ABSTRACT
OBJECTIVE@#To understand the relationships of daily average temperature and relative humidity with outpatient visit frequency of patients with chronic obstructive pulmonary disease, and whether temperature and relative humidity have a lag effect.@*METHODS@#The effects of daily average temperature, relative humidity, and their interaction in Lanzhou between January 2013 and December 2017 on the outpatient visit frequency of chronic obstructive pulmonary disease patients were analyzed using Poisson generalized linear regression model combined with distributed lag non-linear model.@*RESULTS@#There was a non-linear relationship between the daily average temperature and the outpatient visit frequency of chronic obstructive pulmonary disease patients. Between -12 °C and -8 °C, the outpatient visit frequency increased gradually with the decrease of the daily average temperature, and the outpatient visit frequency of chronic obstructive pulmonary disease patients increased by 11.60% per 1 °C of temperature drop. The daily average relative humidity also presented a non-linear effect on the outpatient visit frequency chronic obstructive pulmonary disease patients. When the daily average relative humidity was in the range of 15%-28%, the outpatient visit frequency increased gradually with the decrease of relative humidity, and the outpatient visit frequency of COPD patients increased by 37.05% for every 1% decrease of relative humidity. A synergistic effect was found between air temperature and relative humidity on chronic obstructive pulmonary disease, that is, under different relative humidity, the effect of air temperature was different. When the daily average relative humidity ≤ 50% and the daily average temperature≤11 °C, the effect of air temperature was the most obvious. For every 1 °C drop in temperature, the daily out-patient visit frequency of the whole population increased by 12.68% (5.62% in males and 7.56% in females; 5.24% in population < 65 years and 14.74% in population ≥ 65 years). When the daily average relative humidity > 50% and the daily average temperature ≤ 11 °C, the daily outpatient visit frequency of the whole population increased by 9.00% for every 1 °C drop in temperature (< 65 years, 7.11%; ≥65 years, 10.93%). When the daily average temperature > 11 °C, the temperature had no effect on the daily outpatient visit frequency of chronic obstructive pulmonary disease patients under different relative humidity.@*CONCLUSION@#The presence of a certain extent of interaction is observed between daily average temperature and relative humidity. Low-temperature and dry environment (relative humidity ≤50%, temperature ≤11 °C) as well as low-temperature and high-humidity environment (relative humidity > 50%, temperature ≤11 °C) can both increase the risk of outpatient visit in chronic obstructive pulmonary disease patients.
Subject(s)
Aged , Female , Humans , Male , Air Pollution , China , Humidity , Outpatients , Pulmonary Disease, Chronic Obstructive , TemperatureABSTRACT
Objective To predict the monthly reported echinococcosis cases in China with the autoregressive integrated mov-ing average(ARIMA)model,so as to provide a reference for prevention and control of echinococcosis. Methods SPSS 24.0 software was used to construct the ARIMA models based on the monthly reported echinococcosis cases of time series from 2007 to 2015 and 2007 to 2014,respectively,and the accuracies of the two ARIMA models were compared. Results The model based on the data of the monthly reported cases of echinococcosis in China from 2007 to 2015 was ARIMA(1,0,0)(1,1, 0)12,the relative error among reported cases and predicted cases was-13.97%,AR(1)=0.367(t=3.816,P<0.001),SAR (1)=-0.328(t=-3.361,P=0.001),and Ljung-Box Q=14.119(df=16,P=0.590).The model based on the data of the monthly reported cases of echinococcosis in China from 2007 to 2014 was ARIMA(1,0,0)(1,0,1)12,the relative error among reported cases and predicted cases was 0.56%,AR(1)=0.413(t=4.244,P<0.001),SAR(1)=0.809(t=9.584, P<0.001),SMA(1)=0.356(t=2.278,P=0.025),and Ljung-Box Q=18.924(df=15,P=0.217).Conclusions The different time series may have different ARIMA models as for the same infectious diseases.It is needed to be further verified that the more data are accumulated,the shorter time of predication is,and the smaller the average of the relative error is.The estab-lishment and prediction of an ARIMA model is a dynamic process that needs to be adjusted and optimized continuously accord-ing to the accumulated data,meantime,we should give full consideration to the intensity of the work related to infectious diseas-es reported(such as disease census and special investigation).