Clinical Characteristics and Prognostic Factors of COVID-19 Patients Admitted to the National Treatment Center in The Gambia
Abstract
Background and Aims: Information regarding the clinical features and outcomes of severely ill COVID-19 patients in low-resource settings is limited. The objective of this study was to evaluate the clinical characteristics, comorbidities, and prognostic factors associated with mortality and COVID-19 severity among COVID-19 patients admitted to the national COVID-19 treatment center in The Gambia.
Methods: We conducted a retrospective cross-sectional study on 930 confirmed COVID-19 patients admitted to The Gambia’s national COVID-19 treatment center from March 17, 2020, to March 13, 2023. We assessed the association between patients’ characteristics using the t-test and the chi-square test. Prognostic factors of mortality and COVID-19 severity were investigated using a multivariable logistic regression model. Statistical analyses were performed using the STATA/SE 12.1 statistics/data analysis.
Results: Patients with COVID-19 who had a saturation of peripheral oxygen (SPO2) level below 90 (severe COVID-19) faced a fivefold higher risk of death (adjusted odds ratio [aOR] = 5.15; 95% confidence interval [CI] = 1.08, 24.58; p = 0.040). Additionally, individuals aged 60 years and older had an approximately threefold higher risk of experiencing severe COVID-19 (aOR = 3.43; 95% CI = 2.30, 5.13; p < 0.001), while those with comorbidities showed a 60% higher risk of severe disease (aOR = 1.60; 95% CI = 1.07, 2.38; p = 0.021).
Conclusion: These findings underscore the importance of early detection and management strategies targeting older patients and those with underlying health conditions to mitigate the impact of COVID-19 and improve patient outcomes. Future efforts should prioritize tailored interventions and supportive care for these high-risk populations.
1. Introduction
On December 31, 2019, the discovery of a novel coronavirus (SARS-CoV-2)-infected pneumonia (COVID-19) in Wuhan, China, was reported to the World Health Organization (WHO). On January 30, 2020, the WHO declared COVID-19 as a public health emergency of global concern, and Africa was the last continent to confirm the virus. The continent confirmed its first case in Egypt on February 14, 2020, and from sub-Saharan Africa (SSA), the first case was reported in Nigeria on February 27 [1, 2]. The first case of COVID-19 was diagnosed in The Gambia on March 17, 2020, and as of March 13, 2023, 12,631 confirmed COVID-19 cases with 372 deaths were recorded.
The clinical presentation of COVID-19 ranges from mild infection to severe disease to fatal illness [3]. The most commonly reported symptoms were cough, fever, and dyspnea [3–6]. Critically ill patients were older, more likely to be male, and have underlying comorbidities. The mortality rate ranges from 8.7% to 21% among those patients admitted with pneumonia [3–6]. In a larger report, 49% of all 2087 critically ill patients with COVID-19 in China died [7].
In Western countries, the response to the COVID-19 pandemic has been to increase hospital capacity and to provide more intensive care units (ICUs) and more ventilators [3–6]. There had been little discussion of the provision of oxygen as this is a standard clinical tool widely available in hospitals [3]. However, in SSA, there is a shortage of oxygen in health facilities, and thus, two things should be at the top of the list before ICUs and ventilators [3]. These are personal protective equipment (PPE) for frontline health workers and oxygen for the patients [8]. In The Gambia, there is only one hospital that has a dedicated ICU with eight ICU beds, making an estimated 0.4 ICU beds/100,000 population in the country. This very low ICU bed capacity and lack of human resources and equipment necessary to diagnose and treat the large number of critically ill patients admitted to public hospitals in the country [9, 10] may hinder the countries response to COVID-19.
In The Gambia, Abatan et al. [11] examined transmission rates among 1366 employees of the Medical Research Council in The Gambia (MRCG), where systematic surveillance of symptomatic cases and contact tracing were implemented. Additionally, Lowe et al. [12] conducted a brief evaluation of the measures and challenges facing The Gambia’s COVID-19 response, offering policy and practical recommendations for policymakers and implementers. A qualitative study [13] was also carried out to assess the acceptability of a clinical trial for COVID-19 prevention and treatment in The Gambia, aiming to identify strategies to enhance community engagement in such trials. Various studies [6, 14–16] have described the clinical and epidemiological characteristics, risk factors, case management, and outcomes of COVID-19 in patient cohorts globally. Other research [17, 18] has reviewed the clinical features, diagnosis, and treatment of COVID-19 in different regions. However, limited information is available regarding the clinical characteristics of COVID-19 patients in Africa [19]. In this study, we assessed the clinical features, comorbidities, and prognosis of COVID-19 patients admitted at the national treatment center in The Gambia. The morbidity and mortality data from this study will be of considerable value for the identification of individuals who are at risk of becoming critically ill or dying and who are most likely to benefit from early screening and possible treatment. The overall goal of this study is to identify prognostic factors for poor outcome among COVID-19 patients admitted into the national treatment center in The Gambia.
