Risk Markers of COVID-19, a Study from South-Lebanon
by
, and
Mohamed Chakkour
1,†, 
Ali Salami
2,3,*,†,
Dana Olleik
4,
Israa Kamal
4,
Fatima Y. Noureddine
5,
Ali El Roz
4,6,7 and
Ghassan Ghssein
7,* 1
Department of Biology, Faculty of Arts and Sciences, American University of Beirut, Beirut P.O. Box 11-0236, Lebanon
2
Department of Mathematics, Faculty of Sciences, Lebanese University, Nabatieh P.O. Box 6573/14, Lebanon
3
Department of Mathematics, School of Arts and Sciences, Lebanese International University, Beirut P.O. Box 146404, Lebanon
4
Department of Biology, Faculty of Sciences, Lebanese University, Nabatieh P.O. Box 6573/14, Lebanon
5
Medical Analysis Laboratory, Molecular Genetics Unit, Sheikh Ragheb Harb University Hospital (SRHUH), Nabatieh P.O. Box 1700, Lebanon
6
Department of Biological and Chemical Sciences, School of Arts and Sciences, Lebanese International University, Beirut P.O. Box 146404, Lebanon
7
Department of Laboratory Sciences, Faculty of Public Health, Islamic University of Lebanon, Khalde P.O. Box 30014, Lebanon
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
COVID 2022, 2(7), 867-876; https://doi.org/10.3390/covid2070063
Submission received: 13 May 2022
/
Revised: 6 June 2022
/
Accepted: 24 June 2022
/
Published: 27 June 2022
Abstract
:Background: COVID-19, caused by the novel coronavirus SARS-CoV-2, was declared by WHO in early 2020 as a worldwide pandemic. Several known risk markers are associated with COVID-19 morbidity and mortality, including age, gender, and diseases, such as hypertension, diabetes, and chronic cardiovascular diseases. Recent studies have shown an association between COVID-19 infection and the ABO blood groups. Objective: To assess the prevalence of SARS-CoV-2 among suspected COVID-19 patients as well as the risk markers for COVID-19 associated with ABO blood group, Rhesus factor, and patient’s address during the past year. Methods: 69,019 nasopharyngeal swab samples were collected and analyzed by reverse transcription polymerase chain reaction technique for the detection of SARS-CoV-2 in patients attending a tertiary health care center in South Lebanon during the period between August 2020 and July 2021. Results: Among all tested subjects, the prevalence of SARS-CoV-2 infection was 19.2% (95% CI: 18.9% to 19.5%). Among those with known blood group (N = 17,462), odds of SARS-CoV-2 were higher in group A (Odds Ratio = 1.12, 95% CI: 1.02 to 1.23) and group AB (OR = 1.19, 95% CI: 1.00 to 1.41) relative to the reference group O (OR = 1). Odds of SARS-CoV-2 in the Rh-negative group (OR = 1.02, 95% CI: 0.89 to 1.16) were not significantly different from the Rh-positive group. Among those with known address (N = 30,060), odds of SARS-CoV-2 were lower in residents of remote areas (OR = 0.89, 95% CI: 0.80 to 0.99) relative to central cities. Conclusion: There is a modestly higher risk of SARS-CoV-2 infection associated with blood groups A and AB, and a lower risk associated with living in remote, less crowded regions.
1. Introduction
The novel Coronavirus disease of 2019 (COVID-19), caused by the SARS-CoV-2 virus, has transmitted rapidly across the world, causing over 323 million confirmed cases and over 5.5 million deaths worldwide as of 16 January 2022 (WHO 2022). Lebanon has suffered a surge of cases during the first and second waves of the pandemic, especially during holiday seasons [1]. As of 25 March 2022, Lebanon has recorded 1,089,419 confirmed cases and 10,263 deaths with 10,760 new cases detected only on 3 February 2022 [2,3].
Several known risk factors are associated with COVID-19 morbidity and mortality, including age, gender, and diseases, such as hypertension, diabetes, obesity, and chronic cardiovascular and respiratory diseases [4]. In 2005, Cheng et al. demonstrated a link between ABO blood group and susceptibility to severe acute respiratory syndrome (SARS1), with blood group ‘O’ individuals being less likely to be infected [5].
