Individualizing Risk Prediction for Positive Coronavirus Disease 2019 Testing: Results From 11,672 Patients.

doi:10.1016/J.CHEST.2020.05.580

Journal Article•DOI•

Individualizing Risk Prediction for Positive Coronavirus Disease 2019 Testing: Results From 11,672 Patients.

Lara Jehi¹, Xinge Ji¹, Alex Milinovich¹, Serpil C. Erzurum², Brian P. Rubin¹, Steve Gordon¹, James B. Young¹, Michael W. Kattan¹ - Show less +4 more•Institutions (2)

Cleveland Clinic¹, Cleveland Clinic Lerner Research Institute²

10 Jun 2020-Chest (Elsevier)-Vol. 158, Iss: 4, pp 1364-1375

TL;DR: Relevance of age, race, gender, and socioeconomic characteristics in COVID-19-susceptibility is demonstrated and a potential modifying role of certain common vaccinations and drugs identified in drug-repurposing studies is suggested.

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About: This article is published in Chest.The article was published on 2020-06-10 and is currently open access. It has received 153 citations till now. The article focuses on the topics: Retrospective cohort study & Cohort.

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Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

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Fei Zhou¹, Ting Yu, Ronghui Du, Guohui Fan², Ying Liu, Zhibo Liu¹, Jie Xiang³, Yeming Wang⁴, Bin Song, Xiaoying Gu¹, Xiaoying Gu², Lulu Guan, Yuan Wei, Li Hui¹, Xudong Wu, Jiuyang Xu⁵, Shengjin Tu, Yi Zhang¹, Hua Chen, Bin Cao - Show less +16 more•Institutions (5)

Peking Union Medical College¹, China-Japan Friendship Hospital², Wuhan Jinyintan Hospital³, Capital Medical University⁴, Tsinghua University⁵

01 Jan 2020

TL;DR: Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future.

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Abstract: Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

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4,408 citations

Journal Article•DOI•

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

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Laure Wynants¹, Laure Wynants², Ben Van Calster², Ben Van Calster³, Gary S. Collins⁴, Gary S. Collins⁵, Richard D Riley⁶, Georg Heinze⁷, Ewoud Schuit⁸, Marc J.M. Bonten⁸, Darren Dahly⁹, Johanna A A G Damen⁸, Thomas P. A. Debray⁸, Valentijn M.T. de Jong⁸, Maarten De Vos², Paula Dhiman⁵, Paula Dhiman⁴, Maria C Haller⁷, Michael O. Harhay¹⁰, Liesbet Henckaerts², Pauline Heus⁸, Michael Kammer⁷, Nina Kreuzberger¹¹, Anna Lohmann³, Kim Luijken³, Jie Ma⁵, Glen P. Martin¹², David J. McLernon¹³, Constanza L Andaur Navarro⁸, Johannes B. Reitsma⁸, Jamie C. Sergeant¹², Chunhu Shi¹⁴, Nicole Skoetz⁷, Luc J.M. Smits¹, Kym I E Snell⁶, Matthew Sperrin¹⁵, René Spijker⁸, René Spijker¹⁶, Ewout W. Steyerberg³, Toshihiko Takada⁸, Ioanna Tzoulaki¹⁷, Ioanna Tzoulaki¹⁸, Sander M. J. van Kuijk¹⁹, Bas C T van Bussel¹⁹, Bas C T van Bussel¹, Iwan C. C. van der Horst¹⁹, Florien S. van Royen⁸, Jan Y Verbakel⁴, Jan Y Verbakel², Christine Wallisch²⁰, Christine Wallisch⁷, Jack Wilkinson¹², Robert Wolff, Lotty Hooft⁸, Karel G.M. Moons⁸, Maarten van Smeden⁸ - Show less +52 more•Institutions (20)

07 Apr 2020-BMJ

TL;DR: Proposed models for covid-19 are poorly reported, at high risk of bias, and their reported performance is probably optimistic, according to a review of published and preprint reports.

