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.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).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.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.
Patient retention in care for HIV/AIDS is a critical challenge for antiretroviral treatment programs. Community-based adherence programs (CBAPs) as compared to health care facility-based adherence programs have been considered as one of the options to provide treatment maintenance support for groups of patients on antiretroviral therapy. Such an approach provides a way of enhancing self-management of the patient's condition. In addition, CBAPs have been implemented to support antiretroviral treatment expansion in resource-limited settings. CBAPs involve 30 patients that are allocated to a group and meet at either a facility or a community venue for less than an hour every 2 or 3 months depending on the supply of medication. Our study aimed to establish perceived challenges in moving adherence clubs from health facilities to communities.A qualitative study was conducted in 39 clinics in Mpumalanga and Gauteng Provinces in South Africa between December 2015 and January 2016. Purposive sampling method was used to identify nurses, club managers, data capturers, pharmacists and pharmacy assistants who had been involved in facility-based treatment adherence clubs. Key-informant interviews were conducted. Also, semi-structured interviews were used and thematic content analysis was done.A total of 53 health care workers, 12 (22.6%) males and 41 (77.4%) females, participated in the study. Most of them 49 (92.5%) indicated that participating in community adherence clubs were a good idea. Reduction in waiting time at the health facilities, in defaulter rate, improvement in adherence to treatment as well as reduction in stigma were some of the perceived benefits. However, security of medication, storage conditions and transportation of the prepacked medication to the distribution sites were the areas of concern.Health care workers were agreeable to idea of the moving adherence clubs from health facilities to communities. Although some challenges were identified, these could be addressed by the key stakeholders. However, government and nongovernmental organizations need to exercise caution when transitioning to community-based adherence clubs.
Topic: 30. Infections in hematology (incl. supportive care/therapy) Background: Studies from Asia, Europe and the USA indicate that widely available haematological parameters could be used to determine the clinical severity of Coronavirus disease 2019 (COVID-19) and predict management outcomes. There is limited date from Africa on their usefulness in patients admitted to intensive care units (ICUs). We performed an evaluation of baseline haematological parameters as prognostic biomarkers in ICU COVID-19 patients. Aims: To evaluate baseline “routine” haematological parameters in patients admitted to the ICU of a tertiary academic hospital during the first and second COVID-19 waves and correlate these to the disease severity and outcome. Methods: Demographic, clinical and laboratory data were collected prospectively on patients with confirmed COVID-19, admitted to the adult ICU at Tygerberg Hospital, the 1384 bed teaching hospital for Stellenbosch University Faculty of Medicine and Health Sciences. The study period was March 2020 to February 2021. Robust Poisson regression methods and receiver operating characteristic (ROC) curves were used to explore the association between haematological parameters and COVID-19 severity and mortality. Results: A total of 490 patients (median age 54.1 years) were included of whom 237(48%) were female. The median duration of ICU stay was 6 days and 309/490 (63%) patients died. Raised neutrophil counts and neutrophil/lymphocyte ratios (NLR) were associated with worse outcomes. Independent risk factors associated with mortality were age (ARR 1.01, 95% CI 1.0-1.02; female sex (ARR 1.23, 95% CI 1.05-1.42; p=0.008) and D-dimer levels (ARR 1.01, 95% CI 1.002-1.03; p=0.016). Summary/Conclusion: Our study showed that raised neutrophil count, NLR and D-dimer at the time of ICU admission were associated with higher mortality. Contrary t what has been previously reported, our study revealed that females admitted to the ICU had a higher risk of mortality than males. Keywords: COVID-19, Neutropenia, Lymphocyte, Prognostic factor
Abstract Background Tuberculosis (TB) is one of the leading cause of morbidity and mortality among people living with HIV/AIDS. The growing burden of TB/HIV co-infection continues to strain the healthcare system due to association with long duration of treatment. This is a catalyst for poor treatment adherence, which is a major public health challenge due to its propensity to drive drug resistance. This study evaluated the effects of treatment duration on adherence to treatment among TB/HIV co-infected patients. Method This was a cross sectional study that involved 10427 patients’ ≥18 years of age with HIV infection and co-infected with TB. We measure adherence to clinic appointments as ’good’ if ≥ 90% and poor if < 90%. We used multivariate logistic regression to evaluate factors associated with adherence to clinic appointments. Results 1528 patients were co-infected with TB, of these 17.4% of them attained good adherence to clinic appointments. Patients with TB/HIV co-infection who were on treatment for a longer period were less likely to adhere to clinic appointments (AOR: 0.98 95% CI: 0.97, 0.99). This confirms the notion that advanced disease has a negative impact on adherence to clinic attendance for follow up reviews. Conclusions Duration on treatment among TB/HIV co-infected patients is associated with treatment adherence. It is therefore vital to reinforce public health intervention that would enhance sustained treatment adherence and mitigate its impact on development of drug resistance.
