A spatial analysis of human Schistosoma japonicum infections in Hubei, China, during 2009-2014

Predicting Climate Change Impact on the Habitat Suitability of the Schistosoma Intermediate Host Oncomelania hupensis in the Yangtze River Economic Belt of China

Oncomelania hupensis is the exclusive intermediary host of Schistosoma japonicum in China. The alteration of O. hupensis habitat and population distribution directly affects the safety of millions of individuals residing in the Yangtze River Economic Belt (YREB) and the ecological stability of Yangtze River Basin. Therefore, it is crucial to analyze the influence of climate change on the distribution of O. hupensis in order to achieve accurate control over its population. This study utilized the MaxEnt model to forecast possible snail habitats by utilizing snail distribution data obtained from historical literature. The following outcomes were achieved: The primary ecological factors influencing the distribution of O. hupensis are elevation, minimum temperature of the coldest month, and precipitation of wettest month. Furthermore, future climate scenarios indicate a decrease in the distribution area and a northward shift of the distribution center for O. hupensis; specifically, those in the upstream will move northeast, while those in the midstream and downstream will move northwest. These changes in suitable habitat area, the average migration distance of distribution centers across different climate scenarios, time periods, and sub-basins within the YREB, result in uncertainty. This study offers theoretical justification for the prevention and control of O. hupensis along the YREB.

Spatiotemporal distribution of schistosomiasis transmission risk in Jiangling County, Hubei Province, P.R. China

OBJECTIVE

This study aims to explore the spatiotemporal distribution of schistosomiasis in Jiangling County, and provide insights into the precise schistosomiasis control.

METHODS

The descriptive epidemiological method and Joinpoint regression model were used to analyze the changes in infection rates of humans, livestock, snails, average density of living snails and occurrence rate of frames with snails in Jiangling County from 2005 to 2021. Spatial epidemiology methods were used to detect the spatiotemporal clustering of schistosomiasis transmission risk in Jiangling county.

RESULTS

The infection rates in humans, livestock, snails, average density of living snails and occurrence rate of frames with snails in Jiangling County decreased from 2005 to 2021 with statistically significant. The average density of living snails in Jiangling County was spatially clustered in each year, and the Moran's I varied from 0.10 to 0.26. The hot spots were mainly concentrated in some villages of Xionghe Town, Baimasi Town and Shagang Town. The mean center of the distribution of average density of living snails in Jiangling County first moved from northwest to southeast, and then returned from southeast to northwest after 2014. SDE azimuth fluctuated in the range of 111.68°-124.42°. Kernal density analysis showed that the high and medium-high risk areas of Jiangling County from 2005 to 2021 were mainly concentrated in the central and eastern of Jiangling County, and the medium-low and low risk areas were mainly distributed in the periphery of Jiangling County.

CONCLUSIONS

The epidemic situation of schistosomiasis decreased significantly in Jiangling County from 2005 to 2021, but the schistosomiasis transmission risk still had spatial clustering in some areas. After transmission interruption, targeted transmission risk intervention strategies can be adopted according to different types of schistosomiasis risk areas.

Spatial Analysis of Schistosomiasis in Hunan and Jiangxi Provinces in the People's Republic of China

Understanding the spatial distribution of schistosome infection is critical for tailoring preventive measures to control and eliminate schistosomiasis. This study used spatial analysis to determine risk factors that may impact Schistosoma japonicum infection and predict risk in Hunan and Jiangxi Provinces in the People's Republic of China. The study employed survey data collected in Hunan and Jiangxi in 2016. Independent variable data were obtained from publicly available sources. Bayesian-based geostatistics was used to build models with covariate fixed effects and spatial random effects to identify factors associated with the spatial distribution of infection. Prevalence of schistosomiasis was higher in Hunan (12.8%) than Jiangxi (2.6%). Spatial distribution of schistosomiasis varied at pixel level (0.1 × 0.1 km), and was significantly associated with distance to nearest waterbody (km, β = -1.158; 95% credible interval [CrI]: -2.104, -0.116) in Hunan and temperature (°C, β = -4.359; 95% CrI: -9.641, -0.055) in Jiangxi. The spatial distribution of schistosomiasis in Hunan and Jiangxi varied substantially and was significantly associated with distance to nearest waterbody. Prevalence of schistosomiasis decreased with increasing distance to nearest waterbody in Hunan, indicating that schistosomiasis control should target individuals in close proximity to open water sources as they are at highest risk of infection.

Efforts to eliminate schistosomiasis in Hubei province, China: 2005-2018

BACKGROUND

The Hubei province is one of the most schistosomiasis-epidemic-prone provinces in China. A series of strategies were adopted by the government to curb the rebound schistosomiasis endemic status that has prevailed since the early 2000s. This study aimed to elucidate the trends of schistosomiasis transmission and to appraise the effectiveness of the integrated control strategy in lake and marshland areas.

