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SARS-CoV-2 virus disease (COVID-19) is declared a global pandemic with multiple risk factors. Obesity is considered by several researchers as one of the serious risk factors for SARS-CoV-2 virus complications based on recent empirical studies. Yet, other scholars argue in favor of the existence of an obesity survival paradox and criticize the former group of studies on the grounds that they lack controls for race, socioeconomic status, or quality of care. The objective of the current study is to analyze the potential relationships between different SARS-CoV-2 virus indicators and obesity on a country-wide level based on an OECD report. In an attempt to test the counterintuitive possibility of an obesity survival paradox, the proposed empirical model relaxes the assumption of monotonic change by applying the quadratic design and testing which one of the two competing models (i.e., quadratic or linear) better fits the data. Findings suggest more complex relationships between SARS-CoV-2 virus indices and obesity rates than previously thought. Consequently, ethical guidelines referring to priority in intubation and intensive care treatments—published by the Israeli Ministry of Health in April 2020—should account for these complex relationships between obesity and SARS-CoV-2 virus. Indeed, there is a linear increase in mortality rate from SARS-CoV-2 virus with an elevated prevalence of obesity. Yet, other indicators, such as the number of infected per 10,00,000 persons, rates of severe SARS-CoV-2 virus cases, rates of recovered SARS-CoV-2 virus patients, and SARS-CoV-2 virus, as the cause of death exhibit quadratic, rather than linear, patterns. The reasons for these nonlinear patterns might be explained by several conditions such as increased metabolic reserves, more aggressive treatment, other non-SARS-CoV-2 virus complications for obese persons, and unidentified factors that should be examined in future research.

COVID-19 is declared a global pandemic with multiple risk factors (WHO report coronavirus) [

There are several reasons that may support the former approach, according to which obesity is a risk factor associated with these complications. A summary of a report of more than 72 thousand cases from the Chinese Center for Disease Control and Prevention reported by Wu and McGoogan [

The objective of the current study is to analyze the potential relationships between different SARS-CoV-2 virus indicators and obesity on a country-wide level. The collected indicators are updated to April 28, 2020, and include SARS-CoV-2 virus as the cause of death (of the total number of deaths), number of SARS-CoV-2 virus infections, number of severe cases of illness from SARS-CoV-2 virus, the number of deaths from SARS-CoV-2 virus, and the population of the country as of July 2019. The Organization for Economic Cooperation and Development (OECD) issued a report, which specifies for each country and year the prevalence of overweight and obesity in the population (BMI ≥ 25). The report covers the years 1980–2020. Given the downward bias of self-reported indices, the current analysis uses only the former index.

In an attempt to test the counterintuitive possibility of an obesity survival paradox, the empirical model in the study relaxes the assumption of monotonic change by applying the quadratic design and testing which one of the two competing models (i. e., quadratic or linear) better fits the data. Results of our study confirm the rise in the prevalence of obesity among 23 OECD countries. While in 1980, only 38 percent of the population suffers from overweight and obesity, this percent crosses the 50 percent benchmark (the majority of the population) in 2008 and reaches 55 percent in 2020.

Further results from our study indicate more complex relationships between SARS-CoV-2 virus indices and obesity than previously considered. Differently put, the relationship between COVID-19 indicators and obesity does not necessarily increase or decrease monotonically but rather quadratically. Indeed, there is a linear increase in mortality rate from SARS-CoV-2 virus with the prevalence of obesity. Nevertheless, other indicators, such as the number of infected per 10,00,000 persons, rates of severe SARS-CoV-2 virus cases, rates of recovered SARS-CoV-2 virus patients, and SARS-CoV-2 virus, as the cause of death exhibit quadratic rather than linear patterns.

The remainder of the article is organized as follows: section

The data for this article was obtained from the OECD website (available at

Overweight and obesity are known risk factors for long series of health problems, and the fourth leading risk factor for global mortality [^{2}), overweight is defined as 25 ≤ BMI < 30, and obesity is defined as BMI ≥ 30 (WHO: Obesity and overweight, available at:

Overweight or obese population (measured/self-reported % of population aged 15+, 2018 or latest available). Source: OECD report available at

