太原市大气污染与缺血性心脏病日住院人数的时间序列研究

发布时间：2018-05-29 17:06

本文选题：大气污染 + 缺血性心脏病　；参考：《山西医科大学》2017年硕士论文

【摘要】：目的:1.了解太原市缺血性心脏病住院患者的基本情况。2.了解太原市大气污染的基本情况。3.建立太原市大气污染与缺血性心脏病日住院人数的关系模型,定量分析太原市大气污染对缺血性心脏病日住院人数的影响。方法:采用生态学研究方法,收集2013年9月-2015年8月太原市5所省级三级甲等综合性医院的缺血性心脏病住院患者的电子病历首页资料,同时分别收集同期太原市每日大气污染物的浓度资料与气象资料。描述研究期间太原市大气污染物、气象因素、缺血性心脏病日住院人数的分布情况,采用时间序列研究的广义相加模型建立太原市大气污染对缺血性心脏病日住院人数的影响模型。结果:1.2013年9月-2015年8月共收集太原市缺血性心脏病住院人数14538例,其中男性住院人数多于女性,"g65岁住院人数多于65岁,春冬季住院人数多于夏秋季。2.2013年9月-2015年8月太原市主要大气污染物为SO_2、PM_(10)、O_3、PM_(2.5)。四季主要污染物分布不同:春季主要污染物为PM_(10)与PM_(2.5),夏季主要污染物为O_3与PM_(2.5),秋、冬季主要污染物均为SO_2、PM_(10)与PM_(2.5),但冬季污染较秋季严重。3.按性别分析:单污染模型中,SO_2、PM_(10)、CO、O_3、PM_(2.5)对女性缺血性心脏病日住院人数影响的最强效应滞后期分别为滞后1、4、1、0、4天,污染物浓度每增加一个四分位数间距缺血性心脏病日住院人数增加的RR值分别为1.02(95%CI:1.01-1.05)、1.10(95%CI:1.02-1.18)、1.03(95%CI:1.01-1.08)、1.13(95%CI:1.03-1.23)、1.08(95%CI:1.01-1.16),其暴露-反应系数为污染物浓度每增加10μg/m~3,女性缺血性心脏病日住院人数增加的百分比分别为0.28%、1.22%、0.03%、1.87%、1.55%。双污染模型中,分别调整NO_2、PM_(10)、CO、O_3、PM_(2.5)进入SO_2模型,SO_2、NO_2、CO、O_3进入PM_(10)模型,NO_2进入CO模型,SO_2、NO_2、CO进入O_3模型,SO_2、CO、O_3进入PM_(2.5)模型后调整模型有统计学意义。单污染模型中,SO_2、PM_(10)、CO、O_3对男性缺血性心脏病日住院人数影响最强效应滞后期分别为滞后3、0、2、0天,污染物浓度每增加一个四分位数间距男性缺血性心脏病日住院人数增加的RR值分别为1.02(95%CI:1.00-1.09)、1.13(95%CI:1.02-1.25)、1.04(95%CI:1.01-1.09)、1.11(95%CI:1.04-1.19),其暴露-反应系数为污染物浓度每增加10μg/m~3,男性缺血性心脏病日住院人数增加的百分比分别为0.28%、1.62%、0.04%、1.73%。双污染模型中,分别调整PM_(10)、CO、PM_(2.5)进入SO_2模型,SO_2、NO_2、CO、O_3进入PM_(10)模型,NO_2、PM_(10)、PM_(2.5)进入CO模型,SO_2、NO_2、PM_(10)、CO、PM_(2.5)进入O_3模型后调整模型有统计学意义。4.按年龄分析:单污染模型中,SO_2、PM_(10)、O_3对65岁缺血性心脏病日住院人数影响的最强效应滞后期分别为滞后3、5、3天,污染物浓度每增加一个四分位数间距缺血性心脏病日住院人数增加的RR值分别为1.04(95%CI:1.01-1.09)、1.13(95%CI:1.03-1.25)、1.12(95%CI:1.05-1.20),其暴露-反应系数为污染物浓度每增加10μg/m~3,65岁缺血性心脏病日住院人数增加的百分比分别为0.55%、1.57%、1.73%。双污染模型中,分别调整NO_2、PM_(10)、CO、PM_(2.5)进入SO_2模型,SO_2、NO_2、CO、O_3进入PM_(10)模型,SO_2、NO_2进入O_3模型后的调整模型有统计学意义。单污染模型中,SO_2、CO、PM_(2.5)对≥65岁缺血性心脏病日住院人数影响的最强效应滞后期分别为滞后3、3、0天,污染物浓度每增加一个四分位数间距缺血性心脏病日住院人数增加的RR值分别为1.04(95%CI:1.01-1.07)、1.03(95%CI:1.02-1.09)、1.14(95%CI:1.06-1.22),其暴露-反应系数为污染物浓度每增加10μg/m~3,≥65岁缺血性心脏病日住院人数增加的百分比分别为0.55%、0.03%、2.71%。双污染模型中,分别调整NO_2、PM_(10)、PM_(2.5)进入SO_2模型,SO_2、NO_2、PM_(2.5)进入CO模型,SO_2、NO_2、CO、O_3进入PM_(2.5)模型后的调整模型具有统计学意义。5.按季节分析:单污染模型中,PM_(10)和PM_(2.5)在春季对缺血性心脏病日住院人数影响的最强效应滞后期分别为滞后0、5天,污染物浓度每增加一个四分位数间距缺血性心脏病日住院人数增加的RR值分别为1.02(95%CI:1.01-1.07)、1.07(95%CI:1.01-1.13),其暴露-反应系数为污染物浓度每增加10μg/m~3,春季缺血性心脏病日住院人数增加的百分比分别为0.24%、1.35%。双污染模型中,分别调整SO_2、NO_2、CO、O_3进入PM_(10)模型,SO_2、NO_2、CO、O_3进入PM_(2.5)模型后的调整模型具体统计学意义。单污染模型中,SO_2、O_3、PM_(2.5)在夏季对缺血性心脏病日住院人数影响的最强效应滞后期分别为滞后4、0、2天,污染物浓度每升高一个四分位数间距缺血性心脏病日住院人数增加的RR值分别为1.04(95%CI:1.01-1.16)、1.07(95%CI:1.01-1.11)、1.12(95%CI:1.00-1.28),其暴露-反应系数为污染物浓度每增加10μg/m~3,夏季缺血性心脏病日住院人数增加的百分比分别为0.55%、1.01%、2.32%。双污染模型中,分别调整NO_2、PM_(10)、CO、O_3、PM_(2.5)进入SO_2模型,SO_2、NO_2、CO、O_3进入PM_(2.5)模型,SO_2、NO_2、PM_(10)、CO、PM_(2.5)进入O_3模型后的调整模型具有统计学意义。单污染模型中,PM_(10)、O_3、PM_(2.5)在秋季对缺血性心脏病日住院人数影响的最强效应滞后期分别为滞后0、4、0天,污染物浓度每升高一个四分位数间距缺血性心脏病日住院人数增加的RR值分别为1.21(95%CI:1.03-1.42)、1.16(95%CI:1.04-1.30)、1.17(95%CI:1.01-1.36),其暴露-反应系数为污染物浓度每增加10μg/m~3,秋季缺血性心脏病日住院人数增加的百分比分别为2.53%、2.31%、3.29%。