Abstract

Objective To evaluate effects of the daily average temperature on the daily number of outpatient visits for eczema in Lanzhou city. Methods Clinical data were obtained from outpatients with eczema in the Department of Dermatology of 2 third-grade class-A hospitals in Lanzhou city from January 1st 2007 to December 31st 2015, and meteorological data during this period were also collected. Controlling for confounding factors like long-term trends and day of the week, a distributed lag non-linear model (DLNM) fitted with quasi-Poisson link function was used to assess the effects of daily average temperature on the daily number of outpatient visits for eczema, and the analysis was stratified by season, age and gender. Results The exposure-response relationship between the daily average temperature and daily number of outpatient visits for eczema could be roughly described by aW-shaped curve. Stratification analysis showed that the effect of the daily average temperature on outpatient visits for eczema was strongest in autumn and winter, followed by summer, and weakest in spring. Low temperature may have lagged, cumulative and persistent effects on the daily number of outpatient visits for eczema, with the maximum relative risk (RR) value (1.12[95% CI: 1.03-1.22]) observed at-9 ℃ on lag day 14. With a 1 ℃ decrease in the temperature, 16% (RR= 1.16, 95% CI: 1.00-1.03) , 14% (RR= 1.14, 95% CI: 1.02-1.26) and 13% (RR= 1.13, 95% CI: 1.02-1.25) increases in the daily number of outpatient visits for eczema were observed in men, teenagers and middle-aged adults respectively (P 0.05) . The effect of high temperature usually occurred following exposure without lag periods, and was gradually weakened over lag time (P > 0.05) . Conclusions In Lanzhou, the effect of daily average temperature on outpatient visits for eczema was strongest in autumn and winter. Changes of the daily temperature may be one of risk factors for eczema. Low temperature had lagged effects on the daily number of outpatient visits for eczema, and the effects were strongest on lag day 14. Key words: Temperature; Eczema; Seasons; Distributed lag non-linear models; Daily mean temperature

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