Mumps remains one of the worldwide major health problems over the past decade. Seasonal variations of mumps indicate that meteorological factors play an important role in the development of mumps, but few studies have investigated the relationship between extreme meteorological factors and the incidence of mumps. Daily mumps cases and meteorological factors in Hefei, China, from 2011 to 2016 were obtained. A generalized additive model combined with the distributed lag nonlinear model (DLNM) was used to quantify the risk of extreme meteorological factors on mumps incidence. Nonlinear relationships were observed among all meteorological factors and mumps incidence. We found that extremely low and high temperatures increased the risk of mumps. The relative risks (RRs) of the cumulative effects along 30 lag days were 2.02 (95%CI: 1.14–3.56) and 2.42 (95%CI: 1.37–4.24), respectively. Both short and long sunshine duration had negative correlation on mumps, with cumulative RRs of 0.64 (95%CI: 0.46–0.92) and 0.57 (95%CI: 0.44–0.74), respectively. In the subgroup analysis, males were found to be more sensitive to extreme weather, especially extreme temperatures and sunshine duration. This study suggests that extreme meteorological factors, especially extreme temperatures and sunshine duration, exert a significant impact on the incidence of mumps. When formulating and implementing effective strategies to the prevention and control of mumps, authorities should take the effect caused by extreme meteorological factors into consideration and pay more attention to susceptible populations, such as male children and teenagers.
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We appreciate Hefei Center for Disease Control and Prevention and Hefei Bureau of Meteorology for providing data for our research.
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Wu, H., You, E., Jiang, C. et al. Effects of extreme meteorological factors on daily mumps cases in Hefei, China, during 2011–2016. Environ Sci Pollut Res 27, 4489–4501 (2020). https://doi.org/10.1007/s11356-019-07073-y
- Extreme meteorological factors
- Distributed lag nonlinear model
- Sunshine duration
- Time series analysis