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. 2023 Apr 3;9:23779608231165485. doi: 10.1177/23779608231165485

Correlation Between Diabetes and COVID-19 Indices: A Global Level Ecological Study

Alireza Mirahmadizadeh 1, Mousa Ghelichi-Ghojogh 2, Kimia Jokari 3, Sanaz Amiri 3, Leila Moftakhar 3, Mohammad Javad Moradian 4, Mohammad Habibi 5, Seyed Sina Dehghani 6, Amir Hossein Hassani 6, Alireza Jafari 7, Fatemeh Rezaei 8,✉
PMCID: PMC10074615  PMID: 37032958

Abstract

Introduction

Coronavirus is threatening the global public health as a new and widespread crisis. The researchers must keep in mind that one of the most vulnerable groups to COVID-19 are the people with underlying diseases, especially diabetes.

Objective

This ecological study aimed to investigate the correlation between diabetes and the epidemiological indices of COVID-19.

Methods

This ecological study included 144 countries. Their available data consists of the cumulative incidence rate of cases, cumulative incidence rate of death, recovery rate, case fatality rate, and performed tests of COVID-19, and diabetes. To collect the variables, a data set was provided which included the information of 144 countries based on diabetes and COVID-19 indices. Spearman coefficients were used for assess correlation between diabetes and COVID-19 indices. Also, Scatter plots of diabetes for the studied countries were drawn based on cumulative incidence rate of cases, cumulative incidence rate of death, tests, recovery rate, and case fatality rate of COVID-19.

Results

The results of this ecological study showed in total countries, there was a weak positive correlation between diabetes and cumulative incidence rate of cases and also cumulative incidence rate of death. Correlation between diabetes with test of COVID-19 was very weak. Scatter plots showed a weak liner correlation between diabetes and cumulative incidence rate of cases, cumulative incidence rate of death and test of COVID-19.

Conclusions

In this study, there was a weak positive correlation between diabetes and cumulative incidence rate of cases, cumulative incidence rate of death, and performed test of COVID-19. This disease is an enormous challenge for health policymakers; therefore, it is necessary to develop strategies and practical guidelines specific to each region to take the necessary care, especially for diabetic patients.

Keywords: COVID-19, diabetes, ecologic study

Introduction

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is threatening the global public health as a new and widespread crisis (Ghorbanzadeh et al., 2022; Singhal, 2020). It was first transmitted to humans in December 2019 in Wuhan, Hubei Province of China (Singhal, 2020). The disease spread rapidly, leading to an epidemic throughout China followed by an increasing number of cases in other countries around the world (McIntosh et al., 2020). As of May 2nd, 2021, more than 151 million people worldwide have been infected with the disease, and more than 3 million people have died (World Health Organization, 2011).

Studies have shown that self-care behaviors, older age, and chronic illnesses, including diabetes, hypertension, chronic cardiovascular and respiratory diseases, are the most prominent risk factors for hospitalization and death in COVID-19 (Amin et al., 2022; Rezaei et al., 2021; Zhou et al., 2020). The researchers must keep in mind that one of the most vulnerable groups to COVID-19 are the people with underlying diseases, especially diabetes (Moftakhar et al., 2021).

Review of Literature

Diabetes is one of the most common non-communicable diseases which is currently the fifth leading cause of mortality in the world and its prevalence is increasing rapidly to the point that it is estimated that the number of diabetic patients will increase from 285 million in 2010 to 438 million people in 2030 (Mirsamiyazdi et al., 2021; Moradi & Hasani, 2019). The prevalence of diabetes among COVID-19 patients was reported to be 14.5%–52% and these patients had a higher risk to develop severe COVID-19 (Padilla-Raygoza et al., 2020; Singh & Khunti, 2020). Not only most people with diabetes suffer from a severe form of COVID-19, but also, they face a high risk of mortality (Ruan et al., 2020; Yan et al., 2020); consequently, developing the necessary strategies for the management and control of the disease in these patients is one of the key factors for health policymakers, since countries are different from each other in terms of age, the prevalence of diabetes, and access to health services and facilities. So far, there is a lack of studies on diabetes and five COVID-19 epidemiological indices in global level. So, the researchers conducted this ecological study to examine the correlation between diabetes and the epidemiological indicators of COVID-19, including cumulative incidence, cumulative incidence of death, performed COVID-19 tests, recovery rate, and case fatality rate.

