深圳市龙岗区大气污染物与医院呼吸系统疾病门诊量的广义相加模型分析
|
摘要
目的探讨深圳市龙岗区主要大气污染物(SO_2、NO_2、PM_(10)与PM_(2.5))与医院呼吸系统疾病门诊量的关系。方法收集2013年1月1日-2015年12月31日深圳市龙岗区2家公立医院呼吸系统疾病逐日门诊量资料,深圳市龙岗区逐日大气污染物浓度及逐日气象资料分别来自深圳市环境监测站及气象局,运用时间序列分析广义相加模型对大气污染物日均浓度与呼吸系统疾病门诊量的关系及滞后效应进行分析。结果深圳市龙岗区2013-2015年SO_2、NO_2、PM_(10)与PM_(2.5)浓度中位数分别为8. 08、38. 08、46. 05μg/m~3及31. 04μg/m~3。2家医院三年呼吸系统门诊总量为549 169人次,日门诊量中位数为499人次/d。广义相加模型分析结果表明,除NO_2对呼吸系统疾病门诊量影响差异无统计学意义外,其余三种污染物对呼吸系统疾病门诊量影响均存在滞后效应,污染物每升高10μg/m~3,滞后2 d时SO_2对门诊量影响最强(相对危险度RR为1. 030 7,95%CI:1. 015 7～1. 045 9),滞后3 d时PM_(10)与PM_(2.5)浓度对呼吸系统疾病门诊量影响最强(PM_(10):RR=1. 005 4,95%CI:1. 002 8～1. 008 0,PM_(2.5):RR=1. 006 0,95%CI:1. 002 7～1. 009 4)。结论深圳市龙岗区大气SO_2、PM_(10)与PM_(2.5)浓度对医院呼吸系统疾病门诊量影响存在滞后效应。
Objective To explore the relationship between air pollution( including SO_2,NO_2,PM_(10) and PM_(2.5)) and outpatient visits for respiratory diseases in Longgang District of Shenzhen City. Methods We collected the data regarding daily outpatient visits for respiratory diseases in two hospitals in Longgang District of Shenzhen City,daily air pollution data from Shenzhen Meteorological Bureau and daily meteorological data from Shenzhen Environmental Protection Bureau from January 1,2013 to December31,2015. We performed time-series analysis using a generalized additive model( GAM),and then assessed the association and the lag effect between air pollution and hospital outpatient visits for respiratory diseases. Results The medians of SO_2,NO_2,PM_(10) and PM_(2.5) concentration were 8.08 μg/m~3,38.08 μg/m~3,46.05 μg/m~3 and 31.04 μg/m~3 respectively. The total outpatient visits of the involved hospitals during this three-year period were 549,169,with the median of 499 persons per day. The results of GAM-based analysis indicated a positive association between three air pollutants( SO_2,PM_(10) and PM_(2.5)) and hospital outpatient visits for respiratory diseases. The effect of SO_2 was the largest on lag two days( RR = 1.030,7,95%CI: 1.015,7-1.045,9) and the ones of PM_(10) and PM_(2.5) were the largest on lag three days( PM_(10): RR= 1.005,4,95%CI: 1.002,8-1.008,0,PM_(2.5): RR= 1.006,0,95%CI: 1.002,7-1.009,4). Conclusions The concentration of SO_2,PM_(10) and PM_(2.5) was positively associated with hospital outpatient visits for respiratory diseases in Longgang District in this three-year period,and a lag effect was found in these associations.
Objective To explore the relationship between air pollution( including SO_2,NO_2,PM_(10) and PM_(2.5)) and outpatient visits for respiratory diseases in Longgang District of Shenzhen City. Methods We collected the data regarding daily outpatient visits for respiratory diseases in two hospitals in Longgang District of Shenzhen City,daily air pollution data from Shenzhen Meteorological Bureau and daily meteorological data from Shenzhen Environmental Protection Bureau from January 1,2013 to December31,2015. We performed time-series analysis using a generalized additive model( GAM),and then assessed the association and the lag effect between air pollution and hospital outpatient visits for respiratory diseases. Results The medians of SO_2,NO_2,PM_(10) and PM_(2.5) concentration were 8.08 μg/m~3,38.08 μg/m~3,46.05 μg/m~3 and 31.04 μg/m~3 respectively. The total outpatient visits of the involved hospitals during this three-year period were 549,169,with the median of 499 persons per day. The results of GAM-based analysis indicated a positive association between three air pollutants( SO_2,PM_(10) and PM_(2.5)) and hospital outpatient visits for respiratory diseases. The effect of SO_2 was the largest on lag two days( RR = 1.030,7,95%CI: 1.015,7-1.045,9) and the ones of PM_(10) and PM_(2.5) were the largest on lag three days( PM_(10): RR= 1.005,4,95%CI: 1.002,8-1.008,0,PM_(2.5): RR= 1.006,0,95%CI: 1.002,7-1.009,4). Conclusions The concentration of SO_2,PM_(10) and PM_(2.5) was positively associated with hospital outpatient visits for respiratory diseases in Longgang District in this three-year period,and a lag effect was found in these associations.
