甘肃凉州区气象因素对肺心病门诊人数的影响分析
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摘要
目的探讨甘肃省凉州区气象因素对肺心病门诊人数的影响效应。方法搜集甘肃省凉州区2014-2016年日气象数据(气温、气压、降水量以及日照时数等)和每日肺心病门诊就医人数,采用分布滞后非线性模型(distributed lag nonlinear model, DLNM)分析气象因子对肺心病门诊就医人数的影响关系及滞后效应。结果 2014-2016年凉州区肺心病门诊人数合计20 462例,平均日门诊人数为18.67例,肺心病日门诊人数与气温和日照时数均呈正相关,与气压、相对湿度和降水量呈负相关,其中日均气温对肺心病门诊人数影响最大(r=0.133,P<0.001)。在日平均温度最高,滞后16 d时,相对危险度(relative risk,RR)最高(1.26, 95%CI:1.13～1.40),气温每升高1℃,肺心病门诊人数将增加1.26(95%CI:1.13～1.40)。极端低温(-18℃)时不存在发病危险,在极端高温(29℃),滞后0～15 d时对肺心病门诊人数的相对危险度达到最大。结论气象因子是影响凉州区肺心病门诊人数的重要因素,肺心病的患病风险会因为气温变化而增加,且影响效应会再当天立即发生。高温效应维持时间较短,相对危险度高,而低温对门诊人数的相对危险度则相对较低,并且滞后时间长。
Objective To investigate the effect of meteorological factors on the number of outpatients with pulmonary heart disease in Liangzhou district of Gansu province. Methods We collected the daily meteorological data(temperature, air pressure, precipitation, sunshine hours, etc.) of Liangzhou district of Gansu province and the number of daily outpatients with the pulmonary heart disease from 2014 to 2016, and used the distribution lag model to analyze the impact relationship and hysteresis effect of the meteorological factors on the number of outpatients to pulmonary heart disease clinics. Results The total number of outpatients with pulmonary heart disease was 20 462 in Liangzhou district from 2014 to 2016, and the average number of outpatients per day was 18.67. The number of outpatients with pulmonary heart disease per day was positively correlated with temperature and sunshine hours, and negatively correlated with air pressure, relative humidity and precipitation. Among them, the average daily temperature had the most significant effect on the number of outpatients with pulmonary heart disease(r=0.133, P<0.001). At the highest daily average temperature, lagging 16 days,the relative risk coefficient(RR value) was the highest(1.26, 95% CI:1.13-1.40). For every 1 ℃ increase in temperature, the number of outpatients with pulmonary heart disease increased by 1.26(95% CI: 1.13-1.40). There was no risk of morbidity at an extreme low temperature(-18 ℃), and the relative risk of the number of the pulmonary heart disease outpatients was the greatest at lag 0-15 at an extreme high temperatures(29 ℃). Conclusion Meteorological factor is an important factor affecting the number of outpatients with pulmonary heart disease in Liangzhou district. The risk of pulmonary heart disease will increase due to temperature changes, and the impact will occur immediately on the same day. The high temperature effect is short-lived and the relative risk is high, while the relative risk of low temperature to the number of outpatients is relatively low and the lag time is long.
