Ontology type: schema:ScholarlyArticle
2020-01-23
AUTHORSPeter Georgantopoulos, Jan M. Eberth, Bo Cai, Christopher Emrich, Gowtham Rao, Charles L. Bennett, Kathlyn S. Haddock, James R. Hébert
ABSTRACTBackgroundRacial and socio-economic status (SES) disparities exist in prostate cancer (PrCA) incidence and mortality. Less is known regarding how geographical factors, including neighborhood social vulnerability and distance traveled to receive care, affect PrCA risk. The purpose of this research was to use the Veterans Administration Medical System, which provides a unique means for studying PrCA epidemiology among diverse individuals with ostensibly equal access to healthcare, to determine whether area-level characteristics influence PrCA incidence while accounting for individual-level risk factors.MethodsFrom the US Veteran’s Health Administration (VHA) electronic medical records (EMR) database from January 1999 to December 2015, we identified 3,736 PrCA patients and 104,017 cancer-free controls from South Carolina (SC). The VHA EMRs were linked to the US census which provided area-level factors. US census data were used to construct the Social Vulnerability Index which is a continuous composite measure of area-level vulnerability and was divided into tertiles for modeling purposes. Data were analyzed using a Bayesian multivariate conditional autoregressive model (CAR) which accounted for individual-level factors, area-level factors, spatial random effects, and autocorrelation, which were used to identify areas of higher- or lower-than-expected PrCA incidence after controlling for risk factors.ResultsAs expected, after accounting for age (sixfold and 13-fold increases in men 40–50 years and > 50 years, respectively), race was an important risk factor, with threefold higher odds among Blacks in the fully adjusted model [ORadj 2.98 (2.77, 3.20)]. After accounting for all other factors, residing in a ZIP code tabulated areas (ZCTA) with the greatest level social vulnerability versus the lowest, least vulnerable ZCTA’s, increased PrCA risk by 39% [ORadj 1.39 (1.11, 1.75)].ConclusionsWhile accounting for known risk factors for PrCA, including age, race, and marital status, we found geographic areas in SC characterized by higher than average social vulnerability with higher rates of incident PrCA among veterans. Outreach for screening, education, and care coordination may be needed for veterans in these areas. More... »
PAGES209-220
http://scigraph.springernature.com/pub.10.1007/s10552-019-01263-2
DOIhttp://dx.doi.org/10.1007/s10552-019-01263-2
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1124278193
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/31975155
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Medical and Health Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1117",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Public Health and Health Services",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Adult",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Aged",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Bayes Theorem",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Censuses",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Humans",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Incidence",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Male",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Mass Screening",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Middle Aged",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Prostatic Neoplasms",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Residence Characteristics",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Retrospective Studies",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Risk Factors",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Social Class",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "South Carolina",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Spatial Analysis",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Veterans",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Columbia VA Health Care System, Columbia, SC, USA",
"id": "http://www.grid.ac/institutes/grid.512047.5",
"name": [
"South Carolina Statewide Cancer Prevention and Control Program, University of South Carolina, Columbia, SC, USA",
"Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA",
"Southern Network On Adverse Reactions (SONAR), South Carolina Center of Economic Excellence for Medication Safety, College of Pharmacy, University of South Carolina, Columbia, SC, USA",
"Columbia VA Health Care System, Columbia, SC, USA"
],
"type": "Organization"
},
"familyName": "Georgantopoulos",
"givenName": "Peter",
"id": "sg:person.01027066506.09",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01027066506.09"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA",
"id": "http://www.grid.ac/institutes/grid.254567.7",
"name": [
