Area-based socioeconomic position and adult glioma: a hierarchical analysis of surveillance epidemiology and end results data
Glioma rates vary by demographic factors and geo-political boundaries and this variation suggests higher glioma rates in groups of higher socioeconomic position. The primary goal of this analysis is to investigate the relationship between glioma and county socioeconomic position using U.S. Surveilla...
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| description | Glioma rates vary by demographic factors and geo-political boundaries and this variation suggests higher glioma rates in groups of higher socioeconomic position. The primary goal of this analysis is to investigate the relationship between glioma and county socioeconomic position using U.S. Surveillance Epidemiology and End Results (SEER) data.
Cases were individuals 25+ years diagnosed with glioma between 2000 and 2006 and residing within the SEER-17 catchment area. County-, sex-, race-, age-specific rates were created in order to investigate individual-level associations (population data from U.S. Census 2000). A Bayesian hierarchical Poisson spatial conditionally autoregressive (CAR) model was utilized to simultaneously estimate individual- and county-level associations while controlling for county spatial dependence.
Those residing in counties of the second, third, and fourth highest quartiles of socioeconomic position have glioma incidence rates that are 1.10 (95% CI: 1.02,1.19), 1.11 (95% CI: 1.02,1.20), 1.14 (95% CI: 1.05,1.23) times that of the first quartile, respectively. A CAR model properly controlled for error spatial dependence. Investigated lag times suggest year 2000 census data yields superior model fit.
Demographically adjusted rates of glioma are elevated in counties of higher socioeconomic position. More well-grounded theory concerning the glioma-socioeconomic position association along with socioeconomic data collected at multiple levels is recommended for future studies investigating this relationship. |
| doi_str_mv | 10.1371/journal.pone.0060910 |
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Cases were individuals 25+ years diagnosed with glioma between 2000 and 2006 and residing within the SEER-17 catchment area. County-, sex-, race-, age-specific rates were created in order to investigate individual-level associations (population data from U.S. Census 2000). A Bayesian hierarchical Poisson spatial conditionally autoregressive (CAR) model was utilized to simultaneously estimate individual- and county-level associations while controlling for county spatial dependence.
Those residing in counties of the second, third, and fourth highest quartiles of socioeconomic position have glioma incidence rates that are 1.10 (95% CI: 1.02,1.19), 1.11 (95% CI: 1.02,1.20), 1.14 (95% CI: 1.05,1.23) times that of the first quartile, respectively. A CAR model properly controlled for error spatial dependence. Investigated lag times suggest year 2000 census data yields superior model fit.
Demographically adjusted rates of glioma are elevated in counties of higher socioeconomic position. More well-grounded theory concerning the glioma-socioeconomic position association along with socioeconomic data collected at multiple levels is recommended for future studies investigating this relationship.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0060910</identifier><identifier>PMID: 23585860</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Adults ; Age ; Aged ; Autoregressive models ; Bayes Theorem ; Bayesian analysis ; Black or African American ; Black People ; Brain cancer ; Brain research ; Brain tumors ; Catchment areas ; Catchments ; Census ; Central Nervous System Neoplasms - diagnosis ; Central Nervous System Neoplasms - economics ; Central Nervous System Neoplasms - ethnology ; Central Nervous System Neoplasms - pathology ; Demographics ; Epidemiology ; Error analysis ; Female ; Geospatial data ; Glioma ; Glioma - diagnosis ; Glioma - economics ; Glioma - ethnology ; Glioma - pathology ; Health aspects ; Health risk assessment ; Histology ; Humans ; Ionizing radiation ; Male ; Medical diagnosis ; Medicine ; Middle Aged ; Models, Statistical ; Nervous system ; Political factors ; Population ; Principal components analysis ; Quartiles ; SEER Program - statistics & numerical data ; Social and Behavioral Sciences ; Social Class ; Socioeconomic factors ; Socioeconomics ; Studies ; Surveillance ; Tumors ; United States - epidemiology ; White People</subject><ispartof>PloS one, 2013-04, Vol.8 (4), p.e60910</ispartof><rights>COPYRIGHT 2013 Public Library of Science</rights><rights>2013 Plascak, Fisher. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2013 Plascak, Fisher 2013 Plascak, Fisher</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-67481dd2de588dbe4954882debc22ebc592447d977795a21f7e17a182a610f1b3</citedby><cites>FETCH-LOGICAL-c692t-67481dd2de588dbe4954882debc22ebc592447d977795a21f7e17a182a610f1b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3622005/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3622005/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23585860$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Castresana, Javier S.</contributor><creatorcontrib>Plascak, Jesse J</creatorcontrib><creatorcontrib>Fisher, James L</creatorcontrib><title>Area-based socioeconomic position and adult glioma: a hierarchical analysis of surveillance epidemiology and end results data</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Glioma rates vary by demographic factors and geo-political boundaries and this variation suggests higher glioma rates in groups of higher socioeconomic position. The primary goal of this analysis is to investigate the relationship between glioma and county socioeconomic position using U.S. Surveillance Epidemiology and End Results (SEER) data.
