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/None", 
          "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/None", 
          "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/None", 
          "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/s10552-008-9256-0", 
        "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.10.1007/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/s10552-012-0101-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003794370", 
          "https://doi.org/10.1007/s10552-012-0101-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/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.10.1186/1476-072x-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.10.1186/1476-072x-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-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", 
      "neighborhood social vulnerability", 
      "prostate cancer incidence", 
      "Veterans Administration (VA) medical system", 
      "important risk factor", 
      "PrCa risk", 
      "medical record database", 
      "individual-level risk factors", 
      "cancer-free controls", 
      "continuous composite measures", 
      "cancer incidence", 
      "care coordination", 
      "prostate cancer", 
      "PRCA patients", 
      "higher odds", 
      "area-level predictors", 
      "record database", 
      "US Census data", 
      "marital status", 
      "incidence", 
      "status disparities", 
      "zip codes", 
      "individual-level factors", 
      "patients", 
      "veterans", 
      "high rate", 
      "composite measure", 
      "medical system", 
      "age", 
      "risk", 
      "conditional autoregressive model", 
      "US Census", 
      "PrCa", 
      "random effects", 
      "factors", 
      "tertile", 
      "BackgroundRacial", 
      "MethodsFrom", 
      "geographic areas", 
      "social vulnerability", 
      "cancer", 
      "mortality", 
      "epidemiology", 
      "care", 
      "South Carolina", 
      "ResultsA", 
      "odds", 
      "social vulnerability index", 
      "ConclusionsWhile", 
      "ZCTA", 
      "predictors", 
      "race", 
      "screening", 
      "equal access", 
      "EMRs", 
      "status", 
      "healthcare", 
      "disparities", 
      "census data", 
      "vulnerability", 
      "individuals", 
      "purpose", 
      "index", 
      "data", 
      "control", 
      "area", 
      "outreach", 
      "database", 
      "measures", 
      "rate", 
      "diverse individuals", 
      "effect", 
      "multivariate conditional autoregressive models", 
      "blacks", 
      "spatial random effects", 
      "unique means", 
      "geographical factors", 
      "access", 
      "education", 
      "census", 
      "Carolina", 
      "analysis", 
      "model", 
      "means", 
      "research", 
      "coordination", 
      "spatial analysis", 
      "system", 
      "vulnerability index", 
      "distance", 
      "code", 
      "autoregressive model", 
      "autocorrelation", 
      "socio-economic status (SES) disparities", 
      "Administration Medical System", 
      "PrCA epidemiology", 
      "area-level characteristics influence PrCA incidence", 
      "characteristics influence PrCA incidence", 
      "influence PrCA incidence", 
      "US Veteran\u2019s Health Administration (VHA) electronic medical records (EMR) database", 
      "Veteran\u2019s Health Administration (VHA) electronic medical records (EMR) database", 
      "\u2019s Health Administration (VHA) electronic medical records (EMR) database", 
      "Administration (VHA) electronic medical records (EMR) database", 
      "VHA EMRs", 
      "area-level vulnerability", 
      "Bayesian multivariate conditional autoregressive model", 
      "greatest level social vulnerability", 
      "level social vulnerability", 
      "vulnerable ZCTA\u2019s", 
      "average social vulnerability", 
      "incident PrCA", 
      "South Carolina veterans", 
      "Carolina veterans"
    ], 
    "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": "2022-01-01T18:55", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/article/article_861.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.

