Comorbidities increase the severity of COVID-19: an analysis and epidemiological study conducted in Algeria

Abstract

The COVID-19 pandemic revealed the importance of pre-existing conditions, or comorbidities, as major predictors of disease severity and mortality worldwide. In this context, the major goal of this study was to examine the impact of common comorbidities on the clinical outcomes of COVID-19 patients in Sétif, Algeria, as well as to evaluate the local epidemiological risk profile. We conducted a thorough analysis of 12,932 COVID-19 participants in the Sétif community, focusing on the prevalence and impact of many illnesses, most notably diabetes, cardiovascular disease (CVD), and hypertension. We also looked at geographical inequalities in comorbidity prevalence to help influence local patient care methods. Our data revealed a strong link between these comorbidities and poor COVID-19 outcomes. Patients with heart disease, hypertension, and diabetes had a considerably greater incidence of severe COVID-19, with computed odds ratios (ORs) of 1.50, 1.34, and 2.89, respectively, indicating that diabetes was the best predictor of severity in this cohort. This investigation demonstrated that the presence of comorbidities considerably exacerbated the severity and mortality rate of SARS-CoV-2 infection. These findings highlight the importance of establishing thorough, personalised techniques and more effective diagnostic and treatment approaches for COVID-19 patients that explicitly account for the varied prevalence of underlying health issues.

1. Introduction

A new coronavirus that targets the lower respiratory tract was discovered in Wuhan, Hubei Province, China, in late December 2019. The related disease, SARS-CoV-2, received the name COVID-19 [1,2]. This virus is a positive-sense, single-stranded RNA with a distinctive spiky outer envelope. This structure allows the virus to infect host cells. Its genome measures around 29.8 kb in length and contains 38% G + C. It often has a pleomorphic structure with discrete spikes measuring 8–12 nm in length [3,4].

On March 11, 2020, the World Health Organisation labelled COVID-19 a global pandemic due to its rapid spread and severity [5,6]. By June 6, 2020, about six million cases had been documented, with an estimated fatality rate ranging from 0.5% to 3.5%. These instances were predominantly concentrated in the United States, Europe, and the Eastern Mediterranean [4,7]. As of April 21, 2021, the global COVID-19 case count had reached 142,238,073, with 3,032,124 deaths [8]. Understanding the elements that influence poor outcomes is critical. Nearly half of the hospitalised COVID-19 patients had comorbidities such as cardiovascular disease, hepatitis, lung illness, kidney disease, or obesity [9,10]. Other characteristics associated with COVID-19 severity and reinfection risk include older age, male sex, and vitamin D deficiency [11].

Diabetes has been identified as a significant risk factor [12–14] for infection and a poorer prognosis. This is due to disruptions of innate immunity and to metabolic disorders [9,15,16]. In fact, the US Centres for Disease Control and Prevention have identified type 1 (T1D) and type 2 (T2D) diabetes as risk factors for COVID-19 [9]. The situation is further aggravated by the fact that diabetes, a global health issue, has increased significantly, with an estimated 463 million individuals affected in 2019, and this figure is expected to reach 700 million by 2045 [6].

According to studies, COVID-19 people with diabetes had a 10% mortality rate in the UAE, a 9.7% prevalence of diabetes in China, and an elevated risk of death among patients with type 2 diabetes [17]. In Algeria, where diabetes is becoming more common, there is insufficient data to reduce the impact of COVID-19 in the diabetic community [18]. This study investigated the important significance of pre-existing health disorders, or comorbidities, in predicting COVID-19 patient outcomes during the pandemic. In particular, we look at how diabetes and cardiovascular disease affect the increased severity and mortality associated with SARS-CoV-2 infection in Algeria. We also examine the epidemiological trends and regional distribution in Sétif, with the goal of informing the clinical management of vulnerable patients and comparing our findings to prior research [3,19].

2. Materials and methods

2.1. Study design, period, and participants

This was a retrospective, observational study that took place from July 2020 to December 2022, during the global COVID-19 pandemic. Data were acquired from health records at eleven healthcare facilities (hospitals and primary care clinics) in Algeria's Sétif Province. As of 2021, this province's population was reported to be around 2 million.

