Tamara Thompson*1, Yvonne Dawkins1, Swane Rowe-Gardener1, Lisa Chin-Harty1, Kyaw Kyaw Hoe1, Trevor S. Ferguson1,3, Kelvin Ehikhametalor2, Kelly Ann Gordon-Johnson4, Varough Deyde4
1Department of Medicine, The University of the West Indies, Mona, Kingston 7, Jamaica
2Department of Surgery, Radiology, Anesthesia and Intensive Care, The University of the West Indies, Mona, Kingston 7, Jamaica
3Caribbean Institute for Health Research, The University of the West Indies, Kingston 7, Jamaica
4Centers for Disease Control and Prevention, Caribbean Regional Office (CDC/CRO)
Department of Medicine
University of The West Indies
Mona Campus, Kingston, Jamaica
Email: [email protected]
Co-authors’ email addresses:
Yvonne Dawkins – [email protected]
Swane Rowe-Gardener – [email protected]
Lisa Chin-Harty – [email protected]
Kyaw Kyaw Hoe – [email protected]
Kelvin Ehikhametalor – [email protected]
Trevor Ferguson – [email protected]
Kelly-Ann Gordon-Johnson – [email protected]
Varough Deyde – [email protected]
Copyright: This is an open-access article under the terms of the Creative Commons Attribution License which permits use, distribution, and reproduction in any medium, provided the original work is properly cited.
©2023 The Authors. Caribbean Medical Journal published by Trinidad & Tobago Medical Association
We examined the demographic, clinical characteristics and indicators of poor outcomes among hospitalised adults with COVID-19 infection at the University Hospital of the West Indies, Jamaica.
A retrospective medical record review between March 10 and December 31, 2020 was done and demographic clinical data were collected.
There were 218 males (mean age 59.5 years) and 144 females (mean age 55.7 years). Hypertension, diabetes mellitus, cardiovascular disease, obesity and chronic kidney disease were the most common comorbidities. Cough, shortness of breath, fever and malaise were the most common presenting complaints. Fifty-one per cent of patients were moderately to severely ill on admission; 11% were critically ill; 18% were admitted to the Intensive Care Unit (ICU). Death occurred in 62 (17%) patients (95% CI 13.6-21.4). Among obese participants, there were increased odds of developing respiratory failure (OR 5.2, p < 0.001), acute kidney injury (OR 4.7, p < 0.001), sepsis (OR 2.9, p =0.013) and the need for ICU care (OR 3.7, p < 0.001). Factors independently associated with increased odds of death were age (OR 1.03 per year, p = 0.013) and obesity (OR 2.26, p = 0.017). Mortality also correlated significantly with D-dimer > 1000 ng/mL (OR 2.78; p = 0.03), serum albumin < 40 g/L (OR 3.54; p = 0.03) and serum LDH > 485 U/L OR 1.92, p = 0.11).
Comorbidities were prevalent among COVID-19 cases in this study. Significant correlates of mortality were older age and obesity. Hypoalbuminaemia, elevated D-dimer and serum LDH at admission also portend a poor prognosis.
Keywords: SARS-CoV-2, COVID-19, Jamaica, outcomes, mortality
Jamaica, the largest English-speaking island of the Caribbean, with a population of 2.9 million, recorded its first case of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection on March 10, 2020, a day before the World Health Organization declared its associated coronavirus disease (COVID-19), a pandemic. 1 By December 31, 2020, there was a total of 12,915 confirmed cases of COVID-19 on the island and 303 deaths. 2,3
The clinical presentation of COVID-19 is heterogenous with early reports of cough, myalgia, loss of appetite, diarrhea, fever, headache, and asthenia, accounting for more than 45% of patients. 4,5 Some studies have noted a racial difference in laboratory abnormalities among COVID-19 patients, with higher percentage of White patients having a low white-cell and lymphocyte counts when compared to Blacks, and a higher percentage of Black patients presenting with elevated levels of serum creatinine, aspartate transferase (AST), procalcitonin, and C-reactive protein when compared to Whites. 6
The varied complications of COVID-19 infection are mainly a result of the generalized inflammatory state induced by the virus. These complications include severe viral pneumonia with respiratory failure, multiorgan and systemic dysfunctions with sepsis, septic shock, and death. 7,8 Several studies have described the correlation between COVID-19 disease severity and poor outcomes. Indicators of poor outcomes described include advancing age, male sex, pre-existing comorbidities, lymphopenia, decreased serum albumin and elevated inflammatory markers. 9
We describe results of a retrospective analysis of COVID-19 infected patients admitted to a tertiary teaching hospital in Kingston, Jamaica. These results can inform appropriate approaches to risk stratification and early treatment strategies in the management of COVID-19 with a view to mitigating poor outcomes.
