Hand, foot and mouth disease (HFMD) is a contagious communicable disease, with a high incidence in children aged under 10 years. It is a mainly self-limiting disease but can also cause serious neurological or cardiopulmonary complications in some cases, which can lead to death. Little is known about the burden of HMFD on primary care health care services in the UK. The aim of this work was to describe trends in general practitioner (GP) consultations for HFMD in England from January 2017 to December 2022 using a syndromic surveillance network of GPs. Daily GP consultations for HFMD in England were extracted from 1 January 2017 to 31 December 2022. Mean weekly consultation rates per 100,000 population and 95% confidence intervals (CI) were calculated. Consultation rates and rate ratios (RR) were calculated by age group and sex. During the study period, the mean weekly consultation rate for HFMD (per 100,000 registered GP patients) was 1.53 (range of 0.27 to 2.47). In England, children aged 1–4 years old accounted for the largest affected population followed by children <1 years old. We observed a seasonal pattern of HFMD incidence during the non-COVID years, with a seasonal peak of mean weekly rates between months of September and December. HFMD is typically diagnosed clinically rather than through laboratory sampling. Therefore, the ability to look at the daily HFMD consultation rates provides an excellent epidemiological overview on disease trends. The use of a novel GP-in-hours surveillance system allowed a unique epidemiological insight into the recent trends of general practitioner consultations for HFMD. We demonstrate a male predominance of cases, the impact of the non-pharmaceutical interventions during the COVID-19 pandemic, and a change in the week in which the peak number of cases happens post-pandemic.

Multinomial processing tree models assume that an observed behavior category can arise from one or more processing sequences represented as branches in a tree. These models form a subclass of parametric, multinomial models, and they provide a substantively motivated alternative to loglinear models. We consider the usual case where branch probabilities are products of nonnegative integer powers in the parameters, 0≤θs≤1, and their complements, 1 - θs. A version of the EM algorithm is constructed that has very strong properties. First, the E-step and the M-step are both analytic and computationally easy; therefore, a fast PC program can be constructed for obtaining MLEs for large numbers of parameters. Second, a closed form expression for the observed Fisher information matrix is obtained for the entire class. Third, it is proved that the algorithm necessarily converges to a local maximum, and this is a stronger result than for the exponential family as a whole. Fourth, we show how the algorithm can handle quite general hypothesis tests concerning restrictions on the model parameters. Fifth, we extend the algorithm to handle the Read and Cressie power divergence family of goodness-of-fit statistics. The paper includes an example to illustrate some of these results.

Despite the enormous literature on federalism in constitutional design, and the growing attention to gender equality in constitutional design, there has been remarkably little attention paid to the interaction between the two. This article seeks to provide a summary of the existing literature on this intersection, to apply the insights of that literature to the case of Myanmar, and to offer a contribution concerning the theoretical connections between federalism and gender equality. The analysis generates four primary conclusions. First, federalism is inherently neither good nor bad for gender equality: it all depends on the details of the federal system and the context in which they are applied. Second, there are, nonetheless, some guidelines that can be gleaned from the experiences of countries around the world about the design elements that can make federalism more or less useful for promoting gender equality under different conditions. Third, applying these elements in the case of Myanmar suggests that women's organisations might make common cause with the ethnic minority groups that are negotiating with the government and the army over federalism issues because the women share with these groups certain goals with respect to federal systems. And fourth, there is a connection between gender and federalism, not at the pragmatic or design level, but at the theoretical level. This connection concerns the type of (ideal) orientation that is required of citizens in a federal system and the ways in which that orientation might be valuable for gender equality. It is, then, the character of federal citizens, rather than the federal system itself, that could be inherently beneficial to gender equality.

Measures of effective test length are developed for speeded and power tests, which are independent of the number of items in the test or of the time required for administration. These measures are used in determining reliability for (1) speeded and power tests, where a separately timed short parallel form is administered in addition to the full-length test; (2) power tests, where a subset of items is imbedded within the total test, parallel to the total test; and (3) power tests, where the subset of items is correlated with the complementary parallel subset in the test.

A general approach to the analysis of subjective categorical data is considered, in which agreement matrices of two or more raters are directly expressed in terms of error and agreement parameters. The method provides focused analyses of ratings from several raters for whom ratings have measurement error distributions that may induce bias in the evaluation of substantive questions of interest. Each rater's judgment process is modeled as a mixture of two components: an error variable that is unique for the rater in question as well as an agreement variable that operationalizes the “true” values of the units of observation. The statistical problems of identification, estimation, and testing of such measurement models are discussed.

The general model is applied in several special cases. The most simple situation is that underlying Cohen's Kappa, where two raters place units into unordered categories. The model provides a generalization and systematization of the Kappa-idea to correct for agreement by chance. In applications with typical research designs, including a between-subjects design and a mixed within-subjects, between-subjects design, the model is shown to disentangle structural and measurement components of the observations, thereby controlling for possible confounding effects of systematic rater bias. Situations considered include the case of more than two raters as well as the case of ordered categories. The different analyses are illustrated by means of real data sets.

