Epidemiological Characteristics And Transmission Risk Prediction Of Severe Fever With Thrombocytopenia Syndrome

Background: Emerging infectious diseases(EID) refer to the newly discovered diseases in the world which cause regional or international public health problem in the past 40 years. A series of breaking the original ecological balance changes including the global destruction of natural environment, climate warming, population growth, socio-economic development, and the frequent international communication, combined with the progress of modern detective techniques, greatly increased the kinds of emerging infectious diseases and number of patients. According to statistics, the world has discovered more than 40 types of emerging infectious diseases since the middle of 1970 s, such as Ebola, Nipah virus encephalitis, AIDS, West Nile disease, Mad cow disease, Severe acute respiratory syndrome(SARS), Human infection with avian influenza H5N1, Fever with thrombocytopenia syndrome(SFTS), and Human infection with avian influenza H7N9, which led to a serious threat to human health. At the same time, these EIDs with diversified transmission, various and complex ways of infection, strong transmissive capacity, and many zoonotic diseases are very vulnerable to cross-regionally, cross-borderly, cross-continently and even globally spread. EIDs have attracted more and more global attention, and became serious global public health problems.China has vast territory, multiple heat zones including tropical, subtropical and temperate from south to north, and various species of animals and plants, lead to constantly emergering vector-borne diseases. During recent years, with the development of afforestation, returning farmland to forest and grassland and implementing other policies, the ecological environment has been improved greatly. Meanwhile, with the economic development more and more people choose outdoor tourism. All this changes lead to the number of emerging vector-borne infectious diseases and the frequency increasing significantly. In the face of the serious situation, we need the joint efforts of the whole society to contribute for the human health. On the basis of eatablishing the real-time monitoring systems and developing the pathogen- detective techniques, we should analysis the data effectivly, and improve the efficiency of the monitoring and reporting system. At the same time, we also need to integrate different discipline resources, expand interdisciplinary collaborative research, combine environmental science, ecology, epidemiology, clinical medicine and modern spatial information science into early warning and rapid responsing systems on basis of multi-factor disease statistical models to improve rapid detection of outbreaks, assessm the epidemic accuratly, disposit dimensional prevention and control capacity of epidemic disease reasonablly, and protect people’s life and property for the best.Severe fever with thrombocytopenia syndrome(SFTS) is an emerging tick-borne zoonosis discovered in middle-eastern China. The disease usually presents as fever(>38 ℃), thrombocytopenia, and leukocytopenia, diarrhea, nausea, vomiting, and multi-organ dysfunction syndrome proceeds in some severe patients. The average case fatality rate is approximately 12% but could be as high as 30% for some regions. In 2009, the causative agent was identified as a novel bunyavirus in the genus of phlebovirus, family Bunyaviridae, and designated as the SFTS virus(SFTSV). Ticks have been considered to be the most likely vector. Immediately after noticing the epidemic, the Chinese Ministry of Health initiated a national surveillance program, and formulated “National Guideline for Prevention and Control of Severe Fever with Thrombocytopenia Syndrome(2010 edition)” to guide SFTS prevention, detection and diagnosis and treatment. As of December 2013, SFTS cases had been reported in 14 provinces of China, and epidemic areas are seemingly expanding. Furthermore, SFTS has also been recognized in Japan and South Korea in 2012, and a disease similar to SFTS has been reported in the United States. As an emerging zoonosis, many studies about etiology, clinical diagnosis and treatment had been done, but the epidemiological characteristics and environmental factors in human infection with SFTSV remains unclear. It is important to carry out researches on nationwide epidemiological features combined with field investigation in the severely SFTS-afflicted region, understand the epidemic characteristics of the disease, determine the high