引文
[1]Darrow LA,Klein M,Flanders WD,et al. Air pollution and acute respiratory infections among children 0-4 years of age:an 18-year timeseries study[J]. Am J Epidemiol,2014,180(10):968-977.
[2]彭琦,饶俊,阳志文,等.室内可吸入颗粒物污染与哮喘和肺癌的相关性研究综述[J].实用预防医学,2018,25(8):1022-1024.
[3]Jacobson LS,Hacon SS,de Castro HA,et al. Acute effects of particulate matter and black carbon from seasonal fires on peak expiratory flow of schoolchildren in the Brazilian Amazon[J]. PLoS One,2014,9(8):e104177.
[4]张怡,杨菁,马露.空气污染对生殖健康影响:研究现状与展望[J].中华预防医学杂志,2017,51(3):193-196.
[5]朱鹏飞,张翼,班婕,等.中国空气污染与不良出生结局的研究进展[J].中华流行病学杂志,2017,38(3):393-399.
[6]陈仁杰,阚海东.对《2010年全球疾病负担评估》中我国PM2. 5污染部分的一些看法[J].中华医学杂志,2013,93(34):2689-2690.
[7]杨程,杨兴堂,沈先标.上海市某区2013-2015年大气污染物与医院门诊量的时间序列研究[J].环境与职业医学,2017,34(3):235-238.
[8]王敏珍,郑山,王式功,等.兰州市大气气态污染物与呼吸系统疾病日入院人数的时间序列分析[J].卫生研究,2012,41(5):771-775.
[9]殷丽刚,赵宝新,张燕萍,等.大同市大气SO2与5岁以下儿童呼吸系统疾病住院的浓度-反应关系研究[J].环境与健康杂志,2017,34(3):216-219.
[10]伍燕珍,张金良,赵秀阁,等.我国大气污染与儿童呼吸系统疾病和症状的关系[J].环境与健康杂志,2009,26(6):471-477.
[11]廖玉学,彭朝琼,余淑苑,等.深圳市大气PM10与呼吸系统疾病日门诊量的时间序列分析[J].华南预防医学,2014,55(4):301-305.
[12]杨财平,马蓓蓓,余青,等.宜昌市PM2. 5的污染特征及其风险评价[J].实用预防医学,2017,24(10):1209-1212.
[2]彭琦,饶俊,阳志文,等.室内可吸入颗粒物污染与哮喘和肺癌的相关性研究综述[J].实用预防医学,2018,25(8):1022-1024.
[3]Jacobson LS,Hacon SS,de Castro HA,et al. Acute effects of particulate matter and black carbon from seasonal fires on peak expiratory flow of schoolchildren in the Brazilian Amazon[J]. PLoS One,2014,9(8):e104177.
[4]张怡,杨菁,马露.空气污染对生殖健康影响:研究现状与展望[J].中华预防医学杂志,2017,51(3):193-196.
[5]朱鹏飞,张翼,班婕,等.中国空气污染与不良出生结局的研究进展[J].中华流行病学杂志,2017,38(3):393-399.
[6]陈仁杰,阚海东.对《2010年全球疾病负担评估》中我国PM2. 5污染部分的一些看法[J].中华医学杂志,2013,93(34):2689-2690.
[7]杨程,杨兴堂,沈先标.上海市某区2013-2015年大气污染物与医院门诊量的时间序列研究[J].环境与职业医学,2017,34(3):235-238.
[8]王敏珍,郑山,王式功,等.兰州市大气气态污染物与呼吸系统疾病日入院人数的时间序列分析[J].卫生研究,2012,41(5):771-775.
[9]殷丽刚,赵宝新,张燕萍,等.大同市大气SO2与5岁以下儿童呼吸系统疾病住院的浓度-反应关系研究[J].环境与健康杂志,2017,34(3):216-219.
[10]伍燕珍,张金良,赵秀阁,等.我国大气污染与儿童呼吸系统疾病和症状的关系[J].环境与健康杂志,2009,26(6):471-477.
[11]廖玉学,彭朝琼,余淑苑,等.深圳市大气PM10与呼吸系统疾病日门诊量的时间序列分析[J].华南预防医学,2014,55(4):301-305.
[12]杨财平,马蓓蓓,余青,等.宜昌市PM2. 5的污染特征及其风险评价[J].实用预防医学,2017,24(10):1209-1212.