Figure

Variables | (1) | (2) |
---|---|---|

Overweight | Overweight | |

(Year 1980)^{2} | −0.00392^{∗} | — |

(0.0960) | — | |

(Year 1980) | 0.589^{∗∗∗} | 0.434^{∗∗∗} |

(9.00 | (<0.0001) | |

Constant | 36.91^{∗∗∗} | 37.97^{∗∗∗} |

(<0.0001) | (<0.0001) | |

Observations | 180 | 180 |

Countries | 23 | 23 |

0.616 | 0.609 |

^{∗}^{∗∗}^{∗∗∗}

Changes in overweight and obesity prevalence in 23 OCED countries, 1980–2020. The vertical axis measures the projected relative frequency of overweight and obesity (

To permit appropriate cross-country comparisons, all the variables were transformed to percent according to population size and number of COVID-19 cases. Table

Definition of indicators for COVID-19 and obesity.

Variable | Formula | Definition |
---|---|---|

Infected | Number of COVID-19 cases per 10,00,000 persons. | |

Severe | Relative prevalence of severe COVID-19 cases (1 percent = 1%). | |

Recovered | Relative prevalence of recovery (1 percent = 1%). | |

COVID-19 | Already defined in % (1 percent = 1%) | COVID-19 as the cause of death from the total number of dead persons (1 percent = 1%). |

Mortality | Relative frequency of death from COVID-19 (1 percent = 1%). | |

Overweight | Already defined in % | Relative frequency (1 percent = 1%) of overweight in the country (BMI ≥ 25). |

Year | Irrelevant | The year in which the measurement took place. |

Descriptive statistics stratified by 23 OECD countries.

Countries | Population | INFECTED | SEVERE | RECOVERED | COVID-19 | MORTALITY | OVERWEIGHT |
---|---|---|---|---|---|---|---|

Population of the country | COVID-19 cases per 10,00,000 persons | Relative prevalence of severe COVID-19 cases (1 percent = 1%) | Relative prevalence of recovery (1 percent = 1%) | COVID-19 as the cause of death from the total number of dead persons (1 percent = 1%). | Relative frequency of death from COVID-19 (1 percent = 1%). | Relative frequency (1 percent = 1%) of overweight in the country (BMI ≥ 25). | |

AUS | 25,203,198 | 267 | 0.62 | 84 | 0.5 | 1.25 | 54.05 |

BEL | 11,539,328 | 4,102 | 1.85 | 23 | 43 | 15.49 | 51.00 |

CAN | 37,411,047 | 1,296 | 1.15 | 38 | 3.5 | 5.58 | 59.51 |

CHL | 18,952,038 | 729 | 3.08 | 53 | 0.9 | 1.43 | 67.00 |

CZE | 10,689,209 | 697 | 0.98 | 38 | 1.5 | 2.99 | 52.00 |

DEU | 83,517,045 | 1,901 | 1.52 | 74 | 4 | 3.86 | 59.85 |

EST | 1,325,648 | 1,252 | 0.54 | 14 | 2.2 | 3.01 | 51.30 |

FIN | 5,532,156 | 857 | 1.18 | 53 | 1.3 | 4.07 | 61.56 |

FRA | 65,129,728 | 2,546 | 2.78 | 27 | 30 | 14.05 | 49.00 |

GBR | 675,301,372 | 233 | 0.99 | — | 23 | 13.42 | 57.73 |

HUN | 9,684,679 | 274 | 1.85 | 19 | 1 | 10.99 | 56.53 |

IRL | 4,882,495 | 4,024 | 0.74 | 47 | 15 | 5.61 | 61.33 |

ISR | 8,519,377 | 1,830 | 0.75 | 47 | 3.1 | 1.33 | 56.55 |

JPN | 126,860,301 | 107 | 2.20 | 14 | 0.1 | 2.83 | 22.76 |

KOR | 51,225,308 | 210 | 0.51 | 82 | 0.9 | 2.27 | 30.89 |

LUX | 615,729 | 6,056 | 0.54 | 84 | 23 | 2.36 | 54.10 |

LVA | 1,906,743 | 438 | 0.48 | 32 | 2.2 | 1.56 | 54.60 |

MEX | 127,575,529 | 122 | 2.43 | 59 | 0.5 | 9.23 | 67.94 |

NZL | 4,783,063 | 308 | 0.07 | 82 | — | 1.29 | 62.20 |

PRT | 1,906,743 | 12,756 | 0.71 | 6 | 5.8 | 3.90 | 67.60 |

SVK | 5,457,013 | 254 | 0.43 | 31 | — | 1.45 | 51.07 |

TUR | 83,429,615 | 1,346 | 1.55 | 30 | 3.7 | 2.58 | 59.90 |

USA | 329,064,917 | 3,071 | 1.40 | 14 | 10 | 5.62 | 64.82 |

Descriptive statistics–pooled sample.

