热浪对城市居民健康影响的预测研究
发布时间：2018-08-05 14:47
【摘要】：目的 对我国部分城市的居民死因资料和气象资料进行关联性分析,在此基础上进行高温热浪对城市居民健康影响的模型拟合分析及初步预测分析,探索适合我国大中城市的热浪对居民健康影响的预测模型,为预测预警热浪的健康影响提供基础资料和科学依据。 方法 收集哈尔滨市及北京市朝阳区的居民死因数据和气象数据,利用滞后模型和灰色关联等方法对两市的数据资料进行分析,并应用分段回归模型进行阈值分析,结合高温期间的现场调查结果和访谈信息,以及前期对重庆市和汕头市的研究结果,对多个城市进行广义相加模型拟合分析及运用分布滞后非线性模型进行初步预测分析。 结果 1.现场调查数据显示:高温热浪可以导致热相关疾病尤其是中暑的发病率增加,虽然居民在高温期间会有意识地采取一些应对措施,但中老年人群仍是易感人群。 2.典型城市的气象因素与居民死亡指标的关系分析显示:高温对死亡指标的影响主要集中在滞后0至4天,混杂因素可吸入颗粒物浓度的影响有一定的滞后,其浓度每上升10μg/m3所对应的相对危险度变化在滞后10天左右达较高水平,累积相对危险度在滞后15天左右达峰值,环境温度在滞后0天和1天具有较高的相对危险度,可吸入颗粒物浓度在200μg/m3以下时与环境温度的交互作用较为明显;死亡指标变化的长期趋势与气温和相对湿度有关,此基础上,气压是影响死亡指标短期波动的主要因素。 3.多个城市的环境温度阈值分析显示,北京市、哈尔滨市、重庆市、福州市及汕头市的全死因死亡危险的阈值温度(℃)分别为25.63(±0.809)、23.24(±1.114)、29.29(±2.886)、36.06(±0.281)和31.28(±1.016),当各城市的环境温度分别高于相应的阈值温度时,每升高1℃死亡人数增加的百分数(95%置信区间)分别为0.99(0.46~1.52)、1.12(0.24~2.00)、0.17(-0.34~0.68)、21.70(11.99~32.26)和2.79(1.05~4.57),其中福州市的结果明显偏高,可能是由于数据质量问题,因此该结果有待进一步论证。 4.仅考虑温度因素时模型拟合分析确定的各城市全死因死亡指标的预测因子分别为北京市lag0+lag1+lag24,重庆市lag0,福州市lag0+lag1521,汕头市lag0+lag24+lag814,哈尔滨市lag0+lag1+lag814+lag1521,纳入相对湿度因素后,各城市的预测因子分别为北京市lag0+lag1+lag24+lag1521,重庆市无,福州市无,汕头市lag0+lag814,哈尔滨市lag0+lag1+lag814+lag1521。 5.初步预测分析显示:高温对居民死亡指标的影响主要集中在滞后0至4天左右,分布滞后非线性模型可通过温度的变化及滞后时间的确定估计死亡指标相对危险度的变化,进而确定欲观察时间段内的死亡指标危险程度。北京市和重庆市高温期间对应的最大相对危险度值分别为1.126和1.118,与此相对应的温度并不是最高日平均气温,这种现象主要可能是因为人群对高温的耐受及应对极端高温采取相应的措施,以至极端高温对应的相对危险度有所下降;汕头市、福州市及哈尔滨市高温期间最大的相对危险度分别为1.310、1.269和1.254,分别对应于各城市的最高日平均气温。 结论 1.高温的滞后效应主要在滞后0～4天,为急性效应,可吸入颗粒物污染在气温的影响过程中存在较长的滞后效应;居民死亡指标的长期趋势与气温及相对湿度有关,而气压在气温和相对湿度的影响基础上,主要影响死亡指标的短期波动。 2.仅以气温因素与混杂因子进行广义相加模型拟合时,不同的城市滞后0天温度的效应均有统计意义,而气温效应的滞后时间长短有一定的差异;将相对湿度因素纳入模型时,重庆市和福州市的显著性因子有较大变化,表明相对湿度因素在重庆市和福州市对气温的健康影响有明显的混杂效应。 3.分布滞后非线性模型能够对不同的温度和不同滞后时间的死亡指标的相对危险度进行估计,通过观察日的温度相对于参考日的温度变化及确定一个滞后时间段,了解观察日相对于参考日的相对危险度变化,以此对观察日的居民死亡指标的相对危险度进行预测;通过对模型的初步验证,该模型可以用于预测高温对健康的影响,但数据质量对模型的估计效果有较大影响。
[Abstract]:objective
On the basis of the correlation analysis of the death causes and meteorological data of some urban residents in China, the model fitting analysis and preliminary prediction analysis on the health impact of high temperature heat waves on urban residents' health are carried out to explore the prediction model of the health impact of the heat waves suitable for the large and middle cities in China, in order to predict the health effects of the early warning heat waves. For basic information and scientific basis.