We conducted a retrospective case series study of adults with laboratory confirmed SARS-CoV-2 infection admitted to the University Hospital of the West Indies (UHWI), Kingston, Jamaica from March 10 to December 31, 2020. The primary aims were to describe, the demographic and clinical characteristics as well as risk factors associated with severe disease and mortality among hospitalized COVID-19 patients.
Patient Selection and Procedure
Confirmed cases of COVID-19 infections were identified using the World Health Organization (WHO) case definition. WHO defines a confirmed case of COVID-19 infection as a person with laboratory confirmation of SARS-CoV-2 infection, irrespective of clinical signs and symptoms (10). Real-time polymerase chain reaction (PCR) was used to identify the SARS-CoV-2 Envelope and RNA-dependent RNA polymerase nucleic acid sequences based on the Pan American Health Organization (PAHO) molecular testing protocol. 11
All patients admitted to the UHWI COVID-19 isolation wards with PCR confirmed SARS-CoV-2 tests were identified through hospital admission and laboratory records. From these records, data related to participants’ demographics, vital statistics, laboratory results on admission to the hospital, medical histories, clinical management and trajectory were abstracted.
Patients were included in the study if they were ≥ 18 years old, had laboratory confirmed SARS-CoV-2 infection, and admitted to UHWI between March 10 and December 31, 2020. Patients were excluded from the study if they had missing clinical data or had an inconclusive or negative SARS-CoV-2 PCR test. Ethics approval was obtained from the University of the West Indies Mona Campus Research Ethics Committee: ECP 46 20/21 and the Centers for Disease Control and Prevention (CDC) and was conducted consistent with applicable Federal law and CDC policy before starting the study.
 See e.g., 45 C.F.R. part 46; 21 C.F.R. part 56; 42 U.S.C. §241(d), 5 U.S.C. §552a, 44 U.S.C. §3501 et seq.
Study Variables and Definitions
We retrospectively abstracted patient specific variables from the electronic patient records and laboratory information systems of the UHWI. These variables included demographic data such as age, sex, smoking history, pregnancy and comorbidities (diabetes, hypertension, asthma, obesity, chronic obstructive lung disease, malignancy, HIV, chronic kidney disease, and other chronic medical conditions). The diagnoses of comorbidities were either self-reported by patients or abstracted from previous in-patient or out-patient clinical encounters in the electronic patient records. A full description of these comorbidities such as the degree of disease stability or control was not captured in this study.
Weight, height and waist circumference measurements were not routinely documented for patients in the isolation ward. Obesity, therefore, was based on the physicians’ visual assessment as captured in their clinical notes during patient encounters.
Patient symptoms included respiratory (cough, shortness of breath, chest pain, sore throat and sneezing), gastrointestinal (nausea and vomiting, diarrhoea, abdominal pain) and non-specific symptoms such as loss of taste or smell, joint pain, headache and fever. COVID-19 severity of illness (mild, moderate, severe or critical disease) determination was as per the WHO severity of illness classification. 12 Laboratory variables at the time of admission, included complete blood count, absolute lymphocyte count, electrolytes, liver function tests, D-dimer, lactate dehydrogenase and serum albumin.