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Abstract: Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. Design Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. Data sources PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

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2,183 citations

Journal Article•DOI•

Factors Associated With Death in Critically Ill Patients With Coronavirus Disease 2019 in the US

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Shruti Gupta¹, Salim S. Hayek², Wei Wang¹, Lili Chan³, Kusum S. Mathews³, Michal L. Melamed⁴, Samantha K. Brenner⁵, Amanda K. Leonberg-Yoo⁶, Edward J. Schenck⁷, Jared Radbel⁵, Jochen Reiser⁸, Anip Bansal⁹, Anand Srivastava¹⁰, Yan Zhou¹¹, Anne Sutherland⁵, Adam E. Green¹², Alexandre M. Shehata¹³, Nitender Goyal¹⁴, Anitha Vijayan¹⁵, Juan Carlos Q. Velez¹⁶, Juan Carlos Q. Velez¹⁷, Shahzad Shaefi¹, Chirag R. Parikh¹⁸, Justin Arunthamakun¹⁹, Ambarish M. Athavale, Allon N. Friedman²⁰, Samuel A.P. Short²¹, Zoe A. Kibbelaar²², Samah Abu Omar¹, Andrew J Admon², John P. Donnelly², Hayley B. Gershengorn⁴, Hayley B. Gershengorn²³, Miguel A. Hernán²⁴, Miguel A. Hernán¹, Matthew W. Semler²⁵, David E. Leaf¹ - Show less +33 more•Institutions (25)

01 Nov 2020-JAMA Internal Medicine

TL;DR: This study identified demographic, clinical, and hospital-level risk factors that may be associated with death in critically ill patients with COVID-19 and can facilitate the identification of medications and supportive therapies to improve outcomes.

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Abstract: Importance: The US is currently an epicenter of the coronavirus disease 2019 (COVID-19) pandemic, yet few national data are available on patient characteristics, treatment, and outcomes of critical illness from COVID-19. Objectives: To assess factors associated with death and to examine interhospital variation in treatment and outcomes for patients with COVID-19. Design, Setting, and Participants: This multicenter cohort study assessed 2215 adults with laboratory-confirmed COVID-19 who were admitted to intensive care units (ICUs) at 65 hospitals across the US from March 4 to April 4, 2020. Exposures: Patient-level data, including demographics, comorbidities, and organ dysfunction, and hospital characteristics, including number of ICU beds. Main Outcomes and Measures: The primary outcome was 28-day in-hospital mortality. Multilevel logistic regression was used to evaluate factors associated with death and to examine interhospital variation in treatment and outcomes. Results: A total of 2215 patients (mean [SD] age, 60.5 [14.5] years; 1436 [64.8%] male; 1738 [78.5%] with at least 1 chronic comorbidity) were included in the study. At 28 days after ICU admission, 784 patients (35.4%) had died, 824 (37.2%) were discharged, and 607 (27.4%) remained hospitalized. At the end of study follow-up (median, 16 days; interquartile range, 8-28 days), 875 patients (39.5%) had died, 1203 (54.3%) were discharged, and 137 (6.2%) remained hospitalized. Factors independently associated with death included older age (≥80 vs <40 years of age: odds ratio [OR], 11.15; 95% CI, 6.19-20.06), male sex (OR, 1.50; 95% CI, 1.19-1.90), higher body mass index (≥40 vs <25: OR, 1.51; 95% CI, 1.01-2.25), coronary artery disease (OR, 1.47; 95% CI, 1.07-2.02), active cancer (OR, 2.15; 95% CI, 1.35-3.43), and the presence of hypoxemia (Pao2:Fio2<100 vs ≥300 mm Hg: OR, 2.94; 95% CI, 2.11-4.08), liver dysfunction (liver Sequential Organ Failure Assessment score of 2 vs 0: OR, 2.61; 95% CI, 1.30-5.25), and kidney dysfunction (renal Sequential Organ Failure Assessment score of 4 vs 0: OR, 2.43; 95% CI, 1.46-4.05) at ICU admission. Patients admitted to hospitals with fewer ICU beds had a higher risk of death (<50 vs ≥100 ICU beds: OR, 3.28; 95% CI, 2.16-4.99). Hospitals varied considerably in the risk-adjusted proportion of patients who died (range, 6.6%-80.8%) and in the percentage of patients who received hydroxychloroquine, tocilizumab, and other treatments and supportive therapies. Conclusions and Relevance: This study identified demographic, clinical, and hospital-level risk factors that may be associated with death in critically ill patients with COVID-19 and can facilitate the identification of medications and supportive therapies to improve outcomes.