Abstract Leveraging the potential of virtual platforms in the post‐COVID‐19 era, the Infection and Pulmonary Vascular Diseases Consortium (iPVDc), with the support of the Pulmonary Vascular Research Institute (PVRI), launched a globally accessible educational program to highlight top‐notch research on inflammation and infectious diseases affecting the lung vasculature. This innovative virtual series has already successfully brought together distinguished investigators across five continents – Asia, Europe, South and North America, and Africa. Moreover, these open global forums have contributed to a comprehensive understanding of the complex interplay among immunology, inflammation, infection, and cardiopulmonary health, especially concerning pulmonary hypertension and related pulmonary disorders. These enlightening discussions have not only heightened awareness about the impact of various pathogenic microorganisms, including fungi, parasites, and viruses, on the onset and development of pulmonary vascular diseases but have also cast a spotlight on co‐infections and neglected illnesses like schistosomiasis ‐ a disease that continues to impose a heavy socioeconomic burden in numerous regions worldwide. Thus, the overall goal of this review article is to present the most recent breakthroughs from infectious PVDs as well as bring to light the scientific and educational insights from the 2023 iPVDc/PVRI virtual symposium series, shaping our understanding of these crucial health issues in this more than ever interconnected world.
Background Data on mortality rates are crucial to guide health interventions in crisis-affected and resource-poor settings. The methods currently available to collect mortality data in such settings feature important methodological limitations. We developed and validated a new method to provide near real-time mortality estimates in such settings.
Several observational studies have found an association between maternal Cadmium (Cd) exposure and Small for Gestational Age (SGA). However, these findings are inconsistent. We conducted this meta-analysis to evaluate the relationship between maternal cadmium exposure and SGA risk. A comprehensive search was performed through PubMed, Scopus, Embase, Web of Science, Cochrane Library and OpenGrey to retrieve all pertinent studies published before October 2020. A combined odds ratio (OR) and corresponding 95% confidence interval (CI) were employed to examine this correlation. As a result, nine eligible studies met the inclusion criteria and were included in a systematic review, of those six studies containing sample type of blood were included in meta-analysis, and present meta-analysis showed that maternal cadmium exposure increased the risk of SGA 1.31 times (OR = 1.31; 95% CI = 1.16–1.47 for highest versus lowest category of cadmium). This meta-analysis suggests that maternal Cd exposure may be a risk factor for SGA. However, large prospective studies from different ethnic populations with consideration of other influencing parameters are needed to confirm this finding.
Background: There has been no consensus on the ideal way to measure adherence in resource limited settings (RLS). Viral load is perhaps the most reliable indicator of poor adherence but is not easily accessible in RLS. We aimed to identify routinely collected markers that could be used to assess adherence to ART. Methods & Materials: Retrospective analysis of HIV-positive ART-naïve adults (≥18 years) initiating standard first-line ART at the Themba Lethu Clinic in Johannesburg, South Africa between April 2004 and January 2012. We assessed the association between the last self-reported adherences, change in mean cell volume (MCV) calculated from baseline to 6 months, change in CD4 count calculated from baseline to 6 months and missed visits and poor adherence (defined as a viral load ≥400 copies/ml after 6 months on ART). Poisson regression models with robust error variance were constructed to estimate incidence rate ratio (IRR) and 95% confidence interval (CI). The IRR was used to approximate the relative risk (RR) of poor adherence. Results: A total of 7160 patients were eligible for the study. Of these, 18.9% had poor adherence at 6 months. The marker of poor adherence was change in CD4 count stratified by change in MCV at 6 months (change in CD4 ≥expected and change in MCV <14.5fL; Attributable risk ratio (aRR) 3.11, 95% CI 2.41-4.02, change in CD4
