PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■确认了气候对人畜共患传染病（或可称为气候敏感性人畜共患疾病）的影响。然而，关于布鲁氏菌病与气候之间关系的研究是有限的。我们旨在了解气象因素对布氏杆菌病风险的影响，尤其是在中国东北。
■吉林省2005-2019年布鲁氏菌病月发病数据来源于中国疾病预防控制信息系统（CDC）。月气象资料(平均气温(℃),风速(m/s),相对湿度(%),日照时数（h），空气压力(hPa),和降雨量（mm））在吉林省，中国,2005年至2019年从中国气象信息中心收集（http://数据。cma.cn/)。使用Spearman相关性在几个气象变量中进行选择。采用分布滞后非线性模型(DLNM)估计气象因素对布鲁氏菌病发病风险的滞后和非线性影响。
■2005-2019年吉林省共报告人布鲁氏菌病24921例，4-6月为流行高峰期。低温和低日照时数是布鲁氏菌病的保护因素,其中-13.7°C滞后1个月的最小RR值为0.50（95％CI=0.31-0.82），110.5h滞后2个月的最小RR值为0.61（95％CI=0.41-0.91），分别。高温,高日照时间，低风速是布鲁氏菌病的危险因素。最大RR值为2.91（95％CI=1.43-5.92，滞后=1，25.7°C），1.85（95%CI=1.23-2.80，滞后=2，332.6h），和1.68（95%CI=1.25-2.26，滞后=2，1.4m/s）。极端温度和极端日照时数对布氏杆菌病传播的影响趋势基本一致。
■高温，高日照时间，和低风速更有利于布鲁氏菌病的传播，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认识,为制定相关公共卫生政策提供参考。
■吉林省2005-2019年布鲁氏菌病月发病数据来源于中国疾病预防控制信息系统（CDC）。月气象资料(平均气温(℃),风速(m/s),相对湿度(%),日照时数（h），空气压力(hPa),和降雨量（mm））在吉林省，中国,2005年至2019年从中国气象信息中心收集（http://数据。cma.cn/)。使用Spearman相关性在几个气象变量中进行选择。采用分布滞后非线性模型(DLNM)估计气象因素对布鲁氏菌病发病风险的滞后和非线性影响。
■2005-2019年吉林省共报告人布鲁氏菌病24921例，4-6月为流行高峰期。低温和低日照时数是布鲁氏菌病的保护因素,其中-13.7°C滞后1个月的最小RR值为0.50（95％CI=0.31-0.82），110.5h滞后2个月的最小RR值为0.61（95％CI=0.41-0.91），分别。高温,高日照时间，低风速是布鲁氏菌病的危险因素。最大RR值为2.91（95％CI=1.43-5.92，滞后=1，25.7°C），1.85（95%CI=1.23-2.80，滞后=2，332.6h），和1.68（95%CI=1.25-2.26，滞后=2，1.4m/s）。极端温度和极端日照时数对布氏杆菌病传播的影响趋势基本一致。
■高温，高日照时间，和低风速更有利于布鲁氏菌病的传播，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认识,为制定相关公共卫生政策提供参考。