Methods

Design and Sample

This survey is an ecological study, so all studied variables were aggregate variables. Details to collect the COVID-19 indices have already been published (Mirahmadizadeh, Ghelichi-Ghojogh, et al., 2022; Mirahmadizadeh, Rezaei, et al., 2022). Briefly, to collect the variables in the study, a data set was provided which included the information of each country regarding the cumulative case, cumulative deaths, case fatality rate, recovery rate, and the number of performed COVID-19 tests. Information about COVID-19 for each country was retrieved from https://www.worldometers.info/. The worldometers website was used to collect the COVID-19 indices because the data of five indices related to COVID-19 are available on worldometers by country. COVID-19 indices were collected from the first reported case until November 30th, 2020.

Research Question

In this study, the correlation between COVID-19 indices including cumulative incidence case, cumulative death, cumulative test, recovery rate, case fatality rate, and diabetes were investigated. Data on the prevalence of diabetes, cumulative incidence case, cumulative incidence of death, case fatality rate, and performed COVID-19 tests were available for 144 countries. Also, recovery rate data was available for 140 countries.

Diabetes prevalence information was obtained from https://www.indexmundi.com/ for 2019. IndexMundi is a comprehensive data portal that includes detailed country statistics, graphs, and maps assembled from numerous sources. In this study, the prevalence of diabetes (% of population aged 20 to 79) is defined as the percentage of people aged 20–79 who had type 1 or type 2 diabetes (Ware et al., 1996). In another ecological study for determinants of the severity of COVID-19 data on prevalence of diabetes extracted as the percentage of the population aged 20–79 years (Pana et al., 2021).

Statistical Analysis

Data for all countries were screened. For some countries, data were not available for all COVID-19 indices and diabetes. However, 144 countries were selected that had cumulative incidence rate of cases, cumulative incidence rate of death, case fatality rate, and performed tests of COVID-19, and diabetes data. But four countries had no recovery data.

The correlation coefficient was used for the assessment correlation between two quantitative variables. The normality of the data was checked. Data weren’t normal distribution. So, the spearman correlation coefficient was used to verify the correlation between diabetes and indicators related to COVID-19. Furthermore, scatter plots of diabetes for the studied countries were also drawn based on cumulative incidence case, cumulative incidence death, tests, recovery rate, and case fatality rate of COVID-19. Statistical analyses were performed with Statistical Package for Social Science (IBM SPSS Statistics for Windows, Version 23.0. Armonk, NY: IBM Corp). The significance level was set at 0.05.

Results

Sample Characteristics

The results showed that among all countries surveyed, Luxembourg (54,808 per million) and Belgium (49,661 per million) had the highest cumulative incidence rate of cases for COVID-19, when Fiji (72 per million) and Burundi (57 per million) had the lowest cumulative incidence rate of cases for COVID-19. Also, it revealed that Belgium (1425 per million) followed by Spain (1119 per million) had the highest cumulative incidence rate of death due to COVID-19, when Burundi (0.08 per million) and Papua New Guinea (0.78 per million) had the lowest cumulative incidence rate of death due to COVID-19. Luxembourg (2,180,641 per million) and United Arab Emirates (1,682,881 per million) were the countries with the highest number of performed COVID-19 tests per million among studied countries. The lowest reported number of performed COVID-19 tests per million were from Yemen (560 per million) and Niger (1886 per million). The highest recovery rate was in Singapore (99.86%) and Djibouti (98.33%), while the lowest rate was reported from Belgium (6.48%) and France (7.28%). In addition, the highest case fatality rate of COVID-19 was in Yemen (28.34%) followed by Mexico (9.54%), and the lowest case fatality rate of COVID-19 was in Singapore (0.05%) and Burundi (0.15%).

