Spatial and Temporal Patterns of SARS-CoV-2 transmission in uMgungundlovu, Kwa-Zulu Natal, South Africa (preprint)

Background. Investigating the spatial distribution of SARS-CoV-2 at a local level and describing the pattern of disease occurrence can be used as the basis for efficient prevention and control measures. This research project aims to utilize geospatial analysis to understand the distribution patterns of SARS-CoV-2 and its relationship with certain co-existing factors. Methods. Spatial characteristics of SARS-CoV-2 were investigated over the first four waves of transmission using ESRI ArcGISPro v2.0, including Local Indicators of Spatial Association (LISA) with Morans "I" as the measure of spatial autocorrelation; and Kernel Density Estimation (KDE). In implementing temporal analysis, time series analysis using the Python Seaborn library was used, with separate modelling carried out for each wave. Results. Statistically significant SARS-CoV-2 incidences were noted across age groups with p-values consistently < 0.001. The central region of the district experienced a higher level of clusters indicated by the LISA (Morans I: Wave 1 - 0.22, Wave 2 - 0.2, Wave 3 - 0.11, Wave 4 - 0.13) and the KDE (Highest density of cases: wave 1: 25.1-50, wave 2: 101-150, wave 3: 101-150, wave 4: 50.1-100). Temporal analysis showed more fluctuation at the beginning of each wave with less fluctuation in identified cases within the middle to end of each wave. Conclusion. A Geospatial approach of analysing infectious disease transmission is proposed to guide control efforts (e.g., testing/tracing and vaccine rollout) for populations at higher vulnerability. Additionally, the nature and configuration of the social and built environment may be associated with increased transmission. However, locally specific empirical research is required to assess other relevant factors associated with increased transmission.

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.

The interface between SARS-CoV-2 and non-communicable diseases (NCDs) in a high HIV/TB burden district level hospital setting, Cape Town, South Africa (preprint)

Background COVID-19 experiences on noncommunicable diseases (NCDs) from district-level hospital settings during waves I and II are scarcely documented. The aim of this study is to investigate the NCDs associated with COVID-19 severity and mortality in a district-level hospital with a high HIV/TB burden. Methods This was a retrospective observational study that compared COVID-19 waves I and II at Khayelitsha District Hospital in Cape Town, South Africa. COVID-19 adult patients with a confirmed SARS-CoV-2 polymerase chain reaction (PCR) or positive antigen test were included. In order to compare the inter wave period, clinical and laboratory parameters on hospital admission of noncommunicable diseases, the Student t-test or Mann-Whitney U for continuous data and the X2 test or Fishers' Exact test for categorical data were used. The role of the NCD subpopulation on COVID-19 mortality was determined using latent class analysis (LCA). Findings Among 560 patients admitted with COVID-19, patients admitted during wave II were significantly older than those admitted during wave I. The most prevalent comorbidity patterns were hypertension (87%), diabetes mellitus (65%), HIV/AIDS (30%), obesity (19%), Chronic Kidney Disease (CKD) (13%), Congestive Cardiac Failure (CCF) (8.8%), Chronic Obstructive Pulmonary Disease (COPD) (3%), cerebrovascular accidents (CVA)/stroke (3%), with similar prevalence in both waves except HIV status [(23% vs 34% waves II and I, respectively), p = 0.022], obesity [(52% vs 2.5%, waves II and I, respectively), p <0.001], previous stroke [(1% vs 4.1%, waves II and I, respectively), p = 0.046]. In terms of clinical and laboratory findings, our study found that wave I patients had higher haemoglobin and HIV viral loads. Wave II, on the other hand, had statistically significant higher chest radiography abnormalities, fraction of inspired oxygen (FiO2), and uraemia. The adjusted odds ratio for death vs discharge between waves I and II was similar (0.94, 95%CI: 0.84-1.05). Wave I had a longer average survival time (8.0 vs 6.1 days) and a shorter average length of stay among patients discharged alive (9.2 vs 10.7 days). LCA revealed that the cardiovascular phenotype had the highest mortality, followed by diabetes and CKD phenotypes. Only Diabetes and hypertension phenotypes had the lowest mortality. Conclusion Even though clinical and laboratory characteristics differed significantly between the two waves, mortality remained constant. According to LCA, the cardiovascular, diabetes, and CKD phenotypes had the highest death probability.

The epidemiological and clinical characteristics of COVID-19 in a high HIV/TB burden district level hospital setting (preprint)

Background: The coronavirus disease 2019 (COVID-19) pandemic continues to evolve. Globally, COVID-19 continues to strain even the most resilient healthcare systems, with Omicron being the latest variant. We made a thorough search for literature describing the effects of the COVID-19 in a high human immunodeficiency virus (HIV)/tuberculosis (TB) burden district-level hospital setting. We found scanty literature.Methods: A retrospective observational study was conducted at Khayelitsha District Hospital in Cape Town, South Africa (SA) over the period March 2020 – December 2021. We included confirmed COVID-19 cases with HIV infection aged from 18 years and above. Analysis was performed to identify predictors of mortality or hospital discharge among people living with HIV (PLWH). Predictors investigated include CD4 count, antiretroviral therapy (ART), TB, non-communicable diseases, haematological, and biochemical parameters.Findings: This cohort of PLWH with SARS-CoV-2 infection had a median (IQR) age of 46 (37–54) years, male sex distribution of 29.1%, and a median (IQR) CD4 count of 267 (141–457) cells/mm3. Of 255 patients, 195 (76%) patients were discharged, 60 (24%) patients died. One hundred and sixty-nine patients (88%) were on ART with 73(28%) patients having acquired immunodeficiency syndrome (AIDS). After multivariate analysis, smoking (risk ratio [RR]: 2.86 (1.75–4.69)), neutrophilia [RR]: 1.024 (1.01–1.03), and glycated haemoglobin A1 (HbA1c) [RR]: 1.01 (1.007–1.01) were associated with mortality.Conclusion: The district hospital had a high COVID-19 mortality rate among PLWH. Easy-to-access biomarkers such as CRP, neutrophilia, and HbA1c may play a significant role in informing clinical management to prevent high mortality due to COVID-19 in PLWH at the district-level hospitals.