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      Global, regional, and national estimates of target population sizes for covid-19 vaccination: descriptive study

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          Abstract

          Objective

          To provide global, regional, and national estimates of target population sizes for coronavirus disease 2019 (covid-19) vaccination to inform country specific immunisation strategies on a global scale.

          Design

          Descriptive study.

          Setting

          194 member states of the World Health Organization.

          Population

          Target populations for covid-19 vaccination based on country specific characteristics and vaccine objectives (maintaining essential core societal services; reducing severe covid-19; reducing symptomatic infections and stopping virus transmission).

          Main outcome measure

          Size of target populations for covid-19 vaccination. Estimates use country specific data on population sizes stratified by occupation, age, risk factors for covid-19 severity, vaccine acceptance, and global vaccine production. These data were derived from a multipronged search of official websites, media sources, and academic journal articles.

          Results

          Target population sizes for covid-19 vaccination vary markedly by vaccination goal and geographical region. Differences in demographic structure, presence of underlying conditions, and number of essential workers lead to highly variable estimates of target populations at regional and country levels. In particular, Europe has the highest share of essential workers (63.0 million, 8.9%) and people with underlying conditions (265.9 million, 37.4%); these two categories are essential in maintaining societal functions and reducing severe covid-19, respectively. In contrast, South East Asia has the highest share of healthy adults (777.5 million, 58.9%), a key target for reducing community transmission. Vaccine hesitancy will probably impact future covid-19 vaccination programmes; based on a literature review, 68.4% (95% confidence interval 64.2% to 72.6%) of the global population is willing to receive covid-19 vaccination. Therefore, the adult population willing to be vaccinated is estimated at 3.7 billion (95% confidence interval 3.2 to 4.1 billion).

          Conclusions

          The distribution of target groups at country and regional levels highlights the importance of designing an equitable and efficient plan for vaccine prioritisation and allocation. Each country should evaluate different strategies and allocation schemes based on local epidemiology, underlying population health, projections of available vaccine doses, and preference for vaccination strategies that favour direct or indirect benefits.

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          Most cited references24

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          OpenSAFELY: factors associated with COVID-19 death in 17 million patients

          COVID-19 has rapidly impacted on mortality worldwide. 1 There is unprecedented urgency to understand who is most at risk of severe outcomes, requiring new approaches for timely analysis of large datasets. Working on behalf of NHS England we created OpenSAFELY: a secure health analytics platform covering 40% of all patients in England, holding patient data within the existing data centre of a major primary care electronic health records vendor. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19 related deaths. COVID-19 related death was associated with: being male (hazard ratio 1.59, 95%CI 1.53-1.65); older age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared to people with white ethnicity, black and South Asian people were at higher risk even after adjustment for other factors (HR 1.48, 1.29-1.69 and 1.45, 1.32-1.58 respectively). We have quantified a range of clinical risk factors for COVID-19 related death in the largest cohort study conducted by any country to date. OpenSAFELY is rapidly adding further patients’ records; we will update and extend results regularly.
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            mice: Multivariate Imputation by Chained Equations inR

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              Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls

              Most studies have some missing data. Jonathan Sterne and colleagues describe the appropriate use and reporting of the multiple imputation approach to dealing with them
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                Author and article information

                Contributors
                Role: doctoral candidate
                Role: doctoral candidate
                Role: associate researcher
                Role: masters student
                Role: masters student
                Role: masters student
                Role: masters student
                Role: masters student
                Role: senior staff scientist
                Role: associate professor
                Role: professor
                Journal
                BMJ
                BMJ
                BMJ-UK
                bmj
                The BMJ
                BMJ Publishing Group Ltd.
                0959-8138
                1756-1833
                2020
                15 December 2020
                : 371
                : m4704
                Affiliations
                [1 ]School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, 138 Yixueyuan Road, Xuhui District, 200032 Shanghai, China
                [2 ]Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
                [3 ]Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
                [4 ]Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA
                [5]Correspondence to: H Yu yhj@ 123456fudan.edu.cn
                Author information
                http://orcid.org/0000-0002-6335-5648
                Article
                wanw062749
                10.1136/bmj.m4704
                7736995
                33323388
                4312f3cc-7476-4c79-bffc-e547e512e05e
                © 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.

                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
                : 03 December 2020
                Categories
                Research
                2474
                Special Paper

                Medicine
                Medicine

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