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Interaction analysis is a critical component of clinical and public health research and represents a key topic in precision health and medicine. In applied settings, however, interaction assessment is usually limited to the test of a product term in a regression model and to the presentation of results stratified by levels of additional covariates. Stratification of results often relies on categorizing or making linearity assumptions for continuous covariates, with substantial loss of precision and of relevant information. In time-to-event analysis, moreover, interaction assessment is often limited to the multiplicative hazard scale by inclusion of a product term in a Cox regression model, disregarding the clinically relevant information that is captured by the absolute risk scale. In this paper we present a user-friendly procedure, based on the prediction of individual absolute risks from the Cox model, for the estimation and presentation of interactive effects on both the multiplicative and additive scales in survival analysis. We describe how to flexibly incorporate interactions with continuous covariates, which potentially operate in a nonlinear fashion, provide software for replicating our procedure, and discuss different approaches to deriving CIs. The presented approach will allow clinical and public health researchers to assess complex relationships between multiple covariates as they relate to a clinical endpoint, and to provide a more intuitive and precise depiction of the results in applied research papers focusing on interaction and effect stratification. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study is to investigate the pesticide safety behaviours and related factors among cabbage farmers in Kalaw Township, Myanmar. Background: Unsafe chemical pesticide practices affect farmers' health and the environment. Understanding pesticide safety behaviours is necessary for healthcare providers to protect agriculture workers against unwanted adverse health effects. Methods: The cross‐sectional study was conducted among 195 cabbage farmers from March to May 2019. Safety behaviours and factors applied in the PRECEDE–PROCEDE model as a framework were assessed using questionnaires. Bivariate and multivariate logistic regression was applied to identify any associations. Results: Altogether, 72.1% of the farmers had a low level of safety behaviours. Goggles and masks were rarely used before and during pesticide applications. All of the farmers experienced adverse health effects after using pesticides. The attitudes toward pesticide poisoning; support from family, co‐workers and healthcare providers; and the availability of personal protective equipment were significantly associated with safety behaviours. Conclusion: Our data provide novel empirical evidence for an opportunity to design effective nursing interventions to promote pesticide safety behaviours among Myanmar agricultural workers. Nurses should pay more attention to promoting comprehensive interventions to reduce both exposure and health effects among agricultural workers through the involvement of supported stakeholders. Summary statement: What is already known about this topic? Farmers and agricultural workers have extensively used pesticides in all steps of cultivation and face a greater risk of pesticide exposure, which could result in adverse health effects.Owing to the lack of research on community health promotion conducted in Myanmar, the burden of the consequences and adverse effects of pesticides remains a significant health issue. What this paper adds? Findings reveal a clear framework for better understanding and assessing pesticide safety behaviours among agricultural workers and farmers in Myanmar.Findings contribute new knowledge for nursing community health promotion by enhancing understanding of multilevel factors, including international and external factors, associated with safety behaviours among agricultural workers and farmers. The implications of this paper: Nurses and other healthcare professionals should be aware of the importance of preventing adverse health effects among agricultural workers in the community and interventions aimed at promoting safety behaviours should be focused not only on individuals but also on the involvement of social supports—particularly family and co‐workers.Nursing policymakers should consider strengthening the nursing role in the recognition of pesticide risk exposure and health outcomes, management of pesticide poisoning and exposure and prevention of pesticide‐related health conditions both in nursing education and nursing practice.Local authorities and policymakers should focus their attention on health promotion and health services to promote safety behaviours when using pesticides, and treatment and extension services should be provided for cases of suspected pesticide poisoning. [ABSTRACT FROM AUTHOR]

The keys to constructing numerical schemes for nonlinear partial differential equations are accuracy, handling of the nonlinear terms, and physical properties (energy dissipation or conservation). In this paper, we employ the exponential scalar auxiliary variable (E-SAV) method to solve a semi-linear wave equation. By defining two different variables and combining the Crank−Nicolson scheme, two semi-discrete schemes are proposed, both of which are second-order and maintain Hamiltonian conservation. Two numerical experiments are presented to verify the reliability of the theory. [ABSTRACT FROM AUTHOR]

Bursting is a crucial form of firing in neurons, laden with substantial information. Studying it can aid in understanding the neural coding to identify human behavioral characteristics conducted by these neurons. However, the high-dimensionality of many neuron models imposes a difficult challenge in studying the generative mechanisms of bursting. On account of the high complexity and nonlinearity characteristic of these models, it becomes nearly impossible to theoretically study and analyze them. Thus, this paper proposed to address these issues by focusing on the midbrain dopamine neurons, serving as the central neuron model for the investigation of the bursting mechanisms and bifurcation behaviors exhibited by the neuron. In this study, we considered the dimensionality reduction of a high-dimensional neuronal model and analyzed the dynamical properties of the reduced system. To begin, for the original thirteen-dimensional model, using the correlation between variables, we reduced its dimensionality and obtained a simplified three-dimensional system. Then, we discussed the changing characteristics of the number of spikes within a burst by simultaneously varying two parameters. Finally, we studied the co-dimension-2 bifurcation in the reduced system and presented the bifurcation behavior near the Bogdanov-Takens bifurcation. [ABSTRACT FROM AUTHOR]

