Spatial Clusters and Non-spatial Predictors of Tick-Borne Disease Diagnosis in Indiana.


Journal

Journal of community health
ISSN: 1573-3610
Titre abrégé: J Community Health
Pays: Netherlands
ID NLM: 7600747

Informations de publication

Date de publication:
12 2019
Historique:
pubmed: 5 7 2019
medline: 14 7 2020
entrez: 5 7 2019
Statut: ppublish

Résumé

The purpose of this study was two-fold. First, we sought to identify spatial clusters of self-reported tick-borne disease (TBD) diagnosis in Indiana. Secondly, we determined the significant predictors of self-reported TBD diagnosis in a sample of Indiana residents. Study participants were selected from existing online panels maintained by Qualtrics and completed a cross-sectional survey (n = 3003). Our primary outcome of interest was self-reported TBD diagnosis (Yes/No). Cases and background population were aggregated to the county level. We used a purely spatial discrete Poisson model in SatScan® to determine significant clusters of high-risk TBD diagnosis counties. We also used X

Identifiants

pubmed: 31270657
doi: 10.1007/s10900-019-00691-0
pii: 10.1007/s10900-019-00691-0
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1111-1119

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Auteurs

Oghenekaro Omodior (O)

Department of Recreation, Park, and Tourism Studies, School of Public Health, Indiana University, Bloomington, IN, USA. oomodior@indiana.edu.

Sina Kianersi (S)

Department of Epidemiology and Biostatistics, School of Public Health, Indiana University, Bloomington, IN, USA.

Maya Luetke (M)

Department of Epidemiology and Biostatistics, School of Public Health, Indiana University, Bloomington, IN, USA.

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