Research Question Results

Table 1 shows the correlation coefficient between the indicators related to COVID-19 and diabetes. In total countries, it was shown that cumulative incidence rate of cases, cumulative incidence rate of death, and performed COVID-19 tests had a significant direct correlation with diabetes (p < .05). This means that countries with a high prevalence of diabetes had higher indices such as cumulative incidence rate of cases, cumulative incidence rate of death, and performed COVID-19 tests. However, no significant correlation was observed between diabetes and the recovery rate and case fatality rate (p > .05).

Table 1.

Spearman Correlation of COVID-19 Indices With Diabetes.

All countries Countries with  ≥ 10 million population
Variable N Correlation coefficient p-value Comment a N Correlation coefficient p-value Comment
Cumulative incidence of case (per million)
Cumulative incidence rate of cases (per million) 144 .21 .012 Weak 78 .33 .003 Weak
Cumulative incidence rate of death (per million) 144 .20 .01 Weak 78 .31 .005 Weak
Tests per million 144 .17 .03 Very weak 78 .32 .003 Weak
Recovery rate (%) 144 .09 .29 NS b 78 .07 .55 NS
Case fatality rate (%) 144 .009 .91 NS 78 .04 .69 NS
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a

00–.19 “very weak,” .20–.39 “weak,” .40–.59 “moderate,” .60–.79 “strong,” .80–1.0 “very strong.”

b

Not significant.

In countries with a population of 10 million or more, there was a significant direct correlation between the cumulative incidence rate of cases, the cumulative incidence rate of death, and performed COVID-19 tests with diabetes (p < .05), but no significant correlation was observed between diabetes in these countries and the recovery rate and case fatality rate (p > .05).

In Figure 1, scatter plots of diabetes by the cumulative incidence rate of cases, the cumulative incidence rate of death, the performed COVID-19 tests, recovery rate, and case fatality rate in all countries and countries with a population of 10 million and more are shown separately. Among all countries, the highest R2 was related to the cumulative incidence rate of cases and the performed COVID-19 tests (R2 = .01). Moreover, among countries with a population of 10 million or more, the highest R2 belonged to the cumulative incidence rate of cases and the performed COVID-19 tests (R2 = .007).

Figure 1.

Figure 1.

Open in a new tab

Scatterplot of correlation between diabetes prevalence with indices related to COVID-19. (a, b, c, e) Fiji, Burundi, Thailand, China, Niger, Papua New Guinea, Yemen-Rep, Mali, Benin, South Sudan, Malawi, Liberia, Nigeria, Brunei Darussalam, Togo, Mauritius, New Zealand, Uganda, Rwanda, Angola, Mozambique, Madagascar, Zimbabwe, Korea, Cuba, Haiti, Cameroon, Ethiopia, Zambia, Senegal, Lesotho, Guinea, Australia, Sri Lanka, Egypt, Japan, Afghanistan, Gambia-The, Kenya, Uruguay, Ghana, Myanmar, Pakistan, Mauritania, Indonesia, Uzbekistan, Guinea-Bissau, Bangladesh, Venezuela-RB, Equatorial Guinea, Jamaica, Malaysia, Philippines, Gabon, Botswana, Finland, Trinidad and Tobago, Namibia, Djibouti, El Salvador, Norway, Guatemala, India, Guyana, Kazakhstan, Turkey, Nepal, Tunisia, Mexico, Cyprus, Suriname, Latvia, Estonia, Morocco, Canada, Singapore, Greece, Saudi Arabia, Ecuador, Honduras, Kyrgyz Republic, Iran, Paraguay, Azerbaijan, Libya, Bolivia, Germany, Iraq, South Africa, Dominican Republic, Albania, Denmark, Belarus, Belize, Ireland, Russia, Iceland, Ukraine, United Arab Emirates, Lebanon, Cabo Verde, Slovak Republic, Serbia, Bulgaria, Jordan, Malta, Hungary, Lithuania, United Kingdom, Maldives, Oman, Sweden, Romania, Colombia, Poland, Italy, Moldova, Costa Rica, Bosnia and Herzegovina, Chile, Portugal, Peru, North Macedonia, Brazil, the Netherlands, Argentina, Austria, Croatia, Kuwait, France, Georgia, Spain, Slovenia, Israel, Switzerland, Panama, United States of America, Armenia, Czech Republic, Qatar, Belgium, Bahrain, Luxembourg, Montenegro. (d) All countries in (a) except Spain, United Kingdom, Sweden, the Netherlands. (f) All countries in (a) except countries with  < 10 million population. (i) All countries in (a) except Spain, United Kingdom, Sweden, the Netherlands, and countries with  < 10 million population.

