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      COVID-19 and the worsening of health inequities in Santiago, Chile

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          Abstract

          The COVID-19 pandemic is already responsible for >1 million deaths worldwide. 1 Latin America is one of the most affected regions worldwide, with >300 000 deaths confirmed by September 2020. 1 Latin America is also one of the most urbanized and unequal regions in the world, with wide inequities in longevity in its cities. 2 Wide inequities in COVID-19 outcomes have been reported in other settings. 3 However, policymakers in some Latin American countries have expressed scepticism about the existence of health inequities in COVID-19 mortality. 4 We used mortality, population and census data to show a worsening of pre-existing inequities in mortality in the municipalities that make up the metropolitan region of Santiago (Chile) during the COVID-19 pandemic. We obtained data for the 36 comunas (municipalities) that make up Greater Santiago, the metropolitan area of the capital of Chile, on: (i) mortality by age for the 2016–2020 period from the Department of Statistics and Health Information (DEIS); (ii) population projections by age for the 2016–2020 period from the National Institute of Statistics (INE); and (iii) average years of schooling among adults aged ≥25 years, and the proportion of households living in overcrowding (>2.5 people/bedroom) from the 2017 Chilean census. We selected these two indicators since they represent either good measures of area-level socio-economic status (SES) or are directly linked to COVID-19 transmission. We computed age-adjusted mortality rates, using the 2000 World Health Organization reference population, from January to August for the pre-pandemic (years 2016–2019) and pandemic (2020) periods. We estimated the association between log(mortality) and average years of schooling or proportion overcrowded households using a linear model for each period separately. Data and code for replication are available here: https://github.com/usamabilal/COVID_Chile_Inequities. Figure 1 shows the main results. We found a strong association between SES and mortality in both periods, although this association was stronger in 2020. Specifically, we found a 9.0% lower mortality per 1-year increase in the average schooling years in the pre-pandemic period [relative risk (RR) = 0.91, 95% confidence interval (CI) 0.87 to 0.93] compared with a 13.8% lower mortality in the 2020 period (RR = 0.86, 95% CI 0.83 to 0.89). We also found that a 5% increase in the proportion of overcrowded households was associated with a 22% and 32% higher mortality in the pre-pandemic and pandemic periods, respectively (RR = 1.22, 95% CI 1.16 to 1.28; RR = 1.32, 95% CI 1.23 to 1.42). Figure 1 Area-level socio-economic status and age-adjusted mortality in 2016–2019 and 2020 in the municipalities of Santiago, Chile (A) Average years of schooling for adults >25 years of age; (B) percentage of households living in overcrowded situations. The solid line represents a linear fit. In summary, we found a worsening of pre-existing inequities in mortality in the metropolitan area of Santiago de Chile during the COVID-19 pandemic. The association of years of schooling and overcrowding with mortality in the pre-pandemic period (2016–2019) is consistent with previous research in Santiago, showing wide gaps in life expectancy 2 and infant mortality. 5 Our finding that mortality inequities in 2020 were greater than in previous periods contradicts statements made in September 2020 by the Minister of Health of Chile, which indicated that there is no relationship between mortality due to COVID-19 and poverty, as this would imply discriminatory healthcare due to the place of origin of the patients. 4 This assertion ignores existing structural inequalities in Chile, including inequities in healthcare access and utilization by type of insurance both before 6 and during the pandemic, 7 along with social determinants of health beyond the health system. These include factors driving not only incidence and increased exposure to the virus, 3 such as more precarious employment conditions, loss of income and the need to work outside the home, but also an increased prevalence of conditions that aggravate the consequences of the disease, which show a strong social patterning in Chile. 8 Mitigating health inequities was an explicit objective of the Chilean National Health Strategy 9 and the aggravation of these inequities amidst the COVID-19 pandemic represents a step in the wrong direction. Our analysis is limited by the lack of individual-level mortality data and a validated SES index. However, we are using whole-population vital-registration data in a country with a good vital-registration system, 10 which lends strength to our findings. Interventions to reduce these inequities are greatly needed to avoid the continued widening of these gaps, including but not exclusively focused on healthcare, in addition to addressing the other health and economic consequences of the pandemic, which will likely also affect these vulnerable groups more frequently, intensely and for more prolonged periods of time. Funding U.B. was supported by the Office of the Director of the National Institutes of Health under award number DP5OD26429. U.B., T.A. and A.V. were also supported by the Salud Urbana en América Latina (SALURBAL)/Urban Health in Latin America project, funded by the Wellcome Trust (205177/Z/16/Z). The funding sources had no role in the analysis, writing or decision to submit the manuscript.