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706 citations

Journal Article•DOI•

Racial and Ethnic Disparities in COVID-19-Related Infections, Hospitalizations, and Deaths : A Systematic Review.

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Katherine Mackey¹, Chelsea Ayers, Karli Kondo¹, Somnath Saha¹, Shailesh Advani², Sarah Young, Hunter C. Spencer¹, Max Rusek¹, Johanna Anderson, Stephanie Veazie, Mia Smith¹, Devan Kansagara¹ - Show less +8 more•Institutions (2)

Oregon Health & Science University¹, National Institutes of Health²

01 Mar 2021-Annals of Internal Medicine

TL;DR: A systematic review evaluating racial/ethnic disparities in SARS-CoV-2 infection rates and COVID-19 outcomes, factors contributing to disparities, and interventions to reduce them suggests that impacts of CO VID-19 differ among U.S. racial/ ethnic groups.

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Abstract: BACKGROUND: Data suggest that the effects of coronavirus disease 2019 (COVID-19) differ among U.S. racial/ethnic groups. PURPOSE: To evaluate racial/ethnic disparities in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection rates and COVID-19 outcomes, factors contributing to disparities, and interventions to reduce them. (PROSPERO: CRD42020187078). DATA SOURCES: English-language articles in MEDLINE, PsycINFO, CINAHL, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Scopus, searched from inception through 31 August 2020. Gray literature sources were searched through 2 November 2020. STUDY SELECTION: Observational studies examining SARS-CoV-2 infections, hospitalizations, or deaths by race/ethnicity in U.S. settings. DATA EXTRACTION: Single-reviewer abstraction confirmed by a second reviewer; independent dual-reviewer assessment of quality and strength of evidence. DATA SYNTHESIS: 37 mostly fair-quality cohort and cross-sectional studies, 15 mostly good-quality ecological studies, and data from the Centers for Disease Control and Prevention and APM Research Lab were included. African American/Black and Hispanic populations experience disproportionately higher rates of SARS-CoV-2 infection, hospitalization, and COVID-19-related mortality compared with non-Hispanic White populations, but not higher case-fatality rates (mostly reported as in-hospital mortality) (moderate- to high-strength evidence). Asian populations experience similar outcomes to non-Hispanic White populations (low-strength evidence). Outcomes for other racial/ethnic groups have been insufficiently studied. Health care access and exposure factors may underlie the observed disparities more than susceptibility due to comorbid conditions (low-strength evidence). LIMITATIONS: Selection bias, missing race/ethnicity data, and incomplete outcome assessments in cohort and cross-sectional studies must be considered. In addition, adjustment for key demographic covariates was lacking in ecological studies. CONCLUSION: African American/Black and Hispanic populations experience disproportionately higher rates of SARS-CoV-2 infection and COVID-19-related mortality but similar rates of case fatality. Differences in health care access and exposure risk may be driving higher infection and mortality rates. PRIMARY FUNDING SOURCE: Department of Veterans Affairs, Veterans Health Administration, Health Services Research & Development.

