Assessment of the national schistosomiasis control program in a typical region along the Yangtze River, China

Precision mapping of snail habitat in lake and marshland areas: Integrating environmental and textural indicators using Random Forest modeling

Schistosomiasis japonica continues to pose a significant public health challenge in China, primarily due to the widespread distribution of Oncomelania hupensis, the sole intermediate host of Schistosoma. This study aims to address the constraints of existing remote sensing analyses for identifying snail habitats, which frequently neglect spatial scale and seasonal variations. To this end, we adopt a multi-source data-driven Random Forest approach that integrates bottomland and ground-surface texture data with traditional environmental variables, enhancing the accuracy of snail habitat assessments. We developed four distinct models for the lake and marshland areas of Guichi, China: a baseline model incorporating ground-surface texture, bottomland variables, and environmental variables; Model 1 with only environmental variables; Model 2 adding ground-surface texture and environmental variables; and Model 3 integrating bottomland with environmental variables. The baseline model outperformed the others, achieving a true skill statistic of 0.93, an accuracy of 0.97, a kappa statistic of 0.94, and an area under the curve of 0.99. Our analysis pinpointed critical high-risk snail habitats distributed in a belt-like pattern along major water bodies, near the Yangtze River, QiuPu River, and around Shengjin Lake, Jiuhua River, and Qingtong River. These insights can aid local health authorities in more efficiently allocating limited resources, developing effective snail surveillance and control strategies to combat schistosomiasis. Additionally, this approach can be adapted to localize other endemic hosts with similar ecological characteristics.

Mapping schistosomiasis risk in Southeast Asia: a systematic review and geospatial analysis

BACKGROUND

Schistosomiasis is a water-borne parasitic disease estimated to have infected >140 million people globally in 2019, mostly in sub-Saharan Africa. Within the goal of eliminating schistosomiasis as a public health problem by 2030 in the World Health Organization (WHO) Roadmap for neglected tropical diseases, other regions cannot be neglected. Empirical estimates of the disease burden in Southeast Asia largely remain unavailable.

METHODS

We undertook a systematic review to identify empirical survey data on schistosomiasis prevalence in Southeast Asia using the Web of Science, ScienceDirect, PubMed and the Global Atlas of Helminth Infections, from inception to 5 February 2021. We then conducted advanced Bayesian geostatistical analysis to assess the geographical distribution of infection risk at a high spatial resolution (5 × 5 km) using the prevalence, number of infected individuals and doses needed for preventive chemotherapy.

RESULTS

We identified 494 Schistosoma japonicum surveys in the Philippines and Indonesia, and 285 in Cambodia and Laos for S. mekongi. The latest estimates suggest that 225 [95% credible interval (CrI): 168-285] thousand in the endemic areas of Southeast Asian population were infected in 2018. The highest prevalence of schistosomiasis was 3.86% (95% CrI: 3.40-4.31) in Laos whereas the lowest was 0.29% in Cambodia (95% CrI: 0.22-0.36). The estimated number of praziquantel doses needed per year was 1.99 million (95% CrI: 1.92-2.03 million) for the entire population in endemic areas of Southeast Asia.

CONCLUSIONS

The burden of schistosomiasis remains far from the WHO goal and our estimates highlighted areas to target with strengthened interventions against schistosomiasis.

A comparison of modelling the spatio-temporal pattern of disease: a case study of schistosomiasis japonica in Anhui Province, China

