No abstract

African countries are preparing to face the outbreak of COVID-19, which the World Health Organization declared a Global Public Health Emergency of International Concern on 31 January 2020 [1] and a pandemic 40 days later. [2] COVID-19 is caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The virus, emerging in early December 2019 in Wuhan, Hubei Province, China, [3] has since spread explosively throughout the world. Most of Europe and the USA has been severely affected in recent weeks despite their relatively high income per capita and robust public healthcare systems. In contrast, African countries are poorly resourced, have a sizeable vulnerable population, and in recent years noticeably demonstrated vulnerability to epidemics. [4] Many South Africans of all ages have significant risk factors for severe COVID-19 outcomes. The pre-existing colliding epidemics of HIV infection and tuberculosis (TB), as well as the high prevalence of non-communicable diseases such as hypertension, diabetes and cardiovascular disease, place a large segment of the population at high risk. [5] This open-access article is distributed under Creative Commons licence CC-BY-NC 4.0.

Based on the framework of testlet models, the current study suggests the Bayesian random block item response theory (BRB IRT) model to fit forced-choice formats where an item block is composed of three or more items. To account for local dependence among items within a block, the BRB IRT model incorporated a random block effect into the response function and used a Markov Chain Monte Carlo procedure for simultaneous estimation of item and trait parameters. The simulation results demonstrated that the BRB IRT model performed well for the estimation of item and trait parameters and for screening those with relatively low scores on target traits. As found in the literature, the composition of item blocks was crucial for model performance; negatively keyed items were required for item blocks. The empirical application showed the performance of the BRB IRT model was equivalent to that of the Thurstonian IRT model. The potential advantage of the BRB IRT model as a base for more complex measurement models was also demonstrated by incorporating gender as a covariate into the BRB IRT model to explain response probabilities. Recommendations for the adoption of forced-choice formats were provided along with the discussion about using negatively keyed items.

This study examined whether cutoffs in fit indices suggested for traditional formats with maximum likelihood estimators can be utilized to assess model fit and to test measurement invariance when a multiple group confirmatory factor analysis was employed for the Thurstonian item response theory (IRT) model. Regarding the performance of the evaluation criteria, detection of measurement non-invariance and Type I error rates were examined. The impact of measurement non-invariance on estimated scores in the Thurstonian IRT model was also examined through accuracy and efficiency in score estimation. The fit indices used for the evaluation of model fit performed well. Among six cutoffs for changes in model fit indices, only ΔCFI > .01 and ΔNCI > .02 detected metric non-invariance when the medium magnitude of non-invariance occurred and none of the cutoffs performed well to detect scalar non-invariance. Based on the generated sampling distributions of fit index differences, this study suggested ΔCFI > .001 and ΔNCI > .004 for scalar non-invariance and ΔCFI > .007 for metric non-invariance. Considering Type I error rate control and detection rates of measurement non-invariance, ΔCFI was recommended for measurement non-invariance tests for forced-choice format data. Challenges in measurement non-invariance tests in the Thurstonian IRT model were discussed along with the direction for future research to enhance the utility of forced-choice formats in test development for cross-cultural and international settings.

Atrial fibrillation is the most common postoperative arrhythmia and is associated with increased length of stay, cost, morbidity and mortality. The incidence of postoperative atrial fibrillation for noncardiac, nonthoracic surgeries ranges from 0.4% to 26%. The incidence increases to 20%–50% in cardiac surgery, occurring in approximately 30% of isolated coronary artery bypass grafting (CABG), approximately 40% of isolated valve surgeries and up to 50% of CABG plus valve surgeries. Our aim was to identify risk factors that may predispose patients to postoperative atrial fibrillation and compare the efficacy of previously developed prediction tools to a new bedside prediction tool. We sought to develop a bedside screening tool using 4 easily identifiable variables: body mass index, age, congestive heart failure and hypertension (BACH). We predicted that our model would compare similarly to previously developed and validated prediction models but would be easier to use.