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617 citations

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...Cohort and Cross-sectional Studies of SARS-CoV-2 Infection and Seroprevalence Rates, by Race/Ethnicity Study, Year (Reference) Participants, n Outcomes* Model Adjustments Results Risk for positive SARS-CoV-2 result on PCR Adegunsoye et al, 2020 (19) 4413 OR (95% CI) for testing SARSCOV-2 positive African American/Black versus nonBlack persons and Hispanic persons versus non-Hispanic persons Age, sex, ZIP code African American/Black: 2.16 (1.73–2.70)† Hispanic: 1.00 (0.61–1.63) Ahmed et al, 2020 (20)‡ 20 088 OR (95% CI) for testing SARSCOV-2 positive among Hispanic persons versus nonHispanic White persons Symptoms and known SARS-CoV2 exposure Non-White§: 1.1 (0.8–1.6) Hispanic: 2.0 (1.3–3.1)† Anyane-Yeboa et al, 2020 (21) NR Infection rate per 100 000 population Unadjusted White: 193 (120–266) African American/Black: 530 (312–748)† Asian: 194 (133–254) Hispanic: 652 (363–941) Baggett et al, 2020 (23)‡ 408 OR (95% CI) for testing SARSCOV-2 positive Unadjusted African American/Black: 0.92 (0.58–1.47)|| Asian: 1.71 (0.41–7.04)|| American Indian/Alaska Native: 1.71 (0.23–12.39)|| Hispanic: 0.78 (0.43–1.38)|| Blitz et al, 2020 (24) 4674 OR (95% CI) for testing SARSCOV-2 positive Unadjusted African American/Black: 1.8 (1.3– 2.4)†|| Asian: 0.7 (0.4–1.0)†|| Hispanic: 2.5 (2.0–3.2)†|| Caraballo et al, 2020 (25) 900 OR (95% CI) for testing SARSCOV-2 positive Unadjusted African American/Black: 1.75 (1.22–2.5)†|| Chamie et al, 2020 (26) 3953 OR (95% CI) for testing SARSCOV-2 positive among Hispanic persons versus nonHispanic persons Unadjusted Hispanic: 28 (12–93)† Chow et al, 2020 (27)‡ 1940 individuals from 583 census tracts COVID-19 cases per 100 000 persons, by race/ethnicity NA Asian: 3.0 (1.4–6.1) vs. White: 2.9 (0.71–6.8) Hispanic: 9.4 (95% CI: 7.2–12.6) vs. White: 2.9 (95% CI: 0.71– 6.8) Emeruwa et al, 2020 (29) 673 OR (95% CI) for testing SARSCOV-2 positive Unadjusted African American/Black: 1.39 (0.58–3.34)|| Hispanic: 2.13 (1.14–3.97)†|| Goldfarb et al, 2020 (31) 136 OR (95% CI) for testing SARSCOV-2 positive among Hispanic versus non-Hispanic persons Unadjusted Hispanic: 1.56 (0.78–3.11)|| Golestaneh et al, 2020 (32) 505 992 OR (95% CI) for testing SARSCOV-2 positive Age, sex, diabetes, hypertension, asthma, Charlson Comorbidity Index score, smoking, obesity, neighborhood characteristics (average household size, proportion living under poverty level, proportion who completed high school, proportion with active internet subscription, proportion using public transportation to commute to work, proportion of Black residents) African American/Black: 1.7 (1.5– 2.0)† Hispanic: 1.3 (1.1–1.5)† Gu et al, 2020 (34) 5698 OR (95% CI) for testing SARSCOV-2 positive (PCR or antibody test) Age, sex, persons per square mile; less than high school education; unemployed, annual income below FPL; comorbidity score¶ African American/Black: 3.51 (2.84–4.33)† Continued on following page Annals.org Annals of Internal Medicine • Vol. 174 No. 3 • March 2021 Appendix Table 2–Continued Study, Year (Reference) Participants, n Outcomes* Model Adjustments Results Holtgrave et al, 2020 (35) NR Ratio infection risk Unadjusted African American/Black: 2.35 Hispanic: 3.56 Jehi et al, 2020 (36) 11 672 OR (95% CI) for testing SARSCoV-2 positive among African American/Black persons versus White persons, Asian persons versus White persons, and Hispanic versus non-Hispanic persons Unadjusted African American/Black: 1.42 (1.20–1.68)†|| Asian: 0.76 (0.39–1.49)|| Hispanic: 1.31 (0.96–1.78)|| Martinez et al, 2020 (40) 37 727 OR (95% CI) for