Population Segmentation for COVID-19 Vaccine Outreach: A Clustering Analysis and Implementation in Missouri.
Objectives: The purpose of this work was to segment the Missouri population into unique groups related to COVID-19 vaccine acceptance using data science and behavioral science methods to develop tailored vaccine outreach strategies. Methods: Cluster analysis techniques were applied to a large data s...
| Publicado en: | Journal of Public Health Management & Practice Vol. 29; no. 4; pp. 563 - 572 |
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| Autores principales: | , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Jul/Aug2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=163798967&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 163798967 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10784659 0O3 jtl: Journal of Public Health Management & Practice issn: 10784659 maglogo: N pubinfo: dt: Jul/Aug2023 vid: 29 iid: 4 pid: 5086 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 163798967 163798967 163798967 10.1097/PHH.0000000000001740 163798967 ppf: 563 ppct: 9 formats: tig: atl: Population Segmentation for COVID-19 Vaccine Outreach: A Clustering Analysis and Implementation in Missouri. aug: au: Chessen, Eleanor G. Ganser, Madelyn E. Paulish, Colin A. Malik, Aamia Wishner, Allison G. Turabelidze, George Glenn, Jeffrey J. affil: Deloitte Consulting LLP, Arlington, Virginia (Mss Chessen, Ganser, Malik, and Wishner, Mr Paulish, and Dr Glenn) sug: subj: COVID-19 Pandemic Prevention and Control COVID-19 Vaccines Administration and Dosage Immunization Programs Vaccination Hesitancy Population Health Evaluation Behavioral Sciences Methods Data Science Methods Program Development Human Missouri Cluster Analysis Health Services Accessibility COVID-19 Prevention and Control Attitude to Vaccines Data Analytics ab: Objectives: The purpose of this work was to segment the Missouri population into unique groups related to COVID-19 vaccine acceptance using data science and behavioral science methods to develop tailored vaccine outreach strategies. Methods: Cluster analysis techniques were applied to a large data set that aggregated vaccination data with behavioral and demographic data from the American Community Survey and Deloitte's HealthPrism™ data set. Outreach recommendations were developed for each cluster, specific to each group's practical and motivational barriers to vaccination. Results: Following selection procedures, 10 clusters—or segments—of census tracts across Missouri were identified on the basis of k -means clustering analysis of 18 different variables. Each cluster exhibited unique geographic, demographic, socioeconomic, and behavioral patterns, and outreach strategies were developed on the basis of each cluster's practical and motivational barriers. Discussion: The segmentation analysis served as the foundation for "working groups" comprising the 115 local public health agencies (LPHAs) across the state. LPHAs with similar community segments in their service area were grouped together to discuss their communities' specific challenges, share lessons learned, and brainstorm new approaches. The working groups provided a novel way for public health to organize and collaborate across the state. Widening the aperture beyond Missouri, population segmentation via cluster analysis is a promising approach for public health practitioners interested in developing a richer understanding of the types of populations they serve. By pairing segmentation with behavioral science, practitioners can develop outreach programs and communications campaigns that are personalized to the specific behavioral barriers and needs of the population in focus. While our work focused on COVID-19, this approach has broad applicability to enhance the way public health practitioners understand the populations they serve to deliver more tailored services. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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