Patients of all ages were enrolled if they tested positive for SARS-CoV-2 using real-time reverse transcription‒polymerase chain reaction. During the study period, patients' records from contributing medical sites contained documented clinical and outcome data. Patients with an inadequate clinical record that lacked critical characteristics (e.g. comorbidity status, end outcome) and verified COVID-19 cases who lived outside of Sétif Province were excluded. A total of 12,932 patients were evaluated. The data were collected from 11 healthcare institutions around the province, including hospitals and primary care clinics. These institutions are strategically located in and near Sétif, including Ain-Oulmene, Ain-Azel, Belair, Elhidab, Ain-Kebira, Bougaa, Eleulma, Ain-Abessa, Beni-Ourtilene, and Beni-Aziz (Figure 1).

Figure 1.

Collection sites of nasopharyngeal swabs and bronchoalveolar lavage samples in Sétif Province, and number of samples per site (the sites are numbered in the map).

Patients were diagnosed with SARS-CoV-2 pneumonia using WHO criteria, including dyspnea, tachypnea, decreased oxygen saturation (6 L/min), and real-time polymerase chain reaction (RT-PCR) tests.

2.2. Real-time polymerase chain reaction

Total RNA was extracted from clinical specimens in viral transport medium using magnetic-bead-based chemistry (MagMAX Viral/Pathogen Nucleic Acid Isolation Kit) according to the manufacturer's instructions. To ensure specimen adequacy and rule out PCR inhibition, the assay included a human housekeeping control (RNase P gene).

The DiaSorin Molecular Simplexa™ COVID-19 Direct Kit used a one-step RT-qPCR experiment with reverse transcription and real-time amplification in a closed tube to reduce contamination. Primer-probe sets were created to amplify conserved sections of the SARS-CoV-2 genome, specifically the N gene, RdRp gene, and S gene, with detection utilising fluorogenic hydrolysis probes (TaqMan chemistry) on the Thermo Fisher StepOnePlus Real-Time PCR System.

Amplification curves and cycle threshold (Ct) values were interpreted using kit-specific criteria. In most commercial tests, a Ct value ≤ 38 is considered positive, while amplification above this threshold is considered negative. Furthermore, samples demonstrating amplification of only one viral target or anomalous late amplification were classified as inconclusive, necessitating repeat testing or re-extraction in accordance with laboratory policy.

2.3. Clinical criteria for severe COVID-19

COVID-19 was defined as severe when patients needed to be hospitalised owing to respiratory distress, such as dyspnea (difficulty breathing), as well as other infection-related complications, such as hypoxemia, pneumonia, or multi-organ involvement. Severe cases also met one or more objective clinical criteria, including oxygen saturation < 93%, respiratory rate > 30 breaths/min, PaO₂/FiO₂ ratio < 300 mmHg, or the need for intensive care unit admission or mechanical ventilation [20].

2.4. Statistical analysis

Microsoft Excel was used to handle the data and perform preliminary analyses. Statistical analyses were carried out with IBM SPSS Statistics (Version 20.0). To investigate the independent effect of comorbidities on COVID-19 results, the study used univariate and multivariate logistic regression analyses. The univariate study first looked at the crude, unadjusted relationship between each predictor (age, sex, diabetes) and the severity of COVID-19. Next, a multivariate logistic regression model was created. This critical step analyses multiple predictors simultaneously to calculate adjusted odds ratios (aOR), isolating the true, independent contributions of comorbidities (such as diabetes, hypertension, and heart disease) to increased illness severity while controlling for confounding factors such as age and gender. This methodology provides strong statistical support for the conclusions presented.

A 2 × 2 contingency table was used to calculate the odds ratio (OR) between comorbidities and severe COVID-19 (Table 1). This is characterised as hospitalisation for serious symptoms. It demonstrates how much more likely severe disease is to occur in patients with comorbidities versus those without. An OR greater than 1 indicates that the comorbidity is significantly linked with worse COVID-19.

Table 1.

The 2 × 2 contingency table.

	Severe COVID-19 (+)	Non-severe COVID-19 (−)
Comorbidity (+)	a	b
Comorbidity (–)	c	d

Note: The odd ration (OR) is calculated according to the formula: OR =  (a × d)/(b × c).

