Applications of spatial autocorrelation in global dentistry: a scoping review
1
Issued Date
2025-12-01
Resource Type
eISSN
14726831
Scopus ID
2-s2.0-105019568390
Journal Title
BMC Oral Health
Volume
25
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
BMC Oral Health Vol.25 No.1 (2025)
Suggested Citation
Owasitth R., Lawpoolsri S., Chaisuparat R., Soparat P., Detsomboonrat P. Applications of spatial autocorrelation in global dentistry: a scoping review. BMC Oral Health Vol.25 No.1 (2025). doi:10.1186/s12903-025-07034-7 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/112807
Title
Applications of spatial autocorrelation in global dentistry: a scoping review
Corresponding Author(s)
Other Contributor(s)
Abstract
Background: Spatial analysis in dental research offers critical insights into the geographic distribution of oral health issues. This scoping review aims to identify and classify the applications of spatial analysis in dental research, with a specific focus on spatial autocorrelation methods. Methods: This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines and the Joanna Briggs Institute (JBI) methodology. Literature searches were conducted via PubMed and Scopus, including all available publications through 2025. The inclusion criteria were based on Population, Concept, and Context (PCC) framework. Data were summarized in tables detailing publication chronology, study aims, geographical distribution, dental health aspects, methodological approaches, study design, spatial unit, and software utilized. Results: The search yielded 429 records, of which 29 studies met the inclusion criteria. Most focused on oral cancer, access to oral healthcare, and dental service utilization. Fewer studies addressed oral conditions such as dental caries, dental trauma, periodontitis, edentulism, and cleft lip and palate. The majority of studies originated from Brazil and the United States. A notable increase in publications was observed, particularly after 2010. Conclusions: This review highlights the role of spatial autocorrelation in understanding geographic clustering of oral diseases and disparities in oral healthcare access. Integrating spatial analysis can enhance public health surveillance, identify underserved areas, and inform targeted interventions. Future research should refine spatial methodologies, incorporate big data analytics, and integrate surveillance systems to support evidence-based policymaking, optimize resource allocation, and advance global efforts toward universal oral health coverage.