Recent studies have confirmed an association between ABO blood types and COVID-19 infection risk. In July 2020, Zhao et al. found a greater proportion of A blood group and a lower proportion of O blood group among COVID-19 patients relative to the general populations of Wuhan and Shenzhen in China [6]. Similarly, a genome-wide association study involving data from Italy and Spain showed a lower risk of COVID-19 among O blood type individuals and a higher risk among A blood type individuals [7,8,9,10,11].
ABO blood types are determined by carbohydrate antigens located on the surface of red blood cells. The antigenic determinants of A and B blood groups are the trisaccharide groups GalNAcα1-3-(Fucα1,2)-Galβ- and Galα1-3-(Fucβ1,2)-Galβ-, respectively, while O blood group antigen is a disaccharide (Fucα1,2-Galβ-) [6]. The ABO blood type trait mirrors polymorphisms within the ABO gene, this gene is linked to several different infections or disease severity following infections [8]. Moreover, the ABO gene is linked to a number of several traits that are considered risk factors for COVID-19 morbidity and mortality, such as type 2 diabetes [12], coronary artery disease [13], angiotensin converting enzyme [14], myocardial infarction [13,15], venous thromboembolism [16], and many others [8].
On the other hand, Rhesus (D) phenotypes (positive and negative Rh blood groups) are associated with the susceptibility to very few diseases compared to ABO [17]. For instance, Rh-positive individuals are found to be protected against the effects of latent toxoplasmosis [18]. While a recent population-based cohort study from Ontario, Canada, demonstrated that Rh-negative individuals may be linked with a slightly lower risk for SARS-CoV-2 infection and severe COVID-19 illness [19]. In October 2021, a similar study performed in Beirut, Lebanon, with a sample size of 404 patients concluded that A Rh+ blood group is associated with an increased risk of developing COVID-19 [20].
In this study, we pursued to comprehend the association between SARS-CoV-2 infection (COVID-19) and blood type. To conduct this study, 69,019 nasopharyngeal swab samples were collected and analyzed to identify SARS-CoV-2 in patients visiting the health care center Sheikh Ragheb Harb University Hospital (SRHUH) in Nabatiyeh, South Lebanon, during the period between August 2020 and July 2021. We compared both ABO and Rh (D) blood types, and we investigated initial infection. The objective of our study was to find the overall prevalence of SARS-CoV-2 among suspected COVID-19 patients in South Lebanon, as well as the risk markers for COVID-19 associated with blood group, Rhesus factor, and patient’s address.
2. Materials and Methods
2.1. Study Design and Setting
In this retrospective study, results and associated data (age, gender, blood group, rhesus group, and the address of patients) for 69,019 RT-PCR tests for the detection of SARS-CoV-2 were collected using the Statistic Module system of the healthcare institute. This study was limited to a one-year period between August 2020 and July 2021 in patients visiting the health care center Sheikh Ragheb Harb University Hospital (SRHUH) in Nabatiyeh, South Lebanon.
2.2. Sample Collection and Transportation
Swab samples were taken from individuals suspected of having COVID-19 in order to extract the SARS-CoV-2 genome. Each sample was put in a transport tube containing a sterile solution of normal saline and transferred to the molecular genetics’ unit at the laboratory of the health care center.