METHODS

Surveillance data of schistosomiasis in the Hubei province between 2005 and 2018 were analyzed, including conventional health control measures, integrated strategies, and measures that focused on the infection source. According to the local annual plan for schistosomiasis control in endemic counties, previous measures were human and snail control and surveillance. Residents aged 6-65 years were screened by an immunological detection method called indirect hemagglutination assay (IHA) after the transmission season each year. All residents who tested positive were then asked to provide a fecal sample for examination by the miracidium hatching technique (MHT) to detect the presence of schistosomes. Moreover, systematic snail surveys were conducted as a part of the combined environmental sampling method. The latter included integrated strategies and measures that focused on the infection source. Bovine stool samples were also collected and concurrently assessed using the MHT by the agriculture department, river-hardening slope protection was constructed by the water conservancy department, and forestation promotion was conducted by the forest department. The effectiveness of the integrated control strategy was assessed using two indicators of resident and livestock infection rates and three indicators of snail epidemics across all endemic areas.

RESULTS

From 2005 to 2018, a total of 28. 46 million and 2. 05 million residents were assessed by immunological (IHA) and etiological (MHT) detection techniques, respectively. Snail surveys and molluscicide application were performed in 2. 26 hectares and 0. 37 hectares, respectively. Moreover, 2. 60 million bovines were assessed by etiological detection techniques (MHT). The river-hardening slope protection project was implemented in 503 places, and 46 thousand hectares in endemic areas underwent environmental modification. Forestation was implemented at an area of 0. 15 million hectares. Between 2005 and 2018, the epidemic indicators, including resident and livestock infection rates and the infested areas and infection rate of snails, all presented downward trends. The resident infection rate decreased from 3. 78% in 2005 to 0% in 2016, which persisted through 2018. The livestock infection rate decreased from 5. 63% in 2005 to 0% in 2013, which also persisted through 2018. From 2005 to 2018, the snail-inhabited area was slightly reduced, but the area of infected snails decreased to 0 in 2012; this persisted through 2018. All counties met the goal for schistosomiasis infection control, transmission control, and disruption of schistosomiasis activity in 2008, 2013, and 2018 separately. That means the goal has been achieved in each stage.

CONCLUSIONS

The decline of the schistosomiasis epidemic rate demonstrates that the Chinese government was successful in meeting its public health goal in Hubei province. In the next decade, precision interventions must be implemented in endemic counties with a relatively low epidemic status to achieve the goals of the Outline of the Healthy China 2030 Plan. A similar strategy can be applied in other countries to eliminate schistosomiasis globally.

Spatiotemporal Heterogeneity in Human Schistosoma japonicum Infection at Village Level in Hubei Province, China

The spatiotemporal dynamics of Schistosoma japonicum, combined with temporal heterogeneity among regions of different epidemic areal-types from a microscale viewpoint might capture the local change dynamics and thus aid in optimizing the combinations of precise schistosomiasis control measures. The prevalence data on schistosomiasis infection from 2007 to 2012 in the 30 most endemic counties of Hubei Province, Central China, were appended to the village-level administrative division polygon layer. Anselin local Moran's I, a retrospective space-time scan statistic and a multilevel-growth model analysis framework, was used to investigate the spatiotemporal pattern of schistosomiasis resident infection rate (RIR) at the village level and how natural geographical environment influence the schistosomiasis RIR over time. Two spatiotemporal high-risk clusters and continuous high-rate clusters were identified mainly in the embankment region across flooding areas of lakes connected with the Yangze and Hanjiang Rivers. Moreover, 12 other clusters and outlier evolution modes were detected to be scattered across the continuous high-rate clusters. Villages located in embankment region had the highest initial values and most rapidly reduced RIRs over time, followed by villages located in marshland-and-lake regions and finally by villages located in hilly region. Moreover, initial RIR values and rates of change did significantly vary (p < 0.001 and p < 0.001, respectively) irrespective of their epidemic areal-type. These local spatiotemporal heterogeneities could contribute to the formulation of distinct control strategies based on local transmission dynamics and be applied in other endemic areas of schistosomiasis.

Spatial epidemiological approaches to inform leptospirosis surveillance and control: A systematic review and critical appraisal of methods

Leptospirosis is a global zoonotic disease that the transmission is driven by complex geographical and temporal variation in demographics, animal hosts and socioecological factors. This results in complex challenges for the identification of high-risk areas. Spatial and temporal epidemiological tools could be used to support leptospirosis control programs, but the adequacy of its application has not been evaluated. We searched literature in six databases including PubMed, Web of Science, EMBASE, Scopus, SciELO and Zoological Record to systematically review and critically assess the use of spatial and temporal analytical tools for leptospirosis and to provide general framework for its application in future studies. We reviewed 115 articles published between 1930 and October 2018 from 41 different countries. Of these, 65 (56.52%) articles were on human leptospirosis, 39 (33.91%) on animal leptospirosis and 11 (9.5%) used data from both human and animal leptospirosis. Spatial analytical (n = 106) tools were used to describe the distribution of incidence/prevalence at various geographical scales (96.5%) and to explored spatial patterns to detect clustering and hot spots (33%). A total of 51 studies modelled the relationships of various variables on the risk of human (n = 31), animal (n = 17) and both human and animal infection (n = 3). Among those modelling studies, few studies had generated spatially structured models and predictive maps of human (n = 2/31) and animal leptospirosis (n = 1/17). In addition, nine studies applied time-series analytical tools to predict leptospirosis incidence. Spatial and temporal analytical tools have been greatly utilized to improve our understanding on leptospirosis epidemiology. Yet the quality of the epidemiological data, the selection of covariates and spatial analytical techniques should be carefully considered in future studies to improve usefulness of evidence as tools to support leptospirosis control. A general framework for the application of spatial analytical tools for leptospirosis was proposed.

