Preventive Medicine Research
Original Article

Association between country-level diabetes prevalenceand COVID-19 mortality across 38 OECD member statesin the first year of the pandemic
Nlandu Roger Ngatu1, *, Kanae Kanda2, Steeve Akumwami2, Daniel-Kuezina Tonduangu3, Yukinori Mashima4,
Akitsu Murakami5, Tomohiro Hirao1
1
2
3
4
5

Department of Public Health, Kagawa University Faculty of Medicine, Kagawa 761-0793, Japan
Department of Anesthesiology, Kagawa University Faculty of Medicine, Kagawa, Japan
Department of Internal Medicine, Faculty of Medicine, University of Kinshasa, Democratic Republic of the Congo (DRC)
Department of Medical Informatics, Kagawa University Faculty of Medicine, Kagawa, Japan
Kagawa University Hospital Cancer Center, Kagawa University Faculty of Medicine, Kagawa, Japan

OPEN ACCESS
Citation: Ngatu NR, Kanda K, Akumwami S, etal. Association between country-level diabetes
prevalence and COVID-19 mortality across 38
OECD member states in the first year of thepandemic. Prevent Med Res 1; 34–40: 2023.
DOI: 10.60219/pmr.1.3_34*Correspondence to:
Nlandu Roger Ngatu
Email: ngatu.nlandu@kagawa-u.ac.jp
Tel: +81-90-9778-2097

Abstract
Objective. In December 2019, an outbreak of unprecedented spread, later known
as COVID-19, has taken the world by storm. Pre-existing metabolic disorders
have been reported to be high-risk factors for severe COVID-19. We exploredassociations between sever COVID-19 outcomes and diabetes across 38 countriesof the Organization for Economic Cooperation and Development (OECD). Methods.
This was an ecological study that used freely accessible data from OECD and theWorld Health Organization databases. Outcome variables were case-fatality rate
(CFR) and number of years of healthy life lost due to COVID-19 per 100,000

Editor: Fumihiko Suzuki (Ohu University)(YLLs/100,000). CFR was calculated based on cumulative COVID-19 cases andReceived: July 18, 2023

deaths as of 31 December 2020. Pearson’s correlation analysis was carried out

Accepted: October 27, 2023

to determine the correlates of COVID-19 CFR and YLLs; thereafter, ecological

Published: November 30, 2023

regression analysis was performed to identify the predictors of COVID-19 CFR and

Copyright: © Japanese Society of Preventive
Medicine
https://creativecommons.org/
licenses/by/4.0/

YLLs. Results. Mean population was 35,875,100 (±9,641,900) inhabitants. There
were 1,559,488 (±699,285) COVID-19 cases reported and 43,579.5 (±19,055) deaths,
for a COVID-19 CFR of 2.1 (0.2)% and COVID-19 YLLs of 566.8 (±91.5)/100,000
population. Overweight and obese population rate was 60.1 (±3.7)%, and overall
diabetes prevalence rate was 6.9 (±0.4)%. Pearson correlation analysis showed that
COVID-19 CFR was inversely correlated with country’s “high education completionrate” (rho = -0.41; p < 0.009), GDP per capita (rho = -0.47; p = 0.013), the ratio
nurses/10,000 (rho = -0.36; p = 0.045), and positively correlated with diabetes
prevalence (rho = 0.48; p = 0.003). On the other hand, COVID-19 YLLs wereinversely correlated with elderly population rate (rho = 0.47; p = 0.003), andpositively with diabetes prevalence rate (rho = -0.51; p = 0.011). Furthermore,
the bivariate regression analysis showed that diabetes prevalence was positively
associated with both COVID-19 CFR (beta = 1.01(0.3), 95%CI: 0.38–1.63; p = 0.002)
and COVID-19 YLLs (beta = 8.28(3.5), 95%CI: 2.07–14.40; p = 0.010). Conclusion.
Findings from this study tend to confirm that diabetes is a high-risk factor for severe
COVID-19 outcomes at country level, disease mortality in particular.
Keywords: coronavirus disease 2019, diabetes, ecological study, fatality

