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Modeling the Spatial Distribution of Mood Disorders in Isfahan Province, Iran

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Modeling the Spatial Distribution of Mood Disorders in Isfahan Province, Iran International Journal of Epidemiologic Research doi:10.15171/ijer.2017.07 2017 Summer;4(3):218-226 http://ijer.skums.ac.ir Original Article Modeling the Spatial Distribution of Mood Disorders in Isfahan Province, Iran Mojgan Entezari1, Saba Sepahvand2* 1Geography Department, Physical Geography, University of Isfahan, Isfahan, Iran 2Geography Department, Medical Geography, University of Isfahan, Isfahan, Iran *Corresponding Author: Abstract Saba Sepahvand Email: Background and aims: Physical and social environments are effective on personality traits. What is [email protected] in the framework of medical geography, is physical environment that can have positive effect on the human psyche. It can also have negative effects whose investigation is in the field of medical geographers. Methods: The present study is a descriptive analytical research that discusses the modeling of the vulnerability of mood disorders (depression, bipolar) by using meta-ranking PROMETHEE, and ArcGIS software to study climatic parameters on the spatial distribution of these disorders in Isfahan Province from 2007 to 2011. Results: The prevalence of mood disorders (depression and bipolar disorder) in all the province had Received: 25 January 2017 a direct correlation with each other. Isfahan, Lenjan, and Shahin Shahr were very high-risk cities and Accepted: 28 May 2017 ePublished: 19 June 2017 Nain and Semirom were low-risk cities. The prevalence and incidence of these disorders have a direct correlation with temperature, precipitation and humidity. There was no significant correlation between sunshine hours and incidence of mood disorders in Isfahan province. The percentage of incidence of these disorders was almost twice higher in men than women. Conclusion: Climatic parameters can be one of those factors that are effective in incidence and increasing of mood disorders (depression, bipolar). This issue highlights the need for more study and research in this field. Keywords: Mood disorders, PROMETHEE, Isfahan Introduction and spatial distribution is drawn, a significant difference Several factors can be effective on the health of human can be seen from one point to another point that is psyche. One of the most important factors that not explicable only by genetic and social factors and influence personality traits is environment. In a public it seems that the environmental interferences are the classification, the effective environment on personality main factors in their etiology.3 Therefore, it is necessary is divided into physical environment and social to investigate their distribution in the province environment and what is in the framework of medical and influencing factors, especially environmental geography, is physical environment. The physical factors for development of such diseases. There is environment is part of the natural environment that an assumption that the geographical and climatic includes weather, climate, food and other material conditions can affect mood disorders. Therefore, it facilities.1 In addition to positive effects on the human seems essential to study spatial distribution of mental psyche, in some cases, environment has negative illnesses in the province and the effect of climatic effects. For example, increase of the temperature in parameters and factors on the incidence and spread a period can increase irritability and irrational people of these illnesses and also to draw the map of spatial and thus cause abnormal behavior and reactions of distribution of mood disorders according to climate people, or when the weather is cloudy and rainy, some parameters. people feel angry, upset or sleepy, whereas when it is sunny, they become energetic For several important PROMETHEE Methods mental disorders whose cause is unknown and whose This method starts by expanding the scale measure treatment is uncertain, when their map of incidence for preference evaluation of one option compared Copyright © 2017 The Author(s); Published by Shahrekord University of Medical Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited. Entezari and Sepahvand to other options by converting the product levels for It should be noted that the above equations are for options to scale of 0 to 1 (where 0 represents the worst the criteria that they need to be maximized. For the 4 and 1 the best). criteria that should be minimized, the amount of dj(a,b) Intensity of preferences is represented in a form of in the above equations will be symmetric. matrix according to equation (1) within which all