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[目的]探讨广州市越秀区空气污染物AQI对呼吸系统疾病死亡的影响。[方法]收集2010、2011年广州市越秀区呼吸系统疾病死亡数据和越秀区气象及大气污染物数据,采用广义线性模型模块,拟合Poisson回归分析空气各污染物空气质量指数(AQI)、综合AQI和空气质量分级与呼吸系统疾病死亡的影响。[结果]2010、2011年广州市越秀区因呼吸系统疾病死亡病例有2 678例。SO2AQI与呼吸系统疾病死亡有统计学意义,SO2AQI每升高1个单位,其危险度约增加1%(RR=1.01,P〈0.05);综合AQI的变化和空气质量等级与呼吸系统疾病死亡无统计学意义。[结论]广州市越秀区空气污染物中SO2是呼吸系统相关因素死亡的危险因素。  相似文献   
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目的 探讨智能超声定量技术(AQi)对非心脏手术老年患者左室收缩功能的诊断价值。方法 应用AQi对33例非心脏手术患者术前、术前进行左室收缩末期面积(ESA)、收缩末期容积(ESV)、射血发数(EF),左室面积变化分数(FAC),峰值射血率(PER)进行定量对比分析。结果 术前、术后两组比较,EF、PER、FAC、术后组均低于术前组,ESA,ESV术后组高于术前组(P<0.001)。结论 AQi是评估左室收缩功能的一项有价值的诊断方法。  相似文献   
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目的 分析我国城市空气质量的时空分布特征.方法 基于国家环境保护部公布的2015年全国337个城市的逐日空气质量指数(AQI)数据,运用GIS地图、热图及聚类分析等统计手段,分析我国城市空气质量的时空分布特征.结果 我国城市空气质量水平空间差异明显,具有季节特征.AQI大致呈现北高南低的特征,尤其是华北、西北地区AQI最高,华南、华中、西藏及北方部分边境地区AQI较低.AQI冬、春季高于夏、秋季,月峰值普遍出现在1月和12月,新疆塔克拉玛干沙漠地区月峰值出现在3、4月.西北地区首要污染物以PM10为主,华北、华中首要污染物以PM2.5为主.结论 目前我国部分城市空气污染较严重,建议根据城市情况制定不同的空气污染治理策略.  相似文献   
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BackgroundGlobal climate change concerns are forcing local public health agencies to assess potential disease risk.ObjectiveDetermine if risk of an emergency department asthma diagnosis in Douglas County, NE, was higher during the 2012 heatwave compared to 2011.MethodsRetrospective, observational, case-control design selecting subjects from 2011 and 2012 emergency department (ED) admissions. Risk was estimated by conditional logistic regression.ResultsThe asthma ED risk estimate was 1.23 (95%CI = 0.96–1.57) times higher in 2012 than 2011, for the same calendar period. Asthma ED diagnosis risk was 3.37 (95%CI = 2.27–4.17) times higher among subjects <19years old compared to older subjects, and 3.25 (95%CI = 2.63–4.02) times higher among African-Americans than non-African–Americans, adjusted for heatwave exposure. Absolute humidity appears inversely related to asthma diagnosis risk ( χ2 = 16.6; p < 0.001).ConclusionAsthma ED diagnosis risk was not significantly higher in 2012 compared to 2011. Risk was elevated among subjects less than 19years old, and among African Americans; adjusted for heatwave exposure.  相似文献   
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目的 研究不同空气质量指数(air quality index,AQI)下氡子体的分布特征,评价雾霾天气暴露人群的健康风险。方法 利用EQF3120测量仪监测放射性核素氡及其子体浓度,空气质量指标数据由苏州工业园区环境监测站提供。运用SPSS 16.0对氡及其子体与空气质量指标等数据进行统计描述、主成分分析及简单相关分析。运用氡剂量计算公式估算环境氡暴露致肺部区域有效剂量。结果 苏州市工业园区冬季雾霾天气结合态氡子体与NO2、SO2、O3、PM10和PM2.5均存在相关关系,214Bi与其相关系数分别为0.741、0.681、-0.431、0.597和0.675;且结合态氡子体与PM2.5的相关系数>与PM10的相关系数;当AQI>200,居民在室外接收氡及其短寿命子体的有效剂量为0.63 mSv/年。结论 随着AQI的增大,结合态氡子体浓度增大,未结合态氡子体浓度变化不大;雾霾天气增加了暴露人群吸入氡的辐射风险。  相似文献   
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Background: To explore etiology for providing scientific clues for the prevention of lung cancer. Materials and Methods: Data for lung cancer incidence and meteorological geographic factors from 25 counties in Zhejiang province of China during 2011 were studied. Stepwise multiple regression and correlation analysiswere performed to analyze the geographic distribution and epidemiology of lung cancer. Results: 8,291 new cases (5,998 in males and 2,293 females) of lung cancer during 2011 in Zhejiang province were reported in the 25 studied counties. Reported and standardized incidence rates for lung cancer were 58.0 and 47.0 per 100,000 population, respectively. The incidence of lung cancer increased with age. Geographic distribution analysis shows that the standardized incidence rates of lung cancer in northeastern Zhejiang province were higher than in the southwestern part, such as in Nanhu, Fuyang, Wuxing and Yuyao counties, where the rates were more than 50 per 100,000 population. In the southwestern Zhejiang province, for instance, in Yueqing, Xianju and Jiande counties, the standardized incidence rates of lung cancer were lower than 37 per 100,000 population. Spearman correlation tests showed that forest coverage rate, air quality index (AQI), and annual precipitation level are associated with the incidence of lung cancer. Conclusions: Lung cancer in Zhejiang province showsobvious regional differences. High incidence appears associated with low forest coverage rate, poor air quality and low annual precipitation. Therefore, increasing the forest coverage rate and controlling air pollution may play an important role in lung cancer prevention.  相似文献   
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Background

