Jul 2026· International Journal of Geoinformatics· 0 citations· 19 references
TL;DR
It is indicated that food poisoning in Thailand exhibits non-random, geographically structured distribution attributable to inter-related socioeconomic, dietary, and food-market environmental factors, suggesting stable geographically determined risk factors.
Abstract
Food poisoning represents a persistent public health burden in Thailand, yet provincial-level spatial clustering patterns have not been comprehensively characterized over extended time series. This retrospective analytical study utilized foodborne illness incidence data from the national disease surveillance system (Report 506) of the Department of Disease Control, Ministry of Public Health, covering all 77 provinces for the period 2003–2022
(n = 2,329,463 reported cases). Provincial incidence rates (per 100,000 population) were computed annually and linked to administrative boundary polygon data. Spatial autocorrelation was assessed using the Global Moran's I statistic, and spatial cluster analysis was performed using the Local Indicators of Spatial Association (LISA) with Queen contiguity first-order spatial weights in GeoDa (version 1.14.0). Global Moran's I ranged from 0.317 to 0.522 across all study years (all p < 0.05), indicating statistically significant positive spatial autocorrelation. High–High (H–H) clusters were consistently identified in the northeastern and northern regions, with the northeastern region demonstrating 9–14 provinces per year classified. Low–Low clusters predominated in the southern region throughout the study period. The spatial clustering patterns persisted across five-year sub-periods, suggesting stable geographically determined risk factors. Findings indicate that food poisoning in Thailand exhibits non-random, geographically structured distribution attributable to inter-related socioeconomic, dietary, and food-market environmental factors. These results provide evidence for spatially targeted surveillance and intervention strategies.
The results emphasize the need for geographically targeted, municipality-focused interventions to advance Nepal’s progress toward the End TB Strategy and Sustainable Development Goal 3.
ABSTRACT Objective: To analyze the spatial and temporal patterns and factors associated with tuberculosis treatment interruption in Brazil from 2010 to 2020. Method: Ecological study using geoprocessing. The Joinpoint method was used for temporal analysis. Spatial autocorrelation and scan statistics identified clusters. Spatial and non-spatial regression models, considering p < .05, detected factors associated with the outcome. Results: A stationary trend in tuberculosis treatment interruption was observed across the country, with increases in the Central-West and North regions. Associated socioeconomic indicators included the Gini index, household density > 2, retreatment rate, social vulnerability index, illiteracy rate, percentage of individuals in extreme poverty, and Family Health Strategy coverage. Conclusion: Treatment interruption showed a stationary trend. Spatial regression showed that socioeconomic vulnerability indicators influence the outcome, positively or negatively, depending on the region, which calls for intensified prevention and control efforts in those areas.
Maria Izabel Félix Rocha, Thatiana Araujo Maranhão, Maria Madalena Cardoso da Frota et al.· Cogitare Enfermagem· 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
Investigate spatial and temporal distribution of suicide mortality across the Regional Health Care Networks of São Paulo State, Brazil, from 2012 to 2022, evaluating associations with mental health service availability, psychiatric workforce density, and socioeconomic indicators to emerge as key modifiable factors.
Maria Carolina Vita Nunes, J. Santos, Mirian Matsura Shirassu et al.· Revista Brasileira de Psiqui...· 0 citations
Background Dengue has shifted from Dhaka‐centric to nationwide in Bangladesh. Aim To quantify spatiotemporal patterns and identify high‐risk clusters across all 64 districts from 2021 to 2025, and, extending previous reports with data from 2025, this study aims to observe an ongoing shift in per‐capita risk hotspots. Methods Retrospective ecological study of the Directorate General of Health Services (DGHS) aggregate yearly data on suspected and laboratory‐confirmed dengue admissions (n = 637,626 cases). Incidence per 100,000 population was calculated using 2022 census projections with 1.12% annual growth. Global (Moran’s I) and local (Getis‐Ord Gi∗) spatial autocorrelation and prospective space–time scan statistics (SaTScan) were applied. Results National incidence: 76.4/100,000/year. 2025 rate‐adjusted Moran’s I = 0.328 (p = 0.001). Raw‐case hotspots: Greater Dhaka; population‐adjusted hotspots: Jhalokathi, Barguna (coastal). High‐rate space–time cluster (2024–2025): Dhaka, Manikganj (RR = 3.13, p = 0.001). Dhaka O/E = 4.31 (RR = 6.38). Two low‐rate clusters (north, southeast). Conclusion Urban hyperendemicity, characterized by a high case volume, coupled with the emerging coastal per‐capita risk, necessitates tailored interventions. Intensive vector control is essential in Dhaka, while early‐warning surveillance should be implemented in coastal districts.
P. Hasan, Tazdin Delwar Khan, M. Islam et al.· Journal of Tropical Medicine· 0 citations
Background: While tuberculosis persists as a public health threat in urban Indonesia, the spread of the disease is uneven. It is believed that rapid urbanization and unequal access to medical services have led to varying disease burdens across regions.
Objectives: To examine the spatial distribution and clustering of tuberculosis incidence and its association with population density and primary health care distribution in Semarang City.
Methods: An ecological study was conducted using a spatial analysis approach based on secondary data on TB cases, population, and health facilities in Semarang City for the years 2022-2023. Both global and local spatial autocorrelation analyses were conducted to examine clustering, followed by bivariate analyses to test their relationships with other variables.
Results: Significant spatial clustering of TB incidence was observed in both years (Moran's I = 0.339 in 2022 and 0.465 in 2023; p < 0.001). High-incidence clusters were mainly located in the central and eastern urban areas. Population density showed a significant positive spatial association with TB incidence (Moran's I=0.415 in 2022 and 0.522 in 2023; p=0.001), whereas no significant association was found for PHC distribution.
Conclusion: Tuberculosis incidence in Semarang City exhibited a clustered spatial pattern, particularly in densely populated areas. These findings support geographically targeted TB control strategies. However, because the analysis was based on aggregated ecological data, the results should not be interpreted at the individual level.
Muhammad Auliya Rahman, Muhammad Ashraff Zurkarnain, S. Sulistiyani et al.· Liaquat National Journal of...· 0 citations