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Covid-19 pandemic and the social determinants of health
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Publication date (Electronic):
29 January 2021
Journal:
The BMJ
Publisher:
BMJ Publishing Group Ltd.
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Abstract
Lauren Paremoer and colleagues call for action to create a fairer and more sustainable post-covid world
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Most cited references14
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Racism and Health: Evidence and Needed Research
David Williams, Jourdyn Lawrence, Brigette Davis (2019)
In recent decades, there has been remarkable growth in scientific research examining the multiple ways in which racism can adversely affect health. This interest has been driven in part by the striking persistence of racial/ethnic inequities in health and the empirical evidence that indicates that socioeconomic factors alone do not account for racial/ethnic inequities in health. Racism is considered a fundamental cause of adverse health outcomes for racial/ethnic minorities and racial/ethnic inequities in health. This article provides an overview of the evidence linking the primary domains of racism—structural racism, cultural racism, and individual-level discrimination—to mental and physical health outcomes. For each mechanism, we describe key findings and identify priorities for future research. We also discuss evidence for interventions to reduce racism and describe research needed to advance knowledge in this area.
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Disparities in Incidence of COVID-19 Among Underrepresented Racial/Ethnic Groups in Counties Identified as Hotspots During June 5–18, 2020 — 22 States, February–June 2020
Jazmyn T. Moore, Jessica Ricaldi, Charles E Rose … (2020)
During January 1, 2020–August 10, 2020, an estimated 5 million cases of coronavirus disease 2019 (COVID-19) were reported in the United States.* Published state and national data indicate that persons of color might be more likely to become infected with SARS-CoV-2, the virus that causes COVID-19, experience more severe COVID-19–associated illness, including that requiring hospitalization, and have higher risk for death from COVID-19 ( 1 – 5 ). CDC examined county-level disparities in COVID-19 cases among underrepresented racial/ethnic groups in counties identified as hotspots, which are defined using algorithmic thresholds related to the number of new cases and the changes in incidence. † Disparities were defined as difference of ≥5% between the proportion of cases and the proportion of the population or a ratio ≥1.5 for the proportion of cases to the proportion of the population for underrepresented racial/ethnic groups in each county. During June 5–18, 205 counties in 33 states were identified as hotspots; among these counties, race was reported for ≥50% of cumulative cases in 79 (38.5%) counties in 22 states; 96.2% of these counties had disparities in COVID-19 cases in one or more underrepresented racial/ethnic groups. Hispanic/Latino (Hispanic) persons were the largest group by population size (3.5 million persons) living in hotspot counties where a disproportionate number of cases among that group was identified, followed by black/African American (black) persons (2 million), American Indian/Alaska Native (AI/AN) persons (61,000), Asian persons (36,000), and Native Hawaiian/other Pacific Islander (NHPI) persons (31,000). Examining county-level data disaggregated by race/ethnicity can help identify health disparities in COVID-19 cases and inform strategies for preventing and slowing SARS-CoV-2 transmission. More complete race/ethnicity data are needed to fully inform public