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Background: Bayesian mixture cure rate frailty model is a model used in survival analysis by controlling frailty when the fraction of cured individuals exists. The present study was performed as the first systematic review in survival analysis with cure fraction. The aim of this systematic review was to study and evaluate the related studies on Bayesian mixture cure rate frailty model. Also, this model was used to demonstrate its importance and applicability in determining the variables affecting the survival of patients with gastric cancer. Methods: This systematic review was done based on the PRISMA guideline by considering related searching keywords in PubMed, Scopus, Science Direct, Web of Science, and Google Scholar. Also, Bayesian mixture cure rate frailty model was used to analyze gastric cancer data. Results: In the beginning, 882 studies related to survival analysis of cure rate model were found. Finally, by reading the full-text, only 4 related studies were found based on the inclusion and exclusion criteria. In these studies, semi-parametric models and parametric model with Weibull distribution were used for time-to-event data. Also, based on the results of the model, significant and affective variables on the survival of patients with gastric cancer were found. Conclusion: According to the results of this study, in the cure model, choice of proper distribution for the frailty variable and baseline distribution can influence the results. It was also found that place of residence, chemotherapy, morphology, and metastasis are effective variables on survival of patients with gastric cancer. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study was to apply the Bayesian mixture cure rate frailty model to determine the factors that influence shortterm and long-term survival of patients with gastric cancer Background: Determining the risk factors of gastric cancer is currently considered very important, because the disease has become one of the most dangerous types of mortal cancers. Therefore, it is possible to determine the effective risk factors of short-term and long-term survival in patients through utilizing this model. Methods: The present retrospective study was conducted on 339 gastric cancer patients whose data was recorded in hospitals of Kerman province, Iran, during 2001-2015. In the study, the Bayesian mixture cure rate frailty model was used to determine the effective factors of short-term and long-term survival in patients. Results: In the present study, the event of interest occurred for 57.5% of patients. Over time, the survival rate of cancer patients reached its lowest point, approximately 0.3 at the end of study. According to the results of the present study, variables of chemotherapy (β=-0.35 (-0.75, - 0.03) and OR=1.59 (1.08, 2.19)), morphology (β =-0.98(-1.45, -0.48) and OR=2.99 (1.78, 4.17)), and metastasis (β =0.42(0.10, 0.93) and OR=0.39(0.01, 0.84)) were identified as effective factors in short-term and long-term survival of patients. Conclusion: The effective factors of long-term and short-term survival can be identified by utilizing the Bayesian mixture cure rate frailty model, while it is impossible through conventional models of survival analysis. Chemotherapy, morphology, and metastasis are the most important effective factors of short-term and long-term survival in patients with gastric cancer. [ABSTRACT FROM AUTHOR]

Mixture cure rate models are commonly used to analyze lifetime data with long-term survivors. On the other hand, frailty models also lead to accurate estimation of coefficients by controlling the heterogeneity in survival data. Gamma frailty models are the most common models of frailty. Usually, the gamma distribution is used in the frailty random variable models. However, for survival data which are suitable for populations with a cure rate, it may be better to use a discrete distribution for the frailty random variable than a continuous distribution. Therefore, we proposed two models in this study. In the first model, continuous gamma as the distribution is used, and in the second model, discrete hyper-Poisson distribution is applied for the frailty random variable. Also, Bayesian inference with Weibull distribution and generalized modified Weibull distribution as the baseline distribution were used in the two proposed models, respectively. In this study, we used data of patients with gastric cancer to show the application of these models in real data analysis. The parameters and regression coefficients were estimated using the Metropolis with Gibbs sampling algorithm, so that this algorithm is one of the crucial techniques in Markov chain Monte Carlo simulation. A simulation study was also used to evaluate the performance of the Bayesian estimates to confirm the proposed models. Based on the results of the Bayesian inference, it was found that the model with generalized modified Weibull and hyper-Poisson distributions is a suitable model in practical study and also this model fits better than the model with Weibull and Gamma distributions. [ABSTRACT FROM AUTHOR]

Background: Healthcare-associated infections (HAIs) are among the most critical challenges for patients and healthcare providers. To achieve the goals of the surveillance system, it is necessary to identify its barriers and problems. This study aimed to identify the barriers and problems of the surveillance system for HAIs., Methods: This qualitative study was conducted using the content analysis method to investigate the challenges of this surveillance system from the perspective of 18 infection control nurses from hospitals in different cities of Iran with work experience of 1 to 15 years. Data were collected through semi-structured interviews and analyzed using the Lundman and Graneheim qualitative content analysis method., Results: In this study, we found 2 categories and 7 subcategories. Two categories were barriers related to human resources and organizational barriers to infection control. The 7 subcategories included weakness of medical staff in adherence to health principles, obstacles related to patients, high workload and insufficient motivation, lack of staff knowledge, lack of human resources, functional and logistical weaknesses, and weaknesses in the surveillance system., Conclusion: To reduce problems and improve HAIs reporting, the HAIs surveillance system needs the support of health system officials and managers. This administrative and support focus can establish the framework for removing and lowering other barriers, such as the number of reported cases, physician and staff noncooperation, and the prevalence of HAIs. It can also bring HAIs cases closer to reality., Competing Interests: The authors declare that they have no competing interests., (© 2023 Iran University of Medical Sciences.)

Iran has invariably been under the growing public health threat of cutaneous leishmaniasis (CL), a significant barrier to local development that hinders the prevention and control efforts toward eliminating the disease. So far, no comprehensive and in-depth epidemiological analysis of the CL situation has been carried out nationwide. This study aimed to employ advanced statistical models to analyze the data collected through the Center for Diseases Control and Prevention of Communicable Diseases during 1989-2020. However, we emphasized the current trends, 2013-2020, to study temporal and spatial CL patterns. In the country, the epidemiology of CL is incredibly intricate due to various factors. This fact indicates that the basic infrastructure, the preceding supports, and the implementation plan related to preventive and therapeutic measures need crucial support. The leishmaniasis situation analysis is consistent with desperate requirements for efficient information on the control program in the area. This review provides evidence of temporally regressive and spatially expanding incidence of CL with characteristic geographical patterns and disease hotspots, signifying an urgent need for comprehensive control strategies. This information could be a suitable model and practical experience in the Eastern Mediterranean Region, where over 80% of CL is reported., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Sharifi, Khosravi, Aflatoonian, Salarkia, Bamorovat, Karamoozian, Nekoei Moghadam, Sharifi, Aghaei Afshar, Agha Kuchak Afshari, Gharachorloo, Shirzadi, Amiri, Zainali, Doosti, Zamani and Gouya.)