, reflecting the variability of hot weather. Mortality was greatest in July and August, when the greatest number of multi-day heat episodes occurred. Furthermore, the longer the episode, the greater was the daily risk for mortality.
The method can be used to forecast the risk of heat-related mortality, and to facilitate the development of public health responses to mitigate that risk.
异常炎热的天气时期，尤其是在温带气候地区，会带来发病和死亡负担，在城市地区尤为如此。随着对其成因的争议减少，且全球变暖的迹象已经明显，公共卫生从业者认识和预测“热浪”风险并制定社区保护性应对措施变得势在必行。本研究利用历史数据和先前开发的一种方法，来研究多伦多市过去五十年来经历的炎热天气模式，并评估相关的死亡负担。
基于多伦多的气象数据对气团进行天气学分类，以确定与炎热天气和空气污染相关的年度平均疾病负担（以死亡率升高计）。然后，使用一个模型系统确定每日死亡风险与历史每日天气和空气质量数据之间的系数，该模型系统（针对每种气团）评估导致死亡率每日变化的因素。
在研究期间，平均每年有120例（95%可信区间：105 - 135）与热相关的死亡，年与年之间差异很大，反映了炎热天气的变化。死亡率在7月和8月最高，此时发生的多日炎热事件数量最多。此外，事件持续时间越长，每日死亡风险就越大。
该方法可用于预测与热相关的死亡风险，并有助于制定公共卫生应对措施以降低该风险。
