Patient- and area-level predictors of prostate cancer among South Carolina veterans: a spatial analysis View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2020-01-23

AUTHORS

Peter Georgantopoulos, Jan M. Eberth, Bo Cai, Christopher Emrich, Gowtham Rao, Charles L. Bennett, Kathlyn S. Haddock, James R. Hébert

ABSTRACT

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’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... »

PAGES

209-220

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10552-019-01263-2

DOI

http://dx.doi.org/10.1007/s10552-019-01263-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1124278193

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/31975155


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

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

HOW TO GET THIS DATA PROGRAMMATICALLY:

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

Subject Predicate Object
1 sg:pub.10.1007/s10552-019-01263-2 schema:about N19b49bde68984c1eb43e32a967c04c37
2 N46b9313802164f82814611a892c91985
3 N48209fd5580b431193c1d90743e4cc7f
4 N4e8afd1033964bfd9a3333953d1d9a3e
5 N79acf42c07e34603927415c10c6ab411
6 N85fe04eb54454dedad4dd2c1c78974c4
7 N91cdd0da93b44d9e8c01ed418932c995
8 N98726d46e60843fdae01a2fbbaa40e86
9 Na14d6c1809864f93852f13bc30399100
10 Na440583897b74136bc2d610eff8d5b19
11 Nb903f9609034427bb583cb80b0240778
12 Nbaef7627cc5944b59a5793ae3ab40166
13 Nce876af31fa942268305838444645fde
14 Ncf427528b6264112a9001084527724d4
15 Nd44b5484b5ce43a480698a592cf65677
16 Ne3d2ec33b308490da0a4b3a66f2fb3cd
17 Nee8cab488eb64989b7084b9bf2127ce0
18 anzsrc-for:11
19 anzsrc-for:1117
20 schema:author N9e0943af79af47299f69f2fd1b44f4ec
21 schema:citation sg:pub.10.1007/s10552-004-1291-x
22 sg:pub.10.1007/s10552-008-9256-0
23 sg:pub.10.1007/s10552-009-9369-0
24 sg:pub.10.1007/s10552-012-0101-0
25 sg:pub.10.1186/1476-072x-10-63
26 sg:pub.10.1186/1476-072x-11-15
27 sg:pub.10.1186/1476-072x-5-58
28 sg:pub.10.1186/1476-072x-5-59
29 schema:datePublished 2020-01-23
30 schema:datePublishedReg 2020-01-23
31 schema: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’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.
32 schema:genre article
33 schema:inLanguage en
34 schema:isAccessibleForFree false
35 schema:isPartOf N47a2c01151fc4f7c9b9a51d094b6fa45
36 N610621b6070d45fba5f534f35680c258
37 sg:journal.1100917
38 schema:keywords BackgroundRacial
39 Carolina
40 ConclusionsWhile
41 EMRs
42 MethodsFrom
43 PRCA patients
44 PrCA incidence
45 PrCa
46 PrCa risk
47 ResultsA
48 Social Vulnerability Index
49 South Carolina
50 US Census
51 US Census data
52 Veterans Administration (VA) medical system
53 ZCTAs
54 access
55 age
56 analysis
57 area
58 area-level factors
59 area-level predictors
60 autocorrelation
61 autoregressive model
62 blacks
63 cancer
64 cancer incidence
65 cancer-free controls
66 care
67 care coordination
68 census
69 census data
70 code
71 composite measure
72 conditional autoregressive model
73 continuous composite measures
74 control
75 coordination
76 data
77 database
78 disparities
79 distance
80 diverse individuals
81 education
82 effect
83 electronic medical record database
84 epidemiology
85 equal access
86 factors
87 geographic areas
88 geographical factors
89 healthcare
90 high rate
91 higher odds
92 important risk factor
93 incidence
94 index
95 individual-level factors
96 individual-level risk factors
97 individuals
98 marital status
99 means
100 measures
101 medical record database
102 medical system
103 model
104 mortality
105 multivariate conditional autoregressive models
106 neighborhood social vulnerability
107 odds
108 outreach
109 patients
110 predictors
111 prostate cancer
112 prostate cancer incidence
113 purpose
114 race
115 random effects
116 rate
117 record database
118 research
119 risk
120 risk factors
121 screening
122 social vulnerability
123 spatial analysis
124 spatial random effects
125 status
126 status disparities
127 system
128 tertile
129 unique means
130 veterans
131 vulnerability
132 vulnerability index
133 zip codes
134 schema:name Patient- and area-level predictors of prostate cancer among South Carolina veterans: a spatial analysis
135 schema:pagination 209-220
136 schema:productId N3a709f49323b4068a705d27a80bcaee6
137 N8824edcaa73c4e60bc55375ec66ecae6
138 Nd44bb8b21e5d48b89fee55767869ef78
139 schema:sameAs https://app.dimensions.ai/details/publication/pub.1124278193
140 https://doi.org/10.1007/s10552-019-01263-2
141 schema:sdDatePublished 2022-05-20T07:37
142 schema:sdLicense https://scigraph.springernature.com/explorer/license/
