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BJECTIVES: The Bristol Stool Form Scale (BSFS) is inadequate for non-toilet trained children. The Brussels Infant and Toddler Stool Scale (BITSS) was developed, consisting of 7 photographs of diapers containing stools of infants and toddlers. We aimed to evaluate interobserver reliability of stool consistency assessment among parents, nurses, and medical doctors (MDs) using the BITSS. METHODS: In this multicenter cross-sectional study (2016-2017), BITSS photographs were rated according to the BSFS. The reliability of the BITSS was evaluated using the overall proportion of perfect agreement and the linearly weighted κ statistic. RESULTS: A total of 2462 observers participated: 1181 parents (48.0%), 624 nurses (25.3%), and 657 MDs (26.7%). The best-performing BITSS photographs corresponded with BSFS type 7 (87.5%) and type 4 (87.6%), followed by the BITSS photographs representing BSFS type 6 (75.0%), BSFS type 5 (68.0%), BSFS type 1 (64.8%), and BSFS type 3 (64.6%). The weakest performing BITSS photograph corresponded with BSFS type 2 (49.7%). The overall weighted κ-value was 0.72 (95% CI 0.59-0.85; good agreement). Based on these results, photographs were categorized per stool group as hard (BSFS type 1-3), formed (BSFS type 4), loose (BSFS types 5 and 6), or watery (BSFS type 7) stools. According to this new categorization system, correct allocation for each photograph ranged from 83 to 96% (average: 90%). The overall proportion of correct allocations was 72.8%. CONCLUSIONS: BITSS showed good agreement with BSFS. Using the newly categorized BITSS photographs, the BITSS is reliable for the assessment of stools of non-toilet trained children in clinical practice and research. A multilanguage translated version of the BITSS can be downloaded at https://bitss-stoolscale.com/.

Additional file 1. Table S1. Clinical coding grouping. Table S2. Registers that provided national hospital discharge statistics to the present study. Table S3. Non-included countries in Europe: data provider contacted and reason for exclusion. Table S4. Mean annual number and mean annual rate of hospital discharges for all-cause acute gastroenteritis as first-listed diagnosis in participating European countries between 2004 and 2015. Table S5. Goodness of fit. Table S6. List of Data Providers / Requirements.

The BITSS scale was validated as a reliable instrument to assess stools of non-toilet trained children via assessment of interobserver reliability among 2462 study participants.

Background: Norovirus is the leading cause of community-acquired and nosocomial acute gastroenteritis. Routine testing for norovirus is seldom undertaken, and diagnosis is mainly based on presenting symptoms. This makes understanding the burden of medically attended norovirus-attributable gastroenteritis (MA-NGE) and targeting care and prevention strategies challenging. Methods: We used linked population-based healthcare datasets (Clinical Practice Research Datalink General Practice OnLine Database linked with Hospital Episode Statistics Admitted Patient Care) to model the incidence of MA-NGE associated with primary care consultations or hospitalizations according to age groups in England in the period July 2007-June 2013. Results: Mean annual incidence rates of MA-NGE were 4.9/1000 person-years and 0.7/1000 person-years for episodes involving primary care or hospitalizations, respectively. Incidence rates were highest in children aged 65 years (1.7/1000 person-years). Conclusions: In this particular study, the burden of MA-NGE estimated from healthcare datasets was higher than previously estimated in small cohort studies in England. Routinely collected primary care and hospitalization datasets are useful resources to estimate and monitor the burden of MA-NGE in a population over time.

Analyzing the combined effects of genes and/or environmental factors on the development of complex diseases is a great challenge from both the statistical and computational perspective, even using a relatively small number of genetic and nongenetic exposures. Several data-mining methods have been proposed for interaction analysis, among them, the Multifactor Dimensionality Reduction Method (MDR) has proven its utility in a variety of theoretical and practical settings. Model-Based Multifactor Dimensionality Reduction (MB-MDR), a relatively new MDR-based technique that is able to unify the best of both nonparametric and parametric worlds, was developed to address some of the remaining concerns that go along with an MDR analysis. These include the restriction to univariate, dichotomous traits, the absence of flexible ways to adjust for lower order effects and important confounders, and the difficulty in highlighting epistatic effects when too many multilocus genotype cells are pooled into two new genotype groups. We investigate the empirical power of MB-MDR to detect gene–gene interactions in the absence of any noise and in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Power is generally higher for MB-MDR than for MDR, in particular in the presence of genetic heterogeneity, phenocopy, or low minor allele frequencies. T. Cattaert, F. Van Lishout, J. M. Mahachie John and K. Van Steen acknowledge research opportunities offered by the Belgian Network BioMAGNet (Bioinformatics and Modelling: from Genomes to Networks), funded by the Interuniversity Attraction Poles Programme (Phase VI/4), initiated by the Belgian State, Science Policy Office. Their work was also supported in part by the IST Programme of the European Community, under the PASCAL2 Network of Excellence (Pattern Analysis, Statistical Modelling and Computational Learning), IST-2007-216886. In addition, F. Van Lishout acknowledges support by Alma in Silico, funded by the European Commission and Walloon Region through the Interreg IV Program. The work of M. L. Calle and V. Urrea has been supported by Grant MTM2008-06747-C02-02 from the Ministerio de Educacion y Ciencia, Grant 050831 from La Marato de TV3 Foundation, and Grant 2009SGR-581 from AGAUR-Generalitat de Catalunya. S. Dudek and M. D. Ritchie are supported by NIH grants LM010040 and HL065962.