We propose a hierarchical Bayesian model for analyzing multi-site experimental fMRI studies. Our method takes the hierarchical structure of the data (subjects are nested within sites, and there are multiple observations per subject) into account and allows for modeling between-site variation. Using posterior predictive model checking and model selection based on the deviance information criterion (DIC), we show that our model provides a good fit to the observed data by sharing information across the sites. We also propose a simple approach for evaluating the efficacy of the multi-site experiment by comparing the results to those that would be expected in hypothetical single-site experiments with the same sample size.

Forty-two choice models, each representing stimuli by one-dimensional probability distributions, are obtained by relaxing the assumptions of Thurstone's Case V Law of Comparative Judgment. The models which imply or fail to imply each of nine testable probabilistic conditions are determined. Stochastic transitivity is vulnerable in most of these models. The results suggest discarding weak stochastic transitivity, and in its place using the conjunction of weak stochastic transitivity and the triangular condition. However, unless it is possible to predict which stimuli will produce violations of the conditions, none of the conditions can be rejected on the basis of too frequent intransitive triads of choices.

Formulas for the standard error of measurement of three measures of change—simple difference scores, residualized difference scores, and the measure introduced by Tucker, Damarin, and Messick—are derived. Equating these formulas by pairs yields additional explicit formulas which provide a practical guide for determining the relative error of the three measures in any pretest-posttest design. The functional relationship between the standard error of measurement and the correlation between pretest and posttest observed scores remains essentially the same for each of the three measures despite variations in other test parameters (reliability coefficients, standard deviations), even when pretest and posttest errors of measurement are correlated.

In this paper graphs are given for estimating a correlation ρXY from a double-entry table. 2 Hr, twice the statistic Hr, “relative uncertainty” (the average of the ratios of the conditional uncertainty of each variable to its uncertainty) was computed. Mean 2 Hr's were computed from 100 samples for each of several sample sizes and numbers of categories. Graphs relating mean 2 Hr to \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\hat r_H$$\end{document}, the estimated correlation, for 3–17 categories to each variable and 3 sample sizes—50, 100, and 500—were constructed. 2.5 per cent and 97.5 per cent fiducial limits curves were constructed for establishing confidence limits on ρXY and \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\hat r_H$$\end{document}.

This study develops Markov Chain Monte Carlo (MCMC) estimation theory for the General Condorcet Model (GCM), an item response model for dichotomous response data which does not presume the analyst knows the correct answers to the test a priori (answer key). In addition to the answer key, respondent ability, guessing bias, and difficulty parameters are estimated. With respect to data-fit, the study compares between the possible GCM formulations, using MCMC-based methods for model assessment and model selection. Real data applications and a simulation study show that the GCM can accurately reconstruct the answer key from a small number of respondents.

A general model is presented for homogeneous, dichotomous items when the answer key is not known a priori. The model is structurally related to the two-class latent structure model with the roles of respondents and items interchanged. For very small sets of respondents, iterative maximum likelihood estimates of the parameters can be obtained by existing methods. For other situations, new estimation methods are developed and assessed with Monte Carlo data. The answer key can be accurately reconstructed with relatively small sets of respondents. The model is useful when a researcher wants to study objectively the knowledge possessed by members of a culturally coherent group that the researcher is not a member of.

A Cultural Consensus Theory approach for ordinal data is developed, leading to a new model for ordered polytomous data. The model introduces a novel way of measuring response biases and also measures consensus item values, a consensus response scale, item difficulty, and informant knowledge. The model is extended as a finite mixture model to fit both simulated and real multicultural data, in which subgroups of informants have different sets of consensus item values. The extension is thus a form of model-based clustering for ordinal data. The hierarchical Bayesian framework is utilized for inference, and two posterior predictive checks are developed to verify the central assumptions of the model.

Multinomial processing tree (MPT) models are theoretically motivated stochastic models for the analysis of categorical data. Here we focus on a crossed-random effects extension of the Bayesian latent-trait pair-clustering MPT model. Our approach assumes that participant and item effects combine additively on the probit scale and postulates (multivariate) normal distributions for the random effects. We provide a WinBUGS implementation of the crossed-random effects pair-clustering model and an application to novel experimental data. The present approach may be adapted to handle other MPT models.

A method is provided for estimating the nonspurious correlation of a part of a test with the total test. Two cases are considered: one in which the actual subtest is parallel to the total test, the other in which the actual subtest is not parallel to the total test.

Cultural Consensus Theory (CCT) models have been applied extensively across research domains in the social and behavioral sciences in order to explore shared knowledge and beliefs. CCT models operate on response data, in which the answer key is latent. The current paper develops methods to enhance the application of these models by developing the appropriate specifications for hierarchical Bayesian inference. A primary contribution is the methodology for integrating the use of covariates into CCT models. More specifically, both person- and item-related parameters are introduced as random effects that can respectively account for patterns of inter-individual and inter-item variability.

The problem of combining abilities of group members to maximize the performance of the group as a whole is examined in terms of redundancy in task assignments. In particular, ways of distributing a given number of items of information among a given number of individuals to obtain the maximum probability of each item being recalled by at least one individual are studied. It is shown that there exists an optimal distribution scheme which is independent of the amount of material originally given, the size of the group, and individual differences in ability.

Debate surrounds the early peopling of the Arabian Peninsula. The first evidence of the Levallois lithic technology in the Huqf area of south-eastern Arabia now extends the Middle Palaeolithic record of hominin activity into central Oman and helps to diversify the picture of Arabian prehistory.