Method
The data of death causes and meteorological data of residents in Harbin and Chaoyang District of Beijing city are collected. The data of the two cities are analyzed by means of lag model and grey correlation, and the threshold analysis is carried out by the piecewise regression model, and the field investigation results and interview information during the high temperature are combined, and the research on the city of Chongqing and Shantou in the early stage is also carried out. The results show that the generalized additive model fitting analysis and preliminary prediction analysis are carried out for several cities by using the distributed lag nonlinear model.
Result
1. field survey data show that high temperature heat waves can lead to an increase in the incidence of heat related diseases, especially heatstroke, although the residents will take some measures consciously during the high temperature, but the elderly are still susceptible.
The analysis of the relationship between the meteorological factors and the death index of 2. typical cities showed that the influence of high temperature on the death index was mainly concentrated in 0 to 4 days, and the influence of the concentration of inhalable particulate matter was lagging behind, and the relative risk of the concentration of its concentration was higher at about 10 g/m3 and accumulated to a higher level at about 10 days. The relative risk degree reached a peak at about 15 days, and the ambient temperature had a relatively high relative risk in 0 days and 1 days. The interaction of inhaled particulate matter under 200 g/m3 was more obvious. The long-term trend of the change of death index was related to the temperature and relative humidity. On this basis, the pressure was the influence of death. The main factor of short-term volatility.
The environmental temperature threshold analysis of more than 3. cities showed that the threshold temperature of death risk for all deaths in Beijing, Harbin, Chongqing, Fuzhou and Shantou was 25.63 (+ 0.809), 23.24 (+ 1.114), 29.29 (+ 2.886), 36.06 (+ 0.281) and 31.28 (+ 1.016), when the ambient temperature of each city was higher than the corresponding threshold temperature, respectively. The percentage of increased deaths at 1 degrees centigrade (95% confidence intervals) was 0.99 (0.46~1.52), 1.12 (0.24~2.00), 0.17 (-0.34~0.68), 21.70 (11.99~32.26) and 2.79 (1.05~4.57). The results of Fuzhou were significantly higher, which may be due to data quality questions, so the results need to be further demonstrated.
4. the prediction factors of all urban death factors determined by model fitting analysis of temperature factors are Beijing lag0+lag1+lag24, Chongqing city lag0, Fuzhou city lag0+lag1521, Shantou city lag0+lag24+lag814, Harbin city lag0+lag1+lag814+lag1521, respectively, after the relative humidity factors are included, the prediction factors of each city are Beijing. City lag0+lag1+lag24+lag1521, Chongqing no, Fuzhou no, Shantou city lag0+lag814, Harbin lag0+lag1+lag814+lag1521.
5. the preliminary prediction analysis shows that the influence of high temperature on the death index of residents is mainly concentrated on the lag of 0 to 4 days. The distribution lag nonlinear model can estimate the relative risk degree of death index through the change of temperature and time lag, and then determine the risk of Death Index in the period of desire. Beijing and Chongqing City The corresponding maximum relative risk values are 1.126 and 1.118 respectively during the high temperature period. The corresponding temperature is not the highest daily average temperature. This phenomenon is mainly due to the population's tolerance to high temperature and the corresponding measures to cope with extreme high temperature, and the relative risk of extreme high temperature is reduced; Shantou City, Fuzhou City The maximum relative hazards were 1.310, 1.269 and 1.254, respectively, corresponding to the highest daily mean temperature in each city.
conclusion
1. the lag effect of high temperature is mainly lagging behind 0~4 days, which is an acute effect. There is a long lag effect in the influence of air temperature. The long-term trend of the resident death index is related to the temperature and relative humidity, and the pressure is on the basis of the influence of the temperature and relative humidity, which mainly affects the short-term fluctuation of the death index.
2. only when the temperature factor and the hybrid factor are fitted with the generalized additive model, the effect of the 0 days' temperature in different cities has statistical significance, while the lag time of the temperature effect has a certain difference. When the relative humidity factors are incorporated into the model, the explicit factors of Chongqing and Fuzhou have great changes, indicating the relative humidity factors. There is an obvious mixed effect on the health effects of temperature in Chongqing and Fuzhou.
The 3. distribution lag nonlinear model can estimate the relative risk of the death index of different temperature and different lag time. By observing the temperature change of the day relative to the reference day and determining a lag time, the relative risk of the observation day relative to the reference day is understood, so that the death of the observation day is dead. The relative risk of the index is predicted, and the model can be used to predict the effect of high temperature on health, but the quality of the data has a great influence on the estimation effect of the model.
【学位授予单位】：汕头大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：R188
本文编号：2166118
[Abstract]:objective
On the basis of the correlation analysis of the death causes and meteorological data of some urban residents in China, the model fitting analysis and preliminary prediction analysis on the health impact of high temperature heat waves on urban residents' health are carried out to explore the prediction model of the health impact of the heat waves suitable for the large and middle cities in China, in order to predict the health effects of the early warning heat waves. For basic information and scientific basis.