2. Methods
2.1. Study Setting
The study was conducted at the national COVID-19 treatment center located within the Edward Francis Small Teaching Hospital (EFSTH) in Banjul, which has a total bed capacity of 600. As a leading teaching hospital and a key referral center in the country, it serves patients from all regions of The Gambia and acts as the primary isolation facility for COVID-19 cases. The hospital provides clinical services across various specialties, while also conducting research and offering training for medical students, house officers, medical officers, and residents. Additionally, it houses both an accident and emergency unit, as well as an ICU.
2.2. Study Design
A retrospective cross-sectional study was conducted to assess the clinical features, comorbidities, and prognosis of 930 confirmed COVID-19 patients admitted at the national COVID-19 treatment center, The Gambia, from March 17, 2020, to March 13, 2023.
2.3. Study Population
The study population includes all confirmed COVID-19 patients admitted into the national COVID-19 treatment center.
2.3.1. Confirmed COVID-19
A person with laboratory confirmation of COVID-19 infection irrespective of clinical signs and symptoms. COVID-19 infection was determined using PCR machines [11, 20].
2.4. Data Collection Method
Samples were obtained using nasopharyngeal swabs, oropharyngeal swabs, or both, with FLOQSwabs (COPAN Diagnostics, https://www.copanusa.com) [11]. Each sample was placed in individual tubes containing universal transport medium (COPAN Diagnostics) and transported to the laboratory within 24 h [11]. The sampling techniques were consistent across all cohorts, following similar operational procedures and training. All patients’ demographic data (age and gender); comorbidities such as hypertension, diabetes, pulmonary tuberculosis (PTB), congestive cardiac failure (CCF), chronic renal failure (CRF), lactate dehydrogenase elevating virus (LDV), cerebrovascular accident (stroke) (CVA), and sickle cell disease (SCD); symptoms (cough, fever, dyspnea, chest pain, abdominal pain, vomiting, and sore throat); vital signs (pulse rate, respiratory rate, temperature, and saturation of peripheral oxygen [SPO2]); and clinical outcome (death) were collected from electronic files at the end of the study. Disease severity as per the WHO definition was determined at the time of diagnosis. The investigations done and treatments received by patients were also recorded.
2.4.1. Laboratory Methods for COVID-19 Confirmation
The MRCG laboratories partnered with national public health laboratories to support countrywide testing during the epidemic. Both MRCG and the national laboratories employed identical laboratory methods and assays. Anticipating the spread of the outbreak to the West African subregion, MRCG staff participated in a regional training workshop on COVID-19 diagnostics, organized by the Africa Centres for Disease Control and Prevention (https://africacdc.org) in February 2020 in Dakar, Senegal. Following this, The Gambia developed laboratory protocols for processing and testing suspected SARS-CoV-2 samples in line with the WHO guidelines. These procedures and assays were then implemented in local laboratories [11]. The standard diagnostic test for COVID-19 in The Gambia is real-time reverse transcription PCR (RT-PCR), targeting specific viral gene sequences of SARS-CoV-2.
2.4.2. Inclusion Criteria
All patients with confirmed COVID-19 patients admitted into the national COVID-19 treatment center, The Gambia.
2.4.3. Exclusion Criteria
Any suspected COVID-19 patient with negative results, incomplete data, or unknown COVID-19 results were excluded from the study.
2.5. Variables
The outcome variables in this study are death (coded as 1 if the patient died and 0 otherwise) and COVID-19 severity (coded as 1 if SPO2 < 90 and 0 if SPO2 ≥ 90). The predictors examined include hypertension, diabetes, PTB, CCF, CRF, liver disease (LDV), CVA, SCD, cough, fever, dyspnea, chest pain, abdominal pain, vomiting, sore throat, pulse rate, respiratory rate, temperature, and SPO2 levels. Age and gender were considered as potential confounders.
2.6. Bias
There is a risk of obtaining unrepresentative samples due to varying levels of interest in COVID-19 testing, which could result in sampling bias.
2.7. Ethical Consideration
Ethics approval for this study was granted by the Institutional Review Board of EFSTH.
2.8. Statistical Analysis
The Student’s t-test was used for continuous normally distributed variables, and chi-square test was used for discrete variables. The chi-square and t-tests were used to assess whether there are statistically significant differences in the distribution of outcome variables and their predictors. We determined prognostic factors for death and severity (SPO2 < 90) among COVID-19 patients using a multivariable logistics regression model [21–26]. Statistical significance was defined as p < 0.05. Statistical analysis was performed using the STATA/SE 12.1 statistics/data analysis.