Discussion

This ecological study was conducted to assess the correlation between diabetes and COVID-19 indices, including cumulative incidence rate of cases, cumulative incidence rate of death, cumulative tests performed, recovery rate, case fatality rate in 144 countries (Padilla-Raygoza et al., 2020). Previously, ecological studies were performed in epidemiology and public health fields (Mirahmadizadeh, Ghelichi-Ghojogh, et al., 2022; Mirahmadizadeh, Rezaei, et al., 2022; Rezaei et al., 2019). COVID-19 is the latest pandemic in the world that causes Severe Acute Respiratory Syndrome (SARS). It is rapidly spreading around the world and has involved many people. Among them, the elderly and people with underlying diseases are at higher risk of developing COVID-19 than others (Padilla-Raygoza et al., 2020). Many studies have shown that a high percentage of COVID-19 patients had underlying diabetes and were at risk for more severe complications (Hu et al., 2020; Rezaei et al., 2021; Zhou et al., 2020).

The results showed that diabetes was positively and directly correlated with the cumulative incidence rate of cases, the cumulative incidence rate of death, and the performed COVID-19 tests, while there was no significant correlation between the recovery rate and the case fatality rate. Similar to our results, which showed that the cumulative incidence rate of COVID-19 is higher in diabetics, other studies have shown that many people with COVID-19 have diabetes. The prevalence of diabetes in COVID-19 patients was reported to be 33.9%–55.3% (Jeong et al., 2020). Onder et al. showed that 35.5% of mortality due to COVID-19 occurred in diabetic patients (Hodgson et al., 2015). Other studies have also shown that the prevalence of COVID-19 is higher in diabetics than in non-diabetics. This rate was 10.8% versus 3.6% (Guan et al., 2020) and 81.3% versus 47.6% (Zhu et al., 2020). Consequently, the higher incidence of COVID-19 in diabetics leads to a higher testing rate.

Researchers have indicated several reasons for the higher incidence of COVID-19 in diabetics. One of these reasons is the major role of the Angiotensin Converting Enzyme II. This enzyme is a type of inner membrane glycoprotein, which is expressed in epithelial cells, respiratory system, cardiovascular system, kidneys, and intestines (Pal & Bhansali, 2020; Wan et al., 2020). According to the previous studies, this enzyme is the functional receptor for SARS-CoV-2, which turned it into an entry point for the virus into the body; therefore, coronavirus attaches to these cellular receptors from its spike section and enters the cells. Due to the high level of this enzyme in the body of diabetics, it is predicted that diabetics are more likely to develop COVID-19 (Huang et al., 2020).

Another factor that researchers have mentioned is the deficiency of the immune system in diabetic patients which increases their susceptibility to infections (Casqueiro et al., 2012) since hyperglycemia disturbs the function of phagocytosis in defending the body against pathogens (Rajpal et al., 2020). The ineffective immune system, decreased lung function, and severe respiratory problems, as well as the effects of COVID-19 on blood sugar control and on the pathogenesis of diabetes, are all factors that increase mortality in these patients (Moftakhar et al., 2021).

