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Identifying and prioritising areas of child dental service need: a GIS-based approach
Insights
Geographic Information System (GIS) mapping identified areas in Western Australia with high dental service needs among children. This data helps prioritize services for underserved, high-risk populations, especially school-aged children.
Area of Science:
- Public Health
- Dental Public Health
- Geographic Information Systems (GIS)
Background:
- Dental service access is a critical public health concern for children.
- Identifying areas of unmet need is essential for equitable service provision.
Purpose of the Study:
- To pinpoint and prioritize regions with significant dental service requirements in metropolitan Western Australia's child population.
- To map areas of high need relative to existing School Dental Service clinics.
Main Methods:
- Analysis of a 10-year dataset (2000-2009) of children hospitalized for oral conditions in Perth.
- Utilized Geographic Information System (GIS) tools to map child residential locations, socioeconomic data, and clinic proximity.
Main Results:
- Identified geographical areas with high hospital admission rates for oral conditions but lacking services, particularly among school-aged children.
- High-risk areas, defined by socioeconomic deprivation and high oral-related hospital admissions, were often located within 2km of existing clinics.
- Least-disadvantaged areas with high hospital admission rates were also more likely to be near clinics.
Conclusions:
- Geographic Information System (GIS) methodology effectively identified current community dental service access needs.
- The findings support evidence-based decision-making for planning and implementing service changes to improve access based on identified risks.
Aim:
To identify and prioritise areas of high need for dental services among the child population in metropolitan Western Australia.
Design:
All children hospitalised due to an oral-condition from 2000 to 2009, at metropolitan areas of Perth were included in the analysis of a 10-year data set. QGIS tools mapped the residential location of each child and socioeconomic data in relation to existing services (School Dental Service clinics).
Results:
The tables and maps provide a clear indication of specific geographical areas, where no services are located, but where high hospital-admission rates are occurring, especially among school-age children. The least-disadvantaged areas and areas of high rates of school-age child hospital-admissions were more likely to be within 2km of the clinics than not. More of high-risk-areas (socio-economically deprived areas combined with high oral-related hospital admissions rates), were found within 2km of the clinics than elsewhere.
Conclusion:
The application of GIS methodology has identified a community's current service access needs, and assisted evidence based decision making for planning and implementing changes to increase access based on risk.
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