2.3. RNA Extraction and SARS-CoV-2 Detection by qRT-PCR
Upon reception of the samples, RNA extraction was performed manually once the samples were received, using spin column viral RNA extraction kits (QIAamp Viral RNA Mini kit, cat, no, 52906) according to the manufacturers’ instructions. One-Step Reverse Transcription Real-Time polymerase chain reaction (RT- PCR) was performed to validate the presence of the SARS-CoV-2 RNA in the samples by amplifying various sequences of different genes specific for SARS-CoV-2. We employed the COVID-19 one-step RT-PCR kit (Pishtaz Teb DIAGNOSTICS Research and Development (PJS)) kit in our study. Two thermocycler devices can be used to perform RT-PCR experiment QuantStudio™ 5 Real-Time PCR System for Human Identification (Applied Biosystems™, Thermo Fisher Scientific, Waltham, MA, USA) and the Rotor-Gene Q Real-Time PCR Cycler (Qiagen, Düsseldorf, Germany). The reaction mix contained COVID-19 Reaction Mixture, COVID-19 Probe Mixture, and the RNA sample. Briefly, the following steps were followed in the RT-PCR assays: Reverse transcription, 40–45 cycles of denaturation, annealing, extending, and collecting fluorescence signals on different channels. The results were analyzed according to the manufacturers’ instructions
3. Statistical Analysis
The statistical analyses were performed using the SPSS (IBM Corp., Released 2013, SPSS Statistics for Windows Version 22.0, Armonk, NY, USA). This software was used as well for data management and cleaning. The significance level was set at p < 0.05 for all statistical analyses. Categorical variables were shown as frequencies followed by percentages. Chi-squared tests assessed statistical significance of associations between the hypothesized risk markers (categorical variables) and SARS-CoV-2 RT-PCR test result (positive or negative). Univariate logistic regression was used to test the hypothesized associations between the PCR test result (positive/negative) as the dependent variable and the other variables (age group, gender, blood group, rhesus group, address, and months) as independent variables. Only variables with a significant p-value in the univariate analysis were included in the final multivariate logistic regression.
4. Results
4.1. Demographic Data of All Patients
Sixty-nine thousand nineteen samples were analyzed to detect SARS-CoV-2 infection at the molecular genetics’ unit in the SRHUH between August 2020 and July 2021. Chi-squared tests of independence were performed, and insufficient evidence was found to conclude that the blood-group frequencies differ between SARS-CoV-2-tested and the general population of Nabatieh [21] (Supplementary Table S1). Regarding the age group of participants, the majority of tests (51.0%) were for adults aged 20 to 39 years, followed by mature persons aged 40 to 69 years (31.0%), adolescents aged 10 to 19 years (8.1%), elderly persons aged 70 years and above (5.2%), and finally children aged 0 to 9 years (4.6%). In terms of gender, 35,288 (51.1%) tests belong to males, and 33,729 (48.9%) tests belong to females (Table 1). Regarding the blood group, the majority of samples belong to individuals with O and A blood groups with a percentage of 38.9% and 38.1%, respectively, followed by B (17.0%) and AB (6.1%). In terms of the rhesus group, the majority of samples belong to individuals with rhesus positive (89.2%), and the minority of tests were to those with rhesus negative (10.8%) (Table 1). Regarding the address of participants, 10.2% were addressed in the central area, 63.8% in the cities-close area, and 26.0% in the remote area. The record of the monthly number of performed tests in the molecular genetics unit shows a slight increase between August 2020 and December 2020 (from 2571 to 5528 monthly tests). This number increased significantly between December 2020 and January 2021 (from 5528 to 8085 monthly tests) and continued to rise until reaching a peak of 9993 monthly tests in March 2021, after that the number of monthly tests decreased between March and July 2021 (9993 to 5688 monthly tests) (Figure 1 and Table 1).
4.2. Prevalence of SARS-CoV-2 Infection and the Distribution of Positive Cases Regarding Different Parameters
A total of 13,285 out of 69,019 tested samples (19.2%, 95% CI: 18.9% to 19.5%) were positive for SARS-CoV-2 by RT-PCR over the study period, which indicates the overall prevalence. Out of 13,282 positive cases of known age, 6677 (50.3%) belong to adults, followed by 4224 (31.8%) for mature persons, then come adolescents, elderly persons, and finally children, with a percentage of 10.0%, 4.6%, and 3.4%, respectively (p-value < 0.001), for the null hypothesis of no association between age group and SARS-CoV-2 test result (Table 1).
