Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Using Geographic Information System(GIS) to Explore the Spatial Association Between Neighborhood Contexts and Oral Health Outcomes in a Pediatric Population

Version 1 : Received: 1 November 2020 / Approved: 3 November 2020 / Online: 3 November 2020 (15:33:02 CET)

How to cite: Podskalniy, V.; Pani, S.C.; Lee, J.; Vieira, L.A.C.; Perinpanayagam, H. Using Geographic Information System(GIS) to Explore the Spatial Association Between Neighborhood Contexts and Oral Health Outcomes in a Pediatric Population. Preprints 2020, 2020110169 (doi: 10.20944/preprints202011.0169.v1). Podskalniy, V.; Pani, S.C.; Lee, J.; Vieira, L.A.C.; Perinpanayagam, H. Using Geographic Information System(GIS) to Explore the Spatial Association Between Neighborhood Contexts and Oral Health Outcomes in a Pediatric Population. Preprints 2020, 2020110169 (doi: 10.20944/preprints202011.0169.v1).

Abstract

ABSTRACT: Aims: This study aimed to explore the impacts of neighborhood-level socioeconomic contexts (e.g., income, education) on the therapeutic and preventative dental quality outcome of children aged 3 to 15 years. Materials and Methods Anonymized billing data of 842 patients reporting to a university Children’s Dental over three years met the inclusion criteria. Their access to care (OEV-CH-A), topical fluoride application (TFL-CH-A) and dental treatment burden (TRT-CH-A) were determined by dental quality alliance (DQA) criteria. The three oral health variables were aggregated at a neighborhood-level and analyzed with census data provided by Statistics Canada within a GIS framework. The forward sortation area (FSA) was chosen as a neighborhood spatial unit and regression models were run both the individual and neighborhood level. Results: The individual-level regression models showed significant negative associations between OEV-CH-A (p=0.027) and TFL-CH-A (p=0.001) and the cost of dental care. There was a significant negative association between TRT-CH-A and median household income. Neighborhood-level Ordinary Least Squares (OLS) linear regression models show negative associations of all three dental health variables (OEV-CH-A, TFL-CH-A, TRT-CH-A) with median household income and the number of households without a college degree. Conclusion: GIS and spatial quantitative approaches may be an effective tool to explore the impacts of socioeconomic variables on oral health outcomes.

Subject Areas

Dental treatment outcomes; Geographic Information Systems; Neighborhood contexts

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