Showing 3 results for Kavousi
Sh Seyedagha, A Kavousi , Ar Baghestani , M Nasehi,
Volume 13, Issue 3 (Vol.13, No.3, Atumn 2017)
Abstract
Background and Objectives: Tuberculosis is the most common cause of death among single-factor infectious diseases and is the tenth cause of death among all diseases in the world. The disease is spread mainly from an infected person through close contact with other people living in one place. The aim of this study was to investigate the relationship between the spatial correlation structure and the recovery time of patients with pulmonary tuberculosis in Iran.
Methods: In this applied study, the data of 20554 patients with sputum smear-positive pulmonary tuberculosis in Iran from 1389 to 1393 were used. A parametric accelerated failure time model with spatial frailty and batesian approach was used to analyze the data. The OpenBUGS 1.4 was used for programming and the ArcGIS 9.2 was used for mapping the environmental impact on tuberculosis.
Results: The mean age of the patients was 50.35 years with a standard deviation of 21.6 years. The results showed that the geographical environment, gender, prison condition, degree of smear positivity at diagnosis and location (urban-rural) had a significant impact on the recovery time of pulmonary tuberculosis patients. The recovery time of patients with smear grade 1-9 bacilli, 1+ and 2+ who were treated was significantly shorter than the others.
Conclusion: According to the study, geographical environment and the location have a significant impact on smear positive patients’ recovery time. This impact depends on the degree of smear positivity in some provinces and is independent of it in some other provinces.
S Heidari, A Kavousi, V Rezaei Tabar,
Volume 14, Issue 2 (Vol.14, No.2, 2018)
Abstract
Background and Objectives: Breast cancer is the most common cancer in Iran. It can be prevented by rapid diagnosis of the disease. Thus, it is necessary to determine the causal relationships between variables related to breast cancer. Bayesian network is a data mining tool that shows the causal relationship between different variables. In this paper, a Bayesian network was applied to find causal relationships between breast cancer variables using a genetic algorithm in a graphical model.
Methods: in this applied study, data were collected from 900 breast cancer patients in Kerman Province from 1999 to 2008. For data analysis, we used a probabilistic graphical model representing the causal relationship between variables.
Results: The results showed that surgery was the most important treatment for breast cancer. Based on the conditional and marginal probabilities, the women who underwent surgery had higher hopes of living longer. Moreover, 81% of the patients who did not undergo surgery only received chemotherapy or radiotherapy were less likely to have long lives.
Conclusion: People aged 40-65 years are more likely to have breast cancer. Moreover, the variables of age, surgery, chemotherapy, and radiotherapy had a direct effect on the status of the patients and there were direct edges from these variables to the status of the patients.
Z Naghibifar, S Eskandari, M Sajjadipour, A Kavousi, K Etemad,
Volume 16, Issue 4 (Vol.16, No.4 2021)
Abstract
Background and Objectives: Immune deficiency syndrome is an epidemic disease. During immunodeficiency caused by HIV, infections such as tuberculosis, hepatitis B and hepatitis C may occur. Given that the transmission of these infections is similar to that of HIV, the risk of HIV infection with these infections is high. The purpose of this study was to determine the prevalence of common HIV infections and the related risk factors in HIV positive individuals.
Method:This study was conducted as a retrospective cohort study performed on 3047 HIV patients at Imam Khomeini Counseling Center in Tehran who have been admitted in 2004 -2018.Required data were extracted from patient records and entered into Excel software. For data analysis, SPSS version 21 was used.
Results: The mean age of the patients was 44.24 ± 9.46 years and 77.3% of them were male. Of them, 98 (3.2%) were co-infected with hepatitis B, 961 (31.5%) were co-infected with hepatitis C, and 415 (13.6%) were co-infected with tuberculosis. According to the results, hepatitis B and hepatitis C had a significant association with age, marital status, gender, education, prison history, history of injection, history of addiction, and needle sharing.
Conclusion: Due to the common route of HIV transmission and these infections, there is the possibility of co-infection. The demographic variables and behavioral factors are the most effective risk factors for developing co-infections.
