Literature DB >> 31443110

[Geographic Clusters of Underimmunization Against Influenza in the Elderly: Westphalia-Lippe as an Example].

Sebastian Völker.   

Abstract

BACKGROUND: Underimmunization against influenza has been increasing in recent years. A spatial clustering of underimmunization is suspected. These clusters can pose risks to health care and make achieving immunization quality standards difficult. The objectives of this paper are to (a) identify and describe PLZ-level spatial clusters with high levels of underimmunization against influenza, (b) compare the clusters with other preventive services, and (c) model possible factors influencing underimmunization.
MATERIAL AND METHODS: From the routine data of the Association of Statutory Health Insurance physicians Westphalia-Lippe, patients ≥ 60 years and vaccinations between 2012-2017 were extracted. As a methodology, the spatial scan statistics were chosen, which show high relative risks of underimmunization in clusters. RESULTS AND DISCUSSION: Four statistically significant clusters of underimmunization against influenza were identified, which proved to be stable even after adjustment with the temporal trend of local underimmunization rates. In the flu season 2016/2017, the underimmunization rate in the higher risk clusters (RR>1) was 71.9-74.7% and the rate outside these clusters was 67.0%. As influencing factors on underimmunization, socio-economic factors and vaccination behavior in the preseason were identified. Underimmunization rates are geographically clustered. The spatial scan statistics can be used for the identification of persistent clusters in order to carry out targeted spatial and addressee-specific measures to reduce underimmunization rates. © Georg Thieme Verlag KG Stuttgart · New York.

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Year:  2019        PMID: 31443110     DOI: 10.1055/a-0921-7374

Source DB:  PubMed          Journal:  Gesundheitswesen        ISSN: 0941-3790


  1 in total

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

Authors:  Sebastian Völker; Reinhard Hammerschmidt; Anke Spura
Journal:  Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz       Date:  2021-04-23       Impact factor: 1.513

  1 in total

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