A model of disparities: risk factors associated with COVID-19 infection

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Abstract

Background

By mid-May 2020, there were over 1.5 million cases of (SARS-CoV-2) or COVID-19 across the U.S. with new confirmed cases continuing to rise following the re-opening of most states. Prior studies have focused mainly on clinical risk factors associated with serious illness and mortality of COVID-19. Less analysis has been conducted on the clinical, sociodemographic, and environmental variables associated with initial infection of COVID-19.

Methods

A multivariable statistical model was used to characterize risk factors in 34,503cases of laboratory-confirmed positive or negative COVID-19 infection in the Providence Health System (U.S.) between February 28 and April 27, 2020. Publicly available data were utilized as approximations for social determinants of health, and patient-level clinical and sociodemographic factors were extracted from the electronic medical record.

Results

Higher risk of COVID-19 infection was associated with older age (OR 1.69; 95% CI 1.41–2.02, p < 0.0001), male gender (OR 1.32; 95% CI 1.21–1.44, p < 0.0001), Asian race (OR 1.43; 95% CI 1.18–1.72, p = 0.0002), Black/African American race (OR 1.51; 95% CI 1.25–1.83, p < 0.0001), Latino ethnicity (OR 2.07; 95% CI 1.77–2.41, p < 0.0001), non-English language (OR 2.09; 95% CI 1.7–2.57, p < 0.0001), residing in a neighborhood with financial insecurity (OR 1.10; 95% CI 1.01–1.25, p = 0.04), low air quality (OR 1.01; 95% CI 1.0–1.04, p = 0.05), housing insecurity (OR 1.32; 95% CI 1.16–1.5, p < 0.0001) or transportation insecurity (OR 1.11; 95% CI 1.02–1.23, p = 0.03), and living in senior living communities (OR 1.69; 95% CI 1.23–2.32, p = 0.001).

Conclusion

sisk of COVID-19 infection is higher among groups already affected by health disparities across age, race, ethnicity, language, income, and living conditions. Health promotion and disease prevention strategies should prioritize groups most vulnerable to infection and address structural inequities that contribute to risk through social and economic policy.

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

Fei Zhou, Ting Yu, Ronghui Du … (2020)

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<0·0001), and d-dimer greater than 1 μg/mL (18·42, 2·64–128·55; p=0·0033) on admission. Median duration of viral shedding was 20·0 days (IQR 17·0–24·0) in survivors, but SARS-CoV-2 was detectable until death in non-survivors. The longest observed duration of viral shedding in survivors was 37 days. 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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Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal

Laure Wynants, Ben Van Calster, Marc M. J. Bonten … (2020)

Abstract Objective To review and critically appraise 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 risk of being admitted to hospital for covid-19 pneumonia. Design Rapid systematic review and critical appraisal. Data sources PubMed and Embase through Ovid, Arxiv, medRxiv, and bioRxiv up to 24 March 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 2696 titles were screened, and 27 studies describing 31 prediction models were included. Three models were identified for predicting hospital admission from pneumonia and other events (as proxy outcomes for covid-19 pneumonia) in the general population; 18 diagnostic models for detecting covid-19 infection (13 were machine learning based on computed tomography scans); and 10 prognostic models for predicting mortality risk, progression to severe disease, or length of hospital stay. Only one study used patient data from outside of China. The most reported predictors of presence of covid-19 in patients with suspected disease included age, body temperature, and signs and symptoms. The most reported predictors of severe prognosis in patients with covid-19 included age, sex, features derived from computed tomography scans, C reactive protein, lactic dehydrogenase, and lymphocyte count. C index estimates ranged from 0.73 to 0.81 in prediction models for the general population (reported for all three models), from 0.81 to more than 0.99 in diagnostic models (reported for 13 of the 18 models), and from 0.85 to 0.98 in prognostic models (reported for six of the 10 models). All studies were rated at high 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, and high risk of model overfitting. Reporting quality varied substantially between studies. Most reports did not include a description of the study population or intended use of the models, and calibration of predictions was rarely assessed. 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 proposed models are poorly reported, at high risk of bias, and their reported performance is probably optimistic. Immediate sharing of well documented individual participant data from covid-19 studies is needed for collaborative efforts to develop more rigorous prediction models and validate existing ones. The predictors identified in included studies could be considered as candidate predictors for new models. Methodological guidance should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, studies 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.

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Traveling towards disease: transportation barriers to health care access.

Samina Syed, Ben S Gerber, Lisa K Sharp (2013)

Transportation barriers are often cited as barriers to healthcare access. Transportation barriers lead to rescheduled or missed appointments, delayed care, and missed or delayed medication use. These consequences may lead to poorer management of chronic illness and thus poorer health outcomes. However, the significance of these barriers is uncertain based on existing literature due to wide variability in both study populations and transportation barrier measures. The authors sought to synthesize the literature on the prevalence of transportation barriers to health care access. A systematic literature search of peer-reviewed studies on transportation barriers to healthcare access was performed. Inclusion criteria were as follows: (1) study addressed access barriers for ongoing primary care or chronic disease care; (2) study included assessment of transportation barriers; and (3) study was completed in the United States. In total, 61 studies were reviewed. Overall, the evidence supports that transportation barriers are an important barrier to healthcare access, particularly for those with lower incomes or the under/uninsured. Additional research needs to (1) clarify which aspects of transportation limit health care access (2) measure the impact of transportation barriers on clinically meaningful outcomes and (3) measure the impact of transportation barrier interventions and transportation policy changes.

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Author and article information

Contributors

Yelena Rozenfeld:

ORCID: http://orcid.org/0000-0002-1801-9928

Yelena.Rozenfeld@providence.org

Journal

Journal ID (nlm-ta): Int J Equity Health

Journal ID (iso-abbrev): Int J Equity Health

Title: International Journal for Equity in Health

Publisher: BioMed Central (London )

ISSN (Electronic): 1475-9276

Publication date (Electronic): 29 July 2020

Publication date PMC-release: 29 July 2020

Publication date Collection: 2020

Volume: 19

Electronic Location Identifier: 126

Affiliations

[1 ]Providence Health System (Providence), 1801 Lind Avenue S.W. Valley Office Park - Morin Bldg, 1st Floor Renton, Washington, 98057-9016 USA

[2 ]Providence Health System (Providence) and Ayin Health Solutions, 1801 Lind Avenue S.W. - Valley Office Park - Gamelin Bldg, Executive Office, Renton, Washington, 98057-9016 USA

Author information

Yelena Rozenfeld http://orcid.org/0000-0002-1801-9928

Article

Publisher ID: 1242

DOI: 10.1186/s12939-020-01242-z

PMC ID: 7387879

PubMed ID: 32727486

SO-VID: 37b127b1-f078-4c62-98f4-09964e7012fe

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 28 May 2020

Date accepted : 15 July 2020

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ScienceOpen disciplines: Health & Social care

Keywords: social determinants of health,multivariable model,risk factors,covid-19,disparities,infection

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ScienceOpen disciplines: Health & Social care

Keywords: social determinants of health, multivariable model, risk factors, covid-19, disparities, infection

A model of disparities: risk factors associated with COVID-19 infection

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Most cited references 14

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

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

Traveling towards disease: transportation barriers to health care access.

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