Volume 13, Issue 1

Objectives The first objective is to compare the psychometric properties of two scales, measuring COVID-19-related burnout in a general working population during an economic crisis. The second objective is to compare the relevance through the assessment of statistically significant associations between the independent variables and the validated (scale 1) or non-validated (scale 2) scales taken as dependent variables. Methods This study enrolled 151 Lebanese participants, using a snowball sampling method. Two scales that measure burnout during COVID-19 were used. Results A significantly strong correlation was found between the validated COVID-19 burnout scale (scale 1) and the new pandemic-related burnout scale (scale 2) (r=0.796, p<0.001). A first linear regression on scale 1 (dependent) showed that increased concern about the impact of the economic crisis and COVID-19 (Beta=9.61) was significantly associated with higher COVID-19 burnout. However, higher financial well-being (Beta=−0.23) and working as a full timer (Beta=−7.80) were significantly associated with a lower COVID-19 burnout score. A second regression model on scale 2 (dependent) showed that higher financial well-being was only significantly associated with a lower pandemic-related burnout score (Beta=−0.72). Conclusions Our results showed that more specific scales have better psychometric properties while using non-validated, non-specific scales to evaluate an outcome might lead to biased associations and incorrect conclusions.

Objectives The addition of two-way interactions is a classic problem in statistics, and comes with the challenge of quadratically increasing dimension. We aim to a) devise an estimation method that can handle this challenge and b) to aid interpretation of the resulting model by developing computational tools for quantifying variable importance. Methods Existing strategies typically overcome the dimensionality problem by only allowing interactions between relevant main effects. Building on this philosophy, and aiming for settings with moderate n to p ratio, we develop a local shrinkage model that links the shrinkage of interaction effects to the shrinkage of their corresponding main effects. In addition, we derive a new analytical formula for the Shapley value, which allows rapid assessment of individual-specific variable importance scores and their uncertainties. Results We empirically demonstrate that our approach provides accurate estimates of the model parameters and very competitive predictive accuracy. In our Bayesian framework, estimation inherently comes with inference, which facilitates variable selection. Comparisons with key competitors are provided. Large-scale cohort data are used to provide realistic illustrations and evaluations. The implementation of our method in RStan is relatively straightforward and flexible, allowing for adaptation to specific needs. Conclusions Our method is an attractive alternative for existing strategies to handle interactions in epidemiological and/or clinical studies, as its linked local shrinkage can improve parameter accuracy, prediction and variable selection. Moreover, it provides appropriate inference and interpretation, and may compete well with less interpretable machine learners in terms of prediction.

Objectives A compartmental deterministic model that allows immunity from two stages of infection and carriage, and disease induced death, is used in studying the dynamics of meningitis epidemic process, in a closed population. It allows for difference in the transmission rate of infection to a susceptible by a carrier and an infective. It is generalized to allow a proportion of those susceptible infected to progress directly to infectives in stage I. Methods Two models are used in this study; the generalized model when these proportions are greater than zero (called model 1) and a special case when the proportion is zero (called model 2). The threshold conditions for the spread of carrier and infectives in stage I are derived for both models. Sensitivity analysis is performed on the reproductive number derived from the next generation matrix. The case-carrier ratio profile for various parameters and threshold values are shown. So also, are the graphs of the total number ever infected as influenced by the odds in favor of a carrier over an infective, in transmitting an infection to a susceptible, and the proportion of those susceptible that can progress directly to the infected stage. Results The infection transmission rate, these odds in favor of a carrier, and the carrier conversion rate to an infective in stage I, are identified as key parameters that should be subject of attention for any control intervention strategy. The case-carrier ratio profiles provide evidence of a critical case-carrier ratio attained before the number of reported cases grow to an epidemic level. They also provide visual evidence of epidemiological context, in this case, epidemic incidence (in later part of dry season) and endemic incidence (during rainy season). Conclusions Results from the total proportion ever infected suggest that the special case (model 2) can adequately represent, in essence, the generalized model (model 1) for this study.