risk population, the high risk areas and high risk seasons, and assess the environmental risk factors as well as the transmission of risks to raise the public awareness of the disease, and promote the targeted SFTS prevention and control measures.Objectives: ①To understand the epidemiology characteristics of SFTS in China, including demographic features, temporal distribution feature, spatial distribution feature, and geographic differences. ②To identify the and the spatial-temporal clusters of SFTS in China, and summary the typical environmental landscape in the hotspots. ③To quantitatively evaluate the associations between prevalence of SFTS and the natural environment, climate, social economy, livestock density and tick distribution data. ④To establish risk assessment model and predict risk areas for SFTSV human infections in China.Methods:(1).A field epidemiological survey was conducted to study the epidemiology features of SFTS in Xinyang City, the most severely SFTS-afflicted region in China from 2011 to 2012, and a Poisson regression was applied to explore the associations between SFTS incidence and environmental factors at the township level.(2).Reported SFTS cases in China from January 1, 2010 to December 31, 2013 were obtained from the China Information System for Diseases Control and Prevention(CISDCP), and data concerning demographics, climate, natural environment, landcover, livestock density and tick distribution data were also collected to construct a comprehensively spatial database of SFTS.(3).Multivariate mathematical statistics and modern spatial information technology were combined to describe and analysis the epidemiology features of SFTS in the whole China.(4).We performed the spatial scan statistic to identify spatial-temporal clusters of SFTS in China from 2010 to 2013, and the typical environmental landscape in the clusters was aslo summaried from the field survey and related literature.(5).A boosted regression trees(BRT) model coupled with the maximum entropy method was applied at the county level to assess the risk factors associated with the occurrence of SFTS, then a risk map of human SFTSV infections was created in China.(6).The software used in the study including Microsoft office 2010,Arc GIS 9.2,ENVI 4.8,STATA 10.0,Sa TScan 9.0,R Language 3.1.1, etc.Results:(1).As the most severely SFTS-afflicted region in China, a total of 504 laboratory-confirmed SFTS cases were reported in Xinyang City from 2011 to 2012 which accounting for 98.0% of SFTS cases in Henan Province. The patients’ ages ranged from 7 to 87 years(median 61 years) old, and the age distribution demonstrated that the annual incidence increased with age(χ2 test for trend, P < 0.001).The female-to-male ratio of cases was 1.58. Overwhelming majority of confirmed cases lived in rural areas, and 97.0%(489/504) of the cases were farmers being engaged in agriculture activities.The annual incidence tremendously varied from township to township ranging from 0 to 64.9 per 100,000 people, with an average of 4.2/100,000 people in the study site. The top five incidences were 64.94, 56.93, 43.32, 38.84, and 38.00 per 100,000 persons in the townships of Gaoliangdian, Wanggang, Guanmiao, Hefengqiao, and Yanghe, respectively. All of them occurred during March to November, with epidemic peaking from May to July(71.6%, 361/504). In 2011, case number peaked in July(32.1%, 62/193), while in 2012, the peak occurred in May(37.9%, 118/311). This period is local tea-picking activity, which was performed mostly by elderly women from May to July when H. longicornis is highly active in this region. The multivariate Poisson regression analysis revealed that the spatial variations of SFTS incidence were significantly associated with the shrub, forest, and rain-fed cropland areas. SFTS incidence was found to be positively associated with the proportion of shrub and forest. The association between SFTS incidence and proportion of rainfed cropland showed an inverted-U pattern relationship. With the rainfed cropland proportion increasing, SFTS incidence rose to the peak and then dropped.