Abstract
Aims
The Coronavirus (COVID-19) is a global pandemic requiring global responses. The objective of this paper is to identify the common factors of COVID-19 cases and deaths among the 50 most affected countries.
Methods
We performed Ordinary least squares among a wide range of socio-economic, environmental, climatic and health indicators to explain the number of cases and deaths.
Results
The findings are: (i) obesity is the only significant global denominator for the number of COVID-19 cases and deaths; (ii) the percentage of the population over the age of 65 and number of hospital beds per 1000 population inversely correlated to mortality from COVID-19.
Conclusions
Obesity increases vulnerability to COVID-19 infections and mortality. Global awareness of obesity and social investment in health infrastructure are pre-requisite for a pandemic adaptive future. However, the study is limited to cross-sectional data of April 17, 2020.
Keywords: COVID-19, Pandemic, Diabetes, Obesity, Infectious disease
1. Introduction
The coronavirus (COVID - 19) pandemic is surging globally with more than 141 million confirmed cases and 3.01 million deaths as of April 20, 2021 [1]. Many potential factors are noted in the literature explaining the rate of infection. Population density and urbanization, health (i.e., obesity and diabetes), environmental (i.e. PM2.5), climatic (i.e. temperature and humidity), and socio-economic inequality – all of these factors are widely documented [[2], [3], [4], [5], [6], [7], [8], [9], [10], [11]]. Understandably, most of the confirmed cases are based on test results against symptomatic cases and asymptomatic cases are generally undocumented. Underlying health conditions i.e., diabetes, obesity, and heart diseases can create severe complications and deaths from COVID-19, which are widely documented in the literature (see for example [3,7]). Most of the literature is country-specific and document multiple factors for the infection and death from COVID-19.
Response to the pandemic varies across countries. The common suppressive measures applied with varying success to curb down the COVID-19 infections and subsequent deaths are: complete lockdown over a certain period, restriction on mobility, contact tracing, social distancing and masking, and mass vaccination [12]. Except for mass vaccination, which is limited in supply with varying effectiveness, there is hardly a clear-cut way-out in sight from the pandemic. As the current pandemic is expected to prolong and pandemic frequency is expected to rise in the future [13,14], pandemic adaptive social, economic and environmental planning is of utmost importance.
This article considers a range of social, economic and environmental factors of 50 countries with the highest COVID-19 cases (Fig. 1 ) to explore the common factors of cases and death. In doing so, the article has the potential for a global understanding to channel social energy and finance to develop pandemic adaptive social policy and infrastructure.
Fig. 1.
Selected 50 countries with the highest COVID-19 severity.
2. Methods and data
We have selected 50 countries with the highest infections (as of April 17, 2021) from COVID-19. A total of 14 factors is selected to explain the number of confirmed cases and deaths per 100,000 population (see the factor lists in Table 2, Table 3). These factors are selected from an extensive literature review. Ordinary least square (OLS) is used for regression analysis. SPSS software package is used to perform the statistical analysis. By equally weighing the normalized COVID-19 case and death rates, a COVID-19 severity map (Fig. 1) is created using ArcGIS 10.5.
Table 2.
Factors of COVID-19 cases.