Outcome variables were respiratory failure including acute respiratory distress syndrome (severe pneumonia with oxygenation impairment and ratio of arterial partial pressure of oxygen to fraction of inspired oxygen (PaO2/FiO2) < 300 mmHg), sepsis (known or suspected source of infection with associated systemic inflammatory response syndrome), secondary infections (concurrent infections such as bacteremia and superimposed bacterial pneumonia) and coagulopathy (concurrent excessive bleeding or recognized thrombosis). The outcome of acute kidney injury was based on the clinical assessment documented by the medical team managing the patient. Other clinical outcomes included admission to the intensive care unit and in-hospital mortality whilst in the COVID-19 unit.
Descriptive statistics were obtained for clinical and demographic characteristics using mean and standard deviation for continuous variables and number and per cent for categorical variables. For variables with non-normal distribution, we report medians with the 25th and 75th centiles. Sex-specific estimates and comparisons of sex differences were computed using t-tests for continuous variables and a chi-squared test or Fisher’s exact test for categorical variables as appropriate. We estimated in-patient mortality rates with proportions and 95% confidence intervals (CI) for the period. Bivariate analyses were used to assess potential associations between in-patient mortality and severe disease and identify variables for inclusion in multivariable models. Multivariate logistic regression analyses were used to identify factors associated with in-patient mortality and disease severity. Covariates used in the multivariate analyses were sex, age, and comorbidities such as diabetes mellitus, hypertension, bronchial asthma and obesity. A p-value <0.05 was considered statistically significant. All statistical analyses were done using Stata version 16.
From March 10 to December 31, 2020, a total of 362 SAR-CoV-2 positive patients admitted to the UHWI were identified as being eligible for inclusion in the study. Two hundred and eighteen (60%) participants were males with a mean age of 59.5 (59.5±16.4) years. More males were represented in the 60-69 years age group (25%), compared to all other age groups. The mean age for females was 55.7 (55.7±17.1) years, with the largest representation (27%) being in the 50-59 years age group (Table 1).
Table 1. Baseline Characteristics of Patients Hospitalized with COVID-19 Infection
n = 144 (%)
n = 218 (%)
N = 362 (%)
|Age (Mean ± SD) 1||55.7 ±17.1||59.5 ± 16.4||58.0 ± 16.7||0.983|
|<30||10 (6.9)||12 (5.5)||22 (6)|
|30-39||22 (15.2)||16 (7.3)||38 (10.5)|
|40-49||16 (11.1)||34 (15.6)||50 (13.8)|
|50-59||39 (27.0)||35 (16)||74 (20.4)|
|60-69||23 (15.9)||55 (25.2)||78 (21.5)|
|70-79||24 (16.6)||41 (18.8)||65 (17.9)|
|≥80||10 (6.9)||25 (11.4)||35 (9.6)||0.008|
|Shortness of breath||84 (58.3)||129 (59.1)||213 (58.8)||0.874|
|Cough||91 (63.1)||139 (63.7)||230 (63.5)||0.913|
|Fever||66 (45.8)||120 (55.0)||186 (51.3)||0.086|
|Anorexia||36 (25.0)||71 (32.5)||107 (29.5)||0.122|
|Malaise||34 (23.6)||67 (30.7)||101 (27.9)||0.139|
|Diarrhea||35 (24.3)||43 (19.7)||78 (21.5)||0.299|
|Nausea & vomiting||30 (20.8)||31 (14.2)||61 (16.8)||0.100|
|Chest pain||26 (18.0)||32 (14.6)||58 (16.0)||0.391|
|Headache||18 (12.5)||23 (10.5)||41 (11.3)||0.567|
|Joint pain||13 (9.0)||16 (7.3)||29 (8.0)||0.562|
|Abdominal pain||10 (6.9)||14 (6.4)||24 (6.6)||0.845|
|Sore throat||8 (5.5)||13 (5.9)||21 (5.8)||0.871|
|Runny nose||7 (4.8)||11 (5.0)||18 (4.9)||0.937|
|Loss of taste||6 (4.1)||12 (5.5)||18 (4.9)||0.567|
|Loss of smell||6 (4.1)||7 (3.2)||13 (3.5)||0.632|
|Sneezing||1 (0.6)||0 (0)||1 (0.2)||0.218|