Objectives The prey-predator model provides a mathematical framework for understanding the population dynamics of interacting species, highlighting the delicate balance between predator and prey populations in ecological systems. The four-species predator-prey model extends the Lotka-Volterra framework to explore the dynamics of ecosystems with multiple interacting species. It provides a theoretical foundation for understanding how the populations of multiple prey and predator species influence each other over time. Apart from the traditional methods like direct approach for solving the non-linear system of equations, recent Fuzzy method approaches have been developed. The solution of non-linear systems using classical methods is not easy due to its non-linearity, analytical complexity, chaotic behavior, etc. and the T-S method is very much effective to analyze the non-linear models. Methods In this study, we considered an eco-epidemic model with two populations of prey and one population of predator, with the only infectious disease infecting the first prey population. The four-dimensional Lotka-Volterra predator-prey system’s model stability has been examined using the Takagi-Sugeno (T-S) impulsive control model and the Fuzzy impulsive control model. Following the formulation of the model, the global stability and the Fuzzy solution are carried out through numerical simulations and graphical representations with appropriate discussion for a better understanding the dynamics of our proposed model. Results The Takagi-Sugeno method has diverse applications in modeling, control, pattern recognition, and decision-making in systems where uncertainty and non-linearity play a significant role. Its ability to combine fuzzy logic with traditional mathematical models provides a powerful tool for addressing complex real-world problems. Conclusions The impulse control approach, what is considered within the foundation of fuzzy systems established on T-S model, is found to be suitable for extremely complex and non-linear systems with impulse effects.

Objectives We consider the followingcomparative effectiveness scenario. There are two treatments for a particular medical condition: a randomized experiment has demonstrated mediocre effectiveness for the first treatment, while a non-randomized study of the second treatment reports a much higher success rate. On what grounds might one justifiably prefer the second treatment over the first treatment, given only the information from those two studies, including design details? This situation occurs in reality and warrants study. Methods We consider a particular example involving studies of treatments for Crohn's disease. In order to help resolve these cases of asymmetric evidence, we make three contributions and apply them to our example. Results First, we demonstrate the potential to improve success rates above those found in a randomized trial, given heterogeneous effects. Second, we prove that deliberate treatment assignment can be more efficient than randomization when study results are to be transported to formulate an intervention policy on a wider population. Third, we provide formal conditions under which a temporal-discontinuity design approximates a randomized trial, and we introduce a novel design parameter to inform researchers about the strength of that approximation. Conclusions Overall, our results indicate that while randomization certainly provides special advantages, other study designs such as temporal-discontinuity designs also have distinct advantages, and can produce valuable evidence that informs treatment decisions and intervention policy.

Objectives To create an external control arm (ECA) for a single arm trial, the choice of index date – when a patient becomes eligible for a study, is a complex issue. In real world data (RWD), patients commonly have multiple qualifying lines of therapy (LOT) which can be used to determine the index date. This study assessed the impact of different methods to assign the index date on the effectiveness estimates of the target drug versus conventional therapies and explored the impact of seven methods to assign the index date on the effectiveness estimates of the target drug versus conventional therapies. Methods A study using RWD was conducted in which patients received varied number of LOTs before qualifying for entry into the ECA. Two novel and five established indexing methods were examined for the ECA in this comparative effectiveness research. Baseline characteristics were adjusted by using stabilized inverse probability of treatment weighting (sIPTW). Cox proportional hazards (PH) model was used for time-to-event endpoints and risk ratio (RR) was estimated from a binomial regression for response-based end points. Results Five methods (first eligible line [FEL], restricted-line, all eligible lines, random line, and stratified random line) demonstrated close clinical outcome estimates after adjustment of baseline differences via sIPTW. The FEL resulted in an inability to adjust for number of prior LOTs due to poor overlap of line distribution in this study. The last and second last eligible line cannot be recommended due to their inability to adjust for immortal time bias. Conclusions Multiple methods are available for selecting the most appropriate index date for an ECA, and this empirical study has indicated that certain methods yield comparable outcomes when the treatment effect and sample size are large. It is important for researchers to carefully assess the specifics of their studies and justify their selection of the most appropriate indexing method. Future research including simulations to evaluate the two novel stratified random line and SLEL methods is necessary.