3. Results
3.1. Results From Descriptive Statistics, Chi-Square, and t-Test
Descriptive statistics, chi-square, and t-test results are shown in Tables 1–3. Table 1 presents the descriptive statistics of the demographic characteristics, comorbidities, symptoms, vital signs of the COVID-19 patients by gender (female vs. male). These statistics showed that the mean age of COVID-19 patients considered was 48.6 years (47.3–49.9) and male patients are older (50.51 vs. 45.76 years, p < 0.001). The majority of females experienced abdominal pain (18.77 vs. 13.8, p = 0.044) and vomiting (7.51 vs. 4.32, p = 0.038).
Variable | Total | Gender | p-Value | |
---|---|---|---|---|
Female | Male | |||
Demographic characteristics | ||||
Age | 48.62∓19.46 | 45.76∓19.71 | 50.51∓19.07 | <0.001 ∗∗ |
Age ≥ 60 | 306 (33.89%) | 109 (30.36%) | 197 (36.21%) | 0.069 |
Comorbidities | 370 (39.87%) | 150 (40.21%) | 220 (39.64%) | 0.861 |
HTN | 264 (28.48%) | 112 (30.03%) | 152 (27.44%) | 0.392 |
DM | 165 (17.78%) | 70 (18.77%) | 95 (17.12%) | 0.519 |
PTB | 43 (4.63%) | 13 (3.49%) | 30 (5.41%) | 0.172 |
CCF | 14 (1.54%) | 4 (1.09%) | 10 (1.83%) | 0.372 |
CRF | 31 (3.34%) | 13 (3.49%) | 18 (3.24%) | 0.841 |
LDV | 19 (2.05%) | 10 (2.68%) | 9 (1.62%) | 0.264 |
CVA | 23 (2.48%) | 7 (1.88%) | 16 (2.89%) | 0.332 |
SCD | 9 (0.97%) | 6 (1.61%) | 3 (0.54%) | 0.104 |
Symptoms | ||||
Cough | 459 (49.41%) | 197 (52.82%) | 262 (47.12%) | 0.089 |
Fever | 607 (65.34%) | 245 (65.68%) | 362 (65.11%) | 0.857 |
Dyspnea | 304 (32.79%) | 122 (32.80%) | 182 (32.79%) | 0.999 |
Chest pain | 227 (24.43%) | 102 (27.35%) | 125 (22.48%) | 0.091 |
Abdominal pain | 147 (15.82%) | 70 (18.77%) | 77 (13.85%) | 0.044 |
Vomiting | 52 (5.60%) | 28 (7.51%) | 24 (4.32%) | 0.038 |
Sore throat | 13 (1.40%) | 8 (2.14%) | 5 (0.90%) | 0.113 |
Vital signs | ||||
PR ≥ 100 | 75 (37.69%) | 22 (31.88%) | 53 (40.77%) | 0.218 |
RR ≥ 30 | 12 (6.03%) | 3 (4.35%) | 9 (6.92%) | 0.468 |
SPO2 < 90 | 136 (16.13%) | 47 (13.95%) | 89 (17.59%) | 0.159 |
PR | 92.67∓17.02 | 92.44∓16.70 | 92.82∓17.250 | 0.374 |
RR | 23.10∓8.78 | 23.50∓9.20 | 22.85∓8.51 | 0.781 |
SBP | 130.03∓23.77 | 128.44∓23.16 | 131.09∓24.13 | 0.057 |
DBP | 82.78∓14.85 | 81.88∓12.99 | 83.39∓15.96 | 0.074 |
Temperature | 36.40∓0.10 | 36.21∓0.84 | 36.53∓3.40 | 0.056 |
- Abbreviations: CCF, congestive cardiac failure; CRF, chronic renal failure; CVA, cerebrovascular accident; DM, diabetes; HTN, hypertension; LDV, lactate dehydrogenase elevating virus; PR, pulse rate; PTB, pulmonary tuberculosis; RR, respiratory rate; SCD, sickle cell disease; SPO2, oxygen saturation.
- ∗∗Statistical significance, p-value < 0.05.