|Hypertension||86 (59.7)||114 (52.2)||200 (55.2)||0.164|
|Diabetes||64 (44.4)||86 (39.4)||150 (41.4)||0.345|
|Cardiovascular||32 (22.2)||39 (17.8)||71 (19.6)||0.310|
|Obesity||31 (21.5)||34 (15.6)||65 (17.9)||0.150|
|Chronic kidney disease||12 (12)||22 (16.4)||34 (14.5)||0.343|
|Asthma||16 (11.1)||20 (9.1)||36 (9.9)||0.547|
|Malignancy||11 (7.6)||22 (10.0)||33 (9.1)||0.427|
|COPD||0 (0)||5 (2.2)||5 (1.3)||0.067|
|Smoking history (N=276)|
|Non-smoker||97 (35.1)||119 (43.2)||216 (78.3)||0.001|
|Current smoker||4 (1.4)||17 (6.6)||21 (7.6)||0.001|
|Ex-smoker||6 (2.2)||33 (11.9)||39 (14.1)||0.001|
|Severity of illness at admission|
|Asymptomatic||29 (20.1)||37 (16.9)||66 (18.2)|
|Mild||20 (13.8)||51 (23.3)||71 (19.6)|
|Moderate||35 (24.3)||57 (26.1)||92 (25.4)|
|Severe||42 (29.1)||51 (23.3)||93 (25.6)|
|Critical||18 (12.5)||22 (10.0)||40 (11.0)|
|ICU Admission||25 (17.3)||40 (18.3)||65 (17.9)||0.811|
|Alive||123 (85.4)||177 (81.1)||300 (82.8)|
|Death 2||21 (14.5)||41 (18.8)||62 (17.1)||0.292|
COPD: Chronic Obstructive Pulmonary Disease
Column percentages (%) for Female, Male, Total categories calculated as number of participants in each category / n (sample size) or N (study population) x 100
95% Confidence interval: 1 56.3, 59.8; 2 0.69,2.26
Those with significant p values are indicated in bold with p values < 0.05 indicating that the difference was statistically significant
Among enrolled participants, pre-existing hypertension (55%), diabetes mellitus (41%) cardiovascular disease (20%), obesity (18%) and chronic kidney disease (15%) were the most commonly reported comorbid illnesses (Table 1). These comorbid illnesses were observed primarily in men (Supplemental Figure 1), but the difference was not statistically significant.
Underlying respiratory illnesses were relatively uncommon. A smoking history was obtained for 276 participants, of whom non-smokers accounted for 216 (78%). Those who were identified as current smokers were few (7.6%) and 14% reported a previous smoking history (Table 1).
Among all enrolled participants, cough (64%), shortness of breath (59%) and subjective fever (51%), were the most common symptoms reported, followed by malaise (28%) and gastrointestinal symptoms such as anorexia (30%), diarrhea (22%), nausea and vomiting (17%) and abdominal pain (6.6%). Runny nose (4.9%), loss of taste (4.9%), loss of smell (3.5%) and sneezing (0.2%) were uncommon (Table 1).
On admission, the median serum inflammatory markers for participants were above the normal range, such that the median values for D-dimer (163 tests) was 1466 U/L and serum lactate dehydrogenase (LDH) (159 tests) was 485 ng/ml. Median values for bilirubin, alanine transferase, and alkaline phosphatase were within range. Whilst the median serum aspartate transferase and gamma glutamyl transferase were elevated. Sex differences were observed for some laboratory tests. Males had lower median absolute lymphocyte count (ALC), serum albumin, serum LDH and D-dimer levels compared to their female counterparts (Supplemental Table 1).
Among all participants, 18% were asymptomatic and 20% had mild disease, with both sets being admitted for medical diseases other than COVID-19. Most patients were found to have moderate (25%) or severe disease (26%) while 11% were critically ill at admission. Eighteen per cent of the patients were admitted to the ICU (Table 1).
There was a male predominance for all categories of disease severity, including ICU admissions (62%) (Table 1). Among moderately to critically ill disease categories, the most represented ages were the 60-69 age group (22%) followed by those 50-59 years (20%); only 11% were below the age of 40 years (Supplemental Figure 2).