Another important factor is the inefficient response of inflammatory cytokines in diabetic patients, which increases the risk of other infections (Maddaloni & Buzzetti, 2020; Pal & Bhansali, 2020). Similar to our results, many other studies have shown a high COVID-19 mortality rate in people in whom diabetes disease has been diagnosed (Guan et al., 2020; Huang et al., 2020).

Indeed, patients with diabetes, due to changes in their body, especially in their immune and inflammatory systems (Jeong et al., 2020; Moftakhar et al., 2021), are more prone to severe forms of the disease than non-diabetics, which require artificial respiration and other medical facilities. In Australia, 24% of patients with COVID-19 admitted to ICU wards were found to be diabetic (CDC COVID-19 Response Team et al., 2020), compared with 32% in the United States (Wang et al., 2020). In other studies, the proportion of people with diabetes who were severely ill with COVID-19 versus non-diabetics were estimated to be 42.3% versus 10.8% (Hu et al., 2020), and 16.2% versus 5.7% (Guan et al., 2020). In our study, diabetes has no significant correlation with case fatality rate and recovery rate. There are several reasons, including the age of diabetic patients with COVID-19, the presence of other underlying diseases, and the amount of medical equipment to treat these patients, which varies from country to country. These factors can affect the estimation of these correlations but these data were not available for this study. Therefore, we must note that the association of diabetes with other factors makes the determination of the underlying causes regarding higher mortality rate, higher incidence rate, and recovery rate difficult. To solve this problem, more clinical research is needed.

Strength and Limitations of the Study

One of the strengths of the present study was that for the first time, the relationship between diabetes and COVID-19 epidemiological indices (cumulative incidence rate of cases, cumulative incidence rate of death, cumulative tests performed, case fatality rate, and recovery rate) was examined in 144 countries. Since this study is an ecological study, to generalize the results to the individual level, the researchers may have issues with the ecological fallacy. Also, since our study was conducted at the aggregate level and not at the individual level, the researchers cannot estimate the causal association between risk factors and outcome. It should also be noted that the countries surveyed are different in terms of income level, economic and social status, the number of medical facilities and equipment and even the amount of performed tests, the prevalence of chronic diseases, and age composition. Furthermore, data on diabetes were available for persons aged 20–79 years, while COVID-19 indices were for all ages.

Implications for Practice

It is necessary to develop strategies and practical guidelines specific to each region to take the necessary care for diabetes patients.

Conclusion

In this study, there was a positive correlation between diabetes and cumulative incidence rate of cases, cumulative incidence rate of death, cumulative tests performed. Also, it is shown that there is a serious concern for individuals, while there is still no definitive treatment for this disease. Since there is still a lot of unknowns about COVID-19, so how to manage this disease is an enormous challenge for health policymakers. Therefore, it is necessary to develop strategies and practical guidelines specific to each region to take the necessary care for these patients. The recommendations for home quarantine and regular blood sugar control are serious recommendations for these patients.

Acknowledgments

The authors would like to thank the vice chancellor for research and technology for financial support.

Footnotes

Author Contributions: All authors contributed to the study conception and design. AMA and MGG participated in the design of the study. KJ and SA performed data collection. LM and MJM wrote the manuscript. MH and AHH revised the manuscript. MGG and FR helped with statistical analysis and prepared the illustrations. FR and AJ edited the manuscript. All authors read and approved the final manuscript.

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was approved and financially supported by Shiraz University of Medical Sciences (Grant number: 99-01-106-19536).

Statement of Ethics: This study was approved by the research ethics committee of Shiraz University of Medical Sciences (IR.SUMS.REC.1399.174).

ORCID iD: Fatemeh Rezaei https://orcid.org/0000-0003-2977-2699

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