COVID-19 pandemic in BRICS countries and its association with socio-economic and demographic characteristics, health vulnerability, resources, and policy response

Infectious Diseases of Poverty

Lin zhu¹  Jingmin Zhu²  Wenxin Yan³  Jue Liu⁴ 

[1] Center for Primary Care and Outcomes Research, School of Medicine, Center for Health Policy, Freeman Spogli Institute for International Studies, Stanford University, 450 Jane Stanford Way, 94305-2004, Stanford, CA, USA;Department of Economics, University of Birmingham, B15 2TT, Birmingham, UK;Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Haidian District, No. 38, Xueyuan Road, 100191, Beijing, China;Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Haidian District, No. 38, Xueyuan Road, 100191, Beijing, China;Institute for Global Health and Development, Peking University, No. 5 Yiheyuan Road, Haidian, 100871, Beijing, China;National Health Commission Key Laboratory of Reproductive Health, Peking University, No. 38, Xueyuan Road, Haidian, 100191, Beijing, China;

关键词: COVID-19;    BRICS countries;    Policy response;    Associated factors;   

DOI  :  10.1186/s40249-021-00881-w

来源: Springer

【 摘 要 】

BackgroundLittle attention has been paid to the comparison of COVID-19 pandemic responses and related factors in BRICS (Brazil, Russia, India, China, and South Africa) countries. We aimed at evaluating the association of daily new COVID-19 cases with socio-economic and demographic factors, health vulnerability, resources, and policy response in BRICS countries.MethodsWe conducted a cross-sectional study using data on the COVID-19 pandemic and other indicators of BRICS countries from February 26, 2020 to April 30, 2021. We compared COVID-19 epidemic in BRICS countries and analyzed related factors by log-linear Generalized Additive Model (GAM) models.ResultsIn BRICS countries, India had the highest totally of confirmed cases with 18.76 million, followed by Brazil (14.45 million), Russia (4.81 million), and South Africa (1.58 million), while China (0.10 million) had the lowest figure. South Africa had the lowest rate of administered vaccine doses (0.18 million) among BRICS countries as of April 30, 2021. In the GAM model, a 1 unit increase in population density and policy stringency index was associated with a 5.17% and 1.95% growth in daily new COVID-19 cases (P < 0.001), respectively. Exposure–response curves for the effects of policy stringency index on daily new cases showed that there was a rapid surge in number of daily new COVID-19 cases when the index ranged from 0 to 45. The number of infections climbed slowly when the index ranged from 46 to 80, and decreased when the index was above 80 (P < 0.001). In addition, daily new COVID-19 cases (all P < 0.001) were also correlated with life expectancy at birth (-1.61%), extreme poverty (8.95%), human development index (-0.05%), GDP per capita (-0.18%), diabetes prevalence (0.66%), proportion of population aged 60 and above (2.23%), hospital beds per thousand people (-0.08%), proportion of people with access to improved drinking water (-7.40%), prevalence of open defecation (0.69%), and annual tourist/visitor arrivals (0.003%), after controlling other confounders. Different lag structures showed similar results in the sensitivity analysis.ConclusionsStrong policy response is crucial to control the pandemic, such as effective containment and case management. Our findings also highlighted the importance of reducing socio-economic inequalities and strengthening the resilience of health systems to better respond to public health emergencies globally.Graphic abstract

【 授权许可】

CC BY