Schistosomiasis Surveillance — China, 2015−2018

Demographics and Clinical Characteristics of Patients with Neurocysticercosis: A Retrospective Study from Dali, China

Background

Neurocysticercosis (NCC), a predominant parasitic disease that affects the central nervous system and presents with diverse clinical manifestations, is a major contributor to acquired epilepsy worldwide, particularly in low-, middle-, and upper middle-income nations, such as China. In China, the Yunnan Province bears a significant burden of this disease.

Objective

To describe the demographic, clinical, and radiological features as well as serum and cerebrospinal fluid antibodies to cysticercus in patients with NCC from Dali, Yunnan Province, China.

Materials and Methods

This retrospective study included patients who were diagnosed with NCC at The First Affiliated Hospital of Dali University between January 2018 and May 2023 and were residing in Dali, Yunnan Province, China.

Results

A total of 552 patients with NCC were included, of which 33.3% belonged to Bai ethnicity. The clinical presentation of NCC exhibited variability that was influenced by factors such as the number, location, and stage of the parasites. Epilepsy/seizure (49.9%) was the most prevalent symptom, with higher occurrence in the degenerative stage of cysts (P < 0.001). Compared with other locations, cysticerci located in the brain parenchyma are more likely to lead to seizures/epilepsy (OR = 17.45, 95% CI: 7.96-38.25) and headaches (OR = 3.02, 95% CI: 1.23-7.41). Seizures/epilepsy are more likely in patients with cysts in the vesicular (OR = 2.71, 95% CI: 1.12-6.61) and degenerative (OR = 102.38, 95% CI: 28.36-369.60) stages than those in the calcified stage. Seizures was not dependent on the number of lesions. All NCC patients underwent anthelminthic therapy, with the majority receiving albendazole (79.7%).

Conclusion

This study provides valuable clinical insights into NCC patients in Dali and underscores the significance of NCC as a leading preventable cause of epilepsy.

Three Gorges Dam: Potential differential drivers and trend in the spatio-temporal evolution of the change in snail density based on a Bayesian spatial-temporal model and 5-year longitudinal study

BACKGROUND

Snail abundance varies spatially and temporally. Few studies have elucidated the different effects of the determinants affecting snail density between upstream and downstream areas of the Three Gorges Dam (TGD). We therefore investigated the differential drivers of changes in snail density in these areas, as well as the spatial-temporal effects of these changes.

METHODS

A snail survey was conducted at 200 sites over a 5-year period to monitor dynamic changes in snail abundance within the Yangtze River basin. Data on corresponding variables that might affect snail abundance, such as meteorology, vegetation, terrain and economy, were collected from multiple data sources. A Bayesian spatial-temporal modeling framework was constructed to explore the differential determinants driving the change in snail density and the spatial-temporal effects of the change.

RESULTS

Volatility in snail density was unambiguously detected in the downstream area of the TGD, while a small increment in volatility was detected in the upstream area. Regarding the downstream area of the TGD, snail density was positively associated with the average minimum temperature in January of the same year, the annual Normalized Difference Vegetation Index (NDVI) of the previous year and the second, third and fourth quartile, respectively, of average annual relative humidity of the previous year. Snail density was negatively associated with the average maximum temperature in July of the previous year and annual nighttime light of the previous year. An approximately inverted "U" curve of relative risk was detected among sites with a greater average annual ground surface temperature in the previous year. Regarding the upstream area, snail density was positively associated with NDVI and with the second, third and fourth quartile, respectively, of total precipitation of the previous year. Snail density was negatively associated with slope.

CONCLUSIONS

This study demonstrated a rebound in snail density between 2015 and 2019. In particular, temperature, humidity, vegetation and human activity were the main drivers affecting snail abundance in the downstream area of the TGD, while precipitation, slope and vegetation were the main drivers affecting snail abundance in the upstream area. These findings can assist authorities to develop and perform more precise strategies for surveys and control of snail populations.

Sentinel Surveillance of Schistosomiasis - China, 2021

Introduction

This report analyzes the national surveillance data for schistosomiasis in 2021 to understand the current status and provide evidence for further policy actions to promote elimination. This analysis is in line with the National Surveillance Plan of Schistosomiasis, which was revised in 2020 to adapt to the new stage of moving towards elimination.

Methods

Data from the 2021 national surveillance of schistosomiasis in humans, livestock, and snails were collected from 13 provincial-level administrative divisions (PLADs) and analyzed using descriptive epidemiological methodology. The antibody-positive rate and area of newly discovered and re-emergent snail habitats were calculated.