Objective To investigate the effect of meteorological factors on the number of outpatients with pulmonary heart disease in Liangzhou district of Gansu province. Methods We collected the daily meteorological data(temperature, air pressure, precipitation, sunshine hours, etc.) of Liangzhou district of Gansu province and the number of daily outpatients with the pulmonary heart disease from 2014 to 2016, and used the distribution lag model to analyze the impact relationship and hysteresis effect of the meteorological factors on the number of outpatients to pulmonary heart disease clinics. Results The total number of outpatients with pulmonary heart disease was 20 462 in Liangzhou district from 2014 to 2016, and the average number of outpatients per day was 18.67. The number of outpatients with pulmonary heart disease per day was positively correlated with temperature and sunshine hours, and negatively correlated with air pressure, relative humidity and precipitation. Among them, the average daily temperature had the most significant effect on the number of outpatients with pulmonary heart disease(r=0.133, P<0.001). At the highest daily average temperature, lagging 16 days,the relative risk coefficient(RR value) was the highest(1.26, 95% CI:1.13-1.40). For every 1 ℃ increase in temperature, the number of outpatients with pulmonary heart disease increased by 1.26(95% CI: 1.13-1.40). There was no risk of morbidity at an extreme low temperature(-18 ℃), and the relative risk of the number of the pulmonary heart disease outpatients was the greatest at lag 0-15 at an extreme high temperatures(29 ℃). Conclusion Meteorological factor is an important factor affecting the number of outpatients with pulmonary heart disease in Liangzhou district. The risk of pulmonary heart disease will increase due to temperature changes, and the impact will occur immediately on the same day. The high temperature effect is short-lived and the relative risk is high, while the relative risk of low temperature to the number of outpatients is relatively low and the lag time is long.
引文
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[13] 黄照,刘涛,许燕君,等.基于死亡数据用DLNM构建气象健康指数 [J].环境卫生学杂志,2018,8(5):368-373,380.DOI:10.13421/j.cnki.Hjwsxzz.2018.05.002.Huang Z,Liu T,Xu YJ,et al.Building meteorological health index with DLNM based on death data [J].J Environ Hyg,2018,8(5):368-373,380.DOI:10.13421/j.cnki.Hjwsxzz.2018.05.002.
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[2] 李秋敏,周雪征,何晓金,等.锦州地区慢性阻塞性肺病(COPD)肺心病急性加重期与气象因素关系的探讨 [J].锦州医学院学报,1991,12(5):306-307.Li QM,Zhou XZ,He XJ,et al.Discussion on the relationship between acute exacerbation of pulmonary heart disease and meteorological factors in chronic obstructive pulmonary disease (COPD) in Jinzhou area [J].J Jinzhou Med Coll,1991,12(5):306-307.
[3] Guo YM,Barnett A G,Pan X,et al.The impact of temperature on mortality in Tianjin,China:a case-crossover design with a distributed lag nonlinear model [J].Environ Health Perspect,2011,119(12):1719-1725.DOI:10.1289/ehp.1103598.
[4] 潘洁,杨军,李萌萌,等.分布滞后非线性模型在气象因素对手足口病发病影响研究中的应用 [J].环境与健康杂志,2016,33(5):377-380,470.DOI:10.16241/j.cnki.1001-5914.2016.05.001.Pan J,Yang J,Li MM,et al.Application of distributed lag nonlinear model in the study of the influence of meteorological factors on the incidence of foot and mouth disease [J].J Environ Health,2016,33(5):377- 380,470.DOI:10.16241/j.cnki.1001-5914.2016.05.001.
[5] 丁亚萍,虞明星,郝海燕,等.石家庄市空气PM2.5浓度与儿童呼吸系统疾病门诊量的关系 [J].中华疾病控制杂志,2018,22(7):672-676.DOI:10.16462/j.cnki.zhjbkz.2018.07.005.Ding YP,Yu MX,Hao HY,et al.Relationship between PM2.5 concentration in air and children's respiratory diseases in Shijiazhuang city [J].Chin J Dis Control Prev,2018,22(7):672-676.DOI:10.16462/j.cnki.zhjbkz.2018.07.005.
[6] 罗焕金,曾四清,胡梦珏,等.极端低温对呼吸系统疾病住院人数影响的时间序列分析 [J].华南预防医学,2014,40(6):504-511.DOI:10.13217/j.scjpm.2014.0504.Luo HJ,Zeng SQ,Hu MJ,et al.Time series analysis of the effect of extreme hypothermia on hospitalization of respiratory diseases [J].S Chin J Prev Med,2014,40(6):504-511.DOI:10.13217/j.scjpm.2014.0504.