Variable | N | OECD countries | Mean | Median | (Standard error) |
---|---|---|---|---|---|

Infected | 180 | 23 | 1,275.96^{∗∗∗} [973.82, 1,578.09] | 243.16 | (153.11) |

Severe | 180 | 23 | 1.25^{∗∗∗} [1.14, 1.37] | 0.99 | (0.0577) |

Recovered | 151 | 22 | 46.57^{∗∗∗} [41.75, 51.40] | 38.15 | (2.44) |

COVID-19 | 165 | 21 | 8.73^{∗∗∗} [7.12, 10.34] | 1.5 | (0.815) |

Mortality | 180 | 23 | 5.15^{∗∗∗} [4.51, 5.80] | 2.83 | (0.33) |

Overweight | 180 | 23 | 48.49^{∗∗∗} [46.01, 50.97] | 54.6 | (1.26) |

Year | 180 | 23 | 2004.26 [2002.75, 2005.76] | 2007 | (0.76) |

^{∗∗∗} for rejection of the null hypothesis of equality to zero.

The respective median and average number of infected people per 10,00,000 persons are 243 and 1,276, respectively, and the standard error is 153 (INFECTED). The implication is right-tailed distribution of INFECTED, namely, relatively few countries with large number of infected people per 10,00,000 persons. The relative frequency of severe cases divided by total COVID-19 cases is 1.25 percent, the median is 0.99, and the standard error is 0.0577 percent (SEVERE). Referring to the severity of outcomes, the reason the authors do not provide an exact definition of severe cases is the complexity of such a definition in different countries, as part of personal characteristics including background diseases, and in contrast to COVID-19 cases (defined by the PCR outcomes) and mortality (as clearly defined by the medical profession. The relative frequency of recovered divided by total COVID-19 cases is 46.57 percent, the median is 38.15, and the standard error is 2.44 percent (RECOVERED). The relative frequency of SARS-CoV-2 virus as the cause of death divided by the total number of dead persons in the country is 8.73 percent, the median is 1.5, and the standard deviation is 0.815 percent (COVID-19). The relative frequency of mortality from SARS-CoV-2 virus divided by the total number of SARS-CoV-2 virus cases in the country is 5.15 percent, the median is 2.83 percent, and the standard error is 0.33 percent (MORTALITY). The 95% and 99% confidence intervals of all the SARS-CoV-2 virus indicators demonstrate that the sample means of all the variables across these 23 countries are different from zero. A mean-median comparison indicates a right-tailed distribution of all these variables, namely, relatively few countries with higher prevalence. Finally, the relative frequency of overweight across these 23 countries and based on 1980–2020 measured BMI is 48.49 percent, and the median is 54.6 percent. The standard error is 1.26 percent (OVERWEIGHT). Based on the confidence intervals, one cannot reject the null hypothesis that the relative frequency equals the 50 percent benchmark, namely, a majority of the population suffers from obesity. Finally, this distribution is left-tailed, namely, there are only few countries with lower prevalence of overweight. This provides evidence regarding the extent of the obesity pandemic in OECD countries.

Consider the following two competing empirical models:

To test which of these two models better fits the data for each indicator, the Ramsey RESET test is employed (e.g., [

