Identifying High-Risk Spatiotemporal Clusters of Mushroom Poisoning in Subtropical China: A Retrospective Surveillance Study in Zhejiang Province (2012–2023)
Aug 2026· Foods· Vol 15, pp. 2913· 0 citations· 27 references
Medicine
Abstract
To understand the epidemiological characteristics and patterns of mushroom poisoning in Zhejiang Province from 2012 to 2023, and to overcome the limitations of previous descriptive studies in precise early warning and spatial identification, this study explored the feasibility of identifying spatial distribution characteristics and high-risk spatiotemporal clusters. First, descriptive epidemiological analysis was conducted on 2276 cases from the Foodborne Disease Case Surveillance System and 408 outbreaks from the Foodborne Disease Outbreak Surveillance System reported over the 12-year period to clarify the basic characteristics and trends of poisoning. Subsequently, spatial autocorrelation analysis (Moran’s I) was employed to reveal spatial dependence and clustering patterns. Finally, spatiotemporal scan statistics (SatScan) were used to precisely identify high-risk spatiotemporal clusters, systematically analyzing the spatiotemporal distribution and clustering patterns of mushroom poisoning cases. The results showed a distinct summer–autumn seasonal peak (June–October), attributed to the subtropical monsoon climate with high temperatures and abundant rainfall, which is conducive to mushroom growth. Farmers were the most affected population (47.93%), and homes were the primary poisoning locations (71.7%), reflecting widespread foraging habits and insufficient risk awareness in rural areas. Chlorophyllum molybdites (36.27%) and Russula japonica (10.05%) were the dominant poisoning mushroom species, with gastrointestinal symptoms being the predominant clinical manifestation (84.07%). Spatial analysis revealed significant spatiotemporal clustering of mushroom poisoning in Zhejiang Province. The global Moran’s I index showed significant positive autocorrelation in some years (p < 0.05), with local hotspots mainly distributed in western Zhejiang counties. This pattern is driven by a dual model of environmental suitability and behavioral risk, resulting from the high forest coverage and humid climate of the western Zhejiang mountainous areas providing suitable habitats, combined with long-standing foraging habits among local residents. Retrospective spatiotemporal scanning identified high-risk clusters for each year from 2018 to 2023, with the Lishui area in 2023 being the most significant cluster (Relative Risk (RR) = 15.44, Log-Likelihood Ratio (LLR) = 114.49). The results confirm that mushroom poisoning in Zhejiang Province exhibits a stable and identifiable spatiotemporal clustering pattern, providing a quantitative basis for precise health education and targeted prevention and control in high-risk counties of western Zhejiang during June–October, thereby shifting the approach from passive reporting to targeted intervention.
This study collected data on paragonimiasis cases from 13 medical institutions in Chongqing between 2010 and 2024 to analyse their epidemiological characteristics, spatio-temporal distribution and high-risk areas. The data included demographic characteristics, epidemiological history, clinical manifestations and diagnostic criteria. We established a database using Microsoft Excel, performed time series analysis and Kruskal-Wallis tests with R software, and used ArcGIS for global and local spatial autocorrelation analyses to identify spatio-temporal clustering patterns. A total of 1,986 cases were reported from 13 institutions, including 33 (1.66%) confirmed, 468 (23.57%) clinically diagnosed, and 1,485 (74.77%) suspected cases. Cases were predominantly male (69.74%) and young, with 85.95% under 20 years old and most having low educational attainment. Cases showed a significant declining
OBJECTIVE
To investigate the spatiotemporal epidemiological characteristics and temporal trends of influenza in Fujian Province, China, from 2005 to 2022.
METHODS
Descriptive epidemiological methods were used to analyze the temporal, demographic, occupational, and regional distribution of influenza cases. Spatial autocorrelation analysis, trend surface analysis, and spatiotemporal scan statistics were applied to explore the spatial distribution and clustering characteristics of influenza.
RESULTS
A total of 215,338 influenza cases were reported in Fujian Province during 2005-2022, with the incidence rate showing an overall fluctuating upward trend over time (Z = 4.394, P < 0.001). Children aged 0-5 years and students accounted for the largest proportions of reported cases. Influenza activity demonstrated marked temporal and seasonal variation, with relatively higher activity observed in summer and winter. Trend surface analysis indicated higher case concentrations in eastern and central regions of Fujian Province. Significant positive spatial autocorrelation was identified in multiple years, and six significant spatiotemporal clusters were detected, mainly distributed in southeastern Fujian.
CONCLUSION
Influenza in Fujian Province showed an increasing annual incidence trend, marked temporal fluctuation, and dynamic spatial clustering during 2005-2022. These findings may support more targeted influenza surveillance, prevention, and resource allocation in high-risk populations and areas.
