ObjectivesThis 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.MethodsWe 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.ResultsOur 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.ConclusionThe 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.

The spread of COVID-19 makes it essential to investigate its prevalence. In such investigation research, as far as we know, the widely-used sampling methods didn't use the information sufficiently about the numbers of the previously diagnosed cases, which provides a priori information about the true numbers of infections. This motivates us to develop a new, two-stage sampling method in this paper, which utilizes the information about the distributions of both population and diagnosed cases, to investigate the prevalence more efficiently. The global likelihood sampling, a robust and efficient sampler to draw samples from any probability density function, is used in our sampling strategy, and thus, our new method can automatically adapt to the complicated distributions of population and diagnosed cases. Moreover, the corresponding estimating method is simple, which facilitates the practical implementation. Some recommendations for practical implementation are given. Finally, several simulations and a practical example verify its efficiency.

In many clinical studies, longitudinal biomarkers are often used to monitor the progression of a disease. For example, in a kidney transplant study, the glomerular filtration rate (GFR) is used as a longitudinal biomarker to monitor the progression of the kidney function and the patient's state of survival that is characterized by multiple time-to-event outcomes, such as kidney transplant failure and death. It is known that the joint modelling of longitudinal and survival data leads to a more accurate and comprehensive estimation of the covariates' effect. While most joint models use the longitudinal outcome as a covariate for predicting survival, very few models consider the further decomposition of the variation within the longitudinal trajectories and its effect on survival. We develop a joint model that uses functional principal component analysis (FPCA) to extract useful features from the longitudinal trajectories and adopt the competing risk model to handle multiple time-to-event outcomes. The longitudinal trajectories and the multiple time-to-event outcomes are linked via the shared functional features. The application of our model on a real kidney transplant data set reveals the significance of these functional features, and a simulation study is carried out to validate the accurateness of the estimation method.

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.

Introduction This paper sought to investigate the clinical characteristic differences between suspected and confirmed patients with COVID-19 from CT scan to prevent and treat this infectious disease, since the coronavirus outbreak in the world has seriously affected the quality of life. Methods We proposed to use a retrospective case-control study to give a comparison between suspected patients and confirmed patients in the clinical characteristics. Results (56%) patients were confirmed for COVID-19 from suspected 167 patients. We find that elder people were more likely to be infected by COVID-19. Among the confirmed 94 patients, 2 (2%) patients were admitted to an intensive care unit, and 0 (0%) patients died during the study period. We find that images of CT scan of patients with a COVID-19 are significantly different from patients without a COVID-19. Conclusions To our best knowledge, it is the first time to use the case-control design to study the coronavirus disease, since it is particularly appropriate for investigating infectious disease outbreaks. The clinical treatment experience in this study can supply a guideline for treating COVID-19 as the number of the infected patients is increasing in the world. Compared with other studies, we find that the mortality rate and the intensive care unit rate can be reduced if patients can be treated timely in the right identification and detection with nucleic acid testing and chest CT scan. Therefore, we recommend nucleic acid testing and chest CT scan for the clinical treatment practice from this successful clinical treatment study.

Kidney-alone transplant (KAT) candidates may be disadvantaged by the allocation priority given to multi-organ transplant (MOT) candidates. This study identified potential KAT candidates not receiving a given kidney offer due to its allocation for MOT. Using the Organ Procurement and Transplant Network (OPTN) database, we identified deceased donors from 2002 to 2017 who had one kidney allocated for MOT and the other kidney allocated for KAT or simultaneous pancreas-kidney transplant (SPK) (n = 7,378). Potential transplant recipient data were used to identify the "next-sequential KAT candidate" who would have received a given kidney offer had it not been allocated to a higher prioritized MOT candidate. In this analysis, next-sequential KAT candidates were younger (p < .001), more likely to be racial/ethnic minorities (p < .001), and more highly sensitized than MOT recipients (p < .001). A total of 2,113 (28.6%) next-sequential KAT candidates subsequently either died or were removed from the waiting list without receiving a transplant. In a multivariable model, despite adjacent position on the kidney match-run, mortality risk was significantly higher for next-sequential KAT candidates compared to KAT/SPK recipients (hazard ratio 1.55, 95% confidence interval 1.44, 1.66). These results highlight implications of MOT allocation prioritization, and potential consequences to KAT candidates prioritized below MOT candidates.

