Using a Bayesian analytic approach to identify county-level ecological factors associated with survival among individuals with early-onset colorectal cancer
In the United States (US), incidence of early age of onset colorectal cancer (EOCRC, diagnosed
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| creator | Siddique, Sunny Baum, Laura V M Deziel, Nicole C Kelly, Jill R Warren, Joshua L Ma, Xiaomei |
| description | In the United States (US), incidence of early age of onset colorectal cancer (EOCRC, diagnosed |
| doi_str_mv | 10.1371/journal.pone.0311540 |
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Principal component (PC) analysis was used to reduce dimensionality of 36 county-level social, behavioral, and preventive factors from the Centers for Disease Control and Prevention data. Survival information was derived from the Surveillance, Epidemiology, and End Results Program data from January 1, 2000 to December 31, 2019. The association between the identified PCs and survival was evaluated using multivariable spatial generalized linear mixed models. Counties with residual low and high survival (i.e., unexplained by the PCs) were classified as hotspots and coldspots, respectively.
Four PCs were used to explain the spatial variability in 5-year survival among 75,215 individuals with EOCRC: PC1) poverty, chronic disease, health risk behaviors (β = -0.03, 95% credible interval (CrI): -0.04, -0.03); PC2) younger age, chronic disease-free, minority status (β = -0.01, 95% CrI: -0.02, 0.00); PC3) urban environment, preventive services (β = 0.02, 95% CrI: 0.00, 0.03); and PC4) older age (-0.04, 95% CrI: -0.06, -0.02). Among individuals with distant malignancies, the residual spatial variability remained high for two US counties: 1) Salt Lake County, UT residents experiencing 26.5% (95% CrI: 1.5%, 47.8%) lower odds of survival [hotspot], and 2) Riverside County, CA residents experiencing 37% (95% CrI: 7.97%, 78.8%) higher odds survival [coldspot] after adjustment for county-level factors.
County-level ecological factors are strongly associated with survival among individuals with EOCRC. Yet there is some evidence of survival disparities among individuals with distant malignancies that remain unexplained by the included factors.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0311540</identifier><identifier>PMID: 39471191</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Age ; Age of Onset ; Bayes Theorem ; Bayesian analysis ; Bayesian statistical decision theory ; Cancer ; Cardiovascular disease ; Care and treatment ; Chemotherapy ; Chronic illnesses ; Colorectal cancer ; Colorectal carcinoma ; Colorectal Neoplasms - epidemiology ; Colorectal Neoplasms - mortality ; Disease control ; Disease hot spots ; Disease prevention ; Ecology and Environmental Sciences ; Environmental aspects ; Epidemiology ; Female ; Health behavior ; Health risk assessment ; Health risks ; Human ecology ; Humans ; Male ; Malignancy ; Mathematical models ; Medicine and Health Sciences ; Middle Aged ; Minority & ethnic groups ; Multivariable control ; Older people ; Patient outcomes ; Poverty ; Principal Component Analysis ; Principal components analysis ; Risk Factors ; Risk taking ; SEER Program ; Spatial variability ; Statistical models ; Surveillance ; Survival ; Tumors ; United States - epidemiology ; Urban environments ; Variables</subject><ispartof>PloS one, 2024-10, Vol.19 (10), p.e0311540</ispartof><rights>Copyright: © 2024 Siddique et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2024 Public Library of Science</rights><rights>2024 Siddique et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2024 Siddique et al 2024 Siddique et al</rights><rights>2024 Siddique et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c506t-dedb186eaa540ef2413672a03231bcc1dccdf392b25cb44cd3fec79349ccecbc3</cites><orcidid>0000-0002-4853-6213 ; 0000-0002-5751-9191 ; 0000-0002-6274-6970 ; 0000-0002-5746-1161</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11521299/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11521299/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2915,23847,27905,27906,53772,53774,79349,79350</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39471191$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Chen, Zhuo</contributor><creatorcontrib>Siddique, Sunny</creatorcontrib><creatorcontrib>Baum, Laura V M</creatorcontrib><creatorcontrib>Deziel, Nicole C</creatorcontrib><creatorcontrib>Kelly, Jill R</creatorcontrib><creatorcontrib>Warren, Joshua L</creatorcontrib><creatorcontrib>Ma, Xiaomei</creatorcontrib><title>Using a Bayesian analytic approach to identify county-level ecological factors associated with survival among individuals with early-onset colorectal cancer</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>In the United States (US), incidence of early age of onset colorectal cancer (EOCRC, diagnosed <50 years of age) has been increasing. Using a Bayesian analytic approach, we evaluated the association between county-level ecological factors and survival among individuals with EOCRC and identified hotspot and coldspot counties with unexplained low and high survival, respectively.
Principal component (PC) analysis was used to reduce dimensionality of 36 county-level social, behavioral, and preventive factors from the Centers for Disease Control and Prevention data. Survival information was derived from the Surveillance, Epidemiology, and End Results Program data from January 1, 2000 to December 31, 2019. The association between the identified PCs and survival was evaluated using multivariable spatial generalized linear mixed models. Counties with residual low and high survival (i.e., unexplained by the PCs) were classified as hotspots and coldspots, respectively.
Four PCs were used to explain the spatial variability in 5-year survival among 75,215 individuals with EOCRC: PC1) poverty, chronic disease, health risk behaviors (β = -0.03, 95% credible interval (CrI): -0.04, -0.03); PC2) younger age, chronic disease-free, minority status (β = -0.01, 95% CrI: -0.02, 0.00); PC3) urban environment, preventive services (β = 0.02, 95% CrI: 0.00, 0.03); and PC4) older age (-0.04, 95% CrI: -0.06, -0.02). Among individuals with distant malignancies, the residual spatial variability remained high for two US counties: 1) Salt Lake County, UT residents experiencing 26.5% (95% CrI: 1.5%, 47.8%) lower odds of survival [hotspot], and 2) Riverside County, CA residents experiencing 37% (95% CrI: 7.97%, 78.8%) higher odds survival [coldspot] after adjustment for county-level factors.
