Climate change is a crucial cause of health issues, as reported in many studies. Temperature is one of the important factors related to extreme weather. Chiang Mai, the center of the north of Thailand, is also affected by temperature changes that have led to many outpatient visits. Better information will help the health service to be well-prepared. This research applied typical meteorological data and solar radiation into the distributed lag nonlinear model and a quasi-Poisson regression model. The "hot effect" and "cold effect" on outpatient visits caused by respiratory diseases, dermatophytosis, and intestinal infectious diseases in a public Chiang Mai hospital between January 2015 and December 2019 were then investigated. Of the 185,202 cases, results showed that all of the diseases mentioned had more than 10% of relative risk (RR) in cold effects. However, the RR of dermatophytosis was found to be 114%, a very high risk. In the case of hot effects, the patients of the age 19-29 have relatively high RR over 20% for respiratory diseases and dermatophytosis. It was also observed that cold effects lasted longer than hot effects.
Keywords: Distributed lag nonlinear model (DLNM); Outpatient visits; Regression; Relative risk; Temperature.
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.