Figure 1. Age and Sex adjusted Odds Ratio for Death in the presence of comorbidities in COVID-19 patients
Risk Factors associated with Disease severity
The prevalence of adverse outcomes associated with COVID-19 infection were acute respiratory failure including acute respiratory distress syndrome (ARDS) (26%), acute kidney injury (AKI) (16%), ICU admission (18%) and death (17%). Sepsis, and coagulopathy were uncommon outcomes (Table 2).
Table 2. Relationship Between Clinical Outcomes and Sex Among Hospitalized COVID-19 Patients
|Odds ratio (M vs F)||P-value M vs F|
|Respiratory Failure or ARDS||26.4
AKI: Acute Kidney Injury, ARDS: Acute Respiratory Distress Syndrome, ICU: Intensive Care
Those with significant p-values are indicated in bold with p-values < 0.05 indicating that the difference was statistically significant
There was no significant association between sex and mortality (OR 1.3, p=0.298) (Table 2). There was a significant association between age category and mortality (p=0.031) (Supplemental Table 2). The likelihood of death among those 60-69 and 70-79 years was roughly 2-fold but was not statistically significant. Participants aged ≥ 80 years were almost 6 times more likely to die (p = 0.030) compared to patients in the age 30-39-year category (Supplemental Table 2).
Obesity was associated with increased odds of death (OR 2.26, p < 0.05) (Table 3, Figure 1). This significant association was not observed with other pre-existing comorbidities such as diabetes mellitus, hypertension, chronic obstructive pulmonary disease, and cardiovascular disease.
Table 3. Multivariable Model of Association Between Clinical Outcomes with Age, Sex and Comorbidities
|Respiratory Failure/ ARDS
OR (95% CI)
OR (95% CI)
OR (95% CI)
OR (95% CI)
|1.21 (0.72,2.03)||0.92 (0.42,1.99)||0.61
(1.47, 5.98) **
(1.01, 1.05) *
(1.38, 5.18) **
(1.16, 4.44) *
(2.85, 9.30) ***
(1.25, 6.79) *
(2.29, 9.43) ***
ICU: Intensive Care Unit, ARDS: Acute respiratory distress syndrome, AKI: Acute Kidney Injury
Those with significant p-values are indicated with an asterix (*) with the statistical difference reflected as follows: *p < 0.05; **p < 0.01, ***p < 0.001
The development of sepsis, secondary infections, and coagulopathy as outcomes among hospitalized patients were not significantly associated with sex, age or pre-existing comorbidities. However, obesity was associated with increased odds of developing sepsis as a complication (OR 2.92 (CI 1.25, 6.79; p < 0.05) (Table 3).
Males had a 3-fold likelihood of developing AKI (p < 0.001). AKI was also significantly associated with pre-existing diabetes mellitus (OR 2.7, p < 0.01) and obesity (OR 4.7, p < 0.001). Additionally, among obese participants, the likelihood of developing respiratory failure including ARDS was 5-fold (p < 0.001) (Table 3).
Laboratory values at Admission and outcomes
Mortality was significantly higher in patients who had D-dimer > 1000 ng/ml [19 participants (20%); OR 2.74; p =0.04] and serum albumin < 40 g/L [ 36 participants (21.4%); OR 3.55; p = 0.04] than those who did not (Table 4). This was also observed in the multivariable analysis (Table 5). Serum LDH above the mean of 485 U/L was associated with 2-fold odds of death, but this was not statistically significant. There was also increased odds of developing an ALC <1.0 x 109 cells/liter (OR 2.24, p < 0.05), a WBC < 4 (OR 2.30, p < 0.05) or WBC > 11 x 109 per liter (OR 2.14, p < 0.05) with the presence of obesity (Table 5). This finding was not associated with any other comorbidities.