The construction of spatio-temporal models can be either descriptive or dynamic. In this study we aim to evaluate the differences in model fitting between a descriptive model and a dynamic model of the transmission for intestinal schistosomiasis caused by Schistosoma japonicum in Guichi, Anhui Province, China. The parasitological data at the village level from 1991 to 2014 were obtained by cross-sectional surveys. We used the fixed rank kriging (FRK) model, a descriptive model, and the integro-differential equation (IDE) model, a dynamic model, to explore the space-time changes of schistosomiasis japonica. In both models, the average daily precipitation and the normalized difference vegetation index are significantly positively associated with schistosomiasis japonica prevalence, while the distance to water bodies, the hours of daylight and the land surface temperature at daytime were significantly negatively associated. The overall root mean square prediction error of the IDE and FRK models was 0.0035 and 0.0054, respectively, and the correlation reflected by Pearson's correlation coefficient between the predicted and observed values for the IDE model (0.71; p<0.01) was larger than that for the FRK model (0.53; p=0.02). The IDE model fits better in capturing the geographic variation of schistosomiasis japonica. Dynamic spatio-temporal models have the advantage of quantifying the process of disease transmission and may provide more accurate predictions.

Bayesian maximum entropy-based prediction of the spatiotemporal risk of schistosomiasis in Anhui Province, China

Background

“Schistosomiasis” is a highly recurrent parasitic disease that affects a wide range of areas and a large number of people worldwide. In China, schistosomiasis has seriously affected the life and safety of the people and restricted the economic development. Schistosomiasis is mainly distributed along the Yangtze River and in southern China. Anhui Province is located in the Yangtze River Basin of China, with dense water system, frequent floods and widespread distribution of Oncomelania hupensis that is the only intermediate host of schistosomiasis, a large number of cattle, sheep and other livestock, which makes it difficult to control schistosomiasis. It is of great significance to monitor and analyze spatiotemporal risk of schistosomiasis in Anhui Province, China. We compared and analyzed the optimal spatiotemporal interpolation model based on the data of schistosomiasis in Anhui Province, China and the spatiotemporal pattern of schistosomiasis risk was analyzed.

Methods

In this study, the root-mean-square-error (RMSE) and absolute residual (AR) indicators were used to compare the accuracy of Bayesian maximum entropy (BME), spatiotemporal Kriging (STKriging) and geographical and temporal weighted regression (GTWR) models for predicting the spatiotemporal risk of schistosomiasis in Anhui Province, China.

Results

The results showed that (1) daytime land surface temperature, mean minimum temperature, normalized difference vegetation index, soil moisture, soil bulk density and urbanization were significant factors affecting the risk of schistosomiasis; (2) the spatiotemporal distribution trends of schistosomiasis predicted by the three methods were basically consistent with the actual trends, but the prediction accuracy of BME was higher than that of STKriging and GTWR, indicating that BME predicted the prevalence of schistosomiasis more accurately; and (3) schistosomiasis in Anhui Province had a spatial autocorrelation within 20 km and a temporal correlation within 10 years when applying the optimal model BME.

Conclusions

This study suggests that BME exhibited the highest interpolation accuracy among the three spatiotemporal interpolation methods, which could enhance the risk prediction model of infectious diseases thereby providing scientific support for government decision making.

Dynamic evolution of schistosomiasis distribution under different control strategies: Results from surveillance covering 1991-2014 in Guichi, China

Background

Since the founding of the China, the Chinese government, depending on the changing epidemiological situations over time, adopted different strategies to continue the progress towards elimination of schistosomiasis in the country. Although the changing pattern of schistosomiasis distribution in both time and space is well known and has been confirmed by numerous studies, the problem of how these patterns evolve under different control strategies is far from being understood. The purpose of this study is, therefore, to investigate the spatio-temporal change of the distribution of schistosomiasis with special reference to how these patterns evolve under different control strategies.

Methodology / Principal findings

Parasitological data at the village level were obtained through access to repeated cross-sectional surveys carried out during 1991–2014 in Guichi, a rural district along the Yangtze River in Anhui Province, China. A hierarchical dynamic spatio-temporal model was used to evaluate the evolving pattern of schistosomiasis prevalence, which accounted for mechanism of dynamics of the disease. Descriptive analysis indicates that schistosomiasis prevalence displayed fluctuating high-risk foci during implementation of the chemotherapy-based strategy (1991–2005), while it took on a homogenous pattern of decreasing magnitude in the following period when the integrated strategy was implemented (2006–2014). The dynamic model analysis showed that regularly global propagation of the disease was not present after the effect of proximity to river was taken into account but local pattern transition existed. Maps of predicted prevalence shows that relatively high prevalence (>4%) occasionally occurred before 2006 and prevalence presents a homogenous and decreasing trend over the study area afterwards.

