tetano
Editor, Senior Moderator
Sci Rep
. 2023 Jan 31;13(1):1746.
doi: 10.1038/s41598-023-28362-0.
Influence of social deprivation index on in-hospital outcomes of COVID-19
Parag Goyal[SUP] 1 2 [/SUP], Edward Schenck[SUP] 1 2 [/SUP], Yiyuan Wu[SUP] 3 [/SUP], Yongkang Zhang[SUP] 3 [/SUP], Aayush Visaria[SUP] 4 [/SUP], Duncan Orlander[SUP] 3 [/SUP], Wenna Xi[SUP] 3 [/SUP], Iván Díaz[SUP] 3 [/SUP], Dmitry Morozyuk[SUP] 3 [/SUP], Mark Weiner[SUP] 3 [/SUP], Rainu Kaushal[SUP] 1 2 3 5 [/SUP], Samprit Banerjee[SUP] 6 7 [/SUP]
Affiliations
Abstract
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.
. 2023 Jan 31;13(1):1746.
doi: 10.1038/s41598-023-28362-0.
Influence of social deprivation index on in-hospital outcomes of COVID-19
Parag Goyal[SUP] 1 2 [/SUP], Edward Schenck[SUP] 1 2 [/SUP], Yiyuan Wu[SUP] 3 [/SUP], Yongkang Zhang[SUP] 3 [/SUP], Aayush Visaria[SUP] 4 [/SUP], Duncan Orlander[SUP] 3 [/SUP], Wenna Xi[SUP] 3 [/SUP], Iván Díaz[SUP] 3 [/SUP], Dmitry Morozyuk[SUP] 3 [/SUP], Mark Weiner[SUP] 3 [/SUP], Rainu Kaushal[SUP] 1 2 3 5 [/SUP], Samprit Banerjee[SUP] 6 7 [/SUP]
Affiliations
- PMID: 36720999
- DOI: 10.1038/s41598-023-28362-0
Abstract
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.