health decision-making. Addressing the pandemic’s disproportionate incidence of COVID-19 in communities of color can reduce the community-wide impact of COVID-19 and improve health outcomes. This analysis used cumulative county-level data during February–June 2020, reported to CDC by jurisdictions or extracted from state and county websites and disaggregated by race/ethnicity. Case counts, which included both probable and laboratory-confirmed cases, were cross-referenced with counts from the HHS Protect database (https://protect-public.hhs.gov/). Counties missing race data for more than half of reported cases (126) were excluded from the analysis. § The proportion of the population for each county by race/ethnicity was calculated using data obtained from CDC WONDER ( 6 ). For each underrepresented racial/ethnic group, disparities were defined as a difference of ≥5% between the proportion of cases and the proportion of the population consisting of that group or a ratio of ≥1.5 for the proportion of cases to the proportion of the population in that racial/ethnic group. The county-level differences and ratios between proportion of cases and the proportion of population were used as a base for a simulation accounting for missing data using different assumptions of racial/ethnic distribution of cases with unknown race/ethnicity. An intercept-only logistic regression model was estimated for each race/ethnicity category and county to obtain the intercept regression coefficient and standard error. The simulation used the logistic regression-estimated coefficient and standard error to produce an estimated mean and confidence interval (CI) for the percentage difference between and ratio of proportions of cases and population. This simulation was done for each racial/ethnic group within each county. The lower bound of the CI was used to identify counties with disparities (as defined by percentage differences or ratio). The mean of the estimated differences and mean of the estimated ratios were calculated for all counties with disparities. Analyses were conducted using SAS software (version 9.4; SAS Institute). During June 5–18, a total of 205 counties in 33 states were identified as hotspots. These counties have a combined total population of 93.5 million persons, and approximately 535,000 cumulative probable and confirmed COVID-19 cases. Among the 205 identified hotspot counties, 79 (38.5%) counties in 22 states, with a combined population of 27.5 million persons and approximately 162,000 COVID-19 cases, had race data available for ≥50% of cumulative cases and were included in the analysis (range = 51.3%–97.4%). Disparities in cases were identified among underrepresented racial/ethnic groups in 76 (96.2%) analyzed counties (Table 1). Disparities among Hispanic populations were identified in approximately three quarters of hotspot counties (59 of 79, 74.7%) with approximately 3.5 million Hispanic residents (Table 2). Approximately 2.0 million black persons reside in 22 (27.8%) hotspot counties where black residents were disproportionately affected by COVID-19, approximately 61,000 AI/AN persons live in three (3.8%) hotspot counties where AI/AN residents were disproportionately affected by COVID-19, nearly 36,000 Asian persons live in four (5.1%) hotspot counties where Asian residents were disproportionately affected by COVID-19, and approximately 31,000 NHPI persons live in 19 (24.1%) hotspot counties where NHPI populations were disproportionately affected by COVID-19. TABLE 1 Total population and racial/ethnic disparities* in cumulative COVID-19 cases among 79 counties identified as hotspots during June 5–18, 2020, with any disparity identified — 22 states, February–June 2020 State No. of persons living in analyzed hotspot