Introduction: Most people in the United States live busy stressful lives which can lead to significant health challenges, especially to one’s mental health. After years of being ignored and stigmatized, mental illness has been given the recognition it deserves. It is now associated with the overall health of a person and treated seriously. The same could be said about substance abuse disorder though on a lesser scale. This paper attempts to understand the relationship between drug use and mental health illness differentiated across age groups. The understanding of these causations and relationships could help us better understand the rate and triggers of mental illness in the United States.

Methods: We used data from a 2023 survey, which contains a total of 69,850 completed interviews. The data collected were taken from each state individually with each of them implementing the same survey to collect responses. Instead of looking at all fifty states, we decided to analyze only the highest twenty- one most populated states. Various non-parametric tests were used to analyze age groups and their rate of mental health illnesses and drug use within the country.

Results: We found that tobacco use is associated with mental illness, while alcohol use is associated with attempted suicide. It appears that indicators of drug use are not homogeneous across states for either age group. We also found that the alcohol consumption is related to a declining proportion of attempted suicides.

Conclusion: In this research we found a significant association between drug use and inidcators of mental illness. The association differed across different age groups and also across states. Since we found that the alcohol consumption is related to a declining proportion of attempted suicides, it will be interesting to explore why this is the case and what could be observed to decrease suicidal rates.

Introduction: Breast cancer in men is a rare disease that has been increasing in recent decades. Identifying factors influencing the survival rate of these patients is particularly important considering the small sample size. The aim of this study was to present the results of the conventional Cox- LASSO method and compare it with the newer refined generalized log-rank (RGLR) method for analyzing survival data with a small sample size.
Methods: Available information related to men with breast cancer referred to 3 treatment centers in the country (Iran) between 2012 and 2020 were reviewed. Cox-LASSO and RGLR models were fitted on the data. The analyzes were done using R.4.1.2 software and the significance level of 0.05 was considered.
Results: About 60% of the conflicts are reported on the left side. About 53% of men have been diagnosed at a low stage. The tumor size of 75% of the patients was between 2 and 4.3. Most patients have received modified radical mastectomy (MRM) treatment and adjuvant radiotherapy. 80% of patients had received chemotherapy and most had received anthracycline-taxane base. According to Akaike's criterion, RGLR model (AIC=289.32) was better than Cox-LASSO (AIC=314.76) model. Results of RGLR model indicated that, age (p-value= 0.038, HR >50 vs <50 = 6.75, 95% CI: 2.70–17.30), left laterality (p-value = 0.019, HR left vs right = 3.45, 95% CI: 1.48–8.02), larger tumor size (p-value=0.033, HR T2 vs T1 = 3.70, 95% CI: 2.92–6.68; HR T3 vs T1= 4.34, 95% CI: 3.17–5.95), higher tumor grades (p-value<0.001, HR grade 2 or 3 vs grade1 = 8.67, 95% CI: 5.10–14.71), are influential factors decreasing male breast cancer patient’s survival.
Conclusion: Although the results of the two existing models in the field of small sample size survival analysis (Cox-LASSO and RGLR) are close to each other, the RGLR model has performed better than the Cox-LASSO. With smaller AIC and SE of parameter estimation, RGLR model was choose compared to Cox-LASSO model.

Introduction: The present study discusses the importance of having a predictive method to determine the prognosis of patients with diseases like Covid-19. This method can assist physicians in making treatment decisions that improve survival rates and avoid unnecessary treatments. This research also highlights the importance of calibration, which is often overlooked in model evaluation. Without proper calibration, incorrect decisions can be made in disease treatment and preventive care. Therefore, the current study compares two highly accurate machine learning algorithms, Gradient boosting and Extreme gradient boosting, not only in terms of prediction accuracy but also in terms of model calibration and speed.

Methods: This study involved analyzing data from Covid-19 patients who were admitted to two hospitals in Mashhad city, Razavi Khorasan province, over a span of 18 months. The k-fold cross-validation method was employed on the training dataset (K=5) to conduct the study. The accuracy and calibration of two methods (Gradient boosting and Extreme gradient boosting) in predicting survival were compared using the Concordance Index and calibration.

