Epidemiological analysis of respiratory and intestinal infectious diseases in three counties of Sichuan: the baseline survey of Disaster Mitigation Demonstration Area in western China.

BACKGROUND: Natural disasters can indirectly induce epidemics of infectious diseases through air and water pollution, accelerated pathogen reproduction, and population migration. This study aimed to explore the epidemiological characteristics of the main infectious diseases in Sichuan, a province with a high frequency of natural disasters. METHODS: Data were collected from the local Centers for Disease Control infectious disease reports from Lu, Shifang and Yuexi counties from 2011 to 2015 and from the baseline survey of the Disaster Mitigation Demonstration Area in Western China in 2016. Principal component regression was used to explore the main influencing factors of respiratory infectious diseases (RIDs). RESULTS: The incidence rates of RIDs and intestinal infectious diseases (IIDs) in 2015 were 78.99/100,000, 125.53/100,000, 190.32/100,000 and 51.70/100,000, 206.00/100,000, 69.16/100,000 in Lu, Shifang and Yuexi respectively. The incidence rates of pulmonary tuberculosis (TB) was the highest among RIDs in the three counties. The main IIDs in Lu and Shifang were hand-foot-mouth disease (HFMD) and other infectious diarrhea; however, the main IIDs in Yuexi was bacillary dysentery. The proportions of illiterate and ethnic minorities and per capita disposable income were the top three influencing factors of RIDs. CONCLUSIONS: TB was the key point of RIDs prevention among the three counties. The key preventable IIDs in Lu and Shifang were HFMD and other infectious diarrhea, and bacillary dysentery was the major IIDs in Yuexi. The incidence rates of RIDs was associated with the population composition, the economy and personal hygiene habits.

A Rapid Public Health Needs Assessment Framework for after Major Earthquakes Using High-Resolution Satellite Imagery.

Background: Earthquakes causing significant damage have occurred frequently in China, producing enormous health losses, damage to the environment and public health issues. Timely public health response is crucial to reduce mortality and morbidity and promote overall effectiveness of rescue efforts after a major earthquake. Methods: A rapid assessment framework was established based on GIS technology and high-resolution remote sensing images. A two-step casualties and injures estimation method was developed to evaluate health loss with great rapidity. Historical data and health resources information was reviewed to evaluate the damage condition of medical resources and public health issues. Results: The casualties and injures are estimated within a few hours after an earthquake. For the Wenchuan earthquake, which killed about 96,000 people and injured about 288,000, the estimation accuracy is about 77%. 242/294 (82.3%) of the medical existing institutions were severely damaged. About 40,000 tons of safe drinking water was needed every day to ensure basic living needs. The risk of water-borne and foodborne disease, respiratory and close contact transmission disease is high. For natural foci diseases, the high-risk area of schistosomiasis was mapped in Lushan County as an example. Finally, temporary settlements for victims of earthquake were mapped. Conclusions: High resolution Earth observation technology can provide a scientific basis for public health emergency management in the major disasters field, which will be of great significance in helping policy makers effectively improve health service ability and public health emergency management in prevention and control of infectious diseases and risk assessment.

Mapping risk of leptospirosis in China using environmental and socioeconomic data.

BACKGROUND: Leptospirosis is a water-borne and widespread spirochetal zoonosis caused by pathogenic bacteria called leptospires. Human leptospirosis is an important zoonotic infectious disease with frequent outbreaks in recent years in China. Leptospirosis's emergence has been linked to many environmental and ecological drivers of disease transmission. In this paper, we identified the environmental and socioeconomic factors associated with leptospirosis in China, and predict potential risk area of leptospirosis using predictive models. METHODS: Leptospirosis incidence data were derived from the database of China's web-based infectious disease reporting system, a national surveillance network maintained by Chinese Center for Disease Control and Prevention. We built statistical relationship between occurrence of leptospirosis and nine environmental and socioeconomic risk factors using logistic regression model and Maxent model. RESULTS: Both logistic regression model and Maxent model have high performance in predicting the occurrence of leptospirosis, with AUC value of 0.95 and 0.96, respectively. Annual mean temperature (Bio1) and annual total precipitation (Bio12) are two most important variables governing the geographic distribution of leptospirosis in China. The geographic distributions of areas at risk of leptospirosis predicted from both models show high agreement. The risk areas are located mainly in seven provinces of China: Sichuan Province, Chongqing Municipality, Hunan Province, Jiangxi Province, Guangdong Province, Guangxi Province, and Hainan Province, where surveillance and control programs are urgently needed. Logistic regression model and Maxent model predicted that 403 and 464 counties are at very high risk of leptospirosis, respectively. CONCLUSIONS: Our results highlight the importance of socioeconomic and environmental variables and predictive models in identifying risk areas for leptospirosis in China. The values of Geographic Information System and predictive models were demonstrated for investigating the geographic distribution, estimating socioeconomic and environmental risk factors, and enhancing our understanding of leptospirosis in China.