testing SARSCOV-2 positive Unadjusted African American/Black: 2.21 (2.06–2.38)†|| Hispanic: 7.68 (7.08–8.33)†|| Rentsch et al, 2020 (47)‡ 62 098 OR (95% CI) for testing SARSCOV-2 positive Age and comorbid conditions** African American/Black: 2.83 (2.65–3.03)† Hispanic: 1.80 (1.63–1.99)† Reichberg et al, 2020 (45) 46 793 OR for testing SARS-CoV-2 positive among African American/ Black persons versus nonHispanic White persons and Asian persons versus nonHispanic White persons Unadjusted African American/Black: 1.67 (1.56–1.80)† Asian: 1.31 (1.18–1.47)† Vahidy et al, 2020 (51) 20 228 OR (95% CI) for testing SARSCOV-2 positive among nonHispanic Black persons versus non-Hispanic White persons and Hispanic persons versus non-Hispanic persons Age, sex, ZIP code household income, insurance type, Charlson Comorbidity Index score, hypertension, diabetes, obesity Non-Hispanic Black: 2.23 (1.90– 2.60)† Hispanic: 1.95 (1.72–2.20)† Wang et al, 2020 (53)‡†† 28 336 OR (95% CI) for testing SARSCoV-2 positive Age Black: 1.89 (1.79–2.03)†‡‡ Asian/Pacific Islander: 1.02 (0.90– 1.14)†† Hispanic/Latinx: 7.24 (6.75– 7.69)†‡‡ Risk for positive SARS-CoV-2 antibody test (seroprevalence studies) Flannery et al, 2020 (30) 1293 OR (95% CI) for positive SARSCoV-2 IgG or IgM antibody test Unadjusted African American/Black: 5.2 (2.5– 10.7)†|| Asian: 0.5 (0.1–3.7)|| Hispanic: 5.6 (2.4–13.5)†|| Moscola et al, 2020 (42) 40 329 RR for SARS-CoV-2 IgG antibodies Age, sex, borough/county of residence, job function, PCR test (negative or positive), selfreported suspicion of virus exposure, primary work location, direct patient care, work in a COVID-19–positive unit African American/Black: 1.03 (0.99–1.07) Asian: 0.98 (0.94–1.01) American Indian: 1.01 (0.90–1.13) Pacific Islander: 0.99 (0.89–1.11) Hispanic: 1.02 (0.98–1.05) Rosenberg et al, 2020 (48) 15 101 Proportion of infection-experienced versus proportion of population (adults) Test characteristics Non-Hispanic White: 34 vs. 58 African American/Black: 20 vs. 14 Asian: 8 vs. 9 Hispanic: 37 vs. 17 COVID-19 = coronavirus disease 2019; FPL = federal poverty level; NR = not reported; OR = odds ratio; PCR = polymerase chain reaction; RR = relative risk; SARS-CoV-2 = severe acute respiratory syndrome coronavirus 2....
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...56 Jehi et al, 2020 (36) 11 672 OR (95% CI) for testing SARSCoV-2 positive among African American/Black persons versus White persons, Asian persons versus White persons, and Hispanic versus non-Hispanic persons Unadjusted African American/Black: 1....
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...Among 7 cohort and cross-sectional studies that evaluated SARS-CoV-2 infection risk (according to SARSCoV-2 PCR or serologic tests) in Asian populations, 6 found no difference and 1 identified a higher risk for infection compared with White populations (23, 24, 30, 36, 42, 45, 53)....
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...…2–Continued Study, Year (Reference) Participants, n Outcomes* Model Adjustments Results Holtgrave et al, 2020 (35) NR Ratio infection risk Unadjusted African American/Black: 2.35 Hispanic: 3.56 Jehi et al, 2020 (36) 11 672 OR (95% CI) for testing SARSCoV-2 positive among African American/Black…...
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...Other studies not finding a disparity in infection rates among Hispanic populations had low representation of Hispanic persons (ranging from less than 1% to 9%) and may not have been adequately powered to detect a difference or were small studies of unique participants (19, 23, 31, 36, 45)....
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DOI•