双污染模型中,分别调整SO_2、NO_2、CO、O_3进入PM_(10)模型,SO_2、NO_2、PM_(10)、CO进入O_3模型,NO_2、CO、O_3进入PM_(2.5)模型。单污染模型中,SO_2、PM_(10)、PM_(2.5)在冬季对缺血性心脏病日住院人数影响的最强效应滞后期分别为滞后4、4、4天,污染物浓度每升高一个四分位数间距缺血性心脏病日住院人数增加的RR值分别为1.52(95%CI:1.00-2.18)、1.17(95%CI:1.01-1.36)、1.20(95%CI:1.03-1.39),其暴露-反应系数为污染物浓度每增加10μg/m~3,冬季缺血性心脏病日住院人数增加的百分比分别为7.15%、2.05%、3.87%。双污染模型中,分别调整NO_2、PM_(10)、CO、O_3、PM_(2.5)引入SO_2模型,O_3进入PM_(10)模型,SO_2、NO_2、O_3进入PM_(2.5)模型后的调整模型具有统计学意义。结论:大气污染物SO_2、PM_(10)、CO、O_3、PM_(2.5)均可增加缺血性心脏病日住院人数,具有一定的滞后效应与暴露-反应关系,并且随不同性别、年龄、季节而不同。
[Abstract]:Objective: 1. understand the basic situation of ischemic heart disease in Taiyuan,.2. understand the basic situation of air pollution in Taiyuan city.3. establish the relationship model of air pollution and ischemic heart disease in Taiyuan, quantitative analysis of the influence of air pollution on the number of ischemic heart disease in Taiyuan. Study methods, collected data of the first page of the electronic medical records of inpatients of ischemic heart disease in 5 provincial level three grade first class hospitals in Taiyuan in September 2013 and August, and collect the daily atmospheric pollutant concentration and meteorological data in the same period of Taiyuan, and describe the atmospheric pollutants, meteorological factors and ischemic factors in the period of the study of Taiyuan. The distribution of the number of hospitalized heart disease days, using the generalized additive model of time series study to establish the influence model of air pollution in Taiyuan on the number of ischemic heart disease in Taiyuan. Results: in August -2015 September, 14538 cases of ischemic heart disease in Taiyuan were collected, and the number of hospitalized men was more than that of women, "G65 years old." The number of hospitalized people is more than 65 years old. The number of inpatients in spring and winter is more than that in summer and autumn.2.2013 years and September -2015 years in August. The main air pollutants in Taiyuan city are SO_2, PM_ (10), O_3, PM_ (2.5). The main pollutants in the four seasons are different: the main pollutants in the spring are PM_ (10) and PM_ (2.5), the main pollutants in the summer season are O_3 and PM_ (2.5), and the main pollutants in autumn and winter are SO_2, PM. (10) and PM_ (2.5), but the winter pollution is more severe than the autumn.3. by sex analysis: in the single pollution model, SO_2, PM_ (10), CO, O_3, PM_ (2.5) have the strongest lag behind the lag 1,4,1,0,4 days for the number of patients with ischemic heart disease, and the increase of the concentration of the pollutants is increased by a four quantile interval, and the number of hospitalized days of ischemic heart disease increases. The added RR values were 1.02 (95%CI:1.01-1.05), 1.10 (95%CI:1.02-1.18), 1.03 (95%CI:1.01-1.08), 1.13 (95%CI:1.03-1.23), and 1.08 (95%CI:1.01-1.16). The exposure reaction coefficient was 10 mu per increase of the concentration of