Prevalence and spatial distribution patterns of human echinococcosis at the township level in Sichuan Province, China

Abstract

Background Echinococcosis is a global zoonotic parasitic disease caused by Echinococcus larvae. This disease is highly endemic in Sichuan Province, China. This study investigates the prevalence and spatial distribution characteristics of human echinococcosis at the township level in Sichuan Province, geared towards providing a future reference for the development of precise prevention and control strategies. Methods Human prevalence of echinococcosis was evaluated using the B-ultrasonography diagnostic method in Sichuan Province between 2016 and 2019. All data were collected, collated, and analyzed. A spatial distribution map was drawn to intuitively analyze the spatial distribution features. Eventually, the spatial autocorrelation was specified and local indicators of spatial association (LISA) clustering map was drawn to investigate the spatial aggregation of echinococcosis at the township level in Sichuan Province. Results The prevalence of echinococcosis in humans of Sichuan Province was 0.462%, among which the occurrence of cystic echinococcosis (CE) was 0.221%, while that of alveolar echinococcosis (AE) was 0.244%. Based on the results of the spatial distribution map, a predominance of echinococcosis in humans decreased gradually from west to east and from north to south. The Global Moran’s I index was 0.77 (Z = 32.07, P < 0.05), indicating that the prevalence of echinococcosis in humans was spatially clustered, exhibiting a significant spatial positive correlation. Further, the findings of local spatial autocorrelation analysis revealed that the “high–high” concentration areas were primarily located in some townships in the northwest of Sichuan Province. However, the “low–low” concentration areas were predominantly located in some townships in the southeast of Sichuan Province. Conclusions Our findings demonstrated that the prevalence of echinococcosis in humans of Sichuan Province follows a downward trend, suggesting that the current prevention and control work has achieved substantial outcomes. Nevertheless, the prevalence in humans at the township level is widely distributed and differs significantly, with a clear clustering in space. Therefore, precise prevention and control strategies should be formulated for clusters, specifically strengthening the “high–high” clusters at the township level. Graphic Abstract

A Knowledge Survey on Health Education of Echinococcosis Among Students — Ganzi Tibetan Autonomous Prefecture, Sichuan Province, China, 2020

“…Echinococcosis is a public health problem that seriously threatens human health and restricts economic development (2). It is widespread in pastoral and semi-pastoral areas in western China, of which Ganzi is an important endemic area with a prevalence of 1.05% in humans (3).…”

“…Echinococcosis is a public health problem that seriously threatens human health and restricts economic development ( 2 ). It is widespread in pastoral and semi-pastoral areas in western China, of which Ganzi is an important endemic area with a prevalence of 1.05% in humans ( 3 ). The echinococcosis prevention and control in Sichuan has experienced 4 different periods since 1980 and the basic goal of control was achieved by 2019; educational strategies matching the conditions of different times provided a key impetus in this accomplishment ( 4 ).…”

Temporal and Spatial Evolution of the African Swine Fever Epidemic in Vietnam

“…Spatial autocorrelation analysis performed in the ArcGIS software was used to detect the presence of spatial aggregation in the ASF epidemic [28,29,40]. The global Moran's I is calculated to analyze whether the ASF outbreaks demonstrate aggregation in the whole study area.…”

“…The spatiotemporal analysis method based on the disease maps of the epidemic is helpful for understanding its spread patterns, supporting the cause analysis of its spatiotemporal heterogeneity and control efforts [24][25][26]. For example, center feature analysis can visually reflect changes in the centers of disease distribution [27] and spatial autocorrelation analysis can identify whether the overall distribution of diseases is clustered or dispersed and detect the location of clusters and outliers [28,29]. Spatiotemporal scanning statistical analysis can detect high-risk clusters of disease in continuous time and space [30][31][32].…”

On the Distribution of Echinococcosis in the Republic of Kazakhstan

“…Резкий рост заболеваемости в конце 1990-х связан с ухудшением социально-экономических условий жизни населения и ветеринарно-санитарного надзора вследствие распада СССР. [8] Отчасти он может объясняться усовершенствованием методов диагностики населения в последние годы. [9] По опубликованным данным в 2015 году заболеваемость людей эхинококкозом составляет 5 на 100 000 населения в год, 5949 случаев заболевания людей зарегистрировано в стране с 2007 по 2013 годы [10].…”