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      COVID-19 vaccine acceptance and hesitancy in low- and middle-income countries

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      1 , 2 , 3 , 1 , 1 , 4 , 1 , 5 , 6 , 7 , 3 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 13 , 16 , 15 , 13 , 17 , 18 , 19 , 5 , 20 , 1 , 21 , 14 , 22 , 2 , 23 , 22 , 16 , 12 , 24 , 21 , 25 , 14 , 21 , 4 , 26 , 15 , 27 , 28 , 4 , 29 , 22 , 30 , 30 , 23 , 31 , 31 , 30 , 8 , 21 , 32 , 32 , 33 , 21 , 29 , 34 , 35 , 31 , 34 , 15 , 36 , 37 , 22 , 32 , 22 , 16 , 35 , 12 , 24 , 38 , 34 , 39 , 28 , 2 , 40 , 41 , 3 , 1 , 6 , 42 , , 7 ,
      Nature Medicine
      Nature Publishing Group US
      Epidemiology, Interdisciplinary studies

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          Abstract

          Widespread acceptance of COVID-19 vaccines is crucial for achieving sufficient immunization coverage to end the global pandemic, yet few studies have investigated COVID-19 vaccination attitudes in lower-income countries, where large-scale vaccination is just beginning. We analyze COVID-19 vaccine acceptance across 15 survey samples covering 10 low- and middle-income countries (LMICs) in Asia, Africa and South America, Russia (an upper-middle-income country) and the United States, including a total of 44,260 individuals. We find considerably higher willingness to take a COVID-19 vaccine in our LMIC samples (mean 80.3%; median 78%; range 30.1 percentage points) compared with the United States (mean 64.6%) and Russia (mean 30.4%). Vaccine acceptance in LMICs is primarily explained by an interest in personal protection against COVID-19, while concern about side effects is the most common reason for hesitancy. Health workers are the most trusted sources of guidance about COVID-19 vaccines. Evidence from this sample of LMICs suggests that prioritizing vaccine distribution to the Global South should yield high returns in advancing global immunization coverage. Vaccination campaigns should focus on translating the high levels of stated acceptance into actual uptake. Messages highlighting vaccine efficacy and safety, delivered by healthcare workers, could be effective for addressing any remaining hesitancy in the analyzed LMICs.

          Abstract

          Survey data collected across ten low-income and middle-income countries (LMICs) in Asia, Africa and South America compared with surveys from Russia and the United States reveal heterogeneity in vaccine confidence in LMICs, with healthcare providers being trusted sources of information, as well as greater levels of vaccine acceptance in these countries than in Russia and the United States.

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          Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine

          Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the resulting coronavirus disease 2019 (Covid-19) have afflicted tens of millions of people in a worldwide pandemic. Safe and effective vaccines are needed urgently. Methods In an ongoing multinational, placebo-controlled, observer-blinded, pivotal efficacy trial, we randomly assigned persons 16 years of age or older in a 1:1 ratio to receive two doses, 21 days apart, of either placebo or the BNT162b2 vaccine candidate (30 μg per dose). BNT162b2 is a lipid nanoparticle–formulated, nucleoside-modified RNA vaccine that encodes a prefusion stabilized, membrane-anchored SARS-CoV-2 full-length spike protein. The primary end points were efficacy of the vaccine against laboratory-confirmed Covid-19 and safety. Results A total of 43,548 participants underwent randomization, of whom 43,448 received injections: 21,720 with BNT162b2 and 21,728 with placebo. There were 8 cases of Covid-19 with onset at least 7 days after the second dose among participants assigned to receive BNT162b2 and 162 cases among those assigned to placebo; BNT162b2 was 95% effective in preventing Covid-19 (95% credible interval, 90.3 to 97.6). Similar vaccine efficacy (generally 90 to 100%) was observed across subgroups defined by age, sex, race, ethnicity, baseline body-mass index, and the presence of coexisting conditions. Among 10 cases of severe Covid-19 with onset after the first dose, 9 occurred in placebo recipients and 1 in a BNT162b2 recipient. The safety profile of BNT162b2 was characterized by short-term, mild-to-moderate pain at the injection site, fatigue, and headache. The incidence of serious adverse events was low and was similar in the vaccine and placebo groups. Conclusions A two-dose regimen of BNT162b2 conferred 95% protection against Covid-19 in persons 16 years of age or older. Safety over a median of 2 months was similar to that of other viral vaccines. (Funded by BioNTech and Pfizer; ClinicalTrials.gov number, NCT04368728.)
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            Efficacy and Safety of the mRNA-1273 SARS-CoV-2 Vaccine