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Introduction
Since early 2020, the rapid spread of COVID-19 outbreak that originated in China has caused a global health crisis,
affecting over 200 countries1). Metabolic disorders, particularly diabetes and its associated disorders, are reported
to be strong determinants of COVID-19 morbidity and mortality2). There have been approximately 463,000,000
individuals with diabetes worldwide in 20192,3). With the spread of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) and the high prevalence of obesity and diabetes, there is a possibility for increased incidence of
COVID-19-related severe outcomes in most affected countries. Considering the negative influence of metabolic healthimpairment, efficient strategies should be implemented for the prevention and management of obesity and related
metabolic disorders in countries where the COVID-19 pandemic remains one of major health problems4).
Since the beginning the pandemic, obesity has caused concerns given its high prevalence in SARS-CoV-2-infectedpatients4). Studies conducted in China have shown higher incidence rates of severe pneumonia in patients infectedby SARS-CoV-2-infected patients with overweight and obesity as compared with individuals with normal weight.
Additionally, the coexistence of obesity and diabetes is reported to increase the risk of severe COVID-19 outcomes5,6).
In the present study, we searched to explore the associations between country-level COVID-19 epidemiologicalindicators, and the prevalence of metabolic disorders (rate of overweight/obese population, diabetes prevalence) for
the first year of COVID-19 pandemic, across 38 member states of the Organization for Economic Cooperation andDevelopment (OECD).

Materials and Methods
Study design, data sources and outcome variables
We conducted an ecological study using epidemiological databases related to 38 OECD countries. Data on COVID-19
burden statistics for the first year of the pandemic (year 2020), included years of life lost due to COVID-19(COVID-19 YLLs), demographic and economic indicators, as well as the rates of metabolic disorders. They wereextracted from online freely accessible OECD, WHO and World Bank databases. Based on data related to cumulative
COVID-19 cases and deaths (as of 31 December 2020), COVID-9 case fatality rate (CFR) was calculated for eachcountry as we reported previously7), with the use of the following formula:
CFR % =

Number of COVID‐19 deaths × 100
Number of COVID‐19

Ethical considerations and data analysis
Epidemiological data on country’s demographics, COVID-19 cumulative cases and deaths, and prevalence of metabolic disorders were anonymous and freely accessible online8–10); thus, informed consent was not applicable. Mean
(SD) values are used to present data related to continuous variables. For the purpose of statistical analysis of the data,
OECD countries also grouped in three regions:(1) Asia and Pacific region: Japan, South Korea, Australia, New Zealand.
(2) Region of the Americas (north, central, south America): Mexico, Costa Rica, Canada, USA, Chile, Colombia.
(3) Europe and Middle-east: Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece,
Hungary, Iceland, Ireland, Israel, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Norway, Poland, Portugal,
Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, England.
To assess the relationship between outcome variables and the covariates, overweight/obese population rate anddiabetes prevalence in particular, Pearson’s correlation test was conducted. Afterwards, an ecological regression
analysis was performed to explore the associations between both severe COVID-19 outcomes (CFR, YLLs) and the
explanatory variables. All the statistical analyses were performed with the use of STATA statistical package version 16(StataCorp, TX, USA).

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Results
Sociodemographic characteristics, health system and health indicators of 38 OECD countries
Table 1 shows the distribution of mean values (with minimum and maximum values) of sociodemographic characteristics, health system and COVID-19-related health indicators. Mean population was 35,875.1 (59,436.8) × 103,with an elderly population rate of 17.4 (4.3) %, higher education completion rate of 44.9 (1.0)% and a GDP per capita
of 40,246.5 (23,826.9) USD in the year 2020. Moreover, cumulative COVID-19 cases was 1,559,488 (425,358.5),
whereas cumulative COVID-19 deaths was 43,579.5 (91,386) for a CFR of 2.12 (1.3)% and COVID-19 YLLs of 556.8(556.3) per 1,000.
Correlation between outcome variables and covariates by Pearson correlation analysis
Pearson’s correlation analysis showed, on one hand, inverse correlations between COVID-19 CFR and country’s“high education completion rate” (r = -0.41; p < 0.009), as well as GDP per capita (r = -0.47; p = 0.013), andthe ratio nurses per 10,000 population (r = -0.36; p = 0.045); however, it was positively correlated with diabetes
prevalence (r = 0.48; p = 0.003). On the other hand, COVID-19 YLLs were inversely correlated with country’s elderly
population rate (r = 0.47; p = 0.003), and positively correlated with diabetes prevalence rate (r = 0.51; p = 0.011),
as shown in Table 2. In contrast, when assessing the relationship between overweight/obese population rate and
COVID-19 YLLs, the statistical significance was not reached, despite a relatively high r coefficient value. Furthermore,no correlation was observed between country-level full vaccination rate and COVID-19 outcomes (CFR, YLLs) when
considering OECD countries all together (Table 2).
Fig. 1 shows the two-way graph of the relationship between country-level severe COVID-19 outcomes (CFR, YLLs)
and diabetes prevalence. It presents OECD countries’ COVID-19 CFR and YLLs values and the corresponding 95%
confidence intervals. It was observed that OECD countries with high diabetes prevalence tending to have higher
COVID-19 CFR and YLLs in the first year of the pandemic.
Relationship between outcome variables and metabolic disorders (diabetes, overweight and obesity)
among OECD countries by bivariate regression analysis
As shown in Table 3, of the two metabolic disorders that were entered into the analysis, only diabetes prevalence wasrevealed to be a predictor of both COVID-19 CFR (beta = 1.01 (0.3), 95%CI: 0.38–1.63; p = 0.002) and COVID-19
Table 1. Sociodemographics and health system indicators of 38 OECD countries in 2020
Characteristics/indicators

