Hospital food services and the resulting food waste impact patient satisfaction, health outcomes, healthcare costs, and the environment. This cross-sectional study assessed food waste and patient satisfaction in five public hospitals in Cyprus, involving 844 inpatients. Patient characteristics and responses to the 21-item Acute Care Hospital Foodservice Patient Satisfaction Questionnaire (ACHFPSQ) were recorded. Plate waste was evaluated using photographs and a five-point visual scale (0 to 1) to estimate food consumption. Hunger and overall satisfaction were also assessed. While 77.8% rated food services as good or very good, food quality received the most negative feedback. Only 31.2% finished their main dish entirely; 29.5% and 26.3% left ¼ and ½, respectively. For dessert, 48.2% finished it, while 13.3% left it untouched. These findings reveal a gap between general satisfaction and perceived food quality, underscoring the need for targeted public health strategies to enhance food quality and reduce waste in hospitals.

Cross-cutting issues like nutrition have not been adequately addressed for children with severe visual impairment studying in integrated schools of Nepal. To support advocacy, this study aimed to determine the nutritional status of this vulnerable group, using a descriptive cross-sectional design involving 101 students aged 5–19 years from two integrated public schools near Kathmandu Valley and two in western Nepal. The weight-for-age z-score (WAZ), height-for-age z-score (HAZ), and body mass index-for-age z-score (BAZ) were computed and categorised using World Health Organization cut-off values (overnutrition: z-score > +2.0 standard deviations (SD), healthy weight: z-score −2.0SD to +2.0SD, moderate undernutrition: z-score ≥ −3.0SD to <−2.0SD, severe undernutrition: z-score < −3.0 SD) to assess nutritional status. A child was considered to have undernutrition for any z-scores <−2.0SD. Multivariate logistic regression was used to analyse variables linked to undernutrition. The mean age of participants was 11.86 ± 3.66 years, and the male-to-female ratio was nearly 2:1. Among the participants, 71.29% had blindness, and 28.71% had low vision. The mean BAZ and HAZ scores decreased with age. The WAZ, HAZ, and BAZ scores indicated that 6.46% were underweight, 20.79% were stunted, and 5.94% were thin, respectively. Overall, 23.76% of students had undernutrition and 7.92% had overnutrition. More than three in ten students had malnutrition and stunting was found to be prevalent. Older students and females were more likely to have undernutrition. These findings highlight the need for nutrition interventions within inclusive education settings, particularly targeting girls with visual impairments who may face compounded vulnerabilities.

Random-effects meta-analyses with only a few studies often face challenges in accurately estimating between-study heterogeneity, leading to biased effect estimates and confidence intervals with poor coverage. This issue is especially the case when dealing with rare diseases. To address this problem for normally distributed outcomes, two new approaches have been proposed to provide confidence limits of the global mean: one based on fiducial inference, and the other involving two modifications of the signed log-likelihood ratio test statistic in order to have improved performance with small numbers of studies. The performance of the proposed methods was evaluated numerically and compared with the Hartung–Knapp–Sidik–Jonkman approach and its modification to handle small numbers of studies. The simulation results indicated that the proposed methods achieved coverage probabilities closer to the nominal level and produced shorter confidence intervals compared to those based on existing methods. Two real examples are used to illustrate the proposed methods.

Epidemiological studies have reported an association between the planetary health diet (PHD), diet-related greenhouse gas emissions (GHGEs), and mortality. However, data from individuals from non-Western countries was limited. Therefore, we aimed to examine this association among Japanese individuals using a cross-sectional ecological study of all 47 prefectures in Japan. Prefecture-level data were obtained from government surveys. The dietary amount was estimated based on the weight of food purchased (211 items) from the 2021–2023 Family Income and Expenditure Survey. Adherence to PHD was scored using the EAT-Lancet index (range, 0 [worst] to 42 [best]) and categorised into four groups: ≤ 24 (n = 14, low), 25 (n = 17, medium-low), 26 (n = 10, medium-high), and 27 points (n = 6, high). Diet-related GHGEs were estimated using previously developed GHGE tables for each food item. Mortality data were obtained using the 2022 Vital Statistics. Mortality rate ratio (RR) was calculated using a multivariate Poisson regression model. After adjusting for confounders, compared to the prefecture in the medium-low group of adherence score, those in the low and high groups were associated with a higher mortality RR for all-cause (low group: RR = 1.03 [95% CI (confidence interval) = 1.01–1.05]; high group: RR = 1.03 [95% CI = 1.00–1.07]) and pneumonia. Moreover, although a higher adherence score was inversely associated with GHGE, it was linked to an increased mortality risk from heart disease and stroke. Our findings indicate a reverse J-shaped association between adherence to PHD and mortality.