County-level ecological factors are strongly associated with survival among individuals with EOCRC. Yet there is some evidence of survival disparities among individuals with distant malignancies that remain unexplained by the included factors.</description><subject>Adult</subject><subject>Age</subject><subject>Age of Onset</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Bayesian statistical decision theory</subject><subject>Cancer</subject><subject>Cardiovascular disease</subject><subject>Care and treatment</subject><subject>Chemotherapy</subject><subject>Chronic illnesses</subject><subject>Colorectal cancer</subject><subject>Colorectal carcinoma</subject><subject>Colorectal Neoplasms - epidemiology</subject><subject>Colorectal Neoplasms - mortality</subject><subject>Disease control</subject><subject>Disease hot spots</subject><subject>Disease prevention</subject><subject>Ecology and Environmental Sciences</subject><subject>Environmental aspects</subject><subject>Epidemiology</subject><subject>Female</subject><subject>Health behavior</subject><subject>Health risk assessment</subject><subject>Health risks</subject><subject>Human ecology</subject><subject>Humans</subject><subject>Male</subject><subject>Malignancy</subject><subject>Mathematical models</subject><subject>Medicine and Health Sciences</subject><subject>Middle Aged</subject><subject>Minority & ethnic groups</subject><subject>Multivariable control</subject><subject>Older people</subject><subject>Patient outcomes</subject><subject>Poverty</subject><subject>Principal Component Analysis</subject><subject>Principal components analysis</subject><subject>Risk Factors</subject><subject>Risk taking</subject><subject>SEER Program</subject><subject>Spatial variability</subject><subject>Statistical models</subject><subject>Surveillance</subject><subject>Survival</subject><subject>Tumors</subject><subject>United States - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Siddique, Sunny</au><au>Baum, Laura V M</au><au>Deziel, Nicole C</au><au>Kelly, Jill R</au><au>Warren, Joshua L</au><au>Ma, Xiaomei</au><au>Chen, Zhuo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using a Bayesian analytic approach to identify county-level ecological factors associated with survival among individuals with early-onset colorectal cancer</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2024-10-29</date><risdate>2024</risdate><volume>19</volume><issue>10</issue><spage>e0311540</spage><pages>e0311540-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>In the United States (US), incidence of early age of onset colorectal cancer (EOCRC, diagnosed <50 years of age) has been increasing. Using a Bayesian analytic approach, we evaluated the association between county-level ecological factors and survival among individuals with EOCRC and identified hotspot and coldspot counties with unexplained low and high survival, respectively.
Principal component (PC) analysis was used to reduce dimensionality of 36 county-level social, behavioral, and preventive factors from the Centers for Disease Control and Prevention data. Survival information was derived from the Surveillance, Epidemiology, and End Results Program data from January 1, 2000 to December 31, 2019. The association between the identified PCs and survival was evaluated using multivariable spatial generalized linear mixed models. Counties with residual low and high survival (i.e., unexplained by the PCs) were classified as hotspots and coldspots, respectively.
Four PCs were used to explain the spatial variability in 5-year survival among 75,215 individuals with EOCRC: PC1) poverty, chronic disease, health risk behaviors (β = -0.03, 95% credible interval (CrI): -0.04, -0.03); PC2) younger age, chronic disease-free, minority status (β = -0.01, 95% CrI: -0.02, 0.00); PC3) urban environment, preventive services (β = 0.02, 95% CrI: 0.00, 0.03); and PC4) older age (-0.04, 95% CrI: -0.06, -0.02). Among individuals with distant malignancies, the residual spatial variability remained high for two US counties: 1) Salt Lake County, UT residents experiencing 26.5% (95% CrI: 1.5%, 47.8%) lower odds of survival [hotspot], and 2) Riverside County, CA residents experiencing 37% (95% CrI: 7.97%, 78.8%) higher odds survival [coldspot] after adjustment for county-level factors.
County-level ecological factors are strongly associated with survival among individuals with EOCRC. Yet there is some evidence of survival disparities among individuals with distant malignancies that remain unexplained by the included factors.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>39471191</pmid><doi>10.1371/journal.pone.0311540</doi><tpages>e0311540</tpages><orcidid>https://orcid.org/0000-0002-4853-6213</orcidid><orcidid>https://orcid.org/0000-0002-5751-9191</orcidid><orcidid>https://orcid.org/0000-0002-6274-6970</orcidid><orcidid>https://orcid.org/0000-0002-5746-1161</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS); PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Adult Age Age of Onset Bayes Theorem Bayesian analysis Bayesian statistical decision theory Cancer Cardiovascular disease Care and treatment Chemotherapy Chronic illnesses Colorectal cancer Colorectal carcinoma Colorectal Neoplasms - epidemiology Colorectal Neoplasms - mortality Disease control Disease hot spots Disease prevention Ecology and Environmental Sciences Environmental aspects Epidemiology Female Health behavior Health risk assessment Health risks Human ecology Humans Male Malignancy Mathematical models Medicine and Health Sciences Middle Aged Minority & ethnic groups Multivariable control Older people Patient outcomes Poverty Principal Component Analysis Principal components analysis Risk Factors Risk taking SEER Program Spatial variability Statistical models Surveillance Survival Tumors United States - epidemiology Urban environments Variables |
| title | Using a Bayesian analytic approach to identify county-level ecological factors associated with survival among individuals with early-onset colorectal cancer |
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