of In each of the functions (Table 1), q is indifference decision-making options are compared to each other threshold, p is strict superiority threshold and S is with respect to each target. an amount between p and q. Threshold of minor differences, is the biggest difference that is not ag1 11(a ) g 21 (a ) gk (a1 ) important for decision-maker and superiority threshold a2 . ... (1) is the smallest difference for complete allocation of . ... superiority between two options, and it is sufficient. agn1(a n ) gkn(a ) g1(.) gk (.) It must be noted that the utility function is zero for negative d values.6 In the following, weight of criteria ({w;j=1,2,…,k}) k j In equation (1), A={a ,a ,….,a } is a finite set of with proviso ∑w j =1 is determined and the overcome 1 2 n j =1 options and {g1(.),g2(.),…,gk(.)} is a set of evaluation degree of π (,ab )or the same amount of superiority (a) criteria. to option (b) is calculated in comparison with all the In the next step, the utility function (F) is selected criteria, for all couple options, according to equation from Table 1 and the amount of (P(a,b)) for all couples (4). of options is compared with all of the criteria from k π = equations (2) and (3) and calculated. (,)ab∑ pjj (,) abw j=1 + - Pj( ab , )= F jj [ d ( ab , )]; ∀∈ a, b A (2) Positive (Φ (a)) and negative (Φ (a)) outranking currents are calculated according to equations (5 & 6). dabj(,)= ga jj () − gb () + 1 ≤≤ (3) φπ()a = (,)ax (5) 0Pj (,) ab 1 ∑ n −1 xA∈ Table 1. Types of Generalized Criteria - 00d The first type ordinary standards Pd() 10d 0 dq The second typeU Pd() standardform 1 dq 00d V -shaped standards d Pd() 0 d p p 1 dp 0 dq Criteria coplanar 1 Pd() q d p 2 1 dp 0 dq Criteria V shape with indifference area dq Pd() q d p pq 1 dp 0 dq Criteria kaovssi d 2 Pd() 2 1e2s dq Source: Tomic et al.5 Reference: Tomic et al. 2011(5) In each of the functions (Table 1), q is indifference threshold , p is st rict superiority Int J Epidemiol threshold Res, and Volume S is an 4, Issue 3, 2017 219 amount between p and q. Threshold of minor differences, is the biggest difference that is not important for decision-maker and superiority threshold is the smallest difference for complete allocation of superiority between two options, and it is sufficient. It must be noted that the utility function is zero for negative d values.6 k In the following, weight of criteria ({wj;j=1,2,…,k}) with proviso w j 1 is determined and the j 1 overcome degree of ( (,ab )or the same amount of superiority (a) to option (b) is calculated in comparison with all the criteria, for all couple options, according to Equation (4) Entezari and Sepahvand (classification of indices and options), sustainability − 1 φπ(a )= ∑ (x,a) (6) of results compared to most of other methods, n −1 xA∈ sensitivity analysis as simple and quick as possible, Thus, the pure outranking methods can be obtained taking advantage of graphic design modeling, and according to equation (7). possibility of considering different constraints in decision optimization.8 φφ()a=+− () aa − φ () (7) Given that this method alone cannot analyze the spatial multi-criteria problems, integration of In this method, the full ranking of options, according PROMETHEE outranking methods with GIS is to 2 mentioned terms in equation (8), and superiority recommended for better and more efficient analysis relations (P) and minor differences (I) are obtained. of spatial issues. Therefore, in this study, because of the necessity II aP b ifφφ( a )> (b) (8) of studying the mood disorders, the vulnerability II aI b ifφφ() a= () b model with Promethee method of MCDA family models (multiple-criteria decision-making [MCDM] Bayatani and Sadeghi concluded that prioritizing or multiple-criteria decision analysis [MCDA] is a the causative agents of diseases and finding their sub-discipline of operations research that explicitly local aggregation by using a combination of statistical evaluates multiple conflicting criteria in decision analysis and local aggregation and modeling are making both in daily life and in settings such as possible in a GIS.7 business, government and medicine) was prepared. In recent years, a number of methods were provided In the present study, this method is used to evaluate as outranking methods based on paired comparisons the climatic parameters (temperature, sunny hours, without using the excessive information, that focus minimum and maximum humidity) effect on the on more accurate and realistic modeling decision- incidence of mood disorders (bipolar and depression) making issues. Among the various methods that are in patients hospitalized in Farabi hospital of Isfahan presented in the form of outranking methods, family and Kargrnezhad hospital
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