Mathematical modelling of physical phenomena is based on the laws of physics, but for complicated processes, phenomenological models could enhance the descriptive and prescriptive power of the analysis. This paper describes some hybrid models, where in addition to the physics-driven part, some phenomenological variables (based on observations) are added. The internal variables widely used in continuum mechanics for modelling dissipative processes and the phenomenological variables used in modelling neural impulses are described and compared. The appendices describe two models of neural impulses and test problems for two classical cases: the wave equation and the diffusion equation. These test problems demonstrate the usage of phenomenological variables for describing dissipation as well as amplification. [ABSTRACT FROM AUTHOR]

Randomized controlled trials (RCTs) are considered the gold standard for causal inference. With a sufficient sample size, randomization removes confounding up to the time of randomization and allows the treatment effect to be isolated. However, RCTs may have limited generalizability and transportability and are often not feasible in addiction research due to ethical or logistical constraints. The importance of observational studies from real‐world settings has been increasingly recognized in research on health. This paper provides an overview of modern approaches to designing observational studies that enable causal inference. It illustrates three key techniques, Directed Acyclic Graphs (DAGs), modified Disjunctive Cause Criterion and Target Trial Emulation, and discusses the strengths and limitations of their applications. [ABSTRACT FROM AUTHOR]

This study aims to provide novel insights into variable selection in the semivarying coefficient model. We focus on the problem of variable selection and screening for the constant coefficient part. A common approach in the existing literature is to infer the constant coefficients by transforming the problem into a linear model scenario, utilizing a fine estimator of the varying coefficients. In this paper, we propose an approximation method for the varying coefficient functions using local averaging, which is characterized by its simplicity, rough and computational efficiency. Additionally, we introduce an adaptive lasso estimator and a forward regression algorithm specifically designed for semivarying coefficient models. Theoretical and experimental results highlight the effectiveness of the local averaging method in extending variable selection techniques from the linear model to the semivarying coefficient model. Our proposed approaches demonstrate a significant improvement in inference speed compared with baseline methods, with little loss of asymptotic efficiency. [ABSTRACT FROM AUTHOR]

This paper presents a multi-algorithm fusion model (StackingGroup) based on the Stacking ensemble learning framework to address the variable selection problem in high-dimensional group structure data. The proposed algorithm takes into account the differences in data observation and training principles of different algorithms. It leverages the strengths of each model and incorporates Stacking ensemble learning with multiple group structure regularization methods. The main approach involves dividing the data set into K parts on average, using more than 10 algorithms as basic learning models, and selecting the base learner based on low correlation, strong prediction ability, and small model error. Finally, we selected the grSubset + grLasso, grLasso, and grSCAD algorithms as the base learners for the Stacking algorithm. The Lasso algorithm was used as the meta-learner to create a comprehensive algorithm called StackingGroup. This algorithm is designed to handle high-dimensional group structure data. Simulation experiments showed that the proposed method outperformed other R2, RMSE, and MAE prediction methods. Lastly, we applied the proposed algorithm to investigate the risk factors of low birth weight in infants and young children. The final results demonstrate that the proposed method achieves a mean absolute error (MAE) of 0.508 and a root mean square error (RMSE) of 0.668. The obtained values are smaller compared to those obtained from a single model, indicating that the proposed method surpasses other algorithms in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

A parametric study employing three-dimensional finite element analysis is used in this paper to examine the improvement of soft clay and silty loose sand under a road embankment using deep soil mixing (DSM) columns. Both soils are designed with three variables namely DSM column diameter (D), DSM column length (L) and centre-to-centre distance of the DSM columns (s). The settlement values under the road embankment constructed with and without DSM columns were obtained by numerical analysis. The analyses were also repeated for the DSM columns which were stiffened by adding I-profiles. The results showed that the improvement effect of the DSM columns on settlement is higher in soft clay. For constant improvement area ratio (ar), the maximum improvement is achieved for D = 80 cm (2.62 ft.) and increasing D did not affect the degree of improvement. In both soil types, settlement decreased with increasing L within the weak soil zone. Increasing L into the comparatively stronger soil did not significantly affect the settlement value. Although the settlement decreased with increasing ar in silty loose sand, it reached at maximum in soft clay at ar of thirty-five percent and was not affected thereafter with increasing ar values. [ABSTRACT FROM AUTHOR]

To assess whether a measure of leadership support for worker safety, health, and well-being predicts staff turnover in nursing homes after controlling for other factors. This paper uses administrative payroll data to measure facility-level turnover and uses a survey measure of nursing home leadership commitment to workers. In addition, we use data from Medicare to measure various nursing home characteristics. Nursing homes with at least 30 beds serving adults in California, Ohio, and Massachusetts were invited to participate in the survey. The analysis sample included 495 nursing homes. We used a multivariable ordinary least squares model with turnover rate as the dependent variable. We used an indicator for nursing homes who scored above the median on the measure of leadership that supports worker safety, health, and well-being. Control variables include bed count (deciles), ownership (corporate/noncorporate × for-profit/not-for-profit), percent of residents on Medicaid, state, being in a nonmetropolitan county, and total nurse staffing per patient day in the 2 quarters before the survey. The unadjusted turnover rate was lower for those nursing homes that scored higher on leadership commitment to worker safety, health, and well-being. After controlling for additional variables, greater leadership commitment was still associated with lower turnover but with some attenuation. We find that nursing homes with leadership that communicated and demonstrated commitment to worker safety, health, and well-being had relatively fewer nurses leave during the study period, with turnover rates approximately 10% lower than homes without. These findings suggest that leadership may be a valuable tool for reducing staff turnover. [ABSTRACT FROM AUTHOR]