Conclusions

Proximity to river is still an important determinant for schistosomiasis infection regardless of different types of implemented prevention and control strategies. Between the transition from the chemotherapy-based strategy to the integrated one, we noticed a decreased prevalence. However, schistosomiasis would remain an endemic challenge in these study areas. Further prevention and control countermeasures are warranted.

Schistosomiasis japonica is one of the most serious parasitic diseases in China. The Chinese government has launched three different rounds of national schistosomiasis control programs since 1950s. The latest two are the World Bank Loan Project (WBLP) that ushered in chemotherapy as the main control approach, active from 1992 to 2001, and the integrated control strategy that took its place in 2005. In this study, we investigated changes in the dynamics of schistosomiasis transmission over space and time under these different control strategies. Based on spatio-temporal analyses of the schistosomiasis prevalence data at the village level during 1991–2014 in Guichi, Anhui Province, we built a dynamic model to evaluate the evolving pattern of prevalence. We found that the schistosomiasis prevalence generally showed a north-western shift over the study area during 1991–2005, while there was no such trend during 2006–2014. This global shifting trend disappeared after the effect of proximity to river was taken into account, but local change still existed which was possibly due to the transition between the two latest national control strategies. We conclude that proximity to River is still an important determinant for schistosomiasis prevalence and that although the integrated control strategy is more effective than the WBPL in reducing schistosomiasis prevalence, the disease would remain endemic for the long term without further improvements of the control program.

Polarization of intestinal tumour-associated macrophages regulates the development of schistosomal colorectal cancer

Tumour-associated macrophages (TAMs) can be divided into M1 and M2 TAMs. M2 TAMs play an important role in tumor progression, promoting a pro-angiogenic and immunosuppressive signal in the tumor. Previous studies have shown a correlation between schistosomiasis and colorectal cancer (CRC), but the specific mechanism has not been clarified. The differences between schistosomal CRC and non-schistosomal CRC were explored by analysing the clinicopathological data and survival time prognosis of schistosomal CRC and non-schistosomal CRC patients. The underlying mechanisms leading to the differences were investigated via tissue pathology experiments. Here, we investigated whether TAMs play a role in schistosomal CRC, leading to different clinicopathological features and prognoses in schistosomal CRC and non-schistosomal CRC patients and whether TAMs have a regulatory effect on the development and prognosis of schistosomal CRC. We found that schistosomal CRC and non-schistosomal CRC patients differ in age, sex, TNM staging and prognosis survival. Applying a logistic regression analysis model, the results showed that age, sex, pathological T stage and combined schistosomiasis were independent risk factors for CRC. Prognostic analysis of follow-up patients with schistosomal CRC found that the T stage, M stage and M2 TAMs numbers were independent prognostic factors for overall survival (OS). TAMs are significantly higher in tissues of schistosomal CRC than in non-schistosomal CRC patients, especially M2 TAMs. Studies on schistosomal colorectal tissue found that the expression of M2 TAMs increased with the malignant process of intestinal tissue. In summary, schistosomal CRC and non-schistosomal CRC patients have different clinicopathological features and prognosis, schistosomiasis is a risk factor for CRC and M2 TAMs are independent prognostic factors for OS.

Nomograms to predict 2-year overall survival and advanced schistosomiasis-specific survival after discharge: a competing risk analysis

Background

The prognosis of patients with advanced schistosomiasis is poor. Pre-existing prognosis studies did not differentiate the causes of the deaths. The objectives were to evaluate the 2-year overall survival (OS) and advanced schistosomiasis-specific survival (ASS) in patients with advanced schistosomiasis after discharge through competing risk analysis and to build predictive nomograms.