Results: The Concordance Index values obtained for gradient boosting and Extreme gradient boosting models were 0.734 and 0.736, in the imbalanced and In the balanced data, the Concordance Index values were 0.893 for gradient boosting and 0.894 for Extreme gradient boosting. The surv.calib_beta index, the gradient boosting model had an estimated value of 0.59 in the imbalanced data and 0.66 in the balanced data. The Extreme gradient boosting model had an estimated value of 0.86 in the balanced data and 0.853 in the imbalanced data. The Extreme gradient boosting model was faster in the learning process compared to the gradient boosting model.

Conclusion: The Gradient boosting and Extreme gradient boosting models exhibited similar prediction accuracy and discrimination power, but the Extreme gradient boosting model demonstrated relatively good calibration compare to Gradient boosting model.

Introduction: Data obtained from functional magnetic resonance imaging (fMRI) have a complex structure. Considering the special features of this type of data in analyses is of particular importance. Previous studies on generalized anxiety disorder (GAD) as a prevalent mental disorder using functional neuroimaging have had conflicting results. In this study, we apply a Bayesian spatiotemporal model to this type of data which considers both spatial and temporal dependence among regions which is one of the most essential features to consider.
Methods: In this single-subject study, we analyze data from a patient with GAD and a healthy participant. Both participants are 24-year-old women who are assigned an emotion reactivity task (matching neutral and negative facial expressions) inside a scanner. The spatial Bayesian variable selection method is used to detect blood oxygen level-dependent activation in fMRI data.
Results: Activation areas in neutral and negative facial expressions are provided for both participants by posterior probability map. The results of our study show a greater level of activity in the GAD participant in comparison to the healthy participant in responding to the negative matching task.
Conclusion: the GAD patient showed more neural activity in response to negative facial expressions than the healthy participant in brain regions related to emotional response in the areas of the frontal Pole, middle frontal gyrus, insular cortex, and frontal orbital cortex. Moreover, the inferior frontal gyrus in the patient with GAD showed more reaction to negative emotional stimuli.

Introduction: Elderly people usually feel lonely that can have adverse health effects. The purpose of current paper is to determine the loneliness score in the elderly population of the Ardakan Cohort and the factors affecting it.
Methods: This is a cross sectional study using data from the Ardakan Cohort Study on Ageing (ACSA). Loneliness was measured using a 6-item De Jong Gierveld short scales. The 11-item Duke Social Support Index (DSSI) was used to measures social support of aging. living arrangement, demographic factors and self-rated health was also collected using a checklist. Linear regression was used to examine the relationship between loneliness and predictor factors. The data was analyzed with Stata software version 17 and a p-value of 0.05 was considered as a significant level.
Results: Among the 5,188 participants, 48.13% were male and most of the participants were over 60 years old. Total score of loneliness was 3.27±1.45(95% CI: 3.24 to 3.31). Among covariates, age (p value=0.000), sex (p value=0.000), marital status (p value=0.046), education (p value=0.001) and economic status (p value=0.001) have significant association with loneliness score. People with good self-rated health had a lower loneliness score (p value<0.001). The score of social support has an inverse association with the score of loneliness (p value<0.001). Adults who lived with others had a higher loneliness score (p value<0.001).
Conclusion: According to the results, elderly people who have more social support and have better self-rated health feel less lonely.

Introduction: Analyzing long term survivors such as diabetic patients can't be done using the usual survival models. One approach to analyze it is using defective distribution that doesn't force a pre-assumption of cure fraction to the model. To study more than one random variable interacting together, multivariate distributions may be used. However, most of multivariate distributions have complicated forms, which make the computations difficult. Besides, it may be hard to find a multivariate distribution that fits the data properly, especially in health care field. To get over this problem, one can use copula approach. In literature, to the best of our knowledge, only one paper handled copula defective models and didn't consider the effect of covariates. In this paper, we take into consideration not only existed covariates but also unobserved ones by including frailty term.
Methods: Two new models are introduced. The first model, used Gumbel copula to take the dependence into consideration together with the observed covariates. The second one take into consideration not only the dependence but also the unobserved covariates by integrating frailty term in to the model.
Results: A diabetic retinopathy data is analyzed. The two models indicated the existence of long-term survivals through negative parameters without the need of pre-assuming the existence of it. Including frailty term to the model helped in capturing more dependence between the variables. We compared the results using goodness of fit methods, and the results suggested that the model with frailty term is the best to be used.
Conclusion: The two introduced models correctly detected the existence of cure fraction with less estimated parameters than that in mixture cure fraction models. Also, it has the advantage of not pre-assuming the existence of cure fraction to the model. comparing both models, the model with frailty term fitted the data better.

