Factors affecting Hemoglobin A1c in the longitudinal study of the Iranian population using mixed quantile regression.

Diabetes, a major non-communicable disease, presents challenges for healthcare systems worldwide. Traditional regression models focus on mean effects, but factors can impact the entire distribution of responses over time. Linear mixed quantile regression models (LQMMs) address this issue. A study involving 2791 diabetic patients in Iran explored the relationship between Hemoglobin A1c (HbA1c) levels and factors such as age, sex, body mass index (BMI), disease duration, cholesterol, triglycerides, ischemic heart disease, and treatments (insulin, oral anti-diabetic drugs, and combination). LQMM analysis examined the association between HbA1c and the explanatory variables. Associations between cholesterol, triglycerides, ischemic heart disease (IHD), insulin, oral anti-diabetic drugs (OADs), a combination of OADs and insulin, and HbA1c levels exhibited varying degrees of correlation across all quantiles (p < 0.05), demonstrating a positive effect. While BMI did not display significant effects in the lower quantiles (p > 0.05), it was found to be significant in the higher quantiles (p < 0.05). The impact of disease duration differed between the low and high quantiles (specifically at the quantiles of 5, 50, and 75; p < 0.05). Age was discovered to have an association with HbA1c in the higher quantiles (specifically at the quantiles of 50, 75, and 95; p < 0.05). The findings reveal important associations and shed light on how these relationships may vary across different quantiles and over time. These insights can serve as guidance for devising effective strategies to manage and monitor HbA1c levels.

Barriers and gaps in tuberculosis care and treatment in Iran: A multi-center qualitative study.

Introduction: Patients with Tuberculosis (TB) still have barriers in accessing high quality care and treatment services. In this qualitative study, we investigated barriers in accessing TB health services including confirmatory diagnosis, treatment adherence and recurrence of pulmonary TB using patients, physicians, and policy makers point of view. Materials and methods: In this qualitative research from November to March 2021, 3 policy makers from the Ministry of Health, 12 provincial TB experts and physicians from the TB control program and 33 patients diagnosed with TB from 4 provinces were enrolled for a semi-structured in-depth interview. All interviews were audio recorded and then transcribed. Framework analysis was done by MAXQDA 2018 software to identify key themes. Results: Several barriers reported for TB care and treatment: Poor knowledge of patents about TB symptoms, failure to screen for TB among at-risk patients by physicians, similar symptoms between TB and other lung diseases, low sensitivity of TB diagnostic tests, incomplete case finding and contact-tracing, stigma related to TB, and patients poor adherence due to long TB treatment. In addition, COVID-19 pandemic disrupted TB services and decreased detection, care and treatment services for TB patients. Conclusion: Our findings highlight the need for interventions to increase public and healthcare providers awareness about TB symptoms, using more sensitive diagnostic tests, and interventions to reduce stigma, and improve case finding and contact tracing effort. Improving patients' adherence required better monitoring and shorter effective treatment regimes.

A Cross-Sectional Comparative Study of Morbidity and Mortality of COVID-19 Disease in four Cities of Northern Khorasan Province using Ratio of Coefficient of Variation.

Background: In December 2019, the spread of a new infectious disease was reported in Wuhan, caused by a new coronavirus named COVID-19 by the World Health Organization. This study aims to compare the dispersion of COVID-19 disease among four Iranian cities in North Khorasan named Bojnord, Farooj, Jajarm, and Shirvan. Materials and Methods: This cross-sectional study includes information about the daily morbidity and mortality of COVID-19 in 1124 patients from March to May 2021. The analysis of variance method, Scheffe post hoc technique and Leven's test are used to compare the means and the variances of daily morbidity and mortality of these cities. Finally, the coefficients of variation (CVs) of the morbidity and mortality are compared. Results: The means of daily morbidity in Bojnord, Farooj, Jajarm, and Shirvan cities are 6.387, 0.946, 1.150, and 2.193, respectively. Furthermore, the means of daily mortality in Bajnourd, Farooj, Jajarm, and Shirvan are 0.763, 0.193, 0.161, and 0.290, respectively. The means and the variances of both daily mortality and morbidity are significantly different in all four cities (P < 0.05). Furthermore, CVs of daily morbidity in the cities of Bojnord, Farooj, Jajarm, and Shirvan are 0.665, 1.026, 1.032, and 0.787, respectively. The CVs of daily mortality in these cities are 1.196, 2.052, 2.468, and 1.728, respectively. The CVs of both daily mortality and morbidity are significantly different in all four cities (P < 0.05). Conclusion: The ratio of CVs is a good option for comparing the spread of COVID-19 in different regions with different means and variances.

