Abstract
Studies on scarlet fever with meteorological factors included were few. We aimed to illustrate meteorological factors’ effects on monthly incidence of scarlet fever. Cases of scarlet fever were collected from the report of legal infectious disease in Hefei City from 1985 to 2006; the meteorological data were obtained from the weather bureau of Hefei City. Monthly incidence and corresponding meteorological data in these 22 years were used to develop the model. The model of auto regressive integrated moving average with covariates was used in statistical analyses. There was a highest peak from March to June and a small peak from November to January. The incidence of scarlet fever ranges from 0 to 0.71502 (per 105 population). SARIMAX (1,0,0)(1,0,0)12 model was fitted with monthly incidence and meteorological data optimally. It was shown that relative humidity (β = −0.002, p = 0.020), mean temperature (β = 0.006, p = 0.004), and 1 month lag minimum temperature (β = −0.007, p < 0.001) had effect on the incidence of scarlet fever in Hefei. Besides, the incidence in a previous month (AR(β) = 0.469, p < 0.001) and in 12 months before (SAR(β) = 0.255, p < 0.001) was positively associated with the incidence. This study shows that scarlet fever incidence was negatively associated with monthly minimum temperature and relative humidity while was positively associated with mean temperature in Hefei City, China. Besides, the ARIMA model could be useful not only for prediction but also for the analysis of multiple correlations.
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This work was partly supported by grants from the Natural Science Foundation of Anhui Province in 2013 (code: 1308085MH169) and the Key Project of the Education Department of Anhui Province Natural Science Research (code: KJ2012A165).
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Our research was in compliance with the Helsinki Declaration (Williams 2008) approved by the ethics committee of Anhui Medical University.
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Duan, Y., Huang, Xl., Wang, Yj. et al. Impact of meteorological changes on the incidence of scarlet fever in Hefei City, China. Int J Biometeorol 60, 1543–1550 (2016). https://doi.org/10.1007/s00484-016-1145-8
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DOI: https://doi.org/10.1007/s00484-016-1145-8