Abbas Bahrampour

Introduction: The COVID-19 pandemic significantly impacted mental health, leading to psychiatric complications such as anxiety, depression, and insomnia. Health anxiety, a perceived threat to one's health, increased during the pandemic, influencing individuals'; health informationseeking behavior. This study explores the relationship between health anxiety and health information- seeking behavior among COVID-19 patients. Method: A cross-sectional study was conducted in Kerman, Iran, from September 2021 to July 2022. A total of 288 COVID-19 patients participated through convenience sampling. Data were collected via a validated online questionnaire consisting of three sections: demographic information, health information-seeking behavior (39 items), and health anxiety (Short Form Health Anxiety Inventory). The study utilized descriptive statistics, t-tests, chi-square, ANOVA, and logistic regression for data analysis. Results: The mean total health anxiety score was 22.95±6.36, indicating significant anxiety levels among participants. Regression analysis revealed that information sources, needs, and barriers significantly influenced health anxiety. Notably, national television and consultation with other patients were predictors of health anxiety. Moreover, difficulty distinguishing trustworthy information from misinformation and limited time for information search were significant barriers associated with increased anxiety. Conclusion: The findings suggest a bi-directional relationship between health anxiety and information- seeking behavior. High-quality, timely healthcare information is crucial to alleviate health anxiety during pandemics. Future studies should focus on reducing anxiety through psychological interventions and improving digital health literacy to mitigate misinformation and its psychological impact.

Background: Breast cancer (BC) is the most common cancer in women, and it is important to identify models that can accurately predict mortality in patients with this cancer. The aim of the present study was to use the elastic net regression and artificial neural network (ANN) models in diagnosing and predicting factors affecting BC mortality. Study Design: A cross-sectional study. Methods: The data of 2,836 people with BC during 2014-2018 were analyzed in this study. Information was registered in the cancer registration system of Kerman University of Medical Sciences. Death status was considered the dependent variable, while age, morphology, tumor differentiation, residence status, and residence place were regarded as independent variables. Sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC), precision, and F1-score were used to compare the models. Results: Based on the test set, the elastic net regression determined factors affecting BC mortality (with sensitivity of 0.631, specificity of 0.814, AUC of 0.629, accuracy of 0.792, precision of 0.318, and F1-score of 0.42) and ANN did so (with sensitivity of 0.66, specificity of 0.748, AUC of 0.704, accuracy of 0.738, precision of 0.265, and F1-score of 0.37). Conclusion: The sensitivity and AUC of the ANN model were higher than those of the elastic net regression, but the specificity, accuracy, precision, and F1-score of the elastic net were higher than those of the ANN. According to the purpose of the study, two models can be used simultaneously. Based on the results of models, morphology, tumor differentiation, and age had a greater effect on death.

Background: the urban Health Commission of Wuhan City, China, issued an emergency notice due to an incidence of viral pneumonia of unknown cause in December 2019. The World Health Organization officially named it the 2019 novel coronavirus. Since the course of the disease is not the same in the countries and regions of the world, and the study of this diversity is an important source of information for policymakers and researchers. The current study aims to cluster selected countries of the world based on its incidence and it was done according to the trajectories shape. Methods: the data set analyzed, included new cases of COVID-19(per million people) in 13 countries of the world, which were published on the World Health website from March, 2020, to October, 2021 monthly then analyzed by the k-means clustering method using Fréchet distance and R software V4.0.5. in addition, clustercrit and kmlshape packages were utilized for trajectory clustering. Result: Research results show that, 13 countries of the world were classified into 2 clusters with high and low incidence. The cluster with high incidence included 8(62%) countries. The 2 cluster exhibits the highest outlier in COVID-19 incidence. In the analyzed nations, United States and Brazil exhibited the highest incidence rate in clusters 1 and 2, respectively. Conclusion: The present findings showed that there are 2 patterns in the epidemic of COVID-19. The first pattern includes severe fluctuations and the next pattern includes low fluctuations. The results revealed that the method used in this article has the potential to understand incidence trends regardless of the time of disease onset. Since the Covid-19 infection process experiences fluctuations over time that vary based on when the pandemic began in each country, it's essential to analyze the similarity and shape of infection trends collectively, independent of their starting points.