3. Results

Among the 12,932 samples examined using RT-PCR, 5,738 (44.4%) were positive, and 7,194 (55.6%) were negative. Among the 5738 confirmed COVID-19 patients, 2039 (36%) had persistent chronic diseases, such as heart disease (31%), hypertension (29%), and diabetes (24%).

The year 2021 came out as the most severe, accounting for more than half of all illnesses diagnosed over this time frame, indicating increased viral transmission, most likely caused by new variations. In contrast, in 2022, only 13% of cases were recorded, indicating that public health interventions and vaccine campaigns were effective in reducing the epidemic. The monthly investigations observed clear seasonal patterns, with peaks occurring more frequently throughout the summer months. Contrary to expectations, hot temperatures showed to facilitate viral transmission. Despite a general declining tendency in 2022, sporadic resurgences were seen (Figure 2).

Figure 2.

(A) Number of positive cases identified during the 3-year COVID-19 pandemic in the CLCC laboratory. (Setif). (B) Epidemiological trend: exploring the monthly number of SARS-CoV-2-positive cases over 27 months (July 2020–December 2022).

3.1. Clinical presentation

The most prevalent symptom during hospital admission was fever (75.6%), followed by headache (47.02%), dyspnea (50.94%) and fatigue (70.32%). Cough is a common symptom that affects 71% of patients. Dyspnea (65%) and tiredness (70%) are also prominent symptoms.

Headaches and muscular pains, reported by 55% and 40% of patients, respectively, broaden the range of symptoms. Less common symptoms, such as diarrhoea, stomach pain, nausea, and vomiting, are also reported, albeit in a smaller proportion of people (about 10%). Gastrointestinal problems, including diarrhoea and stomach pain, were also reported (35.10%, 23.68%, and 13.89%, respectively). This highlights the varied clinical picture of COVID-19, in which individuals may have a variety of symptoms rather than a consistent appearance.

3.2. Geographic distribution

The geographical distribution of COVID-19 cases admitted to various care centres revealed that the district of Ain-Oulmene was the epicenter (32.6%) of the epidemic, followed by Ain-Azel and Belair at 16.7% and 16.5%, respectively (Figure 1).

From November 14th to 19th, 2020, there was a significant geographical disparity. While Ain-Oulmene has the most verified cases (1872), Beni-Aziz has only 14, demonstrating the pandemic's asymmetrical spatial distribution. El Eulma stands out not only for its 490 cases but also for its worrisome 32.6% positive rate, the highest of any region. This, together with Ain-Kbira (22.5%) and Bougaa (21.9%), raises worry about potential transmission hotspots.

These observed variations may be influenced by many contextual factors. In certain districts, local socioeconomic activities and population density may accelerate the spread of viruses. Access to healthcare also varies; in many places, there are no hospitals and just primary health centres with inadequate resources, which may hinder case diagnosis and postpone necessary therapy.

3.3. Severe COVID-19 and comorbidity

A total of 1185 patients with severe disease and comorbidities were hospitalised (Figure 3). COVID-19 was associated with heart disease, the most common ailment (31.05%), followed by hypertension (29.96%) and diabetes (24.05%). Comorbidities were seen in 34.3% of all patients and 35.5% of confirmed cases. Thyroid disease (5.74%), bronchial asthma (4.3%), chronic renal failure (2.7%), and chronic obstructive pulmonary disease (COPD) accounted for a smaller proportion. Cardiovascular comorbidities accounted for the majority of patients, while metabolic, pulmonary, renal, and endocrine diseases were less commonly recorded.

Figure 3.

Percentages of comorbidities associated with COVID-19 in hospitalised patients. COPD: chronic obstructive pulmonary disease.

The overall case fatality rate in the cohort was 1.6% (209/12,932), while the mortality rate among COVID-19–positive patients was about 2% (115/5738). Regarding vaccination status, only 2% of the total cohort (244/12,932) of confirmed COVID-19 patients (113 out of 5738) had received vaccination, indicating a very low vaccination coverage in this study population. Owing to the limited number of vaccinated individuals, it was not possible to meaningfully assess the impact of vaccination on disease severity or variant distribution.