            Abstract Background Vaccines are needed to prevent coronavirus disease 2019 (Covid-19) and to protect persons who are at high risk for complications. The mRNA-1273 vaccine is a lipid nanoparticle–encapsulated mRNA-based vaccine that encodes the prefusion stabilized full-length spike protein of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes Covid-19. Methods This phase 3 randomized, observer-blinded, placebo-controlled trial was conducted at 99 centers across the United States. Persons at high risk for SARS-CoV-2 infection or its complications were randomly assigned in a 1:1 ratio to receive two intramuscular injections of mRNA-1273 (100 μg) or placebo 28 days apart. The primary end point was prevention of Covid-19 illness with onset at least 14 days after the second injection in participants who had not previously been infected with SARS-CoV-2. Results The trial enrolled 30,420 volunteers who were randomly assigned in a 1:1 ratio to receive either vaccine or placebo (15,210 participants in each group). More than 96% of participants received both injections, and 2.2% had evidence (serologic, virologic, or both) of SARS-CoV-2 infection at baseline. Symptomatic Covid-19 illness was confirmed in 185 participants in the placebo group (56.5 per 1000 person-years; 95% confidence interval [CI], 48.7 to 65.3) and in 11 participants in the mRNA-1273 group (3.3 per 1000 person-years; 95% CI, 1.7 to 6.0); vaccine efficacy was 94.1% (95% CI, 89.3 to 96.8%; P<0.001). Efficacy was similar across key secondary analyses, including assessment 14 days after the first dose, analyses that included participants who had evidence of SARS-CoV-2 infection at baseline, and analyses in participants 65 years of age or older. Severe Covid-19 occurred in 30 participants, with one fatality; all 30 were in the placebo group. Moderate, transient reactogenicity after vaccination occurred more frequently in the mRNA-1273 group. Serious adverse events were rare, and the incidence was similar in the two groups. Conclusions The mRNA-1273 vaccine showed 94.1% efficacy at preventing Covid-19 illness, including severe disease. Aside from transient local and systemic reactions, no safety concerns were identified. (Funded by the Biomedical Advanced Research and Development Authority and the National Institute of Allergy and Infectious Diseases; COVE ClinicalTrials.gov number, NCT04470427.)
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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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                Author and article information

