Background The identification of statistical models for the accurate forecast and timely determination of the outbreak of infectious diseases is very important for the healthcare system. Thus, this study was conducted to assess and compare the performance of four machine-learning methods in modeling and forecasting brucellosis time series data based on climatic parameters. Methods In this cohort study, human brucellosis cases and climatic parameters were analyzed on a monthly basis for the Qazvin province-located in northwestern Iran-over a period of 9 years (2010-2018). The data were classified into two subsets of education (80%) and testing (20%). Artificial neural network methods (radial basis function and multilayer perceptron), support vector machine and random forest were fitted to each set. Performance analysis of the models were done using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Root Error (MARE), and R 2 criteria. Results The incidence rate of the brucellosis in Qazvin province was 27.43 per 100,000 during 2010-2019. Based on our results, the values of the RMSE (0.22), MAE (0.175), MARE (0.007) criteria were smaller for the multilayer perceptron neural network than their values in the other three models. Moreover, the R 2 (0.99) value was bigger in this model. Therefore, the multilayer perceptron neural network exhibited better performance in forecasting the studied data. The average wind speed and mean temperature were the most effective climatic parameters in the incidence of this disease.

Background: The rate of elder abuse has been increasing worldwide. This study aimed at identifying the group of elders susceptible to abuse and determining the influential factors of elder abuse. Methods: A total of 683 elders, living in rural and urban areas of Qazvin (Iran), participated in this cross- sectional study that was conducted during September to December 2015. They were selected by stratified multistage random sampling method and filled in a standard questionnaire (H-S/EAST). Multiple logistic regression models were used for data analysis in Microsoft SPSS v.18. Type 1 error was considered equal to 0.05. Results: The average age of participants was 68.5±7.6. Also, the prevalence of elder abuse in this study was 38.5 (95% CI: 3.34- 42.3). After eliminating the confounders and applying multiple regression analysis, we found a significant association between elder abuse and factors such as education level (OR= 2.003, 95% CI: 1.177-3.409), residence (OR= 3.53, 95% CI: 1.969-6.324), and age (OR= 0.963, 95% CI: 0.931-0.995). Conclusion: The results of this study indicated a high prevalence of elder abuse in the studied population. By identifying high-risk individuals for elder abuse and planning to improve their quality of life, we will be able to successfully overcome this issue.

Background: We aimed to determine the relation of different sources of academic stress and adolescents´ mental health through mediator variables on the student and school levels. Study design: A cross-sectional study. Methods: Overall, 1724 students aged 12-19 yr were selected from 53 high schools in Qazvin City, northwest instead of central Iran through stratified cluster sampling. The sources of academic stress include family conditions, education system, future concerns, academic competitions, interaction with teachers, school disciplines, peer pressure, parental involvement, and financial problems. Academic self-efficacy and self-concept were the mediator constructs. The students and schools´ information were considered on levels 1 and 2, respectively. A Multilevel Structural Equation Modeling (MSEM) analysis was done. Results: High value of academic stress was associated with reduction of mental health. On the student level, the academic stress caused by the families 0.31 (95% CI: 0.28, 0.34), peers 0.29 (95% CI: 0.27, 0.32), and the education system 0.21 (95% CI: 0.18, 0.24) had the highest impact on the adolescentsˊ mental health, respectively. There was a direct and indirect relation between academic stress and mental health through the self-concept. On the school level, only family conditions stress had a relation with mental health (P=0.015, b=1.08). Academic self-efficacy showed no significant relation in the model. Conclusion: The stress from the family is the most important source of stress associated with adolescent mental health. Self-concept unlike academic self-efficacy had an important mediating role in the relation between different sources of academic stress and adolescents' mental health.

Socioeconomic inequality and child maltreatment have not been studied using the concentration index as an indicator of inequality. The study aimed to assess the association of child maltreatment with socioeconomic status among schoolchildren in Qazvin province, Islamic Republic of Iran. In this cross-sectional study a questionnaire based on the ISPCAN Child Maltreatment Screening Tool-Children's Version and the Juvenile Victimization Questionnaire was filled by 1028 children aged 9-14 years, selected through multistage stratified random sampling. The concentration indices for economic inequality were -0.086 for any type of child maltreatment and -0.155, -0.098 and -0.139 for the physical, psychological and neglect subtypes of maltreatment respectively. The number of children and the economic status of the family also showed a significant association with child maltreatment in all 3 subtypes. Appropriate planning for effective interventions for at-risk children of lower socioeconomic status should be considered by the relevant decision-makers.

Introduction: Coronavirus disease 2019 has now turned into a public health emergency. Isolation of patients is a possible solution for controlling epidemic infectious diseases. We assessed the compliance of isolation and associated factors among patients with COVID-19. Methods: This cross-sectional study was conducted on 320 COVID-19 patients discharged from hospitals of Qazvin province. Patients' isolation, self-care health behaviors, reference to public health services and possible related factors were assessed. Data were analyzed using multiple logistic regression. Results: In this study, 320 patients were enrolled, including 175 men (54.7%). Two hundred and eighty-six patients (89.4%) had complete isolation. Factors such as phone tracking by health center (OR = 1.30; 95% CI: 1.01 to 1.75) and dry cough (OR = 2.36; 95% CI: 1.09 to 5.09) increased odds of complete isolation in COVID-19 patients, but having a COVID-19 patient in the family (OR = 0.32; 95% CI: 0.15 to 0.71) and symptoms of disease like shortness of breath (OR = 0.39; 95% CI: 0.18 to 0.85) and muscle pain (OR = 0.43; 95% CI: 0.20 to 0.95) decreased odds ratio for these patients. Conclusion:Phone tracking by the health center was the most important factor to increase the odds of patient isolation. Thus, the health system should consider improving health workers' knowledge and skills through education.