Cases were individuals 25+ years diagnosed with glioma between 2000 and 2006 and residing within the SEER-17 catchment area. County-, sex-, race-, age-specific rates were created in order to investigate individual-level associations (population data from U.S. Census 2000). A Bayesian hierarchical Poisson spatial conditionally autoregressive (CAR) model was utilized to simultaneously estimate individual- and county-level associations while controlling for county spatial dependence.
Those residing in counties of the second, third, and fourth highest quartiles of socioeconomic position have glioma incidence rates that are 1.10 (95% CI: 1.02,1.19), 1.11 (95% CI: 1.02,1.20), 1.14 (95% CI: 1.05,1.23) times that of the first quartile, respectively. A CAR model properly controlled for error spatial dependence. Investigated lag times suggest year 2000 census data yields superior model fit.
Demographically adjusted rates of glioma are elevated in counties of higher socioeconomic position. More well-grounded theory concerning the glioma-socioeconomic position association along with socioeconomic data collected at multiple levels is recommended for future studies investigating this relationship.</description><subject>Adult</subject><subject>Adults</subject><subject>Age</subject><subject>Aged</subject><subject>Autoregressive models</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Black or African American</subject><subject>Black People</subject><subject>Brain cancer</subject><subject>Brain research</subject><subject>Brain tumors</subject><subject>Catchment areas</subject><subject>Catchments</subject><subject>Census</subject><subject>Central Nervous System Neoplasms - diagnosis</subject><subject>Central Nervous System Neoplasms - economics</subject><subject>Central Nervous System Neoplasms - ethnology</subject><subject>Central Nervous System Neoplasms - pathology</subject><subject>Demographics</subject><subject>Epidemiology</subject><subject>Error analysis</subject><subject>Female</subject><subject>Geospatial data</subject><subject>Glioma</subject><subject>Glioma - 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epidemiology</topic><topic>White People</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Plascak, Jesse J</creatorcontrib><creatorcontrib>Fisher, James L</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Plascak, Jesse J</au><au>Fisher, James L</au><au>Castresana, Javier S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Area-based socioeconomic position and adult glioma: a hierarchical analysis of surveillance epidemiology and end results data</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2013-04-09</date><risdate>2013</risdate><volume>8</volume><issue>4</issue><spage>e60910</spage><pages>e60910-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Glioma rates vary by demographic factors and geo-political boundaries and this variation suggests higher glioma rates in groups of higher socioeconomic position. The primary goal of this analysis is to investigate the relationship between glioma and county socioeconomic position using U.S. Surveillance Epidemiology and End Results (SEER) data.
Cases were individuals 25+ years diagnosed with glioma between 2000 and 2006 and residing within the SEER-17 catchment area. County-, sex-, race-, age-specific rates were created in order to investigate individual-level associations (population data from U.S. Census 2000). A Bayesian hierarchical Poisson spatial conditionally autoregressive (CAR) model was utilized to simultaneously estimate individual- and county-level associations while controlling for county spatial dependence.
Those residing in counties of the second, third, and fourth highest quartiles of socioeconomic position have glioma incidence rates that are 1.10 (95% CI: 1.02,1.19), 1.11 (95% CI: 1.02,1.20), 1.14 (95% CI: 1.05,1.23) times that of the first quartile, respectively. A CAR model properly controlled for error spatial dependence. Investigated lag times suggest year 2000 census data yields superior model fit.
Demographically adjusted rates of glioma are elevated in counties of higher socioeconomic position. More well-grounded theory concerning the glioma-socioeconomic position association along with socioeconomic data collected at multiple levels is recommended for future studies investigating this relationship.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>23585860</pmid><doi>10.1371/journal.pone.0060910</doi><tpages>e60910</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Adult Adults Age Aged Autoregressive models Bayes Theorem Bayesian analysis Black or African American Black People Brain cancer Brain research Brain tumors Catchment areas Catchments Census Central Nervous System Neoplasms - diagnosis Central Nervous System Neoplasms - economics Central Nervous System Neoplasms - ethnology Central Nervous System Neoplasms - pathology Demographics Epidemiology Error analysis Female Geospatial data Glioma Glioma - diagnosis Glioma - economics Glioma - ethnology Glioma - pathology Health aspects Health risk assessment Histology Humans Ionizing radiation Male Medical diagnosis Medicine Middle Aged Models, Statistical Nervous system Political factors Population Principal components analysis Quartiles SEER Program - statistics & numerical data Social and Behavioral Sciences Social Class Socioeconomic factors Socioeconomics Studies Surveillance Tumors United States - epidemiology White People |
| title | Area-based socioeconomic position and adult glioma: a hierarchical analysis of surveillance epidemiology and end results data |
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