Mean (SD)Minimum

Maximum

3,5875.1 (59,436.8)

339

329,065

Sociodemographics
Total population (×103)
Elderly population rate (65 years or older; %)17.4 (4.3)

7.2

28.1

Higher education completion rate (%)

44.9 (10)

23.6

69.8

40,246.5 (23,826.9)

6,428.7

114,705

Number of nurses per 104

8.6 (4.1)

1.3

17.9

Number of doctors per 104

3.4 (0.8)

1.9

5.2

GDP per capita in 2020 (USD)
Health system & COVID-19-related health indicators

Overweight/obese population rate (%)

60.1 (13.9)

26.7

75.2

Cumulative COVID-19 cases (as of Dec. 2020)

1,559,488 (425,358.5)

2,304

2.60e+07

Cumulative COVID-19 deaths (as of Dec. 2020)

43,579.5 (91,386)

577

439,439

Case fatality rate (CFR; %)2.12 (1.3)

0.48

8.51

Years of life lost due to COVID-19/100,000 (YLLs)

556.8 (556.3)

6

2,055

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Table 2. Relationship between COVID-19 health indicators (CFR, YLLs) and country-level sociodemographic, economic and healthsystem indicators by Spearman correlation test
CovariatesRho

p-valueHigh education completion rate

-0.41

0.009

Elderly population rate

-0.17

0.311

GDP per capita

-0.47

0.013

Full vaccination coverage rate

-0.24

0.166

Nurses per 104

-0.36

0.045

Doctors per 104

-0.34

0.228

Overweight/obese population rate

0.34

0.228

Diabetes prevalence (%)

0.48

0.003

(1) Predictors of COVID-19 CFR(2) Predictors of COVID-19 YLLs
High education completion rate

-0.37

0.063

Elderly population rate

0.47

0.003

GDP per capita

-0.26

0.109

Full COVID-19 vaccination coverage rate

-0.15

0.393

Nurses per 104

-0.19

0.267

Doctors per 104

-0.21

0.255

Overweight/obese population

0.50

0.074

Diabetes prevalence

0.51

0.011

*Notes: %, percentage; CFR, case fatality rate; COVID-19 YLLs, years of healthy
life lost due to COVID-19; GDP, gross domestic product.

Fig. 1. Two-way graph of the correlation between severe COVID-19 outcomes (case fatality rate, years of healthy life lost due to
COVID-19) and country-level diabetes prevalence across OECD member states

YLLs (beta = 8.28 (3.5), 95%CI: 2.07–14.4; p = 0.010). Furthermore, higher education completion rate was inversely associated with both COVID-19 CFR (beta = -3.21(1.1), 95%CI: -5.53–(-0.89), p = 0.008) and COVID-19 YLLs
(beta = -2.28(8.5), 95%CI: -40.13–(-5.43); p = 0.012).

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Table 3. Association between COVID-19-related health indicators (CFR, YLLs) and metabolic disorders (overweight/obesity, diabetes) across OECD countries in the first year of the pandemic (2020) by ecological regression analysis.
Covariates

CFR (%)YLLs per 100,000

Beta (SE)

95%CI

p

Beta (SE)

95%CI

p

Elderly population rate

-0.06 (0.05)-0.17–0.04

0.220-39.6 (20.8)-81.91–2.76

0.066

Higher education comple-tion rate

-3.21 (1.1)-5.53–(-0.89)

0.008-22.8 (8.5)

Overweight/obese popula-lation rate

0.04 (0.0)-0.03–0.12

0.228

0.01 (0.0)-0.01(0.0)–0.02-40.13–(-5.43) 0.012
0.074

Diabetes prevalence rate

1.01 (0.3)

0.38–1.63

0.002

8.28 (3.5)

2.07–14.4

0.010*Notes: OR, odds ratio; %, percentage; CFR, case fatality rate; YLLs, years of healthy life lost due to the disease.