pollutants, and the increase in the number of inpatients in the days of ischemic heart disease was 0.28%, 1.22%, 0.03%, 1.87%, and 1.55%. double pollution models, respectively. NO_2, PM_ (10), CO, O_3, PM_ (2.5) entered SO_2 model respectively, SO_2, NO_2, CO, O_3 entered PM_ (10) model. The latter was delayed 3,0,2,0 days respectively. The increased RR value of the number of male ischemic heart disease days with each increase of four quantile spacing was 1.02 (95%CI:1.00-1.09), 1.13 (95%CI:1.02-1.25), 1.04 (95%CI:1.01-1.09), 1.11 (95%CI:1.04-1.19), and the exposure reaction coefficient was 10 mu per increase of the concentration of pollutants. /m~3, the percentage of male ischemic heart disease days increased in 0.28%, 1.62%, 0.04%, and 1.73%. double pollution model, adjusting PM_ (10), CO, PM_ (2.5) into SO_2 model, SO_2, NO_2, CO, O_3 into PM_ (10) model, NO_2, 10, 2.5, 2.5 Study significance.4. according to age analysis: in single pollution model, SO_2, PM_ (10), the strongest effects of O_3 on 65 year old ischemic heart disease days were lagging behind 3,5,3 days respectively. The RR value of the number of inpatients increased by each four quantile interval of four quantile interval was 1.04 (95%CI:1.01-1.09) and 1.13 (95%CI:1). .03-1.25), 1.12 (95%CI:1.05-1.20), the percentage of the exposure response coefficient was 0.55%, 1.57%, and 1.73%. in the 0.55%, 1.57%, and 1.73%. double pollution model, respectively, to adjust NO_2, PM_ (10), CO, PM_ (2.5) into the SO_2 model, SO_2, NO_2, CO, and entered the 10 model. The adjustment model after 3 model was statistically significant. In the single pollution model, SO_2, CO, and PM_ (2.5) were respectively lagging behind 3,3,0 days for the strongest effect in the hospitalization of ischemic heart disease more than 65 years old, and the RR value of the increase of the number of inpatients with each increase of the concentration of four quantiles of the pollutant concentration was 1.04 (95%CI:1.01-1. 07), 1.03 (95%CI:1.02-1.09), 1.14 (95%CI:1.06-1.22), the exposure response coefficient was 10 mu g/m~3 per increase, and the percentage of the number of ischemic heart disease days increased in 0.55%, 0.03%, and 2.71%. were respectively adjusted to NO_2, PM_ (10) and PM_ (2.5) into SO_2 model, SO_2, NO_2, PM_ (2.5) entered the CO model. The adjustment model of NO_2, CO, O_3 after entering PM_ (2.5) model has statistical significance in seasonal analysis: in the single pollution model, the strongest effect lag behind PM_ (10) and PM_ (2.5) in the spring for ischemic heart disease days is lagged 0,5 days respectively, and the concentration of pollutants is increased by an four quantile interval of ischemic heart day. The increased RR values were 1.02 (95%CI:1.01-1.07), 1.07 (95%CI:1.01-1.13), the exposure reaction coefficient was 10 g/m~3 per increase of the concentration of pollutants, the percentage of the increase in the number of inpatients in the day of ischemic heart disease was 