As shown in Table 1, most positive cases belong to the A (39.3%) and O (36.6%) blood groups compared to B (17.5%) and AB (6.6%) (p = 0.044). Rhesus-positive patients account for most positive cases (89.1%), while rhesus-negative patients account for only 10.9%. Regarding the address of participants, the highest percentage of positive cases belong to individuals who were addressed in the close area (66.7%), followed by those who were addressed in a remote area (23.2%), and finally, those who were addressed in the central area (10.0%) (p-value < 0.001) for the null hypothesis of no association between address group and SARS-CoV-2 test result.
Furthermore, the prevalence of positive cases varied by month. As shown in Table 1, the prevalence of positive tests (among all tests during the month) was 3.0% in August 2020, then increased every month to a peak of 32.4% in February 2021, before declining to 3.4% in June 2021, and increasing slightly to 6.0% in July 2021.
Table 1.
Distribution of SARS-CoV-2 test results with respect to age group, gender, address, ABO group, Rh type, and month.
Table 1.
Distribution of SARS-CoV-2 test results with respect to age group, gender, address, ABO group, Rh type, and month.
| Test Result | ||||||
|---|---|---|---|---|---|---|
| Total | Negative | Positive * | p-Value | |||
| Age group | n (%) | 95% CI | n (%) | 95% CI | <0.001 | |
| Children 0–9 years | 3172 | 2723 (85.8) | (84.59–87.01) | 449 (14.2) | (12.99–15.41) | |
| Adolescent 10–19 | 5602 | 4276 (76.3) | (75.19–77.41) | 1326 (23.7) | (22.59–24.81) | |
| Adult 20–39 | 35,217 | 28,540 (81.0) | (80.59–81.41) | 6677 (19.0) | (18.59–19.41) | |
| Mature 40–69 | 21,392 | 17,168 (80.3) | (79.77–80.83) | 4224 (19.7) | (19.17–20.23) | |
| Elderly 70+ | 3621 | 3015 (83.3) | (82.08–84.52) | 606 (16.7) | (15.48–17.92) | |
| Total | 69,004 | 55,722 (80.8) | 13,282 (19.2) | |||
| Gender | n (%) | 95% CI | n (%) | 95% CI | 0.398 | |
| Male | 35,288 | 28,452 (80.6) | (80.19–81.01) | 6836 (19.4) | (18.99–19.81) | |
| Female | 33,727 | 27,279 (80.9) | (80.48–81.32) | 6448 (19.1) | (18.68–19.52) | |
| Total | 69,015 | 55,731 (80.8) | 13,284 (19.2) | |||
| Address | n (%) | 95% CI | n (%) | 95% CI | <0.001 | |
| Central area | 3065 | 2469 (80.6) | (79.20–82.00) | 596 (19.4) | (18.00–20.80) | |
| Close area | 19,167 | 15,206 (79.3) | (78.73–79.87) | 3961 (20.7) | (20.13–21.27) | |
| Remote area | 7828 | 6448 (82.4) | (81.56–83.24) | 1380 (17.6) | (16.76–18.44) | |
| Total | 30,060 | 24,123 (80.2) | 5937 (19.8) | |||
| ABO Blood group | n (%) | 95% CI | n (%) | 95% CI | 0.044 | |
| O | 6791 | 5752 (84.7) | (83.84–85.56) | 1039 (15.3) | (14.44–16.16) | |
| A | 6646 | 5529 (83.2) | (82.30–84.10) | 1117 (16.8) | (15.90–17.70) | |
| B | 2966 | 2469 (83.2) | (81.85–84.55) | 497 (16.8) | (15.45–18.15) | |
| AB | 1059 | 872 (82.3) | (80.00–84.60) | 187 (17.7) | (15.40–20.00) | |
| Total | 17,462 | 14,622 (83.7) | 2840 (16.3) | |||
| Rhesus Group | n (%) | 95% CI | n (%) | 95% CI | 0.831 | |
| Rh− | 1878 | 1569 (83.5) | (81.82–85.18) | 309 (16.5) | (14.82–18.18) | |
| Rh+ | 15,584 | 13,053 (83.8) | (83.22–84.38) | 2531 (16.2) | (15.62–16.78) | |
| Total | 17,462 | 14,622 (83.7) | 2840 (16.3) | |||
