Ana Subtil

Introduction : Loneliness among older adults’ residents in Portuguese Residential Structures for the Elderly (ERPI) is a significant public health concern, exacerbated by increased life expectancy, chronic diseases, and reduced social networks. This study aims to explore loneliness and its associated factors, highlighting the role of specialized nursing in promoting health and quality of life for institutionalized older adults’ individuals. Method This exploratory study, conducted between November 2020 and July 2021, collected quantitative data from 157 elderly ERPI residents (aged ≥ 65 years) using intentional non-probabilistic sampling. The UCLA-Loneliness Scale assessed loneliness, and a sociodemographic and clinical questionnaire characterized the sample. Data were analyzed using SPSS version 24.0, with descriptive statistics, Kolmogorov-Smirnov and Shapiro-Wilk tests for normality, and inferential tests (Mann-Whitney U, chi-square, Fisher’s exact) to explore variable relationships. Results The sample (65.6% female, mean age 84.6 years) showed a high prevalence of negative loneliness feelings (UCLA mean score 35.5, cutoff > 32), particularly among those aged ≥ 85, widowed individuals, and those with multimorbidity (67.5%). A significant association was found between loneliness and multimorbidity (p = 0.016), but not with sociodemographic variables. Women and older participants exhibited higher dependency and loneliness scores. Conclusion Loneliness is prevalent among ERPI residents, with multimorbidity as a key predictor. Specialized nursing is pivotal in implementing holistic, evidence-based interventions to mitigate loneliness, promote self-care, and enhance well-being.

Sample size calculation in biomedical practice is typically based on the problematic Wald method for a binomial proportion, with potentially dangerous consequences. This work highlights the need of incorporating the concept of conditional probability in sample size determination to avoid reduced sample sizes that lead to inadequate confidence intervals. Therefore, new definitions are proposed for coverage probability and expected length of confidence intervals for conditional probabilities, like sensitivity and specificity. The new definitions were used to assess seven confidence interval estimation methods. In order to determine the sample size, two procedures—an optimal one, based on the new definitions, and an approximation—were developed for each estimation method. Our findings confirm the similarity of the approximated sample sizes to the optimal ones. R code is provided to disseminate these methodological advances and translate them into biomedical practice.

Dichotomous diagnostic tests are widely used to detect the presence or absence of a biomedical condition of interest. A rigorous evaluation of the accuracy of a diagnostic test is critical to determine its practical value. Performance measures, such as the sensitivity and specificity of the test, should be estimated by comparison with a gold standard. Since an error-free reference test is frequently missing, approaches based on available imperfect diagnostic tests are used, namely: comparisons with an imperfect gold standard or with a composite reference standard, discrepant analysis, and latent class models. In this work, we compare these methods using a theoretical approach based on analytical expressions for the deviations between the sensitivity and specificity according to each method, and the corresponding true values. We explore the impact on the deviations of varying conditions: tests sensitivities and specificities, prevalence of the condition and local dependence between the tests. An R interactive graphical application is made available for the visualisation of the outcomes. Based on our findings, we discuss the methods validity and potential usefulness.

The main focus of this study is to illustrate the importance of the statistical analysis in the evaluation of the accuracy of malaria diagnostic tests, without admitting a reference test, exploring a dataset ( 3317) collected in São Tomé and Príncipe. Bayesian Latent Class Models (without and with constraints) are used to estimate the malaria infection prevalence, together with sensitivities, specificities, and predictive values of three diagnostic tests (RDT, Microscopy and PCR), in four subpopulations simultaneously based on a stratified analysis by age groups ( , 5 years old) and fever status (febrile, afebrile). In the afebrile individuals with at least five years old, the posterior mean of the malaria infection prevalence is 3.2% with a highest posterior density interval of [2.3–4.1]. The other three subpopulations (febrile 5 years, afebrile or febrile children less than 5 years) present a higher prevalence around 10.3% [8.8–11.7]. In afebrile children under-five years old, the sensitivity of microscopy is 50.5% [37.7–63.2]. In children under-five, the estimated sensitivities/specificities of RDT are 95.4% [90.3–99.5]/93.8% [91.6–96.0] – afebrile – and 94.1% [87.5–99.4]/97.5% [95.5–99.3] – febrile. In individuals with at least five years old are 96.0% [91.5–99.7]/98.7% [98.1–99.2] – afebrile – and 97.9% [95.3–99.8]/97.7% [96.6–98.6] – febrile. The PCR yields the most reliable results in four subpopulations. The utility of this RDT in the field seems to be relevant. However, in all subpopulations, data provide enough evidence to suggest caution with the positive predictive values of the RDT. Microscopy has poor sensitivity compared to the other tests, particularly, in the afebrile children less than 5 years. This type of findings reveals the danger of statistical analysis based on microscopy as a reference test. Bayesian Latent Class Models provide a powerful tool to evaluate malaria diagnostic tests, taking into account different groups of interest.