Publication: Log-linear model of cardiovascular diseases patients’ data in Jigme Dorji wangchuk national referral hospital, Bhutan
Issued Date
2019-01-01
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1943023X
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2-s2.0-85073731160
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Mahidol University
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SCOPUS
Bibliographic Citation
Journal of Advanced Research in Dynamical and Control Systems. Vol.11, No.9 Special Issue (2019), 238-242
Suggested Citation
Lhamo, Suntaree Unhapipat Log-linear model of cardiovascular diseases patients’ data in Jigme Dorji wangchuk national referral hospital, Bhutan. Journal of Advanced Research in Dynamical and Control Systems. Vol.11, No.9 Special Issue (2019), 238-242. doi:10.5373/JARDCS/V11/20192562 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/50679
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Title
Log-linear model of cardiovascular diseases patients’ data in Jigme Dorji wangchuk national referral hospital, Bhutan
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Abstract
© 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved. The main interest of evaluating on cardiovascular disease (CVD) is to bring awareness to public health in Bhutan. CVD is globally a life-threatening disease. According to WHO findings in 2017, 18.59% of people’s death in Bhutan was due to heart diseases. Therefore, we focus this study to obtain the most appropriate model of associations among CVD variables. We propose log-linear model to study CVD risk factors in Bhutan so as to manage from such health issue. Two-way contingency tables were investigated for clinical and personal parameters through independence test. The statistic test of Chi-square (x2)and highest value of Cramer’s V at significance level 0.05 is used to determine association among variables. The interrelated variables are then formulated into two-dimensional log-linear models by computing log- likelihood ratio (D) test, x2 and degrees of freedom (d.f). From general data we found that most of the people are vulnerable to cardiac disease at old age. The recovery from this hazardous disease is over 80%, which estimates 20% of death. The application of two-dimensional log-linear model gave us associated variables such as: ward and status of last contact; length of stay and status of last contact; age and ward in female personal and clinical data. Simultaneously, ward and status of last contact; length of stay and status of last contact; status of last contact and number of disease; age group and length of stay in hospital are associated clinical and personal variables in male patients. We conclude that the parsimonious models obtained in two-dimensional models are saturated models.