| Months | n (%) | 95% CI | n (%) | 95% CI | <0.001 | |
| August 2020 | 2571 | 2493 (97.0) | (96.34–97.66) | 78 (3.0) | (2.34–3.66) | |
| September 2020 | 3164 | 3007 (95.0) | (94.24–95.76) | 157 (5.0) | (4.24–5.76) | |
| October 2020 | 3670 | 3377 (92.0) | (91.12–92.88) | 293 (8.0) | (7.12–8.88) | |
| November 2020 | 4467 | 3833 (85.8) | (84.78–86.82) | 634 (14.2) | (13.18–15.22) | |
| December 2020 | 5528 | 4479 (81.0) | (79.97–82.03) | 1049 (19.0) | (17.97–20.03) | |
| January 2021 | 8085 | 5829 (72.1) | (71.12–73.08) | 2256 (27.9) | (26.92–28.88) | |
| February 2021 | 8163 | 5517 (67.6) | (66.58–68.62) | 2646 (32.4) | (31.38–33.42) | |
| March 2021 | 9993 | 7041 (70.5) | (69.61–71.39) | 2952 (29.5) | (28.61–30.39) | |
| April 2021 | 7744 | 5688 (73.5) | (72.52–74.48) | 2056 (26.5) | (25.52–27.48) | |
| May 2021 | 5556 | 4884 (87.9) | (87.04–88.76) | 672 (12.1) | (11.24–12.96) | |
| June 2021 | 4387 | 4238 (96.6) | (96.06–97.14) | 149 (3.4) | (2.86–3.94) | |
| July 2021 | 5688 | 5345 (94.0) | (93.38–94.62) | 343 (6.0) | (5.38–6.62) | |
| Total | 69,016 | 55,731 (80.8) | 13,285 (19.2) | |||
* In the Positive column, the row percentage represents the prevalence of SARS-CoV-2 infection in the specified group. Categorical variables were shown as number (n) and percentages (%). CI: Confidence Interval.
4.3. Risk Markers of COVID-19
As shown in Table 2, univariate logistic regression models found that among those with known blood group, odds of a positive SARS-CoV-2 test were higher in blood group A (Odds Ratio = 1.12, 95% CI: 1.02 to 1.23) and group AB (OR = 1.19, 95% CI: 1.00 to 1.41) relative to the reference group O (OR = 1). Odds of SARS-CoV-2 in the Rh-negative group (OR = 1.02, 95% CI: 0.89 to 1.16) were not significantly different from the Rh-positive group. Among those with known address, odds of SARS-CoV-2 were lower in residents of remote areas (OR = 0.89, 95% CI: 0.80 to 0.99) relative to central cities. Odds of a positive SARS-CoV-2 test also varied significantly by age group and month of the test. In a multivariate logistic regression model, including ABO blood groups, address areas, age groups (four binary dummy variables), and month of the test (eleven variables) as independent variables, odds of a positive SARS-CoV-2 test still appeared higher in blood group A (OR = 1.09, 95% CI: 0.99 to 1.21) and group AB (OR = 1.15, 95% CI: 0.94 to 1.40) relative to the reference group (combination of group O and group B), though not significantly higher given a threshold of p < 0.05, two-sided. Odds of SARS-CoV-2 remained significantly lower in residents of remote areas (OR = 0.87, 95% CI: 0.78 to 0.98) relative to the reference group (combination of “central area” and “close area”). The magnitudes of the associations (measured by the point estimates of the odds ratios) observed in the univariate analyses were supported by the findings of the multivariate analysis. Therefore, the observed associations are not explainable by confounding. The lack of statistical significance in the multivariate model of the estimated odds ratios pertaining to ABO blood groups is likely the result of reduced statistical power due to the large number of independent variables included in the multivariate model.