Variable | Total | Death | p-Value | |
---|---|---|---|---|
Alive | Death | |||
Demographic characteristics | ||||
Age | 48.60∓19.46 | 46.27∓18.96 | 62.34 ∓ 16.54 | <0.001 |
Age ≥ 60 | 306 (33.85%) | 217 (28.11%) | 89 (67.42%) | <0.001 |
Male | 556 (59.85%) | 465 (58.56%) | 91 (67.41%) | 0.053 |
Comorbidities | 370 (39.83%) | 277 (34.89%) | 93 (68.89%) | <0.001 |
HTN | 264 (28.45%) | 201 (25.35%) | 63 (46.67%) | <0.001 |
DM | 165 (17.76%) | 114 (14.36%) | 51 (37.78%) | <0.001 |
PTB | 43 (4.63%) | 37 (4.66%) | 6 (4.44%) | 0.912 |
CCF | 14 (1.54%) | 6 (0.77%) | 8 (5.93%) | <0.001 |
CRF | 31 (3.34%) | 19 (2.39%) | 12 (8.89%) | <0.001 |
LVD | 19 (2.05%) | 17 (2.14%) | 2 (1.48%) | 0.167 |
CVA | 23 (2.48%) | 13 (1.64%) | 10 (7.41%) | <0.001 |
SCD | 9 (0.97%) | 6 (0.76%) | 3 (2.22%) | 0.108 |
Symptoms | ||||
Cough | 459 (49.35%) | 366 (46.045) | 93 (68.89%) | <0.001 |
Fever | 603 (65.27%) | 488 (61.38%) | 119 (88.155) | <0.001 |
Dyspnea | 304 (32.76%) | 228 (28.75%) | 76 (56.30%) | <0.001 |
Abdominal pain | 147 (15.81%) | 131 (16.48%) | 16 (11.85%) | 0.173 |
Chest pain | 227 (24.41%) | 185 (23.27%) | 42 (31.11%) | 0.050 |
Vomiting | 52 (5.59%) | 44 (5.53%) | 8 (5.93%) | 0.855 |
Sore throat | 13 (1.40%) | 10 (1.26%) | 3 (2.22%) | 0.378 |
Vital signs | ||||
RR ≥ 30 | 12 (6.03%) | 7 (3.76) | 5 (38.46%) | <0.001 |
PR ≥ 100 | 75 (37.69%) | 66 (35.48) | 9 (69.23%) | 0.015 |
SPO2 < 90 | 136 (16.13%) | 73 (10.28) | 63 (47.37%) | <0.001 |
PR | 92.67∓17.02 | 91.17∓16.36 | 100.68∓18.25 | <0.001 |
RR | 23.10∓8.78 | 21.94∓7.01 | 28.54∓13.18 | <0.001 |
Treatment | ||||
Antibiotics | 611 (65.70%) | 480 (60.38%) | 131 (97.04%) | <0.001 |
Heparin | 357 (38.39%) | 253 (31.82%) | 104 (77.04%) | <0.001 |
Steroids | 362 (38.92%) | 250 (31.45%) | 112 (82.96%) | <0.001 |
Oxygen | 218 (23.44%) | 118 (14.84%) | 100 (74.7%) | <0.001 |
- Abbreviations: CCF, congestive cardiac failure; CRF, chronic renal failure; CVA, cerebrovascular accident; DM, diabetes; HTN, hypertension; LDV, lactate dehydrogenase elevating virus; PR, pulse rate; PTB, pulmonary tuberculosis; RR, respiratory rate; SCD, sickle cell disease; SPO2, oxygen saturation.
Variable | Total | SPO2 | p-Value | |
---|---|---|---|---|
SPO2 ≥ 90 | SPO2 < 90 | |||
Demographic characteristics | ||||
Age | 49.83 ∓ 19.35 | 47.58∓19.17 | 61.26 ∓ 15.99 | <0.001 |
Age ≥ 60 | 299 (36.46%) | 212 (30.99%) | 87 (63.97%) | <0.001 |
Male | 506 (60.02%) | 417 (58.98%) | 89 (65.44%) | 0.159 |
Comorbidities | 361 (42.87%) | 280 (39.66%) | 81 (59.56%) | <0.001 |
HTN | 259 (30.80%) | 200 (28.37%) | 59 (43.38%) | <0.001 |
DM | 161 (19.12%) | 117 (16.57%) | 44 (32.35%) | <0.001 |
PTB | 41 (4.87%) | 36 (5.10%) | 5 (3.68%) | 0.480 |
CCF | 14 (1.69%) | 9 (1.30%) | 5 (3.68%) | 0.050 |
CRF | 31 (3.68%) | 25 (3.54%) | 6 (4.41%) | 0.621 |
LVD | 19 (2.26%) | 19 (2.69%) | 0 (0.00%) | 0.053 |
CVA | 23 (2.73%) | 17 (2.41%) | 6 (4.41%) | 0.190 |
SCD | 9 (1.07%) | 8 (1.13%) | 1 (1.07%) | 0.679 |
Symptoms | ||||
Cough | 444 (52.67%) | 350 (49.50%) | 94 (69.12%) | <0.001 |
Fever | 581 (68.92%) | 464 (65.63%) | 117 (86.03%) | <0.001 |
Dyspnea | 299 (35.55%) | 205 (29.08%) | 94 (69.12%) | <0.001 |
Abdominal pain | 146 (17.32%) | 119 (16.83%) | 27 (19.85%) | 0.394 |
Chest pain | 224 (26.57%) | 175 (24.75%) | 49 (36.03%) | 0.006 |
Vomiting | 50 (5.93%) | 48 (6.79%) | 2 (1.47%) | 0.016 |
Sore throat | 12 (1.42%) | 10 (1.41%) | 2 (1.47%) | 0960 |
Vital signs | ||||
PR ≥ 100 | 75 (37.88) | 60 (34.48%) | 15 (62.50%) | 0.008 |
RR ≥ 30 | 12 (6.06) | 3 (1.72%) | 9 (37.50%) | <0.001 |
RR | 23.15∓8.81 | 22.02∓7.63 | 27.84∓11.467 | <0.001 |
PR | 92.74∓17.06 | 91.79∓16.63 | 97.67∓18.43 | <0.001 |
