An approach for estimating vaccination coverage for communities using school-level data and population mobility...

Area of Science:

• Epidemiology
• Geographic Information Systems (GIS)
• Public Health

Background:

• School-level vaccination data offer high spatial resolution but create a spatial mismatch with residential areas.
• Existing methods using public school districts inadequately represent all students due to private schools and school choice programs.

Purpose of the Study:

• To develop and validate a novel approach for estimating community-level childhood vaccination coverage.
• To resolve the spatial mismatch inherent in school-level vaccination data.
• To align vaccination coverage estimates with the geographic scale of public health policy.

Main Methods:

• Integrated school-level vaccination data with population commuting information.
• Developed a mobility-adjusted model to estimate community-level vaccine coverage.
• Conducted a case study using diphtheria, tetanus, and pertussis (DTP) vaccination data for kindergarten students in California.

Main Results:

• The mobility-adjusted model produced community-level DTP coverage estimates.
• Statewide DTP coverage was estimated at 92.37%.
• This estimate closely matched the 92.44% coverage calculated from the original school-level data.

Conclusions:

• The proposed method effectively resolves the spatial mismatch in childhood vaccination data.
• Mobility-adjusted estimates provide a more accurate representation of community-level vaccination coverage.
• This approach enhances the geographic relevance of vaccination data for public health interventions.