This Time Must Be Different: Disparities During the COVID-19 Pandemic
2020; American College of Physicians; Volume: 173; Issue: 3 Linguagem: Inglês
10.7326/m20-2247
ISSN1539-3704
Autores Tópico(s)COVID-19 and healthcare impacts
ResumoIdeas and Opinions28 April 2020This Time Must Be Different: Disparities During the COVID-19 PandemicFREEKirsten Bibbins-Domingo, PhD, MD, MASKirsten Bibbins-Domingo, PhD, MD, MASUniversity of California, San Francisco, San Francisco, California (K.B.)Author, Article, and Disclosure Informationhttps://doi.org/10.7326/M20-2247 SectionsAboutVisual AbstractPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail After reports of racial and ethnic disparities in the U.S. pandemic, a large, nationally representative survey provided empirical evidence regarding the sources of these disparities (1). The authors found that increased likelihood of exposure to the virus, increased susceptibility to severe consequences of the infection, and lack of health care access were all important contributors, and they concluded with pointed, domain-specific recommendations to mitigate these disparities. The clarity of this path forward would be alluring and reassuring were the historical nature of these observations not so alarming. These data are not based on the coronavirus disease 2019 (COVID-19) pandemic; rather, they describe the nation's experience of the 2009 H1N1 influenza pandemic.Unfortunately, things have not changed for the better. African Americans and Latinos are overrepresented among cases of and deaths from COVID-19, both nationally and in many of the areas hardest hit by the pandemic (2, 3). In New York City, African American and Latino residents have the highest age-adjusted rates of hospitalized and nonhospitalized COVID-19, and age-adjusted death rates for African Americans are more than twice those for white and Asian residents (4). Throughout the United States, data by race and ethnicity are incomplete and highly dependent on what information is collected at the local level—a glaring omission in data collection that was highlighted for remediation during the 2009 H1N1 pandemic (1).The likely causes of the disparities are also distressingly similar. Minority communities are more likely to be exposed to the virus because they are overrepresented in the low-wage, essential workforce at the front lines, including low-wage health care workers who often move between clinics, hospitals, and nursing homes to make a living, thereby magnifying their risk (5). Poor communities may face challenges implementing social distancing because of housing density and overcrowding, and minority populations are overrepresented in congregate settings, such as homeless shelters and prisons, that increase exposure risk. Minority communities may be more susceptible to severe forms of COVID-19 because of existing disparities in underlying conditions known to be associated with COVID-19 mortality, including hypertension, cardiovascular disease, kidney disease, and diabetes. Although largely preventable or amenable to medical management, these chronic conditions are more common, less likely to be controlled, and more likely to occur at younger ages in these communities. Health care access is also a probable contributor to COVID-19 mortality given the limited availability of both testing and treatments. Much of the testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has occurred in the context of a health care evaluation, resulting in barriers for those without insurance. Although data are not yet available, concerns about the equitable distribution of ventilators and treatments have also been raised.We simply cannot afford to bear witness to yet another manifestation of health inequities. This time must be different because we are living in a global pandemic of massive proportion and uncertain duration, the management of which will require ongoing, effective, and equitable attention to the areas of greatest need if we are to avoid even more devastating consequences. This time must be different because the increasing diversity of the U.S. population and our essential workers reminds us of our interdependence and means that focusing on minority communities is essential both to relieve suffering in these communities and to effectively manage this crisis. This time must be different because the economic underpinning of these disparities has worsened over the past decade and threatens to deteriorate further in the face of the anticipated global depression, likely exacerbating the COVID-19 disparities we are already witnessing.It is time to learn from the lessons of past epidemics and their disproportionate effect on minority