| Coefficientsa | |||||
|---|---|---|---|---|---|
| Model | Unstandardized Coefficients |
Standardized Coefficients |
T | Sig. | |
| B | Std. Error | Beta | |||
| (Constant) | −15264.749 | 14833.788 | −1.029 | .311 | |
| Population density | 2.648 | 2.298 | .178 | 1.152 | .257 |
| % of population over the age of 65 | 125.447 | 115.669 | .275 | 1.085 | .286 |
| % of urban population | −48.762 | 40.777 | -.235 | −1.196 | .240 |
| Health care index | 42.911 | 60.769 | .134 | .706 | .485 |
| Life expectancy at birth | 129.572 | 199.356 | .187 | .650 | .520 |
| % of population with diabetes between age 20-79 | −64.963 | 159.406 | -.073 | -.408 | .686 |
| Obesity rate | 224.019 | 83.739 | .560 | 2.675 | .011 |
| Death from cardio-vascular diseases per 100000 | 1.377 | 7.635 | .033 | .180 | .858 |
| GDP per capita | -.006 | .050 | -.035 | -.111 | .913 |
| Human Development Index | 534.979 | 14438.005 | .016 | .037 | .971 |
| Air quality PM2.5 | −38.214 | 30.289 | -.230 | −1.262 | .215 |
| Air connectivity index | 44.778 | 52.705 | .151 | .850 | .401 |
| Governance effectiveness | −203.983 | 1323.193 | -.052 | -.154 | .878 |
| Gini coefficient | 6390.984 | 6153.476 | .173 | 1.039 | .306 |
Dependent variable: cases per 100,000 population.
Table 3.
Factors of COVID-19 deaths.
| Coefficientsa | |||||
|---|---|---|---|---|---|
| Unstandardized Coefficients |
Standardized Coefficients |
T | Sig. | ||
| B | Std. Error | Beta | |||
| (Constant) | 310.463 | 404.456 | .768 | .448 | |
| % of population over the age 65 | 8.235 | 2.991 | .801 | 2.753 | .009 |
| Health care index | 1.506 | 1.293 | .211 | 1.164 | .252 |
| No. of hospital beds per 1000 population | −10.354 | 5.375 | -.440 | −1.926 | .062 |
| No. of doctors per 10000 population | -.003 | 1.014 | -.001 | -.003 | .998 |
| Life expectancy at birth | −7.911 | 5.673 | -.464 | −1.394 | .172 |
| % of people with diabetes 20-79 | −2.375 | 3.424 | -.119 | -.694 | .493 |
| Obesity rate | 3.647 | 1.860 | .408 | 1.961 | .058 |
| Air Quality PM2.5 | -.990 | .637 | -.268 | −1.554 | .129 |
| Population density | .026 | .050 | .079 | .529 | .600 |
| % of urban population | -.275 | .886 | -.060 | -.310 | .758 |
| Death from cardio-vascular diseases per 100000 | -.100 | .200 | -.107 | -.499 | .621 |
| GDP per capita | .000 | .001 | -.073 | -.277 | .784 |
| Human Development index | 307.907 | 324.776 | .405 | .948 | .350 |
| Governance Effectiveness | −34.346 | 28.406 | -.394 | −1.209 | .235 |
Dependent variable: deaths per 100,000 population.
We have used online open-source data from several sources for the selected factors. Table 1 is the descriptive statistics of the data used in this research. We refrained from data normalization because of the in-built scale of the dataset, and the min-max data normalization does not generate different results.
Table 1.
Descriptive statistics.
| Data | Minimum | Maximum | Mean | Std. Deviation | Source |
|---|---|---|---|---|---|
| Confirmed COVID-19 | 303598 | 32256982 | 2601562 | 5120209 | 1 |
| COVID-19 deaths | 1365 | 579357 | 56363 | 99283 | |
| Population density | 4.13 | 1239.58 | 165.52 | 211.52 | 2 |
| % of population over the age of 65 | 1.16 | 28 | 13.45 | 6.89 | |
| % of urban population | 34.47 | 98.04 | 73.01 | 15.14 | |
| Life expectancy at birth | 63.86 | 84.21 | 77.21 | 4.53 | |
| % of population with diabetes between age 20-79 | 3.9 | 19.9 | 8.35 | 3.55 | |
| No. of hospital beds per 1000 population | 0.6 | 13.4 | 4.03 | 3 | |
| No. of Doctors per 1000 population | 4.3 | 54.8 | 27.56 | 13.43 | |
| GDP per capita | 1482 | 82818 | 21523 | 19560 | |
| Air quality PM2.5 | 6.18 | 90.87 | 23.59 | 18.88 | |
| Health care index | 42.7 | 80.99 | 64.37 | 9.81 | 3 |
| Obesity rate | 3.6 | 36.2 | 22.74 | 7.85 | 4 |
| Death from cardio-vascular diseases per 100000 | 30.99 | 328.39 | 125.85 | 74.95 | 5 |
| Human Development Index | 0.56 | 0.96 | 0.83 | 0.09 | 6 |
| Air connectivity index | 1.79 | 77.78 | 7.69 | 10.6 | [15] |
| Governance effectiveness | −1.34 | 1.97 | 0.48 | 0.8 | 7 |
| Gini coefficient | 0.48 | 0.9 | 0.74 | 0.09 | 8 |
3. Results
3.1. Factors of COVID-19 cases
The COVID-19 cases per 100,000 population show that the Czech Republic has the highest infection rate followed by the United States. The OLS of factors affecting the COVID-19 cases are summarized in Table 2. With a 95 % confidence interval (R2 = 0.519, ANOVA significance at 0.009), only the obesity rate is positively and significantly affecting the number of COVID-19 cases.
