Covid-19 vaccination priorities defined on machine learning
Rev. saúde pública (Online)
;
56: 1-13, 2022. tab, graf
Article
Dans Anglais
| LILACS, BBO
| ID: biblio-1365958
ABSTRACT
ABSTRACT OBJECTIVE Defining priority vaccination groups is a critical factor to reduce mortality rates. METHODS We sought to identify priority population groups for covid-19 vaccination, based on in-hospital risk of death, by using Extreme Gradient Boosting Machine Learning (ML) algorithm. We performed a retrospective cohort study comprising 49,197 patients (18 years or older), with RT-PCR-confirmed for covid-19, who were hospitalized in any of the 336 Brazilian hospitals considered in this study, from March 19th, 2020, to March 22nd, 2021. Independent variables encompassed age, sex, and chronic health conditions grouped into 179 large categories. Primary outcome was hospital discharge or in-hospital death. Priority population groups for vaccination were formed based on the different levels of in-hospital risk of death due to covid-19, from the ML model developed by taking into consideration the independent variables. All analysis were carried out in Python programming language (version 3.7) and R programming language (version 4.05). RESULTS Patients' mean age was of 60.5 ± 16.8 years (mean ± SD), mean in-hospital mortality rate was 17.9%, and the mean number of comorbidities per patient was 1.97 ± 1.85 (mean ± SD). The predictive model of in-hospital death presented area under the Receiver Operating Characteristic Curve (AUC - ROC) equal to 0.80. The investigated population was grouped into eleven (11) different risk categories, based on the variables chosen by the ML model developed in this study. CONCLUSIONS The use of ML for defining population priorities groups for vaccination, based on risk of in-hospital death, can be easily applied by health system managers
Texte intégral:
Disponible
Indice:
LILAS (Amériques)
Sujet Principal:
Vaccins contre la COVID-19
/
COVID-19
Type d'étude:
Étude observationnelle
/
Étude pronostique
/
Facteurs de risque
Limites du sujet:
Adulte
/
Adulte très âgé
/
Humains
Pays comme sujet:
Amérique du Sud
/
Brésil
langue:
Anglais
Texte intégral:
Rev. saúde pública (Online)
Thème du journal:
Sa£de P£blica
Année:
2022
Type:
Article
Pays d'affiliation:
Brésil
Institution/Pays d'affiliation:
Centro Internacional de Longevidade/BR
/
Fundação Lucas Machado/BR
/
Instituto de Acreditação e Gestão em Saúde/BR
/
Instituto de Assistência Médica ao Servidor Público Estadual de São Paulo./BR
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