Method
The data of death causes and meteorological data of residents in Harbin and Chaoyang District of Beijing city are collected. The data of the two cities are analyzed by means of lag model and grey correlation, and the threshold analysis is carried out by the piecewise regression model, and the field investigation results and interview information during the high temperature are combined, and the research on the city of Chongqing and Shantou in the early stage is also carried out. The results show that the generalized additive model fitting analysis and preliminary prediction analysis are carried out for several cities by using the distributed lag nonlinear model.
Result
1. field survey data show that high temperature heat waves can lead to an increase in the incidence of heat related diseases, especially heatstroke, although the residents will take some measures consciously during the high temperature, but the elderly are still susceptible.
The analysis of the relationship between the meteorological factors and the death index of 2. typical cities showed that the influence of high temperature on the death index was mainly concentrated in 0 to 4 days, and the influence of the concentration of inhalable particulate matter was lagging behind, and the relative risk of the concentration of its concentration was higher at about 10 g/m3 and accumulated to a higher level at about 10 days. The relative risk degree reached a peak at about 15 days, and the ambient temperature had a relatively high relative risk in 0 days and 1 days. The interaction of inhaled particulate matter under 200 g/m3 was more obvious. The long-term trend of the change of death index was related to the temperature and relative humidity. On this basis, the pressure was the influence of death. The main factor of short-term volatility.
The environmental temperature threshold analysis of more than 3. cities showed that the threshold temperature of death risk for all deaths in Beijing, Harbin, Chongqing, Fuzhou and Shantou was 25.63 (+ 0.809), 23.24 (+ 1.114), 29.29 (+ 2.886), 36.06 (+ 0.281) and 31.28 (+ 1.016), when the ambient temperature of each city was higher than the corresponding threshold temperature, respectively. The percentage of increased deaths at 1 degrees centigrade (95% confidence intervals) was 0.99 (0.46~1.52), 1.12 (0.24~2.00), 0.17 (-0.34~0.68), 21.70 (11.99~32.26) and 2.79 (1.05~4.57). The results of Fuzhou were significantly higher, which may be due to data quality questions, so the results need to be further demonstrated.
4. the prediction factors of all urban death factors determined by model fitting analysis of temperature factors are Beijing lag0+lag1+lag24, Chongqing city lag0, Fuzhou city lag0+lag1521, Shantou city lag0+lag24+lag814, Harbin city lag0+lag1+lag814+lag1521, respectively, after the relative humidity factors are included, the prediction factors of each city are Beijing. City lag0+lag1+lag24+lag1521, Chongqing no, Fuzhou no, Shantou city lag0+lag814, Harbin lag0+lag1+lag814+lag1521.
5. the preliminary prediction analysis shows that the influence of high temperature on the death index of residents is mainly concentrated on the lag of 0 to 4 days. The distribution lag nonlinear model can estimate the relative risk degree of death index through the change of temperature and time lag, and then determine the risk of Death Index in the period of desire. Beijing and Chongqing City The corresponding maximum relative risk values are 1.126 and 1.118 respectively during the high temperature period. The corresponding temperature is not the highest daily average temperature. This phenomenon is mainly due to the population's tolerance to high temperature and the corresponding measures to cope with extreme high temperature, and the relative risk of extreme high temperature is reduced; Shantou City, Fuzhou City The maximum relative hazards were 1.310, 1.269 and 1.254, respectively, corresponding to the highest daily mean temperature in each city.
conclusion
1. the lag effect of high temperature is mainly lagging behind 0~4 days, which is an acute effect. There is a long lag effect in the influence of air temperature. The long-term trend of the resident death index is related to the temperature and relative humidity, and the pressure is on the basis of the influence of the temperature and relative humidity, which mainly affects the short-term fluctuation of the death index.
2. only when the temperature factor and the hybrid factor are fitted with the generalized additive model, the effect of the 0 days' temperature in different cities has statistical significance, while the lag time of the temperature effect has a certain difference. When the relative humidity factors are incorporated into the model, the explicit factors of Chongqing and Fuzhou have great changes, indicating the relative humidity factors. There is an obvious mixed effect on the health effects of temperature in Chongqing and Fuzhou.
The 3. distribution lag nonlinear model can estimate the relative risk of the death index of different temperature and different lag time. By observing the temperature change of the day relative to the reference day and determining a lag time, the relative risk of the observation day relative to the reference day is understood, so that the death of the observation day is dead. The relative risk of the index is predicted, and the model can be used to predict the effect of high temperature on health, but the quality of the data has a great influence on the estimation effect of the model.
【学位授予单位】：汕头大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：R188
【参考文献】
相关期刊论文 前2条
1 刘志刚,陈思静,钱妙芬;气候因素与人体疾病研究现状与展望[J];成都气象学院学报;1998年01期
2 郑有飞;气象与人类健康及其研究[J];气象科学;1999年04期
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