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      Disparities In Outcomes Among COVID-19 Patients In A Large Health Care System In California : Study examines disparities in access and outcomes for COVID-19 patients who are members of racial and ethnic minorities and socioeconomically disadvantaged groups.

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          Abstract

          As the novel coronavirus disease (COVID-19) pandemic spreads throughout the United States, evidence is mounting that racial and ethnic minorities and socioeconomically disadvantaged groups are bearing a disproportionate burden of illness and death. We conducted a retrospective cohort analysis of COVID-19 patients at Sutter Health, a large integrated health system in northern California, to measure potential disparities. We used Sutter's integrated electronic health record to identify adults with suspected and confirmed COVID-19, and we used multivariable logistic regression to assess risk of hospitalization, adjusting for known risk factors, such as race/ethnicity, sex, age, health, and socioeconomic variables. We analyzed 1,052 confirmed cases of COVID-19 from the period January 1-April 8, 2020. Among our findings, we observed that compared with non-Hispanic white patients, non-Hispanic African American patients had 2.7 times the odds of hospitalization, after adjustment for age, sex, comorbidities, and income. We explore possible explanations for this, including societal factors that either result in barriers to timely access to care or create circumstances in which patients view delaying care as the most sensible option. Our study provides real-world evidence of racial and ethnic disparities in the presentation of COVID-19.

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          Is Open Access

          Implicit bias in healthcare professionals: a systematic review

          Background Implicit biases involve associations outside conscious awareness that lead to a negative evaluation of a person on the basis of irrelevant characteristics such as race or gender. This review examines the evidence that healthcare professionals display implicit biases towards patients. Methods PubMed, PsychINFO, PsychARTICLE and CINAHL were searched for peer-reviewed articles published between 1st March 2003 and 31st March 2013. Two reviewers assessed the eligibility of the identified papers based on precise content and quality criteria. The references of eligible papers were examined to identify further eligible studies. Results Forty two articles were identified as eligible. Seventeen used an implicit measure (Implicit Association Test in fifteen and subliminal priming in two), to test the biases of healthcare professionals. Twenty five articles employed a between-subjects design, using vignettes to examine the influence of patient characteristics on healthcare professionals’ attitudes, diagnoses, and treatment decisions. The second method was included although it does not isolate implicit attitudes because it is recognised by psychologists who specialise in implicit cognition as a way of detecting the possible presence of implicit bias. Twenty seven studies examined racial/ethnic biases; ten other biases were investigated, including gender, age and weight. Thirty five articles found evidence of implicit bias in healthcare professionals; all the studies that investigated correlations found a significant positive relationship between level of implicit bias and lower quality of care. Discussion The evidence indicates that healthcare professionals exhibit the same levels of implicit bias as the wider population. The interactions between multiple patient characteristics and between healthcare professional and patient characteristics reveal the complexity of the phenomenon of implicit bias and its influence on clinician-patient interaction. The most convincing studies from our review are those that combine the IAT and a method measuring the quality of treatment in the actual world. Correlational evidence indicates that biases are likely to influence diagnosis and treatment decisions and levels of care in some circumstances and need to be further investigated. Our review also indicates that there may sometimes be a gap between the norm of impartiality and the extent to which it is embraced by healthcare professionals for some of the tested characteristics. Conclusions Our findings highlight the need for the healthcare profession to address the role of implicit biases in disparities in healthcare. More research in actual care settings and a greater homogeneity in methods employed to test implicit biases in healthcare is needed.
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            Examining the Presence, Consequences, and Reduction of Implicit Bias in Health Care: A Narrative Review.

            Recent evidence suggests that one possible cause of disparities in health outcomes for stigmatized groups is the implicit biases held by health care providers. In response, several health care organizations have called for, and developed, new training in implicit bias for their providers. This review examines current evidence on the role that provider implicit bias may play in health disparities, and whether training in implicit bias can effectively reduce the biases that providers exhibit. Directions for future research on the presence and consequences of provider implicit bias, and best practices for training to reduce such bias, will be discussed.
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              Determinants of Receipt of Recommended Preventive Services: Implications for the Affordable Care Act

              Objectives. We examined preventive care use by nonelderly adults (aged 18–64 years) before the Affordable Care Act (ACA) and considered the contributions of insurance coverage and other factors to service use patterns. Methods. We used data from the 2005–2010 Medical Expenditure Panel Survey to measure the receipt of 8 recommended preventive services. We examined gaps in receipt of services for adults with incomes below 400% of the federal poverty level compared with higher incomes. We then used a regression-based decomposition analysis to consider factors that explain the gaps in service use by income. Results. There were large income-related disparities in preventive care receipt for nonelderly adults. Differences in insurance coverage explain 25% to 40% of the disparities in preventive service use by income, but education, age, and health status are also important drivers. Conclusions. Expanding coverage to lower-income adults through the ACA is expected to increase their preventive care use. However, the importance of education, age, and health status in explaining income-related gaps in service use indicates that the ACA cannot address all barriers to preventive care and additional interventions may be necessary.
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                Author and article information

                Journal
                Health Affairs
                Health Affairs
                Health Affairs (Project Hope)
                0278-2715
                1544-5208
                May 21 2020
                : 10.1377/hlthaff
                Affiliations
                [1 ]Kristen M. J. Azar () is a research scientist at the Sutter Health Center for Health Systems Research, in Walnut Creek, California, and a doctoral student in the Department of Epidemiology and Biostatistics at the University of California San Francisco (UCSF), in San Francisco, California.
                [2 ]Zijun Shen is a statistical analyst at the Sutter Health Center for Health Systems Research.
                [3 ]Robert J. Romanelli is a research scientist at the Sutter Health Center for Health Systems Research and an associate adjunct professor in the Clinical Pharmacy Department at UCSF.
                [4 ]Stephen H. Lockhart is chief medical officer at Sutter Health in Sacramento, California.
                [5 ]Kelly Smits is a communication specialist at Sutter Health in Sacramento.
                [6 ]Sarah Robinson is a statistical analyst at the Sutter Health Center for Health Systems Research.
                [7 ]Stephanie Brown is a physician at the Alta Bates Medical Center, Sutter Health, in Oakland, California.
                [8 ]Alice R. Pressman is codirector of the Sutter Health Center for Health Systems Research and an associate adjunct professor in the Department of Epidemiology and Biostatistics, UCSF.
                Article
                10.1377/hlthaff.2020.00598
                32437224
                f572924c-6de4-4a1c-838b-6359800bdb08
                © 2020
                History

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