Figures

Variables | (1) | (2) |
---|---|---|

Infected | Infected | |

Overweight^{2} | −2.269^{∗∗∗} | — |

(0.00274) | — | |

Overweight | 236.2^{∗∗∗} | 36.92^{∗∗∗} |

(0.000457) | (3.51 × 10^{−5}) | |

Constant | −4,202^{∗∗∗} | −514.1 |

(0.00134) | (0.251) | |

Observations | 180 | 180 |

OECD Countries | 23 | 23 |

R-squared | 0.137 | 0.092 |

^{∗}^{∗∗}^{∗∗∗}

Variables | (1) | (2) |
---|---|---|

Severe | Severe | |

Overweight^{2} | 0.00204^{∗∗∗} | — |

(<0.0001) | — | |

Overweight | −0.197^{∗∗∗} | −0.0179^{∗∗∗} |

(<0.0001) | (5.86 × 10^{−8}) | |

Constant | 5.437^{∗∗∗} | 2.122^{∗∗∗} |

(<0.0001) | (<0.0001) | |

Observations | 180 | 180 |

OECD Countries | 23 | 23 |

R-squared | 0.409 | 0.153 |

^{∗}^{∗∗}^{∗∗∗}

Variables | (1) | (2) |
---|---|---|

Recovered | Recovered | |

Overweight^{2} | −0.0600^{∗∗∗} | — |

(2.91 × 10^{−8}) | — | |

Overweight | 5.919^{∗∗∗} | 0.644^{∗∗∗} |

(1.05 × 10^{−9}) | (1.62 × 10^{−9}) | |

Constant | −80.42^{∗∗∗} | 16.49^{∗∗∗} |

(9.55 × 10^{−6}) | (0.0114) | |

Observations | 151 | 151 |

OECD Countries | 22 | 22 |

R-squared | 0.305 | 0.143 |

^{∗}^{∗}^{∗∗∗}

Variables | (1) | (2) |
---|---|---|

COVID-19 | COVID-19 | |

Overweight^{2} | −0.0213^{∗∗∗} | — |

(5.14 × 10^{−9}) | — | |

Overweight | 2.137^{∗∗∗} | 0.272^{∗∗∗} |

(5.79 × 10^{−11}) | (2.05 × 10^{−9}) | |

Constant | −38.43^{∗∗∗} | −4.199^{∗∗∗} |

(7.72 × 10^{−10}) | (0.0539) | |

Observations | 165 | 165 |

OECD Countries | 21 | 21 |

0.351 | 0.198 |

^{∗}^{∗∗}^{∗∗∗}

Variables | (1) | (2) |
---|---|---|

Mortality | Mortality | |

Overweight^{2} | −0.00196 | — |

(0.224) | — | |

Overweight | 0.261^{∗} | 0.0890^{∗∗∗} |

(0.0678) | (2.45 | |

Constant | −2.340 | 0.839 |

(0.399) | (0.372) | |

Observations | 180 | 180 |

OECD Countries | 23 | 23 |

R-squared | 0.125 | 0.118 |

^{∗}^{∗∗}^{∗∗∗}

COVID-19 cases per 10,00,000 persons vs. prevalence of overweight and obesity. The vertical axis measures the projected probability of COVID-19 cases per 10,00,000 persons based on the formula:

Rate of severe COVID-19 cases vs. prevalence of overweight and obesity. The vertical axis measures the projected probability of severe cases of COVID-19 divided by total cases of COVID-19 in percentage points. The horizontal axis measures the prevalence of overweight and obesity (

Rate of recovered COVID-19 patients vs. prevalence of overweight and obesity. The vertical axis measures the projected probability of recovery divided by total cases of COVID-19 in percentage points. The horizontal axis measures the prevalence of overweight and obesity (

COVID-19 as the cause of death vs. prevalence of overweight and obesity. The vertical axis measures the projected probability of SARS-COV-2 virus as the cause of death rather than other reasons. The horizontal axis measures the prevalence of overweight and obesity (

Mortality rate from COVID-19 vs. prevalence of overweight and obesity. The vertical axis measures the projected probability of mortality from COVID-19 based on the formula for each OECD country:

Figure

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Finally, Figure

In this section, we compare the differences and similarities between the current manuscript applied to OECD countries, and Arbel et al. [

USA is a member of the OECD with a high prevalence of overweight and obesity (third place after Mexico and Chile with 70% of the population considered to be overweight, namely,

Referring to the OECD countries, the information considered is the prevalence of overweight (

In contrast to the US research, which focused on one country, the current study exhibits more extensive global perspective. OECD countries span four different continents: Asia, Europe, Australia, and America (North, Central, and South).

With respect to the scope of morbidity measured as COVID-19 cases per 1 million persons, the difference is as follows. While in research conducted regarding US states, the anticipated scope of morbidity declines monotonically from 6,200 cases per million persons in states with 20% prevalence of obesity to fewer than 1,700 cases per million persons in states with 40% prevalence of obesity, in OECD countries, the decline is not monotonic for the full range. Accordingly, starting from 52% prevalence of overweight, there is a decline from a maximum of 1,947 to 1,217 COVID-19 cases per 1 million persons in countries with 70% prevalence of overweight. One possible explanation is cultural differences and concurrent differences in eating habits, among residents in the various OECD countries.

