Measuring COVID-19 vaccination coverage: an enhanced age-adjusted two-step floating catchment area model

Area of Science:

• Public Health
• Geographic Information Systems (GIS)
• Spatial Analysis

Background:

• Limited research exists on COVID-19 vaccine accessibility in metropolitan areas.
• Identifying optimal vaccination center locations is crucial for equitable vaccine distribution.

Purpose of the Study:

• To assess spatial access to COVID-19 vaccination centers in Mashhad, Iran.
• To identify the most suitable healthcare facilities for vaccination services.

Main Methods:

• Utilized an enhanced two-step floating catchment area (EFCA) model incorporating 2021 age demographics.
• Employed a weighted decision-matrix and analytic hierarchy process (AHP) to select suitable vaccination centers based on service area, accessibility, capacity, and road proximity.
• Applied Global Moran's Index (GMI) to analyze spatial autocorrelation of accessibility.

Main Results:

• Analysis considered scenarios with public hospitals only, public healthcare centers only, or both.
• Including both hospitals and healthcare centers demonstrated high accessibility in central urban areas.
• The proposed model showed a GMI of 0.53 (Z=162.42, P<0.01), indicating enhanced accessibility and reduced spatial inequality.

Conclusions:

• Peripheral and low-income areas exhibited the least access to vaccination centers.
• Spatial access measurement using GIS provides critical data for urban public health planning.
• The developed model aids in identifying optimal vaccination sites for efficient coverage, particularly when resources are limited.