Disparities in COVID-19 Outcomes by Race, Ethnicity, and Socioeconomic Status: A Systematic-Review and Meta-analysis.

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Shruti Magesh¹, Shruti Magesh², Daniel John¹, Daniel John², Wei Tse Li¹, Wei Tse Li², Yuxiang Li², Yuxiang Li¹, Aidan Mattingly-App¹, Aidan Mattingly-App², Sharad Jain³, Eric Y. Chang¹, Eric Y. Chang², Weg M. Ongkeko², Weg M. Ongkeko¹ - Show less +11 more•Institutions (3)

University of California, San Diego¹, Veterans Health Administration², University of California, Davis³

01 Nov 2021

TL;DR: In this article, the authors evaluated the association of race and ethnicity with COVID-19 outcomes and examined the association between race, ethnicity, COVID19 outcomes, and socioeconomic determinants.

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Abstract: Importance COVID-19 has disproportionately affected racial and ethnic minority groups, and race and ethnicity have been associated with disease severity. However, the association of socioeconomic determinants with racial disparities in COVID-19 outcomes remains unclear. Objective To evaluate the association of race and ethnicity with COVID-19 outcomes and to examine the association between race, ethnicity, COVID-19 outcomes, and socioeconomic determinants. Data sources A systematic search of PubMed, medRxiv, bioRxiv, Embase, and the World Health Organization COVID-19 databases was performed for studies published from January 1, 2020, to January 6, 2021. Study selection Studies that reported data on associations between race and ethnicity and COVID-19 positivity, disease severity, and socioeconomic status were included and screened by 2 independent reviewers. Studies that did not have a satisfactory quality score were excluded. Overall, less than 1% (0.47%) of initially identified studies met selection criteria. Data extraction and synthesis Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Associations were assessed using adjusted and unadjusted risk ratios (RRs) and odds ratios (ORs), combined prevalence, and metaregression. Data were pooled using a random-effects model. Main outcomes and measures The main measures were RRs, ORs, and combined prevalence values. Results A total of 4 318 929 patients from 68 studies were included in this meta-analysis. Overall, 370 933 patients (8.6%) were African American, 9082 (0.2%) were American Indian or Alaska Native, 101 793 (2.4%) were Asian American, 851 392 identified as Hispanic/Latino (19.7%), 7417 (0.2%) were Pacific Islander, 1 037 996 (24.0%) were White, and 269 040 (6.2%) identified as multiracial and another race or ethnicity. In age- and sex-adjusted analyses, African American individuals (RR, 3.54; 95% CI, 1.38-9.07; P = .008) and Hispanic individuals (RR, 4.68; 95% CI, 1.28-17.20; P = .02) were the most likely to test positive for COVID-19. Asian American individuals had the highest risk of intensive care unit admission (RR, 1.93; 95% CI, 1.60-2.34, P Conclusions and relevance In this study, members of racial and ethnic minority groups had higher risks of COVID-19 positivity and disease severity. Furthermore, socioeconomic determinants were strongly associated with COVID-19 outcomes in racial and ethnic minority populations.

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Individualizing Risk Prediction for Positive Coronavirus Disease 2019 Testing: Results From 11,672 Patients.

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