5. Discussion
As the pandemic persists with its harmful effects prevailing among all the different health and economic sectors worldwide, the urge to understand the link between blood groups and SARS-CoV-2 infection increased in hopes of finding better treatments to save humanity. In an attempt to explain this association between the ABO blood groups and COVID-19 susceptibility, several mechanisms have been proposed, and these include the production of glycan antigens by SARS-CoV-2, influence of the coagulation system, genetic variations in ABO gene and the presence of anti-A antibodies [22]. Based on our findings along with all the aforementioned studies, we tend to agree with the proposed mechanism, which involves the existence of anti-A antibodies in the serum of non-A blood group individuals.
A previous study by Guillon et al. in 2008 demonstrates solid evidence of the neutralizing effect of anti-A antibodies and their ability to inhibit the S protein of the SARS-CoV from binding to ACE 2 receptors [23]. According to Miotto et al., evidence reveals that the interaction between the SARS-CoV-2 S protein and its membrane receptor ACE2 could be interrupted by anti-A blood group antibodies that naturally occur in the serum of non-A blood groups such as O and B individuals [24]. In fact, another study by Gérard et al. stated that SARS-CoV-2 was less prevalent among blood groups O and B, which develop the anti-A antibodies, while it was higher in the groups lacking the anti-A antibodies such as A and AB [25], which explains the high prevalence of COVID-19 among individuals with blood groups A and AB as shown by our results. Moreover, a review by Zhang et al. suggests that non-A blood type individuals are less susceptible to COVID-19 infection due to the presence of anti-A antibodies in their serum [26].
Almost all the studies, including our current study, state that individuals with blood group A are more prone to infection, while those with blood group O are less likely to get infected, without taking into account individuals with blood group B whose immune system is also capable of producing the anti-A antibodies. This is explained by the fact that the anti-A antibodies present in blood group O are from the IgG class, while those in the blood group B are from the IgM class, thus anti-A antibodies from the blood group O are more protective than the antibodies produced by the blood group B [25].
Another possible mechanism is that while SARS-CoV-2 is replicating in the host epithelium, it produces glycan antigens similar to those of the host A or B antigens, according to the blood group of the host [27]. As a result, when a viral particle having the A glycan infects an individual with blood group O or B, the host’s anti-A antibodies will inhibit the interaction between the viral S protein and its corresponding ACE 2 receptors in host cells, and thus, the individual will develop, to some extent, protection against infection. This protection will be absent in individuals with blood groups A and AB where anti-A antibodies are absent [28]. This may explain the higher prevalence of COVID-19 infections in individuals with blood groups A and AB who lack the anti-A antibodies compared with those of blood group O who are capable of developing anti-A IgG antibodies.
Our results show that residents of remote areas are less subjected to COVID-19 infection compared to those living in the central city or in the close areas near the hospital. This inequality of COVID-19 prevalence based on the patient’s address can be explained by the fact that cities are usually crowded due to the huge number of citizens and closely built houses. In addition to that, the probability of meeting infected individuals in crowded places, such as markets and public spaces, is higher in the city compared to remote areas. Thus, the spread of SARS-CoV-2 is easier in crowded cities compared to less crowded territories, in fact, a study from Mumbai, India, demonstrates a significant increase in COVID-19 seropositivity in slums (highly populated poor residential areas) compared to less populated rural areas within the same city [29]. Moreover, cluster transmission due to crowded accommodations in specific areas of Stockholm, Sweden, accounted for a higher seroprevalence of SARS-CoV-2 compared to less crowded regions within the same district [30]. A systematic review by Franceschi et al. highlights the heterogeneous prevalence of COVID-19 within a population based on several demographic, social, and economic factors. The research team demonstrates a direct relationship between living in crowded low-income neighborhoods and the increase in COVID-19 infection rate [31]. In our case, all the regions from where the patients are admitted (central area, close area, and remote area) share the same economic status but differ in the number of residents and whether these residents are crowded or not. Thus, living in remote areas, which are less crowded, is considered a protective factor against infection.
