Introduction:The COVID-19 infectious epidemic has become a serious worry all over the world, including Iran. The high outbreak of disease ranked Iran as second in Asia and 11th in the world. Given the growing progress of this epidemic in infecting and killing individuals, it is essential to forecast the delay effect of the number of hospitalized upon the hospitalized mortality rate.

 Methods:   In this study, we used the daily Hospitalization cases of COVID-19 of IRAN for the period of 15-May 2020 to 5-Oct 2020 which were obtained from the online database. Five distribution delay models were compared for estimating and forecasting.

Results: Based on measurement errors DDM selected as the best model for forecasting the number of death. According to this model, the long-run effects show that observing the effect of hospitalization counts on death counts takes an average of five days and the long-run hospitalized mortality rate was 12%.

Conclusion: The overall hospitalized mortality rate of COVID-19 in Iran is less than the global rate of 15%. The mean of delay effect of daily hospitalization on mortality is approximately 5 days. Our findings showed distributed delay model (DDM) has better performance in the forecasting of the future behavior of the Coronavirus mortality, and providing to government and health care decision- makers the possibility to predict the outcomes of their decision on public health.

Introduction: Quality of life (QOL) is an important index in society that need for evaluation in all age groups people especially in medical university students as a people that their physical and mental health is related with community health. This study aims to investigate the quality of life (QOL) of Ardabil University of Medical Sciences.

Methods: This is a cross-sectional study that has been conducted on 200 students who selected by random sampling method from Ardabil medical university students. The QOL was measured by WHOQOL-BREF which its validity and Reliability were investigated and approved. This questionnaire include 26 questions in four dimensions (physical, mental, social and environmental health). Collected data we analyzed by statistical test such as t-test for compare the mean of QOL score among demographic data.

Results: Of all students, 57% were male and 91.5% were single. Of all students, 56% had desired quality of life. The relationships between QOL and variables such as gender, educational level, marital status and age of students wasn’t significant. The mean difference of four dimension scores among two sexes was statistically significant. The mean of Physical health dimension score was 11.6±2.1, Psychological was 12.3±2.4, Social relationships was 13.1±3.4 and environment was 12.7±3.2. The mean of total score of QOL in all students was 12.4±2.3.

Conclusion: Results showed that the QOL of all students were in high level and in four dimension of QOL the female students had significant higher score than male students.

Background and aims: In Iran, breast cancer accounts for 24.4% of all cancers and contributes to 14.2% of cancer-associated mortality in women. A major challenge facing the health system is to examine the health status of patients with breast cancer, which often involves the axillary lymph nodes. The number of involved nodes should be clinically predicted to ascertain postoperative radiotherapy and chemotherapy. The present study employed regression models to investigate the determinants of the number of lymph nodes involved in patients with breast cancer.

Methods: This retrospective study recruited patients diagnosed with breast cancer during 2005-2015 referring to Shafa Hospital affiliated to Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. The outcome variable was the number of involved lymph nodes. Regression models for count outcomes, were utilized for investigating the related factors to the number of involved lymph nodes in patients with breast cancer.

Results: A sample of 165 patients was eligible for the present study. The Akaike information criterion (AIC) of the zero-inflated negative binomial (ZINB) model was the lowest. The logistic part showed that absence of metastasis significantly increased the chance of node-negative breast cancer (P=0.027). The negative binomial part revealed an increase of 86% in the risk of a greater number of involved nodes in stage III breast cancer compared to stages I and II, suggesting that the patients were at a high risk (P=0.006).

Conclusion: Metastasis status and tumor grade significantly relate to the number of lymph nodes involved in breast cancer. Determining the factors associated with nodal involvement is crucial for the early diagnosis of breast cancer by clinicians.

Objective: Rapid technological advances in the last century and the large amount of information have made it difficult to analyze a large number of independent variables. In such circumstances, the existence of interactions of different degrees in the model is expected, in this case, the Cox model cannot be useful and the nonparametric method of random survival forest can be a useful alternative. This study compares the prediction error of random survival forest with Cox and Weibull models in predicting the time to the first recurrence in patients with epithelial ovarian cancer.

Method: In this retrospective study, the records of patients with epithelial ovarian cancer who referred to Imam Hossein Hospital in Tehran from 2007 to 2018 were used. To investigate the factors affecting the first recurrence of these patients, RSF was fitted to the data. Finally, prediction error of Cox, Weibull and RSF were compared using C-Index and Brier score.

Results: Brier score was calculated 0.16 for RSF, and 0.24 for Cox, also C-Index was calculated 0.34 for RSF and 0.42 for Cox. Brier score was calculated 0.092 for Cox and 0.089 for Weibull, so the prediction error of RSF was lower than both Cox and Weibull models.

Conclusion: Random survival forest with a suitable fit on many variables and without the need for a special default with a prediction error less than the Weibull and Cox methods can predict the response variable when confronted with high-dimensional data.

