Loading…
Assessing Smoking Status and Risk of SARS-CoV-2 Infection: A Machine Learning Approach among Veterans
The role of smoking in the risk of SARS-CoV-2 infection is unclear. We used a retrospective cohort design to study data from veterans' Electronic Medical Record to assess the impact of smoking on the risk of SARS-CoV-2 infection. Veterans tested for the SARS-CoV-2 virus from 02/01/2020 to 02/28...
Saved in:
Published in: | Healthcare (Basel) 2022-07, Vol.10 (7), p.1244 |
---|---|
Main Authors: | , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c496t-a43686d48a401b4a791c33fa8c633971bab2e3010c836e76e7bc5d5f2b6f71e43 |
---|---|
cites | cdi_FETCH-LOGICAL-c496t-a43686d48a401b4a791c33fa8c633971bab2e3010c836e76e7bc5d5f2b6f71e43 |
container_end_page | |
container_issue | 7 |
container_start_page | 1244 |
container_title | Healthcare (Basel) |
container_volume | 10 |
creator | Nono Djotsa, Alice B S Helmer, Drew A Park, Catherine Lynch, Kristine E Sharafkhaneh, Amir Naik, Aanand D Razjouyan, Javad Amos, Christopher I |
description | The role of smoking in the risk of SARS-CoV-2 infection is unclear. We used a retrospective cohort design to study data from veterans' Electronic Medical Record to assess the impact of smoking on the risk of SARS-CoV-2 infection. Veterans tested for the SARS-CoV-2 virus from 02/01/2020 to 02/28/2021 were classified as: Never Smokers (NS), Former Smokers (FS), and Current Smokers (CS). We report the adjusted odds ratios (aOR) for potential confounders obtained from a cascade machine learning algorithm. We found a 19.6% positivity rate among 1,176,306 veterans tested for SARS-CoV-2 infection. The positivity proportion among NS (22.0%) was higher compared with FS (19.2%) and CS (11.5%). The adjusted odds of testing positive for CS (aOR:0.51; 95%CI: 0.50, 0.52) and FS (aOR:0.89; 95%CI:0.88, 0.90) were significantly lower compared with NS. Four pre-existing conditions, including dementia, lower respiratory infections, pneumonia, and septic shock, were associated with a higher risk of testing positive, whereas the use of the decongestant drug phenylephrine or having a history of cancer were associated with a lower risk. CS and FS compared with NS had lower risks of testing positive for SARS-CoV-2. These findings highlight our evolving understanding of the role of smoking status on the risk of SARS-CoV-2 infection. |
doi_str_mv | 10.3390/healthcare10071244 |
format | article |
fullrecord | <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_43bd0ddb47514c6bb92a6e078d6af8be</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_43bd0ddb47514c6bb92a6e078d6af8be</doaj_id><sourcerecordid>2693997078</sourcerecordid><originalsourceid>FETCH-LOGICAL-c496t-a43686d48a401b4a791c33fa8c633971bab2e3010c836e76e7bc5d5f2b6f71e43</originalsourceid><addsrcrecordid>eNplkk2LFDEQhhtR3GXdP-BBGrx4ac1XJx0PwjD4MTAi7OheQyVdPdOz3cmYdAv-ezM767KrIVBJ5X0fqkIVxUtK3nKuybsdwjDtHESkhCjKhHhSnDPGVKUJZ08fnM-Ky5T2JC9NecPr58UZr5umVoqeF7hICVPq_bbcjOHmNk4wzakE35ZXfbopQ1duFlebahmuK1aufIdu6oN_Xy7Kr-B2vcdyjRD90bs4HGLIyRLGkK_XOGEEn14UzzoYEl7exYvix6eP35dfqvW3z6vlYl05oeVUgeCyka1oQBBqBShNHecdNE7mnhW1YBlyQolruESVt3V1W3fMyk5RFPyiWJ24bYC9OcR-hPjbBOjNbSLErYE49W5AI7htSdtaoWoqnLRWM5BIVNNK6BqLmfXhxDrMdsTWoZ8iDI-gj198vzPb8MtoTrWsdQa8uQPE8HPGNJmxTw6HATyGORkmdc10LRnN0tf_SPdhjj5_1VHFtVa5sKxiJ5WLIaWI3X0xlJjjUJj_hyKbXj1s497ydwT4H0_jtGs</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2693997078</pqid></control><display><type>article</type><title>Assessing Smoking Status and Risk of SARS-CoV-2 Infection: A Machine Learning Approach among Veterans</title><source>Publicly Available Content (ProQuest)</source><source>PubMed Central</source><source>Coronavirus Research Database</source><creator>Nono Djotsa, Alice B S ; Helmer, Drew A ; Park, Catherine ; Lynch, Kristine E ; Sharafkhaneh, Amir ; Naik, Aanand D ; Razjouyan, Javad ; Amos, Christopher I</creator><creatorcontrib>Nono Djotsa, Alice B S ; Helmer, Drew A ; Park, Catherine ; Lynch, Kristine E ; Sharafkhaneh, Amir ; Naik, Aanand D ; Razjouyan, Javad ; Amos, Christopher I</creatorcontrib><description>The role of smoking in the risk of SARS-CoV-2 infection is unclear. We used a retrospective cohort design to study data from veterans' Electronic Medical Record to assess the impact of smoking on the risk of SARS-CoV-2 infection. Veterans tested for the SARS-CoV-2 virus from 02/01/2020 to 02/28/2021 were classified as: Never Smokers (NS), Former Smokers (FS), and Current Smokers (CS). We report the adjusted odds ratios (aOR) for potential confounders obtained from a cascade machine learning algorithm. We found a 19.6% positivity rate among 1,176,306 veterans tested for SARS-CoV-2 infection. The positivity proportion among NS (22.0%) was higher compared with FS (19.2%) and CS (11.5%). The adjusted odds of testing positive for CS (aOR:0.51; 95%CI: 0.50, 0.52) and FS (aOR:0.89; 95%CI:0.88, 0.90) were significantly lower compared with NS. Four pre-existing conditions, including dementia, lower respiratory infections, pneumonia, and septic shock, were associated with a higher risk of testing positive, whereas the use of the decongestant drug phenylephrine or having a history of cancer were associated with a lower risk. CS and FS compared with NS had lower risks of testing positive for SARS-CoV-2. These findings highlight our evolving understanding of the role of smoking status on the risk of SARS-CoV-2 infection.</description><identifier>ISSN: 2227-9032</identifier><identifier>EISSN: 2227-9032</identifier><identifier>DOI: 10.3390/healthcare10071244</identifier><identifier>PMID: 35885771</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Age ; Body mass index ; Cancer ; Coronaviruses ; COVID-19 ; Datasets ; Diabetes ; Feature selection ; Hispanic Americans ; Hypertension ; Infections ; Machine learning ; Pandemics ; Pre-existing conditions ; R&D ; Research & development ; Respiratory diseases ; SARS Coronavirus 2 ; Severe acute respiratory syndrome coronavirus 2 ; smoking ; Variables ; veteran</subject><ispartof>Healthcare (Basel), 2022-07, Vol.10 (7), p.1244</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 by the authors. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c496t-a43686d48a401b4a791c33fa8c633971bab2e3010c836e76e7bc5d5f2b6f71e43</citedby><cites>FETCH-LOGICAL-c496t-a43686d48a401b4a791c33fa8c633971bab2e3010c836e76e7bc5d5f2b6f71e43</cites><orcidid>0000-0003-1157-159X ; 0000-0002-8540-7023</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2693997078?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2693997078?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,38516,43895,44590,53791,53793,74412,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35885771$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Nono Djotsa, Alice B S</creatorcontrib><creatorcontrib>Helmer, Drew A</creatorcontrib><creatorcontrib>Park, Catherine</creatorcontrib><creatorcontrib>Lynch, Kristine E</creatorcontrib><creatorcontrib>Sharafkhaneh, Amir</creatorcontrib><creatorcontrib>Naik, Aanand D</creatorcontrib><creatorcontrib>Razjouyan, Javad</creatorcontrib><creatorcontrib>Amos, Christopher I</creatorcontrib><title>Assessing Smoking Status and Risk of SARS-CoV-2 Infection: A Machine Learning Approach among Veterans</title><title>Healthcare (Basel)</title><addtitle>Healthcare (Basel)</addtitle><description>The role of smoking in the risk of SARS-CoV-2 infection is unclear. We used a retrospective cohort design to study data from veterans' Electronic Medical Record to assess the impact of smoking on the risk of SARS-CoV-2 infection. Veterans tested for the SARS-CoV-2 virus from 02/01/2020 to 02/28/2021 were classified as: Never Smokers (NS), Former Smokers (FS), and Current Smokers (CS). We report the adjusted odds ratios (aOR) for potential confounders obtained from a cascade machine learning algorithm. We found a 19.6% positivity rate among 1,176,306 veterans tested for SARS-CoV-2 infection. The positivity proportion among NS (22.0%) was higher compared with FS (19.2%) and CS (11.5%). The adjusted odds of testing positive for CS (aOR:0.51; 95%CI: 0.50, 0.52) and FS (aOR:0.89; 95%CI:0.88, 0.90) were significantly lower compared with NS. Four pre-existing conditions, including dementia, lower respiratory infections, pneumonia, and septic shock, were associated with a higher risk of testing positive, whereas the use of the decongestant drug phenylephrine or having a history of cancer were associated with a lower risk. CS and FS compared with NS had lower risks of testing positive for SARS-CoV-2. These findings highlight our evolving understanding of the role of smoking status on the risk of SARS-CoV-2 infection.