Background The interaction between COVID-19, non-communicable diseases, and chronic infectious diseases such as HIV and tuberculosis is unclear, particularly in low-income and middle-income countries in Africa. South Africa has a national HIV prevalence of 19% among people aged 15-49 years and a tuberculosis prevalence of 0•7% in people of all ages. Using a nationally representative hospital surveillance system in South Africa, we aimed to investigate the factors associated with in-hospital mortality among patients with COVID-19. MethodsIn this cohort study, we used data submitted to DATCOV, a national active hospital surveillance system for COVID-19 hospital admissions, for patients admitted to hospital with laboratory-confirmed SARS-CoV-2 infection between March 5, 2020, and March 27, 2021. Age, sex, race or ethnicity, and comorbidities (hypertension, diabetes, chronic cardiac disease, chronic pulmonary disease and asthma, chronic renal disease, malignancy in the past 5 years, HIV, and past and current tuberculosis) were considered as risk factors for COVID-19-related in-hospital mortality. COVID-19 in-hospital mortality, the main outcome, was defined as a death related to COVID-19 that occurred during the hospital stay and excluded deaths that occurred because of other causes or after discharge from hospital; therefore, only patients with a known in-hospital outcome (died or discharged alive) were included. Chained equation multiple imputation was used to account for missing data and random-effects multivariable logistic regression models were used to assess the role of HIV status and underlying comorbidities on COVID-19 in-hospital mortality. FindingsAmong the 219 265 individuals admitted to hospital with laboratory-confirmed SARS-CoV-2 infection and known in-hospital outcome data, 51 037 (23•3%) died. Most commonly observed comorbidities among individuals with available data were hypertension in 61 098 (37•4%) of 163 350, diabetes in 43 885 (27•4%) of 159 932, and HIV in 13 793 (9•1%) of 151 779. Tuberculosis was reported in 5282 (3•6%) of 146 381 individuals. Increasing age was the strongest predictor of COVID-19 in-hospital mortality. Other factors associated were HIV infection (adjusted odds ratio 1•34, 95% CI 1•27-1•43), past tuberculosis (1•26, 1•15-1•38), current tuberculosis (1•42, 1•22-1•64), and both past and current tuberculosis (1•48, 1•32-1•67) compared with never tuberculosis, as well as other described risk factors for COVID-19, such as male sex; non-White race; underlying hypertension, diabetes, chronic cardiac disease, chronic renal disease, and malignancy in the past 5 years; and treatment in the public health sector. After adjusting for other factors, people with HIV not on antiretroviral therapy (ART; adjusted odds ratio 1•45, 95% CI 1•22-1•72) were more likely to die in hospital than were people with HIV on ART. Among people with HIV, the prevalence of other comorbidities was 29•2% compared with 30•8% among HIV-uninfected individuals. Increasing number of comorbidities was associated with...

Background The first wave of COVID-19 in South Africa peaked in July, 2020, and a larger second wave peaked in January, 2021, in which the SARS-CoV-2 501Y.V2 (Beta) lineage predominated. We aimed to compare in-hospital mortality and other patient characteristics between the first and second waves.Methods In this prospective cohort study, we analysed data from the DATCOV national active surveillance system for COVID-19 admissions to hospital from March 5, 2020, to March 27, 2021. The system contained data from all hospitals in South Africa that have admitted a patient with COVID-19. We used incidence risk for admission to hospital and determined cutoff dates to define five wave periods: pre-wave 1, wave 1, post-wave 1, wave 2, and post-wave 2. We compared the characteristics of patients with COVID-19 who were admitted to hospital in wave 1 and wave 2, and risk factors for in-hospital mortality accounting for wave period using random-effect multivariable logistic regression.Findings Peak rates of COVID-19 cases, admissions, and in-hospital deaths in the second wave exceeded rates in the first wave: COVID-19 cases, 240•4 cases per 100 000 people vs 136•0 cases per 100 000 people; admissions, 27•9 admissions per 100 000 people vs 16•1 admissions per 100 000 people; deaths, 8•3 deaths per 100 000 people vs 3•6 deaths per 100 000 people. The weekly average growth rate in hospital admissions was 20% in wave 1 and 43% in wave 2 (ratio of growth rate in wave 2 compared with wave 1 was 1•19, 95% CI 1•18-1•20). Compared with the first wave, individuals admitted to hospital in the second wave were more likely to be age 40-64 years (adjusted odds ratio [aOR] 1•22, 95% CI 1•14-1•31), and older than 65 years (aOR 1•38, 1•25-1•52), compared with younger than 40 years; of Mixed race (aOR 1•21, 1•06-1•38) compared with White race; and admitted in the public sector (aOR 1•65, 1•41-1•92); and less likely to be Black (aOR 0•53, 0•47-0•60) and Indian (aOR 0•77, 0•66-0•91), compared with White; and have a comorbid condition (aOR 0•60, 0•55-0•67).For multivariable analysis, after adjusting for weekly COVID-19 hospital admissions, there was a 31% increased risk of in-hospital mortality in the second wave (aOR 1•31, 95% CI 1•28-1•35). In-hospital case-fatality risk increased from 17•7% in weeks of low admission (<3500 admissions) to 26•9% in weeks of very high admission (>8000 admissions; aOR 1•24, 1•17-1•32).Interpretation In South Africa, the second wave was associated with higher incidence of COVID-19, more rapid increase in admissions to hospital, and increased in-hospital mortality. Although some of the increased mortality can be explained by admissions in the second wave being more likely in older individuals, in the public sector, and by the increased health system pressure, a residual increase in mortality of patients admitted to hospital could be related to the new Beta lineage.