Temperature | 36.40∓2.70 | 36.37∓2.91 | 36.58∓0.941 | 0.216 |
Treatment | ||||
Antibiotics | 596 (70.70%) | 470 (66.48%) | 126 (92.65%) | <0.001 |
Heparin | 355 (42.11%) | 248 (35.08%) | 107 (78.68%) | <0.001 |
Steroids | 357 (42.35%) | 246 (34.79%) | 111 (81.62%) | <0.001 |
- Abbreviations: CCF, congestive cardiac failure; CRF, chronic renal failure; CVA, cerebrovascular accident; DM, diabetes; HTN, hypertension; LDV, lactate dehydrogenase elevating virus; PR, pulse rate; PTB, pulmonary tuberculosis; RR, respiratory rate; SCD, sickle cell disease; SPO2, oxygen saturation.
The results from Table 2 show that the mean age of those who died was significantly higher (62.34 vs. 46.27, p < 0.001), with mortality being particularly elevated among individuals aged 60 and above (67.42% vs. 28.11%). Mortality rates were also higher among patients with comorbidities, except for those with PTB, liver disease (LVD), and cerebrovascular disease (CSD). Among clinical symptoms, patients who experienced cough, fever, and dyspnea had higher mortality rates, and abnormal vital signs were linked to increased mortality. The analysis further indicated that the use of antibiotics, heparin, steroids, and oxygen was significantly associated with mortality.
Table 3 shows that the mean age of individuals with severe COVID-19 (SPO2 < 90) was significantly higher (61.26 vs. 47.58, p < 0.001), and a larger proportion of those aged 60 and above were associated with severe disease (63.97% vs. 30.99%, p < 0.001). The majority of individuals with SPO2 < 90 also had comorbidities, particularly hypertension and diabetes. Clinical symptoms such as cough, fever, dyspnea, and chest pain were significantly linked to COVID-19 severity. Additionally, vital signs like pulse rate and respiratory rate were associated with disease severity. Most COVID-19 patients with SPO2 < 90 were treated with antibiotics, heparin, steroids, and oxygen.
3.2. Results From Multivariable Logistic Regression Model
The results from multivariable logistic regression models for death and SPO2 are shown in Tables 4 and 5. The findings from Table 4 revealed notable risk factors for mortality among individuals with COVID-19. Specifically, individuals aged 60 years and older showed an approximately fourfold increased risk of death (adjusted odds ratio [aOR] = 3.56; 95% confidence interval [CI] = 0.86, 14.72; p = 0.272). Females exhibited a threefold increased risk of death (aOR = 2.70; 95% CI = 0.46, 15.92; p = 0.272). Moreover, COVID-19 patients with a pulse rate of 100 and above had a twofold increased risk of death (aOR = 2.39; 95% CI = 0.55, 10.50; p = 0.248), while those with a respiratory rate of 30 and above faced a sixfold increased risk (aOR = 5.59; 95% CI = 0.89, 35.09; p = 0.067). Additionally, COVID-19 patients with an SPO2 level less than 90 had a fivefold increased risk of death (aOR = 5.15; 95% CI = 1.08, 24.58; p = 0.040). These results highlight significant associations between these factors and mortality risk in COVID-19 patients.
Variable | unaOR (95% CI) | p-Value | aOR (95%CI) | p-Value |
---|---|---|---|---|
Gender | 1.46 (0.99, 2.15) | 0.054 | 2.70 (0.46, 15.92) | 0.272 |
Age >60 years | 5.29 (3.56, 7.87) | <0.001 ∗∗ | 3.56 (0.86, 14.72) | 0.079 |
Comorbidities | 4.13 (2.79, 6.12) | <0.001 ∗∗ | 1.00 (0.24, 4.13) | 0.999 |
Pulse rate ≥ 100 | 4.09 (1.21, 13.79) | 0.023 ∗∗ | 2.39 (0.55, 10.50) | 0.248 |
Respiratory rate ≥ 30 | 15.98 (4.149, 61.56) | <0.001 ∗∗ | 5.59 (0.89, 35.09) | 0.067 |
SPO2 < 90 ∗ | 7.85 (5.17, 11.93) | <0.001 ∗∗ | 5.15 (1.08, 24.58) | 0.040 |
- ∗SPO2: oxygen saturation.
