Geographic analyses as a foundation for evidence-based public health interventions: the example identification and typology of risk clusters for mumps, measles, and rubella

Background Ideally, health services and interventions to improve immunization rates should be tailored to local target populations, such as spatial clusters. However, to date, little attention has been paid to spatial clusters of underimmunization and have instead been typified based on small-scale data. Aim Using the example of vaccination against measles, mumps, and rubella (MMR) in children, the present study aims to (1) identify the spatial distribution of insufficient MMR vaccination in Westphalia-Lippe on a small scale, (2) identify specific, spatial risk clusters with insufficient vaccination protection, and (3) describe spatial-neighborhood influencing factors of the different risk clusters as starting points for public health interventions. Material and methods Account data from the Kassenarztliche Vereinigung Westfalen-Lippe (KVWL) were used as a basis. Birth cohorts 2013-2016 of children with statutory health insurance were formed and aggregated at postcode level (n = 410). Statistically significant, spatially compact clusters and relative risks (RRs) of underimmunization were identified. Local risk models were estimated in binary logistic regressions based on spatial-neighborhood variables. Results and discussion Two significant clusters of underimmunization were identified for each of the vaccination rates at least one MMR vaccination and both MMR vaccinations. Significant risk factors for low immunization rates included age structure, socioeconomic variables, population density, medical coverage, and value attitude. The proposed methodology is suitable for describing spatial variations in vaccination behavior based on identified typologies for targeted evidence-based interventions.

Automated summary

This study focused on identifying spatial clusters of underimmunization for MMR vaccination in children in Westphalia-Lippe, and found significant risk factors such as age structure, socioeconomic variables, and population density. The proposed methodology can help guide evidence-based interventions to improve vaccination rates in specific areas.