"South Carolina Statewide Cancer Prevention and Control Program, University of South Carolina, Columbia, SC, USA",
"Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA"
],
"type": "Organization"
},
"familyName": "Eberth",
"givenName": "Jan M.",
"id": "sg:person.0737533572.20",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0737533572.20"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA",
"id": "http://www.grid.ac/institutes/grid.254567.7",
"name": [
"Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA"
],
"type": "Organization"
},
"familyName": "Cai",
"givenName": "Bo",
"id": "sg:person.01114577174.95",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01114577174.95"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "College of Health and Public Affairs, University of Central Florida, Orlando, USA",
"id": "http://www.grid.ac/institutes/grid.170430.1",
"name": [
"College of Health and Public Affairs, University of Central Florida, Orlando, USA"
],
"type": "Organization"
},
"familyName": "Emrich",
"givenName": "Christopher",
"id": "sg:person.07351167613.31",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07351167613.31"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA",
"id": "http://www.grid.ac/institutes/grid.254567.7",
"name": [
"Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA"
],
"type": "Organization"
},
"familyName": "Rao",
"givenName": "Gowtham",
"id": "sg:person.0767264137.17",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0767264137.17"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Columbia VA Health Care System, Columbia, SC, USA",
"id": "http://www.grid.ac/institutes/grid.512047.5",
"name": [
"Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA",
"Southern Network On Adverse Reactions (SONAR), South Carolina Center of Economic Excellence for Medication Safety, College of Pharmacy, University of South Carolina, Columbia, SC, USA",
"Columbia VA Health Care System, Columbia, SC, USA"
],
"type": "Organization"
},
"familyName": "Bennett",
"givenName": "Charles L.",
"id": "sg:person.01100705232.37",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01100705232.37"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Columbia VA Health Care System, Columbia, SC, USA",
"id": "http://www.grid.ac/institutes/grid.512047.5",
"name": [
"Columbia VA Health Care System, Columbia, SC, USA"
],
"type": "Organization"
},
"familyName": "Haddock",
"givenName": "Kathlyn S.",
"id": "sg:person.01317027407.74",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01317027407.74"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA",
"id": "http://www.grid.ac/institutes/grid.254567.7",
"name": [
"South Carolina Statewide Cancer Prevention and Control Program, University of South Carolina, Columbia, SC, USA",
"Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA"
],
"type": "Organization"
},
"familyName": "H\u00e9bert",
"givenName": "James R.",
"id": "sg:person.01064021255.05",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01064021255.05"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/s10552-009-9369-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1004069429",
"https://doi.org/10.1007/s10552-009-9369-0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10552-004-1291-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1017160670",
"https://doi.org/10.1007/s10552-004-1291-x"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10552-008-9256-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000168956",
"https://doi.org/10.1007/s10552-008-9256-0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1476-072x-5-58",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044298735",
"https://doi.org/10.1186/1476-072x-5-58"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1476-072x-5-59",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002819870",
"https://doi.org/10.1186/1476-072x-5-59"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1476-072x-10-63",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003564548",
"https://doi.org/10.1186/1476-072x-10-63"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10552-012-0101-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003794370",
"https://doi.org/10.1007/s10552-012-0101-0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1476-072x-11-15",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1035582295",
"https://doi.org/10.1186/1476-072x-11-15"
],
"type": "CreativeWork"
}
],
"datePublished": "2020-01-23",
"datePublishedReg": "2020-01-23",
