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      The impact of access to financial services on mitigating COVID-19 mortality globally

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          Abstract

          The COVID-19 pandemic has disproportionately affected different social and demographic groups, deepening the negative health implications of social and economic inequalities and highlighting the importance of social determinants of health. Despite a deep literature on pandemic-related disparities, specifically regarding social determinants and health outcomes, the influence of the accessibility of financial services on health outcomes during COVID-19 remains largely unexplored. Modeling (pre-omicron) COVID-19 mortality across 142 nations, we assess the impact of national-level usage and access to formal financial services. Two financial access indexes constructed through principal component analysis capture (1) usage of and access to formal financial tools and (2) reliance on alternative and informal financial tools. On average, nations with higher pre-pandemic use of and access to formal financial services had substantially lower population mortality risk from COVID-19, controlling for key population health, demographic, and socioeconomic covariates. The scale of effect is similar in magnitude—but opposite in direction—to major risk factors identified in previous literature, such as lung cancer, hypertension, and income inequality. Findings suggest that financial services deserve greater attention both in the public health literature related to COVID-19 and more broadly in policy discussions about fostering better public health overall.

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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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            Comorbidity and its impact on 1590 patients with Covid-19 in China: A Nationwide Analysis

            Background The coronavirus disease 2019 (Covid-19) outbreak is evolving rapidly worldwide. Objective To evaluate the risk of serious adverse outcomes in patients with coronavirus disease 2019 (Covid-19) by stratifying the comorbidity status. Methods We analysed the data from 1590 laboratory-confirmed hospitalised patients 575 hospitals in 31 province/autonomous regions/provincial municipalities across mainland China between December 11th, 2019 and January 31st, 2020. We analyse the composite endpoints, which consisted of admission to intensive care unit, or invasive ventilation, or death. The risk of reaching to the composite endpoints was compared according to the presence and number of comorbidities. Results The mean age was 48.9 years. 686 patients (42.7%) were females. Severe cases accounted for 16.0% of the study population. 131 (8.2%) patients reached to the composite endpoints. 399 (25.1%) reported having at least one comorbidity. The most prevalent comorbidity was hypertension (16.9%), followed by diabetes (8.2%). 130 (8.2%) patients reported having two or more comorbidities. After adjusting for age and smoking status, COPD [hazards ratio (HR) 2.681, 95% confidence interval (95%CI) 1.424–5.048], diabetes (HR 1.59, 95%CI 1.03–2.45), hypertension (HR 1.58, 95%CI 1.07–2.32) and malignancy (HR 3.50, 95%CI 1.60–7.64) were risk factors of reaching to the composite endpoints. The HR was 1.79 (95%CI 1.16–2.77) among patients with at least one comorbidity and 2.59 (95%CI 1.61–4.17) among patients with two or more comorbidities. Conclusion Among laboratory-confirmed cases of Covid-19, patients with any comorbidity yielded poorer clinical outcomes than those without. A greater number of comorbidities also correlated with poorer clinical outcomes.
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              A global database of COVID-19 vaccinations

              An effective rollout of vaccinations against COVID-19 offers the most promising prospect of bringing the pandemic to an end. We present the Our World in Data COVID-19 vaccination dataset, a global public dataset that tracks the scale and rate of the vaccine rollout across the world. This dataset is updated regularly and includes data on the total number of vaccinations administered, first and second doses administered, daily vaccination rates and population-adjusted coverage for all countries for which data are available (169 countries as of 7 April 2021). It will be maintained as the global vaccination campaign continues to progress. This resource aids policymakers and researchers in understanding the rate of current and potential vaccine rollout; the interactions with non-vaccination policy responses; the potential impact of vaccinations on pandemic outcomes such as transmission, morbidity and mortality; and global inequalities in vaccine access.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: SupervisionRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: Software
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: Software
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: Software
                Role: ConceptualizationRole: InvestigationRole: Writing – original draft
                Role: Editor
                Journal
                PLOS Glob Public Health
                PLOS Glob Public Health
                plos
                PLOS Global Public Health
                Public Library of Science (San Francisco, CA USA )
                2767-3375
                17 March 2023
                2023
                : 3
                : 3
                : e0001137
                Affiliations
                [1 ] Department of Economics, Martindale Center for the Study of Private Enterprise, Lehigh University, Bethlehem, PA, United States of America
                [2 ] Data for Impact Fellows, Lehigh University, Bethlehem, PA, United States of America
                Universidad Nacional de Colombia, COLOMBIA
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-8264-0420
                https://orcid.org/0000-0002-3816-732X
                https://orcid.org/0000-0003-2274-8661
                https://orcid.org/0000-0002-4078-639X
                https://orcid.org/0000-0002-8493-370X
                Article
                PGPH-D-22-01480
                10.1371/journal.pgph.0001137
                10022804
                36963020
                e9c3aeed-38fa-4036-8aaf-f51668c8dad6
                © 2023 Watkins et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 9 September 2022
                : 16 February 2023
                Page count
                Figures: 1, Tables: 6, Pages: 18
                Product
                Funding
                Funded by: Lehigh University’s Martindale Center for the Study of Private Enterprise
                Award Recipient :
                Funded by: Lehigh University’s Martindale Center for the Study of Private Enterprise
                Award Recipient :
                Funded by: Lehigh University’s Martindale Center for the Study of Private Enterprise
                Award Recipient :
                Funded by: Lehigh University’s Martindale Center for the Study of Private Enterprise
                Award Recipient :
                Funded by: Lehigh University’s Martindale Center for the Study of Private Enterprise
                Award Recipient :
                Funded by: Lehigh University’s Martindale Center for the Study of Private Enterprise
                Award Recipient :
                Fellowship funding for the Data for Impact Fellows (KN, HA, RG, JP, BT) was provided by internal funding from Lehigh University’s Martindale Center for the Study of Private Enterprise, which is directed by the lead author (TW). Except for the role of the lead author, no funding source played any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors received no specific external funding for this work.
                Categories
                Research Article
                Social Sciences
                Economics
                Finance
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Viral Diseases
                Covid 19
                Biology and Life Sciences
                Population Biology
                Population Metrics
                Death Rates
                Medicine and Health Sciences
                Health Care
                Socioeconomic Aspects of Health
                Medicine and Health Sciences
                Public and Occupational Health
                Socioeconomic Aspects of Health
                Medicine and Health Sciences
                Public and Occupational Health
                Global Health
                Medicine and Health Sciences
                Critical Care and Emergency Medicine
                Medicine and Health Sciences
                Epidemiology
                Medical Risk Factors
                Cancer Risk Factors
                Medicine and Health Sciences
                Oncology
                Cancer Risk Factors
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Lung and Intrathoracic Tumors
                Custom metadata
                All data used in the study is from publicly available sources, as comprehensively listed in the manuscript. Minimal data set and source details also archived at OpenICPSR: https://www.openicpsr.org/openicpsr/project/183982/version/V1/view.

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