Geospatial analysis of blindness within rural and urban counties
<h4>Purpose</h4> To determine the associations of blindness within rural and urban counties using a registry of blind persons and geospatial analytics. <h4>Methods</h4> We used the Oregon Commission for the Blind registry to determine the number of persons who are legally bli...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550029/?tool=EBI |
| _version_ | 1811342214719078400 |
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| author | Facundo G. Sanchez Stuart K. Gardiner Shaban Demirel Jack P. Rees Steven L. Mansberger |
| author_facet | Facundo G. Sanchez Stuart K. Gardiner Shaban Demirel Jack P. Rees Steven L. Mansberger |
| author_sort | Facundo G. Sanchez |
| collection | DOAJ |
| description | <h4>Purpose</h4> To determine the associations of blindness within rural and urban counties using a registry of blind persons and geospatial analytics. <h4>Methods</h4> 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. <h4>Results</h4> 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). <h4>Conclusion</h4> 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. |
| first_indexed | 2024-04-13T19:06:29Z |
| format | Article |
| id | doaj.art-f249c8b7845848a692d0fa0ffa11c3cb |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-13T19:06:29Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-f249c8b7845848a692d0fa0ffa11c3cb2022-12-22T02:33:57ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011710Geospatial analysis of blindness within rural and urban countiesFacundo G. SanchezStuart K. GardinerShaban DemirelJack P. ReesSteven L. Mansberger<h4>Purpose</h4> To determine the associations of blindness within rural and urban counties using a registry of blind persons and geospatial analytics. <h4>Methods</h4> 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. <h4>Results</h4> 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). <h4>Conclusion</h4> 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.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550029/?tool=EBI |
| spellingShingle | Facundo G. Sanchez Stuart K. Gardiner Shaban Demirel Jack P. Rees Steven L. Mansberger Geospatial analysis of blindness within rural and urban counties PLoS ONE |
| 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 |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550029/?tool=EBI |
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