Objective: Our aim is to perform an analysis, using big data, of cases diagnosed with primary hypothyroidism and aged 18 and over who presented to our hospital, by evaluating the laboratory and socio-demographic data of the patients. Clustering analysis was performed in the big dataset for the purpose of structure-search study on the subject.

Methods: According to ICD-10 diagnoses of hypothyroidism between 2005-2018 in our hospital 130159 patients aged 18 and over with E03 and E06 diagnosis codes were included in the study. Since drugs containing levothyroxine used in primary hypothyroidism treatment have an effect on the measured hormone levels, in our study, TSH, fT3 and fT4 laboratory values in the first diagnosis of cases who had not received any treatment as part of the diagnosis according to demographics were analysed. Patients with one or more missing laboratory values were excluded, and data of 2680 patients with complete data and TSH values above 4.94 mU/L were retained. Analysis was made with the k-means clustering technique, with the data separated into two sets. k-means clustering was performed by including age, TSH, fT3 and fT4 variables. Cliff’s Delta effect size coefficients and confidence intervals were calculated to perform size of the difference.

Results: The higher prevalence of primary hypothyroidism in female and the peak in hypothyroidism at 4-5 decades in both genders were observed. In which ages were low, fT3 and fT4 values were higher, whereas TSH values were lower in male. In which ages were low, TSH values were higher, whereas fT4 values were lower in female.

Conclusion: This study is the first big data analysis study carried out about primary hypothyroidism in our country. Despite the difficulties in implementation, it should not be forgotten that studies like these are important methods for enabling data to be created in our country.

Background: There are some overlaps between celiac disease and irritable bowel syndrome symptoms (IBS). It can lead to misdiagnosis or delayed diagnosis of celiac disease. In some guidelines, it is recommended to screen for celiac in IBS cases. For assessment of the necessity for such diagnostic approaches in patients, this study was done to evaluate the epidemiological and clinical characteristics of celiac disease among IBS cases in Zanjan, Iran.

Methods: In this descriptive cross-sectional study, 121 cases with IBS attending to gastroenterology clinics since 2015 to 2018 were enrolled. The laboratory tests and upper digestive endoscopy were performed for all patients. Endoscopic biopsy specimens were taken from the duodenum, and the samples were examined to confirm diagnosis of celiac disease. Data analysis was done by SPSS software.

Results: Of 121 studied patients, 51.2% were male. The mean age of the patients was 36.65 ± 10.09 years old. The most common IBS subtype was mixed (80.2%). According to the serology results and Marsh grading, 4.1% and 1.6% had celiac disease and potential celiac disease, respectively. There were statistically significant differences among celiac disease in gastroesophageal reflux disease and abdominal discomfort/cramping.

Conclusion: The incidence of celiac disease was evaluated 4.1 cases per each 100 patients with IBS, which was higher than recent similar studies, and screening for celiac disease in these patients is advisable. However, further studies with larger sample size are required to attain more definite results.

Background: Duration of breastfeeding is an important health indicator of mother and child. There are various indirect epidemiological methods available to estimate the duration of breastfeeding from cross sectional data. 

Objective: To estimate the distribution of duration of breastfeeding at national level cross sectional data and compare various available technique. The impact of the sampling frame (ascertain of the individual understudy) is also evaluated. 

Method: National Family Health Survey (NFHS-IV) data is used. Duration of breastfeeding of only those children who were born before 60 months from survey date were included in the study. The technique of Current Status Data, Life Table Analysis, and Kaplan Meier (KM) estimator is applied to assess the distribution of duration of breastfeeding. 

Result: The mean estimate is 32.84, 33.14 and 33.64 months by Kaplan Maier Estimator, Current Status Data and Life Table Analysis respectively. The Current Status and Life Table method are better than Kaplan Meier Estimator as it is doesn’t based on recall data and heaping present in the data. 

Conclusion: One must be very cautions while estimating the various epidemiological parameters from cross section data set. The assumptions of the methodology as per data available should be evaluate. If such data is not available, the available methodology may be modified. Regression analysis based on Current Status data technique may be used to assess the impact of various clinical and epidemiological factors (such as nutrition of mother, health status of mother etc.) on duration of breastfeeding.  

Background: Calibration of clinical prediction models often entails assessing goodness of fit with independent, non-identically distributed Bernoulli random variables. We here investigate two statistics studied by Copas in this setting.

Materials and Methods: We present distribution theory and a simulation study to compare the operating characteristics of the Copas statistics.

Results: In our simulation study with relatively small sample sizes, we found a simple Cornish-Fisher approximation tail quantiles of the distributions of the Copas statistics to perform adequately. Upon illustrating their use in a calibration study relating to prediction of atherosclerotic cardiovascular disease risk, power properties appear to reflect differential weighting accorded to observations, as evinced with other goodness-of-fit statistics.

Conclusion: The Copas statistics are easily implemented, have proven value in other contexts, and appear to be underutilized in calibration studies. They ought to be part of the armamentarium of calibration tools for all researchers