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Enregistrement W4313534946 · doi:10.1371/journal.pone.0279968

Community risks for SARS-CoV-2 infection among fully vaccinated US adults by rurality: A retrospective cohort study from the National COVID Cohort Collaborative

2023· article· en· W4313534946 sur OpenAlexaff
Alfred Anzalone, Jing Sun, Amanda J. Vinson, William H. Beasley, William B. Hillegass, Kimberly Murray, Brian Hendricks, Melissa Haendel, Carol Geary, Kristina L. Bailey, Corrine Hanson, Lucio Miele, Ronald Horswell, Julie A. McMurry, J. Zachary Porterfield, Michael T. Vest, H. Timothy Bunnell, Jeremy Harper, Bradley S. Price, Susan L. Santangelo, Clifford J. Rosen, James C. McClay, Sally Hodder

Notice bibliographique

RevuePLoS ONE · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueSARS-CoV-2 and COVID-19 Research
Établissements canadiensNova Scotia Health Authority
Organismes subventionnairesWake Forest Clinical and Translational Science Institute, Wake Forest School of MedicineNational Center for Advancing Translational SciencesNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesFrontiers Clinical and Translational Science Institute, University of KansasClinical and Translational Science Institute, Boston UniversitySouth Carolina Clinical and Translational Research Institute, Medical University of South CarolinaCenter for Clinical and Translational Sciences, University of Texas Health Science Center at HoustonCenter for Clinical and Translational Science, Mayo ClinicColorado Clinical and Translational Sciences InstituteCenter for Clinical and Translational Science, University of MassachusettsUniversity of Colorado DenverCenter for Clinical and Translational ResearchLeonard M. Miller School of MedicineOregon Clinical and Translational Research InstituteWeill Cornell Medical CollegeUniversity of Illinois at Urbana-ChampaignUniversity of Oklahoma Health Sciences CenterNational Institutes of HealthStony Brook UniversityLouisiana Clinical and Translational Science CenterTufts Medical CenterInstitute of Translational Health SciencesChildren's National HospitalUniversity of Arkansas for Medical SciencesVanderbilt University Medical CenterTranslational Research Institute, University of Arkansas for Medical SciencesNorthShore University HealthSystemSouthern California Clinical and Translational Science InstituteUniversity at BuffaloUniversity of RochesterAurora Health CareUniversity of North Carolina at Chapel HillChildren’s Hospital of Wisconsin Research InstituteUniversity of MiamiUniversity of South CarolinaRutgers, The State University of New JerseyInstitute for Clinical and Translational Research, University of Wisconsin, MadisonPennsylvania State UniversityVanderbilt Institute for Clinical and Translational ResearchUniversity of CincinnatiInstitute of Clinical and Translational SciencesUniversity of MichiganUniversity of OklahomaUniversity of Southern CaliforniaHarvard CatalystUniversity of MinnesotaU.S. Department of Veterans AffairsUniversity of PennsylvaniaGeorge Washington UniversityMichigan Institute for Clinical and Health ResearchUniversity of UtahJohns Hopkins UniversityBill and Melinda Gates FoundationUniversity of WashingtonOhio State UniversityWake Forest UniversityNorthwestern UniversityVanderbilt UniversityAccelerated Innovation Research Initiative Turning Top Science and Ideas into High-Impact ValuesUniversity of ChicagoGeorgia Clinical and Translational Science AllianceIrving Medical Center, Columbia UniversityVirginia Commonwealth UniversityTulane UniversityBrown UniversityUniversity of Wisconsin-MadisonChildren's Hospital ColoradoPenn State Clinical and Translational Science InstituteUniversity of Texas Medical BranchWest Virginia Clinical and Translational Science InstituteUniversity of Nebraska Medical CenterRush UniversityUniversity of Texas Health Science Center at HoustonNational Institute on Alcohol Abuse and AlcoholismSchool of Medicine, Indiana UniversityWashington University in St. LouisCarilion Clinic
Mots-clésMedicineRuralityVaccinationHazard ratioProportional hazards modelCohortRetrospective cohort studyDemographyCohort studyLogistic regressionOdds ratioConfidence intervalEnvironmental healthRural areaInternal medicineImmunology

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: While COVID-19 vaccines reduce adverse outcomes, post-vaccination SARS-CoV-2 infection remains problematic. We sought to identify community factors impacting risk for breakthrough infections (BTI) among fully vaccinated persons by rurality. METHODS: We conducted a retrospective cohort study of US adults sampled between January 1 and December 20, 2021, from the National COVID Cohort Collaborative (N3C). Using Kaplan-Meier and Cox-Proportional Hazards models adjusted for demographic differences and comorbid conditions, we assessed impact of rurality, county vaccine hesitancy, and county vaccination rates on risk of BTI over 180 days following two mRNA COVID-19 vaccinations between January 1 and September 21, 2021. Additionally, Cox Proportional Hazards models assessed the risk of infection among adults without documented vaccinations. We secondarily assessed the odds of hospitalization and adverse COVID-19 events based on vaccination status using multivariable logistic regression during the study period. RESULTS: Our study population included 566,128 vaccinated and 1,724,546 adults without documented vaccination. Among vaccinated persons, rurality was associated with an increased risk of BTI (adjusted hazard ratio [aHR] 1.53, 95% confidence interval [CI] 1.42-1.64, for urban-adjacent rural and 1.65, 1.42-1.91, for nonurban-adjacent rural) compared to urban dwellers. Compared to low vaccine-hesitant counties, higher risks of BTI were associated with medium (1.07, 1.02-1.12) and high (1.33, 1.23-1.43) vaccine-hesitant counties. Compared to counties with high vaccination rates, a higher risk of BTI was associated with dwelling in counties with low vaccination rates (1.34, 1.27-1.43) but not medium vaccination rates (1.00, 0.95-1.07). Community factors were also associated with higher odds of SARS-CoV-2 infection among persons without a documented vaccination. Vaccinated persons with SARS-CoV-2 infection during the study period had significantly lower odds of hospitalization and adverse events across all geographic areas and community exposures. CONCLUSIONS: Our findings suggest that community factors are associated with an increased risk of BTI, particularly in rural areas and counties with high vaccine hesitancy. Communities, such as those in rural and disproportionately vaccine hesitant areas, and certain groups at high risk for adverse breakthrough events, including immunosuppressed/compromised persons, should continue to receive public health focus, targeted interventions, and consistent guidance to help manage community spread as vaccination protection wanes.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,031
Score d'incertitude au seuil0,063

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,112
Tête enseignante GPT0,380
Écart entre enseignants0,268 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations9
Publié2023
Routes d'admission1
Résumé présentoui

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