Introduction: Cardiovascular diseases such as coronary heart disease, heart failure, arrhythmia, and cardiomyopathy all include hypertension as a key risk factor. Research has shown that the early detection and treatment of hypertension and its risk factors, as well as public health policies to reduce behavioral risk factors, have led to a gradual reduction in mortality caused by heart disease and stroke in high-income countries in the past three decades. Trends in hypertension incidence have been monitored at the national level in Iran. The aim of this study examine province-level disparities in Hypertension incidence from 2004 to 2016.
Methods: Use the Non-Communicable Diseases Risk-Factors Surveillance in the Islamic Republic of Iran STEPs registry data. to estimate the incidence rate of hypertension for all provinces in 2004, 2006-2009, 2011, and 2016 using a Bayesian spatial model with Markov chain Monte Carlo algorithm in OpenBUGS version 3.2.3 and R version 4.2.2.
Results: The estimated Hypertension incidence rate in total increased from 19.87 per 1000 people (95% credible interval 14.28, 25.48) in 2004 to 193.02 (171.92, 220.48) in 2016. According to the estimates of 2016, we found that the provinces of Markazi, Ardabil, and Semnan had the highest rate of hypertension, and the provinces of Hormozgan, and Sistan-Baluchistan had the lowest rate. Our findings show that Khorasan, North, Alborz, and Semnan have the most significant percentage change in incidence rate from 2004-2016.
Conclusion: To reduce the prevalence of hypertension in Iranian regions, it is crucial to develop regular hypertension screening programs, especially among the elderly.

Introduction: In low- and middle-income countries, a large proportion of road users include pedestrians, cyclists, and motorcyclists, and nearly half of road traffic fatalities occur among motorcyclists. This study aimed to examine the pattern of motorcyclists' death due to accidents in East Azerbaijan, Iran between 2006 and 2021 and present a forecast.
Methods: We used death data due to motorcycle accidents of Legal Medicine Department between 2006 and 2021. For time series analysis, the Box-Jenkins model was used and three stages of identification, estimation, and diagnosis were successively performed and repeated several times to achieve the best prediction model.
The Box-cox transformation method was used to stabilize the variance, and the first-order seasonal differential method with a period of 12 was used to control the seasonality. Due to seasonal variations, the Seasonality Auto-Regressive Integrated Moving Average model: SARIMA (p, d, q) (P, D, Q)s was employed and the death trend was predicted for 36 months. The candidate models were compared based on Log-likelihood, AIC, and BIC indices. STATA 17 was used for data analysis.
Results: About 18.6% of all accident deaths are attributed to motorcycle accidents. The death rate for all causes of accidents and motorcycle accidents were 23.13 and 4.30 per 100,000 population, respectively. Seven models were considered as candidates. The SARIMA (0, 0, 0) (1, 1, 1)12 model was selected as the best model due to better fit and used to predict the number and trend of motorcycle accident deaths. Motorcycle accident deaths are predicted to decrease gradually in the next 36 months, from June 2021 to May 2024, affected by seasonal changes.
Conclusion: The trend of death due to motorcycle accidents from 2006 to 2021 in East Azerbaijan was declining, and it is predicted to decrease slightly in the next three years as well. As this reduction may be attributed to many factors, it is recommended to investigate effective factors in future studies.

The coronavirus (COVID-19) pandemic is accompanied with increasing morbidity and mortality and has impacted the lives of people worldwide. Health care personnel including medical school students are at high risk of exposure and transmission of the coronavirus, hence the interest in studying knowledge and attitudes towards COVID-19 to have protection for themselves, their work colleagues.
Materials and Methods: descriptive cross-sectional study in November 2021 among medical students, conducted by a self-administered questionnaire, cluster sampling stratified on years of study was performed.
Result: 300 students responded with a 75% response rate, with a mean age of 21.3 ± 1.4 years. About 85% of the respondents had good knowledge (GK) and 106 (35.3%) had good practice (GP) using thresholds of 85% and 77% in each case. To combat the COVID-19 pandemic, 76.7% of the study participants improved their regular hand washing, and 94.7% of them used a face mask since the outbreak; There was a significant difference between hand washing with soap and water and COVID infection. The prevalence of anti-coronavirus vaccination was 87.7%, CI= [83.4-90.9].
conclusion, medical students showed a satisfactory level of knowledge and adherence to the recommendations for protection against COVID-19 by following appropriate strategies for preventing its spread.