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      Inequalities in COVID-19 mortality: defining a global research agenda

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          Abstract

          The global death toll of the coronavirus disease 2019 (COVID-19) pandemic is very high, with over 6 million officially registered deaths and estimates of excess mortality ranging from 10 million to 20 million. 1 – 3 Yet this burden has not been equally distributed between countries or across race, ethnicity, socioeconomic status and social class within countries. 4 – 6 Evidence from several countries indicate disparities in exposure, susceptibility and capacity to treat and contain infection, severe illness, hospitalization and death stemming from the disease. 7 Leading scholars have described COVID-19 as a syndemic (that is, where social and biological factors interact to produce poor health outcomes), as mortality and morbidity from the pandemic feed into and exacerbate existing inequalities in social conditions and chronic disease rates. 4 – 6 An early systematic review revealed stark social inequalities in mortality in the early months of the pandemic among a subset of high-income countries. 7 A recent World Health Organization (WHO) evidence brief identified evidence of poorer COVID-19-related outcomes within countries for lower income individuals, marginalized ethnic minorities, indigenous people, low-paid essential workers, migrants, populations affected by emergencies (including conflicts), incarcerated populations and people experiencing homelessness and housing insecurity. 8 Although evidence suggests that the pandemic has exacerbated social inequalities in mortality, a global synthesis of the trajectory of COVID-19 is needed. Furthermore, quantitative data synthesis is required to understand the global magnitude of inequalities in COVID-19 mortality, as measured with respect to a diverse set of social stratifiers (that is, measures of socioeconomic position, such as educational attainment or wealth). We also need more clarity to ascertain the global picture of the theoretical and methodological approaches underpinning COVID-19 mortality inequality research. 4 The Technical Advisory Group on COVID-19 Mortality Assessment advises and supports efforts by WHO and the United Nations Department of Economic and Social Affairs on matters related to COVID-19 mortality. Working Group 5, on inequality in COVID-19 mortality between and within countries, provides evidence-based recommendations regarding the study of demographic, socioeconomic and geographical inequalities in COVID-19 mortality. 9 Here, we detail the global research agenda defined by this working group to assess the state of existing scientific knowledge regarding social inequalities in COVID-19 mortality, synthesize research about the scope and magnitude of inequalities, and identify key gaps for ongoing data collection and study. A team of researchers housed at the Centre for Global Health Inequalities Research at the Norwegian University for Science and Technology in Trondheim is undertaking this work along with the leadership of the Global Public Health Observatory of the Johns Hopkins Bloomberg School of Public Health, 10 under supervision of the Inequality Working Group within the Technical Advisory Group 9 and in collaboration with a global network of researchers. We suggest that a two-phase, systematic assessment is well suited to address the research questions. The first phase will be aimed at determining the existing frameworks and data coverage describing social inequalities in COVID-19 mortality, and which social stratifiers these frameworks have focused on. The second phase will be focused on quantitatively synthesizing the effect sizes of a key set of social stratifiers for COVID-19 mortality. The first phase consists of charting the landscape of frameworks and stratifiers that have been used to measure COVID-19 inequalities. A systematic search of the literature will be carried out leveraging several databases such as: PubMed®, Web of Science, Scopus, Embase®, Global Health, EconLit and Sociology Source Ultimate. The search will be limited to papers published on the subject of review since February 2020 without any restrictions on language, sample size or characteristics. The phenomenon of interest is adult COVID-19 mortality based on social position, broadly defined using a wide range of social markers, including educational attainment, household wealth, income, race, ethnicity, urbanicity, employment/occupational status and insurance status, as available. Both individual and area-level measures will be assessed in this phase. Age and sex will be assessed where they are studied intersectionally with other social dimensions, such as income or education. A set of pilot searches identifying key papers on social inequalities in adult COVID-19 mortality will guide the development of a list of social stratifiers and theoretical frameworks. Theoretical frameworks will likely include the syndemic approach, intersectionality, fundamental cause theory, social determinants and straightforward social epidemiological measurement approaches. Study designs for represented research will include cohort studies, cross-sectional studies, randomized controlled trials and non-randomized trials. Extracted quantitative measures of effect size will include relative risk, hazard ratio, odds ratio and rate ratio as they describe official direct COVID-19 