- ∗∗Significance (p-value < 0.05).
Variable | unaOR (95% CI) | p-Value | aOR (95% CI) | p-Value |
---|---|---|---|---|
Gender | 1.32 (0.90, 1.93) | 0.160 | 1.22 (0.82, 1.81) | 0.329 |
Age >60 years | 3.95 (2.69, 5.81) | <0.001 ∗∗ | 3.43 (2.30, 5.13) | <0.001 ∗∗ |
Comorbidities ∗ | 2.24 (1.54, 3.26) | <0.001 ∗∗ | 1.60 (1.07, 2.38) | 0.021 ∗∗ |
- ∗SPO2: oxygen saturation.
- ∗∗Significance (p-value < 0.05).
The analysis from a multivariable logistic regression model (as presented in Table 5) regarding COVID-19 severity revealed significant findings. Individuals aged 60 years and older exhibited an approximately threefold increased risk of COVID-19 severity (aOR = 3.43; 95% CI = 2.30, 5.13; p < 0.001). Females showed a 22% increased risk of COVID-19 severity, although this finding was not statistically significant (aOR = 1.22; 95% CI = 0.82, 1.81; p = 0.329). Notably, COVID-19 patients with comorbidities had a 60% increased risk of COVID-19 severity (aOR = 1.60; 95% CI = 1.07, 2.38; p = 0.021). These findings highlight the link between age, comorbidities, and COVID-19 severity.
4. Discussion
The study underscores the vital role of SPO2 levels in predicting mortality risk in COVID-19 patients. This study revealed that patients with SPO2 levels below 90% faced a fivefold higher risk of death, which is consistent with previous research showing the significant prognostic value of SPO2 in assessing the severity and potential mortality of COVID-19 [27]. For instance, Fernando Mejía and colleagues [28], in their study of hospitalized adult COVID-19 patients at a public hospital in Lima, Peru, discovered that oxygen saturation levels below 90% at the time of admission were linked to a higher risk of mortality. Similarly, an investigation by Junior Carbajal et al. [29] into the risk factors for in-hospital mortality among patients with COVID-19 and type 2 diabetes mellitus (T2DM) identified that a partial pressure of oxygen below 60 mmHg was an independent risk factor for in-hospital mortality. Furthermore, researchers [30] developed an early predictive model using baseline clinical factors and found that a SPO2 below 94% was an independent predictor of mortality in COVID-19 patients. Jiang Xie et al. [31] research on in-hospital mortality also reported that higher SPO2 levels following oxygen supplementation were associated with a reduced risk of death, independent of age and sex. Additional studies by Takahisa Mikami et al. [32] in New York City, Peter M. Mphekgwana et al. [33] in South Africa, and a global systematic review and meta-analysis of 76 studies by Adam Booth et al. [34] further confirmed that SPO2 levels below 94% are associated with an increased mortality risk. These collective findings emphasize the crucial role of SPO2 as an early indicator for identifying patients at high risk of death from COVID-19, highlighting the importance of timely medical intervention to improve patient outcomes. Maintaining adequate oxygen saturation (95%–100%) [35–37] is essential not only for improving survival chances but also for informing clinical decisions and prioritizing care for the most vulnerable patients.
We found that age was a significant risk factor for COVID-19 severity (SPO2 < 90). Specifically, COVID-19 patients aged 60 and above were associated with an approximately threefold increased risk of severe outcomes. This aligns with numerous studies that have linked older age to a heightened risk of severe COVID-19. For instance, a retrospective study by Sevda et al. [38], using data from 6906 hospitalized adults across a community health system in five states in the western United States, highlighted variations in the significance and predictive value of risk factors between older and younger patients. Additionally, a rapid review, meta-analysis, and meta-regression of 12 studies by Karla et al. [39] showed that the unadjusted impact of age indicated a 5.2% and 13.4% higher risk of disease severity and death per additional year, respectively. However, after adjusting for major age-related risk factors such as diabetes, hypertension, cardiovascular/CSD, compromised immunity, previous respiratory conditions, and renal disease, the risk increase for severity was reduced to 2.7% (in two studies), and there was no additional risk of death per year (in five studies). The authors emphasized that the modest impact of age on COVID-19 severity, once key comorbidities were considered, should inform age-targeted preventive strategies, including age-related work policies.