Table 4. Association Between Mortality and Laboratory Variables
|OR||95% CI||P value|
|D-dimer >1000 ng/ml||6 (8.7)||19 (20.4)||2.74||1.03, 7.27||0.043|
|serum albumin < 40 g/L||3 (7.1)||36 (21.4)||3.55||1.04, 12.14||0.044|
|ALC < 1.0 x 109 cells/liter||28 (15.4)||24 (23.8)||1.71||0.93, 3.15||0.083|
|LDH > 485 U/L||12 (15.0)||20 (25.3)||1.92||0.87, 4.26||0.108|
|WBC > 12 x 109 per liter||32 (17.1)||28 (16.8)||0.98||0.56, 1.70||0.931|
ALC: Absolute Lymphocyte count, LDH: Lactate Dehydrogenase, WBC: White Blood Cell
N: number of patients with abnormal laboratory values as described on admission to hospital
n: number of deaths among patients with abnormal laboratory values on admission
Percentages (%): calculated based on n/Nx100
p-values are from the logistic regression model for association between laboratory characteristic and death. Those with significant p-values are indicated in bold with p-values < 0.05 indicating that the difference was statistically significant.
Table 5. Multivariable Model for Laboratory Characteristics and Association with Death Adjusted for Age, Sex and Comorbidities
|Risk Factors||D-Dimer >1000 ng/ml
|Albumin <40 g/L
|LDH >485 U/L
|ALC <1.0 x 109 cells/liter||WBC <4 x 109 per liter
|WBC >11 x 109 per liter|
|Odds for Death||2.85 (0.98, 8.31)||3.16 (0.87, 11.4)||2.01 (0.88, 4.60)||1.58 (0.83, 3.00)||1.34 (0.33, 5.50)||0.94 (0.52, 1.68)|
|Age (years)||1.02 (0.99, 1.06)||1.02 (0.99, 1.05)||1.01 (0.98, 1.04)||1.02 (0.99, 1.04)||1.03 (1.01, 1.05) *||1.02 (1.00, 1.04) *|
|Sex||1.15 (0.44, 3.03)||0.83 ((0.39, 1.78)||1.06 (0.46, 2.44)||1.44 (0.72, 2.85)||1.35 (0.74, 2.46)||1.41 (0.77, 2.06)|
|Bronchial Asthma||4.25 (1.12, 15.5)||2.14 (0.73, 6.24)||2.74 (0.87, 8.59)||1.62 (0.59, 4.50)||1.90 (0.80, 4.50)||1.80 (0.75, 4.32)|
|Hypertension||0.85 (0.30, 2.44)||0.71 (0.29, 1.66)||0.70 (0.26, 1.88)||0.66 (0.32, 1.36)||0.77 (0.40, 1.50)||0.85 (0.44, 1.67)|
|Diabetes||1.68 (0.63, 4.47)||1.26 (0.57, 2.77)||2.00 (0.86, 4.81)||1.74 (0.88, 3.44)||1.50 (0.80, 2.79)||1.53 (0.82, 2.88)|
|Obesity||2.55 (0.99, 6.59)||1.91 (0.85, 4.30)||1.38 (0.56, 3.45)||2.24 (1.08, 4.48) *||2.30 (1.17, 2.79) *||2.14 (1.06, 4.32) *|
ALC = Absolute Lymphocyte count, LDH = Lactate Dehydrogenase, WBC = White Blood Cell
Those with significant p-values (p<0.05) are indicated with an asterix (*)
This single-centre retrospective study is the first to summarise the demographic, clinical characteristics, and outcomes among adult hospitalized COVID-19 patients in Jamaica, the largest English-Speaking Caribbean island. These hospitalizations were in the first year of the pandemic. The epidemic curve demonstrated a propagated spread of the infection after August 2020 following the phased re-opening of the Jamaican borders and the relaxation of some public health measures. During this time, the ancestral SARS-CoV-2 and later Alpha variants were in circulation.
