空气污染物与气象因素对慢性阻塞性肺疾病急性加重就诊人次影响的时间序列研究

Impact of Air Pollutants and Meteorological Factors on the Number of Visits for Acute Exacerbation of Chronic Obstructive Pulmonary Disease:a Time-Series Study

目的采用广义相加模型(GAM)、分布滞后非线性模型(DLNM)和双变量响应面模型分析空气污染物与气象因素对慢性阻塞性肺疾病急性加重(AECOPD)就诊人次的影响。方法从医院信息管理系统获取2021—2023年柳州市工人医院收治的AECOPD患者的临床资料,统计AECOPD就诊人次、日平均AECOPD就诊人次。于空气质量在线检测分析平台收集2021—2023年柳州市空气污染物数据,统计二氧化硫(SO_(2))、二氧化氮(NO_(2))、一氧化碳(CO)、臭氧日最大8小时(O3-8h)、PM10、PM_(2.5)日平均浓度。于国家气象科学数据中心收集2021—2023年柳州市气象因素数据,统计气温日较差、日平均相对湿度。采用R 4.1.2软件中的dlnm、mgcv、ggplot2、rsm包进行GAM、DLNM、双变量响应面模型分析。结果Lag6时,SO_(2)日平均浓度对AECOPD就诊人次存在单日滞后效应(P<0.05),且Lag6为最强单日滞后效应日〔比值比(OR)=1.011,95%CI(1.003~1.019)〕。Lag7时,NO_(2)日平均浓度对AECOPD就诊人次存在单日滞后效应(P<0.05),且Lag7为最强单日滞后效应日〔OR=1.004,95%CI(1.000~1.008)〕。Lag1、Lag6时,CO日平均浓度对AECOPD就诊人次存在单日滞后效应(P<0.05),且Lag6为最强单日滞后效应日〔OR=2.142,95%CI(1.176~3.904)〕。Lag1时,高日平均相对湿度(100.0%)、低日平均相对湿度(28.0%)对AECOPD就诊人次存在单日滞后效应(P<0.05),且Lag1为最强单日滞后效应日〔高日平均相对湿度(100%):相对危险度(RR)=1.363,95%CI(1.010~1.840);低日平均相对湿度(28.0%):RR=1.016,95%CI(1.000~1.033)〕。低气温日较差(1℃)与低SO_(2)日平均浓度共存的情况下,AECOPD就诊人次达到最大;低气温日较差(1℃)与高NO_(2)、CO、O3-8h、PM10、PM_(2.5)日平均浓度共存的情况下,AECOPD就诊人次达到最大;低日平均相对湿度(28.0%)与高SO_(2)、NO_(2)、PM10、PM_(2.5)日平均浓度共存的情况下,AECOPD就诊人次达到最大;高日平均相对湿度(100.0%)与高CO、O3-8h日平均浓度共存的情况下,AECOPD就诊人次达到最大。结论Lag6为SO_(2)、CO日平均浓度对AECOPD就诊人次的最强单日滞后效应日,Lag7为NO_(2)日平均浓度对AECOPD就诊人次的最强单日滞后效应日,O3-8h、PM10、PM_(2.5)日平均浓度对AECOPD就诊人次不存在单日滞后效应;Lag1为高日平均相对湿度(100.0%)及低日平均相对湿度(28.0%)对AECOPD就诊人次的最强单日滞后效应日。低气温日较差与特定低或高浓度空气污染物、低或高日平均相对湿度与特定高浓度空气污染物可协同增加AECOPD就诊人次。

Objective To analyze the impact of air pollutants and meteorological factors on the number of visits for acute exacerbation of chronic obstructive pulmonary disease(AECOPD)by the generalized additive model(GAM),distributed lag non-linear model(DLNM),and bivariate response surface model.Methods The clinical data of AECOPD patients admitted to Liuzhou Worker's Hospital from 2021 to 2023 were obtained from the hospital information management system.The number of visits for AECOPD and the daily average number of visits for AECOPD were counted.The air pollution data of Liuzhou City from 2021 to 2023 were collected on the online air quality detection and analysis platform.The daily average concentrations of sulfur dioxide(SO_(2)),nitrogen dioxide(NO_(2)),carbon monoxide(CO),daily maximum 8-hour mean ozone concentration(O3-8h),PM10,and PM_(2.5) were counted.The meteorological factor data of Liuzhou City from 2021 to 2023 were collected from the National Meteorological Science Data Center.The daily temperature range and daily average relative humidity were counted.The dlnm,mgcv,ggplot2,and rsm packages in R 4.1.2 software were used for GAM,DLNM,and bivariate response surface model analysis.Results At Lag6,the SO_(2) daily average concentration had single-day lag effect on the number of visits for AECOPD daily average(P<0.05),and Lag6 was the day with the strongest single-day lag effect[OR=1.011,95%CI(1.003-1.019)].At Lag7,the NO_(2) daily average concentration had single-day lag effect on the number of visits for AECOPD(P<0.05),and Lag7 was the day with the strongest single-day lag effect[odds ratio(OR)=1.004,95%CI(1.000-1.008)].At Lag1 and Lag6,there was a single-day lag effect of CO daily average concentration on the number of visits for AECOPD(P<0.05),and Lag6 was the day with the strongest single-day lag effect[OR=2.142,95%CI(1.176-3.904)].There was a single-day lag effect of high daily average relative humidity(100.0%)and low daily average relative humidity(28.0%)on the number of visits for AECOPD at Lag1(P<0.05),and Lag1 was the day with the strongest single-day lag effect[high daily average relative humidity(100.0%):relative risk(RR)=1.363,95%CI(1.010-1.840);low daily average relative humidity(28.0%):RR=1.016,95%CI(1.000-1.033)].Under the coexistence of low daily temperature range(1℃)and low daily average concentration of SO_(2),the number of visits for AECOPD reached its maximum;under the coexistence of low daily temperature range(1℃)and high daily average concentrations of NO_(2),CO,O3-8h,PM10,and PM_(2.5),the number of visits for AECOPD reached its maximum;under the coexistence of low daily average relative humidity(28.0%)and high daily average concentrations of SO_(2),NO_(2),PM10,and PM_(2.5),the number of visits for AECOPD reached its maximum;under the coexistence of high daily average relative humidity(100.0%)and high daily average concentrations of CO and O3-8h,the number of visits for AECOPD reached its maximum.Conclusion Lag6 is the day with the strongest single-day lag effect of SO_(2) and CO daily average concentrations on the number of visits for AECOPD.Lag7 is the day with the strongest single-day lag effect of NO_(2) daily average concentration on the number of visits for AECOPD.The daily average concentrations of O3-8h,PM10,and PM_(2.5) have no single-day lag effect on the number of visits for AECOPD.Lag1 is the day with the strongest single-day lag effect of high daily average relative humidity(100.0%)and low daily average relative humidity(28.0%)on the number of visits for AECOPD.Low daily temperature range and specific low or high concentration air pollutants,and low or high daily average relative humidity and specific high concentration air pollutants can synergistically increase the number of visits for AECOPD.