[7] Giorgini P,Rubenfire M,Das R,et al.Particulate matter air pollution and ambient temperature:opposing effects on blood pressure in high-risk cardiac patients [J].J Hypertens,2015,33(10):2032-2038.DOI:10.1097/HJH.0000000000000663.
[8] 张欣,吴磊,余超.南昌极端气温与冠心病的病例交叉研究 [J].中国老年学杂志,2013,33(24):6242-6245.DOI:10.3969/j.issn.1005-9202.2013.24.078.Zhang X,Wu L,Yu C.A case study of cases of extreme temperature and coronary heart disease in Nanchang [J].Chin J Gerontol,2013,33(24):6242-6245.DOI:10.3969/j.issn.1005-9202.2013.24.078.
[9] Han MH,Yi HJ,Yong K,et al.Association between hemorrhagic stroke occurrence and meteorological factors and pollutants [J].BMC Neurol,2016,16(1):59.DOI:10.1186/s12883-016-0579-2.
[10] 程健.合肥市2006-2012年温度变化对细菌性痢疾的时间序列研究 [D].合肥:安徽医科大学,2015.Cheng J.Time series of temperature changes in Hefei from 2006 to 2012 on bacterial dysentery [D].Hefei:Anhui Medical University,2015.
[11] Gasparrini A.Distributed lag linear and non-linear models in R:the package dlnm [J].J Stat Softw,2011,43(8):1-20.DOI:10.18637/jss.v043.i08.
[12] 董继元,陈永聪,张本忠,等.兰州市气温对脑卒中发病的滞后效应研究 [J].气候变化研究进展,2017,13(4):366-374.DOI:10.12006/j.issn.1673-1719.2016.222.Dong JY,Chen YC,Zhang BZ,et al.Study on the lag effect of temperature in Lanzhou city on stroke [J].Clim Change Res,2017,13(4):366-374.DOI:10.12006/j.issn.1673-1719.2016.222.
[13] 黄照,刘涛,许燕君,等.基于死亡数据用DLNM构建气象健康指数 [J].环境卫生学杂志,2018,8(5):368-373,380.DOI:10.13421/j.cnki.Hjwsxzz.2018.05.002.Huang Z,Liu T,Xu YJ,et al.Building meteorological health index with DLNM based on death data [J].J Environ Hyg,2018,8(5):368-373,380.DOI:10.13421/j.cnki.Hjwsxzz.2018.05.002.
[14] 陈静,韩军彩,张素果,等.基于暑热指数的河北省中暑气象等级预报指标研究 [J].气象与环境学报,2013,29(5):86-91.DOI:10.3969 /j.issn.1673-503X.2013.05.014.Chen J,Han JC,Zhang SG,et al.Research on the forecast index of heatstroke level in Hebei province based on heat index [J].J Meteorol Environ,2013,29(5):86-91.DOI:10.3969/j.issn.1673-503X.2013.05.014.
[15] Pascal M,Wagner V,Le Tertre A,et al.Definition of temperature thresholds:the example of the French heat wave warning system [J].Int J Biom,2013,57(1):21-29.DOI:10.1007/s00484-012-0530-1.
[16] 钟其梅,赵天鑫,王岚.气象因素与妊娠期高血压疾病相关性的研究进展 [J].环境与职业医学,2017,34(1):79-82.DOI:10.13213/j.cnki.jeom.2017.16406.Zhong QM,Zhao TX,Wang L.Research progress on the relationship between meteorological factors and hypertensive disorder complicating pregnancy [J].Environ Occup Med,2017,34(1):79-82.DOI:10.13213/j.cnki.jeom.2017.16406.
[17] Huang F,Luo Y,Guo Y,et al.Particulate matter and hospital admissions for stroke in Beijing,China:modification effects by ambient temperature [J].J Am Heart Assoc,2016,5(7):e003437.DOI:10.1161/JAHA.116.003437.