中华临床医师杂志（电子版）
中華臨床醫師雜誌（電子版）
중화림상의사잡지（전자판）
CHINESE JOURNAL OF CLINICIANS(ELECTRONIC VERSION)
2015年
4期
581-584
,共4页
刘健%韩佰花%李玉琴%唐彤宇
劉健%韓佰花%李玉琴%唐彤宇
류건%한백화%리옥금%당동우
消化性溃疡%高血压,门静脉%上消化道出血%季节变化%医疗气象预报
消化性潰瘍%高血壓,門靜脈%上消化道齣血%季節變化%醫療氣象預報
소화성궤양%고혈압,문정맥%상소화도출혈%계절변화%의료기상예보
Peptic ulcer%Hypertension,portal%Upper gastrointestinal bleeding%Seasonal fluctuation%Medical weather forecast
目的：探讨吉林省上消化道出血（UGIB）的季节性发病规律及其与气象因素的相关性。方法对吉林大学第一医院2011年1月至2012年12月确诊为UGIB的681例住院患者的临床资料、入院时的月份和季度归属及同期的气象资料进行统计分析，比较各个月份、季度之间 UGIB发病的差异，并分析各气象因素与 UGIB 发病之间的关系。结果 UGIB 的发病例数在不同季节有统计学差异（χ2=25.11，P＜0.01）。秋、冬季出血发生率明显高于春、夏季（58.30%vs.41.70%， P＜0.01），且在10月达高峰，在4月达低谷。Spearman双变量相关分析显示UGIB发病与平均大气压关联最显著（rho=0.738，P=0.000），其次是平均气温（rho=－0.533，P=0.007），再次是人体舒适度指数（rho=－0.462，P=0.023），而平均风速（rho=－0.359，P=0.085）、平均相对湿度（rho=0.168， P=0.431）和平均气温日较差（rho=－0.005，P=0.98）与UGIB发病无明显关联。将平均大气压和平均气温代入多元线性回归分析，可建立回归方程：UGIB 月发病数=－1211.401＋0.349×月平均气温＋1.254×月平均大气压。结论吉林省UGIB的发病存在显著的季节差异，且与月平均大气压呈正相关，与月平均气温呈负相关。通过多元线性回归分析，可以建立预测方程，进行医疗气象预报。
目的：探討吉林省上消化道齣血（UGIB）的季節性髮病規律及其與氣象因素的相關性。方法對吉林大學第一醫院2011年1月至2012年12月確診為UGIB的681例住院患者的臨床資料、入院時的月份和季度歸屬及同期的氣象資料進行統計分析，比較各箇月份、季度之間 UGIB髮病的差異，併分析各氣象因素與 UGIB 髮病之間的關繫。結果 UGIB 的髮病例數在不同季節有統計學差異（χ2=25.11，P＜0.01）。鞦、鼕季齣血髮生率明顯高于春、夏季（58.30%vs.41.70%， P＜0.01），且在10月達高峰，在4月達低穀。Spearman雙變量相關分析顯示UGIB髮病與平均大氣壓關聯最顯著（rho=0.738，P=0.000），其次是平均氣溫（rho=－0.533，P=0.007），再次是人體舒適度指數（rho=－0.462，P=0.023），而平均風速（rho=－0.359，P=0.085）、平均相對濕度（rho=0.168， P=0.431）和平均氣溫日較差（rho=－0.005，P=0.98）與UGIB髮病無明顯關聯。將平均大氣壓和平均氣溫代入多元線性迴歸分析，可建立迴歸方程：UGIB 月髮病數=－1211.401＋0.349×月平均氣溫＋1.254×月平均大氣壓。結論吉林省UGIB的髮病存在顯著的季節差異，且與月平均大氣壓呈正相關，與月平均氣溫呈負相關。通過多元線性迴歸分析，可以建立預測方程，進行醫療氣象預報。
목적：탐토길림성상소화도출혈（UGIB）적계절성발병규률급기여기상인소적상관성。방법대길림대학제일의원2011년1월지2012년12월학진위UGIB적681례주원환자적림상자료、입원시적월빈화계도귀속급동기적기상자료진행통계분석，비교각개월빈、계도지간 UGIB발병적차이，병분석각기상인소여 UGIB 발병지간적관계。결과 UGIB 적발병례수재불동계절유통계학차이（χ2=25.11，P＜0.01）。추、동계출혈발생솔명현고우춘、하계（58.30%vs.41.70%， P＜0.01），차재10월체고봉，재4월체저곡。Spearman쌍변량상관분석현시UGIB발병여평균대기압관련최현저（rho=0.738，P=0.000），기차시평균기온（rho=－0.533，P=0.007），재차시인체서괄도지수（rho=－0.462，P=0.023），이평균풍속（rho=－0.359，P=0.085）、평균상대습도（rho=0.168， P=0.431）화평균기온일교차（rho=－0.005，P=0.98）여UGIB발병무명현관련。장평균대기압화평균기온대입다원선성회귀분석，가건립회귀방정：UGIB 월발병수=－1211.401＋0.349×월평균기온＋1.254×월평균대기압。결론길림성UGIB적발병존재현저적계절차이，차여월평균대기압정정상관，여월평균기온정부상관。통과다원선성회귀분석，가이건립예측방정，진행의료기상예보。
Objective To verify the possible existence of a seasonal pattern in the onset of upper gastrointestinal bleeding (UGIB) and its correlation with meteorological factors in Jilin province. Methods The study included 681 patients whose diagnosis was UGIB. They were consecutively admitted to First Hospital of Jilin University between January, 2011 and December, 2012. The difference between the incidence of each month and each quarter was told, and statistical analysis of the relationship between the incidence of UGIB and meteorological data was also made retrospectively. Results The number of UGIB cases occurred in spring, summer, autumn, winter was different, and the difference was statistically significant (χ2=25.11, P<0.01). Moreover, the incidence of UGIB in autumn and winter was significantly higher than in spring and summer (58.30%vs. 41.70%, P<0.05), and its peak was in October; its trough in April. Furthermore, Spearman bivariate correlation analysis showed that the association between the onset of UGIB with the mean meteorological factors was that atmospheric pressure (rho=0.738, P=0.000), followed by temperature (rho=-0.533, P=0.007), next was human comfort index (rho=-0.462, P=0.023), but the association between the incidence of UGIB and wind speed (rho=-0.359, P=0.085), relative humidity (rho=0.168, P=0.431) and temperature diurnal difference (rho=-0.005, P=0.98) was poor. Finally, the linear regression equation was established, the incidence of UGIB per month=-1 211.401+0.349×the mean temperature per month+1.254×the mean atmospheric pressure per <br> month. Conclusion There was seasonal fluctuation in upper gastrointestinal bleeding. Besides, the incidence of UGIB and the mean atmospheric pressure was positive correlated, while the mean temperature was negative correlated. In addition, by multivariate linear regression analysis, we can establish the predictive equation, which can be used for medical meteorological forecast.
