The disproportionate effect of COVID-19 mortality on ethnic minorities: Genetics or health inequalities?
Notice bibliographique
Résumé
The cases of novel coronavirus disease 2019 (COVID-19) continue to increase across the world, infecting nearly 5.5 million individuals and more than 350,000 death. The earlier studies indicate that significant risk factors for severe COVID-19 are older adults, and people with co-morbidities (regardless of age) including chronic lung diseases, heart diseases, severe obesity (body mass index 40 or higher), and diabetes. When it comes to the role of race/ethnicity, the data is limited, which could be disproportionately affecting ethnic minorities as observed in England during 2009 Influenza A (H1N1) pandemic [[1]Zhao H. Harris R.J. Ellis J. Pebody R.G. Ethnicity, deprivation and mortality due to 2009 pandemic influenza A(H1N1) in England during the 2009/2010 pandemic and the first post-pandemic season.Epidemiol Infect. 2015; 143: 3375-3383Crossref PubMed Scopus (40) Google Scholar]. Earlier reports from the United Kingdom indicated black, Asian and minority ethnic (BAME) are the hardest hit with COVID-19 both in terms of critically ill as well as higher mortality [[2]Liverpool L. Why are ethnic minorities worse affected?.New Sci. 2020; 246: 11Crossref PubMed Google Scholar]. In the recent systematic review, by Pan et al. conducted an extensive systematic review of the literature related to ethnicity and COVID-19 during the period of 1st December 2019 through 15th May 2020 [[3]Pan D. Sze S. Minhas J. et al.The impact of ethnicity on clinical outcomes in COVID-19: a systematic review.EClinicalMedicine. 2020; https://doi.org/10.1016/j.eclinm.2020.100404Summary Full Text Full Text PDF Scopus (356) Google Scholar]. They included a total of 162 articles that were deemed related to their study aim (search sources included databases, medical journals, preprints and grey literature). Of these articles, a total of 58 articles reported race/ethnicity. Interestingly, race/ethnicity was not reported in the articles published in China, at the begining of the pandemic. Only recently, in April 2020 onward, which overlaps with the timing emergence of the pandemic in Europe and USA. As a summary of their findings, Pan et al. report there is emerging evidence of disproportionate clinical outcomes among the ethnic minorities in several countries against COVID-19, which has been suggested by others as well [[4]Kirby T. Evidence mounts on the disproportionate effect of COVID-19 on ethnic minorities.Lancet Respir Med. 2020; Summary Full Text Full Text PDF Scopus (331) Google Scholar,[5]Wadhera R.K. Wadhera P. Gaba P. et al.Variation in COVID-19 Hospitalizations and Deaths Across New York City Boroughs.JAMA. 2020; Crossref Scopus (472) Google Scholar]. The review also discussed in detail the utility of race/ethnicity, primarily when such pandemic is affecting the world, which is considered a highly globalized society. Therefore, they suggested including ethnicity as a variable in the government surveillance systems. We would like to suggest that there could be several explanations for the disproportionate burden of COVID-19 in ethnic minorities that include social, economic and health inequalities as well as genetic predisposition, biological or pathophysiological differences in response to infection. The ethnic minorities have higher burden of comorbidities like diabetes, cardiovascular disease and morbid obesity. The studies indicated that ethnic minorities had increased prevalence of vitamin D deficiency [[6]Martineau A.R. Jolliffe D.A. Hooper R.L. et al.Vitamin D supplementation to prevent acute respiratory tract infections: systematic review and meta-analysis of individual participant data.Bmj. 2017; 356: i6583Crossref PubMed Scopus (1271) Google Scholar], increased inflammatory burdens [[7]Schmeer K.K. Tarrence J. Racial-ethnic Disparities in Inflammation: evidence of Weathering in Childhood?.J Health Soc Behav. 2018; 59: 411-428Crossref PubMed Scopus (40) Google Scholar], could increase the risk of COVID-19 disease severity in those populations. Also, we would like to acknowledge that the COVID-19 pandemic did make the topic of social disparities as a priority topic where it did reflect the vulnerability of the minority groups. This includes the proportion of minor race/ethnic groups, where their work conditions did not allow them to (i) practice a physical distancing; (ii) work from home; (iii) isolate and protect older family members (when they multi-generational households live together); (iv) isolate those who are sick; (v) lack of paid sick leave; or (vi) they are not provided with the proper protective measures [[8]Yancy C.W. COVID-19 and African Americans.JAMA. 2020; Crossref PubMed Scopus (1380) Google Scholar]. Given that the number of cases and the case fatality ratio is relatively lower in the African countries [[9]Mehtar S. Preiser W. Lakhe N.A. et al.Limiting the spread of COVID-19 in Africa: one size mitigation strategies do not fit all countries.The Lancet Global Health. 2020; ([Epub ahead of print.])https://doi.org/10.1016/S2214-109X(20)30212-6Summary Full Text Full Text PDF PubMed Scopus (103) Google Scholar] and other low- and middle-income countries, despite fragile health system, the response required to be tailored to the region of interest addressing the social and health inequalities with a proper plan. Race/ethnicity is a complex issue. Ellison, a pioneer in the field of the utility of race/ethnicity in public policy and biomedical research, argues that the utility of race/ethnicity has limited reliability, in addition to triggering stereotyping and discrimination [[10]Ellison G.T.H. ‘Population profiling’ and public health risk: when and how should we use race/ethnicity?.Crit Public Health. 2005; 15: 65-74Crossref Scopus (31) Google Scholar]. Therefore, it is recommended to avoid using it when it comes to population profiling for public health purposes. Yet, to be able to reduce such risks, and to increase the benefits of using race/ethnicity, it is suggested to (i) use race/ethnicity when it comes to assessing the risk of discriminative treatment (i.e. to assess whether patients would receive a discriminative treatment at a hospital due to their race/ethnicity); and (ii) when race/ethnicity is needed as a proxy for variables that is not possible to measure them, nor to find alternative variables for them (i.e. to predict socio-economic differentials like housing, income, and/or education, when it is not possible to ask about these factors). In the long-term, the COVID-19 issue will be resolved after approving a vaccine and/or treatment. Yet, historically morbidity and mortality tend to be higher among ethnic minority, in comparison with the general population, and especially during public health emergencies. Appropriate public health and social interventions are essential to address the issue in the long run to provide better health for all. All authors declare no conflicts of interest. The impact of ethnicity on clinical outcomes in COVID-19: A systematic reviewData on ethnicity in patients with COVID-19 in the published medical literature remains limited. However, emerging data from the grey literature and preprint articles suggest BAME individuals are at an increased risk of acquiring SARS-CoV-2 infection compared to White individuals and also worse clinical outcomes from COVID-19. Further work on the role of ethnicity in the current pandemic is of urgent public health importance. Full-Text PDF Open Access
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».