Objectives:The aim of this systematic review and meta-analysis is to assess the prevalence of loneliness in many countries worldwide which have different ways of assessing it.Design:Systematic review and meta-analysis.Setting:We searched seven electronic databases for English peer-reviewed studies published between 1992 and 2021.Participants:We selected English-language peer-reviewed articles, with data from non-clinical populations of community-dwelling older adults (>60 years), and with “loneliness” or “lonely” in the title.Measurements:A multilevel random-effects meta-analysis was used to estimate the prevalence of loneliness across studies and to pool prevalence rates for different measurement instruments, data collection methods, and countries.Results:Our initial search identified 2,021 studies of which 45 (k = 101 prevalence rates) were included in the final meta-analysis. The estimated pooled prevalence rate was 31.6% (n = 168,473). Measurement instrument was a statistically significant moderator of the overall prevalence of loneliness. Loneliness prevalence was lowest for single-item questions and highest for the 20-item University of California-Los Angeles Loneliness Scale. Also, differences between modes of data collection were significant: the loneliness prevalence was significantly the highest for face-to-face data collection and the lowest for telephone and CATI data collection. Our moderator analysis to look at the country effect indicated that four of the six dimensions of Hofstede also caused a significant increase (Power Distance Index, Uncertainty Avoidance Index, Indulgence) or decrease (Individualism) in loneliness prevalence.Conclusions:This study suggests that there is high variability in loneliness prevalence rates among community-dwelling older adults, influenced by measurement instrument used, mode of data collection, and country.

Objective: The aim of this study was to identify clinical and laboratory phenotype distribution patterns and their usefulness as prognostic markers in COVID-19 patients admitted to the intensive care unit (ICU) at Tygerberg Hospital, Cape Town.Methods and results: A latent class analysis (LCA) model was applied in a prospective, observational cohort study. Data from 343 COVID-19 patients were analysed. Two distinct phenotypes (1 and 2) were identified, comprising 68.46% and 31.54% of patients, respectively. The phenotype 2 patients were characterized by increased coagulopathy markers (D-dimer, median value 1.73 ng/L vs 0.94 ng/L; p < 0.001), end-organ dysfunction (creatinine, median value 79 mol/L vs 69.5 mu mol/L; p < 0.003), under-perfusion markers (lactate, median value 1.60 mmol/L vs 1.20 mmol/L; p < 0.001), abnormal cardiac function markers (median N-terminal pro-brain natriuretic peptide (NT-proBNP) 314 pg/ml vs 63.5 pg/ml; p < 0.001 and median high-sensitivity cardiac troponin (Hs-TropT) 39 ng/L vs 12 ng/L; p < 0.001), and acute inflammatory syndrome (median neutrophil-to-lymphocyte ratio 15.08 vs 8.68; p < 0.001 and median monocyte value 0.68 x 10(9)/L vs 0.45 x 10(9)/L; p < 0.001).Conclusion: The identification of COVID-19 phenotypes and sub-phenotypes in ICU patients could help as a prognostic marker in the day-to-day management of COVID-19 patients admitted to the ICU.

The correct specification of covariance structures in linear mixed models (LMMs) is critical for accurate longitudinal data analysis. These data, characterised by repeated measurements on subjects over time, demand careful handling of inherent correlations to avoid biased estimates and invalid inferences. Incorrect covariance structure specification can lead to inflated type I error rates, reduced statistical power, and inefficient estimation, ultimately compromising the reliability of statistical inferences. Traditional methods for selecting appropriate covariance structures, such as AIC and BIC, often fall short, particularly as model complexity increases or sample sizes decrease. Studies have shown that these criteria can misidentify the correct structure, resulting in suboptimal parameter estimates and poor assessment of standard errors for fixed effects. Additionally, relying on trial-and-error comparisons in LMMs can lead to overfitting and arbitrary decisions, further undermining the robustness of model selection and inference. To address this challenge, we introduce LiMMCov, an interactive app that uniquely integrates time-series concepts into the process of covariance structure selection. Unlike existing tools, LiMMCov allows researchers to explore and model complex structures using autoregressive models, a novel feature that enhances the accuracy of model specification. The app provides interactive visualisations of residuals, offering insights into underlying patterns that traditional methods may overlook. LiMMCov facilitates a systematic approach to covariance structure selection with a user-friendly interface and integrated theoretical guidance. This paper details the development and features of LiMMCov, demonstrates its application with an example dataset, and discusses its potential impact on research. The app is freely accessible at https://zq9mvv-vub0square.shinyapps.io/LiMMCov-research-tool/.

Background: Children with cancer frequently experience pain, which may persist into survivorship. Furthermore, many undergo body composition changes throughout their disease trajectory. However, little is still known about the interplay between pain and body composition. Methods: This cross-sectional case–control study compared pain and anthropometric characteristics between 30 children with cancer (8–18 years) and 30 age- and sex-matched healthy controls and examined whether pain was associated with anthropometric characteristics and cancer diagnosis. Pain in the past two weeks, chronic pain, and pressure pain thresholds (PPTs) at the tibialis anterior and trapezius pars descendens muscles were assessed. Anthropometric measures included waist circumference, fat %, fat-free mass, muscle mass, body water %, and Body Mass Index. Results: Children with cancer had a higher prevalence of chronic pain (p = 0.011), lower PPTs at the tibialis anterior (p = 0.030), and fewer pain locations (p = 0.037). They also showed lower body water % (p = 0.020), and higher waist circumference (p = 0.012) and fat % (p = 0.026). Cancer diagnosis and lower muscle mass were associated with lower PPTs at both locations (tibialis anterior: p = 0.016, β = −0.305; p = 0.033, β = 0.267; trapezius pars descendens: p = 0.020, β = −0.286; p = 0.004, β = 0.361, respectively). Conclusions: Children with cancer differ from their healthy peers in both pain and body composition profiles. These findings underscore the need for systematic pain assessment and body composition monitoring in pediatric oncology and may help identify children at risk for heightened pain sensitivity and adverse body composition changes who could benefit from early, targeted interventions.