Estimating the burden of disease attributable to unsafe water and lack of sanitation and hygiene in South Africa in 2000

Objectives To estimate the burden of disease attributable to unsafe water, sanitation and hygiene (WSH) by age group for South Africa in 2000. Design World Health Organization comparative risk assessment methodology was used to estimate the disease burden attributable to an exposure by comparing the observed risk factor distribution with a theoretical lowest possible population distribution. A scenario-based approach was applied for estimating diarrhoeal disease burden from unsafe WSH. Six exposure scenarios were defined based on the type of water and sanitation infrastructure and environmental faecal-oral pathogen load. For ‘intestinal parasites’ and schistosomiasis, the burden was assumed to be 100% attributable to exposure to unsafe WSH. Setting South Africa. Outcome measures Disease burden from diarrhoeal diseases, intestinal parasites and schistosomiasis, measured by deaths and disability-adjusted life years (DALYs). Results 13 434 deaths were attributable to unsafe WSH accounting for 2.6% (95% uncertainty interval 2.4 - 2.7%) of all deaths in South Africa in 2000. The burden was especially high in children under 5 years, accounting for 9.3% of total deaths in this age group and 7.4% of burden of disease. Overall, the burden due to unsafe WSH was equivalent to 2.6% (95% uncertainty interval 2.5 - 2.7%) of the total disease burden for South Africa, ranking this risk factor seventh for the country. Conclusions Unsafe WSH remains an important risk factor for disease in South Africa, especially in children under 5. High priority needs to be given to the provision of safe and sustainable sanitation and water facilities and to promoting safe hygiene behaviours, particularly among children.

Structure matters: Adoption of structured classification Approach in the context of cognitive presence classification

Within online learning communities, receiving timely and meaningful insights into the quality of learning activities is an important part of an effective educational experience. Commonly adopted methods – such as the Community of Inquiry framework – rely on manual coding of online discussion transcripts, which is a costly and time consuming process. There are several efforts underway to enable the automated classification of online discussion messages using supervised machine learning, which would enable the real-time analysis of interactions occurring within online learning communities. This paper investigates the importance of incorporating features that utilise the structure of on-line discussions for the classification of "cognitive presence" – the central dimension of the Community of Inquiry framework focusing on the quality of students' critical thinking within online learning communities. We implemented a Conditional Random Field classification solution, which incorporates structural features that may be useful in increasing classification performance over other implementations. Our approach leads to an improvement in classification accuracy of 5.8% over current existing techniques when tested on the same dataset, with a precision and recall of 0.630 and 0.504 respectively.

Nutritional status of children with cystic fibrosis measured by total body potassium as a marker of body cell mass: Lack of sensitivity of anthropometric measures

Objective: To investigate measures aimed at defining the nutritional status of cystic fibrosis (CF) populations, this study compared standard anthropometric measurements and total body potassium (TBK) as indicators of malnutrition. Methods: Height, weight, and TBK measurements of 226 children with CF from Royal Children's Hospital, Brisbane, Australia, were analyzed. Z scores for height for age, weight for age, and weight for height were analyzed by means of the National Centre for Health Statistics reference. TBK was measured by means of whole body counting and compared with predicted TBK for age. Two criteria were evaluated with respect to malnutrition: (1) a z score < -2.0 and (2) a TBK for age <80% of predicted. Results: Males and females with CF had lower mean height-for-age and weight-for-age z scores than the National Centre for Health Statistics reference (P < .01), but mean weight-for-height z score was not significantly different. There were no significant gender differences. According to anthropometry, only 7.5% of this population were underweight and 7.6% were stunted. However, with TBK as an indicator of nutritional status, 29.9% of males and 22.0% of females were malnourished. Conclusion: There are large differences in the percentage of patients with CF identified as malnourished depending on whether anthropometry or body composition data are used as the nutritional indicator. At an individual level, weight-based indicators are not sensitive indicators of suboptimal nutritional status in CF, significantly underestimating the extent of malnutrition. Current recommendations in which anthropometry is used as the indicator of malnutrition in CF should be revised.