Schwellnus, M.P. et al. (2018). Match injury incidence during the Super Rugby tournament is high : a prospective cohort study over five seasons involving 93 641 player-hours. Abstract: Objectives To determine the incidence and nature of injuries in the Super Rugby tournament over a 5-year period. Methods 482 male professional rugby union players from six South African teams participating in the Super Rugby tournament were studied (1020 player-seasons). Medical staff of participating teams (2012-2016 tournaments) recorded all time loss injuries (total injuries and match injuries) and exposure hours (93 641 total playing hours; 8032 match hours). Injury incidence, injured player proportion, severity (time lost), anatomical location, tissue type and activity/phase during which injury occurred are reported.Results The overall incidence of match injuries (per 1000 player-hours; 95% CI) for each year was as follows:

Background The rising burden of the ongoing COVID-19 epidemic in South Africa has motivated the application of modeling strategies to predict the COVID-19 cases and deaths. Reliable and accurate short and long-term forecasts of COVID-19 cases and deaths, both at the national and provincial level, are a key aspect of the strategy to handle the COVID-19 epidemic in the country. Methods In this paper we apply the previously validated approach of phenomenological models, fitting several non-linear growth curves (Richards, 3 and 4 parameter logistic, Weibull and Gompertz), to produce short term forecasts of COVID-19 cases and deaths at the national level as well as the provincial level. Using publicly available daily reported cumulative case and death data up until 22 June 2020, we report 5, 10, 15, 20, 25 and 30-day ahead forecasts of cumulative cases and deaths. All predictions are compared to the actual observed values in the forecasting period. Results We observed that all models for cases provided accurate and similar short-term forecasts for a period of 5 days ahead at the national level, and that the three and four parameter logistic growth models provided more accurate forecasts than that obtained from the Richards model 10 days ahead. However, beyond 10 days all models underestimated the cumulative cases. Our forecasts across the models predict an additional 23,551–26,702 cases in 5 days and an additional 47,449–57,358 cases in 10 days. While the three parameter logistic growth model provided the most accurate forecasts of cumulative deaths within the 10 day period, the Gompertz model was able to better capture the changes in cumulative deaths beyond this period. Our forecasts across the models predict an additional 145–437 COVID-19 deaths in 5 days and an additional 243–947 deaths in 10 days. Conclusions By comparing both the predictions of deaths and cases to the observed data in the forecasting period, we found that this modeling approach provides reliable and accurate forecasts for a maximum period of 10 days ahead.

Background. Ongoing quantification of the disease burden attributable to smoking is important to monitor and strengthen tobacco control policies.Objectives. To estimate the attributable burden due to smoking in South Africa for 2000, 2006 and 2012.Methods. We estimated attributable burden due to smoking for selected causes of death in South African (SA) adults aged ≥35 years for 2000, 2006 and 2012. We combined smoking prevalence results from 15 national surveys (1998 - 2017) and smoking impact ratios using national mortality rates. Relative risks between smoking and select causes of death were derived from local and international data. Results. Smoking prevalence declined from 25.0% in 1998 (40.5% in males, 10.9% in females) to 19.4% in 2012 (31.9% in males, 7.9% in females), but plateaued after 2010. In 2012 tobacco smoking caused an estimated 31 078 deaths (23 444 in males and 7 634 in females), accounting for 6.9% of total deaths of all ages (17.3% of deaths in adults aged ≥35 years), a 10.5% decline overall since 2000 (7% in males; 18% in females). Age-standardised mortality rates (and disability-adjusted life years (DALYs)) similarly declined in all population groups but remained high in the coloured population. Chronic obstructive pulmonary disease accounted for most tobacco-attributed deaths (6 373), followed by lung cancer (4 923), ischaemic heart disease (4 216), tuberculosis (2 326) and lower respiratory infections (1 950). The distribution of major causes of smoking-attributable deaths shows a middle- to high-income pattern in whites and Asians, and a middle- to low-income pattern in coloureds and black Africans. The role of infectious lung disease (TB and LRIs) has been underappreciated. These diseases comprised 21.0% of deaths among black Africans compared with only 4.3% among whites. It is concerning that smoking rates have plateaued since 2010. Conclusion. The gains achieved in reducing smoking prevalence in SA have been eroded since 2010. An increase in excise taxes is the most effective measure for reducing smoking prevalence. The advent of serious respiratory pandemics such as COVID-19 has increased the urgency of considering the role that smoking cessation/abstinence can play in the prevention of, and post-hospital recovery from, any condition.