Regarding the prevalence of SARS-CoV-2 infection during the period of the study, our results are congruent with a previous study performed by our team in which the number of positive cases was low in the period between September 2020 and December 2020 during which the Lebanese authorities enforced a lockdown, and the number of cases drastically increased in the period of January 2021 after Christmas’ and holidays’ celebrations due to crowded gatherings in closed spaces [1]. In our previous study, we also identified a potential SARS-CoV-2 variant which is directly correlated with the increase in COVID-19 cases [1], a fact which explains the increased prevalence of the disease during February and March 2021. On the contrary, the strict decline in the number of cases in the period from March 2021 till July 2021 can be linked to three main reasons. First, the natural immunity developed by the previously infected individuals in the area, second, the start and increase in the rate of administering COVID-19 vaccines, and third, the increased awareness among the public regarding social distancing, personal hygiene, and quarantine. In fact, the Lebanese vaccination campaign started on 14 February 2021, and gradually, the percentage of vaccinated individuals increased from 1.6% in March 2021 to 16.5% by the end of July 2021 [32]. Nevertheless, a modeling study done at the American University of Beirut predicts a slight decrease in daily infections as the percentage of vaccinations significantly increases [33]. Another study from Austria, Luxemburg, and Sweden demonstrates that vaccination will considerably, but not immediately, help to limit the infection [34], thus, vaccination plays a minor role in the decline of cases observed in our data and may be majorly linked to the other two aforementioned factors.
6. Conclusions
To our knowledge, this study is considered the first of a kind in Lebanon regarding the sample size. In order to identify SARS-CoV-2, 69,019 nasopharyngeal swab samples were analyzed. In this study, we found evidence for associations between ABO blood groups and COVID-19 prevalence. Using data from SRHUH, we found a modestly increased SARS-CoV-2 infection risk among blood groups A and AB relative to group O. We also concluded that living in remote non-crowded regions is modestly protective against SARS-CoV-2 infection and we emphasized the importance of abiding by lockdowns and COVID-19 precautions to curb infections. Further studies should be done over a further extended period of time to significantly assess the role of vaccines in limiting COVID-19 infection since our current study is not enough to come up with substantial conclusions in this context.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/covid2070063/s1, Table S1: Chi-square tests to evaluate dependence between blood type and having received a test for SARS-CoV-2.
Author Contributions
Conceptualization: G.G. and A.E.R.; Methodology: F.Y.N., I.K., D.O. and A.S.; Validation: A.E.R., A.S. and G.G.; Formal analysis: M.C., A.E.R., A.S. and G.G.; Investigation: G.G.; Data curation: F.Y.N., I.K., D.O. and A.S.; Software: A.S.; Writing—original draft preparation: M.C., A.E.R., A.S. and G.G.; Writing—review and editing: All authors; Supervision: A.E.R., A.S. and G.G.; Project administration: G.G. and A.S. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
The study was approved by the ethical and scientific committee of the Sheikh Ragheb Harb University Hospital (SRHUH). Ethical clearance was taken as per the norms and in accordance with relevant guidelines and regulations of the SRHUH. This study is done in a manner that ensures the confidentiality of patients.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data that support the findings of this study are available from the corresponding authors (A.S. and G.G.) upon reasonable request.
Acknowledgments
We would like to express our gratitude to the healthcare center “Sheikh Ragheb Harb University Hospital” for their support in the conduction of this study.
Conflicts of Interest
The authors declare no conflict of interest.
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Figure 1.
Distribution of SARS-CoV-2 tests and SARS-CoV-2 positive cases regarding the months.
Table 2.
Results of univariate and multivariate logistic regression analyses with SARS-CoV-2 test result as the dependent variable.
Table 2.