Analysis of comorbidities revealed that patients with heart disease were significantly more likely to develop severe COVID-19, with an adjusted odds ratio (aOR). This result indicates that, even after accounting for confounding factors such as age and sex, heart disease independently increased the risk of severe outcomes by 1.50 (95% CI: 1.29–1.73). Similarly, hypertension was associated with increased severity (OR = 1.34, 95% CI: 1.17–1.54). Diabetes showed the strongest association, nearly tripling the odds of severe disease compared with patients without diabetes (OR = 2.89, 95% CI: 2.41–3.46). These findings indicate that all three comorbidities independently contribute to a higher risk of severe COVID-19, with diabetes emerging as the most prominent risk factor (Table 2).

Table 2.

Influence of major comorbidities on COVID-19 severity.

Comorbidity	Sever_with (a)	Sever_without (c)	Non sever_with (b)	Non sever_without (d)	Odd ratio
Heart disease	381	1406	562	3102	1.49
Hypertension	439	985	965	2907	1.34
Diabetes	317	843	268	2059	2.89

4. Discussion

This comprehensive study showed the importance of comorbidities in SARS-CoV-2 outcomes in Setif. Surprisingly, these findings reveal the prevalence of disease vulnerabilities associated with specific prior conditions.

According to Navand et al. (2020) [6], COVID-19's most common clinical symptoms were fever, dry cough, and shortness of breath, with 75% of verified patients having fever and 71% having cough. Our findings were consistent with this article in terms of detecting probable consequences such as acute respiratory distress syndrome (ARDS), acute heart damage, secondary infections, and coagulation abnormalities. Most importantly, symptom prevalence varies by community and across time. Individuals' COVID-19 symptoms can be modified by age, underlying health issues, and the virus strain in circulation. As a result, drawing a diagnosis based solely on the presence of symptoms is not suggested.

We observed that heart disease, hypertension, and diabetes were all independently linked with an increased risk of severe COVID-19, with odds ratios of 1.50, 1.34, and 2.89, respectively. These findings demonstrate that underlying cardiovascular and metabolic disorders are powerful predictors of poor outcomes, with diabetes appearing to be the most significant risk factor. Several meta-analyses have found similar relationships, with cardiovascular disease, hypertension, and diabetes significantly increasing COVID-19 severity and mortality rates. For example, Li et al. (2020) [21] revealed pooled odds ratios of 3.21 for coronary heart disease, 2.27 for hypertension, and 2.34 for diabetes, whereas Singh et al. (2020) [22] found that diabetes and hypertension were consistently associated with poor outcomes, such as intensive care unit admission and mortality.

A number of factors could explain the relatively high odds ratio for diabetes in our population. Chronic hyperglycaemia weakens both innate and adaptive immunity by reducing neutrophil chemotaxis, macrophage activity, and T-cell activation [23]. Diabetes also promotes a pro-inflammatory state by increasing cytokine release, which predisposes patients to an excessive inflammatory response known as a ‘cytokine storm’ [24]. Furthermore, the activation of ACE2 receptors and proteases such as furin in diabetic tissues may promote SARS-CoV-2 binding and entry, resulting in larger viral loads and longer viral shedding [25,26]. Diabetic microvascular disease and endothelial dysfunction, both characteristics of long-term diabetes, can increase COVID-19-related coagulation abnormalities, exacerbating pulmonary and systemic consequences [27]. These interconnected circuits offer a compelling scientific explanation for the roughly threefold increase in severity reported in diabetes patients.

Diabetes incidence in Algeria is extremely high. It rose from 6.8% in 1990 to 12.29% in 2005, ranking second among chronic diseases among those aged 35–70 years [18]. The prevalence of diabetes reached 7.4% in 2021, according to the National IDF (International Diabetes Federation, 2023). A 2001 study in Setif found an 8.2% prevalence of type 2 diabetes, with an age-related increase but no statistically significant differences according to sex or urban‒rural distribution in eastern Algeria. This percentage is slightly lower than Algeria's national average of 9.7% [18]. Identifying these associations, such as the potential interplay of SARS-CoV-2, ACE2, and DPP4 in viral infection and glycaemic control, is crucial for developing viable treatment options, particularly in diabetes-prone countries such as Algeria.

