Huang Shanjun, Wang Hao, Li Zhuo, Wang Zhaohan, Ma Tian, Song Ruifang, Lu Menghan, Han Xin, Zhang Yiting, Wang Yingtong, Zhen Qing, Shui Tiejun
Department of Epidemiology and Biostatistics, Key Laboratory of Zoonosis, Ministry of Education, School of Public Health, Jilin University, Changchun, Jilin, PR China.
State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Key Laboratory for Zoonoses Research of the Ministry of Education, Changchun, PR China.
Heliyon. 2024 Apr 15;10(8):e29611. doi: 10.1016/j.heliyon.2024.e29611. eCollection 2024 Apr 30.
The impact of climate on zoonotic infectious diseases (or can be referred to as climate-sensitive zoonotic diseases) is confirmed. Yet, research on the association between brucellosis and climate is limited. We aim to understand the impact of meteorological factors on the risk of brucellosis, especially in northeastern China.
Monthly incidence data for brucellosis from 2005 to 2019 in Jilin province was obtained from the China Information System for Disease Control and Prevention (CDC). Monthly meteorological data (average temperature (°C), wind velocity (m/s), relative humidity (%), sunshine hours (h), air pressure (hPa), and rainfall (mm)) in Jilin province, China, from 2005 to 2019 were collected from the China Meteorological Information Center (http://data.cma.cn/). The Spearman's correlation was used to choose among the several meteorological variables. A distributed lag non-linear model (DLNM) was used to estimate the lag and non-linearity effect of meteorological factors on the risk of brucellosis.
A total of 24,921 cases of human brucellosis were reported in Jilin province from 2005 to 2019, with the peak epidemic period from April to June. Low temperature and low sunshine hours were protective factors for the brucellosis, where the minimum RR values were 0.50 (95 % CI = 0.31-0.82) for -13.7 °C with 1 month lag and 0.61 (95 % CI = 0.41-0.91) for 110.5h with 2 months lag, respectively. High temperature, high sunshine hours, and low wind velocity were risk factors for brucellosis. The maximum RR values were 2.91 (95 % CI = 1.43-5.92, lag = 1, 25.7 °C), 1.85 (95 % CI = 1.23-2.80, lag = 2, 332.6h), and 1.68 (95 % CI = 1.25-2.26, lag = 2, 1.4 m/s). The trends in the impact of extreme temperature and extreme sunshine hours on the transmission of brucellosis were generally consistent.
High temperature, high sunshine hours, and low wind velocity are more conducive to the transmission of brucellosis with an obvious lag effect. The results will deepen the understanding of the relationship between climate and brucellosis and provide a reference for formulating relevant public health policies.
气候对人畜共患传染病（或可称为气候敏感型人畜共患疾病）的影响已得到证实。然而，关于布鲁氏菌病与气候之间关联的研究有限。我们旨在了解气象因素对布鲁氏菌病风险的影响，尤其是在中国东北地区。
从中国疾病预防控制信息系统获取2005年至2019年吉林省布鲁氏菌病的月发病率数据。从中国气象数据网（http://data.cma.cn/）收集2005年至2019年中国吉林省的月气象数据（平均温度（℃）、风速（米/秒）、相对湿度（%）、日照时数（小时）、气压（百帕）和降雨量（毫米））。使用Spearman相关性在多个气象变量中进行选择。采用分布滞后非线性模型（DLNM）来估计气象因素对布鲁氏菌病风险的滞后和非线性效应。
2005年至2019年吉林省共报告24921例人间布鲁氏菌病病例，流行高峰期为4月至6月。低温和低日照时数是布鲁氏菌病的保护因素，其中，滞后1个月时，-13.7℃对应的最小相对风险（RR）值为0.50（95%置信区间=0.31-0.82）；滞后2个月时，110.5小时对应的最小RR值为0.61（95%置信区间=0.41-0.91）。高温、高日照时数和低风速是布鲁氏菌病的危险因素。最大RR值分别为2.91（95%置信区间=1.43-5.92，滞后1个月，25.7℃）、1.85（95%置信区间=1.23-2.80，滞后2个月，332.6小时）和1.68（95%置信区间=1.25-2.26，滞后2个月，1.4米/秒）。极端温度和极端日照时数对布鲁氏菌病传播的影响趋势总体一致。
高温、高日照时数和低风速更有利于布鲁氏菌病的传播，且具有明显的滞后效应。研究结果将加深对气候与布鲁氏菌病关系的理解，并为制定相关公共卫生政策提供参考。