mortality as well as excess mortality. Preprints and other doi (digital object identifier)-referenced articles will be included; however, viewpoint pieces will be excluded. Two researchers – with a third in case of discrepancy – will screen all titles and abstracts of identified references. Researchers working in pairs and applying the inclusion and exclusion criteria identified will also perform full-text reading. After the selection of included studies, the information regarding month(s) and year(s) of data assessed, country, population and age group, study design and method used, risk estimate, confidence intervals and sample size will be extracted out of each study and included in a database. Two qualitative review rounds will take place, one mid-term review and one at the end of the extraction phase. The first phase will conclude with a summary of the social stratifiers, geographical coverage and theoretical frameworks employed in the existing corpus of work. Leveraging the extracted database, we will quantify the geographical coverage of work describing inequalities in COVID-19 mortality. Given limitations in data infrastructure, we expect to find a preponderance of studies for high-income countries, which would represent a critical gap that should be improved in research moving forward. We will also be able to describe the social stratifiers that have been assessed for each world region. Finally, we can show which kinds of inequality metrics, and which frameworks have been employed worldwide. This first stage assessment aims to serve as a guide for ongoing research on inequalities, to describe the existing state of knowledge and identify key gaps as well as strengths in the current corpus of studies. The second phase will quantify the global magnitude of inequalities in COVID-19 mortality. In this phase, we propose to quantitatively synthesize results describing inequalities in COVID-19 mortality globally for a key set of social stratifiers. The final designation regarding the choice of stratifiers will be made once the database has been established, allowing for the assessment of the most represented indicators. However, we expect educational attainment, income, wealth, and employment type and status to serve as key indices. In this phase we propose to focus on individual-level measures, not area-level (such as postal code or municipality) measures, to improve comparability and standardization of measures. In line with previous meta-regression analyses published by researchers involved in this endeavour 11 we will leverage the Meta Regression – Bayesian Trimmed Regularized framework, which was developed as part of comparative risk assessment work conducted for the Global Burden of Disease Study. 12 Using mixed-effect meta-regression, we will combine all measures of the relationship between COVID-19 mortality and a given social stratifier, adjusting for study design, the inclusion of study-level confounders and covariates, the uncertainty associated with each point estimate of measured effect and heterogeneity between studies. Consistent with prior applications of this approach, 11 cross-walking will be used to standardize differences in effect size based on outcome measure type, for example between direct COVID-19 mortality and excess mortality. This method is critical as direct COVID-19 mortality estimates are known to underestimate total pandemic-related deaths, with a social gradient in undercounting and out-of-hospital death. An invitation to collaboration This study of global health inequalities must be conducted as a global, collaborative endeavour to be successful. A key aspect of this research will entail the development of a COVID-19 Mortality Inequality Collaborator Network, consisting of interested researchers with relevant expertise from diverse world regions and academic backgrounds. Collaborators will participate in identifying and addressing data gaps, reviewing model analyses, guiding the interpretation of findings and developing peer-reviewed articles. We encourage interested candidates, especially those from underrepresented backgrounds and from low- and middle-income countries, to join this project’s collaborator network. A doctoral or master’s degree, or equivalent experience, and expertise in the measurement of social inequalities in mortality in country of origin or professional context are desired characteristics of collaborator network candidates. At a minimum, collaborator network members can expect to provide structured feedback at several stages of the research process, including reviewing identified data sources, analytical outputs and manuscript drafts. More information can be found at the working group’s website. 9 Although this work will be initially limited to studying inequalities in mortality stemming from the COVID-19 pandemic, the study will lay the groundwork for subsequent research regarding inequalities in morbidity, which is especially relevant in light of the long-term sequelae experienced by many COVID-19 patients. We expect this research endeavour will result in a comprehensive summary of research describing inequalities in COVID-19 mortality, summarizing the status of current research in the field, and identifying key gaps for future efforts in this area. These results will be invaluable to the Inequality Working Group in making recommendations regarding key priority areas moving forward, as well as opportunities to coordinate data generation and analyses between countries.