Hypertension and pre-existing cardiovascular disease appeared as significant factors for severe COVID-19, albeit with a lesser influence than diabetes. Hypertension was the most common comorbidity among critically sick patients, accounting for up to 29.96% of those referred to intensive care, and it is still one of the most common chronic illnesses worldwide [15]. Dysregulation of the renin‒angiotensin system (RAAS) is one possible cause. SARS-CoV-2 binds to host cells via the ACE2 receptor, therefore viral entry may impair ACE2's protective effects, changing the RAAS balance toward excess angiotensin II activity, inflammation, and fibrosis [28]. This disturbance may also contribute to organ harm and failure in severe circumstances. Hypertension is also related with immunological changes, such as aberrant T-cell activation, which can exacerbate the inflammatory response to infection [29]. In patients with established cardiovascular disease, viral myocardial invasion, systemic inflammation, and hypoxia can all lead to abrupt cardiac damage [30]. More difficult is the widespread use of ACE inhibitors and angiotensin receptor blockers (ARBs) in hypertensive and cardiac patients. These drugs can affect ACE2 expression, suggesting the possibility that they could increase susceptibility to SARS-CoV-2 infection by promoting viral entry [31].

Recent statistics from the Setif ‘acute myocardial infarction’ (AMI) registry indicate the regional burden of cardiovascular disease. Throughout 2015 and 2018, 40.2% of 6886 patients hospitalised to the University Hospital of Sétif with cardiovascular illness had an acute myocardial infarction. This pathology is the most prevalent comorbidity among COVID-19 patients. Although no heart attacks have been observed during earlier coronavirus epidemics (SARS-CoV-1, MERS-CoV), certain studies have found that SARS-CoV-2 infection is likely to produce myocarditis in those infected [32]. Furthermore, a significant proportion of COVID-19 patients develop heart damage [33]. Despite the growing frequency of COVID-19-related cardiovascular disorders, the number of studies on this topic is small when compared to diabetes and hypertension research [34].

It is critical to recognise the study's limitations, which stem mostly from its retrospective design based on medical records. First, the data, which were obtained from 11 different medical sites, may have been influenced by reporting bias or insufficient documentation, potentially influencing the accuracy of the comorbidity diagnoses and severity ratings. Second, the study included only patients from Sétif Province; therefore, the findings may not be fully applicable to the entire Algerian population or other worldwide contexts due to regional differences in demography and healthcare access. Although we used multivariate analysis to control for key confounders such as age and gender, the available dataset may not have included all potential confounding variables, such as lifestyle factors, treatment protocols, or specific immunological markers, that could impact the relationship between comorbidities and COVID-19 severity.

5. Conclusions

This study provides clear evidence that comorbidities, specifically diabetes, hypertension, and cardiovascular disease (CVD), significantly and independently enhance the risk of catastrophic clinical outcomes and mortality among COVID-19 patients in Algeria. The key finding is that diabetes has the greatest individual impact, with the highest adjusted odds ratio for severe COVID-19. These data highlight that the projected worsening of outcomes is most likely caused by biological mechanisms such as immunological dysfunction and endothelial damage, which are inherent in these chronic illnesses. Given Algeria's high prevalence of chronic conditions, managing the COVID-19 pandemic and future infectious outbreaks requires a dual strategy. The implications for clinical care include the need for targeted prevention, optimal chronic illness management, and the formation of multidisciplinary care teams to boost vulnerable patients' resilience.

Funding Statement

This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU-DDRSP2601).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data sets during and/or analysed during the current study are available from the corresponding author upon reasonable request.

Institutional review board statement

The experimental protocol was approved by the ethics committee of our university (N°Ethi/CHU/26-MAR-2024).

Informed consent statement

Informed consent was obtained from all participants prior to enrolment in the study in accordance with institutional ethical guidelines.

Ethical approval

Our research on human samples for SARS-CoV-2 PCR and epidemiological studies agreed with all applicable legal and ethical standards, including providing informed consent, ensuring data confidentiality, and adhering to biosafety protocols. This study received approval from the relevant ethical review board (CHU-2603/2024).

Author contributions

Conceptualisation: M.A., A.K., A.M. and K.H.; methodology: M.A., M.A. and K.H.; writing – original draft: M.A.T.I.; project administration: A.K. and K.H.; writing – review & editing: M.A., F.B., A.D. and K.H.; Visualisation: A.M. and H.B.; supervision: N.B.H., A.L. and K.H.; funding acquisition: N.B.H., F.B. and H.B.; Software: A.D.; R.A.M.A. All authors have read and agreed to the published version of the manuscript.