counties* No. of (col %) hotspot counties analyzed† No. of counties with disparities in COVID-19 cases among each racial/ethnic group§ Hispanic Black NHPI Asian AI/AN Alabama 500,000–1,000,000 1 (1.3) — 1 — — — Arizona 1,000,000–3,000,000 5 (6.3) 3 — — — 3 Arkansas 500,000–1,000,000 4 (5.1) 4 — 2 — — California 1,000,000–3,000,000 1 (1.3) 1 — — — — Colorado 100,000–500,000 1 (1.3) 1 — 1 — — Florida >3,000,000 6 (7.6) 3 2 — — — Georgia 100,000–500,000 1 (1.3) 1 — — — — Iowa 50,000–100,000 1 (1.3) 1 — — — — Kansas 500,000–1,000,000 2 (2.5) 2 — 2 — — Massachusetts 500,000–1,000,000 2 (2.5) — 2 — — — Michigan 1,000,000–3,000,000 5 (6.3) — 5 1 — — Minnesota 3,000,000 18 (22.8) 18 — 3 1 — Ohio 1,000,000–3,000,000 3 (3.8) 3 2 — 1 — Oregon 1,000,000–3,000,000 6 (7.6) 6 1 4 1 — South Carolina 1,000,000–3,000,000 9 (11.4) 6 4 2 — — Tennessee 500,000–1,000,000 3 (3.8) 3 — — — — Texas 500,000–1,000,000 2 (2.5) — 1 — — — Utah 1,000,000–3,000,000 4 (5.1) 4 1 3 — — Virginia <50,000 1 (1.3) — — — — — Wisconsin 100,000–500,000 1 (1.3) 1 — — — — Total (approximate) 27,500,000 79 (100) 59 22 19 4 3 Abbreviations: AI/AN = American Indian/Alaska Native; COVID-19 = coronavirus disease 2019; NHPI = Native Hawaiian/other Pacific Islanders. * Disparities were defined as percentage difference of ≥5% between the proportion of cases and the proportion of the population or a ratio ≥1.5 for the proportion of cases to the proportion of the population) for underrepresented racial/ethnic groups in each county. † Counties with race/ethnicity data available for ≥50% of cases. § Racial/ethnic groups are not mutually exclusive in a given county. TABLE 2 Number of persons in each racial/ethnic group living in 79 counties identified as hotspots during June 5–18, 2020 with disparities* — 22 states, February–June 2020 Racial/Ethnic group No. (%)† of counties with disparities§ identified Approximate no. of persons living in hotspot counties with disparities Hispanic/Latino 59 (74.7) 3,500,000 Black/African American 22 (27.8) 2,000,000 American Indian/Alaska Native 3 (3.8) 61,000 Asian 4 (5.1) 36,000 Native Hawaiian/Other Pacific Islander 19 (24.1) 31,000 Total — 5,628,000 Abbreviation: COVID-19 = coronavirus disease 2019. * Disparities were defined as percentage difference of ≥5% between the proportion of cases and the proportion of the population or a ratio ≥1.5 for the proportion of cases to the proportion of the population) for underrepresented racial/ethnic groups in each county. † Percentage of the 79 counties. § Disparities are in respective racial/ethnic groups and are not mutually exclusive; some counties had disparities in more than one racial/ethnic group. The mean of the estimated differences between the proportion of cases and proportion of the population consisting of each underrepresented racial/ethnic group in all counties with disparities ranged from 4.5% (NHPI) to 39.3% (AI/AN) (Table 3). The mean of the estimated ratio of the proportion of cases to the proportions of population were also generated for each underrepresented racial/ethnic group and ranged from 2.3 (black) to 8.5 (NHPI). TABLE 3 Proportion of cumulative COVID-19 cases compared with proportion of population in 79 counties identified as hotspots during June 5–18, 2020 with racial/ethnic disparities* — 22 states February–June 2020 Racial/Ethnic group Mean of estimated differences, † % (range) Mean of estimated ratios of proportion of cases to proportion of population§ (range) Hispanic/Latino 30.2 (8.0–68.2) 4.4 (1.2–14.6) Black/African American 14.5 (2.3–31.7) 2.3 (1.2–7.0) American Indian/Alaska Native 39.3 (16.4–57.9) 4.2 (1.9–6.4) Asian 4.7 (2.7–6.8) 2.9 (2.0–4.7) Native Hawaiian/Other Pacific Islander 4.5 (0.1–31.5) 8.5 (2.7–18.4) Abbreviation: COVID-19 = coronavirus disease 2019.