Referring to the scope of COVID-19 mortality, the trend is reversed. While in US states, there is a monotonic decline with a higher prevalence of obesity starting from an anticipated 5.35 percent (20% prevalence of obesity) to less than 3.46 percent (40% prevalence of obesity), in OECD countries, there is a monotonic increase from an anticipated 3.06 percent (25% prevalence of overweight) to 7.07 percent (70% prevalence of overweight). A potential explanation is political regime differences in OECD countries. As of 2021, the OECD consists of 38 members, of which, on a 2020 scale of 1 = the best, 7 = the worst ranking in terms of political rights, 30 members are defined as “one,” 4 members are defined as “two,” 3 members are defined as “3”(Columbia, Hungary, and Mexico), and one country is defined as “5” (Turkey).

As the relevant literature demonstrates, in more democratic countries, the tendency to invest in health infrastructure is higher, in light of the need for reelection, e.g., [

The objective of the current study is to analyze the potential relationships between different SARS-CoV-2 virus indicators and obesity on a country-wide level, using the OECD countries as the basis for the comparison. The dependent variables in our model are the following: (1) infections by SARS-CoV-2 virus per 10,00,000 persons, (2) rate of severe SARS-CoV-2 virus case, (3) rate of recovered SARS-CoV-2 virus patients, (4) rate of mortality from SARS-CoV-2 virus (rather than other reasons), and (5) mortality from SARS-CoV-2 virus of the total SARS-CoV-2 virus cases. The independent variable includes overweight and obesity prevalence on a scale of between 25 percent (Japan) and approximately 70 percent (USA, Mexico, and Chile). Given the counterintuitive possibility of an obesity survival paradox [

Findings suggest that relationships between SARS-CoV-2 virus indices and obesity rates are more complex than previously thought. Consequently, ethical guidelines referring to priority in intubation and intensive care treatments—published by the Israeli Ministry of Health in April 2020—should account for these complex relationships between obesity and SARS-CoV-2 virus. Indeed, there is a linear increase in mortality rate from SARS-CoV-2 virus with the prevalence of obesity. Nevertheless, other indicators, such as the number of infected per 10,00,000 persons from the population, rates of severe SARS-CoV-2 virus cases, rates of recovered SARS-CoV-2 virus patients, and SARS-CoV-2 virus, as the cause of death exhibit quadratic rather than linear patterns. Given the (1) decrease in the number of infected per 10,00,000 persons for overweight and obesity prevalence of 50–70%, (2) drop in the rate of severe cases for overweight and obesity prevalence of 25–50%, and (3) increase in the recovery rates for overweight and obesity prevalence of 25–50%, these patterns give rise to the possibility of the obesity survival paradox.

Referring to pneumonia and different types of cancer, including melanoma, the literature identifies the potential existence of the obesity survival paradox, namely, the better prospects of obese persons to recover and lower prospects to be infected, e.g., [

Social and Behavioral Aspects of the Individual: the lower inclination of obese persons to engage in physical activity and walks outside the home (e.g., [

Behavioral Patterns in the Health System: more aggressive treatment regimens following the conventional definition of obesity as a global pandemic and a risk factor for a long series of health problems, including elevated mortality risk (e.g., [

Physiological Considerations: metabolic reserves of obese persons, which may protect against mortality (e.g., [

Several limitations of this research may be pointed out. This study is a cross-sectional description of OECD countries, which was prepared at an earlier stage of the COVID-19 pandemic. Examining the evolvement of the pandemic in OECD countries is a subject for future research upon arrival of new data. On the one hand, the use of data from April 2020 has a significant advantage, namely, the period where the only variant was the delta variant. On the other hand, since the current research cannot address the developments of new variants, vaccinations, etc., this may be considered part of the limitations of the research. Also, the comparison is between OECD countries, which enacted different intervention policies. While Brazil has experienced peak levels in some states today, in Israel, the lockdowns and massive vaccination operation were successful in reducing the pandemic. Finally, the model lacks confounders, such as population heterogeneity within the various countries.

The data used to support the findings of this study, obtained from the OECD website, are available from the corresponding author upon request.

The authors declare that they have no conflicts of interest that they relate to the research described in this paper.

A preprint of this manuscript was published via Research Square at