Background. Identifying women with gestational diabetes mellitus (GDM) allows interventions to improve perinatal outcomes. A fasting plasma glucose (FPG) level ≥5.1 mmol/L is 100% specific for a diagnosis of GDM. The International Association of Diabetes and Pregnancy Study Groups acknowledges that FPG <4.5 mmol/L is associated with a low probability of GDM. Objectives. The validity of selective screening based on the presence of risk factors was compared with the universal application of FPG ≥4.5 mmol/L to identify women with GDM. FPG ≥4.5 mmol/L or the presence of one or more risk factors was assumed to indicate an intermediate to high risk of GDM and therefore the need for an oral glucose tolerance test (OGTT). Methods. Consecutive black South African (SA) women were recruited to a 2-hour 75 g OGTT at 24 -28 weeks' gestation in an urban community health clinic. Of 969 women recruited, 666 underwent an OGTT, and of these 589 were eligible for analysis. The glucose oxidase laboratory method was used to measure plasma glucose concentrations. The World Health Organization GDM diagnostic criteria were applied. All participants underwent a risk factor assessment. The χ 2 test was used to determine associations between risk factors and a positive diagnosis of GDM. The sensitivity and specificity of a positive diagnosis of GDM were calculated for FPG ≥4.5 mmol/L, FPG ≥5.1 mmol/L, and the presence of one or more risk factors. Results. The prevalence of overt diabetes mellitus and GDM was 0.5% and 7.0%, respectively. Risk factor-based selective screening indicated that 204/589 (34.6%) of participants needed an OGTT, but 18/41 (43.9%) of positive GDM diagnoses were missed. Universal screening using the FPG threshold of ≥4.5 mmol/L indicated that 152/589 (25.8%) of participants needed an OGTT, and 1/41 (2.4%) of positive diagnoses were missed. An FPG of ≥5.1 mmol/L identified 36/41 (87.8%) of GDM-positive participants. The sensitivity and specificity of the presence of one or more risk factors were 56% and 67%, respectively. The sensitivity and specificity of FPG ≥4.5 mmol/L were 98% and 80%, respectively. Conclusions. Universal screening using FPG ≥4.5 mmol/L had greater sensitivity and specificity in identifying GDM-affected women and required fewer women to undergo a resource-intensive diagnostic OGTT than risk factor-based selective screening. A universal screening strategy using FPG ≥4.5 mmol/L may be more efficient and cost-effective than risk factor-based selective screening for GDM in black SA women.

ObjectivesTo determine whether a team illness prevention strategy (TIPS) would reduce the incidence of acute illness during the Super Rugby tournament.MethodsWe studied 1340 male professional rugby union player seasons from six South African teams that participated in the Super Rugby tournament (2010–2016). Medical staff recorded all illnesses daily (126 850 player days) in a 3-year control (C: 2010–2012; 47 553 player days) and a 4-year intervention (I: 2013–2016; 79 297 player days) period. A five-element TIPS was implemented in the I period, following agreement by consensus. Incidence rate (IR: per 1000 player days; 95% CI) of all acute illnesses, illness by main organ system, infectious illness and illness burden (days lost due to illness per 1000 player days) were compared between C and I period.ResultsThe IR of acute illness was significantly lower in the I (5.5: 4.7 to 6.4) versus the C period (13.2: 9.7 to 18.0) (p<0.001). The IR of respiratory (C=8.6: 6.3 to 11.7; I=3.8: 3.3 to 4.3) (p<0.0001), digestive (C=2.5: 1.8 to 3.6; I=1.1: 0.8 to 1.4) (p<0.001), skin and subcutaneous tissue illness (C=0.7: 0.4 to 1.4; I=0.3: 0.2 to 0.5) (p=0.0238), all infections (C=8.4: 5.9 to 11.9; I=4.3: 3.7 to 4.9) (p<0.001) and illness burden (C=9.2: 6.8 to 12.5; I=5.7: 4.1 to 7.8) (p=0.0314) were significantly lower in the I versus the C period.ConclusionA TIPS during the Super Rugby tournament was associated with a lower incidence of all acute illnesses (59%), infectious illness (49%) and illness burden (39%). Our findings may have important clinical implications for other travelling team sport settings.