A study by Liu et al. [40] involving 4806 COVID-19 patients without chronic comorbidities identified that age ≥47 was linked to the progression from nonsevere to severe illness using LASSO and stepwise logistic regression models. Wolff et al. [40], in their systematic review of 28 studies, also reported that age was a primary risk factor for severe and fatal COVID-19 cases. Furthermore, according to Diendéré et al. [41], hospital-based cross-sectional study in Burkina Faso showed that patients over 65 years had an approximately eightfold increased risk of severe hypoxemia.
Other researchers, including Terada and colleagues in Japan [42], Davarpanah and colleagues in Iran [43], Sohrabi and colleagues in Tehran [44], and Booth [34] and colleagues [34] global review of 76 studies, have consistently identified age as a significant risk factor for severe COVID-19 at admission, with age over 75 associated with an approximately threefold increased risk of severe outcomes. Conversely, a systematic review, meta-analysis, and meta-regression [45] on COVID-19 risk factors in China found that age did not significantly impact disease severity.
The findings underscore that while age is a strong independent risk factor, the overall impact can be influenced by underlying conditions. Preventive and clinical measures should therefore prioritize older adults and incorporate strategies that consider age alongside comorbidities. Conversely, findings like those from Zhang et al. [45], which showed no significant age impact after statistical adjustments, highlight that the implications of age might vary by population and methodology. Overall, these insights are critical for shaping targeted health policies, age-specific interventions, and resource allocation during pandemics.
The findings also indicated that COVID-19 patients with pre-existing comorbidities were approximately twice as likely to experience severe COVID-19. Multiple studies [46] have confirmed the high prevalence of comorbidities among those with severe COVID-19, with common conditions including cardiovascular disease [47, 48], hypertension [49, 50], diabetes [30, 47, 49], chronic obstructive pulmonary disease (COPD) [47, 49, 50], malignancy [47], CSD [49], and chronic kidney disease [50]. A meta-analysis [51] highlighted that hypertension, diabetes, and cardiovascular disease significantly raise the risk of severe COVID-19, ICU admission, and mortality across all age groups. Additionally, obesity (BMI ≥ 30) has been associated with worse outcomes [52], driven by immune system dysregulation, chronic inflammation, and high ACE2 expression in adipose tissue [53]. Reduced immune function in patients with diabetes and hypertension weakens their resistance to infections, while chronic conditions can impair vascular and cardiac function, increasing the risk of severe COVID-19 [54].The virus’s potential to damage organs, including the liver, kidneys, and heart, further elevates the risk of severe and fatal outcomes for individuals with existing organ-related comorbidities [55]. A study [56] in Bangladesh examining comorbidities and COVID-19 severity among recovered patients found that comorbidities were linked to greater severity, with diabetes and cardiovascular disease notably increasing the likelihood of severe illness. Similarly, research [57] on the impact of comorbidities across ethnic groups demonstrated an elevated risk of severe COVID-19 in both South Asian and White participants, with a relatively higher risk observed in South Asian individuals.
These findings underscore the importance of targeted prevention and management strategies for individuals with underlying health conditions. Prioritizing the monitoring and protection of these high-risk groups is crucial for reducing severe health outcomes. The results advocate for comprehensive health strategies that emphasize early identification and targeted interventions for those with pre-existing conditions while addressing disparities across demographic groups.
Our findings align with studies from other low- and middle-income countries (LMICs). For example, research conducted in LMICs such as Peru [28], China [31], and South Africa [33] also found that an SPO2 level below 94% is linked to an increased risk of mortality. Regarding age as a risk factor for COVID-19 severity, studies from countries like Burkina Faso [41], Iran [43], and Tehran [44] have reported that older COVID-19 patients are more likely to experience severe hypoxemia. Additionally, findings from other LMICs, including China [49, 50] and Bangladesh [56], support our results, indicating that COVID-19 patients with pre-existing comorbidities are at a higher risk of developing severe disease. Research on COVID-19 patients in Africa [58] revealed that older age was associated with increased risk of mortality.
The centralized healthcare approach employed in The Gambia, where all COVID-19 patients were admitted to a single treatment center, offers distinct advantages for managing the pandemic. This setup likely facilitated consistent protocols for diagnosis, treatment, and data collection, contributing to the reliability of findings and the ability to generalize outcomes for the Gambian population. It also enabled efficient resource allocation, centralized monitoring, and the ability to rapidly implement changes to clinical protocols based on emerging evidence. In contrast, findings from decentralized or fragmented healthcare systems may vary significantly due to challenges such as inconsistent care standards, variable resource availability, and diverse patient management strategies. For instance, in decentralized systems, discrepancies in record-keeping can compromise the accuracy of findings and hinder comparisons. Decentralized systems may struggle with equitable distribution of such resources, particularly in regions with significant urban–rural disparities and variations in healthcare quality and access across regions could lead to disparities in COVID-19 outcomes. For example, rural facilities might lack the advanced equipment and trained personnel found in urban centers, potentially resulting in higher mortality rates. Also, decentralized systems may face delays in disseminating and implementing new protocols across multiple facilities, and findings from decentralized systems may reflect the heterogeneity of healthcare delivery, making it difficult to disentangle the effects of patient characteristics from healthcare system variables. For regions with fragmented healthcare systems, efforts should focus on enhancing coordination and standardization across facilities to mitigate disparities.