communities. We need robust data to guide these efforts, but better information must be coupled with urgent and effective action to decrease exposure, susceptibility, and limitations in health care to achieve the desired results. For our public health efforts at mitigation and containment to be most effective, resources must be invested in the communities hardest hit by COVID-19 to redress past underinvestment and the ongoing impact of the economic crisis. Our clinical and public health sectors that have been relentlessly focused on addressing the acute issues of COVID-19 over the past months must refocus to also address prevention and treatment of the underlying cardiovascular and metabolic conditions that are the major contributors to morbidity and mortality in these communities.As we plan for a SARS-CoV-2 vaccine, we must heed the lessons from past vaccination campaigns. During the 1970s, the gap in measles vaccination rates between minority and white children was as high as 18 percentage points. Consequently, the U.S. measles epidemic of 1989 to 1991 that resulted in more than 55 000 cases included 4- to 7-fold higher rates among minority children than white children. Today, gaps in measles vaccination rates by race and ethnicity are nonexistent thanks in part to a dual strategy of boosting universal childhood vaccination and implementing targeted measures in minority communities. These targeted approaches have included increased funding to urban health departments; development of local action plans; linkage of vaccination to other programs like the Special Supplemental Nutrition Program for Women, Infants, and Children; increased reimbursement for Medicaid providers; reduced vaccine prices for Medicaid programs; adjustment of hours in public health clinics to meet the local needs of populations; ongoing monitoring and surveillance through annual surveys; and broad engagement with community organizations with specific targeted messages to minority communities (6). Unfortunately, influenza vaccinations and most other adult vaccinations have not seen similar success. Although influenza vaccination rates improved in the 2018 to 2019 season compared with prior years, the rate overall was only 45.3% (far short of the 70% goal of Healthy People 2020), and rates were substantially lower among African American, Latino, and American Indian/Alaska Native adults (7). Achieving the desired population benefit of a SARS-CoV-2 vaccine will require an implementation strategy that addresses the current gaps in overall rates of adult vaccination, as well as specific issues in minority communities. Establishing and nurturing trust and partnerships within affected communities will be critical because diminished trust in health care born from a legacy of unethical experimentation, including the Tuskegee study, has been identified as an important contributor to vaccine hesitancy among African Americans (8, 9).To borrow the words of Dr. Martin Luther King Jr., "We are now faced with the fact that tomorrow is today. We are confronted with the fierce urgency of now. In this unfolding conundrum of life and history, there is such a thing as being too late. This is no time for apathy or complacency. This is a time for vigorous and positive action" (10). Can we eschew our collective amnesia, acknowledge the persistence and pervasive nature of our health and health care disparities, and draw on our experience to overcome? Or will the failure of our collective will define us as a generation that refused to care and refused to act?References1. Quinn SC, Kumar S, Freimuth VS, et al. Racial disparities in exposure, susceptibility, and access to health care in the US H1N1 influenza pandemic. Am J Public Health. 2011;101:285-93. [PMID: 21164098] doi:10.2105/AJPH.2009.188029 CrossrefMedlineGoogle Scholar2. Centers for Disease Control and Prevention. Cases of coronavirus disease (COVID-19) in the U.S. Accessed at www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html on 20 April 2020. Google Scholar3. Centers for Disease Control and Prevention. Provisional death counts for coronavirus disease (COVID-19): weekly state-specific data updates by select demographic and geographic characteristics. Accessed at www.cdc.gov/nchs/nvss/vsrr/covid_weekly on 20 April 2020. Google Scholar4. City of New York. COVID-19: data. Accessed at www1.nyc.gov/site/doh/covid/covid-19-data.page on 20 April 2020. Google Scholar5. U.S. Bureau of Labor Statistics. Labor force statistics from the current population survey. Updated 22 January 2020. Accessed at www.bls.gov/cps/cpsaat11.htm on 20 April 2020. Google Scholar6. Hutchins SS, Jiles R, Bernier R. Elimination of measles and of disparities in measles childhood vaccine coverage among racial and ethnic minority populations in the United States. J Infect Dis. 2004;189 Suppl 1:S146-52. [PMID: 15106103] MedlineGoogle Scholar7. Centers for Disease Control and Prevention. Flu vaccination coverage, United States, 2018–19 influenza season. 