Comparison of the Effective Reproduction Number (Rt) Estimation Methods of COVID-19 Using Simulation Data Based on Available Data from Iran, USA, UK, India, and Brazil.

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.

A Cluster-wise Linear Regression Model to Investigate the Effect of Demographical and Clinical Variables on the Average Depression Score.

Background: Depression is a prevalent illness in the world. Given the importance of mental disorders, many researchers have investigated the effects of different variables on average depression scores. In this study, we decided to investigate the effect of some explanatory variables on the average depression score. Methods: The data were provided from the second phase of the Kerman Coronary Artery Diseases Risk Factors study (KERCADRS), which took place between 2014 and 2018. To obtain more precise connections between depression ratings and predictor variables, we employed a cluster-wise linear regression model. Results: The total number of the participants in this study was 9811, out of whom 2144 were allocated to cluster 1, 4540 to cluster 2, and 3127 to cluster 3. The average depression score was 13.76 ± 7.6 in cluster 1, 4.39 ± 4.7 in cluster 2, and 10.83 ± 6.7 in cluster 3. However, the average depression score for all the data was 8.5 ± 7.2. In all the clusters, the average depression score of females was significantly greater than that of men (P < 0.001). In cluster 1, the age category of 35-54 years, in cluster 2, the age category of 55-80 years, and in cluster 3, the age category of 15-34 years had a maximum average depression score. Conclusion: We may classify the 3 clusters as having a low (cluster 2), moderate (cluster 3), or high (cluster 1) depression score, according to the age group with the highest artery diseases risk. The patients were 55-80 years, 15-34 years, and 35-54 years in cluster 2 (low), cluster 3 (moderate), and cluster 1 (high), respectively.

An Evaluation of The Diagnostic Value of Sputum Smears Microscopy and Pcr Relative to Sputum Culture in The Diagnosis of Pulmonary Tuberculosis: A Systematic Review and Meta-Analysis in Iran.

Background: Tuberculosis is one of the oldest known diseases in humans, and early detection of tuberculosis is one of the main measures to decrease the spread of tuberculosis. In many parts of the world, including Iran, the diagnosis of tuberculosis is based on the detection of acid-fast bacillus in sputum smear microscopy and PCR. this study aimed to synthesize evidence on the diagnostic accuracy of sputum smear and PCR compared to sputum culture for the diagnosis of PT in Iranian patients. Methods: This systematic review and meta-analysis was conducted based on PRISMA guideline for systematic review and meta-analysis. Eligible studies were cross-sectional original diagnostic studies published in English and Persian in Iran which examined the sensitivity or specificity(study outcome) of sputum smear microscopy or PCR( as the test) relative to sputum culture (as the gold standard/comparator) among Iranian patients suspected of having tuberculosis( study population). Studies whose data were not complete or extractable were excluded. Results: A total of 3518 subjects were evaluated from 15 eligible studies. The pooled sensitivity of sputum smear and PCR was 75.12 (95% CI: 66.68-83.56) and 88.02 (95% CI: 82.87-93.27), respectively. The specificity for sputum smear and PCR was 93.94 (95% CI: 91.26-96.63) and 91.82 (95% CI: 87.29-96.35) respectively. The sensitivity of both sputum smears was higher in studies published after 2010, and had higher quality. The specificity of sputum smear was a bit lower in studies published after2010 but higher in studies with higher quality. The specificity of PCR was higher in studies published after 2010 but higher in studies with higher quality. Conclusion:The increased sensitivity of sputum smear and PCR during recent years suggests the improvement of preparation and laboratory methods in recent years. However, the imperfect sensitivity of these tests highlights the need for a more accurate diagnostic method for the detection of pulmonary tuberculosis in Iran.

Determination of the trend of incidence of cutaneous leishmaniasis in Kerman province 2014-2020 and forecasting until 2023. A time series study.

