Understanding racial disparities in severe maternal morbidity using Bayesian network analysis

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dc.contributor.authorRezaeiahari, Mandanaen
dc.contributor.authorBrown, Clare C.en
dc.contributor.authorAli, Mir M.en
dc.contributor.authorDatta, Jyotishkaen
dc.contributor.authorTilford, J. Micken
dc.coverage.countryUnited Statesen
dc.coverage.stateArkansasen
dc.date.accessioned2021-12-07T14:27:03Zen
dc.date.available2021-12-07T14:27:03Zen
dc.date.issued2021-10-01en
dc.date.updated2021-12-07T14:26:57Zen
dc.description.abstractPrevious studies have evaluated the marginal effect of various factors on the risk of severe maternal morbidity (SMM) using regression approaches. We add to this literature by utilizing a Bayesian network (BN) approach to understand the joint effects of clinical, demographic, and area-level factors. We conducted a retrospective observational study using linked birth certificate and insurance claims data from the Arkansas All-Payer Claims Database (APCD), for the years 2013 through 2017. We used various learning algorithms and measures of arc strength to choose the most robust network structure. We then performed various conditional probabilistic queries using Monte Carlo simulation to understand disparities in SMM. We found that anemia and hypertensive disorder of pregnancy may be important clinical comorbidities to target in order to reduce SMM overall as well as racial disparities in SMM.en
dc.description.versionPublished versionen
dc.format.extentPages e0259258en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0259258en
dc.identifier.eissn1932-6203en
dc.identifier.issn1932-6203en
dc.identifier.issue10 Octoberen
dc.identifier.orcidDatta, Jyotishka [0000-0001-5991-5182]en
dc.identifier.otherPMC8550416en
dc.identifier.otherPONE-D-21-09462 (PII)en
dc.identifier.pmid34705872en
dc.identifier.urihttp://hdl.handle.net/10919/106855en
dc.identifier.volume16en
dc.language.isoenen
dc.publisherPLoSen
dc.relation.urihttps://www.ncbi.nlm.nih.gov/pubmed/34705872en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subject.meshAdolescenten
dc.subject.meshAdulten
dc.subject.meshArkansasen
dc.subject.meshBayes Theoremen
dc.subject.meshFemaleen
dc.subject.meshHealth Status Disparitiesen
dc.subject.meshHumansen
dc.subject.meshInsuranceen
dc.subject.meshMaternal Healthen
dc.subject.meshMiddle Ageden
dc.subject.meshMinority Healthen
dc.subject.meshMorbidityen
dc.subject.meshPregnancyen
dc.subject.meshPregnancy Complicationsen
dc.titleUnderstanding racial disparities in severe maternal morbidity using Bayesian network analysisen
dc.title.serialPLoS ONEen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherJournal Articleen
dcterms.dateAccepted2021-10-15en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Scienceen
pubs.organisational-group/Virginia Tech/Science/Statisticsen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Science/COS T&R Facultyen

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