Background:Various studies have shown a seasonal pattern in suicide in the developed societies; however, this pattern is not taken into consideration in most countries including Iran.Objectives:The current paper studied the seasonal pattern of committing suicide in Northern Iran.Materials and Methods:The present study was a longitudinal study with time series features. Subject included suicide attempts recorded by emergency wards of all hospitals in Mazandaran province, Iran. The variable time, in this study, was defined as each month of study years (2005 - 2011), which included 84 monthly time points. To analyze data, the Student’s independent t-test and analysis of variance (ANOVA) test were used.Results:Of the 14,437 suicide attempts reported during the seven-year period, 5359 (37.1%) were related to males. Suicide attempts reached a peak in June (1418 cases) and November (1352 cases), but were at their lowest level in March (991 cases) (P = 0.877).Conclusions:The suicide seasonality range is broad in this part of Iran. Moreover, there were two noticeable suicide peaks in June and November.

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Introduction: Hypertension is a chronic disease that causes a great deal of harm to the individual, which is the leading cause of disability and death worldwide, accounting for one eighth of all deaths. This disease is not treatable but it is manageable. Therefore, this study aimed to investigate factors related to hypertension based on health belief model in Yazd. Methods: This descriptive cross-sectional study was performed on 400 people over 30 years old in Yazd city. Sampling was done by cluster method. Subjects were included in the study according to the address of the headers. Questionnaires were completed by self-report and interview. Data collection tools were demographic questionnaire, awareness questions and health belief model. SPSS 15 software was used for data analysis. Statistical indices, Mann-Whitney U test, Kruskal-Wallis and Spearman correlation were used to analyze the results. Results: The mean and standard deviation of age were 44.08 ±11.6 years. There was a significant relationship between demographic variables of age, gender, occupation, education, marital status and history of hypertension in relatives. Among the constructs of health belief model, only the perceived barriers showed a significant relationship with blood pressure (P <0.01). Conclusion: Since there is a significant relationship between perceived barriers and hypertension, it is suggested that future interventions be designed to reduce perceived barriers (No need to change lifestyles, lack of time for physical activity, cost of regular blood pressure control, and no family accompanying diet changes) and blood pressure control.

Chronic renal failure (CRF) is an irreversible kidney condition that leads to end-stage renal disease (ESRD) [1]. ESRD patients require replacement interventions, such as kidney transplant or hemodialysis. Globally, ESRD is a substantial issue in the medical field. In the absence of replacement interventions for these patients, ESRD leads to death [2]. There were about 3,730,000 patients in ESRD by the end of 2016. Taiwan, Japan, and the United States have the highest ESRD prevalence in the world [3]. In Iran, ESRD prevalence is 610 per million people, which is greater than the global

Background: Longitudinal data structure is frequently observed in health science. This introduces correlation to the data that needs to be handled in modelling process. Recently, machine learning approaches have been introduced in the context of longitudinal data for prediction of the response variable purpose. In this paper a mixed-effects least squares support vector regression model is presented for three-level longitudinal data. In the proposed model, multiple random-effect terms are used for considering the existing correlation structures in longitudinal data. The proposed model is flexible in modelling (non-)linear and complex relationships between predictors and response, while it takes into account the hierarchical structure of data and is computationally efficient.  Methods Both random intercept and random trend models with a special correlation structure of errors are illustrated. A real data example on human Brucellosis rate is analysed and two simulation studies are performed to illustrate the proposed model. The fitting and generalisation performance of the proposed model are investigated and compared with the ordinary least squares support vector regression and linear mixed-effects models.  Results: Based on the human Brucellosis rate example and two simulation studies, the proposed models had the best performance in generalisation. Also, the fitting performances of the proposed models were better than that of the classic models.  Conclusion: Our study revealed that in the presence of nonlinear relationship between covariates and outcome, the proposed MLS-SVR model has the best fitting and generalisation performance and can capture correlation of the data.

COVID-19 has been considered the most important issue in the last two years. Some characteristics and factors can play a pivotal role in the survival time and mortality of COVID-19 patients. The Delta variant was one of the most important variants of COVID-19. This study aimed to investigate the risk factors of COVID-19 survival before and after the spread of the Delta variant. In this historical cohort study, 6,117 hospitalized patients with positive COVID-19 PCR tests between January and September 2021 participated. Some characteristics such as age, sex, death by COVID-19, and presence/absence of some comorbidities were registered for the patients. Log-rank test and Cox proportional hazards model were done to check the effect of the potential risk factors on the survival of COVID-19 patients by considering the onset of symptoms to death as the time variable. The mean age of patients was 47.29 (SD=18.70). 53% of patients were female, 4.6% were admitted to the ICU, and 3.6% died from COVID-19. Age (HR=9.81, p<.001), cardiovascular disease (HR=2.86, p<.001), chronic kidney disease (HR=6.21, p<.001), diabetes (HR=2.16, p=0.002), hypertension (HR=2.67, p<.001), ICU admission (HR=12.92, p<.001), pO2<93% (HR=6.75, p<.001), and intubation (HR=21.54, P<.001) were risk factors that were influential on the survival of COVD-19 patients before the spread of the Delta variant. Although the effect of some variables changed after the spread of the Delta variant, some of them, like chronic kidney disease and hypertension were no longer significant. Although the effect of some comorbidities was significant only in the crude models, they were not influential in the adjusted model. Conversely, in the presence of other risk factors, especially age, most of the comorbidities were not significant in the adjusted model. Older age, ICU admission, intubation, and pO2<93% are the most important variables which played a pivotal role in the survival of individuals infected by COVID-19.

Background: The news of several people being affected by unusual pneumonia at the beginning of the year 2020 in China led to the introduction of a new type of coronavirus as the cause of a new respiratory disease. With the rapid spread of the disease in China and then to other parts of the world, the new coronavirus became scientifically known as severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) and the resulting disease as coronavirus disease 2019 (COVID-19). This disease has a variety of symptoms and can cause SARS. Objectives: This study aimed to investigate the chronic diseases affecting the mortality rate of this disease. Methods: This retrospective cohort study was performed on 7222 eligible patients admitted to hospitals in Sirjan, Iran, from March 2020 to June 2021 with a positive polymerase chain reaction for COVID-19. The researcher extracted information from the patients' files using the questionnaire of demographic characteristics, clinical signs and symptoms, and the history of underlying diseases. The data were analyzed by the SPSS software using descriptive and analytical statistical tests, namely chi-squared, independent samples t-test, and univariate and multivariate logistic regression tests. Results: Out of 7,222 patients with COVID-19, 294 died, of whom 152 were male and 142 were female (OR = 1.23, CI: 0.97 - 1.55). Moreover, 220 people out of 294 dead cases were over 60 years old (OR = 8.92, CI: 6.82 - 11.67). The most common diseases were hypertension (OR = 3.63, CI: 2.81 - 4.69) and diabetes (OR = 3.16, CI: 2.38 - 4.20). The most common symptoms among those who died were respiratory distress (51.4%) and cough (34.7%). Conclusions: The mortality rate is directly related to the underlying diseases in COVID-19, and the most common underlying disease in the statistical population of this study was hypertension. In addition, respiratory distress is a common symptom in dead patients, which can be used as a predictor of death.