Background Many nutrients have powerful immunomodulatory actions with the potential to alter susceptibility to coronavirus disease 2019 (COVID-19) infection, progression to symptoms, likelihood of severe disease, and survival. Objective The aim was to review the latest evidence on how malnutrition across all its forms (under- and overnutrition and micronutrient status) may influence both susceptibility to, and progression of, COVID-19. Methods We synthesized information on 13 nutrition-related components and their potential interactions with COVID-19: overweight, obesity, and diabetes; protein-energy malnutrition; anemia; vitamins A, C, D, and E; PUFAs; iron; selenium; zinc; antioxidants; and nutritional support. For each section we provide: 1) a landscape review of pertinent material; 2) a systematic search of the literature in PubMed and EMBASE databases, including a wide range of preprint servers; and 3) a screen of 6 clinical trial registries. All original research was considered, without restriction to study design, and included if it covered: 1) severe acute respiratory syndrome coronavirus (CoV) 2 (SARS-CoV-2), Middle East respiratory syndrome CoV (MERS-CoV), or SARS-CoV viruses and 2) disease susceptibility or 3) disease progression, and 4) the nutritional component of interest. Searches took place between 16 May and 11 August 2020. Results Across the 13 searches, 2732 articles from PubMed and EMBASE, 4164 articles from the preprint servers, and 433 trials were returned. In the final narrative synthesis, we include 22 published articles, 38 preprint articles, and 79 trials. Conclusions Currently there is limited evidence that high-dose supplements of micronutrients will either prevent severe disease or speed up recovery. However, results of clinical trials are eagerly awaited. Given the known impacts of all forms of malnutrition on the immune system, public health strategies to reduce micronutrient deficiencies and undernutrition remain of critical importance. Furthermore, there is strong evidence that prevention of obesity and type 2 diabetes will reduce the risk of serious COVID-19 outcomes. This review is registered at PROSPERO as CRD42020186194.

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B y the end of October 2020, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic had spread to 6 continents and caused >45 million coronavirus disease (COVID-19) cases and 1.1 million deaths (1). Despite having 15.6% of the worldwide population (2), by October 31, 2020, Africa had only 3.9% (1.76 million) of the world's COVID-19 cases and 3.6% (42,233) of deaths during the pandemic (1). Data suggest that the pandemic is evolving differently in sub-Saharan Africa compared with the rest of the world and that the outbreak started later (3).Of note, severe COVID-19 cases seem to occur less frequently in Africa than in the rest of the world (4). Several factors have been proposed to explain this. Age is likely a major factor because older persons are at higher risk for severe disease, but Africa has an extremely young population; >60% of persons are <25 years of age (5). However, variation of CO-VID-19 severity with age alone does not fully explain the observed differences (4). Clinical cases and deaths in Africa likely are underreported because systematic surveillance is limited and no systematic death registration exists; thus, the true SARS-CoV-2 burden probably is underestimated (4). Nevertheless, local health systems in Africa, which have a lower capacity to deal with COVID-19 patients than healthcare systems in high-resource settings, were not overwhelmed, even at the peak of the epidemic (6). Although potential

IntroductionAccording to the WHO, 50% of deaths worldwide (40.1% in developing countries) are due to chronic non-communicable diseases (NCDs). Of these chronic NCDs, cardiovascular diseases remain the leading cause of death and disability in developed countries. The Framingham study has shown the importance of hypercholesterolemia as a primary risk factor. In Senegal, the epidemiology of dyslipidemia and obesity are still poorly understood due to the lack of comprehensive studies on their impact on the general population. This motivated this study to look into the key epidemiologic and socio-demographic determinants of these risk factors.MethodsIt was a cross-sectional descriptive epidemiological survey which included 1037 individuals selected by cluster sampling. Data were collected using a questionnaire following the WHO STEPwise approach. Socio-demographic, health and biomedical variables were collected. P value <0.05 was considered to be statistically significant.ResultsThe average age was 48 years with a female predominance (M: F of 0.6). The literacy rate was 65.2% and 44.7% of participants were from rural areas. The prevalence of hypercholesterolemia, hyperLDLemia, hypoHDLemia, hypertriglyceridemia and mixed hyperlipidemia were 56%, 22.5%, 12.4%, 7.11% and 1.9% respectively. One in four was obese (BMI> 30kg/m2) and 34.8% had abdominal obesity. The main factors significantly associated with dyslipidemia were obesity, urban dwelling, physical inactivity and a family history of dyslipidemia.ConclusionThe prevalence of dyslipidemia, obesity and other risk factors in the population was high needing immediate care for those affected and implementation of prevention strategies.

Multimorbidity (presence of ≥2 long term conditions (LTCs)) is a growing global health challenge, yet we know little about the experiences of those living with multimorbidity in low- and middle-income countries (LMICs). We therefore explore: 1) experiences of men and women living with multimorbidity in urban and rural Malawi including their experiences of burden of treatment and 2) examine the utility of Normalization Process Theory (NPT) and Burden of Treatment Theory (BOTT) for structuring analytical accounts of these experiences. We conducted in depth, semi-structured interviews with 32 people in rural (n = 16) and urban settings (n = 16); 16 males, 16 females; 15 under 50 years; and 17 over 50 years. Data were analysed thematically and then conceptualised through the lens of NPT and BOTT. Key elements of burden of treatment identified included: coming to terms with and gaining an understanding of life with multimorbidity; dealing with resulting disruptions to family life; the work of seeking family and community support; navigating healthcare systems; coping with lack of continuity of care; enacting self-management advice; negotiating medical advice; appraising treatments; and importantly, dealing with the burden of lack of treatments/services. Poverty and inadequate healthcare provision constrained capacity to deal with treatment burden while supportive social and community networks were important enabling features. Greater access to health information/education would lessen treatment burden as would better resourced healthcare systems and improved standards of living. Our work demonstrates the utility of NPT and BOTT for aiding conceptualisation of treatment burden issues in LMICs but our findings highlight that ‘lack’ of access to treatments or services is an important additional burden which must be integrated in accounts of treatment burden in LMICs.