Discussion
The present study explored the relationship between severe COVID-19 outcomes, namely case fatality rate and thenumber of years of healthy life lost due to SARS-CoV-2 infection on one hand, and metabolic disorders (diabetes,
overweight/obesity) across 38 countries that belong to the OECD organization in the first year of the pandemic(2020). We found that countries with low tertiary education rate, low GDP per capita, as well those with low ratio ofnurses per 10,000 tended to have COVID-19-related fatalities. These findings suggest that country’s high education
completion rate, the socioeconomic status of the population and the importance of the nursing workforce might have
had an influence on the outcomes of COVID-19 among infected populations of OECD member states in the firstyear of the pandemic. A meta-analysis conducted by Takkavatakarn and colleagues11) that investigated the global
incidence and mortality of COVID-19 has also shown an inverse correlation between country’s GDP per capita and
COVID-19 mortality.
Additionally, another study conducted in Brazil that investigated the impact of socioeconomic inequality on hospital case-fatality among SARS-CoV-2-infected children and adolescents showed that municipalities with lower GDP
per capita had worse indicators of hospital-based COVID-19 case fatality rates12). In most health crises, nurses havebeen at the frontline to respond to emergencies; thus, increasing the nursing workforce and the competency with
COVID-19 CFR and YLLs. A previous study conducted by Sourij and colleagues14) that assessed the predictors of
in-hospital mortality in COVID-19 patients showed that mortality rate was markedly high in diabetic patients. Furthermore, a systematic review and meta-analysis that included 22 studies and conducted by Saha and colleagues15)
found that, among hospitalized patients with COVID-19, mortality rate was highest in diabetic individuals in Europe,
the United States and Asian continent.
Our study did not find any relationship between country-level vaccination coverage rate and the outcome variablesin the year 2020. Previous reports have contradictory results in terms of booster vaccine necessity in some categoriesof the population. For example, a Brazilian study16) that investigated the relationship between COVID-19 vaccination
coverage and the disease mortality among elderly citizens. On the other hand, a meta-analysis conducted by Zhu etal.17) that included seven studies showed that booster vaccine could reduce infection rates, especially against Omicron
variant, but it was less effective against Delta variant, suggesting that the efficacy of COVID-19 vaccine booster should
be considered when making vaccination plans, especially in young adults and children.
Our study also revealed that old age and diabetes were among high-risk conditions for COVID-19 fatality and othersevere outcomes in OECD countries. Both covariates were positively correlated with COVID-19 CFR and COVID-19 YLLs.
Moreover, the regression analysis showed that countries with high diabetes prevalence rate in the first year of thepandemic tended to have higher COVID-19 CFR and YLLs.
The present study has some limitation. Country-level data used in this study do not provide the characteristics
on individual COVID-19 cases; thus, the findings should be interpreted with caution, and they cannot be applied to38

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countries other than OECD member states. Additionally, of the metabolic disorders, only obesity and diabetes weretaken into account as covariates. Data on other conditions such as hypertension were not included in the analysis.

Conclusions
Findings from this study, which used country-level health system and health indicators and COVID-19-related epidemiologic indicators, suggest that old age and diabetes are major predictors of serious COVID-19 outcomes, diseasemortality in particular. An integrated anti-COVID approach that includes tackling prevalent metabolic disorders in
the fight against the pandemic is needed to reduce COVID-19-related disability and mortality.
Acknowledgment
We would like to thank Dr Joel Ngoie N’shissho who serves as the international president of the Congolese diaspora (FICE), USA,and Dr Nsadi Fwene Bethier who serves as the president of Congo Medical Association for their support to our COVID-19 and
NCDs research.

Author Contributions
Conceptualization: NRN., DKT; SA., AM.; Data curation: KK., YM., SA.; Formal analysis: NRN, AM.; Methodology: KK., AM., YM.;
Supervision: NRN.; Writing original draft: NRN., DKT.; Manuscript editing: TH.

Funding
This study was not funded.

Conflict of Interest
Authors declare no conflict of interest.

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