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Comparative Study
. 2021 Jul 8;10(1):97.
doi: 10.1186/s40249-021-00881-w.

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

Jingmin Zhu  1 , Wenxin Yan  2 , Lin Zhu  3 , Jue Liu  4   5   6
Affiliations
Comparative Study

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

Jingmin Zhu et al. Infect Dis Poverty. .

Abstract

Background: Little 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.

Methods: We 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.

Results: In 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.

Conclusions: Strong 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.

Keywords: Associated factors; BRICS countries; COVID-19; Policy response.

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Conflict of interest statement

None declared.

Figures

Fig. 1
Fig. 1
Trends of daily new cases in BRICS countries. BRICS Brazil, Russia, India, China, and South Africa
Fig. 2
Fig. 2
Exposure–response curves on the effects of policy stringency index in daily new COVID-19 cases. The x-axis represents the policy stringency index. The y-axis shows the contribution of the smoother to the fitted values
See this image and copyright information in PMC

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