Abstract: Objective　To examine the association between epidemiological characteristics of hemorrhagic fever with renal syndrome (HFRS) and meteorological factors in Shenzhen City during the period from 2012 to 2019. Methods　Average atmospheric pressure, average air temperature, average relative humidity, precipitation, wind speed, and sunshine duration were captured from Meteorological Bureau of Shenzhen City each month from 2012 to 2019. The average monthly rodent densities in Shenzhen City from 2012 to 2019 were acquired from the Vector Surveillance Management System of Guangdong Provincial Center for Disease Control and Prevention, and the monthly HFRS incidence was retrieved from Shenzhen Municipal Disease Surveillance System from 2012 to 2019. The correlation between meteorological factors and the monthly incidence of HFRS was examined using Spearman's rank correlation in Shenzhen City, and the temporal trends in monthly HFRS incidence and the degrees of freedom for the rodent density were determined in Shenzhen City with a generalized additive model. The optimal lag time was identified using excess risk (ER) and its 95% confidence interval (CI), and univariate and multivariate models were fitted to evaluate the impact of meteorological factors on HFRS incidence in Shenzhen City. Results　The median number of incident HFRS cases was 3.00 (interquartile range, 3.25) in Shenzhen City from 2012 to 2019, with an average air temperature of (23.44 ± 4.91) ℃, average relative humidity of (76.05 ± 7.61)%, median precipitation of 4.10 (interquartile range, 6.83) mm, average wind speed of (1.97 ± 0.26) m/s, average sunshine duration of (5.17 ± 1.64) h, and median monthly rodent density of 1.74% (interquartile range, 2.52%). Spearman's rank correlation analysis showed that the average air temperature positively correlated with average relative humidity (rs = 0.420, P < 0.05), precipitation (rs = 0.658, P < 0.05) and sunshine duration (rs = 0.633, P < 0.05), and the atmospheric pressure negatively correlated with average air temperature (rs = -0.925, P < 0.05), relative humidity (rs = -0.614, P < 0.05), precipitation (rs = -0.789, P < 0.05) and sunshine duration (rs = -0.437, P < 0.05), while the average relative humidity correlated positively with precipitation (rs = 0.724, P < 0.05) and negatively with sunshine duration (rs = -0.218, P < 0.05). Univariate modeling analysis showed that the ERs and their 95% CI were 0.639%（0.540%，0.737%） for average atmospheric pressure, -7.157%（-8.113%，-6.190%） for average air temperature, -3.603%（-4.219%，-2.985%） for average relative humidity, -5.889%（-7.085%， -4.669%） for precipitation, 21.881%（-5.149%，56.612%） for average wind speed, and -13.877%（-16.641%，-11.022%） for sunshine duation (all P values < 0.05). Multivariate modeling analysis showed that in the ensemble model combining average atmospheric pressure and precipitation, the highest ER (6.686%) was caused by increased average atmospheric pressure, and the highest absolute ER values for average air temperature (6.615%), average relative humidity (3.107%) and precipitation (5.386%) were seen after adjustment only for sunshine duration (all P values < 0.05), while the highest absolute ER for sunshine duration (11.875%) was found after adjustment for precipitation (P < 0.05). Conclusions　An increase in average air temperature, relative humidity, precipitation and sunshine duration resulted in a reduced incidence rate of HFRS in Shenzhen City from 2012 to 2019, and an increase in average atmospheric pressure increased the incidence of HFRS. Meteorological factors are important determinants affecting HFRS incidence in Shenzhen City.

Key words: Hemorrhagic fever with renal syndrome, Meteorological factor, Generalized additive model, Shenzhen City

摘要： 目的　了解2012—2019年深圳市肾综合征出血热（hemorrhagic fever with renal syndrome，HFRS）疫情流行特征与气象因素之间的关系。方法　于深圳市气象监测局获取2012—2019年该市各月份平均气压、气温、相对湿度、降雨量、风速、日照时长等气象因素数据，于广东省疾病预防控制中心病媒生物监测管理系统获取2012—2019年深圳市月均鼠密度资料，于深圳市疾病监测系统获取该市同期HFRS月发病数据。采用Spearman秩相关分析气象因素与深圳市HFRS月度发病数间的相关关系；采用广义相加模型（generalized additive model，GAM）确定深圳市HFRS月发病数的时间趋势、鼠密度自由度，以超额风险（excess risk，ER）及其95%置信区间（confidence interval，CI）确定最佳滞后时间，拟合单变量模型和多变量模型，评价深圳市气象因子对HFRS发病的影响。结果　2012—2019年深圳市HFRS发病数为3.00（3.25）例；平均气温为（23.44 ± 4.91）℃，平均相对湿度为（76.05 ± 7.61）%，中位降雨量为4.10（6.83）mm，平均风速为（1.97 ± 0.26）m/s，平均日照时长为（5.17 ± 1.64）h；月中位鼠密度为1.74%（2.52%）。Spearman秩相关分析结果显示，平均气温与平均相对湿度（rs = 0.420，P < 0.05）、降雨量（rs = 0.658，P < 0.05）、日照时长（rs = 0.633，P < 0.05）均呈正相关关系；平均气压与平均气温（rs = -0.925，P < 0.05）、平均相对湿度（rs = -0.614，P < 0.05）、降雨量（rs = -0.789，P < 0.05）和日照时间（rs = -0.437，P < 0.05）均呈负相关关系；平均相对湿度与降雨量（rs = 0.724，P < 0.05）呈正相关关系，与日照时长（rs = -0.218，P < 0.05）呈负相关关系。单变量模型建模结果显示，平均气压、平均气温、平均相对湿度、降雨量、平均风速和日照时长的ER及其95% CI分别为0.639（0.540，0.737）、-7.157（-8.113，-6.190）、-3.603（-4.219，-2.985）、-5.889（-7.085， -4.669）、21.881（-5.149，56.612）和-13.877（-16.641，-11.022），均有统计学意义（P均< 0.05）。多变量模型建模结果显示，在包含平均气压和降雨量的模型中，平均气压升高引起的ER最高，为6.686%；在仅调整日照时长时，平均气温、平均相对湿度和降雨量所引起的ER绝对值均最大，分别为6.615%、3.107%和5.386%；在调整降雨量后，日照时长所引起的ER绝对值最大，为11.875%（P均< 0.05）。结论　2012—2019年深圳市平均气温、平均相对湿度、降雨量和日照时长增加可导致该市HFRS发病水平降低，而平均气压升高可导致该市HFRS发病水平升高。气象因素是影响HFRS流行的重要影响因素之一。

关键词: 肾综合征出血热, 气象因素, 广义相加模型, 深圳市