3.2. Factors of COVID-19 deaths
Columbia has the highest and Indonesia has the lowest deaths per 100,000 population from COVID-19. The OLS results of the factors affecting COVID-19 deaths are summarized in Table 3. With a 95 % confidence interval (R2 = 0.574, ANOVA significance = 0.002), only the percentage of population over the age of 65 positively and significantly affects the COVID-19 deaths. However, with a 90 % confidence interval, the number of hospital beds per 1000 population negatively and the obesity rate positively affect the number of COVID-19 deaths.
4. Discussion
Among the 14 factors, the obesity rate stands out to be the only common factor affecting the number of COVID-19 cases among the topmost affected 50 countries. However, country-specific studies on the correlation between obesity and COVID-19 infection are numerous (see for example [3]). Additionally, per capita GDP is positively correlated (person correlation 0.43 at 0.01 significance) to COVID-19 infection, meaning the richer countries are more susceptible to infection than the poor ones. However, per capita GDP and obesity rate are not significantly correlated, which indicates that obesity is not linked to economic status.
The age factor affecting the severity of illness and death from COVID-19 is widely documented. The study findings also indicate that age dividend – the higher the age the higher the risk – significantly determines the death rate from COVID-19 [16]. Secondly, countries with low hospital beds per 1000 population signifies low health investment, and consequently have higher mortality from COVID-19. The topmost countries with COVID-19 mortality are Columbia, Lebanon, Belgium, Morocco, Belarus, Slovakia, Chile, UAE, Poland, Mexico, Bangladesh and so on, respectively. Finally, the obesity rate appears to be a common factor for both the number of COVID-19 cases and deaths.
5. Conclusion
This research has two conclusive findings on the 50 most COVID-19 affected countries. First, obesity increases susceptibility to COVID-19 infection and consequent deaths. Secondly, lack of health infrastructure i.e., per capita hospital beds increases COVID-19 mortality. Our paper calls for global awareness on obesity and increasing social investments for a pandemic adaptive future.
This research lacks panel data analysis. Therefore, temporal variation of COVID-19 cases and deaths are not accounted for. With the new coronavirus variants i.e., the Delta variant, there is a need for spatio-temporal analysis on COVID-19 cases and deaths. Secondly, the factors of COVID-19 cases and deaths of this study are at the country level, which provides little understanding on spatial distribution i.e., the disparity in health care facilities and concentration of COVID-19 cases between urban and rural areas.
Declarations
Funding
None.
Data availability
The dataset of this study is available from the corresponding author upon reasonable request.
Conflicts of interest
The authors declare no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Ethical approval
The authors declare No ethical approval required. Ethical approval for this type of study is not required by our institution.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The dataset of this study is available from the corresponding author upon reasonable request.
Conflicts of interest
The authors declare no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Ethical approval
The authors declare No ethical approval required. Ethical approval for this type of study is not required by our institution.