337 TRIPLES      22 PREDICATES      167 URIs      151 LITERALS      24 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10552-019-01263-2 schema:about N01a0c27c124748efbd904246db313e60
2 N1031dc91b4f843e09e8141850bedc193
3 N264c296ca9ac4107bc2087ed2f7e5c74
4 N2fbc51c0b3fa4cfeada3cedd93eb480d
5 N32854f4058454f13914e87d7689de81c
6 N45aaea33dea6466cbcca028a00ff431e
7 N585fc400b5b743da8a776b7ca8939768
8 N58ad56d512154eb39574929c377cbbed
9 N5fb05e07826244119103c40cf0fd4468
10 Nb519072461ac41a2a0669a7e59969d8f
11 Nba7aee6cab844ae3b943a89a0991508d
12 Nbf7ddb06054e4dbe97c462cfcf8dcb0b
13 Ncc2209119cf844d2b38dc8411c588a78
14 Nce23dfc8c191406289ed96dbd04e3ef1
15 Nd414190473ab46918508dd6788c5ecc9
16 Ne6178ed13ea443f99049a74504d879a7
17 Nf7f1dc6e0ec248f7b52b9d4f41fbc272
18 anzsrc-for:11
19 anzsrc-for:1117
20 schema:author Neefd7ffea1ef4648b4708cbe69ca4ce1
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 N62cf96a23bf34451a6940e6c4a532104
36 Nc23a680bdb5d49efacffe37f21767dee
37 sg:journal.1100917
38 schema:keywords Administration (VHA) electronic medical records (EMR) database
39 Administration Medical System
40 BackgroundRacial
41 Bayesian multivariate conditional autoregressive model
42 Carolina
43 Carolina veterans
44 ConclusionsWhile
45 EMRs
46 MethodsFrom
47 PRCA patients
48 PrCA epidemiology
49 PrCA incidence
50 PrCa
51 PrCa risk
52 ResultsA
53 South Carolina
54 South Carolina veterans
55 US Census
56 US Census data
57 US Veteran’s Health Administration (VHA) electronic medical records (EMR) database
58 VHA EMRs
59 Veterans Administration (VA) medical system
60 Veteran’s Health Administration (VHA) electronic medical records (EMR) database
61 ZCTA
62 access
63 age
64 analysis
65 area
66 area-level characteristics influence PrCA incidence
67 area-level factors
68 area-level predictors
69 area-level vulnerability
70 autocorrelation
71 autoregressive model
72 average social vulnerability
73 blacks
74 cancer
75 cancer incidence
76 cancer-free controls
77 care
78 care coordination
79 census
80 census data
81 characteristics influence PrCA incidence
82 code
83 composite measure
84 conditional autoregressive model
85 continuous composite measures
86 control
87 coordination
88 data
89 database
90 disparities
91 distance
92 diverse individuals
93 education
94 effect
95 electronic medical record database
96 epidemiology
97 equal access
98 factors
99 geographic areas
100 geographical factors
101 greatest level social vulnerability
102 healthcare
103 high rate
104 higher odds
105 important risk factor
106 incidence
107 incident PrCA
108 index
109 individual-level factors
110 individual-level risk factors
111 individuals
112 influence PrCA incidence
113 level social vulnerability
114 marital status
115 means
116 measures
117 medical record database
118 medical system
119 model
120 mortality
121 multivariate conditional autoregressive models
122 neighborhood social vulnerability
123 odds
124 outreach
125 patients
126 predictors
127 prostate cancer
128 prostate cancer incidence
129 purpose
130 race
131 random effects
132 rate
133 record database
134 research
135 risk
136 risk factors
137 screening
138 social vulnerability
139 social vulnerability index
140 socio-economic status (SES) disparities
141 spatial analysis
142 spatial random effects
143 status
144 status disparities
145 system
146 tertile
147 unique means
148 veterans
149 vulnerability
150 vulnerability index
151 vulnerable ZCTA’s
152 zip codes
153 ’s Health Administration (VHA) electronic medical records (EMR) database
154 schema:name Patient- and area-level predictors of prostate cancer among South Carolina veterans: a spatial analysis
155 schema:pagination 209-220
156 schema:productId N242d2f5a00fe462e85e0a5743cc425c0
157 N80bd6ba7513a4d33aacbb558c30c3999
158 N97fcfee7d4154a3486df67d7e9170b52
159 schema:sameAs https://app.dimensions.ai/details/publication/pub.1124278193
160 https://doi.org/10.1007/s10552-019-01263-2
161 schema:sdDatePublished 2022-01-01T18:55
162 schema:sdLicense https://scigraph.springernature.com/explorer/license/
163 schema:sdPublisher N85975b58967543bfa0c650633b2cb8fb
164 schema:url https://doi.org/10.1007/s10552-019-01263-2
165 sgo:license sg:explorer/license/
166 sgo:sdDataset articles
167 rdf:type schema:ScholarlyArticle
168 N01a0c27c124748efbd904246db313e60 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
169 schema:name Bayes Theorem
170 rdf:type schema:DefinedTerm
171 N1031dc91b4f843e09e8141850bedc193 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
172 schema:name South Carolina
173 rdf:type schema:DefinedTerm
174 N16372185439c40fab2b3e90fe2cddbf4 rdf:first sg:person.07351167613.31