INTRODUCTION: Cutaneous leishmaniasis (CL) is currently a health problem in several parts of Iran, particularly Kerman. This study was conducted to determine the incidence and trend of CL in Kerman during 2014-2020 and its forecast up to 2023. The effects of meteorological variables on incidence was also evaluated. MATERIALS AND METHODS: 4993 definite cases of CL recorded from January 2014 to December 2020 by the Vice-Chancellor for Health at Kerman University of Medical Sciences were entered. Meteorological variables were obtained from the national meteorological site. The time series SARIMA methods were used to evaluate the effects of meteorological variables on CL. RESULTS: Monthly rainfall at the lag 0 (ß = -0.507, 95% confidence interval:-0.955,-0.058) and monthly sunny hours at the lag 0 (ß = -0.214, 95% confidence interval:-0.308,-0.119) negatively associated with the incidence of CL. Based on the Akaike information criterion (AIC) the multivariable model (AIC = 613) was more suitable than univariable model (AIC = 690.66) to estimate the trend and forecast the incidence up to 36 months. CONCLUSION: The decreasing pattern of CL in Kerman province highlights the success of preventive, diagnostic and therapeutic interventions during the recent years. However, due to endemicity of disease, extension and continuation of such interventions especially before and during the time periods with higher incidence is essential.

Forecasts of cardiac and respiratory mortality in Tehran, Iran, using ARIMAX and CNN-LSTM models.

Cardiovascular diseases belong to the leading causes of disability and premature death worldwide, including in Iran. It is predicted that the burden of the disease in Iran in 2025 will be more than doubled compared to 2005. Therefore, many forecasting models have been used to predict disease progression, estimate mortality rates, and assess risk factors. Our study focused on two time series prediction on models: autoregressive integrated moving average with exogenous variable (ARIMAX) and Convolutional neural network-long short-term memory network (CNN-LSTM). ARIMAX (6,1,6) had the best MSE of 0.655 among time series regression models. The prediction of this model shows a significant association in lag 4 and lag 6. Nitrogen dioxide (NO2) was also significant in lag 6, while CNN-LSTM had a much better MSE of 0.21. For the time series analysis and forecasts studied in this paper, deep learning models provided more accurate results than classical methods such as ARIMAX.

Investigating the relationship between the ethical atmosphere of the hospital and the ethical behavior of Iranian nurses.

BACKGROUND: The ethical guidelines and ethical atmosphere of the hospital affect the quality of nursing care. Improving the health of patients in most cases depends on the observance of ethical points and ethical behaviors by nurses. The aim of this study was to determine the effect of hospital ethics and ethical training guidelines on improving the quality of nursing care and nurses' professional ethics. MATERIALS AND METHODS: This is a descriptive-analytical study. This study was performed by multi-stage relative cluster sampling on 260 qualified nurses in 2014. Data were collected using the Hospital Ethical Climate Questionnaire and the Judgments about Nursing Decisions Questionnaire and analyzed using SPSS software version 19. RESULTS: The results showed that nurses' perceptions of the ethical climate of hospitals were relatively positive. The ethical behavior of nurses was moderate and good. Nurses' perception of the ethical climate scores was not related to the ideal ethical behavior of nurses (r = 0.11, P = 0.86). The ethical climate perceptions of nurses were related to real workplace ethical behavior score (r = 0.188, P = 0.002). The results also showed that ethical guidelines can improve the quality of nursing care. CONCLUSION: Considering the results of this study, it seems that holding nursing ethics training programs can be a big step toward promoting the professional behavior of nurses and the observance of professional ethics by nurses.

Role of judgment in promoting nurses' decisions and ethical behavior.

BACKGROUND: Ethical decision-making and behavior of nurses are major factors, which can effect on the quality of nursing care. It seems that there is a correlation between demographic variables and ethical decision-making and moral behaviors of nurses. Promoting patients' health is one of the issues related to nurses' ethical behaviors. The aim of this study was to determine the role of judgment in promoting nurses' decisions and ethical behavior. MATERIALS AND METHODS: This was a descriptive, cross-sectional study in which 260 nurses were selected based on the inclusion criteria. Sampling method was available. The data collection tool was the Hospital Ethics Committee Survey Questionnaire. Data were analyzed by SPSS software version 20. RESULTS: In this study, judgments and ethical behaviors of nurses were evaluated at the moderate and good level. Gender, marriage status, education level, and nursing position were effective in judgments and ethical behavior of nurses. Age, job experience, and participation in the ethics workshop had no significant effect on ethical behavior and moral judgment. The mean score of moral belief of nurses participating in this study was 181.56 ± 17.60, and their mean moral practice in the real environment was 168.5 ± 17.77. CONCLUSION: The judgment competencies in ethical situation of nurses should be promoted to a higher level. It seems that more advanced educational methods are needed to achieve this goal. The findings from this study show the necessity of nurses' ability to improve their behavior and moral judgment. It also shows that nurses need more reinforcement based on the demographic variables.

Short-term and long-term survival of patients with gastric cancer.