26 September 2019. Accessed at www.cdc.gov/flu/fluvaxview/coverage-1819estimates.htm on 25 April 2020. Google Scholar8. Freimuth VS, Jamison AM, An J, et al. Determinants of trust in the flu vaccine for African Americans and whites. Soc Sci Med. 2017;193:70-79. [PMID: 29028558] doi:10.1016/j.socscimed.2017.10.001 CrossrefMedlineGoogle Scholar9. Quinn SC, Jamison A, Freimuth VS, et al. Exploring racial influences on flu vaccine attitudes and behavior: results of a national survey of white and African American adults. Vaccine. 2017;35:1167-1174. [PMID: 28126202] doi:10.1016/j.vaccine.2016.12.046 CrossrefMedlineGoogle Scholar10. King ML Jr. Beyond Vietnam. The Stanford University Martin Luther King, Jr. Research and Education Institute. Accessed at https://kinginstitute.stanford.edu/king-papers/documents/beyond-vietnam on 20 April 2020. Google Scholar Comments 0 Comments Sign In to Submit A Comment Shinya Yamamoto M.D.Division of Infectious Diseases and Applied Immunology, Research Hospital, Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan4 May 2020 Coronavirus disease 2019 (COVID-19): Differential mortality rates in different regions We appreciate the article by Kirsten Bibbins-Domingo, which adds to our knowledge about the characteristics of patients with COVID-19. This article will have a great effect on the practice of COVID-19 treatment. The author presented racial and ethnic disparities among patients in the U.S.A. and suggested correction of health inequities. However, we would like express some concerns regarding this work. The number of total deaths may reflect the severity of infection in that particular country. Mortality from COVID-19 (which differs in different regions, even within a country) is relatively lower in East Asia than that in European and American countries. As of May 3, 2020, the global mortality (cases/million) is 20.8 (1). The mortality in Italy, France, and the USA is 467, 376, and 196, respectively, whereas that in China, South Korea, and Japan is 3.22, 4.88, and 3.62, respectively. The reason underlying this significant difference is unknown and the role of racial differences is unclear. Several confounding factors (social factors such as health care access) can affect these results. Therefore, finding a modifiable factor is essential for clinically understanding the nature of COVID-19. Could this difference arise due to differences in the infecting coronavirus genomes? A previous study reported that SARS-Cov-2 can be divided into two major genotypes: L and S. The L type was predominant during the early days of the epidemic in China (2). However, the clinical implications of these findings are vague. Several reports have strongly associated older age with increased mortality (3). The population aging rate in Italy is higher than that in other countries. Paradoxically, mortality among the Japanese is lower even though Japan has the highest elderly population worldwide. Obesity is also known as a risk factor for hospitalization and mechanical ventilation. The obesity rates in Western countries are relatively higher than those in East Asia, which may also contribute to increasing mortality; this trend is similar to that of Pandemic 2009H1N1 (4). Obesity is also associated with other risk factors (cardiovascular diseases, diabetes mellitus), which are compounding (e.g., elderly people with obesity). Delayed hospital admission and antiviral treatment were reported as risk factors for severe illness during Pandemic 2009H1N1 (5). The number of COVID-19 patients exceeds the capacity for treatment in some countries, resulting in increased mortality. It is unknown whether these hypotheses are confounding reasons and further in-depth studies are needed to elucidate the underlying reasons.References [1] Department of Medical Genome Sciences, Research Institute for Frontier Medicine,Sapporo Medical University School of Medicine. Transition of new coronavirus COVID-19 deaths per population by country https://web.sapmed.ac.jp/canmol/coronavirus/death_e.html (Last accessed May 2 2020).[2] Tang X, Wu C, Li X, et al. On the origin and continuing evolution of SARS-CoV-2.Natl Sci Rev. 2020. [3] Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: Summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. JAMA. 2020;323(13):1239. https://www.ncbi.nlm.nih.gov/pubmed/32091533. doi: 10.1001/jama.2020.2648.