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Growth measurements of children, such as weight and height, are monitored regularly, particularly in infancy, to assess whether or not a child's growth is normal when compared with a reference population of the same age and sex. Here, after a suitable power transformation to normality of the reference population, we model temporal evolution of the standardized deviation (Z-score) of the transformed measurement of a normal child from the reference population as a Gaussian process with zero mean and unit variance. This paper concentrates on modelling and fitting the serial correlation structure of the process, with the benefit that monitoring growth at specific ages is not crucial, statistically. Exploratory analysis of various observed correlation matrices has suggested that a particular two-parameter Markovian form is a good representation of the correlation function in infancy. The main implication for growth monitoring is that we only need to condition on the most recent Z-score to inform a clinician's judgement about a child's growth based on its current Z-score. Inferences about the correlation parameters derive from likelihood methods based either on observed Z-scores or, if raw data are unavailable, on an observed correlation matrix. The Markov model is compared with a previously studied six-parameter correlation model. Data from major child growth studies in Newcastle and Cambridge are used to illustrate the methods and compare predictions from the two models. We argue that the Markov model serves as a pragmatic choice for growth monitoring in infancy.

Objective: Patients with functional constipation can be classified according to symptoms and physiological parameters as either having a disorder of defecation or having normal defecation. It is hypothesized that the disordered defecation, where it exists, is a causative factor of the constipation. However, the utility of this classification has yet to be proven in terms of predicting response to therapy. The definitions are non-specific and based on tests that are done in an artificial setting and with derived normal ranges. It is therefore possible that the symptoms and physiological parameters of a defecatory disorder may occur as a continuous spectrum in these patients, rather than defining a discrete entity or subtype. The aim of this study was to use cluster analysis and factor analysis of defecatory symptoms and physiological parameters to look for evidence of subgroups in patients with functional constipation., Material and Methods: Consecutive patients presenting to a specialist constipation clinic and satisfying the inclusion criteria were assessed to determine the severity of defecatory symptoms, and underwent isotope defecating proctography and the Sitzmark transit study. Assessments were made contemporaneously and results of any test not performed within 6 weeks of the initial assessment were excluded. Principle components analysis and cluster analysis were performed to look for evidence of subgroups. Relationships between evacuatory symptoms, index parameters, and test results were explored. The detailed and unselected nature of the analyses produced hundreds of test results, and statistically significant results were critically evaluated in this context., Results: A total of 116 patients were studied (age range 18-73 years, mean 40.5 years). Based on the results of the transit study and proctography, 38% of patients showed evidence of slow transit constipation, 20% FDD (functional defecation disorder), 29% both, and 12% neither. Principle components analysis did not demonstrate an obvious dimension reduction for the variables tested. Cluster analysis (over 150 solutions tested) failed to show evidence of clustering. There were no useful predictive relationships between evacuatory symptoms, index parameters and test results., Conclusions: We used multiple statistical analyses to look for clustering and predictive relationships between clinical and physiological parameters in consecutive patients with functional constipation and found no evidence of the existence of a subgroup of patients with a defecatory disorder. This may be due to weaknesses in the study design, poor validity of the assessments performed, or that defecatory features do not identify a distinct pathophysiological entity, but rather are manifested variably as a continuous spectrum.

Background: 'Supported' employment stipulates that disabled people should have real jobs for real pay. This paper models kinds of supported employment, assesses how the support and placement features affect its outcomes and its quality from the perspective of the employees, and provides a dynamic model to help explore what types of interventions might promote greater social inclusion for people with learning and other disabilities., Method: Bayesian belief networks (BBNs) provide the general framework for modelling the relationships between the variables and features of interest. The structure, probabilistic specification and quality indicators were elicited from project advisory groups, including people with learning disability, and took into account a pilot survey of 30 individuals. A subsequent survey of 1,461 supported employees was used to update the model and to provide actual assessments of quality of placement., Results: We present the BBN methodology in some detail, as novel to this discipline. We show how the model was constructed, and its implications for supported employment. We derive indices for quality of placement, taking into account the views of clients. We show how survey and individual results can be used to update the model. Use of the model suggests that quality of placement is, on average, relatively high, with small differences between groups with differing primary disability., Conclusions: The BBN is the appropriate methodology to model complex relationships and interventions for problems such as these. The model developed in this study can be used to assess and improve the fit between people and jobs, both at the individual level and for groups of employees, and can take into account different kinds of quality for different stakeholders.

Background and Study Aims: The advent of magnification endoscopy may allow the macroscopic detection of unrecognised villous atrophy in patients with unsuspected coeliac disease. In addition, it may also be possible to use this method to assess the degree of villous atrophy. The aim of this study was to determine the accuracy of zoom endoscopy for the macroscopic evaluation of villous atrophy, in comparison with histological evaluation., Patients and Methods: The zoom endoscope provided a magnification capability of x 115. A scoring system (Z score) was devised for grading the appearances of villous atrophy: "Z1" for normal mucosa, "Z2" for stunted villi, "Z3" for markedly stunted villi (with ridges and pits) and "Z4" for a flat mucosa. A total of 53 consecutive patients with treated coeliac disease were followed up over almost 2 years using the Olympus GIF-Q240Z zoom endoscope; a total of 80 procedures were carried out. Four biopsies from the second part of the duodenum were taken from each patient for histological assessment. Histological assessment of villous atrophy was made by a pathologist blinded to the Z score. The correlation between the Z score and the histological score was assessed using the weighted kappa method., Results: The kappa score for the correlation between the macroscopic assessment of villous atrophy and the histology was 0.631, indicating fair to good reproducibility. Agresti's method revealed a very strong baseline association between the two methods ( P < 0.001). Zoom endoscopy had a positive predictive value of 83 % and a negative predictive value of 77 % in detecting villous atrophy., Conclusions: Our findings suggest that zoom endoscopy may be valuable in assessing the degree of villous atrophy. However, further studies are needed to assess its efficacy in routine practice as a screening or case-finding tool.

With the increasing development of water quality standards for all forms of water bodies and all forms of their uses, there is a greater need for models that can predict water quality in relation to these standards. This paper proposes an empirical method based on logistic regression techniques that deliver probabilities that given quality standards are met (i.e. water above or below the given standard). This technique is applied to data from a saline intrusion barrage (River Wansbeck, Northumberland, England) to predict low water quality events on the basis of UK water standards. The approach accurately classifies the overwhelming majority of pollution events that are caused by processes internal to the system. Misclassification can be ascribed to external processes, such as combined sewer outfalls. The general applicability of this method is compared to physically-based models.