Brown Gary, Fearn Vanessa, Wells Claudia
Office for National Statistics.
Health Stat Q. 2010 Winter(48):58-80. doi: 10.1057/hsq.2010.21.
This article reports research carried out to inform possible methods of describing seasonal mortality in relation to extremes of temperature. In particular, since different methods are currently used to assess excess winter mortality and heatwave related mortality, we aimed to find out whether a single method could be used to measure all seasonal mortality in relation to temperature. In order to do this the project investigated whether there are temperatures above or below which excess deaths occur, and explored whether it is possible to predict reliably how many deaths would occur at extreme temperatures.
Daily and monthly Central England Temperatures for 1998 to 2007 were supplied by the Met Office Hadley Centre and daily death occurrence data between 1993 and 2007 was extracted from the death registrations database held by the Office for National Statistics (ONS). Least squares regression, based on the previous five years of data, was used to predict expected mortality, and excess mortality was calculated as the difference between the expected mortality and the observed mortality on any given day. Statistically significant increases in both daily deaths and temperatures were investigated with the probability of excess mortality assessed on those days. Two regression models were calculated, one for deaths and temperature and one for excess deaths and temperature.
Five days with statistically significant excess mortality were identified over the period 1 January 1998 to 31 December 2007, the largest being on 31 December 1999. Three of the five days identified coincided with extremely hot weather occurring in August 2003 and July 2006. However, more extreme temperatures were seen on some days with no excess mortality, so predicting mortality using extreme temperatures alone would cause frequent false positive results. Regression models based on daily death and temperature explained only 8 per cent of the variance in summer mortality and 7 per cent of the variance in winter mortality. The models based on excess deaths and temperature explained 20 per cent of the variance in excess mortality in summer, but only 1 per cent of the variance in excess mortality in winter.
There is a weak but significant relationship between temperature and mortality in both the summer and winter months. While in winter mortality does increase as it gets colder, winter mortality is variable and high mortality can occur on relatively mild days. Similarly, in the summer high temperatures are often associated with relatively increased mortality, but a single hot day does not always lead to excess deaths. Daily mortality cannot be predicted from temperature alone: the prevalence of influenza in winter and factors such as air pollution in summer should also be considered.
本文报告了一项研究，旨在为描述与极端温度相关的季节性死亡率提供可能的方法。特别是，由于目前使用不同的方法来评估冬季超额死亡率和与热浪相关的死亡率，我们旨在查明是否可以使用单一方法来衡量与温度相关的所有季节性死亡率。为了做到这一点，该项目调查了是否存在高于或低于某个温度时会出现超额死亡的情况，并探讨了是否有可能可靠地预测在极端温度下会发生多少死亡。
1998年至2007年英格兰中部每日和每月的温度数据由英国气象局哈德利中心提供，1993年至2007年的每日死亡发生数据从国家统计局（ONS）持有的死亡登记数据库中提取。基于前五年的数据，使用最小二乘法回归来预测预期死亡率，并将超额死亡率计算为任何给定日期预期死亡率与观察到的死亡率之间的差值。对每日死亡人数和温度的统计显著增加进行了调查，并评估了这些日子超额死亡率的概率。计算了两个回归模型，一个是关于死亡人数和温度的，另一个是关于超额死亡人数和温度的。
在1998年1月1日至2007年12月31日期间，确定了5天具有统计显著的超额死亡率，其中最大的一天是1999年12月31日。确定的5天中有3天与2003年8月和2006年7月出现的极端炎热天气相吻合。然而，在一些没有超额死亡率的日子里也出现了更极端的温度，因此仅使用极端温度来预测死亡率会导致频繁的假阳性结果。基于每日死亡人数和温度的回归模型仅解释了夏季死亡率方差的8%和冬季死亡率方差的7%。基于超额死亡人数和温度的模型解释了夏季超额死亡率方差的20%，但仅解释了冬季超额死亡率方差的1%。
在夏季和冬季月份，温度与死亡率之间存在微弱但显著的关系。虽然在冬季死亡率确实会随着天气变冷而增加，但冬季死亡率是可变的，在相对温和的日子里也可能出现高死亡率。同样，在夏季高温通常与相对较高的死亡率相关，但单一的炎热天气并不总是导致超额死亡。不能仅根据温度来预测每日死亡率：冬季流感的流行情况以及夏季的空气污染等因素也应予以考虑。
