By Jingmin Zhu, Wenxin Yan, Lin Zhu & Jue Liu
Abstract
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.
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.
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.
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.
Coronavirus disease 2019 (COVID-19) has become a global public health issue. Figures from the World Health Organization (WHO) showed that more than 150.99 million COVID-19 cases have been confirmed worldwide, with over 3.17 million deaths as of May 1, 2021. Comprehensive measures have been taken across the world to curb the spread of the new severe acute respiratory syndrome coronavirus (SARS-CoV-2). The COVID-19 pandemic has affected countries in different ways. BRICS constitute a political and economic grouping of countries (Brazil, Russia, India, China, and South Africa) undergoing rapid economic development, making up almost half of the world’s population. As of April 30, the number of SARS-CoV-2 infections in BRICS countries has reached 39.77 million, accounting for more than a quarter (26.3%) of the global total.
The influence of BRICS countries in the international arena has risen enormously in recent decades. However, little attention has been paid to the comparison of COVID-19 pandemic responses and related factors in BRICS countries, despite their increasing global significance as individual countries and as an economic grouping. Comparing COVID-19 situations and exploring its related factors in BRICS countries are important to better understand the spread of SARS-CoV-2 and pandemic control. This work is aimed at evaluating the association of daily new COVID-19 cases with socio-economic and demographic factors, health vulnerability, resources, and policy response among BRICS countries through a comparative study.
To the best of our knowledge, this is the first study that evaluated the association of daily new COVID-19 cases with socio-economic and demographic factors, health vulnerability, resources, and policy response to the pandemic in BRICS countries. Our findings indicated that socio-economic and demographic factors, health and resource vulnerabilities, population mobility, and policy responses were associated with the COVID-19 pandemic. All results of the GAM model remained stable in the sensitivity analysis. After controlling the effect of other potential confounders, daily new cases had a positive relationship with the policy stringency index when the latter was below 80. In contrast, there was a negative relationship between daily new COVID-19 cases and the policy stringency index when the index reached above 80. These results indicated that strong policy response at the national level is crucial to reduce COVID-19 infections and control the pandemic.
Previous findings on the associations among socio-economic and demographic factors, health vulnerability, resources, and COVID-19 policy response were contentious. Rocha et al. found that the initial spread of COVID-19 infections in the country was most affected by patterns of socio-economic vulnerability rather than population age structure and prevalence of existing chronic diseases. Kayral et al. reported the association between the policy stringency and total cases in E7 countries during the COVID-19 period. In this study, we also found that countries with the most disadvantaged communities, vulnerable health system, and less resources (including health system as well water and sanitation) were associated with more COVID-19 cases in BRICS countries. Our results were consistent with previous findings. It supported the evidence that socio-economic vulnerability might have impact on the disproportionate burden on states and municipalities.
We found that a 1 unit increase in population density and extreme poverty were associated with a 5.17% and 8.95% growth in daily new COVID-19 cases respectively. Furthermore, a 1 percent rise in the proportion of population aged 60 and above and diabetes prevalence were associated with a 2.23% and 0.66% increase in daily new COVID-19 cases, respectively. One possible explanation was that the macro socio-economic environment and health system resilience at the national level had a higher impact on the pandemic than individual level, because the impact of individual health vulnerability might be affected by the possibility of exposure to the virus. Socio-economic and demographic factors, health system vulnerability, as well as lack of water and sanitation resources could reflect the macro environment and system resilience. Mujica et al. found that socio-economic inequalities were associated with health outcomes (mortality) in BRICS countries. Our findings highlighted the importance of reducing socio-economic inequalities and strengthening the resilience of health systems to better respond to public health emergencies in the future.
Brazil, Russia, India, China, and South Africa had nearly 45% of the world’s population. Comparative studies on BRICS countries’ health outcomes (such as mortality of chronic diseases) had been conducted but there were no studies focusing on COVID-19. Romaniuk et al. compared the health system outcomes of BRICS countries in 2000−2017 and found that Russia, China, and Brazil had better healthcare performance as opposed to India and South Africa. However, the entire group’s overall healthcare performance did not fare well compared to developed countries. They also found that the health system outcomes (such as infant mortality rate and maternal mortality rate) appeared to correlate on a statistically significant scale with GDP per capita. During the COVID-19 pandemic, India, Brazil, and Russia were three of the top five countries with the highest disease burden of COVID-19, along with the United States and France. Moreover, the cumulative total COVID-19 cases in BRICS countries accounted for more than a quarter (26.3%) of the global total as of April 30, 2021.
Each BRICS country has its specific characteristics in terms of health performance and policy response to COVID-19. For instance, China had the largest total population with high population mobility and faced significant challenges on COVID-19 prevention and control. China adopted stringent policies and containment strategies, including active case detection and management, lockdown, intercity travel ban, practicing physical distancing and personal protection measures (including hand hygiene, face mask use, etc.). China managed to control its COVID-19 outbreak within three months. While China had the highest absolute number of vaccinations among BRICS countries, it still faced challenges on the relatively low level of vaccination coverage due to its large population. On the other hand, India faced the biggest challenge in coping with COVID-19 among BRICS countries, given its healthcare system vulnerability and high disease burden of COVID-19 as well as low immunization coverage.
There are some limitations in our study. First, COVID-19 and related indicators data were based on the national level instead of provincial or city level. We were not able to conduct analysis at provincial or city level. Second, some indicators (such as daily coverage of PCR test on SARS-CoV-2, daily vaccination coverage) were not included in the GAM model due to unavailable data. Third, bilateral correlations were only done in BRICS countries and statistical significance might be underpowered because of limited countries included.
In BRICS countries, daily new COVID-19 cases were affected by socio-economic and demographic status, health vulnerabilities, resources, and policy response. It is important to reduce socio-economic inequalities and strengthen the resilience of health systems to better respond to public health emergencies. Future studies are needed to explore the way of strengthening public health and health systems resilience in all countries to utilize interventions on curbing the pandemic effectively.
Jingmin Zhu - Department of Economics, University of Birmingham, UK.
Wenxin Yan - Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China.
Lin Zhu - Center for Primary Care and Outcomes Research, School of Medicine, Center for Health Policy, Freeman Spogli Institute for International Studies, Stanford University, USA.
Jue Liu - Department of Epidemiology and Biostatistics, School of Public Health, Peking University, China.
Infectious Diseases of Poverty