175 rdf:rest Nefe8e9682e03430ab3d3c70fb4b60fbe
176 N242d2f5a00fe462e85e0a5743cc425c0 schema:name doi
177 schema:value 10.1007/s10552-019-01263-2
178 rdf:type schema:PropertyValue
179 N264c296ca9ac4107bc2087ed2f7e5c74 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
180 schema:name Prostatic Neoplasms
181 rdf:type schema:DefinedTerm
182 N2fbc51c0b3fa4cfeada3cedd93eb480d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
183 schema:name Social Class
184 rdf:type schema:DefinedTerm
185 N32854f4058454f13914e87d7689de81c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
186 schema:name Retrospective Studies
187 rdf:type schema:DefinedTerm
188 N45aaea33dea6466cbcca028a00ff431e schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
189 schema:name Spatial Analysis
190 rdf:type schema:DefinedTerm
191 N4eff9f2cd4c34543ac0ec9b0e62af831 rdf:first sg:person.0737533572.20
192 rdf:rest N4fc9dac13b6f4189bae11efdd33d07cb
193 N4fc9dac13b6f4189bae11efdd33d07cb rdf:first sg:person.01114577174.95
194 rdf:rest N16372185439c40fab2b3e90fe2cddbf4
195 N52e904d3a5174d10bb31fd36c562a80e rdf:first sg:person.01100705232.37
196 rdf:rest N7ffc26bf677546ec80cc701794ebffdf
197 N585fc400b5b743da8a776b7ca8939768 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
198 schema:name Male
199 rdf:type schema:DefinedTerm
200 N58ad56d512154eb39574929c377cbbed schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
201 schema:name Adult
202 rdf:type schema:DefinedTerm
203 N5fb05e07826244119103c40cf0fd4468 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
204 schema:name Censuses
205 rdf:type schema:DefinedTerm
206 N62cf96a23bf34451a6940e6c4a532104 schema:issueNumber 3
207 rdf:type schema:PublicationIssue
208 N7ffc26bf677546ec80cc701794ebffdf rdf:first sg:person.01317027407.74
209 rdf:rest Nbc988223fd8344fb9b23a847bf4ebf5e
210 N80bd6ba7513a4d33aacbb558c30c3999 schema:name dimensions_id
211 schema:value pub.1124278193
212 rdf:type schema:PropertyValue
213 N85975b58967543bfa0c650633b2cb8fb schema:name Springer Nature - SN SciGraph project
214 rdf:type schema:Organization
215 N97fcfee7d4154a3486df67d7e9170b52 schema:name pubmed_id
216 schema:value 31975155
217 rdf:type schema:PropertyValue
218 Nb519072461ac41a2a0669a7e59969d8f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
219 schema:name Veterans
220 rdf:type schema:DefinedTerm
221 Nba7aee6cab844ae3b943a89a0991508d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
222 schema:name Residence Characteristics
223 rdf:type schema:DefinedTerm
224 Nbc988223fd8344fb9b23a847bf4ebf5e rdf:first sg:person.01064021255.05
225 rdf:rest rdf:nil
226 Nbf7ddb06054e4dbe97c462cfcf8dcb0b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
227 schema:name Middle Aged
228 rdf:type schema:DefinedTerm
229 Nc23a680bdb5d49efacffe37f21767dee schema:volumeNumber 31
230 rdf:type schema:PublicationVolume
231 Ncc2209119cf844d2b38dc8411c588a78 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
232 schema:name Incidence
233 rdf:type schema:DefinedTerm
234 Nce23dfc8c191406289ed96dbd04e3ef1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
235 schema:name Humans
236 rdf:type schema:DefinedTerm
237 Nd414190473ab46918508dd6788c5ecc9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
238 schema:name Mass Screening
239 rdf:type schema:DefinedTerm
240 Ne6178ed13ea443f99049a74504d879a7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
241 schema:name Risk Factors
242 rdf:type schema:DefinedTerm
243 Neefd7ffea1ef4648b4708cbe69ca4ce1 rdf:first sg:person.01027066506.09
244 rdf:rest N4eff9f2cd4c34543ac0ec9b0e62af831
245 Nefe8e9682e03430ab3d3c70fb4b60fbe rdf:first sg:person.0767264137.17
246 rdf:rest N52e904d3a5174d10bb31fd36c562a80e
247 Nf7f1dc6e0ec248f7b52b9d4f41fbc272 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
248 schema:name Aged
249 rdf:type schema:DefinedTerm
250 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
251 schema:name Medical and Health Sciences
252 rdf:type schema:DefinedTerm
253 anzsrc-for:1117 schema:inDefinedTermSet anzsrc-for:
254 schema:name Public Health and Health Services
255 rdf:type schema:DefinedTerm
256 sg:journal.1100917 schema:issn 0957-5243
257 1573-7225
258 schema:name Cancer Causes & Control
259 schema:publisher Springer Nature
260 rdf:type schema:Periodical
261 sg:person.01027066506.09 schema:affiliation grid-institutes:None
262 schema:familyName Georgantopoulos
263 schema:givenName Peter
264 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01027066506.09
265 rdf:type schema:Person
266 sg:person.01064021255.05 schema:affiliation grid-institutes:grid.254567.7
267 schema:familyName Hébert
268 schema:givenName James R.