Methods

Data was extracted from a previously constructed database from Hubei province. Patients were enrolled from September 2014 to January 2015, with follow up to January 2017. OS and ASS were primary outcome measures. Nomograms for estimating 2-year OS and ASS rates after discharge were established based on univariate and multivariate Cox regression model and Fine and Gray’s model. Their predictive performances were evaluated using C-index and validated in both internal and external validation cohorts.

Results

The training cohort included 1487 patients with advanced schistosomiasis. Two-year mortality rate of the training cohort was 8.27% (123/1487). Competing events accounted for 26.83% (33/123). Older age, splemomegaly clinical classification, abnormal serum DBil, AST, ALP and positive HBsAg were significantly associated with 2-year OS. Older age, splemomegaly clinical classification, abnormal serum AST, ALP and positive HBsAg were significantly associated with 2-year ASS. The established nomograms were well calibrated, and had good discriminative ability, with a C-index of 0.813 (95% CI 0.803–0.823) for 2-year OS prediction and 0.834 (95% CI 0.824–0.844) for 2-year ASS prediction. Their predictive performances were well validated in both internal and external validation cohorts.

Conclusion

The effective predictors of 2-year OS and ASS were discovered through competing risk analysis. The nomograms could be used as convenient predictive tools in clinical practice to guide follow-up and aid accurate prognostic assessment.

Risk prediction of two types of potential snail habitats in Anhui Province of China: Model-based approaches

Elimination of the intermediate snail host of Schistosoma is the most effective way to control schistosomiasis and the most important first step is to accurately identify the snail habitats. Due to the substantial resources required for traditional, manual snail-searching in the field, and potential risk of miss-classification of potential snail habitats by remote sensing, more convenient and precise methods are urgently needed. Snail data (N = 15,000) from two types of snail habitats (lake/marshland and hilly areas) in Anhui Province, a typical endemic area for schistosomiasis, were collected together with 36 environmental variables covering the whole province. Twelve different models were built and evaluated with indices, such as area under the curve (AUC), Kappa, percent correctly classified (PCC), sensitivity and specificity. We found the presence-absence models performing better than those based on presence-only. However, those derived from machine-learning, especially the random forest (RF) approach were preferable with all indices above 0.90. Distance to nearest river was found to be the most important variable for the lake/marshlands, while the climatic variables were more important for the hilly endemic areas. The predicted high-risk areas for potential snail habitats of the lake/marshland type exist mainly along the Yangtze River, while those of the hilly type are dispersed in the areas south of the Yangtze River. We provide here the first comprehensive risk profile of potential snail habitats based on precise examinations revealing the true distribution and habitat type, thereby improving efficiency and accuracy of snail control including better allocation of limited health resources.

Asian Schistosomiasis: Current Status and Prospects for Control Leading to Elimination

Schistosomiasis is an infectious disease caused by helminth parasites of the genus Schistosoma. Worldwide, an estimated 250 million people are infected with these parasites with the majority of cases occurring in sub-Saharan Africa. Within Asia, three species of Schistosoma cause disease. Schistosoma japonicum is the most prevalent, followed by S. mekongi and S. malayensis. All three species are zoonotic, which causes concern for their control, as successful elimination not only requires management of the human definitive host, but also the animal reservoir hosts. With regard to Asian schistosomiasis, most of the published research has focused on S. japonicum with comparatively little attention paid to S. mekongi and even less focus on S. malayensis. In this review, we examine the three Asian schistosomes and their current status in their endemic countries: Cambodia, Lao People's Democratic Republic, Myanmar, and Thailand (S. mekongi); Malaysia (S. malayensis); and Indonesia, People's Republic of China, and the Philippines (S. japonicum). Prospects for control that could potentially lead to elimination are highlighted as these can inform researchers and disease control managers in other schistosomiasis-endemic areas, particularly in Africa and the Americas.