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          Most cited references9

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          Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

          Summary Background Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease. Methods GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk–outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk–outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk–outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden. Findings The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10·8 million (95% uncertainty interval [UI] 9·51–12·1) deaths (19·2% [16·9–21·3] of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8·71 million (8·12–9·31) deaths (15·4% [14·6–16·2] of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253–350) DALYs (11·6% [10·3–13·1] of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0–9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10–24 years, alcohol use for those aged 25–49 years, and high systolic blood pressure for those aged 50–74 years and 75 years and older. Interpretation Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public. Funding Bill & Melinda Gates Foundation.
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            Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21

            (2022)
            Background Mortality statistics are fundamental to public health decision making. Mortality varies by time and location, and its measurement is affected by well known biases that have been exacerbated during the COVID-19 pandemic. This paper aims to estimate excess mortality from the COVID-19 pandemic in 191 countries and territories, and 252 subnational units for selected countries, from Jan 1, 2020, to Dec 31, 2021. Methods All-cause mortality reports were collected for 74 countries and territories and 266 subnational locations (including 31 locations in low-income and middle-income countries) that had reported either weekly or monthly deaths from all causes during the pandemic in 2020 and 2021, and for up to 11 year previously. In addition, we obtained excess mortality data for 12 states in India. Excess mortality over time was calculated as observed mortality, after excluding data from periods affected by late registration and anomalies such as heat waves, minus expected mortality. Six models were used to estimate expected mortality; final estimates of expected mortality were based on an ensemble of these models. Ensemble weights were based on root mean squared errors derived from an out-of-sample predictive validity test. As mortality records are incomplete worldwide, we built a statistical model that predicted the excess mortality rate for locations and periods where all-cause mortality data were not available. We used least absolute shrinkage and selection operator (LASSO) regression as a variable selection mechanism and selected 15 covariates, including both covariates pertaining to the COVID-19 pandemic, such as seroprevalence, and to background population health metrics, such as the Healthcare Access and Quality Index, with direction of effects on excess mortality concordant with a meta-analysis by the US Centers for Disease Control and Prevention. With the selected best model, we ran a prediction process using 100 draws for each covariate and 100 draws of estimated coefficients and residuals, estimated from the regressions run at the draw level using draw-level input data on both excess mortality and covariates. Mean values and 95% uncertainty intervals were then generated at national, regional, and global levels. Out-of-sample predictive validity testing was done on the basis of our final model specification. Findings Although reported COVID-19 deaths between Jan 1, 2020, and Dec 31, 2021, totalled 5·94 million worldwide, we estimate that 18·2 million (95% uncertainty interval 17·1–19·6) people died worldwide because of the COVID-19 pandemic (as measured by excess mortality) over that period. The global all-age rate of excess mortality due to the COVID-19 pandemic was 120·3 deaths (113·1–129·3) per 100 000 of the population, and excess mortality rate exceeded 300 deaths per 100 000 of the population in 21 countries. The number of excess deaths due to COVID-19 was largest in the regions of south Asia, north Africa and the Middle East, and eastern Europe. At the country level, the highest numbers of cumulative excess deaths due to COVID-19 were estimated in India (4·07 million [3·71–4·36]), the USA (1·13 million [1·08–1·18]), Russia (1·07 million [1·06–1·08]), Mexico (798 000 [741 000–867 000]), Brazil (792 000 [730 000–847 000]), Indonesia (736 000 [594 000–955 000]), and Pakistan (664 000 [498 000–847 000]). Among these countries, the excess mortality rate was highest in Russia (374·6 deaths [369·7–378·4] per 100 000) and Mexico (325·1 [301·6–353·3] per 100 000), and was similar in Brazil (186·9 [172·2–199·8] per 100 000) and the USA (179·3 [170·7–187·5] per 100 000). Interpretation The full impact of the pandemic has been much greater than what is indicated by reported deaths due to COVID-19 alone. Strengthening death registration systems around the world, long understood to be crucial to global public health strategy, is necessary for improved monitoring of this pandemic and future pandemics. In addition, further research is warranted to help distinguish the proportion of excess mortality that was directly caused by SARS-CoV-2 infection and the changes in causes of death as an indirect consequence of the pandemic. Funding Bill & Melinda Gates Foundation, J Stanton, T Gillespie, and J and E Nordstrom
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              Offline: COVID-19 is not a pandemic