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          An interactive web-based dashboard to track COVID-19 in real time

          In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.
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            Inequalities in life expectancy in six large Latin American cities from the SALURBAL study: an ecological analysis

            Summary Background Latin America is one of the most unequal regions in the world, but evidence is lacking on the magnitude of health inequalities in urban areas of the region. Our objective was to examine inequalities in life expectancy in six large Latin American cities and its association with a measure of area-level socioeconomic status. Methods In this ecological analysis, we used data from the Salud Urbana en America Latina (SALURBAL) study on six large cities in Latin America (Buenos Aires, Argentina; Belo Horizonte, Brazil; Santiago, Chile; San José, Costa Rica; Mexico City, Mexico; and Panama City, Panama), comprising 266 subcity units, for the period 2011–15 (expect for Panama city, which was for 2012–16). We calculated average life expectancy at birth by sex and subcity unit with life tables using age-specific mortality rates estimated from a Bayesian model, and calculated the difference between the ninth and first decile of life expectancy at birth (P90–P10 gap) across subcity units in cities. We also analysed the association between life expectancy at birth and socioeconomic status at the subcity-unit level, using education as a proxy for socioeconomic status, and whether any geographical patterns existed in cities between subcity units. Findings We found large spatial differences in average life expectancy at birth in Latin American cities, with the largest P90–P10 gaps observed in Panama City (15·0 years for men and 14·7 years for women), Santiago (8·9 years for men and 17·7 years for women), and Mexico City (10·9 years for men and 9·4 years for women), and the narrowest in Buenos Aires (4·4 years for men and 5·8 years for women), Belo Horizonte (4·0 years for men and 6·5 years for women), and San José (3·9 years for men and 3·0 years for women). Higher area-level socioeconomic status was associated with higher life expectancy, especially in Santiago (change in life expectancy per P90–P10 change unit-level of educational attainment 8·0 years [95% CI 5·8–10·3] for men and 11·8 years [7·1–16·4] for women) and Panama City (7·3 years [2·6–12·1] for men and 9·0 years [2·4–15·5] for women). We saw an increase in life expectancy at birth from east to west in Panama City and from north to south in core Mexico City, and a core-periphery divide in Buenos Aires and Santiago. Whereas for San José the central part of the city had the lowest life expectancy and in Belo Horizonte the central part of the city had the highest life expectancy. Interpretation Large spatial differences in life expectancy in Latin American cities and their association with social factors highlight the importance of area-based approaches and policies that address social inequalities in improving health in cities of the region. Funding Wellcome Trust.
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              Life expectancy and mortality in 363 cities of Latin America

              The concept of a so-called urban advantage in health ignores the possibility of heterogeneity in health outcomes across cities. Using a harmonized dataset from the SALURBAL project, we describe variability and predictors of life expectancy and proportionate mortality in 363 cities across nine Latin American countries. Life expectancy differed substantially across cities within the same country. Cause-specific mortality also varied across cities, with some causes of death (unintentional and violent injuries and deaths) showing large variation within countries, whereas other causes of death (communicable, maternal, neonatal and nutritional, cancer, cardiovascular disease and other noncommunicable diseases) varied substantially between countries. In multivariable mixed models, higher levels of education, water access and sanitation and less overcrowding were associated with longer life expectancy, a relatively lower proportion of communicable, maternal, neonatal and nutritional deaths and a higher proportion of deaths from cancer, cardiovascular disease and other noncommunicable diseases. These results highlight considerable heterogeneity in life expectancy and causes of death across cities of Latin America, revealing modifiable factors that could be amenable to urban policies aimed toward improving urban health in Latin America and more generally in other urban environments.
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                Author and article information

                Journal
                Int J Epidemiol
                Int J Epidemiol
                ije
                International Journal of Epidemiology
                Oxford University Press
                0300-5771
                1464-3685
                04 February 2021
                : dyab007
                Affiliations
                [1 ] Urban Health Collaborative, Drexel Dornsife School of Public Health , Philadelphia, PA, USA
                [2 ] Department of Epidemiology and Biostatistics, Drexel Dornsife School of Public Health , Philadelphia, PA, USA
                [3 ] Escuela de Salud Pública, Universidad de Chile, Santiago, Chile
                [4 ] Departamento de Salud Pública, Pontificia Universidad Católica de Chile, Santiago de Chile, Chile
                [5 ] Centro de Desarrollo Urbano Sustentable (CEDEUS) , Santiago de Chile, Chile
                Author notes
                Corresponding author. 3600 Market St, Suite 730, Philadelphia, PA, 19104, USA. E-mail: ubilal@ 123456drexel.edu
                Author information
                http://orcid.org/0000-0002-9868-7773
                Article
                dyab007
                10.1093/ije/dyab007
                7928917
                33537771
                935383cf-2f61-4063-ac14-abd10b9ff9af
                © The Author(s) 2021; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association

                This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.

                This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model ( https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

                History
                : 03 January 2021
                : 08 January 2021
                Page count
                Pages: 3
                Funding
                Funded by: Office of the Director of the National Institutes of Health;
                Award ID: DP5OD26429
                Funded by: the Salud Urbana en América Latina (SALURBAL)/Urban Health in Latin America;
                Funded by: Wellcome Trust (;
                Award ID: 205177/Z/16/Z)
                Categories
                Letter to the Editor
                AcademicSubjects/MED00860
                Custom metadata
                PAP

                Public health
                mortality,health equity,inequalities,urban health,latin america,chile
                Public health
                mortality, health equity, inequalities, urban health, latin america, chile

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