The observation of an excess of hospitalized cases of COVID-19 among older adults has been corroborated in other studies. One large retrospective USA analysis revealed a mean age of 61.2 (±17.9) years with younger ages of 41.7 (± 16.3) and 50.6 (range 16-94) years recorded among hospitalised patients in Bangladesh and Cuba. 13–15 The disproportionate severity of COVID-19 infection among older age groups may be due in part to the increased number of comorbidities among the aged, and biological factors such as lower numbers of functional cilia, immunosenescence, inflame-aging and lower levels of angiotensin -converting enzyme 2 (ACE-2). 16,17
In this study, a male preponderance was observed among all admissions including patients with comorbidities and severe COVID-19 infection. A meta-analysis of over 3 million reported global cases of COVID-19 infections however, showed that males and females had an equivalent risk of infection, although males did demonstrate higher odds of ICU admissions and mortality. 18 These findings were also noticed in previous human coronavirus outbreaks, involving SARS-CoV-1 and MERS-CoV.19,20 Biological suggestions for these sex differences include the presence of X-linked genes which code for a stronger innate and adaptive immune system response. Additionally, the ACE-2 gene being X-linked, coupled with the upregulation of ACE-2 by estrogen results in less severe lung injury among females compared to males. 21
The top 4 reported comorbidities in this study were hypertension, diabetes, cardiovascular diseases, and obesity. These findings are consistent with that seen in other studies and reflect the chronic illnesses also seen in the wider Jamaican population. 22–24 Additionally, in a 2016 – 2017 national survey, 54 % of Jamaicans were classified as falling into the following categories of weight – overweight (BMI ≥ 25 kg/m2), pre-obese (BMI ≥ 25.0 – 29.9 kg/m2) or obese (BMI ≥ 30 kg/m2). 24 The estimated prevalence of obesity in the current study (17.9 %) was a crude measure based on physicians’ visual assessment and likely underestimated patients who were overweight or pre-obese. Despite this, our findings which link obesity with poorer COVID-19 outcomes are plausible and consistent with other reviews found in the medical literature which defined obesity using objective anthropometric measures. 25,26
Among obese participants, there were increased odds of developing respiratory failure (OR 5.2, p < 0.001), acute kidney injury (OR 4.7, p < 0.001), sepsis (OR 2.9, p =0.013) and the need for ICU care (OR 3.7, p < 0.001). It is known that obesity is associated with chronic inflammation, alteration in immune cell function, compromised T cell regulation and induction of a pro-inflammatory state of peripheral blood mononuclear cells. 27–29 Additionally, decreased chest wall and lung compliance among obese patients, sets the stage for ventilatory compromise and death. 30
Cough, shortness of breath, fever and malaise ranked among the top reported symptoms among the study participants, however, non-respiratory manifestations were also observed. The presence of SARS-CoV-2 RNA has been detected in non-respiratory tissues via PCR. In these sites, tissue-specific injury may occur in severe disease due in part to virus induce microangiopathy, direct viral cytopathic effects, and a dysregulated inflammatory response induced by injured endothelial cells and activated immune cells. 31 This explains the range of non-respiratory pathologies seen among COVID-19 patients and their corresponding laboratory abnormalities. In this study, gastrointestinal symptoms were reported second to respiratory symptoms and AKI was a notable complication particularly among males, diabetic and older patients.Concerning laboratory derangements, lymphocyte depletion, elevations in D-dimer, abnormal liver biochemistries, elevated troponins, acute tubular injury, and elevations in acute phase reactants, have all been shown to be poor prognostic markers among COVID-19 patients.
Lymphocyte depletion with ALC < 1 cells/L has consistently been shown to be a marker of severe and critical COVID-19 infection. 32 This study demonstrated 1.7-fold odds of death among participants with ALC < 1 cells/L, though this did not reach statistical significance. Other important biomarkers of COVID-19 disease severity include serum LDH, hypoalbuminemia and D-dimer. Elevated D-dimer, a marker of coagulation dysfunction, can predict critical illness and fatal outcomes among COVID-19 patients with a sensitivity of 75% and specificity of 83% (33,34). Hypoalbuminemia is an independent risk factor for death in COVID-19. 35,36 A pooled analysis of COVID-19 disease severity and mortality reported that elevated LDH was associated with more than 6-fold increased odds of severe disease and more than 16-fold odds of mortality. 37 This study demonstrated that the median D-dimer on admission was more than 2 times above the upper limit of normal and that a D-dimer >1000 ng/ml was a predictor of death. Further, serum albumin < 40 g/L and elevations of serum LDH above the mean of 485 U/L was associated with 2-fold odds of death. These readily accessible and inexpensive laboratory investigations, therefore, act as useful stratification tools for COVID-19 patients at admission. Serial trends of these investigations throughout hospitalisation, however, would likely offer additional insights.