* Disparities were defined as percentage difference of ≥5% between the proportion of cases and the proportion of the population or a ratio ≥1.5 for the proportion of cases to the proportion of the population) for underrepresented racial/ethnic groups in each county. † The mean of the estimated differences between the proportion of cases in a given racial/ethnic group and the proportion of persons in that racial/ethnic group in the overall population among all counties with disparities identified by the analysis. For example, if Hispanic/Latino persons make up 20% of the population in a given county and 30% of the cases in that county, then the difference would be 10% and the county is considered to have a disparity. § The ratio of the estimated proportion of cases to the proportion of population for each racial/ethnic group among all counties with disparities identified by the analysis. For example, if American Indian/Alaskan Native persons made up 0.5% of the population in a given county and 1.5% of the cases in that county, then the ratio of proportions would be 3.0, and the county is considered to have a disparity. Discussion These findings illustrate the disproportionate incidence of COVID-19 among communities of color, as has been shown by other studies, and suggest that a high percentage of cases in hotspot counties are among persons of color ( 1 – 5 , 7 ). Among all underrepresented racial/ethnic groups in these hotspot counties, Hispanic persons were the largest group living in hotspot counties with a disparity in cases identified within that population (3.5 million persons). This finding is consistent with other evidence highlighting the disproportionate incidence of COVID-19 among the Hispanic population ( 2 , 7 ). The disproportionate incidence of COVID-19 among black populations is well documented ( 1 – 3 ). The findings from this analysis align with other data indicating that black persons are overrepresented among COVID-19 cases, associated hospitalizations, and deaths in the United States. The analysis found few counties with disparities among AI/AN populations. This finding is likely attributable to the smaller proportions of cases and populations of AI/AN identified in hotspot counties, as well as challenges with data for this group, including a lack of surveillance data and misclassification problems in large data sets. ¶ Asian populations were disproportionately affected by COVID-19 in a small number of hotspot counties. Few studies have assessed COVID-19 disparities among Asian populations in the United States.** The Asian racial category is broad, and further subgroup analyses might provide additional insights regarding the incidence of COVID-19 in this population. Disparities in COVID-19 cases in NHPI populations were identified in nearly one quarter of hotspot counties. For some hotspot counties with small NHPI populations, this finding might be related, in part, to the analytic methodology used. Using a ratio of ≥1.5 in the proportion of population and proportion of cases to indicate disparities is sensitive to small differences in these groups. More complete county-level race/ethnicity data are needed to fully evaluate the disproportionate incidence of COVID-19 among communities of color. Disparities in COVID-19–associated mortality in hotspot counties were not assessed because the available county-level mortality data disaggregated by race/ethnicity were not sufficient to generate reliable estimates. Existing national analyses highlight disparities in mortality associated with COVID-19; similar patterns are likely to exist at the county level ( 5 ). As more complete data are made available in the future, county-level analyses examining disparities in mortality might be possible. COVID-19 disparities among underrepresented racial/ethnic groups likely result from a multitude of conditions that lead to increased risk for exposure to SARS-CoV-2, including structural factors, such as economic and housing policies and the built environment, †† and social factors such as essential worker employment status requiring in-person work (e.g., meatpacking, agriculture, service, and health care), residence in multigenerational and multifamily households, and overrepresentation in congregate living environments with an increased risk for transmission ( 4 , 7 – 9 ). Further, long-standing discrimination and social inequities might contribute to factors that increase risk for severe disease and death, such as limited access to health care, underlying medical conditions, and higher levels of exposure to pollution and environmental hazards §§ ( 4 ). The conditions contributing to disparities likely vary widely within and among groups, depending on location and other contextual factors. Rates of SARS-CoV-2 transmission vary by region and time, resulting in nonuniform disease outbreak patterns across the United States. Therefore, using epidemiologic indicators to identify hotspot counties currently affected by SARS-CoV-2 transmission can inform a data-driven emergency response. Tailoring strategies to control SARS-CoV-2 transmission could reduce the overall incidence of COVID-19 in communities. Using these data to identify disproportionately affected groups at the county level can guide the allocation of resources, development of