                Contributors
                ahmed.mobarak@yale.edu
                saad.omer@yale.edu
                Journal
                Nat Med
                Nat Med
                Nature Medicine
                Nature Publishing Group US (New York )
                1078-8956
                1546-170X
                16 July 2021
                16 July 2021
                2021
                : 27
                : 8
                : 1385-1394
                Affiliations
                [1 ]GRID grid.13388.31, ISNI 0000 0001 2191 183X, WZB Berlin Social Science Center, ; Berlin, Germany
                [2 ]GRID grid.479464.c, ISNI 0000 0004 5903 5371, Innovations for Poverty Action (IPA), ; New York, NY USA
                [3 ]International Growth Centre (IGC), Freetown, Sierra Leone
                [4 ]GRID grid.4818.5, ISNI 0000 0001 0791 5666, Wageningen University & Research, ; Wageningen, the Netherlands
                [5 ]GRID grid.410682.9, ISNI 0000 0004 0578 2005, International Center for the Study of Institutions and Development (ICSID), HSE University, ; Moscow, Russia
                [6 ]GRID grid.21729.3f, ISNI 0000000419368729, Columbia University, ; New York, NY USA
                [7 ]GRID grid.47100.32, ISNI 0000000419368710, Yale Institute for Global Health, ; New Haven, CT USA
                [8 ]Busara Center for Behavioral Economics, Lagos, Nigeria
                [9 ]GRID grid.411782.9, ISNI 0000 0004 1803 1817, Department of Sociology, , University of Lagos, ; Lagos, Nigeria
                [10 ]Busara Nigeria, Lagos, Nigeria
                [11 ]Agricultural and Rural Development Secretariat, Federal Capital Territory Administration, Abuja, Nigeria
                [12 ]GRID grid.10772.33, ISNI 0000000121511713, Nova School of Business and Economics, ; Lisbon, Portugal
                [13 ]GRID grid.73263.33, ISNI 0000 0004 0424 0001, The Institute for Fiscal Studies, ; London, UK
                [14 ]GRID grid.440540.1, Lahore University of Management Sciences, ; Lahore, Pakistan
                [15 ]GRID grid.477385.a, Innovations for Poverty Action (IPA) Uganda, ; Kampala, Uganda
                [16 ]Morsel Research & Development, Lucknow, India
                [17 ]GRID grid.11914.3c, ISNI 0000 0001 0721 1626, University of St Andrews, ; St Andrews, UK
                [18 ]Redes Peru, Lima, Peru
                [19 ]GRID grid.419684.6, ISNI 0000 0001 1214 1861, Stockholm School of Economics and Misum, ; Stockholm, Sweden
                [20 ]GRID grid.5342.0, ISNI 0000 0001 2069 7798, Ghent University, Department of Economics, ; Ghent, Belgium
                [21 ]Innovations for Poverty Action (IPA) Colombia, Bogotá, Colombia
                [22 ]Institute of Development and Economic Alternatives, Lahore, Pakistan
                [23 ]Innovations for Poverty Action (IPA) Sierra Leone, Freetown, Sierra Leone
                [24 ]NOVAFRICA, Lisbon, Portugal
                [25 ]GRID grid.8217.c, ISNI 0000 0004 1936 9705, Trinity College Dublin, ; Dublin, Ireland
                [26 ]GRID grid.442296.f, ISNI 0000 0001 2290 9707, Institute of Public Administration and Management, , University of Sierra Leone, ; Freetown, Sierra Leone
                [27 ]GRID grid.499852.b, Centre for the Study of Labour and Mobility (CESLAM), ; Kathmandu, Nepal
                [28 ]GRID grid.5386.8, ISNI 000000041936877X, Cornell University, ; Ithaca, NY USA
                [29 ]GRID grid.185648.6, ISNI 0000 0001 2175 0319, University of Illinois Chicago, ; Chicago, IL USA
                [30 ]GRID grid.477380.f, Innovations for Poverty Action (IPA) Rwanda, ; Kigali, Rwanda
                [31 ]Associação NOVAFRICA para o Desenvolvimento Empresarial e Económico de Moçambique, Maputo, Mozambique
                [32 ]Innovations for Poverty Action (IPA) Burkina Faso, Ouagadougou, Burkina Faso
                [33 ]GRID grid.440573.1, NYU Abu Dhabi, ; Abu Dhabi, United Arab Emirates
                [34 ]Centre for Economic Research in Pakistan (CERP), Lahore, Pakistan
                [35 ]Yale Research Initiative on Innovation and Scale (Y-RISE), New Haven, CT USA
                [36 ]GRID grid.16750.35, ISNI 0000 0001 2097 5006, Princeton University, ; Princeton, NJ USA
                [37 ]GRID grid.10548.38, ISNI 0000 0004 1936 9377, Institute for International Economic Studies (IIES), , Stockholm University, ; Stockholm, Sweden
                [38 ]GRID grid.429997.8, ISNI 0000 0004 1936 7531, Tufts University, ; Medford, MA USA
                [39 ]GRID grid.214458.e, ISNI 0000000086837370, University of Michigan, ; Ann Arbor, MI USA
                [40 ]GRID grid.16753.36, ISNI 0000 0001 2299 3507, Kellogg School of Management at Northwestern University, ; Evanston, IL USA
                [41 ]GRID grid.13063.37, ISNI 0000 0001 0789 5319, London School of Economics and Political Science, ; London, UK
                [42 ]GRID grid.47100.32, ISNI 0000000419368710, Yale University, ; New Haven, CT USA
                Author information
                http://orcid.org/0000-0002-0759-4142
                http://orcid.org/0000-0002-2096-1402
                http://orcid.org/0000-0002-6757-1284
                http://orcid.org/0000-0002-7282-2762
                http://orcid.org/0000-0003-3974-3170
                http://orcid.org/0000-0003-4875-9916
                http://orcid.org/0000-0003-1326-7348
                http://orcid.org/0000-0001-7950-9798
                http://orcid.org/0000-0003-2128-7322
                http://orcid.org/0000-0002-7484-229X
                http://orcid.org/0000-0001-7217-5035
                http://orcid.org/0000-0001-7029-2326
                http://orcid.org/0000-0002-1916-3438
                http://orcid.org/0000-0002-5383-3474
                Article
                1454
                10.1038/s41591-021-01454-y
                8363502
                34272499
                24d595ac-54cd-4dd2-ba55-ffea9e47ca6c
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 March 2021
                : 28 June 2021
                Funding
                Funded by: Funding was provided by Beyond Conflict, Bill and Melinda Gates Foundation, Columbia University, Givewell.org, Ghent University, HSE University Basic Research Program, International Growth Centre, Jameel Poverty Action Lab Crime and Violence Initiative, London School of Economics and Political Science, Mulago Foundation, NOVAFRICA at the Nova School of Business and Economics, NYU Abu Dhabi, Oxford Policy Management, Princeton University, Social Science Research Council, Trinity College Dublin COVID19 Response Funding, UK Aid, UKRI GCRF/Newton Fund, United Nations Office for Project Services,Weiss Family Fund, WZB Berlin Social Science Center, Yale Institute for Global Health, Yale Macmillan Center, and anonymous donors to IPA and Y-RISE.
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                © The Author(s), under exclusive licence to Springer Nature America, Inc. 2021

                Medicine
                epidemiology,interdisciplinary studies
                Medicine
                epidemiology, interdisciplinary studies

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