This cross-sectional study aimed to investigate the correlation between magnesium consumption and periodontitis in different body mass index (BMI) and waist circumference (WC) groups. 8385 adults who participated in the National Health and Nutrition Examination Survey during 2009–2014 were included. The correlation between dietary magnesium intake and periodontitis was first tested for statistical significance by descriptive statistics and weighted binary logistic regression. Subgroup analysis and interaction tests were performed to investigate whether the association was stable in different BMI and WC groups. There was a statistical difference in magnesium intake between periodontitis and non-periodontitis populations. In model 3, participants with the highest magnesium consumption had an odds ratio of 0.72 (0.57-0.92) for periodontitis compared to those with the lowest magnesium consumption. However, in subgroup analysis, the relationship between magnesium intake and periodontitis remained significant only in the non-general obese (BMI ≤ 30 kg/m2) and non-abdominal obese populations (WC ≤ 102 cm in men and ≤ 88 cm in women). Dietary magnesium intake might decrease the periodontitis prevalence in the American population, and this beneficial periodontal health role of magnesium consumption might only be evident in non-general obese and non-abdominal obese populations.

This study aimed to assess the extent to which first-morning void (FMV) urine samples can estimate sodium and potassium excretion compared with 24-hour (24-h) urine samples at the population level. We conducted a cross-sectional study collecting urine samples (FMV and 24-h) and two non-consecutive 24-h dietary recalls in a sub-sample from the Portuguese IAN-AF sampling frame. Six predictive equations were used to estimate 24-h sodium and potassium excretion from FMV urine samples. Pearson correlation coefficients were calculated to compare the association between FMV and 24-h urine collections. Cross-classifications into tertiles were computed to calculate the agreement between measured and estimated excretion with and without calibration. Pearson correlation coefficients were calculated to compare the excretion estimation from FMV and reported intake from 24-h dietary recalls. Bland–Altman plots assessed the agreement between two-day dietary recall and the best-performing calibrated equation. Data from eighty-six subjects aged 18–84 were analysed. Estimated sodium and potassium concentrations from the predictive equations moderate or strongly correlated with the measured 24-h urine samples. The Toft equation was the most predictive and reliable, displaying a moderate correlation (r=0.655) with no risk of over or underestimation of sodium excretion (p=0.096). Tanaka and Kawasaki equations showed a similar moderate correlation (r=0.54 and r=0.58, respectively) but tended to underestimate the 24-h urine excretion of potassium (p<0.001). Calibrated predictive equations using FMV urine samples provide a moderately accurate alternative and resource-efficient option for large-scale nutritional epidemiology studies when 24-h urine collection is impractical.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with significant social, communicative, and behavioral challenges, and its prevalence is increasing globally at an alarming rate. Children with ASD often have nutritional imbalances, and multiple micronutrient deficiencies. Among these, zinc (Zn2+) deficiency is prominent and has gained extensive scientific interest over the past few years. Zn2+ supports numerous proteins, including enzymes and transcription factors, and controls neurogenesis and cell differentiation. It modulates synaptic transmission and plasticity by binding to receptors, ion channels, and transporters. These interactions are crucial, as changes in these processes may contribute to cognitive and behavioral abnormalities in neurodevelopmental disorders, including ASD. Notably, mutations in genes linked to ASD result in Zn2+ dyshomeostasis, altering pivotal biological processes. In addition, Zn2+ promotes gut health by maintaining gut wall integrity, preventing inflammation and leaky gut, preventing translocation of gut bacteria and their metabolites into systemic circulation, and supporting cognitive processes via the gut–brain axis. Zn2+ deficiency during pregnancy alters gut microbiota composition, induces pro-inflammatory cytokine production, may affect neuronal functioning, and is associated with ASD etiology in offspring, as well as the exacerbation of autistic traits in genetically predisposed children. This review focuses on Zn2+ dyshomeostasis, discussing various Zn2+-dependent dysfunctions underlying distinct autistic phenotypes and describing recent progress in the neurobiology of individuals with ASD and animal models.