This study is to be interpreted in the context of several limitations. The study was conducted during a period when the ancestral and later Alpha SARS-CoV-2 variants were circulating in Jamaica. These findings therefore may not be representative of subsequent variants, particularly Delta and Omicron. Only patients diagnosed via SARS-CoV-2 PCR testing were included in the study. Excluded were hospitalized patients with a COVID-19-like illness who had a negative PCR, positive antigen or antibody test and any other adjunctive diagnostics consistent with COVID-19 such as specific radiographic findings. Not all patients had data for the laboratory investigations assessed or the full range of investigations, limiting a more fulsome review of all the laboratory data which could correlate with clinical outcomes. Our study did not use traditional anthropometric measures such as the body mass index (BMI) or waist circumference in the assessment of obesity. This was due to inconsistent documentation of these measurements in the patients’ charts. Moderate to severe forms of obesity may be easier to recognize on visual assessment, with milder forms being misclassified. The accuracy of this visual assessment by the physicians therefore may not be reliable. The study site is a major referral hospital in Jamaica, accepting severe and critically ill patients for specialist care. The population of COVID-19 patients sampled for this study therefore may include a bias towards the more severely and critically affected. Mortality thus could be excessive in this population. With limitations in ICU bed capacity and the surge of patients in the hospitals, many critically ill COVID-19 patients were managed outside of the ICU setting. ICU admissions as an outcome measure for this study was thus grossly underestimated. The study population included only Jamaican patients admitted to a single hospital site, thus affecting the generalisability of our results to larger populations affected by COVID-19 infection.
In conclusion, in this retrospective case series of patients admitted to a Jamaican teaching hospital with COVID-19, we demonstrated a male predominance and identified hypertension, diabetes mellitus, cardiovascular disease and obesity as the most common comorbidities. Significant correlates of poor outcomes were hypoalbuminemia, elevated D-dimer and serum LDH whilst older age and obesity were independent risk factors of mortality. This evidence should be interpreted in the context of the study limitations and may not be generalisable to the wider population. However, the findings may guide clinicians and policy makers involved in the care and management of these complex patients.
The authors thank Heather Stewart for their assistance in collating data for this manuscript. We also thank Anthony Myers for the development of the data abstraction tool and providing overall database management and technical support. The co-authors from the University of the West Indies, Faculty of Medical Sciences, acknowledge and are grateful for the support, oversight and guidance from the collaborators and co-authors from the Centers for Disease Control and Prevention on this study.
Disclosures: The authors have declared that no competing interests exist.
Ethics Approval: Approval was obtained from the University of the West Indies Mona Campus Research Ethics Committee: ECP 46 20/21 and the Centers for Disease Control and Prevention (CDC). Approval from the CDC was consistent with applicable federal law and CDC policy.
Financial Support: The study was supported by the Centers for Disease Control and Prevention CDC#19JM3710P1001. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the Centers for Disease Control and Prevention.
Informed Consent: Not applicable
All co-authors assume collective responsibility for the manuscript and are accountable for all aspects of its work.
Tamara Thompson: Conceptualized the study, designed the study protocol, drafted and finalized the manuscript
Yvonne Dawkins: Contributed to drafting the study protocol, and drafting of the manuscript
Swane Rowe-Gardener: Contributed to drafting the study protocol, data analysis and drafting of the manuscript
Lisa Chin-Harty: Contributed to drafting the study protocol, data analysis and drafting of the manuscript
Kyaw Kyaw Hoe: Responsible for data management, ensured data accuracy, contributed to statistical analysis, contributed to manuscript writing
Trevor Ferguson: Contributed to study protocol development, responsible for all data cleaning and analyses, edited the final version of the manuscript
Kelvin Ehikhametalor: contributed to data collection and analysis/interpretation
Kelly-Ann Gordon-Johnson: revised serial versions of the study protocol and manuscript for intellectual content
Varough Deyde: revised serial versions of the study protocol and manuscript for intellectual content
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