AIM: The aim of this study was to apply the Bayesian mixture cure rate frailty model to determine the factors that influence short-term 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 (ß=-0.35 (-0.75, -0.03) and OR=1.59 (1.08, 2.19)), morphology (ß =-0.98(-1.45, -0.48) and OR=2.99 (1.78, 4.17)), and metastasis (ß =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.

Development of the Quality of Life Questionnaire (SF-36) for Patients with ß-Thalassemia Major and ß-Thalassemia Intermedia Based on Extended Rasch Analysis.

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 ß-thalassemia major (ß-TM) and ß-thalassemia intermedia (ß-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.

Survival Analysis of Gastric Cancer Patients Using Illness-Death Model In Kerman City, Southeast Iran during 2001-2016.

BACKGROUND Gastric cancer (GC) is the fifth most common cancer and the third most common cause of cancer death worldwide. The aim of this study was to investigate the factors affecting the survival of patients with GC using the illness-death model (metastasis as an intermediate event). METHODS In this retrospective cohort study, 339 patients with GC who were referred to Shahid Bahonar and Afzalipour Hospitals in Kerman, Southeast Iran during 2001-2016 were included. Demographic, therapeutic, and clinical data were collected from the patients' medical records. To evaluate the factors affecting patients' survival and the relationship between the factors, the illness-death model (metastasis as an intermediate event) was used. RESULTS One, three, and five-year survival rates in patients with GC were estimated to be 63%, 40%, and 30%, respectively. The results of analysis of illness-death model showed that age (HR = 0.98, 95% CI: 0.97- 0.99; p = 0.007) and histological grade (HR = 1.77; 95% CI: 1.68- 2.67; p =0.007) affected metastasis whereas history of cigarette smoking (HR = 1.89; 95% CI: 1.08- 3.3; p = 0.02) and chemotherapy (HR = 0.63; 95% CI: 0.4-0.93; p = 0.02) affected death hazard without metastasis. History of opium use (HR=2.11; 95% CI: 1.17- 3.8; p = 0.002), family history of GC (HR = 2.48; 95% CI: 1.2-5.15; p = 0.01) and histological grade (HR = 1.85; 95% CI: 1.11- 3.08; p = 0.02) were identified as factors affecting death hazard in patients with metastasis. CONCLUSION According to the results, patients' age at the time of diagnosis and histological grade have a significant effect on the occurrence of metastasis. In addition, using the disease-death model, a complete understanding of the behavior and effects of the studied variables on different disease states can be realized.

Bayesian mixture cure rate frailty models with an application to gastric cancer data.

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.

Characterization of miR-200 family members as blood biomarkers for human and laying hen ovarian cancer.

MicroRNA-200 (miR-200) family is highly expressed in ovarian cancer. We evaluated the levels of family members relative to the internal control miR-103a in ovarian cancer and control blood specimens collected from American and Hong Kong Chinese institutions, as well as from a laying hen spontaneous ovarian cancer model. The levels of miR-200a, miR-200b and miR-200c were significantly elevated in all human cancer versus all control blood samples. Further analyses showed significantly higher miR-200 levels in Chinese control (except miR-429) and cancer (except miR-200a and miR141) samples than their respective American counterparts. Subtype-specific analysis showed that miR-200b had an overall elevated level in serous cancer compared with controls, whereas miR-429 was significantly elevated in clear cell and endometrioid cancer versus controls. MiR-429 was also significantly elevated in cancer versus control in laying hen plasma samples, consistent with the fact that endometrioid tumor is the prevalent type in this species. A neural network model consisting of miR-200a/200b/429/141 showed an area under the curve (AUC) value of 0.904 for American ovarian cancer prediction, whereas a model consisting of miR-200b/200c/429/141 showed an AUC value of 0.901 for Chinese women. Hence, miR-200 is informative as blood biomarkers for both human and laying hen ovarian cancer.

Estimating the Loss in Expectation of Life and Relative Survival Rate among Hemodialysis Patients in Iran.

BACKGROUND: Information regarding the prognosis and burden of diseases can be used by policymakers to determine competing health priorities. We aimed to assess the Relative Survival Rate (RSR) and loss of expectation of life (LEL) to evaluate the prognosis and burden of diseases in Hemodialysis (HD) patients. STUDY DESIGN: A retrospective cohort study. METHODS: We recruited 648 HD patients referred to three referral centers in Kerman City, Iran, from 2008 to 2019. RSR, was defined as the ratio of the observed and the expected survival rates of general population for persons of the same age and sex as patients in the current study. LEL was determined as the difference between corresponding life expectancies (LE). The extended Cox proportional hazard model was used to identify variables associated with the outcome. RESULTS: Variables associated with outcome were diabetic status and age. In the 5th year of the follow-up study, the overall RSR was 0.57. In general, for HD patients, the estimation of LE and LEL was 22.6 and 12.36 year, respectively. CONCLUSION: HD patients, especially older patients, showed a very poor prognosis, with a large amount of lost life expectancy. Therefore, they need more care and attention from health authorities. It is suggested to estimate the cost of eliminating the risk factors causing kidney diseases.