Importance The new coronavirus outbreak has seriously affected the quality of life in China. Wuhan is the disaster area, where the number of cases has increased rapidly. However, the current measures of infected patients in Wuhan are still underestimated. Objective To estimate the overall infected patients in Wuhan from several sampled data. The correct estimated infected patients can be helpful for the government to arrange the needed beds in hospital wards to meet the actual needs. Design We proposed to use the sampling survey to estimate the overall infected patients in Wuhan. The sampling survey is a kind of non-comprehensive survey. It selected some units from all the survey objects to carry out the survey and made the estimation and inference to all the survey objects. Sampling surveys can obtain information that reflects the overall situation, although it is not a comprehensive survey. Setting We estimated the overall infection rate in Wenzhou city, which has a better data collection system. Simultaneously, another different samples of Wuhan tourists to Singapore will be used to validate the infection rate in Wenzhou city. Combined these two samples, we give the estimation of the number of infected patients in Wuhan and other prefecture-level cities in Hubei Province. Participants The number of people who returned from Wuhan to Wenzhou was selected from the daily notification of the pneumonia epidemic caused by a new coronavirus infection in the city. Exposures for observational studies The daily rate of the pneumonia epidemic caused by the new coronavirus infection in Wenzhou City. The numerator is the number of people diagnosed and whether each person diagnosed had a history of living in Wuhan. The denominator is the total number of people returning to Wenzhou from Wuhan. Based on this rate, it is reasonable to predict the number of the infected patients. Main Outcome(s) and Measure(s) According to the most conservative estimate from our proposed sampling method, at least total 54,000 infected patients are in Wuhan. Therefore, the current 8,000 beds in hospital wards and the 20,000 beds in square-class hospitals are far away from meeting the actual needs.

BACKGROUND:Computed 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.OBJECTIVE:The 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.METHODS:A 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.RESULTS:A 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).CONCLUSIONS:Taken 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.

BACKGROUNDCT 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 since the coronavirus outbreak in the world.OBJECTIVEWe wanted to compare CT scan findings of patients that 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 selection for variables was used to identify the good predictors from all available predictors. The area under the curve (AUC) with 95% confidence interval was calculated for each of the selected predictors and these combined selected key predictors.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 by COVID-19. Among the 94 confirmed positive patients, 2 (2%) patients were admitted to an intensive care unit, and 0 (0%) patients died during the study period. We found that the presence, distribution, and location of CT lesions was associated with the presence of COVID-19. White blood cell counts, cough, and a contact history of Wuhan also were the top predictors for COVID-19. The overall AUC of these selected predictors is 0.97 (0.93, 1.00).CONCLUSIONSTaken together with nucleic acid testing, we find 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 non-specific symptoms.CLINICALTRIAL

Recurrent glomerulonephritis (GN) is a common cause of graft loss after kidney transplantation. Steroids are critical to GN management before transplantation, but it is unclear if early steroid withdrawal after transplantation increases the risk of graft loss in patients with GN. Here USRDS data were used to examine the association of early steroid withdrawal with death censored graft loss and all cause graft loss in GN and non-GN adult, non-diabetic, non-sensitized first kidney-only transplant recipients from 1998-2012. A 2-stage propensity score-based matching algorithm was used to match early steroid withdrawal to steroid-maintained patients in the GN and non-GN groups. Multivariate Cox models using a robust variance estimator to account for matched pairs were used to examine the association of early steroid withdrawal with death censored or all cause graft loss in patients with (6388 patients each in early steroid withdrawal and steroid groups) or without GN (6590 each in early steroid withdrawal and steroid groups). Early steroid withdrawal was not associated with an increased risk of death censored or all cause graft loss in patients with or without GN. These findings were consistent across GN types and after accounting for transplant center. Thus, our findings support consideration of early steroid withdrawal in patients with GN at high risk of the adverse consequences of prolonged steroid exposure.

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.