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2016年潍坊市空气污染对儿童呼吸系统疾病门诊量的影响 被引量:20

Effects of air pollution on outpatient visits of respiratory diseases among children in Weifang in 2016
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摘要 [目的]探讨潍坊市空气污染对儿童呼吸系统疾病门诊量的影响。[方法]通过潍坊市妇幼保健院收集潍坊市0~14岁的儿童呼吸系统疾病门诊资料,从全国城市空气质量实时发布平台收集潍坊市2016年大气污染资料及气象数据。采用Spearman秩相关分析大气污染物与气象因素之间的相关性;采用单因素和多因素广义相加模型分析大气污染物对儿童呼吸系统疾病门诊量的影响。[结果]2016年全年共收集潍坊市妇幼保健院0~14岁呼吸系统疾病患者132 524例。Spearman相关分析结果表明,二氧化硫(SO_2)、二氧化氮(NO_2)、一氧化碳(CO)、臭氧(O_3)、粗颗粒物(PM_(10))及细颗粒物(PM_(2.5))质量浓度之间的相关性均有统计学意义(P<0.05或P<0.01)。单因素广义相加模型分析结果表明,PM_(2.5)、PM_(10)、SO_2、NO_2对呼吸系统疾病日门诊量的风险在滞后1 d达到最大,RR及95%CI分别为1.025(1.024~1.026)、1.014(1.013~1.015)、1.065(1.063~1.067)、1.057(1.053~1.060),随着滞后天数的增加,影响逐渐减小。多因素广义相加模型分析结果表明,PM_(2.5)、PM_(10)、SO_2、NO_2、O_3日均质量浓度每增加10μg/m^3,呼吸系统疾病日门诊量增加,RR及95%CI分别为1.045(1.040~1.051)、1.004(1.001~1.007)、1.010(1.006~1.015)、1.041(1.033~1.049)、1.004(1.002~1.005)。随着温度、降雨量、湿度的增加,儿童呼吸系统疾病的日门诊量减少,RR及95%CI分别为0.978(0.976~0.979)、0.926(0.899~0.953)、0.992(0.991~0.992)。儿童呼吸系统疾病的日门诊量亦会受到星期几效应的影响(P<0.05)。[结论]PM_(2.5)、PM_(10)、SO_2、NO_2、O_3会增加儿童患呼吸系统疾病的风险,温度、降雨量、相对湿度的增加会降低儿童患呼吸系统疾病的风险。 [ Objective ] To evaluate the effects of air pollutants on outpatient visits of children's respiratory diseases in Weifang. [ Methods ] Respiratory disease outpatient data of children aged 0-14 years were retrieved from Weifang Maternity and Child Care Hospital in 2016. Meteorological data and air pollution data in 2016 were collected from the National Urban Air Quality Real- time Distribution Platform. The correlations between air pollutants and meteorological factors were analyzed by Spearman's rank correlation. The effects of air pollutants on children's respiratory diseases outpatient visit volume were analyzed by single-factor and multi-factor generalized additive models (GAM). [ Results ] There were 132524 children of 0-14 years old with respiratory diseases visiting Weifang Maternity and Child Care Hospital in 2016. The Spearman correlation analysis results showed that there were significant correlations among sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), coarse particulate matters (PM10), and fine particulate matters (PM2.5) concentrations (P〈0.05 or P〈0.01). The single-factor GAM analysis results showed that PM2.5, PM10, SO2 and NO2 had the maximum effect on dally outpatient visit volume of children's respiratory diseases on lay 1 day, with the RR and 95%CI being 1.025 (1.024-1.026), 1.014 (1.013-1.015), 1.065 (1.063-1.067), and 1.057 (1.053-1.060), respectively, and the risks decreased gradually with the extension of lag days. The multi-factor GAM analysis results showed that the risks of respiratory diseases for each 10 Ixg/m3 increase of PM25, PM10, SO2, NO2, and 03 concentrations were 1.045 (1.040-1.051), 1.004(1.001-1,007), 1.010(1.006-1.015), 1.041 (1.033-1.049), and 1.004 (1.002-1.005), respectively. The risks of respiratory diseases for the increase of temperature, rainfall, and relative humidity were 0.978 (0.976-0.979), 0.926 (0.899-0.953), and 0.992 (0.991-0.992), respectively. The daily outpatient visit volume of children's respiratory diseases was also affected by day-of-the-week effect (P〈0.05). [ Conclusion ] Increased risks of children's respiratory diseases may be caused by increased levels of PM2.5, PM10, SO2, NO2, and O3, as well as decreased levels of temperature, rainfall, and relative humidity.
出处 《环境与职业医学》 CAS CSCD 北大核心 2018年第1期24-28,共5页 Journal of Environmental and Occupational Medicine
基金 山东省医药卫生科技发展计划项目(编号:2015WS0084) 潍坊医学院2015年度大学生科技创新基金项目(编号:KX2015034)
关键词 空气污染 广义相加模型 呼吸系统疾病 儿童 门诊量 air pollution generalized additive model respiratory disease child outpatient visit
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