culturally and linguistically tailored prevention activities, and implementation of focused testing efforts. The findings in this report are subject to at least five limitations. First, more than half of the hotspot counties did not report sufficient race data and were therefore excluded from the analysis. In addition, many hotspot counties included in the analyses were missing data on race for a significant proportion of cases (mean = 28.3%; range = 2.6%–48.7%). These data gaps might result from jurisdictions having to reconcile data from multiple sources for a large volume of cases while data collection and management processes are rapidly evolving. ¶¶ Second, health departments differ in the way race/ethnicity are reported, making comparisons across counties and states more difficult. Third, differences in how race/ethnicity data are collected (e.g., self-report versus observation) likely varies by setting and could lead to miscategorization. Fourth, differences in access to COVID-19 testing could lead to underestimates of prevalence in some underrepresented racial/ethnic populations. Finally, the number of cases that had available race/ethnicity data for the period of study of hotspots (June 5–18) was too small to generate reliable estimates, so cumulative case counts by county during February–June 2020 were used to identify disparities. This approach describes the racial/ethnic breakdown of cumulative cases only. Therefore, these data might not provide an accurate estimate of disparities during June 5–18, which could be under- or overestimated, or change over time. Developing culturally responsive, targeted interventions in partnership with trusted leaders and community-based organizations within communities of color might reduce disparities in COVID-19 incidence. Increasing the proportion of cases for which race/ethnicity data are collected and reported can help inform efforts in the short-term to better understand patterns of incidence and mortality. Existing health inequities amplified by COVID-19 highlight the need for continued investment in communities of color to address social determinants of health*** and structural racism that affect health beyond this pandemic ( 4 , 8 ). Long-term efforts should focus on addressing societal factors that contribute to broader health disparities across communities of color. Summary What is already known about this topic? Long-standing health and social inequities have resulted in increased risk for infection, severe illness, and death from COVID-19 among communities of color. What is added by this report? Among 79 counties identified as hotspots during June 5–18, 2020 that also had sufficient data on race, a disproportionate number of COVID-19 cases among underrepresented racial/ethnic groups occurred in almost all areas during February–June 2020. What are the implications for public health practice? Identifying health disparities in COVID-19 hotspot counties can inform testing and prevention efforts. Addressing the pandemic’s disproportionate incidence among communities of color can improve community-wide health outcomes related to COVID-19.
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Ethnic Disparities in Hospitalisation for COVID-19 in England: The Role of Socioeconomic Factors, Mental Health, and Inflammatory and Pro-inflammatory Factors in a Community-based Cohort Study
Camille Lassale, Bamba Gaye, Mark Hamer … (2020)
Highlights • Differentials in COVID-19 incidence, hospitalisation and mortality according to ethnicity are being reported but their origin is uncertain. • In a large, community-based cohort, Black and Asian individuals had a markedly higher risk of COVID-19 hospitalisation. • After adjustment for socioeconomic, inflammatory-related lifestyle, comorbidities, and biomarkers, Black individuals still experienced more than a doubling of risk compared to white individuals. • The effect for the Asian group was diminished. • If replicated, this has implications for health policy, including the targeting of prevention advice and vaccination coverage.
Author and article information
Contributors
Lauren Paremoer: Role: senior lecturer
Sulakshana Nandi: Role: co-chair of global steering council
Hani Serag: Role: director of programmes
Fran Baum: Role: director
Journal
Journal ID (nlm-ta): BMJ
Journal ID (iso-abbrev): BMJ
Journal ID (publisher-id): BMJ-UK
Journal ID (hwp): bmj
Title:
The BMJ
Publisher:
BMJ Publishing Group Ltd.
ISSN
(Print):
0959-8138
ISSN
(Electronic):
1756-1833
Publication date Collection: 2021
Publication date
(Electronic):
29
January
2021
Volume: 372
Electronic Location Identifier: n129
Affiliations
Author notes
Correspondence to: S Nandi
sulakshana@
123456phmovement.org
Article
Publisher ID: nand0611053DOI: 10.1136/bmj.n129
PMC ID: 7842257
PubMed ID: 33509801
SO-VID: ef75f6fe-f620-4e63-b645-f53695e74abb
Copyright © © Author(s) (or their employer(s)) 2019. Re-use permitted under CC
BY-NC. No commercial re-use. See rights and permissions. Published by
BMJ.
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This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
History
Categories
Subject:
Analysis
Subject:
2474
Series title:
Covid-19: The Road to Equity and Solidarity
ScienceOpen disciplines: Medicine
Data availability:
ScienceOpen disciplines: Medicine