              As the world approaches 1 million deaths from COVID-19, we must confront the fact that we are taking a far too narrow approach to managing this outbreak of a new coronavirus. We have viewed the cause of this crisis as an infectious disease. All of our interventions have focused on cutting lines of viral transmission, thereby controlling the spread of the pathogen. The “science” that has guided governments has been driven mostly by epidemic modellers and infectious disease specialists, who understandably frame the present health emergency in centuries-old terms of plague. But what we have learned so far tells us that the story of COVID-19 is not so simple. Two categories of disease are interacting within specific populations—infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and an array of non-communicable diseases (NCDs). These conditions are clustering within social groups according to patterns of inequality deeply embedded in our societies. The aggregation of these diseases on a background of social and economic disparity exacerbates the adverse effects of each separate disease. COVID-19 is not a pandemic. It is a syndemic. The syndemic nature of the threat we face means that a more nuanced approach is needed if we are to protect the health of our communities. © 2020 Peter Scholey Partnership/Getty Images 2020 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The notion of a syndemic was first conceived by Merrill Singer, an American medical anthropologist, in the 1990s. Writing in The Lancet in 2017, together with Emily Mendenhall and colleagues, Singer argued that a syndemic approach reveals biological and social interactions that are important for prognosis, treatment, and health policy. Limiting the harm caused by SARS-CoV-2 will demand far greater attention to NCDs and socioeconomic inequality than has hitherto been admitted. A syndemic is not merely a comorbidity. Syndemics are characterised by biological and social interactions between conditions and states, interactions that increase a person's susceptibility to harm or worsen their health outcomes. In the case of COVID-19, attacking NCDs will be a prerequisite for successful containment. As our recently published NCD Countdown 2030 showed, although premature mortality from NCDs is falling, the pace of change is too slow. The total number of people living with chronic diseases is growing. Addressing COVID-19 means addressing hypertension, obesity, diabetes, cardiovascular and chronic respiratory diseases, and cancer. Paying greater attention to NCDs is not an agenda only for richer nations. NCDs are a neglected cause of ill-health in poorer countries too. In their Lancet Commission, published last week, Gene Bukhman and Ana Mocumbi described an entity they called NCDI Poverty, adding injuries to a range of NCDs—conditions such as snake bites, epilepsy, renal disease, and sickle cell disease. For the poorest billion people in the world today, NCDIs make up over a third of their burden of disease. The Commission described how the availability of affordable, cost-effective interventions over the next decade could avert almost 5 million deaths among the world's poorest people. And that is without considering the reduced risks of dying from COVID-19. © 2020 Allison Michael Orenstein/Getty Images 2020 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The most important consequence of seeing COVID-19 as a syndemic is to underline its social origins. The vulnerability of older citizens; Black, Asian, and minority ethnic communities; and key workers who are commonly poorly paid with fewer welfare protections points to a truth so far barely acknowledged—namely, that no matter how effective a treatment or protective a vaccine, the pursuit of a purely biomedical solution to COVID-19 will fail. Unless governments devise policies and programmes to reverse profound disparities, our societies will never be truly COVID-19 secure. As Singer and colleagues wrote in 2017, “A syndemic approach provides a very different orientation to clinical medicine and public health by showing how an integrated approach to understanding and treating diseases can be far more successful than simply controlling epidemic disease or treating individual patients.” I would add one further advantage. Our societies need hope. The economic crisis that is advancing towards us will not be solved by a drug or a vaccine. Nothing less than national revival is needed. Approaching COVID-19 as a syndemic will invite a larger vision, one encompassing education, employment, housing, food, and environment. Viewing COVID-19 only as a pandemic excludes such a broader but necessary prospectus. © 2020 xavierarnau/Getty Images 2020 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
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                Author and article information

                Journal
                Bull World Health Organ
                Bull World Health Organ
                BLT
                Bulletin of the World Health Organization
                World Health Organization
                0042-9686
                1564-0604
                01 October 2022
                02 September 2022
                : 100
                : 10
                : 648-650
                Affiliations
                [a ]Center for Social Medicine and Humanities, University of California, B7-435, UCLA Semel Institute , Los Angeles, CA 90095-1759 United States of America (USA).
                [b ]Centre for Global Health Inequalities Research, Norwegian University for Science and Technology , Trondheim, Norway.
                [c ]Nuffield Department of Population Health, University of Oxford , Oxford, England.
                [d ]New York, USA.
                [e ]Statistics Division, United Nations Economic and Social Commission for Asia and the Pacific , Bangkok, Thailand.
                [f ]Institute for Health Research, University of Costa Rica , San Jose, Costa Rica.
                [g ]Statistics Division, United Nations Economic Commission for Latin America and the Caribbean , Santiago, Chile.
                [h ]Department of Social Determinants of Health, World Health Organization , Geneva, Switzerland.
                [i ]Department of Data and Analytics, World Health Organization , Geneva, Switzerland.
                [j ]Department of Population and Development Studies, University of Kinshasa, Kinshasa, Democratic Republic of the Congo.
                [k ]Bloomberg School of Public Health, Johns Hopkins University , Baltimore, USA.
                Author notes
                Correspondence to Joseph Friedman (email: joseph.robert.friedman@ 123456gmail.com ).
                Article
                BLT.22.288211
                10.2471/BLT.22.288211
                9511668
                36188017
                52269a25-515b-40fb-9928-902e7baa7a1f
                (c) 2022 The authors; licensee World Health Organization.

                This is an open access article distributed under the terms of the Creative Commons Attribution IGO License ( http://creativecommons.org/licenses/by/3.0/igo/legalcode), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In any reproduction of this article there should not be any suggestion that WHO or this article endorse any specific organization or products. The use of the WHO logo is not permitted. This notice should be preserved along with the article's original URL.

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                : 04 July 2022
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