Prior studies have shown that plant-based diets are associated with lower cardiovascular risk. However, these diets encompass a large diversity of foods with contrasted nutritional quality that may differentially impact health. We aimed to investigate the pooled cross-sectional association between metabolic syndrome (MetS), its components and healthy and unhealthy plant-based diet indices (hPDI and uPDI), using data from two French cohorts and one representative study from the French population. This study included 16 358 participants from the NutriNet-Santé study, 1769 participants from the Esteban study and 1565 participants from the STANISLAS study who underwent a clinical visit. The MetS was defined according to the International Diabetes Federation definition. The associations between these plant-based diet indices and MetS were estimated by multivariable Poisson and logistic regression models, stratified by gender. Meta-analysis enabled the computation of a pooled prevalence ratio. A higher contribution of healthy plant foods (higher hPDI) was associated with a lower probability of having MetS (PRmen: 0·85; 95 % CI: 0·75, 0·94, PRwomen: 0·72; 95 % CI: 0·67, 0·77), elevated waist circumferences and elevated blood pressure. In women, a higher hPDI was associated with a lower probability of having elevated triacylglyceride (TAG), low HDL-cholesterolaemia and hyperglycaemia; and a higher contribution of unhealthy plant foods was associated with a higher prevalence of MetS (PRwomen: 1·13; 95 % CI: 1·01, 1·26) and elevated TAG. A greater contribution of healthy plant floods was associated with protective effects on metabolic syndrome, especially in women. Gender differences should be further investigated in relation to the current sustainable nutrition transition.

India’s nutrition transition has led to an increased burden of overweight/obesity (body mass index of ≥23 kg/m2), driven by lifestyle factors like poor diet, inactivity, and substance use, prompting public health interventions. However, these interventions lack supporting evidence, especially in rural areas, hindering effective strategies for this population. To address this evidence gap, this study used cohort data (baseline: 2018–19, follow-up: 2022–23) from the Birbhum Population Project (West Bengal, India) to analyse lifestyle risk factors and their association with incidence and remission of overweight/obesity among adults aged ≥18 years (sample: 8,974). Modified Poisson regression model was employed to attain the study objective. From 2017–2018 to 2022–2023, the prevalence of overweight/obesity increased from 15.2% (95% CI: 14.1%–16.4%) to 21.0% (95% CI: 19.7%–22.3%) among men and from 24.1% (95% CI: 22.9%–25.2%) to 33.8% (95% CI: 32.5%–35.1%) among women. Overall, 23.0% (95% CI: 21.8%–24.3%) of adults experienced incidence of overweight/obesity, while 13.9% (95% CI: 12.4%–15.6%) experienced remission. Use of motor vehicles among unemployed participants was associated with incident overweight/obesity (relative risk or RR: 1.058; 95% CI: 1.023–1.095; P: 0.001). Vigorous activity at home (including gardening, yard work, and household chores) was linked to higher odds of recovering from overweight/obesity (RR: 1.065; 95% CI: 1.008–1.125; P: 0.025). Frequent tobacco use (often/daily vs. none) was inversely associated with remission of overweight-obesity (RR: 0.689; 95% CI: 0.484–0.980; P: 0.038), as was each 1 ml in alcohol consumption (RR: 0.995; 95% CI: 0.991–0.999; P: 0.022). Discouraging habitual motor vehicle use may help prevent overweight/obesity, while promoting home-based activities may aid remission, particularly for women who are at higher risk for overweight/obesity.

This cross-sectional ecological study described fruit and vegetable (F&V) intake variability across 144 cities in 8 Latin American countries and by city-level contextual variables. Data sources came from health surveys and census data (Argentina, Brazil, Chile, Colombia, El Salvador, Guatemala, Mexico, and Peru). Self-reported frequency of F&V intake was harmonised across surveys. Daily F&V intake was considered as consumption 7 d of the week. Using a mixed-effects model, we estimated age and sex-standardised city prevalences of daily F&V intake. Through Kruskal–Wallis tests, we compared city F&V daily intake prevalence by tertiles of city variables related to women’s empowerment, socio-economics, and climate zones. The median prevalence for daily F&V intake was 55.7% across all cities (22.1% to 85.4%). Compared to the least favourable tertile of city conditions, F&V daily intake prevalence was higher for cities within the most favourable tertile of per capita GDP (median = 65.7% vs. 53.0%), labour force participation (median = 68.7% vs. 49.4%), women achievement-labour force score (median = 63.9% vs. 45.7%), and gender inequality index (median = 58.6% vs. 48.6%). Also, prevalences were higher for temperate climate zones than arid climate zones (median = 65.9% vs. 50.6%). No patterns were found by city level of educational attainment, city size, or population density. This study provides evidence that the prevalence of daily F&V intake varies across Latin American cities and may be favoured by higher socio-economic development, women’s empowerment, and temperate weather. Interventions to improve F&V intake in Latin America should consider the behaviour disparities related to underlying local social, economic, and climate zone characteristics.

