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Influence of social deprivation index on in-hospital outcomes of COVID-19

While it is known that social deprivation index (SDI) plays an important role on risk for acquiring Coronavirus Disease 2019 (COVID-19), the impact of SDI on in-hospital outcomes such as intubation and mortality are less well-characterized. We analyzed electronic health record data of adults hospita...

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Detalles Bibliográficos
Autores principales: Goyal, Parag, Schenck, Edward, Wu, Yiyuan, Zhang, Yongkang, Visaria, Aayush, Orlander, Duncan, Xi, Wenna, Díaz, Iván, Morozyuk, Dmitry, Weiner, Mark, Kaushal, Rainu, Banerjee, Samprit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9887560/
https://www.ncbi.nlm.nih.gov/pubmed/36720999
http://dx.doi.org/10.1038/s41598-023-28362-0
Descripción
Sumario:While it is known that social deprivation index (SDI) plays an important role on risk for acquiring Coronavirus Disease 2019 (COVID-19), the impact of SDI on in-hospital outcomes such as intubation and mortality are less well-characterized. We analyzed electronic health record data of adults hospitalized with confirmed COVID-19 between March 1, 2020 and February 8, 2021 from the INSIGHT Clinical Research Network (CRN). To compute the SDI (exposure variable), we linked clinical data using patient’s residential zip-code with social data at zip-code tabulation area. SDI is a composite of seven socioeconomic characteristics determinants at the zip-code level. For this analysis, we categorized SDI into quintiles. The two outcomes of interest were in-hospital intubation and mortality. For each outcome, we examined logistic regression and random forests to determine incremental value of SDI in predicting outcomes. We studied 30,016 included COVID-19 patients. In a logistic regression model for intubation, a model including demographics, comorbidity, and vitals had an Area under the receiver operating characteristic curve (AUROC) = 0.73 (95% CI 0.70–0.75); the addition of SDI did not improve prediction [AUROC = 0.73 (95% CI 0.71–0.75)]. In a logistic regression model for in-hospital mortality, demographics, comorbidity, and vitals had an AUROC = 0.80 (95% CI 0.79–0.82); the addition of SDI in Model 2 did not improve prediction [AUROC = 0.81 (95% CI 0.79–0.82)]. Random forests revealed similar findings. SDI did not provide incremental improvement in predicting in-hospital intubation or mortality. SDI plays an important role on who acquires COVID-19 and its severity; but once hospitalized, SDI appears less important.