(2).Between 2010 and 2013, a total of 1,768 laboratory-confirmed SFTS cases were reported in China. During this 4 years period, the annual number of SFTS cases steadily increased from 53 cases(in 2010) to 676 cases(in 2013) per year. The national SFTS epidemic curve revealed significant seasonality, with 67.2% cases occurring between May and July. The median age of the patients was 61 years(range, 1-93), and the majority of cases were 50 years of age or older(1,494, 84.5%). The incidences increased from younger to older age groups(χ2 test for trend, P = 0.017, 0.020, respectively), but no sex difference was observed with a female-to-male ratio of 1.06:1. Most cases were farmers or forest workers(94.4%), who lived in rural areas and engaged in the agricultural activities. Of the 1,768 confirmed SFTS cases, 145 deaths were confirmed to be associated with SFTS, with an overall fatality rate of 8.2%. Annual case fatality rates exhibited a downward trend from 15.1% to 7.1% from 2010 to 2013. Among the affected provinces, the sex ratio in SFTS cases was dramatically different between Henan Province and all other provinces, with a female-to-male ratio of 1.62(425/263) in the former, and 0.82(485/595) in all other provinces combined(p-value<0.001). Liaoning Province had the lowest ratio of 0.71(50/70). Also in Henan Province, the CFR was 2.8%, significantly lower than other provinces which ranged from 8.7 to 12.3%. In addition, the SFTS epidemic peaked later in provinces with higher latitudes. From south to north, the peak months were May to July in Henan, Anhui and Hubei provinces combined(most cases appeared in the junction region of the three provinces), between June and July in Shandong Province, and between July and August in Liaoning Province.(3).The spatial-temporal cluster analysis identified three clusters encompassing 59 counties mainly in the middle-eastern China, where 2.9% of the national total population accounted for 69.1% of SFTS cases in China. The primary cluster(cluster 1) was located at the junction of Henan, Hubei and Anhui provinces including 17 counties. This cluster had a 152.9 relative risk(RR) value(p<0.001), and spanned from April 2011 to October 2013. Cluster 2 was located in 18 counties of Jiaodong peninsula in Shandong Province with a RR of 30.0(p<0.001), and emerged between May 2010 and November 2013. Cluster 3 was located in the central of Shandong Province. It included 24 counties with a RR of 12.4(p<0.001), occurring between May 2011 and November 2013. Spearman correlation analyses within the three clusters showed that climate factors(Temperature,rainfall, relative humidity and sunshine hours)were significantly associated with the temporal variation in SFTS incidence.(4). Based on the BRT model, the occurrence of SFTSV human infection was found to be significantly associated with eight predictors: temperature, rainfall, relative humidity, sunshine hours, elevation, distribution of H. longicornis ticks, cattle density, and coverage of forest, all with average BRT weights > 5.0%. The model-fitted risks were plotted versus each predictor showing non-linear relationships between the predictors and the risk of SFTS. The predicted risk of SFTS occurrence first increased significantly and then plateaued in response to increase in rainfall, sunshine hours, cattle density or percentage coverage of forest. With increasing temperature and relative humidity, the predicted risk also increased first, but dropped dramatically passing the peak. A strong negative relationship was found for elevation, with the highest risk corresponding to low elevations of 100~400m, and then a declining trend for higher elevations. In addition, a relative higher risk was found in areas where H. longicornis ticks had been previously identified. The estimated AUC value of 0.985(95% CI 0.975-0.994) indicated an excellent predictive accuracy of the model. On the basis of the average predicted probabilities, a risk map for SFTSV human infections in China was created at the county level. The high risk areas cover a wide range of middle-eastern China, overlapping very well with the observed SFTS cases. Furthermore, some areas in Anhui, Zhejiang, Jiangsu, Liaoning and Jilin provinces may also see SFTS cases emerging.Conclusion: Based on the modern spatial information technology, this study mainly focused on the epidemiology of SFTS, an emerging tick borne disease. We provided a comprehensive epidemiological description and geographic difference of human SFTSV infections in Xinyang City and China by using the field epidemiological survey data and the national surveillance data, respectively. Using the spatial-temporal cluster analysis, the study identified the spatial-temporal clusters of SFTS in China, and assessed the relationships between climate factors and the temporal variation in SFTS incidence. Poisson regression model and BRT model evaluated the environmental factors of SFTS in Xinyang City and the high risk areas for SFTSV human infections in China, respectively.