[4] Venkata C, Sampathkumar P, Afessa B. Hospitalized patients with 2009 H1N1 influenza infection: The Mayo Clinic experience. Mayo Clin Proc 2010;85:798‐805. [5] Yu H, Feng Z, Uyeki TM, et al. Risk factors for severe illness with 2009 pandemic influenza A (H1N1) virus infection in china. Clin Infect Dis. 2011;52(4):457-465. https://www.jstor.org/stable/29777321. doi: 10.1093/cid/ciq144.Shinya Yamamoto, M.DDepartment of Infectious Diseases and Applied Immunology, IMSUT Hospital of The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, JapanTel. +81-3-5449-5338; Fax. +81-3-5449-5427Email: [email protected] Shinya Yamamoto MD, Makoto Saito MDDivision of Infectious Diseases and Applied Immunology, Research Hospital, Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan14 May 2020 Coronavirus disease 2019 (COVID-19): Differential mortality rates in different regions We appreciate that Kirsten Bibbins-Domingo highlighted racial and ethnic disparities in the U.S.A. and suggested correction of health inequities. We believe understanding the difference between countries, in addition to that within a country as discussed, will also lead to a potential improvement of healthcare in the current (and future) pandemic.The number of total deaths may reflect the severity of infection in that particular country. Mortality from COVID-19 (which differs in different regions, even within a country) is relatively lower in East Asia than that in European and American countries. As of May 2, 2020, the global mortality (cases/million) is 30.6 (1). The mortality in Italy, France, and the USA is 467, 376, and 196, respectively, whereas that in China, South Korea, and Japan is 3.22, 4.88, and 3.62, respectively. The reason underlying this significant difference is unknown and the role of racial differences is unclear. Several confounding factors (social factors such as health care access) can affect these results. Therefore, finding a modifiable factor is essential for clinically understanding the nature of COVID-19.Could this difference arise due to differences in the infecting coronavirus genomes? A previous study reported that SARS-Cov-2 can be divided into two major genotypes: L and S. The L type was predominant during the early days of the epidemic in China (2). However, the clinical implications of these findings are vague. Several reports have strongly associated older age with increased mortality (3). The population aging rate in Italy is higher than that in other countries. Paradoxically, mortality among the Japanese is lower even though Japan has the highest elderly population worldwide. Obesity is also known as a risk factor for hospitalization and mechanical ventilation. The obesity rates in European countries are relatively higher than those in East Asia, which may also contribute to increasing mortality; this trend is similar to that of the 2009 H1N1 Pandemic (4). Obesity is also associated with other risk factors (cardiovascular diseases, diabetes mellitus), which are confounding (e.g., elderly people with obesity). Delayed hospital admission and antiviral treatment were reported as risk factors for severe illness during the 2009 H1N1 Pandemic (5). The number of COVID-19 patients exceeds the capacity for treatment in some countries, resulting in increased mortality. It is unknown whether these hypotheses are confounding reasons and further in-depth studies are needed to elucidate the underlying reasons. References [1] Johns Hopkins University. Coronavirus Resource Center. COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). Accessed at https://coronavirus.jhu.edu/map.html on 2 May 2020. [2] Tang X, Wu C, Li X, et al. On the origin and continuing evolution of SARS-CoV-2.Natl Sci Rev. 2020. doi: 10.1093/nsr/nwaa036. [3] Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in china: Summary of a report of 72 314 cases from the Chinese center for disease control and prevention. JAMA. 2020;323(13):1239. [PMID:32091533] doi: 10.1001/jama.2020.2648. [4] Venkata C, Sampathkumar P, Afessa B. Hospitalized patients with 2009 H1N1 influenza infection: The Mayo Clinic experience. Mayo Clin Proc 2010;85:798‐805. [PMID:20664021] doi: 10.4065/mcp.2010.0166 [5] Yu H, Feng Z, Uyeki TM, et al. Risk factors for severe illness with 2009 pandemic influenza A (H1N1) virus infection in china. Clin Infect Dis. 2011;52(4):457-465. [PMID:21220768] doi: 10.1093/cid/ciq144. Gwenetta Curry, (1,2) Joht Singh Chandan, (3) Neeraj Balmukund Bhala. (3)1. Usher Institute, University of Edinburgh, UK 2. Department of Gender and Race Studies, University of Alabama, AL, USA, 3. Institute of Applied Health Research, University of Birmingham, UK B15 2TT18 May 2020 Transatlantic