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Liver Cancer Incidence and Area-Level Geographic Disparities in Pennsylvania—A Geo-Additive Approach

Many neighborhood socioeconomic index measures (nSES) that capture neighborhood deprivation exist but the impact of measure selection on liver cancer (LC) geographic disparities remains unclear. We introduce a Bayesian geoadditive modeling approach to identify clusters in Pennsylvania (PA) with high...

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Detalles Bibliográficos
Autores principales: Ortiz, Angel G., Wiese, Daniel, Sorice, Kristen A., Nguyen, Minhhuyen, González, Evelyn T., Henry, Kevin A., Lynch, Shannon M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7588924/
https://www.ncbi.nlm.nih.gov/pubmed/33081168
http://dx.doi.org/10.3390/ijerph17207526
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author Ortiz, Angel G.
Wiese, Daniel
Sorice, Kristen A.
Nguyen, Minhhuyen
González, Evelyn T.
Henry, Kevin A.
Lynch, Shannon M.
author_facet Ortiz, Angel G.
Wiese, Daniel
Sorice, Kristen A.
Nguyen, Minhhuyen
González, Evelyn T.
Henry, Kevin A.
Lynch, Shannon M.
author_sort Ortiz, Angel G.
collection PubMed
description Many neighborhood socioeconomic index measures (nSES) that capture neighborhood deprivation exist but the impact of measure selection on liver cancer (LC) geographic disparities remains unclear. We introduce a Bayesian geoadditive modeling approach to identify clusters in Pennsylvania (PA) with higher than expected LC incidence rates, adjusted for individual-level factors (age, sex, race, diagnosis year) and compared them to models with 7 different nSES index measures to elucidate the impact of nSES and measure selection on LC geospatial variation. LC cases diagnosed from 2007–2014 were obtained from the PA Cancer Registry and linked to nSES measures from U.S. census at the Census Tract (CT) level. Relative Risks (RR) were estimated for each CT, adjusted for individual-level factors (baseline model). Each nSES measure was added to the baseline model and changes in model fit, geographic disparity and state-wide RR ranges were compared. All 7 nSES measures were strongly associated with high risk clusters. Tract-level RR ranges and geographic disparity from the baseline model were attenuated after adjustment for nSES measures. Depending on the nSES measure selected, up to 60% of the LC burden could be explained, suggesting methodologic evaluations of multiple nSES measures may be warranted in future studies to inform LC prevention efforts.
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spelling pubmed-75889242020-10-29 Liver Cancer Incidence and Area-Level Geographic Disparities in Pennsylvania—A Geo-Additive Approach Ortiz, Angel G. Wiese, Daniel Sorice, Kristen A. Nguyen, Minhhuyen González, Evelyn T. Henry, Kevin A. Lynch, Shannon M. Int J Environ Res Public Health Article Many neighborhood socioeconomic index measures (nSES) that capture neighborhood deprivation exist but the impact of measure selection on liver cancer (LC) geographic disparities remains unclear. We introduce a Bayesian geoadditive modeling approach to identify clusters in Pennsylvania (PA) with higher than expected LC incidence rates, adjusted for individual-level factors (age, sex, race, diagnosis year) and compared them to models with 7 different nSES index measures to elucidate the impact of nSES and measure selection on LC geospatial variation. LC cases diagnosed from 2007–2014 were obtained from the PA Cancer Registry and linked to nSES measures from U.S. census at the Census Tract (CT) level. Relative Risks (RR) were estimated for each CT, adjusted for individual-level factors (baseline model). Each nSES measure was added to the baseline model and changes in model fit, geographic disparity and state-wide RR ranges were compared. All 7 nSES measures were strongly associated with high risk clusters. Tract-level RR ranges and geographic disparity from the baseline model were attenuated after adjustment for nSES measures. Depending on the nSES measure selected, up to 60% of the LC burden could be explained, suggesting methodologic evaluations of multiple nSES measures may be warranted in future studies to inform LC prevention efforts. MDPI 2020-10-16 2020-10 /pmc/articles/PMC7588924/ /pubmed/33081168 http://dx.doi.org/10.3390/ijerph17207526 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ortiz, Angel G.
Wiese, Daniel
Sorice, Kristen A.
Nguyen, Minhhuyen
González, Evelyn T.
Henry, Kevin A.
Lynch, Shannon M.
Liver Cancer Incidence and Area-Level Geographic Disparities in Pennsylvania—A Geo-Additive Approach
title Liver Cancer Incidence and Area-Level Geographic Disparities in Pennsylvania—A Geo-Additive Approach
title_full Liver Cancer Incidence and Area-Level Geographic Disparities in Pennsylvania—A Geo-Additive Approach
title_fullStr Liver Cancer Incidence and Area-Level Geographic Disparities in Pennsylvania—A Geo-Additive Approach
title_full_unstemmed Liver Cancer Incidence and Area-Level Geographic Disparities in Pennsylvania—A Geo-Additive Approach
title_short Liver Cancer Incidence and Area-Level Geographic Disparities in Pennsylvania—A Geo-Additive Approach
title_sort liver cancer incidence and area-level geographic disparities in pennsylvania—a geo-additive approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7588924/
https://www.ncbi.nlm.nih.gov/pubmed/33081168
http://dx.doi.org/10.3390/ijerph17207526
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