'/%3e%3cuse xlink:href='%23a' transform='rotate(72 0 0)'/%3e%3cuse xlink:href='%23a' transform='rotate(-72 0 0)'/%3e%3cuse xlink:href='%23a' transform='scale(-1 1) rotate(72)'/%3e%3c/g%3e%3c/defs%3e%3cpath fill='%23012169' d='M0 0h1200v600H0z'/%3e%3cpath stroke='%23FFF' stroke-width='60' d='m0 0 600 300M0 300 600 0' clip-path='url(%23b)'/%3e%3cpath stroke='%23C8102E' stroke-width='40' d='m0 0 600 300M0 300 600 0' clip-path='url(%23c)'/%3e%3cpath stroke='%23FFF' stroke-width='100' d='M300 0v300M0 150h600' clip-path='url(%23b)'/%3e%3cpath stroke='%23C8102E' stroke-width='60' d='M300 0v300M0 150h600' clip-path='url(%23b)'/%3e%3cuse xlink:href='%23d' fill='%23FFF' transform='matrix(45.4 0 0 45.4 900 120)'/%3e%3cuse xlink:href='%23d' fill='%23C8102E' transform='matrix(30 0 0 30 900 120)'/%3e%3cg transform='rotate(82 900 240)'%3e%3cuse xlink:href='%23d' fill='%23FFF' transform='rotate(-82 519.022 -457.666) scale(40.4)'/%3e%3cuse xlink:href='%23d' fill='%23C8102E' transform='rotate(-82 519.022 -457.666) scale(25)'/%3e%3c/g%3e%3cg transform='rotate(82 900 240)'%3e%3cuse xlink:href='%23d' fill='%23FFF' transform='rotate(-82 668.57 -327.666) scale(45.4)'/%3e%3cuse xlink:href='%23d' fill='%23C8102E' transform='rotate(-82 668.57 -327.666) scale(30)'/%3e%3c/g%3e%3cuse xlink:href='%23d' fill='%23FFF' transform='matrix(50.4 0 0 50.4 900 480)'/%3e%3cuse xlink:href='%23d' fill='%23C8102E' transform='matrix(35 0 0 35 900 480)'/%3e%3c/svg%3e)
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)
'%3e%3cg id='e'%3e%3cg id='c' fill='none' stroke='%23fff' stroke-width='2'%3e%3cpath id='b' d='M0 1h26M1 10V5h8v4h8V5h-5M4 9h2m20 0h-5V5h8m0-5v9h8V0m-4 0v9' transform='scale(1.4)'/%3e%3cpath id='a' d='M0 7h9m1 0h9' transform='scale(2.8)'/%3e%3cuse xlink:href='%23a' y='120'/%3e%3cuse xlink:href='%23b' y='145.2'/%3e%3c/g%3e%3cg id='d'%3e%3cuse xlink:href='%23c' x='56'/%3e%3cuse xlink:href='%23c' x='112'/%3e%3cuse xlink:href='%23c' x='168'/%3e%3c/g%3e%3c/g%3e%3cuse xlink:href='%23d' x='168'/%3e%3cuse xlink:href='%23e' x='392'/%3e%3c/g%3e%3cg fill='%23da0000' transform='matrix(45 0 0 45 315 180)'%3e%3cg id='f'%3e%3cpath d='M-.548.836A.912.912 0 0 0 .329-.722 1 1 0 0 1-.548.836'/%3e%3cpath d='M.618.661A.764.764 0 0 0 .422-.74 1 1 0 0 1 .618.661M0 1l-.05-1L0-.787a.31.31 0 0 0 .118.099V-.1l-.04.993zM-.02-.85 0-.831a.144.144 0 0 0 .252-.137A.136.136 0 0 1 0-.925'/%3e%3c/g%3e%3cuse xlink:href='%23f' transform='scale(-1 1)'/%3e%3c/g%3e%3c/svg%3e)
'%3e%3cpath fill='%23001489' d='M0 0v6h9V0z'/%3e%3cpath fill='%23e03c31' d='M0 0v3h9V0z'/%3e%3cg stroke='%23fff' stroke-width='2'%3e%3cpath id='d' d='m0 0 4.5 3L0 6m4.5-3H9'/%3e%3cuse xlink:href='%23b' stroke='%23ffb81c' clip-path='url(%23c)'/%3e%3c/g%3e%3cuse xlink:href='%23d' fill='none' stroke='%23007749' stroke-width='1.2'/%3e%3c/g%3e%3c/svg%3e)
'/%3e%3c/defs%3e%3cpath fill='%23012169' d='M0 0h10080v5040H0z'/%3e%3cpath stroke='%23fff' stroke-width='.6' d='m0 0 6 3m0-3L0 3' clip-path='url(%23b)' transform='scale(840)'/%3e%3cpath stroke='%23e4002b' stroke-width='.4' d='m0 0 6 3m0-3L0 3' clip-path='url(%23c)' transform='scale(840)'/%3e%3cpath stroke='%23fff' stroke-width='840' d='M2520 0v2520M0 1260h5040'/%3e%3cpath stroke='%23e4002b' stroke-width='504' d='M2520 0v2520M0 1260h5040'/%3e%3cg fill='%23fff'%3e%3cuse xlink:href='%23d' x='2520' y='3780'/%3e%3cuse xlink:href='%23a' x='7560' y='4200'/%3e%3cuse xlink:href='%23a' x='6300' y='2205'/%3e%3cuse xlink:href='%23a' x='7560' y='840'/%3e%3cuse xlink:href='%23a' x='8680' y='1869'/%3e%3cuse xlink:href='%23e' x='8064' y='2730'/%3e%3c/g%3e%3c/svg%3e)
