Effective and appropriate hand-washing practice for schoolchildren is important in preventing infectious diseases such as diarrhoea, which is the second most common cause of death among school-age children in sub-Saharan Africa. The objective of the review was to identify hand hygiene intervention strategies to reduce infectious diseases such as diarrhoea and respiratory tract infections among schoolchildren aged 6–12 years in developing countries. Published research articles were searched from databases covering a period from as far back as the creation of the databases to November 2015. Eight randomized controlled trials (RCT/CRCT) from developing countries met the inclusion criteria. The Jadad Scale for appraising RCT/CRCT studies revealed methodological challenges in most studies, such that 75% (6/8) were rated as low-quality articles. The review found that hand hygiene can reduce the incidence of diarrhoea and respiratory conditions. Three hand hygiene intervention strategies utilized were training, funding and policy, with training and funding implemented more commonly than policy. These strategies were not only used in isolation but also in combination, and they qualified as multi-level interventions. Factors that influenced hand washing were contextual, psychosocial and technological. Findings can inform school health workers in categorizing and prioritizing activities into viable strategies when implementing multi-level hand-washing interventions. This review also adds to the existing evidence that multi-level hand-washing interventions can reduce the incidence of diarrhoea, respiratory infections, and school absenteeism. Further evidence-based studies are needed with improved methodological rigour in developing countries, to inform policy in this area. Full article

Objectives: To examine the associations between objectively-assessed moderate-to-vigorous physical activity (MVPA) and perceived/objective measures of neighbourhood recreational facilities categorized into indoor or outdoor, public, residential or commercial facilities. The associations between facility perceptions and objectively-assessed numbers of recreational facilities were also examined. Method: A questionnaire was used on 480 adults to measure local facility perceptions, with 154 participants wearing ActiGraph accelerometers for ≥4 days. The objectively-assessed number of neighbourhood recreational facilities were examined using direct observations and Geographical Information System data. Results: Both positive and negative associations were found between MVPA and perceived/objective measures of recreational facilities. Some associations depended on whether the recreational facilities were indoor or outdoor, public or residential facilities. The objectively-assessed number of most public recreational facilities was associated with the corresponding facility perceptions, but the size of effect was generally lower than for residential recreational facilities. Conclusions: The objectively-assessed number of residential outdoor table tennis courts and public indoor swimming pools, the objectively-assessed presence of tennis courts and swimming pools, and the perceived presence of bike lanes and swimming pools were positive determinants of MVPA. It is suggested to categorize the recreational facilities into smaller divisions in order to identify unique associations with MVPA. Full article

In the medical field, many outcome variables are dichotomized, and the two possible values of a dichotomized variable are referred to as classes. A dichotomized dataset is class-imbalanced if it consists mostly of one class, and performance of common classification models on this type of dataset tends to be suboptimal. To tackle such a problem, resampling methods, including oversampling and undersampling can be used. This paper aims at illustrating the effect of resampling methods using the National Health and Nutrition Examination Survey (NHANES) wave 2009–2010 dataset. A total of 4677 participants aged ≥20 without self-reported diabetes and with valid blood test results were analyzed. The Classification and Regression Tree (CART) procedure was used to build a classification model on undiagnosed diabetes. A participant demonstrated evidence of diabetes according to WHO diabetes criteria. Exposure variables included demographics and socio-economic status. CART models were fitted using a randomly selected 70% of the data (training dataset), and area under the receiver operating characteristic curve (AUC) was computed using the remaining 30% of the sample for evaluation (testing dataset). CART models were fitted using the training dataset, the oversampled training dataset, the weighted training dataset, and the undersampled training dataset. In addition, resampling case-to-control ratio of 1:1, 1:2, and 1:4 were examined. Resampling methods on the performance of other extensions of CART (random forests and generalized boosted trees) were also examined. CARTs fitted on the oversampled (AUC = 0.70) and undersampled training data (AUC = 0.74) yielded a better classification power than that on the training data (AUC = 0.65). Resampling could also improve the classification power of random forests and generalized boosted trees. To conclude, applying resampling methods in a class-imbalanced dataset improved the classification power of CART, random forests, and generalized boosted trees. Full article