This study employed geospatial approaches to assess the risk and spatial distribution of Dengue Fever (DF) and Dengue Hemorrhagic Fever (DHF) in Phayao Province, Thailand. Epidemiological data from 2016 to 2024, comprising 3,600 reported cases, were analysed alongside demographic and climatic variables. Temporal analysis revealed a major epidemic in 2023 lasting 20 weeks, coinciding with peak rainfall, with adolescents and young adults (13-24 years old) being the most affected group. Spatial autocorrelation (Moran's I) indicated significant clustering of dengue morbidity rates, with hotspots concentrated in urbanised districts such as Mueang Phayao, Dok Khamtai and Chiang Kham, while Kernel Density Estimation (KDE) highlighted shifts of hotspots toward eastern districts in later years. Local Indicators of Spatial Association (LISA) identified 24 high-high clusters in 2019, predominantly in Mae Chai District. Case-control analysis further revealed that socio-economic conditions, housing environments and inconsistent preventive behaviours influenced dengue incidence, with strong community participation linked to more effective prevention. These findings underscore the spatial heterogeneity of dengue transmission and provide geospatial evidence to guide targeted vector control, strengthen community-based interventions, and support evidence-based public health strategies in northern Thailand.
Phaisarn Jeefoo, Watcharaporn Preedapirom Jeefoo, S. Mekruksavanich et al.· Geospatial Health· 0 citations
Objective To analyze the spatiotemporal clustering of mumps in Hunan Province from 2015 to 2024 and its influencing factors and to explore the policy effect of adjusting the immunization schedule. Methods Spatial autocorrelation, spatiotemporal clustering, geographically and temporally weighted regression (GTWR), and interrupted time series (ITS) analyses were performed on county-level mumps surveillance data obtained from Hunan Province for 2015–2024 to identify mumps clustering, quantify socioeconomic drivers and assess the two-dose mumps-containing vaccine (MuCV) policy impact. Results In total 157,502 cases were reported, and 89.7% of patients were aged 0–14 years. The global Moran's I increased from 0.20 to 0.67 (P < 0.01), indicating intensified clustering. Despite the decreasing provincial incidence, high-high hotspot geographic coverage expanded because of widening spatial disparities. The primary hotspot was in northeastern Hunan (Nov 2016–Dec 2019). GTWR revealed that per capita gross domestic product (GDP) and population mobility intensity had positive local effects in northeastern counties (adjusted R2 = 0.45). ITS showed an immediate significant decline after the 2020 policy (β2 = −2.50, P < 0.01). Conclusions Persistent hotspots in northeastern Hunan may be driven by per capita GDP and intercounty mobility. The two-dose policy reduced overall incidence but exacerbated regional inequalities.
Linlong Liang, Xiaoyan Liu, Fuqiang Liu et al.· Preventive medicine reports· 0 citations
Purpose: This study aimed to analyze the spatio-temporal patterns of dengue virus infection via kernel density estimation to assess the relative risk distribution and identify transmission hotspots in the Bobonaro municipality.
Methods: A retrospective analysis was conducted on confirmed dengue cases (n=311) reported from seven community health centers and one referral hospital in Bobonaro from January 2022 to December 2024. Kernel density estimation with optimal bandwidth selection was employed to map the relative risk distributions and identify spatial clusters. Demographic patterns across age categories were analyzed using negative binomial regression with a quadratic age-rank term, and sex distribution was analyzed using an exact two-proportion binomial test.
Results: Annual dengue cases increased by 41.48% over the study period, with significant seasonal patterns observed during the dry and rainy seasons. Case counts increased from the infancy category toward a peak in the youth category (5–14 years, 158 cases, 50.8% of all cases) before declining in older age groups, a pattern confirmed by negative binomial regression with a quadratic age-rank term (incidence rate ratio [IRR] 8.45, 95% CI 2.70–25.37, p<0.001 for the linear term and 0.74, 95% CI 0.65–0.85, p<0.001 for the quadratic term). Females accounted for a slightly higher proportion of cases (51.8%, n=161) than males (48.2%, n=150), a difference that was not statistically significant (p= 0.571). Spatial analysis revealed persistent hotspots in southeastern Bobonaro, with new clusters emerging in the northern regions by 2023 and transmission typically contained within an 80 m radius.
Conclusion: This study identified clear demographic vulnerabilities and dynamic spatial patterns of dengue transmission in Bobonaro, demonstrating the utility of GIS-based spatial analysis for strengthening surveillance and guiding targeted vector control in resource-constrained settings.
Zito Viegas da Cruz, I. M. Adnyana· Berita Kedokteran Masyarakat· 0 citations
Strengthening surveillance capacity, improving mushroom species identification, and implementing region-specific health education and risk communication strategies may help reduce the burden of mushroom poisoning.
Ruyue Hu, Wen Chen, Li Lin et al.· Frontiers in Public Health· 0 citations
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