0.24% respectively. In the 1.35%. double pollution model, SO_2, NO_2, CO, and O_3 entered the PM_ (10) model respectively. The adjustment model after the PM_ (2.5) model was statistically significant. In the single pollution model, the strongest effect lag behind the SO_2, O_3, and PM_ (2.5) in the summer was 4,0,2 days respectively, and the RR value of the increase of the number of ischemic heart disease days with each increase of four quantile spacing was 1.04 (9). 5%CI:1.01-1.16), 1.07 (95%CI:1.01-1.11), 1.12 (95%CI:1.00-1.28), its exposure response coefficient is 10 mu per increase of the concentration of pollutants, the percentage of the increase in the number of inpatients in the summer of ischemic heart disease is 0.55%, 1.01%, and 2.32%. double pollution model, NO_2, PM_ (10), CO, O_3, PM_ (2.5) into the SO_2 model. SO_2, SO_2, PM_ (2.5) are entered. The PM_ (2.5) model, SO_2, NO_2, PM_ (10), CO, PM_ (2.5) entered the O_3 model with statistical significance. In the single pollution model, the strongest effect lag time of PM_ (10), O_3, PM_ (2.5) on the number of ischemic heart disease days in autumn is divided into lagging 0,4,0 days, and the concentration of pollutants is increased by a four quantile interval. The increase in the number of hospitalized heart disease days was 1.21 (95%CI:1.03-1.42), 1.16 (95%CI:1.04-1.30) and 1.17 (95%CI:1.01-1.36). The exposure response coefficient was 10 mu per increase of the concentration of pollutants, and the percentage of the increase in the number of inpatients in the autumn ischemic heart disease was 2.53%, 2.31%, and 3.29%. double pollution models, respectively, to adjust SO_2, NO_2, CO respectively. O_3 entered the PM_ (10) model, SO_2, NO_2, PM_ (10), and CO entered the O_3 model, NO_2, CO, O_3 entered the PM_ (2.5) model. In the single pollution model, SO_2, 10, and 2.5 were lagging behind the strongest effects in the winter hospitalization for ischemic heart disease, and the concentration of pollutants increased by a four quantile interval of ischemic heart disease. The increased RR values of daily inpatients were 1.52 (95%CI:1.00-2.18), 1.17 (95%CI:1.01-1.36) and 1.20 (95%CI:1.03-1.39). The exposure response coefficient was 10 mu per increase of the concentration of pollutants, and the percentage of the increase in the number of inpatients in winter ischemic heart disease was 7.15%, 2.05%, and 3.87%. double pollution models, respectively, NO_2, PM_ (10), CO, O_3, P. M_ (2.5) introduced the SO_2 model, O_3 entered the PM_ (10) model, SO_2, NO_2, and O_3 entered PM_ (2.5) model with statistical significance. Conclusion: air pollutants SO_2, PM_ (10), CO, O_3, 2.5) can increase the number of ischemic heart disease days, with a certain lag effect and exposure response relationship, and with different sex, age, season. The festival is different.
【学位授予单位】：山西医科大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R122;R541.9

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