Pandji Wibawa Dhewantara1, Wenbiao Hu2, Wenyi Zhang3, Wen-Wu Yin4, Fan Ding5, Abdullah Al Mamun6, Ricardo J Soares Magalhães7. 1. UQ Spatial Epidemiology Laboratory, School of Veterinary Science, The University of Queensland, Gatton, QLD, 4343, Australia; Pangandaran Unit of Health Research and Development, National Institute of Health Research and Development (NIHRD), Ministry of Health of Indonesia, West Java, 46396, Indonesia. Electronic address: p.dhewantara@uq.edu.au. 2. School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia. Electronic address: w2.hu@qut.edu.au. 3. Center for Disease Control and Prevention of PLA, Beijing, 100071, People's Republic of China. Electronic address: zwy0419@126.com. 4. Chinese Center for Disease Control and Prevention, Beijing, 102206, People's Republic of China. Electronic address: yinww@chinacdc.cn. 5. Chinese Center for Disease Control and Prevention, Beijing, 102206, People's Republic of China. Electronic address: dingfan@chinacdc.cn. 6. Institute for Social Science Research, The University of Queensland, Indooroopilly, QLD, 4068, Australia. Electronic address: mamun@sph.uq.edu.au. 7. School of Veterinary Science, The University of Queensland, Gatton, Queensland, 4343, Australia; Children's Health and Environment Program, Child Health Research Centre, The University of Queensland, South Brisbane, QLD, 4101, Australia. Electronic address: r.magalhaes@uq.edu.au.
Abstract
BACKGROUND: In the past three decades, the incidence rate of notified leptospirosis cases in China have steeply declined and are now circumscribed to discrete areas in the country. Previous research showed that climate and environmental variation may play an important role in leptospirosis transmission. However, quantitative associations between climate, environmental factors and leptospirosis in the high-risk areas in China, is still poorly understood. OBJECTIVE: To quantify the temporal effects of climate and remotely-sensed physical environmental factors on human leptospirosis in the high-risk counties in China. METHODS: Time series seasonal decomposition was performed to explore the seasonality pattern of leptospirosis incidence in Mengla County, Yunnan and Yilong County, Sichuan for the period 2006-2016. Time series cross-correlation analysis was carried out to examine lagged effects of rainfall, relative humidity, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI) and land surface temperature (LST) on leptospirosis. The associations of climatic and physical environment factors with leptospirosis in each county were assessed by using a generalized linear regression model with negative binomial link, adjusted by seasonal components. RESULTS: Leptospirosis incidence in both counties showed strong and unique annual seasonality. Our results show that in Mengla County leptospirosis notifications exhibits a bi-modal temporal pattern while in Yilong County it follows a typical single epidemic curve. After adjusting for seasonality, the final best-fitting model for Mengla County indicated that leptospirosis notifications were significantly associated with present LST values (incidence rate ratio, IRR = 0.857, 95% confidence interval (CI):0.729-0.929) and rainfall at a lag of 6-months (IRR = 0.989; 95% CI: 0.985-0.993). The incidence of leptospirosis in Yilong was associated with rainfall at 1-month lag (IRR = 1.013, 95% CI: 1.003-1.023), LST (3-months lag) (IRR = 1.193, 95% CI: 1.095-1.301), and MNDWI (5-months lag) (IRR = 7.960, 95% CI: 1.241-47.66). CONCLUSIONS: Our study identified lagged effects between leptospirosis incidence and climate and remotely-sensed environmental factors in the two most endemic counties in China. Rainfall in combination with satellite derived physical environment factors provided better insight of the local epidemiology as well as good predictors for leptospirosis outbreak in both counties. This would also be an avenue for the development of leptospirosis early warning systems to support leptospirosis control in China.
BACKGROUND: In the past three decades, the incidence rate of notified leptospirosis cases in China have steeply declined and are now circumscribed to discrete areas in the country. Previous research showed that climate and environmental variation may play an important role in leptospirosis transmission. However, quantitative associations between climate, environmental factors and leptospirosis in the high-risk areas in China, is still poorly understood. OBJECTIVE: To quantify the temporal effects of climate and remotely-sensed physical environmental factors on humanleptospirosis in the high-risk counties in China. METHODS: Time series seasonal decomposition was performed to explore the seasonality pattern of leptospirosis incidence in Mengla County, Yunnan and Yilong County, Sichuan for the period 2006-2016. Time series cross-correlation analysis was carried out to examine lagged effects of rainfall, relative humidity, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI) and land surface temperature (LST) on leptospirosis. The associations of climatic and physical environment factors with leptospirosis in each county were assessed by using a generalized linear regression model with negative binomial link, adjusted by seasonal components. RESULTS:Leptospirosis incidence in both counties showed strong and unique annual seasonality. Our results show that in Mengla County leptospirosis notifications exhibits a bi-modal temporal pattern while in Yilong County it follows a typical single epidemic curve. After adjusting for seasonality, the final best-fitting model for Mengla County indicated that leptospirosis notifications were significantly associated with present LST values (incidence rate ratio, IRR = 0.857, 95% confidence interval (CI):0.729-0.929) and rainfall at a lag of 6-months (IRR = 0.989; 95% CI: 0.985-0.993). The incidence of leptospirosis in Yilong was associated with rainfall at 1-month lag (IRR = 1.013, 95% CI: 1.003-1.023), LST (3-months lag) (IRR = 1.193, 95% CI: 1.095-1.301), and MNDWI (5-months lag) (IRR = 7.960, 95% CI: 1.241-47.66). CONCLUSIONS: Our study identified lagged effects between leptospirosis incidence and climate and remotely-sensed environmental factors in the two most endemic counties in China. Rainfall in combination with satellite derived physical environment factors provided better insight of the local epidemiology as well as good predictors for leptospirosis outbreak in both counties. This would also be an avenue for the development of leptospirosis early warning systems to support leptospirosis control in China.