The Prognostic Factors Affecting the Survival of Kurdistan Province COVID-19 Patients: A Cross-sectional Study From February to May 2020.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a new viral disease and in a short period of time, the world has been affected in various economic, social, and health aspects. This disease has a high rate of transmission and mortality. The aim of this study is to investigate the factors affecting the survival of COVID-19 patients in Kurdistan province. METHODS: In this retrospective study, the data including demographic features and the patient's clinical background in terms of co-morbidities such as diabetes, cancer, chronic lung disease (CLD), coronary heart disease (CHD), chronic kidney disease (CKD) and weak immune system (WIS) were extracted from electronic medical records. We use Cox's regression proportional hazard (PH) to model. RESULTS: In this study, out of 1831 patients, 1019 were males (55.7%) and 812 were females (44.3%) with an average age of 52.74 ± 22.16 years. For survival analysis, data from people infected with COVID-19 who died or were still being treated were used. According to Cox's regression analysis, age variables (hazard ratio [HR]: 1.03, CI: 1.02-1.04), patients with a history of diabetes (HR: 2.16, CI: 1.38-3.38), cancer (HR: 3.57, CI: 1.82-7.02), CLD (HR: 2.21, CI: 1.22-4) and CHD (HR: 2.20, CI: 1.57-3.09) were significant and affected the hazard of death in patients with COVID-19 and assuming that the other variables in the model are constant, the hazard of death increases by 3% by increasing one unit (year), and the hazard of death in COVID-19 patients with CHD, diabetes, cancer, CLD is 2.16, 3.57, 2.2 and 2.21, respectively. CONCLUSION: According to findings, it is necessary to evaluate the prevalence of COVID-19 in patients with CLD, diabetes, cancer, CHD, and elder, as patients with these characteristics may face a greater risk of death. Therefore, we suggest that elders and people with those underlying illnesses need to be under active surveillance and screened frequently.

Assessing the Factors Affecting Cesarean Section Selection in Iranian Women Using Multilevel Count Models with Excess Zeros.

BACKGROUND: Iran has ranked second in the frequency of cesarean delivery (CD) and this rate in 2014 has increased by 56 percent. The CD has multiple complications for the woman and newborn, and due to the women's readmission after surgery impose additional costs to the countries. Although CD has many complications and is not recommended by obstetrician and midwives; some factors affect the choice of this method of delivery. METHODS: We used data from the Iranian Institute for Health Sciences Research (IIHSR) in 2015. We studied the effects of factors such as socioeconomic and demographic factors and supplemental insurance status in the choice of CD. We used multilevel Zero-Inflated models for the modeling of data. RESULTS: The employed women resident in urban areas with the high-income and age greater than 34-yr old and supplemental insurance more likely chose CD. On the other hand, women with high education level, women who use at least one media (e.g. Radio, television, etc.) and women that use contraceptive methods have chosen the less CD. CONCLUSION: Our findings highlighted the importance of supplemental insurance and socio-economic status in choosing a CD by women. However, in some cases especially in the rich class of society, the high cost of this type of delivery does not affect the choice decrease of it, and governments should adopt rigorous policies in using this method.

Robust inference in the joint modeling of multilevel zero-inflated Poisson and Cox models.

A popular method for simultaneously modeling of correlated count response with excess zeros and time to event is by means of the joint models. In these models, the likelihood-based methods (such as expectation-maximization algorithm and Newton-Raphson) are used for estimating the parameters, but in the presence of contaminations, these methods are unstable. To overcome this challenge, we extend the M-estimator methods and propose a robust estimator approach to obtain a robust estimation of the regression parameters in the joint model. Our proposed algorithm has two steps (Expectation and Solution). In the expectation step, the likelihood function is expected by conditioning on the observed data and in the solution step, the parameters are computed, with solving robust estimating equations. Therefore, this algorithm achieves robustness by applying robust estimating equations and weighted likelihood in the S-step. Simulation studies under various situations of contaminations show that the robust algorithm gives us consistent estimates with a smaller bias than likelihood-based methods. The application section uses data on factors affecting fertility and birth spacing.

Prevalence of Common Nosocomial Infections and Evaluation of Antibiotic Resistance Patterns in Patients with Secondary Infections in Hamadan, Iran.

INTRODUCTION: The prevalence of nosocomial infections in patients hospitalized to three hospitals of Shahid Beheshti, Farshchian, and Be' saat in Hamadan was investigated for 2 years (2018 to 2020). MATERIALS AND METHODS: The samples were cultured and characterized using morphological and diagnostic biochemical tests. The analysis of the frequency of the isolates and their antibiotic resistance were calculated using SPSS (version 22) at a significant level of P-value < 0.05. RESULTS: Bacterial isolates were collected from the 1194 clinical specimens, of which 1394 were isolated from urine, 16 from CSF, and 588 from tracheal aspiration. Also, 654 (54.8%) isolates were obtained from females and 540 (45.2%) from males with the age range 15-73 years (P> 0.05). The results showed that 22.1% were gram-positive and 77.9% were gram-negative. In our study, the frequency of Klebsiella pneumoniae bacteria was higher than in some studies, and this indicates the genetic changes and resistance of this bacterium to many antibiotics. CONCLUSION: To prevent further spread of resistance, increase the effectiveness of antibiotics and prevent multidrug resistance, it is essential to establish a precise schedule for the use of antibiotics and assess the resistance pattern periodically in each region based on the antibiotic resistance pattern.

Robust inference in the multilevel zero-inflated negative binomial model.

A popular way to model correlated count data with excess zeros and over-dispersion simultaneously is by means of the multilevel zero-inflated negative binomial (MZINB) distribution. Due to the complexity of the likelihood of these models, numerical methods such as the EM algorithm are used to estimate parameters. On the other hand, in the presence of outliers or when mixture components are poorly separated, the likelihood-based methods can become unstable. To overcome this challenge, we extend the robust expectation-solution (RES) approach for building a robust estimator of the regression parameters in the MZINB model. This approach achieves robustness by applying robust estimating equations in the S-step instead of estimating equations in the M-step of the EM algorithm. The robust estimation equation in the logistic component only weighs the design matrix (X) and reduces the effect of the leverage points, but in the negative binomial component, the influence of deviations on the response (Y) and design matrix (X) are bound separately. Simulation studies under various settings show that the RES algorithm gives us consistent estimates with smaller biases than the EM algorithm under contaminations. The RES algorithm applies to the data of the DMFT index and the fertility rate data.