Background: Coronavirus disease 2019 (COVID-19) has a high rate of mortality, but data regarding its prognosis are scarce. This study aimed to identify the risk factors associated with mortality and survival of COVID-19 patients in a southern province of Iran. Materials and Methods: This was a cross-sectional study with a secondary database of 3172 people who were admitted to hospitals for COVID-19 treatment. Relative risk (RR) was computed to estimate mortality risk between groups, and the log-rank test was used to compare the survival functions in different levels of categorical variables. Finally, the multivariable extended Cox model was employed to determine the factors that affected the survival of COVID-19 patients. Results: The 10- and 15-day survival rate of 3172 patients was 89% and 80%, respectively, and the median survival time was 30 days. Based on the extended Cox model, age, oxygen therapy, intensive care unit (ICU) admission, invasive ventilation, chronic cardiac disease, chronic kidney disease, and cancer were the most important variables affecting the survival of COVID-19 patients. Conclusion: Older people (older than 40 years) and those with comorbidities (e.g., chronic cardiac disease, chronic kidney disease, and cancer) had a higher mortality rate; therefore, health-related organizations need to plan for the prevention and early detection of comorbidities to reduce the burden of diseases such as the pandemic of COVID-19 as much as possible in the future.

Background: Accurate determination of the effective reproduction number (Rt) is a very important strategy in the epidemiology of contagious diseases, including coronavirus disease 2019 (COVID-19). This study compares different methods of estimating the Rt of susceptible population to identify the most accurate method for estimating Rt. Study Design: A secondary study. Methods: The value of Rt was estimated using attack rate (AR), exponential growth (EG), maximum likelihood (ML), time-dependent (TD), and sequential Bayesian (SB) methods, for Iran, the United States, the United Kingdom, India, and Brazil from June to October 2021. In order to accurately compare these methods, a simulation study was designed using forty scenarios. Results: The lowest mean square error (MSE) was observed for TD and ML methods, with 15 and 12 cases, respectively. Therefore, considering the estimated values of Rt based on the TD method, it was found that Rt values in the United Kingdom (1.33; 95% CI: 1.14-1.52) and the United States (1.25; 95% CI: 1.12-1.38) substantially have been more than those in other countries, such as Iran (1.07; 95% CI: 0.95-1.19), India (0.99; 95% CI: 0.89-1.08), and Brazil (0.98; 95% CI: 0.84-1.14) from June to October 2021. Conclusion: The important result of this study is that TD and ML methods lead to a more accurate estimation of Rt of population than other methods. Therefore, in order to monitor and determine the epidemic situation and have a more accurate prediction of the incidence rate, as well as control COVID-19 and similar diseases, the use of these two methods is suggested to more accurately estimate Rt.

Multiple imputation by chained equations (MICE) is the most common method for imputing missing data. In the MICE algorithm, imputation can be performed using a variety of parametric and nonparametric methods. The default setting in the implementation of MICE is for imputation models to include variables as linear terms only with no interactions, but omission of interaction terms may lead to biased results. It is investigated, using simulated and real datasets, whether recursive partitioning creates appropriate variability between imputations and unbiased parameter estimates with appropriate confidence intervals. We compared four multiple imputation (MI) methods on a real and a simulated dataset. MI methods included using predictive mean matching with an interaction term in the imputation model in MICE (MICE-interaction), classification and regression tree (CART) for specifying the imputation model in MICE (MICE-CART), the implementation of random forest (RF) in MICE (MICE-RF), and MICE-Stratified method. We first selected secondary data and devised an experimental design that consisted of 40 scenarios (2 × 5 × 4), which differed by the rate of simulated missing data (10%, 20%, 30%, 40%, and 50%), the missing mechanism (MAR and MCAR), and imputation method (MICE-Interaction, MICE-CART, MICE-RF, and MICE-Stratified). First, we randomly drew 700 observations with replacement 300 times, and then the missing data were created. The evaluation was based on raw bias (RB) as well as five other measurements that were averaged over the repetitions. Next, in a simulation study, we generated data 1000 times with a sample size of 700. Then, we created missing data for each dataset once. For all scenarios, the same criteria were used as for real data to evaluate the performance of methods in the simulation study. It is concluded that, when there is an interaction effect between a dummy and a continuous predictor, substantial gains are possible by using recursive partitioning for imputation compared to parametric methods, and also, the MICE-Interaction method is always more efficient and convenient to preserve interaction effects than the other methods.