Air quality indices based on the maximum of sub-indices of pollutants are easy to produce and help quantify the degree of air pollution. However, they discount the additive effects of multiple pollutants and are only sensitive to changes in highest sub-index.

Objectives

We propose a simple and concise method to construct an air quality index that takes into account additive effects of multiple pollutants and evaluate the extent to which this index predicts health effects.

Materials and methods

We obtained concentrations of four criteria pollutants: particulate matter with aerodynamic diameter ≤ 10 μm (PM10), sulphur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3) and daily admissions to Hong Kong hospitals for cardiovascular and respiratory diseases for all ages and those 65 years or older for years 2001–2012. We derived sub-indices of the four criteria pollutants, calculated by normalizing pollutant concentrations to their respective short-term WHO Air Quality Guidelines (WHO AQG). We aggregated the sub-indices using the root-mean-power function with an optimal power to form an overall air quality index. The optimal power was determined by minimizing the sum of over- and under-estimated days. We then assessed associations between the pollution bands of the index and cardiovascular and respiratory admissions using a time-stratified case-crossover design adjusted for ambient temperature, relative humidity and influenza epidemics. Further, we conducted case-crossover analyses using the Hong Kong air quality data with the respective standards and classification of pollution bands of the China Air Quality Index (AQI), the United Kingdom Daily AQI (DAQI), and the United States Environmental Protection Agency (USEPA) AQI.

Results

The mean concentrations of PM10 and SO2 based on maximum 3-h mean exceeded the WHO AQG by 37% and 50%, respectively. We identified the combined condition of observed high-pollution days as either at least one pollutant > 1.5 × WHO AQG or at least two pollutants > 1.0 × WHO AQG to characterize the typical pollution profiles over the study period, which resulted in the optimal power = 3.0. The distribution of days in different pollution bands of the index was: 5.8% for “Low” (0–50), 37.6% for “Moderate” (51–100), 31.1% for “High” (101–150), 14.7% for “Very High” (151–200), and 10.8% for “Serious” (201+). For cardiovascular and respiratory admissions, there were significant associations with the pollution bands of the index for all ages and those 65 years or older. The trends of increasing pollution bands in relation to increasing excess risks of cardiovascular and respiratory admissions were significant for the proposed index, the China AQI, the UK DAQI and the USEPA AQI (P value for test for linear trend < 0.0001), suggesting a dose-response relation.

Conclusions

We have developed a simple and concise method to construct an air quality index that accounts for multiple pollutants to quantify air quality conditions for Hong Kong. Further developments are needed in order to support the extension of the method to other settings.  相似文献   
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