Results of univariate and multivariate logistic regression analyses with SARS-CoV-2 test result as the dependent variable.
| Univariate Analysis | Multivariate Analysis | ||||
|---|---|---|---|---|---|
| Variable | Categories | OR (95% CI) | p-Value | OR (95% CI) | p-Value |
| Age group | Children 0–9 years | 1 | - | 1 | - |
| Adolescent 10–19 | 1.88 (1.67–2.12) | <0.001 | 1.94 (1.21–3.10) | 0.006 | |
| Adult 20–39 | 1.42 (1.28–1.57) | <0.001 | 2.03 (1.36–3.03) | 0.001 | |
| Mature 40–69 | 1.49 (1.34–1.66) | <0.001 | 1.90 (1.27–2.86) | 0.002 | |
| Elderly 70+ | 1.22 (1.07–1.39) | 0.003 | 1.58 (1.02–2.44) | 0.039 | |
| Gender | Male | 1 | - | ||
| Female | 0.984 (0.947–1.022) | 0.398 | |||
| ABO Blood Group | O | 1 | - | 1 | - |
| B | 1.11 (0.99–1.25) | 0.069 | |||
| A | 1.12 (1.02–1.23) | 0.017 | 1.09 (0.99–1.21) | 0.092 | |
| AB | 1.19 (1.00–1.41) | 0.049 | 1.15 (0.94–1.40) | 0.176 | |
| Rhesus Group | Rh+ | 1 | - | ||
| Rh− | 1.02 (0.89–1.16) | 0.814 | |||
| Address | Central Area | 1 | - | 1 | - |
| Close Area | 1.08 (0.98–1.19) | 0.120 | |||
| Remote Area | 0.89 (0.80–0.99) | 0.027 | 0.87 (0.78–0.98) | 0.018 | |
| Months | August 2020 | 0.19 (0.15–0.24) | <0.001 | 0.03 (0.01–0.12) | <0.001 |
| September 2020 | 0.32 (0.26–0.38) | <0.001 | 0.17 (0.10–0.29) | <0.001 | |
| October 2020 | 0.53 (0.45–0.61) | <0.001 | 0.49 (0.34–0.69) | <0.001 | |
| November 2020 | 1 | - | 1 | - | |
| December 2020 | 1.42 (1.27–1.58) | <0.001 | 1.25 (0.97–1.62) | 0.087 | |
| January 2021 | 2.34 (2.12–2.58) | <0.001 | 2.59 (2.06–3.25) | <0.001 | |
| February 2021 | 2.90 (2.63–3.19) | <0.001 | 3.39 (2.70–4.25) | <0.001 | |
| March 2021 | 2.54 (2.31–2.79) | <0.001 | 2.65 (2.12–3.32) | <0.001 | |
| April 2021 | 2.19 (1.98–2.41) | <0.001 | 2.23 (1.77–2.82) | <0.001 | |
| May 2021 | 0.83 (0.74–0.94) | <0.001 | 0.91 (0.69–1.20) | 0.495 | |
| June 2021 | 0.21 (0.18–0.26) | <0.001 | 0.18 (0.11–0.29) | <0.001 | |
| July 2021 | 0.39 (0.34–0.45) | <0.001 | 0.26 (0.17–0.39) | <0.001 | |
OR: Odds Ratio. CI: Confidence Interval.
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Chakkour, M.; Salami, A.; Olleik, D.; Kamal, I.; Noureddine, F.Y.; Roz, A.E.; Ghssein, G. Risk Markers of COVID-19, a Study from South-Lebanon. COVID 2022, 2, 867-876. https://doi.org/10.3390/covid2070063
AMA Style
Chakkour M, Salami A, Olleik D, Kamal I, Noureddine FY, Roz AE, Ghssein G. Risk Markers of COVID-19, a Study from South-Lebanon. COVID. 2022; 2(7):867-876. https://doi.org/10.3390/covid2070063
Chicago/Turabian StyleChakkour, Mohamed, Ali Salami, Dana Olleik, Israa Kamal, Fatima Y. Noureddine, Ali El Roz, and Ghassan Ghssein. 2022. "Risk Markers of COVID-19, a Study from South-Lebanon" COVID 2, no. 7: 867-876. https://doi.org/10.3390/covid2070063
APA StyleChakkour, M., Salami, A., Olleik, D., Kamal, I., Noureddine, F. Y., Roz, A. E., & Ghssein, G. (2022). Risk Markers of COVID-19, a Study from South-Lebanon. COVID, 2(7), 867-876. https://doi.org/10.3390/covid2070063
