Discovery of under immunized spatial clusters using network scan statistics.
Background: Clusters of under-vaccinated children are emerging in a number of states in the United States due to rising rates of vaccine hesitancy and refusal. As the measles outbreaks in California and other states in 2015 and in Minnesota in 2017 showed, such clusters can pose a significant public...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 19; no. 1; pp. 1 - 15 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
2/4/2019
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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=134549375&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134549375 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 2/4/2019 vid: 19 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 134549375 134549375 NLM30717725 134549375 10.1186/s12911-018-0706-7 NLM30717725 134549375 ppf: 1 ppct: 14 formats: tig: atl: Discovery of under immunized spatial clusters using network scan statistics. aug: au: Cadena, Jose Falcone, David Marathe, Achla Vullikanti, Anil affil: Lawrence Livermore National Laboratory, 7000 East Ave, 94550, Livermore, CA, USA sug: subj: Schools Statistics and Numerical Data Models, Theoretical Child Statistics Methods Washington Human Minnesota Cluster Analysis Validation Studies Comparative Studies Evaluation Research Multicenter Studies Clinical Assessment Tools Scales Child: 6-12 years ab: Background: Clusters of under-vaccinated children are emerging in a number of states in the United States due to rising rates of vaccine hesitancy and refusal. As the measles outbreaks in California and other states in 2015 and in Minnesota in 2017 showed, such clusters can pose a significant public health risk. Prior methods have used publicly-available school immunization data for analysis (except for a few, which use private healthcare patient records). School immunization data has limited demographic information-as a result, such analyses are not able to provide demographic characteristics of significant clusters. Further, the resolution of the clusters identified by prior methods is limited since they are typically restricted to disks or well-rounded shapes.Methods: We use realistic population models for Minnesota (MN) and Washington (WA) state, which provide a model of activities for all individuals in the population. We combine this with school level immunization data for these two states, to estimate vaccine coverage at the level of census block groups. A scan statistic method defined on networks is used for finding significant clusters of under-immunized block groups, without any restrictions on shape. Further we provide the demographic characteristics of these clusters.Results: We find 2 significant under-vaccinated clusters in MN and 3 in WA. These are very irregular in shape, in contrast to the circular disks reported in prior work, which rely on the SatScan approach. Some of the clusters found by our method are not contained in those computed using SatScan, a state-of-the-art software tool used in similar studies in other states.Conclusions: The emergence of under-immunized clusters is a growing concern for public health agencies because they can act as reservoirs of infection and increase the risk of infection into the wider population. Higher resolution clusters computed using our network based approach and population models provide new insights on the structure and characteristics of such clusters and enable targeted interventions. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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