Length of Stay Analysis of COVID-19 Intensive Care Unit Admissions Using Count Regression and Hurdle Regression Models: A Study in a Tertiary Hospital, Cape Town, South Africa (preprint)

Objective: To evaluate the variables influencing the length of stay (LoS) for COVID-19 ICU patients at Tygerberg Hospital (Cape Town) and to identify the covariates that significantly influenced it and any potential risk factors associated with LoS. Methods and Results: Poisson, negative binomial (NB), Hurdle–Poisson, and Hurdle–NB regression models were used to model the LoS in this prospective cohort study. The fitted models were compared using the Akaike information criterion (AIC), Vuong’s test criteria, and Rootograms. Based on the chosen performance criteria, the NB model provided the best fit outperforming other candidate models. The baseline LoS count was 8 days. On average, antibiotics reduced LoS by 0.74-fold (95% CI 0.62-0.89) compared to not taking antibiotics. The second wave had a significant effect on the average LoS, which decreased by 0.36-fold (95% CI 0.14-0.93) compared to the first wave. Average LoS increased by 1.01-fold (95% CI 1.01-1.02) for every one-year increase in the age of the patient and by 1.02-fold (95% CI 1.01-1.03) for every 1 unit increase in neutrophils. A 1 ng/L increase in log (TropT) levels decreased the average LoS by 0.87-fold (95% CI 0.81-0.93) similarly, a unit increase in the PF ratio decreased the average LoS by 0.998-fold (95% CI 0.997-0.999) respectively. Conclusion: The study identified common clinical characteristics associated with length of stay in ICU for COVID-19 patients, including age at admission, PF ratio, neutrophils, TropT, Wave, and antibiotic use. These results can aid in identifying risk factors for increased length of stay, assist in healthcare systems planning, and aid in evaluating different models for analysing this type of data.

Burden, Causation, and Particularities of long-COVID in African populations: A rapid systematic review (preprint)

BackgroundThe global estimated prevalence of long COVID-19 is 43%, and the most common symptoms found globally are fatigue, confusion, or lack of confusion, and dyspnea, with prevalence rates of 23%, 14%, and 13%, respectively. However, long COVID still lacks an overall review in African populations. The aim of this review was to determine the prevalence of long COVID, its most common symptoms, comorbidities, and pathophysiological mechanisms. MethodsA systematic review of long COVID in African populations was conducted. The random effects model was used to calculate the pooled prevalence rates (95% CI). If the results could not be pooled, a narrative synthesis was performed. ResultsWe included 14 studies from 7 African countries, totaling 6,030 previously SARS-CoV-2 infected participants and 2,954 long COVID patients. Long COVID had a pooled prevalence of 41% [26%-56%]. Fatigue, dyspnea, and confusion or lack of concentration were the most common symptoms, with prevalence rates (95% CI) of 41% [26%-56%], 25% [12%-38%], and 40% [12%-68%], respectively. Long COVID was associated with advanced age, being female, more than three long COVID symptoms in the acute phase, initial fatigue and dyspnea, post-recovery stress, sadness, and sleep disturbances, and loss of appetite at symptoms onset, mild, moderate, and severe, pre-existing obesity, hypertension, diabetes mellitus, and the presence of any chronic illness (P [≤]0.05). According to our review, high micro clot and platelet poor plasma (PPP) viscosity explain the pathophysiology of long COVID. ConclusionLong COVID prevalence in Africa was comparable to the global prevalence. However, the prevalence of the most common symptoms was higher in Africa. Comorbidities associated with long COVID may lead to additional complications in African populations due to hypercoagulation and thrombosis.