The quality of life (QoL) questionnaire (SF-36) contains 36 questions in eight subscales. It requires much time to fill in by the respondent. The objective of this study was to use Rasch models to develop a questionnaire that brings the desired outcome of the QoL of people. Therefore, a new questionnaire was provided that is more motivating and time-saving for respondents than SF-36. The QoL of life assessment data of 325 β-thalassemia major (β-TM) and β-thalassemia intermedia (β-TI) patients in Kerman, Iran, was used as research data. In this study, the appropriate questions were classified in a limited dimensional framework using the exploratory factor analysis (EFA). The correctness of the factor structure was assessed by confirmatory factor analysis (CFA). The quality of the questions was evaluated by Rasch modeling [partial credit model (PCM)] and item analysis to ensure the validity and reliability of the questionnaire. Finally, the Pearson correlation coefficient was used to compare the new questionnaire with the previous one. This process resulted in the development of a new questionnaire with five subscales and 20 questions. The construct validity of the new questionnaire was good. The reliability index of the questionnaire was 0.75, and the Pearson correlation coefficient between the QoL scores gained from the previous and the new questionnaires was 0.93 that indicates the strength of the correlation. The use of Rasch analysis in this study resulted in the development of a new reliable and valid questionnaire.

Background: The aim of this study was to evaluate the goodness of fit of Bayesian mixture and non-mixture cure models to find the factors affecting dialysis patient’s survival time where a significant proportion of the population has a long-term survival.Study Design: A retrospective cohort study. Methods: The data of 252 dialysis patients were used among whom 35 cases died. Since in this study a part of the population had long-term survival, Bayesian cure models were used and evaluated using DIC index. The data were analyzed by R and Openbugs Softwares. Results: Of the 252 dialysis patients, 136(54%) were males and the mean (SD) age was 53.39 (18.09) years. The patient’s follow-up time mean (SD) was 10.93(7.82) years. The 10 and 20-year survival rate of these patients were 87% and 73%, respectively. The findings show that the best fitting belonged to the Bayesian Non-mixture Cure Model (BNCM) with Dagum distribution. The variables of age, Body Mass Index, dialysis duration, frequency of dialysis, age of onset of dialysis, and occupation affected patients' survival based on BNCM with Dagum distribution.Conclusions: The results demonstrated that the BNCM with Dagum distribution can be a good selection model to analyze survival data, where there is the possibility of a fraction of cure.

Background: Breast cancer is a top biomedical research priority, and it is a major health problem. Therefore, the present study aimed to determine the prognostic factors of breast cancer survival using cure models. Methods: In this retrospective cohort analytic study, data of 140 breast cancer patients were collected from Ali Ibn Abi Taleb hospital, Rafsanjan, Southeastern Iran. Since in this study, a part of the population had long-term survival, cure models were used and evaluated using DIC index. The data were analyzed using Openbugs Software. Results: In this study, of 140 breast cancer patients, 23 (16.4%) cases died of breast cancer. Based on the findings, the Bayesian nonmixture cure model, with type I Dagum distribution, was the best fitted model. The variables of BMI, number of children, number of natural deliveries, tumor size, metastasis, consumption of canned food, tobacco use, and breastfeeding affected patients’ survival based on type I Dagum distribution. Conclusion: The results of the present study demonstrated that the Bayesian nonmixture cure model, with type I Dagum distribution, can be a good model to determine factors affecting the survival of patients when there is the possibility of a fraction of cure. In this study, it was found that adapting a healthy lifestyle (eg, avoiding canned foods and smoking) can improve the survival of breast cancer patients.