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.
Tuberculosis (TB) remains a major public health challenge in Nepal, marked by substantial geographic heterogeneity. Despite ongoing control efforts, spatial clustering patterns and socio-environmental determinants at the municipal level are not well understood. This study assessed spatial clustering of TB notification rates and their association with sociodemographic, housing, and environmental factors across all 753 municipalities of Nepal.
Data of notified TB cases were extracted from National Tuberculosis Control Centre from FY 2019/20 to FY 2023/24. Spatial autocorrelation analyses were conducted using annual TB notification rates, while spatial regression models were based on five-year averaged data.
Spatial autocorrelation and clustering were examined using Global Moran’s I, Getis-Ord Gi*, and Local Indicators of Spatial Association (LISA). Associations between TB notification rates and socio-demographic, housing, and environmental factors were evaluated using Ordinary Least Squares, Spatial Lag, and Spatial Error Models.
A total of 172,155 TB cases were reported over the five-year period (FY 2019/20-2023/24), with national notification rates increasing from 92 to 139 per 100,000 population. Significant and persistent positive spatial autocorrelation was observed annually (Moran’s I: 0.42-0.53; p < 0.001). High-High clusters were consistently concentrated in the densely populated Terai municipalities of Madhesh and Lumbini Provinces, whereas Low-Low clusters dominated the remote mountain regions of Karnali and Sudurpashchim. The Spatial Error Model (SEM) provided the best fit (Pseudo-R² = 0.62), revealing that population density (β=0.005, p<0.001), liquefied petroleum gas use (β=60.85, p<0.001), and nighttime land surface temperature (β=1.69, p<0.05) were significantly associated with higher TB notification rates. Traditional housing materials (mud walls: β=-38.40, p<0.001) and cow dung fuel use (β=-48.43, p<0.05) showed negative associations, this may reflect diagnostic access barriers rather than lower disease incidence.
Tuberculosis in Nepal demonstrates significant and persistent spatial clustering at the local municipal level, driven by population density, housing, energy access, and climatic conditions. These results emphasize the need for geographically targeted, municipality-focused interventions to advance Nepal’s progress toward the End TB Strategy and Sustainable Development Goal 3.3
Not applicable.
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
Mushroom poisoning is a significant foodborne disease in China, with southwestern regions being high-risk areas. These incidents place ongoing pressure on primary healthcare facilities and the public health surveillance system. This study aimed to examine area-level factors associated with reported mushroom poisoning case incidence using aggregated prefecture-level data in Sichuan Province. This ecological panel study used surveillance data from the National Foodborne Disease Outbreak Surveillance System in Sichuan Province from 2019 to 2024. The unit of analysis was prefecture-level cities, and all variables were aggregated at the regional level. Potential determinants were selected based on the Social Determinants of Health framework. Panel regression models were used to estimate associations between area-level variables and reported mushroom poisoning case incidence. From 2019 to 2024, Sichuan Province reported 1,029 mushroom poisoning incidents involving 3,551 cases. The average annual reported incidence rate was 7.07 per million people, showing a fluctuating upward trend over the study period. In the multivariable analysis, per capita GDP (β = 0.109, 95% CI: 0.016 to 0.202) was positively associated with the reported mushroom poisoning case incidence, while the climate index (β = −0.072, 95% CI: −0.118 to −0.026) and urbanization rate (β = −0.140, 95% CI: −0.224 to −0.056) were negatively associated with incidence. The medical staff allocation index (β = 0.069, 95% CI: 0.010 to 0.129) and education level index (β = 0.064, 95% CI: 0.010 to 0.119) were positively associated with the reported incidence rate. Between 2019 and 2024, the reported mushroom poisoning case incidence in Sichuan Province exhibited an increasing trend with substantial regional variation. Regional differences in reported incidence may be associated with a combination of socioeconomic conditions, environmental context, and healthcare system characteristics. 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
OBJECTIVE
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.
METHODS
Longitudinal retrospective ecological study using annual data from 19 Regional Health Care Networks. Suicide mortality (ICD-10 X60-X84) rates per 100,000 inhabitants were analyzed using descriptive epidemiology, exploratory spatial data analysis, and fixed-effects panel regression models with spatial error specifications and one-year lagged independent variables.
RESULTS
Suicide rates increased from 4.8 to 6.3 per 100,000 (32%). Female aged 10-14 years experienced a 332% increase. Spatial clustering revealed persistent high-risk areas in inland regions. In panel regression models (within-R² = 0.53), psychiatrist density in the public health system demonstrated a protective association (β = -0.20, p < 0.05), as did psychiatric bed rates in lagged models (β = -0.01, p < 0.05). Psychosocial Care Centers coverage showed no significant association. GDP per capita was positively associated with suicide rates.
CONCLUSIONS
Psychiatric workforce availability and acute care capacity emerge as key modifiable factors. Increase among female adolescents and persistent geographic disparities warrant targeted prevention strategies. Findings provide actionable evidence for suicide prevention resource allocation in middle-income settings.
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