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0 0.5 1 1.5 2+ Mortality 6% Improvement Relative Risk c19hcq.org Wang et al. HCQ for COVID-19 LATE TREATMENT Is late treatment with HCQ beneficial for COVID-19? Retrospective 7,592 patients in the USA No significant difference in mortality Wang et al., medRxiv, doi:10.1101/2020.06.11.20128926 Favors HCQ Favors control
Comorbidity and Sociodemographic determinants in COVID-19 Mortality in an US Urban Healthcare System
Wang et al., medRxiv, doi:10.1101/2020.06.11.20128926 (Preprint)
Wang et al., Comorbidity and Sociodemographic determinants in COVID-19 Mortality in an US Urban Healthcare System, medRxiv, doi:10.1101/2020.06.11.20128926 (Preprint)
Jun 2020   Source   PDF  
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Database analysis of 7,592 patients in NYC, showing adjusted HCQ mortality odds ratio OR 0.96, p = 0.82, and HCQ+AZ OR 0.94, p = 0.63 This study is excluded in the after exclusion results of meta analysis: confounding by indication is likely and adjustments do not consider COVID-19 severity at baseline.
risk of death, 5.8% lower, RR 0.94, p = 0.63, treatment 1,866, control 5,726, odds ratio converted to relative risk.
Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
Wang et al., 10 Jun 2020, retrospective, database analysis, USA, preprint, 3 authors.
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Abstract: medRxiv preprint doi: https://doi.org/10.1101/2020.06.11.20128926; this version posted June 12, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Comorbidity and Sociodemographic determinants in COVID-19 Mortality in an US Urban Healthcare System Short title: Comorbidity and Sociodemographic determinants in COVID-19 mortality An-Li Wang, PhD1,2; Xiaobo Zhong, DrPH4,5; Yasmin L Hurd, PhD1,2,3,6 1. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA 2. Addiction Institute of Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA 3. Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, USA 4. Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, USA 5. Tisch cancer institute, Icahn School of Medicine at Mount Sinai, New York, USA 6. Behavioral Health System, Mount Sinai, New York, USA Correspond to An-Li Wang, PhD Department of Psychiatry Addiction Institute of Mount Sinai Icahn School of Medicine at Mount Sinai 1399 Park Ave Suit 320-B New York, USA 1 NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.06.11.20128926; this version posted June 12, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Email: an-li.wang@mssm.edu, anliwang@gmail.com Tel: +1 212-585-4668 Conflict of Interest: All authors acknowledge no conflict of interest. Funding: This study is supported by the institutional fund of the Icahn School of Medicine at Mount Sinai. Total word count: 2,805 2 medRxiv preprint doi: https://doi.org/10.1101/2020.06.11.20128926; this version posted June 12, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. ABSTRACT Background: New York City is the US epicenter of the coronavirus disease 2019 (COVID-19) pandemic. Early international data indicated that comorbidity contributes significantly to poor prognosis and fatality in patients infected with SARS-CoV-2. It is not known to what degree medical comorbidity and sociodemographic determinants impact COVID-19 mortality in the US. Methods: Evaluation of de-identified electronic health records of 7,592 COVID-19 patients confirmed by SARS-CoV-2 lab tests in New York City. Medical comorbidites and outcome of mortality, and other covariates, including clinical, sociodemographic, and medication measures were assessed by bivariate and multivariate logistic regression models. Results: Of common comorbid conditions (hypertension, chronic kidney disease, chronic obstructive pulmonary disease, asthma, obesity, diabetes, HIV, cancer), when adjusted for covariates, chronic kidney disease remained significantly associated with increased odds of mortality. Patients who had more than one comorbidities, former smokers, treated with Azithromycin without Hydroxychloroquine, reside within..
Late treatment
is less effective
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