Dynamics of spatiotemporal distribution of schistosomiasis in Hubei Province, China

Schistosomiasis caused by parasitic flatworms of blood flukes, remains a major public health concern in China. The significant progress in controlling schistosomiasis in China over the past decades has resulted in the remarkable reduction in the prevalence and intensity of Schistosoma japonicum infection to an extremely low level. Therefore, the elimination of schistosomiasis has been promoted by the Chinese national government. Hubei Province is the major endemic area, that is, along the middle and low reaches of the Yangtze River in the lake and marshland regions of southern China. Eliminating the transmission of schistosomiasis in Hubei Province is challenging. The current issue is to determine the distributions and clusters of schistosomiasis transmission. In this study, we assessed the spatial distribution of schistosomiasis and the risk at the county level in Hubei Province from 2011 to 2015 to provide guidance on the elimination of schistosomiasis transmission in lake and marshland regions. Spatial database of human S.japonicum infection from 2011 to 2015 at the county level in the study area was built based on the annual schistosomias is surveillance data. Moran's I, the global spatial autocorrelation statistics, was utilized to describe the spatial autocorrelation of human S. japonicum infection. In addition, purely spatial scan statistics combined with space-time scan statistics were used to determine the epidemic clusters. Infection rates of S. japonicum decreased in each endemic county in Hubei from 2011 to 2015. Human S. japonicum infection rate showed statistical significance by global autocorrelation analysis during the study period (Moran's I > 0, P < 0.01). This result suggested that there were spatial clusters present in the distribution of S. japonicum infection for the five years. Purely spatial analysis of human S. japonicum infection showed one most likely cluster and one secondary cluster from 2011 to 2015, which covered four and one counties, respectively. Spatiotemporal clustering analysis determined one most likely cluster and one secondary cluster both in 2011-2012, which appeared in 4 and 5 counties, respectively. However, the number of clustering foci decreased with time, and no cluster was detected after 2013.The clustering foci were both located at the Jianghan Plain, along the middle reaches of the Yangtze River and its connecting branch Hanbei River. Spatial distribution of human S. japonicum infections did not change temporally at the county level in Hubei Province. A declining trend in spatiotemporal clustering was observed between 2011 and 2015. However, effective control strategies and integrated prevention should be continuously performed, especially at the Jianghan Plain area along the Yangtze and Hanbei River Basin. Multivariate statistical analysis was carried out to investigate the risk of missing examinations, missing treatment, and unstandardized treatment events. The results showed that age, education level and Sanitary latrines are risk factors for missing examinations (b > 0, OR >1), and treatment times in past and feeding cattle in village group are protective factors (b < 0, OR <1). We also found that age and education level are risk factors for missing treatment (b > 0, OR >1). Study of the risk for un-standardized treatment revealed that occupation is risk factors (b > 0, OR >1), though, education level is protective factors (b < 0, OR <1). Therefore, precise prevention and control should be mainly targeted at these special populations.

Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province

BACKGROUND

In order to better assist medical professionals, this study aimed to develop and compare the performance of three models-a multivariate logistic regression (LR) model, an artificial neural network (ANN) model, and a decision tree (DT) model-to predict the prognosis of patients with advanced schistosomiasis residing in the Hubei province.

METHODOLOGY/PRINCIPAL FINDINGS

Schistosomiasis surveillance data were collected from a previous study based on a Hubei population sample including 4136 advanced schistosomiasis cases. The predictive models use LR, ANN, and DT methods. From each of the three groups, 70% of the cases (2896 cases) were used as training data for the predictive models. The remaining 30% of the cases (1240 cases) were used as validation groups for performance comparisons between the three models. Prediction performance was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Univariate analysis indicated that 16 risk factors were significantly associated with a patient's outcome of prognosis. In the training group, the mean AUC was 0.8276 for LR, 0.9267 for ANN, and 0.8229 for DT. In the validation group, the mean AUC was 0.8349 for LR, 0.8318 for ANN, and 0.8148 for DT. The three models yielded similar results in terms of accuracy, sensitivity, and specificity.

CONCLUSIONS/SIGNIFICANCE

Predictive models for advanced schistosomiasis prognosis, respectively using LR, ANN and DT models were proved to be effective approaches based on our dataset. The ANN model outperformed the LR and DT models in terms of AUC.