Showing 3 results for Cancer
M Bazyar, A Pourreza, Iraj Harirchi, F Akbari, M Mahmoudi,
Volume 11, Issue 1 (3-2012)
Abstract
Background: With more than 12 million new cases of cancers and nearly 7.6 million deaths all around the world in 2007, cancer currently is the third leading cause of death in the world. This study was conducted to determine medical and non-medical direct costs of cancer patients’ hospitalized in the cancer institute affiliated with Imam Khomeini hospital.
Materials and Methods: This was a cross-sectional study. All patients over 18 years old with kind of head, neck, and stomach cancers that undertaken of oncology treatments in the cancer institute which affiliated ” Imam Khomeini Hospital”. Initially eligible patients invited to participate in this study. The data was collected through structured interviews with patients and or their carers. The data, then, was analyzed by SPSS software.
Results: The average medical and non-medical direct out-of-pocket costs during primary treatment were 2,609,000 and 245,000 Tomans per patient, respectively. Furthermore, the direct average of medical costs for patients who lived in Tehran and other cities were 3,313,000 and 1,870,000 Tomans while the direct average of non-medical costs for patients who lived in Tehran and other cities were 136,000 and 360,000 Tomans, respectively.
Conclusion: The new policies for costs coverage related to cancer patients’, particularly the medical insurance organizations, financial supports from finance intuits like as banks or charity organizations, appropriate distribution of cancer’s centers or providing accommodation to cancer patients who are referred from the remote sites in other cities, and also achieving the equities in health sectors could be reduced the financial costs of cancer patients and might be helped them to manage of cancers efficiently and effectively
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Volume 14, Issue 2 (8-2015)
Abstract
Background: In the telemedicine process, using digital techniques in disease diagnosis caused to have felt needs of archiving and storing patient information and high bandwidth in data transfer.
Methods: This study aimed at introducing an efficient way of multi-stage compression of mammographic image data based LM algorithm and artificial neural networks. At First, data derived from mammographic images given to multi-layer neural network has achieved the possibility of forming with minimum damage and high degree of compaction in the first layer.
Results: The compression process of the mammography images was implemented using images of 128 women aged 46.41±6.55 yrs with BMI 36.78 ±5.5 from three specialized clinics in Sabzevar. The analysis yielded a mean square error (MSE) of 4.24 with the highest difference ratio of 33.46 and compression ratio of 8: 1in the output of the algorithm. The system performance based on the accurate design of the software was acceptable therefore; it demonstrated high efficiency in practice.
Conclusion: The diagnosis in the discovery stage is highly consistent with the diagnosis in real based on reliability of software output in the compression and release, and considering the fact of mammographic images are not completely degraded during compression; therefore, this system has the capacity to be implemented achieving mammography images in hospitals and justify its application.
Omid Mazlumi, Mehraban Parsamehr, Akbar Zare-Shahabadi,
Volume 22, Issue 1 (5-2023)
Abstract
Background: Cancer is often stigmatized in many societies and this has unfortunate consequences for sufferers. The aim of this research was to know the factors related to the social stigma of cancer.
Materials and Methods: The research method was correlation-analytical, and the sampling method was multi-stage cluster. Data were collected using CSI and CAM standard questionnaires.The statistical population included three categories of ordinary citizens, medical staff, and companions of patients in Tehran; Using Cochran's formula, the sample size was 384, 201, 384 people, respectively. In order to fit the model and measure the relationships between the variables, the method of structural equation modeling was used in the form of AMOS software.
Findings: Goodness of fit indices (chi-square/df=2.851, Rmsea=0.08, Cfi=0.945) all indicated the appropriate fit of the model. Except for the variable of inequality in treatment, other independent variables had a significant relationship with stigma. The r2 explanatory coefficient showed that the variables of habitus, optimism, cancer awareness, religiosity, social support, and social capital together predicted 48% of stigma changes. Habitus and social support with standard coefficients (beta) of 0.48 and -0.28 had the highest and lowest contribution in explaining stigma, respectively. Based on the mean difference test, the amount of stigma among ordinary people was more than the other two groups.
Conclusion: Awareness of different aspects of cancer disease (such as symptoms, causative factors), removing false stereotypes about cancer (such as cancer means death), constant communication with cancer patients, and receiving the necessary social support from various sources, were the most important tools necessary to reduce the stigma of cancer.
