Analysis and Modeling of the Spatial Distribution of Respiratory Diseases Associated with Environmental Factors Case Study: Kurdistan Province

Subject Areas : ارزیابی پی آمدهای محیط زیستی

abdolah faraji ¹ , Mohamad kamangar ² , firozeh ghaderi ³

1 - Assistant Professor, Faculty of Human Science, Zanjan University, Zanjan, Iran.
2 - Ph.D., Student, Faculty of, Human Zanjan University, Zanjan, Iran. *(Corresponding Authors)
3 - Researcher, SID, Kurdistan, Iran.

Received: 2019-04-02 Accepted : 2019-07-03 Published : 2020-09-22

Keywords: Respiratory Disease, dust, Kurdistan, Spatial Analysis, Moran Index,

Abstract :

Background and Objective: Human physical and mental health greatly depends on the climatic conditions of its bio-location. Identifying environmental factors creating or exacerbating diseases can be useful in optimizing decision making for prevention and control. The purpose of this study is to determine the spatial resolution of respiratory diseases and its relation with environmental factors in order to understand spatial distribution, cluster discovery and spatial prediction modeling. Method: The population of patients with respiratory diseases referred to the medical centers and the study area of ​​Kurdistan province between 2007 and 1396. Regarding the dispersion of patients from spatial and moron standard deviations, we used spatial regression method to determine the spatial and morphometric variability of the samples using independent variables of dust, height, direction of inclination and temperature. Findings: The results showed that the area of ​​the ellipsoid is three times the standard deviation of the northwest of the southeast, indicating that more than 99% of these diseases are spreading in this direction. Moran index 0.82 also indicates spatial autocorrelation and disease numbers at a significant level of 99%. In spatial modeling to predict the spatial dispersion of a positive symptom disease, the coefficients obtained for dust and temperature with the disease indicate a direct relationship and the negative coefficients between elevation and slope indicate an indirect relationship with the disease. Modeling also showed that dust is the most important parameter in predicting the disease. Discussion and Conclusion: The value of R2 = 0.88 indicates that the extracted model is able to fully predict the dependent variable, respiratory disease, in Kurdistan province, taking into account independent environmental variables. Using the prediction map, the regions with respiratory disease can be better identified in order to improve the decision-making process for allocating and distributing spatial services.

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