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Geospatial analysis of blindness within rural and urban counties

PURPOSE: To determine the associations of blindness within rural and urban counties using a registry of blind persons and geospatial analytics. METHODS: We used the Oregon Commission for the Blind registry to determine the number of persons who are legally blind, as well as licensure data to determi...

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Autores principales: Sanchez, Facundo G., Gardiner, Stuart K., Demirel, Shaban, Rees, Jack P., Mansberger, Steven L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550029/
https://www.ncbi.nlm.nih.gov/pubmed/36215279
http://dx.doi.org/10.1371/journal.pone.0275807
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author Sanchez, Facundo G.
Gardiner, Stuart K.
Demirel, Shaban
Rees, Jack P.
Mansberger, Steven L.
author_facet Sanchez, Facundo G.
Gardiner, Stuart K.
Demirel, Shaban
Rees, Jack P.
Mansberger, Steven L.
author_sort Sanchez, Facundo G.
collection PubMed
description PURPOSE: To determine the associations of blindness within rural and urban counties using a registry of blind persons and geospatial analytics. METHODS: We used the Oregon Commission for the Blind registry to determine the number of persons who are legally blind, as well as licensure data to determine the density of eye care providers (optometrists and ophthalmologists) within each county of the State of Oregon. We used geospatial statistics, analysis of variance, and logistic regression to determine the explanatory variables associated with blindness within counties. RESULTS: We included 8350 individuals who are legally blind within the state of Oregon in the calendar year 2015. The mean observed prevalence of registered blindness was 0.21% and ranged almost 9-fold from 0.04% to 0.58% among counties (p < .001). In univariate models, higher blindness was associated with increasing median age (p = .027), minority race (p < .001), decreased median household income (p < .001), increased poverty within a county (p < .001), and higher density of ophthalmologists (p = .003). Density of optometrists was not associated with prevalence of blindness (p = .89). The final multivariable model showed higher blindness to be associated with lower median household income, higher proportion of black race, and lower proportion of Hispanic race (p < .001 for all). CONCLUSION: Geospatial analytics identified counties with higher and lower than expected proportions of blindness even when adjusted for sociodemographic factors. Clinicians and researchers may use the methods and results of this study to better understand the distribution of individuals with blindness and the associated factors to help design public health interventions.
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spelling pubmed-95500292022-10-11 Geospatial analysis of blindness within rural and urban counties Sanchez, Facundo G. Gardiner, Stuart K. Demirel, Shaban Rees, Jack P. Mansberger, Steven L. PLoS One Research Article PURPOSE: To determine the associations of blindness within rural and urban counties using a registry of blind persons and geospatial analytics. METHODS: We used the Oregon Commission for the Blind registry to determine the number of persons who are legally blind, as well as licensure data to determine the density of eye care providers (optometrists and ophthalmologists) within each county of the State of Oregon. We used geospatial statistics, analysis of variance, and logistic regression to determine the explanatory variables associated with blindness within counties. RESULTS: We included 8350 individuals who are legally blind within the state of Oregon in the calendar year 2015. The mean observed prevalence of registered blindness was 0.21% and ranged almost 9-fold from 0.04% to 0.58% among counties (p < .001). In univariate models, higher blindness was associated with increasing median age (p = .027), minority race (p < .001), decreased median household income (p < .001), increased poverty within a county (p < .001), and higher density of ophthalmologists (p = .003). Density of optometrists was not associated with prevalence of blindness (p = .89). The final multivariable model showed higher blindness to be associated with lower median household income, higher proportion of black race, and lower proportion of Hispanic race (p < .001 for all). CONCLUSION: Geospatial analytics identified counties with higher and lower than expected proportions of blindness even when adjusted for sociodemographic factors. Clinicians and researchers may use the methods and results of this study to better understand the distribution of individuals with blindness and the associated factors to help design public health interventions. Public Library of Science 2022-10-10 /pmc/articles/PMC9550029/ /pubmed/36215279 http://dx.doi.org/10.1371/journal.pone.0275807 Text en © 2022 Sanchez et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Sanchez, Facundo G.
Gardiner, Stuart K.
Demirel, Shaban
Rees, Jack P.
Mansberger, Steven L.
Geospatial analysis of blindness within rural and urban counties
title Geospatial analysis of blindness within rural and urban counties
title_full Geospatial analysis of blindness within rural and urban counties
title_fullStr Geospatial analysis of blindness within rural and urban counties
title_full_unstemmed Geospatial analysis of blindness within rural and urban counties
title_short Geospatial analysis of blindness within rural and urban counties
title_sort geospatial analysis of blindness within rural and urban counties
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550029/
https://www.ncbi.nlm.nih.gov/pubmed/36215279
http://dx.doi.org/10.1371/journal.pone.0275807
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