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Geographical variation of cerebrovascular disease in New York State: the correlation with income

BACKGROUND: Income is known to be associated with cerebrovascular disease; however, little is known about the more detailed relationship between cerebrovascular disease and income. We examined the hypothesis that the geographical distribution of cerebrovascular disease in New York State may be predi...

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Autores principales: Han, Daikwon, Carrow, Shannon S, Rogerson, Peter A, Munschauer, Frederick E
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1276810/
https://www.ncbi.nlm.nih.gov/pubmed/16242043
http://dx.doi.org/10.1186/1476-072X-4-25
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author Han, Daikwon
Carrow, Shannon S
Rogerson, Peter A
Munschauer, Frederick E
author_facet Han, Daikwon
Carrow, Shannon S
Rogerson, Peter A
Munschauer, Frederick E
author_sort Han, Daikwon
collection PubMed
description BACKGROUND: Income is known to be associated with cerebrovascular disease; however, little is known about the more detailed relationship between cerebrovascular disease and income. We examined the hypothesis that the geographical distribution of cerebrovascular disease in New York State may be predicted by a nonlinear model using income as a surrogate socioeconomic risk factor. RESULTS: We used spatial clustering methods to identify areas with high and low prevalence of cerebrovascular disease at the ZIP code level after smoothing rates and correcting for edge effects; geographic locations of high and low clusters of cerebrovascular disease in New York State were identified with and without income adjustment. To examine effects of income, we calculated the excess number of cases using a non-linear regression with cerebrovascular disease rates taken as the dependent variable and income and income squared taken as independent variables. The resulting regression equation was: excess rate = 32.075 - 1.22*10(-4)(income) + 8.068*10(-10)(income(2)), and both income and income squared variables were significant at the 0.01 level. When income was included as a covariate in the non-linear regression, the number and size of clusters of high cerebrovascular disease prevalence decreased. Some 87 ZIP codes exceeded the critical value of the local statistic yielding a relative risk of 1.2. The majority of low cerebrovascular disease prevalence geographic clusters disappeared when the non-linear income effect was included. For linear regression, the excess rate of cerebrovascular disease falls with income; each $10,000 increase in median income of each ZIP code resulted in an average reduction of 3.83 observed cases. The significant nonlinear effect indicates a lessening of this income effect with increasing income. CONCLUSION: Income is a non-linear predictor of excess cerebrovascular disease rates, with both low and high observed cerebrovascular disease rate areas associated with higher income. Income alone explains a significant amount of the geographical variance in cerebrovascular disease across New York State since both high and low clusters of cerebrovascular disease dissipate or disappear with income adjustment. Geographical modeling, including non-linear effects of income, may allow for better identification of other non-traditional risk factors.
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spelling pubmed-12768102005-11-03 Geographical variation of cerebrovascular disease in New York State: the correlation with income Han, Daikwon Carrow, Shannon S Rogerson, Peter A Munschauer, Frederick E Int J Health Geogr Research BACKGROUND: Income is known to be associated with cerebrovascular disease; however, little is known about the more detailed relationship between cerebrovascular disease and income. We examined the hypothesis that the geographical distribution of cerebrovascular disease in New York State may be predicted by a nonlinear model using income as a surrogate socioeconomic risk factor. RESULTS: We used spatial clustering methods to identify areas with high and low prevalence of cerebrovascular disease at the ZIP code level after smoothing rates and correcting for edge effects; geographic locations of high and low clusters of cerebrovascular disease in New York State were identified with and without income adjustment. To examine effects of income, we calculated the excess number of cases using a non-linear regression with cerebrovascular disease rates taken as the dependent variable and income and income squared taken as independent variables. The resulting regression equation was: excess rate = 32.075 - 1.22*10(-4)(income) + 8.068*10(-10)(income(2)), and both income and income squared variables were significant at the 0.01 level. When income was included as a covariate in the non-linear regression, the number and size of clusters of high cerebrovascular disease prevalence decreased. Some 87 ZIP codes exceeded the critical value of the local statistic yielding a relative risk of 1.2. The majority of low cerebrovascular disease prevalence geographic clusters disappeared when the non-linear income effect was included. For linear regression, the excess rate of cerebrovascular disease falls with income; each $10,000 increase in median income of each ZIP code resulted in an average reduction of 3.83 observed cases. The significant nonlinear effect indicates a lessening of this income effect with increasing income. CONCLUSION: Income is a non-linear predictor of excess cerebrovascular disease rates, with both low and high observed cerebrovascular disease rate areas associated with higher income. Income alone explains a significant amount of the geographical variance in cerebrovascular disease across New York State since both high and low clusters of cerebrovascular disease dissipate or disappear with income adjustment. Geographical modeling, including non-linear effects of income, may allow for better identification of other non-traditional risk factors. BioMed Central 2005-10-21 /pmc/articles/PMC1276810/ /pubmed/16242043 http://dx.doi.org/10.1186/1476-072X-4-25 Text en Copyright © 2005 Han et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Han, Daikwon
Carrow, Shannon S
Rogerson, Peter A
Munschauer, Frederick E
Geographical variation of cerebrovascular disease in New York State: the correlation with income
title Geographical variation of cerebrovascular disease in New York State: the correlation with income
title_full Geographical variation of cerebrovascular disease in New York State: the correlation with income
title_fullStr Geographical variation of cerebrovascular disease in New York State: the correlation with income
title_full_unstemmed Geographical variation of cerebrovascular disease in New York State: the correlation with income
title_short Geographical variation of cerebrovascular disease in New York State: the correlation with income
title_sort geographical variation of cerebrovascular disease in new york state: the correlation with income
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1276810/
https://www.ncbi.nlm.nih.gov/pubmed/16242043
http://dx.doi.org/10.1186/1476-072X-4-25
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