143 schema:sdPublisher N44dccab0faf14ed2aada1e04ef73f431
144 schema:url https://doi.org/10.1007/s10552-019-01263-2
145 sgo:license sg:explorer/license/
146 sgo:sdDataset articles
147 rdf:type schema:ScholarlyArticle
148 N19b49bde68984c1eb43e32a967c04c37 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
149 schema:name Veterans
150 rdf:type schema:DefinedTerm
151 N2a02390a82794511a4c81c9cd4fb453d rdf:first sg:person.01317027407.74
152 rdf:rest N57b0ab7cdbd1462ca907d194679e7388
153 N301f026a664e474f9aa0bc71964023e2 rdf:first sg:person.0767264137.17
154 rdf:rest N8d83983569a046ad902629de96d9e3ab
155 N3a709f49323b4068a705d27a80bcaee6 schema:name doi
156 schema:value 10.1007/s10552-019-01263-2
157 rdf:type schema:PropertyValue
158 N44dccab0faf14ed2aada1e04ef73f431 schema:name Springer Nature - SN SciGraph project
159 rdf:type schema:Organization
160 N46b9313802164f82814611a892c91985 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
161 schema:name Spatial Analysis
162 rdf:type schema:DefinedTerm
163 N47a2c01151fc4f7c9b9a51d094b6fa45 schema:issueNumber 3
164 rdf:type schema:PublicationIssue
165 N48209fd5580b431193c1d90743e4cc7f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
166 schema:name Residence Characteristics
167 rdf:type schema:DefinedTerm
168 N4b1acafefd4d40c7b0a141d386c4bc98 rdf:first sg:person.07351167613.31
169 rdf:rest N301f026a664e474f9aa0bc71964023e2
170 N4e8afd1033964bfd9a3333953d1d9a3e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
171 schema:name Aged
172 rdf:type schema:DefinedTerm
173 N57b0ab7cdbd1462ca907d194679e7388 rdf:first sg:person.01064021255.05
174 rdf:rest rdf:nil
175 N610621b6070d45fba5f534f35680c258 schema:volumeNumber 31
176 rdf:type schema:PublicationVolume
177 N79acf42c07e34603927415c10c6ab411 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
178 schema:name Humans
179 rdf:type schema:DefinedTerm
180 N83a949268cf740099e869fd6bdc5e8ec rdf:first sg:person.01114577174.95
181 rdf:rest N4b1acafefd4d40c7b0a141d386c4bc98
182 N85fe04eb54454dedad4dd2c1c78974c4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
183 schema:name Middle Aged
184 rdf:type schema:DefinedTerm
185 N8824edcaa73c4e60bc55375ec66ecae6 schema:name dimensions_id
186 schema:value pub.1124278193
187 rdf:type schema:PropertyValue
188 N8d83983569a046ad902629de96d9e3ab rdf:first sg:person.01100705232.37
189 rdf:rest N2a02390a82794511a4c81c9cd4fb453d
190 N91cdd0da93b44d9e8c01ed418932c995 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
191 schema:name Social Class
192 rdf:type schema:DefinedTerm
193 N98726d46e60843fdae01a2fbbaa40e86 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
194 schema:name South Carolina
195 rdf:type schema:DefinedTerm
196 N9e0943af79af47299f69f2fd1b44f4ec rdf:first sg:person.01027066506.09
197 rdf:rest Na87444c4e7c74f2db095f5b15c7e4f45
198 Na14d6c1809864f93852f13bc30399100 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
199 schema:name Censuses
200 rdf:type schema:DefinedTerm
201 Na440583897b74136bc2d610eff8d5b19 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
202 schema:name Bayes Theorem
203 rdf:type schema:DefinedTerm
204 Na87444c4e7c74f2db095f5b15c7e4f45 rdf:first sg:person.0737533572.20
205 rdf:rest N83a949268cf740099e869fd6bdc5e8ec
206 Nb903f9609034427bb583cb80b0240778 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
207 schema:name Retrospective Studies
208 rdf:type schema:DefinedTerm
209 Nbaef7627cc5944b59a5793ae3ab40166 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
210 schema:name Prostatic Neoplasms
211 rdf:type schema:DefinedTerm
212 Nce876af31fa942268305838444645fde schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
213 schema:name Mass Screening
214 rdf:type schema:DefinedTerm
215 Ncf427528b6264112a9001084527724d4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
216 schema:name Incidence
217 rdf:type schema:DefinedTerm
218 Nd44b5484b5ce43a480698a592cf65677 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
219 schema:name Male
220 rdf:type schema:DefinedTerm
221 Nd44bb8b21e5d48b89fee55767869ef78 schema:name pubmed_id
222 schema:value 31975155
223 rdf:type schema:PropertyValue
224 Ne3d2ec33b308490da0a4b3a66f2fb3cd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
225 schema:name Adult
226 rdf:type schema:DefinedTerm
227 Nee8cab488eb64989b7084b9bf2127ce0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
228 schema:name Risk Factors
229 rdf:type schema:DefinedTerm
230 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
231 schema:name Medical and Health Sciences
232 rdf:type schema:DefinedTerm
233 anzsrc-for:1117 schema:inDefinedTermSet anzsrc-for:
234 schema:name Public Health and Health Services
235 rdf:type schema:DefinedTerm
236 sg:journal.1100917 schema:issn 0957-5243
237 1573-7225
238 schema:name Cancer Causes & Control
239 schema:publisher Springer Nature
240 rdf:type schema:Periodical
241 sg:person.01027066506.09 schema:affiliation grid-institutes:grid.512047.5
242 schema:familyName Georgantopoulos
243 schema:givenName Peter
244 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01027066506.09
245 rdf:type schema:Person
246 sg:person.01064021255.05 schema:affiliation grid-institutes:grid.254567.7
247 schema:familyName Hébert
248 schema:givenName James R.
249 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01064021255.05
250 rdf:type schema:Person
251 sg:person.01100705232.37 schema:affiliation grid-institutes:grid.512047.5
252 schema:familyName Bennett
253 schema:givenName Charles L.
254 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01100705232.37
255 rdf:type schema:Person
256 sg:person.01114577174.95 schema:affiliation grid-institutes:grid.254567.7
257 schema:familyName Cai
258 schema:givenName Bo
259 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01114577174.95
260 rdf:type schema:Person
261 sg:person.01317027407.74 schema:affiliation grid-institutes:grid.512047.5
262 schema:familyName Haddock
263 schema:givenName Kathlyn S.
264 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01317027407.74
265 rdf:type schema:Person
266 sg:person.07351167613.31 schema:affiliation grid-institutes:grid.170430.1
267 schema:familyName Emrich
268 schema:givenName Christopher
269 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07351167613.31
270 rdf:type schema:Person
271 sg:person.0737533572.20 schema:affiliation grid-institutes:grid.254567.7
272 schema:familyName Eberth
273 schema:givenName Jan M.
274 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0737533572.20
275 rdf:type schema:Person
276 sg:person.0767264137.17 schema:affiliation grid-institutes:grid.254567.7
277 schema:familyName Rao
278 schema:givenName Gowtham
279 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0767264137.17
280 rdf:type schema:Person
281 sg:pub.10.1007/s10552-004-1291-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1017160670
282 https://doi.org/10.1007/s10552-004-1291-x
283 rdf:type schema:CreativeWork
284 sg:pub.10.1007/s10552-008-9256-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000168956
285 https://doi.org/10.1007/s10552-008-9256-0
286 rdf:type schema:CreativeWork
287 sg:pub.10.1007/s10552-009-9369-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004069429
288 https://doi.org/10.1007/s10552-009-9369-0
289 rdf:type schema:CreativeWork
290 sg:pub.10.1007/s10552-012-0101-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003794370
291 https://doi.org/10.1007/s10552-012-0101-0
292 rdf:type schema:CreativeWork
293 sg:pub.10.1186/1476-072x-10-63 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003564548
294 https://doi.org/10.1186/1476-072x-10-63
295 rdf:type schema:CreativeWork
296 sg:pub.10.1186/1476-072x-11-15 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035582295
297 https://doi.org/10.1186/1476-072x-11-15
298 rdf:type schema:CreativeWork
299 sg:pub.10.1186/1476-072x-5-58 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044298735
300 https://doi.org/10.1186/1476-072x-5-58
301 rdf:type schema:CreativeWork
302 sg:pub.10.1186/1476-072x-5-59 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002819870
303 https://doi.org/10.1186/1476-072x-5-59
304 rdf:type schema:CreativeWork
305 grid-institutes:grid.170430.1 schema:alternateName College of Health and Public Affairs, University of Central Florida, Orlando, USA
306 schema:name College of Health and Public Affairs, University of Central Florida, Orlando, USA
307 rdf:type schema:Organization
308 grid-institutes:grid.254567.7 schema:alternateName Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
309 schema:name Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
310 South Carolina Statewide Cancer Prevention and Control Program, University of South Carolina, Columbia, SC, USA
311 rdf:type schema:Organization
312 grid-institutes:grid.512047.5 schema:alternateName Columbia VA Health Care System, Columbia, SC, USA
313 schema:name Columbia VA Health Care System, Columbia, SC, USA
314 Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
315 South Carolina Statewide Cancer Prevention and Control Program, University of South Carolina, Columbia, SC, USA
316 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
317 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...