<p><strong>Objectives.</strong> Anxiety disorders are the most common childhood psychiatric disorders. Previous research suggests that South African rates may be high. Our study examined the prevalence and severity of anxiety and depression among Grade 11 and 12 learners attending schools in central Bloemfontein. Learners’ perception of the important stressors as well as the most relevant coping strategies were investigated.<strong></strong></p><p><strong> Methods.</strong> A cross-sectional study was conducted by using self-assessment rating scales and questionnaires. The Hospital Anxiety and Depression Scale (HADS) was used to screen for anxiety and depressive symptoms. Participants were provided with an additional list of possible stressors and coping skills, from which they identified those applicable to themselves. All students enrolled in Grades 11 and 12 at the selected schools during August 2009 were eligible for inclusion.</p><p><strong>Results.</strong> Five hundred and fifteen learners participated in the study, of whom 32.0% presented with moderate or severe anxiety and 5.3% with moderate or severe depressive symptoms. Mild symptoms were reported by an additional 29.0% on the anxiety subscale and 14% on the depression subscale of the HADS. Academic workload was reported as the main source of stress (81.4%).</p><p><strong>Conclusions.</strong> Although the study has limitations in terms of methodology and size, resulting in undetermined validity, it indicates possible higher prevalence rates for anxiety and depression than in previous South African studies and worldwide prevalence rates for adolescents. Pupils were generally hesitant to seek help from formal assistance structures provided by the schools, and preferred discussing problems with parents or friends.</p>

Conclusion. In only 16% of cases, observati were found unaccountable because of epilepsy (automatisms) or postictal confusional states. Our findings confirmed an increased prevalence of violent behaviour during seizure-free periods. This contributes to the evidence that factors associated with epilepsy, rather than epilepsy itself, play an important role in the possible increased risk of violent behaviour in people with epilepsy.

articles Erectile dysfunction (ED) is defined as the consistent inability to achieve and/or maintain an erection sufficient for satisfactory sexual performance, over a 3-month period.1 ED is a common sexual disorder with a prevalence rate of 40% among 40-yearold men, increasing to 70% among 70-year-olds. Methods. An obser vational analytical study was undertaken of 100 consecutive male patients of all ages presenting with ED (with a score less than 20 on the 5-item intensity scale for ED). Age, race, marital and employment status were noted as well as social habits including smoking and alcohol use. The presence of known medical conditions and surgical procedures was ascertained. All current prescription medication was recorded. Panic disorder, obsessive-compulsive disorder, generalised anxiety disorder and social phobia were rated using the Mini International Neuropsychiatric Interview, while the Hamilton Rating Scale for Depression was used to rate depressive symptoms.Results. Thirty-three per cent of respondents had depressive symptoms, and of this group 36% had a co-morbid anxiety disorder. In total, 21% of patients had an anxiety disorder.Anxiety disorders were more common with moderate to severe ED. No anxiety disorders occurred in patients with mild ED. The majority of participants suffering from severe ED were evenly spread in age from 30 to 69 years.Participants suffering from moderate to severe ED were more likely to have medical conditions, most notably hypertension. Conclusion.The results of this study suggest that men suffering from ED are likely to have a co-morbid psychiatric disorder (42%), with the prevalence of depressive symptoms (33%) and anxiety disorders (21%) being higher than in the general population. Significant concomitant medical conditions (most notably hypertension) were more common in men with moderate to severe ED.