Differential item functioning (DIF) analysis is an important step in establishing the validity of measurements. Most traditional methods for DIF analysis use an item-by-item strategy via anchor items that are assumed DIF-free. If anchor items are flawed, these methods will yield misleading results due to biased scales. In this article, based on the fact that the item’s relative change of difficulty difference (RCD) does not depend on the mean ability of individual groups, a new DIF detection method (RCD-DIF) is proposed by comparing the observed differences against those with simulated data that are known DIF-free. The RCD-DIF method consists of a D-QQ (quantile quantile) plot that permits the identification of internal references points (similar to anchor items), a RCD-QQ plot that facilitates visual examination of DIF, and a RCD graphical test that synchronizes DIF analysis at the test level with that at the item level via confidence intervals on individual items. The RCD procedure visually reveals the overall pattern of DIF in the test and the size of DIF for each item and is expected to work properly even when the majority of the items possess DIF and the DIF pattern is unbalanced. Results of two simulation studies indicate that the RCD graphical test has Type I error rate comparable to those of existing methods but with greater power.

Behavioral and psychological researchers have shown strong interests in investigating contextual effects (i.e., the influences of combinations of individual- and group-level predictors on individual-level outcomes). The present research provides generalized formulas for determining the sample size needed in investigating contextual effects according to the desired level of statistical power as well as width of confidence interval. These formulas are derived within a three-level random intercept model that includes one predictor/contextual variable at each level to simultaneously cover various kinds of contextual effects that researchers can show interest. The relative influences of indices included in the formulas on the standard errors of contextual effects estimates are investigated with the aim of further simplifying sample size determination procedures. In addition, simulation studies are performed to investigate finite sample behavior of calculated statistical power, showing that estimated sample sizes based on derived formulas can be both positively and negatively biased due to complex effects of unreliability of contextual variables, multicollinearity, and violation of assumption regarding the known variances. Thus, it is advisable to compare estimated sample sizes under various specifications of indices and to evaluate its potential bias, as illustrated in the example.

In practice, it is common that a best fitting structural equation model (SEM) is selected from a set of candidate SEMs and inference is conducted conditional on the selected model. Such post-selection inference ignores the model selection uncertainty and yields too optimistic inference. Using the largest candidate model avoids model selection uncertainty but introduces a large variation. Jin and Ankargren (Psychometrika 84:84–104, 2019) proposed to use frequentist model averaging in SEM with continuous data as a compromise between model selection and the full model. They assumed that the true values of the parameters depend on $n^{-1/2}$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$n^{-1/2}$$\end{document} with n being the sample size, which is known as a local asymptotic framework. This paper shows that their results are not directly applicable to SEM with ordinal data. To address this issue, we prove consistency and asymptotic normality of the polychoric correlation estimators under the local asymptotic framework. Then, we propose a new frequentist model averaging estimator and a valid confidence interval that are suitable for ordinal data. Goodness-of-fit test statistics for the model averaging estimator are also derived.

Researchers have widely used exploratory factor analysis (EFA) to learn the latent structure underlying multivariate data. Rotation and regularised estimation are two classes of methods in EFA that they often use to find interpretable loading matrices. In this paper, we propose a new family of oblique rotations based on component-wise $L^p$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$L^p$$\end{document} loss functions $(0<p\le 1)$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$(0 < p\le 1)$$\end{document} that is closely related to an $L^p$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$L^p$$\end{document} regularised estimator. We develop model selection and post-selection inference procedures based on the proposed rotation method. When the true loading matrix is sparse, the proposed method tends to outperform traditional rotation and regularised estimation methods in terms of statistical accuracy and computational cost. Since the proposed loss functions are nonsmooth, we develop an iteratively reweighted gradient projection algorithm for solving the optimisation problem. We also develop theoretical results that establish the statistical consistency of the estimation, model selection, and post-selection inference. We evaluate the proposed method and compare it with regularised estimation and traditional rotation methods via simulation studies. We further illustrate it using an application to the Big Five personality assessment.

