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ObjectivesMethodsResultsConclusionThis study examined the factors affecting the use of personal protective equipment (PPE) among US agriculture producers, specifically focusing on chemical, respiratory, and hearing protection. The primary goals were to identify demographic and farm-related factors linked to lower PPE usage and to explore the associations between PPE use and self-reported injuries or diseases in this occupational context.We developed generalized multilevel multinomial logistic regressions for the agricultural safety and health survey data from 2018 and 2020. Our models aimed to explore associations between PPE use, covariates, and their impact on skin diseases, hearing loss, and respiratory diseases. The hierarchical structure of the data was accommodated by designating the survey year as the level-3 variable and the state of residence as the level-2 variable, further delineating the nested structure of the respondents. We proposed using Adaptive Quadrature methods to approximate marginal maximum likelihood function, along with Gauss-Hermite quadrature weights when estimating fixed-effects and random effects in the proposed models. Missing data in this hierarchical structure were addressed through a multiple imputation method.Our findings revealed older age is associated with lower PPE usage across all types. Males exhibited higher PPE use, particularly for chemical (OR: 1.26, 95% CI: 1.08, 1.47) and respiratory protection (OR: 1.33, 95% CI: 1.18, 1.52). Producers on grain-only farms showed increased chemical PPE use (OR: 1.26, 95% CI: 1.10, 1.43) compared to those on livestock-only farms. Additionally, individuals spending 0%–24% of their worktime on the farm/ranch demonstrated lower PPE use than those who spent more time engaged in farm-related activities.The study underscores the importance of addressing low PPE usage among agriculture producers. Interventions tailored to specific groups, such as female producers, livestock-only farms, older-aged producers, and part-time producers, can effectively improve PPE use. By identifying these high-risk groups, interventions can be better adapted and targeted to enhance the adoption of PPE, subsequently reducing the risk of health hazards in the agriculture sector. [ABSTRACT FROM AUTHOR]

BackgroundComputed tomography (CT) scans are increasingly available in clinical care globally. They enable a rapid and detailed assessment of tissue and organ involvement in disease processes that are relevant to diagnosis and management, particularly in the context of the COVID-19 pandemic. ObjectiveThe aim of this paper is to identify differences in the CT scan findings of patients who were COVID-19 positive (confirmed via nucleic acid testing) to patients who were confirmed COVID-19 negative. MethodsA retrospective cohort study was proposed to compare patient clinical characteristics and CT scan findings in suspected COVID-19 cases. A multivariable logistic model with LASSO (least absolute shrinkage and selection operator) selection for variables was used to identify the good predictors from all available predictors. The area under the curve (AUC) with 95% CI was calculated for each of the selected predictors and the combined selected key predictors based on receiver operating characteristic curve analysis. ResultsA total of 94 (56%) patients were confirmed positive for COVID-19 from the suspected 167 patients. We found that elderly people were more likely to be infected with COVID-19. Among the 94 confirmed positive patients, 2 (2%) patients were admitted to an intensive care unit. No patients died during the study period. We found that the presence, distribution, and location of CT lesions were associated with the presence of COVID-19. White blood cell count, cough, and a travel history to Wuhan were also the top predictors for COVID-19. The overall AUC of these selected predictors is 0.97 (95% CI 0.93-1.00). ConclusionsTaken together with nucleic acid testing, we found that CT scans can allow for the rapid diagnosis of COVID-19. This study suggests that chest CT scans should be more broadly adopted along with nucleic acid testing in the initial assessment of suspected COVID-19 cases, especially for patients with nonspecific symptoms.

This article is motivated by jointly modelling longitudinal and time-to-event clinical data of patients with diabetes and end-stage renal disease. All patients are on the waiting list for the pancreas transplant after kidney transplant, and some of them have a pancreas transplant before kidney transplant failure or death. Scant literature has studied the dynamical joint relationship of the estimated glomerular filtration rates trajectory, the effect of pancreas transplant, and time-to-event outcomes, although it remains an important clinical question. In an attempt to describe the association in the multiple outcomes, we propose a new joint model with a longitudinal submodel and an accelerated failure time submodel, which are linked by some latent variables. The accelerated failure time submodel is used to determine the relationship of the time-to-event outcome with all predictors. In addition, the piecewise linear function in the survival submodel is used to calculate the dynamic hazard ratio curve of a time-dependent side event, because the effect of the side event on the time-to-event outcome is non-proportional. The model parameters are estimated with a Monte Carlo EM algorithm. The finite sample performance of the proposed method is investigated in simulation studies. Our method is demonstrated by fitting the joint model for the clinical data of 13,635 patients with diabetes and the end-stage renal disease. [ABSTRACT FROM AUTHOR]

This functional joint model paper is motivated by a chronic kidney disease study post kidney transplantation. The available kidney organ is a scarce resource because millions of end-stage renal patients are on the waiting list for kidney transplantation. The life of the transplanted kidney can be extended if the progression of the chronic kidney disease stage can be slowed, and so a major research question is how to extend the transplanted kidney life to maximize the usage of the scarce organ resource. The glomerular filtration rate is the best test to monitor the progression of the kidney function, and it is a continuous longitudinal outcome with repeated measures. The patient's survival status is characterized by time-to-event outcomes including kidney transplant failure, death with kidney function, and death without kidney function. Few studies have been carried out to simultaneously investigate these multiple clinical outcomes in chronic kidney disease stage patients based on a joint model. Therefore, this paper proposes a new functional joint model from this clinical chronic kidney disease study. The proposed joint models include a longitudinal sub-model with a flexible basis function for subject-level trajectories and a competing-risks sub-model for multiple time-to event outcomes. The different association structures can be accomplished through a time-dependent function of shared random effects from the longitudinal process or the whole longitudinal history in the competing-risks sub-model. The proposed joint model that utilizes basis function and competing-risks sub-model is an extension of the standard linear joint models. The application results from the proposed joint model can supply some useful clinical references for chronic kidney disease study post kidney transplantation.