269 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01064021255.05
270 rdf:type schema:Person
271 sg:person.01100705232.37 schema:affiliation grid-institutes:None
272 schema:familyName Bennett
273 schema:givenName Charles L.
274 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01100705232.37
275 rdf:type schema:Person
276 sg:person.01114577174.95 schema:affiliation grid-institutes:grid.254567.7
277 schema:familyName Cai
278 schema:givenName Bo
279 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01114577174.95
280 rdf:type schema:Person
281 sg:person.01317027407.74 schema:affiliation grid-institutes:None
282 schema:familyName Haddock
283 schema:givenName Kathlyn S.
284 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01317027407.74
285 rdf:type schema:Person
286 sg:person.07351167613.31 schema:affiliation grid-institutes:grid.170430.1
287 schema:familyName Emrich
288 schema:givenName Christopher
289 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07351167613.31
290 rdf:type schema:Person
291 sg:person.0737533572.20 schema:affiliation grid-institutes:grid.254567.7
292 schema:familyName Eberth
293 schema:givenName Jan M.
294 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0737533572.20
295 rdf:type schema:Person
296 sg:person.0767264137.17 schema:affiliation grid-institutes:grid.254567.7
297 schema:familyName Rao
298 schema:givenName Gowtham
299 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0767264137.17
300 rdf:type schema:Person
301 sg:pub.10.1007/s10552-004-1291-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1017160670
302 https://doi.org/10.1007/s10552-004-1291-x
303 rdf:type schema:CreativeWork
304 sg:pub.10.1007/s10552-008-9256-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000168956
305 https://doi.org/10.1007/s10552-008-9256-0
306 rdf:type schema:CreativeWork
307 sg:pub.10.1007/s10552-009-9369-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004069429
308 https://doi.org/10.1007/s10552-009-9369-0
309 rdf:type schema:CreativeWork
310 sg:pub.10.1007/s10552-012-0101-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003794370
311 https://doi.org/10.1007/s10552-012-0101-0
312 rdf:type schema:CreativeWork
313 sg:pub.10.1186/1476-072x-10-63 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003564548
314 https://doi.org/10.1186/1476-072x-10-63
315 rdf:type schema:CreativeWork
316 sg:pub.10.1186/1476-072x-11-15 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035582295
317 https://doi.org/10.1186/1476-072x-11-15
318 rdf:type schema:CreativeWork
319 sg:pub.10.1186/1476-072x-5-58 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044298735
320 https://doi.org/10.1186/1476-072x-5-58
321 rdf:type schema:CreativeWork
322 sg:pub.10.1186/1476-072x-5-59 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002819870
323 https://doi.org/10.1186/1476-072x-5-59
324 rdf:type schema:CreativeWork
325 grid-institutes:None schema:alternateName Columbia VA Health Care System, Columbia, SC, USA
326 schema:name Columbia VA Health Care System, Columbia, SC, USA
327 Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
328 South Carolina Statewide Cancer Prevention and Control Program, University of South Carolina, Columbia, SC, USA
329 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
330 rdf:type schema:Organization
331 grid-institutes:grid.170430.1 schema:alternateName College of Health and Public Affairs, University of Central Florida, Orlando, USA
332 schema:name College of Health and Public Affairs, University of Central Florida, Orlando, USA
333 rdf:type schema:Organization
334 grid-institutes:grid.254567.7 schema:alternateName Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
335 schema:name Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
336 South Carolina Statewide Cancer Prevention and Control Program, University of South Carolina, Columbia, SC, USA
337 rdf:type schema:Organization
 




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


...