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HIV Med ; 2022 Apr 08.
Article in English | MEDLINE | ID: covidwho-1784645


BACKGROUND: We investigated the effect of HIV on COVID-19 outcomes with attention to selection bias due to differential testing and comorbidity burden. METHODS: This was a retrospective cohort analysis using four hierarchical outcomes: positive SARS-CoV-2 test, COVID-19 hospitalization, intensive care unit (ICU) admission and hospital mortality. The effect of HIV status was assessed using traditional covariate-adjusted, inverse probability-weighted (IPW) analysis based on covariate distributions for testing bias (testing IPWs), HIV infection status (HIV-IPWs) and combined models. Among people living with HIV (PWH), we evaluated whether CD4 count and HIV plasma viral load (pVL) discriminated between those who did and those who did not develop study outcomes using receiver operating characteristic analysis. RESULTS: Between March and November 2020, 63 319 people were receiving primary care services at the University of California San Diego (UCSD), of whom 4017 were PWH. The PWH had 2.1 times the odds of a positive SARS-CoV-2 test compared with those without HIV after weighting for potential testing bias, comorbidity burden and HIV-IPW [95% confidence interval (CI): 1.6-2.8]. Relative to people without HIV, PWH did not have an increased rate of COVID-19 hospitalization after controlling for comorbidities and testing bias [adjusted incidence rate ratio (aIRR) = 0.5, 95% CI: 0.1-1.4]. PWH did not have a different rate of ICU admission (aIRR = 1.08, 95% CI: 0.31-3.80) or of in-hospital death (aIRR = 0.92, 95% CI: 0.08-10.94) in any examined model. Neither CD4 count nor pVL predicted any of the hierarchical outcomes among PWH. CONCLUSIONS: People living with HIV have a higher risk of COVID-19 diagnosis than those without HIV but the outcomes are similar in both groups.

JAMIA Open ; 4(2): ooab036, 2021 Apr.
Article in English | MEDLINE | ID: covidwho-1266122


Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that are critical to COVID-19 research. The ontology contains over 50 000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for severe acute respiratory syndrome coronavirus 2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of 9 academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network.