Mortality risk factors in patients with gastric cancer using Bayesian and ordinary Lasso logistic models: a study in the Southeast of Iran.

AIM: The aim of this study was to apply two types of statistical models to determine the factors that influence the mortality rate in patients with gastric cancer. BACKGROUND: In Iran, gastric cancer ranks the first and second most prevalent among men and women, respectively. It is the first cause of death in Iran in both gendersival. METHODS: In this retrospective study, data were obtained from 339 (216 male) patients diagnosed with gastric cancer in the city of Kerman (South-East of Iran) during 2001-2015. In this study, ordinary and Bayesian Lasso (least absolute shrinkage and selection operator) logistic regression models, with goodness-of-fit indices, were compared and the models' risk factors were also determined. RESULTS: The mean age of the participants was 62.84 ±14.53 years, and 12.4% of them were younger than 45 years. Also, the mortality rate was 57.7%. Gender, morphology of the tumor, and time of diagnosis were found to be significant factors in the mortality of the patients in both models. This study found that the Bayesian Lasso model had better fitness. CONCLUSION: The high mortality rate of gastric cancer and its high prevalence at age below 45 years are alarming. Thus, great attention should be paid to prevention, early diagnosis, especially in females, and adenocarcinoma to improve the survival of patients with gastric cancer.

Factors Affecting Long-Survival of Patients with Breast Cancer by Non-Mixture and Mixture Cure Models Using the Weibull, Log-logistic and Dagum Distributions: A Bayesian Approach.

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.

The Relation between Hearing Loss and Smoking among Workers Exposed to Noise, Using Linear Mixed Models.

INTRODUCTION: Noise is one of the most common and harmful physical factors in the working environment and has physical and psychological effects on individuals. In this study, the audiometry results of industrial workers were modeled and the effect of noise and other factors on hearing loss was examined. MATERIALS AND METHODS: This was a longitudinal study based on the records of workers who had worked over 10 years in the industry and had recorded audiometries since their employment. Data was analyzed through linear mixed models. RESULTS: During each year of noise exposure, hearing loss was 1.9 db at 4000 Hz; 0.059 in low frequencies and 0.62 db in high frequencies. At 8000 Hz the effect of the age at employment on hearing loss was significant (P=0.014). At low frequencies the interaction of smoking and age at employment was significantly related to hearing loss (P˂0.001). CONCLUSION: This study showed that despite acquaintance with safety measures, workers still face hearing loss in industry and employers should put workers under more surveillance for using protective gear. Smoking might be another risk factor for hearing loss.

Comparison of Penalized Cox Regression Methods in Low-Dimensional Data with Few-Events: An Application to Dialysis Patients' Data.

BACKGROUND: Dialysis is a dominant therapeutic method in patients with chronic renal failure. The ratio of those who experienced the event to the predictor variables is expressed as event per variable (EPV). When EPV is low, one of the common techniques which may help to manage the problem is penalized Cox regression model (PCRM). The aim of this study was to determine the survival of dialysis patients using the PCRM in low-dimensional data with few events. STUDY DESIGN: A cross-sectional study. METHODS: Information of 252 dialysis patients of Bandar Abbas hospitals, southern Iran, from 2010-16 were used. To deal with few mortality cases in the sample, the PCRM (lasso, ridge and elastic net, adaptive lasso) were applied. Models were compared in terms of calibration and discrimination. RESULTS: Thirty-five (13.9%) mortality cases were observed. Dialysis data simulations revealed that the lasso had higher prediction accuracy than other models. For one unit of increase in the level of education, the risk of mortality was reduced by 0.32 (HR=0.68). The risk of mortality was 0.26 (HR=1.26) higher for the unemployed than the employed cases. Other significant factors were the duration of each dialysis session, number of dialysis sessions per week and age of dialysis onset (HR=0.93, 0.95 and 1.33). CONCLUSION: The performance of penalized models, especially the lasso, was satisfying in low-dimensional data with low EPV based on dialysis data simulation and real data, therefore these models are the good choice for managing of this type of data.