4.1. Strength of the Study
One of the key strengths of this study is that all COVID-19 patients in The Gambia were admitted to a single national treatment center, providing a centralized point of care that reflects the national experience. This centralized model allows for consistent data collection, uniform treatment protocols, and the application of standardized diagnostic criteria across all patients. By including every confirmed COVID-19 case in the country, the study minimizes selection bias that could arise from regional variations in healthcare access or treatment practices.
This comprehensive inclusion ensures that the findings are representative of the population as a whole, offering a clearer understanding of how COVID-19 affects individuals across different demographics and regions within The Gambia. Additionally, it enhances the generalizability of the results to the entire national context, as the treatment center serves as the primary isolation and care facility for the country. The centralized approach also allows for more efficient resource allocation and management, ensuring that all patients receive the same level of care, which helps eliminate disparities that could emerge if treatment were fragmented across multiple healthcare facilities.
However, while this is a strength in terms of uniformity and representativeness, it also means that the study’s findings are specific to a centralized healthcare setting. The results might differ in decentralized or fragmented healthcare systems, where resource availability, care protocols, and patient management may vary. Nonetheless, the strength of having a unified treatment center in this study is that it provides an integrated view of the national response to the pandemic, highlighting key factors such as healthcare capacity, patient outcomes, and prognostic indicators on a national scale.
4.2. Limitations of the Study
There were limited laboratory investigations due to inadequate equipment. Laboratory investigations are crucial for accurately diagnosing, classifying, and monitoring the progression of diseases such as COVID-19. Inadequate access to necessary diagnostic tools and laboratory infrastructure can significantly impact the quality and comprehensiveness of the data collected, which in turn may affect the conclusions drawn from the study. This limitation may also hinder the ability to assess a broader range of factors that could influence patient outcomes, such as underlying comorbidities or the presence of coinfections, which are important for understanding the full scope of COVID-19’s impact.
Additionally, inadequate equipment may have slowed down the response times for testing, leading to delays in diagnosis and treatment. Timely and accurate lab results are essential for informing clinical decisions, especially in a pandemic setting where early intervention can improve patient outcomes.
However, it is important to recognize that the study still provides valuable insights within the limitations of the available resources. This limitation also underscores the need for continued investment in healthcare infrastructure, particularly laboratory capacity, to improve the accuracy of future studies and strengthen the healthcare system’s ability to respond to emerging health threats.
5. Conclusions
The findings from the study strongly highlight the critical role of oxygen saturation (SPO2) levels, age, and pre-existing comorbidities in predicting the severity and mortality risk in COVID-19 patients. These findings advocate for targeted preventive measures, including early identification and continuous monitoring of high-risk patients. Clinical guidelines should emphasize maintaining adequate SPO2 levels and prioritizing resources for older adults and individuals with pre-existing health conditions. Moreover, age-specific and comorbidity-focused interventions can enhance patient outcomes and inform policies that better allocate healthcare resources during pandemics. Addressing health disparities and ensuring equitable care for vulnerable populations is paramount in reducing severe health outcomes and mortality.
Nomenclature
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- CCF:
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- Congestive cardiac failure
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- CRF:
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- Chronic renal failure
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- CVA:
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- Cerebrovascular accident
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- SPO:
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- Oxygen saturation
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- COVID-19:
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- Coronavirus disease 2019
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- WHO:
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- World Health Organization
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- ICU:
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- Intensive care unit
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- SSA:
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- Sub-Saharan Africa
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- SARI:
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- Severe acute respiratory illness.
Ethics Statement
Ethics approval for this study was granted by the Institutional Review Board of Edward Francis Small Teaching Hospital (EFSTH).
Disclosure
The authors have nothing to report.
Conflicts of Interest
The authors declare no conflicts of interest.
Author Contributions
S.O.B. was involved in all stages of the study. A.J., R.N., L.E.S.J., O.N., Y.F.B.M.J., S.J., S.J., M.D., E.B., C.R., and M.B., M.C. participated in the design of the study and data collection. A.K.I. performed the data analysis and interpretation and writing of the manuscript. All authors contributed to the writing of the manuscript. All authors read and approved the final manuscript.
Funding
No funding was received for this research.
Acknowledgments
The team appreciates the support of the staff of the records office and Internal Medicine Department, University of The Gambia, Fatou Ceesay, and Fanta Sisawo.
Open Research
Data Availability Statement
The dataset for this publication is available on request from the corresponding authors.