</description><subject>Age</subject><subject>Body mass index</subject><subject>Cancer</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Datasets</subject><subject>Diabetes</subject><subject>Feature selection</subject><subject>Hispanic Americans</subject><subject>Hypertension</subject><subject>Infections</subject><subject>Machine learning</subject><subject>Pandemics</subject><subject>Pre-existing conditions</subject><subject>R&D</subject><subject>Research & development</subject><subject>Respiratory diseases</subject><subject>SARS Coronavirus 2</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>smoking</subject><subject>Variables</subject><subject>veteran</subject><issn>2227-9032</issn><issn>2227-9032</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>COVID</sourceid><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNplkk2LFDEQhhtR3GXdP-BBGrx4ac1XJx0PwjD4MTAi7OheQyVdPdOz3cmYdAv-ezM767KrIVBJ5X0fqkIVxUtK3nKuybsdwjDtHESkhCjKhHhSnDPGVKUJZ08fnM-Ky5T2JC9NecPr58UZr5umVoqeF7hICVPq_bbcjOHmNk4wzakE35ZXfbopQ1duFlebahmuK1aufIdu6oN_Xy7Kr-B2vcdyjRD90bs4HGLIyRLGkK_XOGEEn14UzzoYEl7exYvix6eP35dfqvW3z6vlYl05oeVUgeCyka1oQBBqBShNHecdNE7mnhW1YBlyQolruESVt3V1W3fMyk5RFPyiWJ24bYC9OcR-hPjbBOjNbSLErYE49W5AI7htSdtaoWoqnLRWM5BIVNNK6BqLmfXhxDrMdsTWoZ8iDI-gj198vzPb8MtoTrWsdQa8uQPE8HPGNJmxTw6HATyGORkmdc10LRnN0tf_SPdhjj5_1VHFtVa5sKxiJ5WLIaWI3X0xlJjjUJj_hyKbXj1s497ydwT4H0_jtGs</recordid><startdate>20220704</startdate><enddate>20220704</enddate><creator>Nono Djotsa, Alice B S</creator><creator>Helmer, Drew A</creator><creator>Park, Catherine</creator><creator>Lynch, Kristine E</creator><creator>Sharafkhaneh, Amir</creator><creator>Naik, Aanand D</creator><creator>Razjouyan, Javad</creator><creator>Amos, Christopher I</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7XB</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>KB0</scope><scope>M2O</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-1157-159X</orcidid><orcidid>https://orcid.org/0000-0002-8540-7023</orcidid></search><sort><creationdate>20220704</creationdate><title>Assessing Smoking Status and Risk of SARS-CoV-2 Infection: A Machine Learning Approach among Veterans</title><author>Nono Djotsa, Alice B S ; Helmer, Drew A ; Park, Catherine ; Lynch, Kristine E ; Sharafkhaneh, Amir ; Naik, Aanand D ; Razjouyan, Javad ; Amos, Christopher I</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c496t-a43686d48a401b4a791c33fa8c633971bab2e3010c836e76e7bc5d5f2b6f71e43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Age</topic><topic>Body mass index</topic><topic>Cancer</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Datasets</topic><topic>Diabetes</topic><topic>Feature selection</topic><topic>Hispanic Americans</topic><topic>Hypertension</topic><topic>Infections</topic><topic>Machine learning</topic><topic>Pandemics</topic><topic>Pre-existing conditions</topic><topic>R&D</topic><topic>Research & development</topic><topic>Respiratory diseases</topic><topic>SARS Coronavirus 2</topic><topic>Severe acute respiratory syndrome coronavirus 2</topic><topic>smoking</topic><topic>Variables</topic><topic>veteran</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nono Djotsa, Alice B S</creatorcontrib><creatorcontrib>Helmer, Drew A</creatorcontrib><creatorcontrib>Park, Catherine</creatorcontrib><creatorcontrib>Lynch, Kristine E</creatorcontrib><creatorcontrib>Sharafkhaneh, Amir</creatorcontrib><creatorcontrib>Naik, Aanand