"description": "BackgroundRacial and socio-economic status (SES) disparities exist in prostate cancer (PrCA) incidence and mortality. Less is known regarding how geographical factors, including neighborhood social vulnerability and distance traveled to receive care, affect PrCA risk. The purpose of this research was to use the Veterans Administration Medical System, which provides a unique means for studying PrCA epidemiology among diverse individuals with ostensibly equal access to healthcare, to determine whether area-level characteristics influence PrCA incidence while accounting for individual-level risk factors.MethodsFrom the US Veteran\u2019s Health Administration (VHA) electronic medical records (EMR) database from January 1999 to December 2015, we identified 3,736 PrCA patients and 104,017 cancer-free controls from South Carolina (SC). The VHA EMRs were linked to the US census which provided area-level factors. US census data were used to construct the Social Vulnerability Index which is a continuous composite measure of area-level vulnerability and was divided into tertiles for modeling purposes. Data were analyzed using a Bayesian multivariate conditional autoregressive model (CAR) which accounted for individual-level factors, area-level factors, spatial random effects, and autocorrelation, which were used to identify areas of higher- or lower-than-expected PrCA incidence after controlling for risk factors.ResultsAs expected, after accounting for age (sixfold and 13-fold increases in men 40\u201350\u00a0years and >\u200950\u00a0years, respectively), race was an important risk factor, with threefold higher odds among Blacks in the fully adjusted model [ORadj 2.98 (2.77, 3.20)]. After accounting for all other factors, residing in a ZIP code tabulated areas (ZCTA) with the greatest level social vulnerability versus the lowest, least vulnerable ZCTA\u2019s, increased PrCA risk by 39% [ORadj 1.39 (1.11, 1.75)].ConclusionsWhile accounting for known risk factors for PrCA, including age, race, and marital status, we found geographic areas in SC characterized by higher than average social vulnerability with higher rates of incident PrCA among veterans. Outreach for screening, education, and care coordination may be needed for veterans in these areas.",
"genre": "article",
"id": "sg:pub.10.1007/s10552-019-01263-2",
"inLanguage": "en",
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1100917",
"issn": [
"0957-5243",
"1573-7225"
],
"name": "Cancer Causes & Control",
"publisher": "Springer Nature",
"type": "Periodical"
},
{
"issueNumber": "3",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "31"
}
],
"keywords": [
"risk factors",
"area-level factors",
"PrCA incidence",
"electronic medical record database",
"prostate cancer incidence",
"Veterans Administration (VA) medical system",
"important risk factor",
"PrCa risk",
"medical record database",
"continuous composite measures",
"individual-level risk factors",
"neighborhood social vulnerability",
"cancer-free controls",
"cancer incidence",
"care coordination",
"prostate cancer",
"area-level predictors",
"higher odds",
"PRCA patients",
"record database",
"US Census data",
"marital status",
"incidence",
"zip codes",
"status disparities",
"individual-level factors",
"patients",
"veterans",
"high rate",
"composite measure",
"medical system",
"age",
"conditional autoregressive model",
"US Census",
"risk",
"PrCa",
"Social Vulnerability Index",
"random effects",
"BackgroundRacial",
"social vulnerability",
"tertile",
"factors",
"MethodsFrom",
"geographic areas",
"mortality",
"epidemiology",
"cancer",
"ResultsA",
"care",
"South Carolina",
"odds",
"ConclusionsWhile",
"predictors",
"race",
"ZCTAs",
"screening",
"status",
"EMRs",
"equal access",
"healthcare",
"disparities",
"individuals",
"census data",
"vulnerability",
"purpose",
"index",
"data",
"database",
"control",
"area",
"outreach",
"diverse individuals",
"measures",
"rate",
"spatial random effects",
"multivariate conditional autoregressive models",
"effect",
"blacks",
"geographical factors",
"unique means",
"access",
"education",
"census",
"Carolina",
"analysis",
"model",
"means",
"research",
"coordination",
"vulnerability index",
"spatial analysis",
"system",
"distance",
"code",
"autoregressive model",
"autocorrelation"
],
"name": "Patient- and area-level predictors of prostate cancer among South Carolina veterans: a spatial analysis",
"pagination": "209-220",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1124278193"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s10552-019-01263-2"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"31975155"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s10552-019-01263-2",
"https://app.dimensions.ai/details/publication/pub.1124278193"
],
"sdDataset": "articles",
"sdDatePublished": "2022-05-20T07:37",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_847.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1007/s10552-019-01263-2"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10552-019-01263-2'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10552-019-01263-2'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10552-019-01263-2'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10552-019-01263-2'
This table displays all metadata directly associated to this object as RDF triples.
317 TRIPLES
22 PREDICATES
147 URIs
131 LITERALS
24 BLANK NODES