Identifying priority and bright spot areas for improving diabetes care: a geospatial approach

Area of Science:

• Geographic Information Systems (GIS)
• Public Health
• Health Disparities Research

Background:

• Diabetes management varies significantly across communities.
• Identifying high-performing and low-performing areas is crucial for targeted interventions.
• Existing methods may not effectively capture local-level variations in diabetes care.

Purpose of the Study:

• To present a novel geospatial methodology for identifying priority (poor-performing) and bright spot (well-performing) communities for diabetes management.
• To apply this methodology at the ZIP Code Tabulation Area (ZCTA) level.
• To serve as the initial phase of a focused rapid assessment process (fRAP) for diabetes care improvement.

Main Methods:

• Utilized geographical information systems (GIS) mapping and spatial analyses.
• Employed a spatial empirical Bayes approach for data adjustment.
• Applied Local Moran's I to detect spatial clusters and outliers in diabetes prevalence and A1c control.

Main Results:

• Successfully identified five priority ZCTAs and three bright spot ZCTAs within the Lehigh Valley Health Network.
• Priority ZCTAs were predominantly in urban cores with large Hispanic populations.
• Bright spot ZCTAs were found in rural areas with fewer patients.

Conclusions:

• The novel geospatial approach effectively identifies areas needing targeted diabetes management interventions.
• This method can help mitigate health disparities by focusing on local factors influencing diabetes care.
• The fRAP methodology provides a framework for community-level diabetes management improvement.