Title of article :
Mapping the Obesity in Iran by Bayesian Spatial Model
Author/Authors :
FARHADIAN, Maryam Dept. Of Biostatistics and Epidemiology - School of Public Health - Hamadan University of Medical Science, Hamadan, Iran , MOGHIMBEIGI, Abbas Dept. Of Biostatistics and Epidemiology - School of Public Health - Hamadan University of Medical Science, Hamadan, Iran , ALIABADI, Mohsen Dept. Of Occupational Health - School of Public Health - Hamadan University of Medical Science, Hamadan, Iran
Pages :
7
From page :
581
To page :
587
Abstract :
Background: One of the methods used in the analysis of data related to diseases, and their underlying reasons is drawing geographical map. Mapping diseases is a valuable tool to determine the regions of high rate of infliction requiring therapeutic interventions. The objective of this study was to investigate obesity pattern in Iran by drawing geographical maps based on Bayesian spatial model to recognize the pattern of the understudy symptom more carefully. Methods: The data of this study consisted of the number of obese people in provinces of Iran in terms of sex based on the reports of non-contagious disease's risks in 30 provinces by the Iran MSRT disease center in 2007. The analysis of data was carried out by software R and Open BUGS. In addition, the data required for the adjacency matrix were produced by Geo bugs software. Results: The greatest percentage of obese people in all age ranges (15-64) is 17.8 for men in Mazandaran and the lowest is 4.9 in Sistan and Baluchestan. For women the highest and lowest are 29.9 and 11.9 in Mazandaran and Hormozgan, respectively. Mazandaran was considered the province of the greatest odds ratio of obesity for men and women. Conclusion: Recognizing the geographical distribution and the regions of high risk of obesity is the prerequisite of decision making in management and planning for health system of the country. The results can be applied in allocating correct resources between different regions of Iran
Keywords :
Mapping , Obesity , Bayesian Spatial Model , Iran
Journal title :
Astroparticle Physics
Serial Year :
2013
Record number :
2419282
Link To Document :
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