Spatial Autocorrelation and High-Risk Area Identification of Food Poisoning in Thailand, 2003-2022.
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| Title: | Spatial Autocorrelation and High-Risk Area Identification of Food Poisoning in Thailand, 2003-2022. |
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| Authors: | Timpong, O.1 Orathai.tim@stu.nida.ac.th, Pochanart, P.1 |
| Source: | International Journal of Geoinformatics. Jun2026, Vol. 22 Issue 6, p1-132. 9p. |
| Subject Terms: | *Food poisoning, *Spatial analysis (Statistics), *Epidemiology, *Public health surveillance, *Geographic spatial analysis, *Disease mapping, *Thai people |
| Geographic Terms: | Thailand |
| 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. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 195141097 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Spatial Autocorrelation and High-Risk Area Identification of Food Poisoning in Thailand, 2003-2022. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Timpong%2C+O%2E%22">Timpong, O.</searchLink><relatesTo>1</relatesTo><i> Orathai.tim@stu.nida.ac.th</i><br /><searchLink fieldCode="AR" term="%22Pochanart%2C+P%2E%22">Pochanart, P.</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Geoinformatics%22">International Journal of Geoinformatics</searchLink>. Jun2026, Vol. 22 Issue 6, p1-132. 9p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Food+poisoning%22">Food poisoning</searchLink><br />*<searchLink fieldCode="DE" term="%22Spatial+analysis+%28Statistics%29%22">Spatial analysis (Statistics)</searchLink><br />*<searchLink fieldCode="DE" term="%22Epidemiology%22">Epidemiology</searchLink><br />*<searchLink fieldCode="DE" term="%22Public+health+surveillance%22">Public health surveillance</searchLink><br />*<searchLink fieldCode="DE" term="%22Geographic+spatial+analysis%22">Geographic spatial analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Disease+mapping%22">Disease mapping</searchLink><br />*<searchLink fieldCode="DE" term="%22Thai+people%22">Thai people</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Thailand%22">Thailand</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.52939/ijg.v22i6.5045 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 1 Subjects: – SubjectFull: Food poisoning Type: general – SubjectFull: Spatial analysis (Statistics) Type: general – SubjectFull: Epidemiology Type: general – SubjectFull: Public health surveillance Type: general – SubjectFull: Geographic spatial analysis Type: general – SubjectFull: Disease mapping Type: general – SubjectFull: Thai people Type: general – SubjectFull: Thailand Type: general Titles: – TitleFull: Spatial Autocorrelation and High-Risk Area Identification of Food Poisoning in Thailand, 2003-2022. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Timpong, O. – PersonEntity: Name: NameFull: Pochanart, P. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16866576 Numbering: – Type: volume Value: 22 – Type: issue Value: 6 Titles: – TitleFull: International Journal of Geoinformatics Type: main |
| ResultId | 1 |