reflections on ethnic/racial disparities: wider lens and policy action are key globally Dr Bibbins-Domingo's article on COVID-19 highlighted the complicated causes that underline the marked ethnic/racial disparities in parts and populations of the USA (1) that are a source for concern globally. Whilst the primary sources for the US data suggest slightly moderated increased risks for populations compared to the initial newspaper-cited reports, we wanted to highlight corresponding United Kingdom (UK) data (2) for the Annals readership.At a national level, provisional analysis from the UK Office of National Statistics has shown that the risk of death involving COVID-19 has marked ethnic/racial disparities: when taking age into account, Black males were 4.2 times more likely to die from covid-19 than White males and Black women were 4.3 times more likely to die than White women. (3) These increased risks does not apply to Black ethnicity alone in the UK: (3) people of Bangladeshi, Pakistani, Indian, and Mixed ethnicities also had statistically significant raised risk of death involving COVID-19 compared with those of White ethnicity. (3) These UK results show that the difference between ethnic groups in COVID-19 mortality is partly a result of socioeconomic disadvantage and other circumstances, but a large remaining part of the difference remains unexplained.These risks are also being corroborated in large UK hospital datasets: the pseudonymised health data of over 17.4 million adults, which included 5683 hospital deaths attributed to COVID-19, found similar estimates of risk for Black and Asian populations after adjusting for age, deprivation and clinical risk factors. (4) Indeed, these disparities do not apply to mortality alone but also healthcare resource utilisation and disease severity. The latest national UK ITU audit reports indicate that Asian and Black populations in the UK are at substantially increased risk of ITU admission compared to their white counterparts. (5)The reasons are complex, but the link between deprivation and increased risk of death from COVID-19 are also clear. As in the US, ethnic minority communities in the UK have some of the highest levels of unemployment and are more likely to live in deprived neighbourhoods (3). In many ways, the UK and USA should be applauded for having more open and transparent big data than other places in the world where others will avoid these difficult considerations. However, more multidimensional sociological, clinical and population health research is needed to understand why COVID-19 disproportionately impacts diverse Black and Asian communities across the Atlantic, with implications for global health policy and actions. Word count – currently 400 1. Bibbins-Domingo K. This Time Must Be Different: Disparities During the COVID-19 Pandemic. Annals of Internal Medicine 28 Apr 2020 2. Bhala, Curry et al. Sharpening the global focus on ethnicity and race in the time of COVID-19. The Lancet May 8. 3. Office for National Statistics. https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/articles/coronavirusrelateddeathsbyethnicgroupenglandandwales/2march2020to10april2020. 4. The OpenSAFELY Collaborative, Elizabeth Williamson, Alex J Walker, Krishnan J Bhaskaran, et al. OpenSAFELY: factors associated with COVID-19-related hospital death in the linked electronic health records of 17 million adult NHS patients. medRxiv 2020.05.06.20092999; https://doi.org/10.1101/2020.05.06.20092999 5. Intensive Care National Audit and Research Care (ICNARC) report on COVID-19 in Critical Care. May 15, 2020. Author, Article, and Disclosure InformationAuthors: Kirsten Bibbins-Domingo, PhD, MD, MASAffiliations: University of California, San Francisco, San Francisco, California (K.B.)Acknowledgment: The author thanks Ms. Amy Markowitz for helpful edits to earlier drafts of this manuscript.Disclosures: The author has disclosed no conflicts of interest. The form can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M20-2247.Corresponding Author: Kirsten Bibbins-Domingo, PhD, MD, MAS, Department of Epidemiology and Biostatistics, University of California, San Francisco, 550 16th Street, 2nd Floor, Box #0560, San Francisco, CA 94158; e-mail, Kirsten.Bibbins-Domingo@ucsf.edu.Author Contributions: Conception and design: K. Bibbins-Domingo.Drafting of the article: K. Bibbins-Domingo.Critical revision of the article for important intellectual content: K. Bibbins-Domingo.Final approval of the article: K. Bibbins-Domingo.Administrative, technical, or logistic support: K. Bibbins-Domingo.This article was published at Annals.org on 28 April 2020. 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