Zhidong Liu1, Jiahui Lao1, Ying Zhang2, Yanyu Liu1, Jing Zhang1, Hui Wang3, Baofa Jiang4. 1. Department of Epidemiology, School of Public Health, Shandong University, Jinan, Shandong Province, People's Republic of China; Shandong University Climate Change and Health Center, Jinan, Shandong Province, People's Republic of China. 2. School of Public Health, China Studies Centre, The University of Sydney, New South Wales, Australia. 3. Department of Medical Administration, Second Hospital of Shandong University, No. 247 BeiYuan Road, 250033 Jinan, Shandong Province, People's Republic of China. Electronic address: huiwang1982@163.com. 4. Department of Epidemiology, School of Public Health, Shandong University, Jinan, Shandong Province, People's Republic of China; Shandong University Climate Change and Health Center, Jinan, Shandong Province, People's Republic of China. Electronic address: bjiang@sdu.edu.cn.
Abstract
BACKGROUND: Little information about the effects of floods on typhoid fever is available in previous studies. This study aimed to examine the relationships between floods and typhoid fever and to identify the vulnerable groups in Yongzhou, China. METHODS: Weekly typhoid fever data, flood data and meteorological data during the flood season (April to September) from 2005 to 2012 were collected for this study. A Poisson generalized linear model combined with a distributed lag non-linear model was conducted to quantify the lagged and cumulative effects of floods on typhoid fever, considering the confounding effects of long-term trend, seasonality, and meteorological variables. The model was also used to calculate risk ratios of floods for weekly typhoid fever cases among various subpopulations. RESULTS: After adjusting for long-term trend, seasonality, and meteorological variables, floods were associated with an increased number of typhoid fever cases with a risk ratio of 1.46 (95% CI: 1.10-1.92) at 1-week lag and a cumulative risk ratio of 1.76 (95% CI: 1.21-2.57) at lag 0-1 weeks. Males, people aged 0-4 years old, people aged 15-64 years old, farmers, and children appeared to be more vulnerable than the others. CONCLUSIONS: Our study indicates that floods could significantly increase the risks of typhoid fever with lag effects of 1 week in the study areas. Precautionary measures should be taken with a focus on the identified vulnerable groups in order to control the transmission of typhoid fever associated with floods.
BACKGROUND: Little information about the effects of floods on typhoid fever is available in previous studies. This study aimed to examine the relationships between floods and typhoid fever and to identify the vulnerable groups in Yongzhou, China. METHODS: Weekly typhoid fever data, flood data and meteorological data during the flood season (April to September) from 2005 to 2012 were collected for this study. A Poisson generalized linear model combined with a distributed lag non-linear model was conducted to quantify the lagged and cumulative effects of floods on typhoid fever, considering the confounding effects of long-term trend, seasonality, and meteorological variables. The model was also used to calculate risk ratios of floods for weekly typhoid fever cases among various subpopulations. RESULTS: After adjusting for long-term trend, seasonality, and meteorological variables, floods were associated with an increased number of typhoid fever cases with a risk ratio of 1.46 (95% CI: 1.10-1.92) at 1-week lag and a cumulative risk ratio of 1.76 (95% CI: 1.21-2.57) at lag 0-1 weeks. Males, people aged 0-4 years old, people aged 15-64 years old, farmers, and children appeared to be more vulnerable than the others. CONCLUSIONS: Our study indicates that floods could significantly increase the risks of typhoid fever with lag effects of 1 week in the study areas. Precautionary measures should be taken with a focus on the identified vulnerable groups in order to control the transmission of typhoid fever associated with floods.