D</creatorcontrib><creatorcontrib>Razjouyan, Javad</creatorcontrib><creatorcontrib>Amos, Christopher I</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Nursing & Allied Health Database</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Research Library</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Healthcare (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nono Djotsa, Alice B S</au><au>Helmer, Drew A</au><au>Park, Catherine</au><au>Lynch, Kristine E</au><au>Sharafkhaneh, Amir</au><au>Naik, Aanand D</au><au>Razjouyan, Javad</au><au>Amos, Christopher I</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessing Smoking Status and Risk of SARS-CoV-2 Infection: A Machine Learning Approach among Veterans</atitle><jtitle>Healthcare (Basel)</jtitle><addtitle>Healthcare (Basel)</addtitle><date>2022-07-04</date><risdate>2022</risdate><volume>10</volume><issue>7</issue><spage>1244</spage><pages>1244-</pages><issn>2227-9032</issn><eissn>2227-9032</eissn><abstract>The role of smoking in the risk of SARS-CoV-2 infection is unclear. We used a retrospective cohort design to study data from veterans' Electronic Medical Record to assess the impact of smoking on the risk of SARS-CoV-2 infection. Veterans tested for the SARS-CoV-2 virus from 02/01/2020 to 02/28/2021 were classified as: Never Smokers (NS), Former Smokers (FS), and Current Smokers (CS). We report the adjusted odds ratios (aOR) for potential confounders obtained from a cascade machine learning algorithm. We found a 19.6% positivity rate among 1,176,306 veterans tested for SARS-CoV-2 infection. The positivity proportion among NS (22.0%) was higher compared with FS (19.2%) and CS (11.5%). The adjusted odds of testing positive for CS (aOR:0.51; 95%CI: 0.50, 0.52) and FS (aOR:0.89; 95%CI:0.88, 0.90) were significantly lower compared with NS. Four pre-existing conditions, including dementia, lower respiratory infections, pneumonia, and septic shock, were associated with a higher risk of testing positive, whereas the use of the decongestant drug phenylephrine or having a history of cancer were associated with a lower risk. CS and FS compared with NS had lower risks of testing positive for SARS-CoV-2. These findings highlight our evolving understanding of the role of smoking status on the risk of SARS-CoV-2 infection.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>35885771</pmid><doi>10.3390/healthcare10071244</doi><orcidid>https://orcid.org/0000-0003-1157-159X</orcidid><orcidid>https://orcid.org/0000-0002-8540-7023</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2227-9032 |
ispartof | Healthcare (Basel), 2022-07, Vol.10 (7), p.1244 |
issn | 2227-9032 2227-9032 |
language | eng |
recordid | cdi_doaj_primary_oai_doaj_org_article_43bd0ddb47514c6bb92a6e078d6af8be |
source | Publicly Available Content (ProQuest); PubMed Central; Coronavirus Research Database |
subjects | Age Body mass index Cancer Coronaviruses COVID-19 Datasets Diabetes Feature selection Hispanic Americans Hypertension Infections Machine learning Pandemics Pre-existing conditions R&D Research & development Respiratory diseases SARS Coronavirus 2 Severe acute respiratory syndrome coronavirus 2 smoking Variables veteran |
title | Assessing Smoking Status and Risk of SARS-CoV-2 Infection: A Machine Learning Approach among Veterans |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-19T16%3A48%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Assessing%20Smoking%20Status%20and%20Risk%20of%20SARS-CoV-2%20Infection:%20A%20Machine%20Learning%20Approach%20among%20Veterans&rft.jtitle=Healthcare%20(Basel)&rft.au=Nono%20Djotsa,%20Alice%20B%20S&rft.date=2022-07-04&rft.volume=10&rft.issue=7&rft.spage=1244&rft.pages=1244-&rft.issn=2227-9032&rft.eissn=2227-9032&rft_id=info:doi/10.3390/healthcare10071244&rft_dat=%3Cproquest_doaj_%3E2693997078%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c496t-a43686d48a401b4a791c33fa8c633971bab2e3010c836e76e7bc5d5f2b6f71e43%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2693997078&rft_id=info:pmid/35885771&rfr_iscdi=true |