Results

In 2021, a total of 31,661 local residents and 101,558 transient population were screened for antibodies using indirect hemagglutination assay (IHA). Of those who tested positive, 745 local residents and 438 transient population underwent further parasitological examination, with only one stool-positive result in the transient population. Additionally, 12,966 livestock were examined using the miracidia hatching test, with no positives detected. The total area of newly discovered and re-emergent snail habitats was 957,702 m² and 4,381,617 m², respectively. No infected snails were found using the microscopic dissection method, but six pooled snail samples were reported as positive using the loop-mediated isothermal amplification method for detecting specific sequences of Schistosoma. japonicum, in Anhui and Jiangxi Provinces.

Conclusions

The prevalence of schistosomiasis among humans and livestock was found to be low, however, a potential transmission risk was identified in certain areas. To reduce the risk of transmission, a comprehensive control strategy should be continued and new techniques should be implemented in the surveillance and early warning system.

Elimination of Schistosomiasis Japonica in China: From the One Health Perspective

Schistosomiasis japonica is caused by infection of Schistosoma japonicum (S. japonicum), which infected 12 million residents in the 1950s in China and was a heavy burden to public health and socioeconomic development (1). After more than seven decades of effort to control schistosomiasis, the prevalence of schistosomiasis has been reduced dramatically in China. Among the 450 endemic counties (including city and district-level jurisdictions), 74.89% (337/450), 21.87% (98/450), and 3.33% (15/450) have achieved the criteria of elimination, transmission interruption, and transmission control of schistosomiasis, respectively. As the overall endemic status of schistosomiasis remains at a low level, the strategies shifted from snail control to morbidity control and then to an integrated strategy that emphasized infection source control. However, being a vector-borne and zoonotic disease, schistosomiasis japonica is intricately linked to multiple factors including biological, natural, and socioeconomic risk factors. In order to eliminate schistosomiasis earlier and more thoroughly, the One Health approach should be adopted, which focuses on solving complex health problems from a macro-level perspective of interactions among human, animal, and environment, emphasizing multi-institution, interdisciplinary, and cross-regional collaboration and communication.

Identification of the high-risk area for schistosomiasis transmission in China based on information value and machine learning: a newly data-driven modeling attempt

BACKGROUND

Schistosomiasis control is striving forward to transmission interruption and even elimination, evidence-lead control is of vital importance to eliminate the hidden dangers of schistosomiasis. This study attempts to identify high risk areas of schistosomiasis in China by using information value and machine learning.

METHODS

The local case distribution from schistosomiasis surveillance data in China between 2005 and 2019 was assessed based on 19 variables including climate, geography, and social economy. Seven models were built in three categories including information value (IV), three machine learning models [logistic regression (LR), random forest (RF), generalized boosted model (GBM)], and three coupled models (IV + LR, IV + RF, IV + GBM). Accuracy, area under the curve (AUC), and F1-score were used to evaluate the prediction performance of the models. The optimal model was selected to predict the risk distribution for schistosomiasis.

RESULTS

There is a more prone to schistosomiasis epidemic provided that paddy fields, grasslands, less than 2.5 km from the waterway, annual average temperature of 11.5-19.0 °C, annual average rainfall of 1000-1550 mm. IV + GBM had the highest prediction effect (accuracy = 0.878, AUC = 0.902, F1 = 0.920) compared with the other six models. The results of IV + GBM showed that the risk areas are mainly distributed in the coastal regions of the middle and lower reaches of the Yangtze River, the Poyang Lake region, and the Dongting Lake region. High-risk areas are primarily distributed in eastern Changde, western Yueyang, northeastern Yiyang, middle Changsha of Hunan province; southern Jiujiang, northern Nanchang, northeastern Shangrao, eastern Yichun in Jiangxi province; southern Jingzhou, southern Xiantao, middle Wuhan in Hubei province; southern Anqing, northwestern Guichi, eastern Wuhu in Anhui province; middle Meishan, northern Leshan, and the middle of Liangshan in Sichuan province.

CONCLUSIONS

The risk of schistosomiasis transmission in China still exists, with high-risk areas relatively concentrated in the coastal regions of the middle and lower reaches of the Yangtze River. Coupled models of IV and machine learning provide for effective analysis and prediction, forming a scientific basis for evidence-lead surveillance and control.