In a recent paper, Bedrick derived the asymptotic distribution of Lord's modified sample biserial correlation estimator and studied its efficiency for bivariate normal populations. We present a more detailed examination of the properties of Lord's estimator and several competitors, including Brogden's estimator. We show that Lord's estimator is more efficient for three nonnormal distributions than a generalization of Pearson's sample biserial estimator. In addition, Lord's estimator is reasonably efficient relative to the maximum likelihood estimator for these distributions. These conclusions are consistent with Bedrick's results for the bivariate normal distribution. We also study the small sample bias and variance of Lord's estimator, and the coverage properties of several confidence interval estimates.

In this paper, we apply sequential one-sided confidence interval estimation procedures with β-protection to adaptive mastery testing. The procedures of fixed-width and fixed proportional accuracy confidence interval estimation can be viewed as extensions of one-sided confidence interval procedures. It can be shown that the adaptive mastery testing procedure based on a one-sided confidence interval with β-protection is more efficient in terms of test length than a testing procedure based on a two-sided/fixed-width confidence interval. Some simulation studies applying the one-sided confidence interval procedure and its extensions mentioned above to adaptive mastery testing are conducted. For the purpose of comparison, we also have a numerical study of adaptive mastery testing based on Wald's sequential probability ratio test. The comparison of their performances is based on the correct classification probability, averages of test length, as well as the width of the “indifference regions.” From these empirical results, we found that applying the one-sided confidence interval procedure to adaptive mastery testing is very promising.

Social scientists are frequently interested in assessing the qualities of social settings such as classrooms, schools, neighborhoods, or day care centers. The most common procedure requires observers to rate social interactions within these settings on multiple items and then to combine the item responses to obtain a summary measure of setting quality. A key aspect of the quality of such a summary measure is its reliability. In this paper we derive a confidence interval for reliability, a test for the hypothesis that the reliability meets a minimum standard, and the power of this test against alternative hypotheses. Next, we consider the problem of using data from a preliminary field study of the measurement procedure to inform the design of a later study that will test substantive hypotheses about the correlates of setting quality. The preliminary study is typically called the “generalizability study” or “G study” while the later, substantive study is called the “decision study” or “D study.” We show how to use data from the G study to estimate reliability, a confidence interval for the reliability, and the power of tests for the reliability of measurement produced under alternative designs for the D study. We conclude with a discussion of sample size requirements for G studies.

It has long been part of the item response theory (IRT) folklore that under the usual empirical Bayes unidimensional IRT modeling approach, the posterior distribution of examinee ability given test response is approximately normal for a long test. Under very general and nonrestrictive nonparametric assumptions, we make this claim rigorous for a broad class of latent models.

In applications of item response theory (IRT), it is often of interest to compute confidence intervals (CIs) for person parameters with prescribed frequentist coverage. The ubiquitous use of short tests in social science research and practices calls for a refinement of standard interval estimation procedures based on asymptotic normality, such as the Wald and Bayesian CIs, which only maintain desirable coverage when the test is sufficiently long. In the current paper, we propose a simple construction of second-order probability matching priors for the person parameter in unidimensional IRT models, which in turn yields CIs with accurate coverage even when the test is composed of a few items. The probability matching property is established based on an expansion of the posterior distribution function and a shrinkage argument. CIs based on the proposed prior can be efficiently computed for a variety of unidimensional IRT models. A real data example with a mixed-format test and a simulation study are presented to compare the proposed method against several existing asymptotic CIs.

Reporting effect size index estimates with their confidence intervals (CIs) can be an excellent way to simultaneously communicate the strength and precision of the observed evidence. We recently proposed a robust effect size index (RESI) that is advantageous over common indices because it’s widely applicable to different types of data. Here, we use statistical theory and simulations to develop and evaluate RESI estimators and confidence/credible intervals that rely on different covariance estimators. Our results show (1) counter to intuition, the randomness of covariates reduces coverage for Chi-squared and F CIs; (2) when the variance of the estimators is estimated, the non-central Chi-squared and F CIs using the parametric and robust RESI estimators fail to cover the true effect size at the nominal level. Using the robust estimator along with the proposed nonparametric bootstrap or Bayesian (credible) intervals provides valid inference for the RESI, even when model assumptions may be violated. This work forms a unified effect size reporting procedure, such that effect sizes with confidence/credible intervals can be easily reported in an analysis of variance (ANOVA) table format.