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Aims 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. Methods 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). Results 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. Conclusions 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.

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(1) Background: We aim to identify clinical and laboratorial parameters to distinguish Kingella kingae from pyogenic septic arthritis (SA). (2) Methods: A longitudinal, observational, single-centre study of children < 5 years old with microbiological positive SA admitted to a paediatric hospital from 2013–2020 was performed. Clinical and laboratorial data at admission and at 48 h, as well as on treatment and evolution, were obtained. (3) Results: We found a total of 75 children, 44 with K. kingae and 31 with pyogenic infections (mostly MSSA, S. pneumoniae and S. pyogenes). K. kingae affected younger children with low or absent fever, low inflammatory markers and a favourable prognosis. In the univariate analyses, fever, septic look, CRP and ESR at admission and CRP at 48 h were significantly lower in K. kingae SA. In the multivariate analyses, age > 6 months ≤ 2 years, apyrexy and CRP ≤ 100 mg/L were significative, with an overall predictive positive value of 86.5%, and 88.4% for K. kingae. For this model, ROC curves were capable of differentiating (AUC 0.861, 95% CI 0.767–0.955) K. kingae SA from typical pathogens. (4) Conclusions: Age > 6 months ≤ 2 years, apyrexy and PCR ≤ 100 mg/L were the main predictive factors to distinguish K. kingae from pyogenic SA < 5 years. These data need to be validated in a larger study.