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Estimation of Mental Disorders Prevalence in High School Avicenna J Neuro Psych Physio. 2016 August; 3(3):e45127. doi: 10.5812/ajnpp.45127. Published online 2016 August 27. Research Article Estimation of Mental Disorders Prevalence in High School Students Using Small Area Methods: A Hierarchical Bayesian Approach Ali Reza Soltanian,1,2,* Azadeh Naderi,2 and Ghodratollah Roshanaei1,2 1Associate Professor (PhD), Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, IR Iran 2Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, IR Iran *Corresponding author: Ali Reza Soltanian, Street of Shahid Fahmideh, Lona Park, Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Postal code: 6517669664, Hamadan, IR Iran. Tel: +98-8138380025, Fax: +988138380509, E-mail: [email protected] Received 2016 June 29; Revised 2016 July 16; Accepted 2016 August 15. Abstract Background: Adolescence is one of the most important periods in the course of human evolution and the prevalence of mental disorders among adolescence in different regions of Iran, especially in southern Iran. Objectives: This study was conducted to determine the prevalence of mental disorders among high school students in Bushehr province, south of Iran. Methods: In this cross-sectional study, 286 high school students were recruited by a multi-stage random sampling in Bushehr province in 2015. A general health questionnaire (GHQ-28) was used to assess mental disorders. The small area method, under the hierarchical Bayesian approach, was used to determine the prevalence of mental disorders and data analysis. Results: From 286 questionnaires only 182 were completely filed and evaluated (the response rate was 70.5%). Of the students, 58.79% and 41.21% were male and female, respectively. Of all students, the prevalence of mental disorders in Bushehr, Dayyer, Deylam, Kan- gan, Dashtestan, Tangestan, Genaveh, and Dashty were 0.48, 0.42, 0.45, 0.52, 0.41, 0.47, 0.42, and 0.43, respectively. Conclusions: Based on this study, the prevalence of mental disorders among adolescents was increasing in Bushehr Province coun- ties. The lack of a national policy in this way is a serious obstacle to mental health and wellbeing access. Keywords: Small Area Technique, Hierarchical Bayesian, Mental Disorders, Adolscent 1. Background Furthermore, in a comprehensive study that was accom- plished in 2010, it was found that depression is the second Adolescence is one of the most important periods in leading cause of disability in the world (Figure 1)(12). Based the course of human evolution, while the tension is higher, on this study, Iran is among the countries where years of commitment and identity will become weaker (1). Almost disability due to depressive disorders is high (12). all societies are paying particular attention to the issues of public health, particularly to mental health (2-4). For this reason world health organization (WHO) has named Mental disorders impose a high economic burden on 2017 as the year of combating depression (5). Public health individuals and society as well as disabling in people (13). is multi-dimensional and anxiety and depression are the There are no statistics regarding the direct and indirect most important aspects of it (6). With a glimpse of the cost of mental disorders in Iran, however, the total cost of studies, it is observed that anxiety and depression are the major depression disorders was about 210.5 billion dollars most common mental disorders among young people (6). in USA in 2010 (14). In addition, a report according to the WHO shows that ap- proximately 1 in 5 young people suffer from mental disor- Various studies show that the prevalence of mental dis- ders (7). orders in different regions of Iran, especially in southern The recent studies show that the average age of onset Iran is high and is becoming an epidemic. Therefore Solta- of symptoms of depression is 16 years among 2% - 8% of nian et al. (15) reported the prevalence of mental disorders people (6,8). Relatively high prevalence of mental disor- among high school students in 2005 to be about 40.7% and ders has been reported in the world (4). In a meta-analysis, Mousavi-Bazaz et al. reported it at about 51.1% in 2015 (16). it was observed that the prevalence of mental disorders The literature review shows that the prevalence of depres- among children and adolescents in the world is 13.4% (CI sion among young Iranians is 55/43%. This disorder is more 95%: 11.3 - 15.9) (4). common in girls (17). In addition, some studies showed For this reason, recent studies have focused on the that the prevalence of mental disorders is increasing in prevalence of mental disorders and related factors (9-11). southern Iran (11). Copyright © 2016, Hamadan 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. Soltanian AR et al. Figure 1. World Map Marking Countries with Statistically Higher or Lower Years with Disability The map is adapted from Ferrari AJ et al. 2013 (12). 2. Objectives Previous studies confirmed the validity of GHQ-28 (18). Peo- ple who had a history of mental illness were excluded. Fur- Due to the increasing prevalence of mental disorders thermore, this study was conducted 1 month prior to the in south Iran, continuously monitoring the spread of the students’ examinations or quizzes. The 12th graders were disease is necessary. Furthermore, when the sample size excluded due to their final quizzes and examinations. is small, we should use advanced statistical methods to fix After collecting the questionnaires, only 182 question- the problem. Therefore, we use the hierarchical Bayesian naires were evaluated. In this study, sample size collapses method to estimate the prevalence of mental disorders due to the fact that some questionnaires were not fulfilled among high school students in Bushehr province, south of (the response rate was 63.6%). Therefore, we tried to elimi- Iran. nate this defect using the small area method by the hierar- chical Bayesian approach as follow section. 3. Methods 3.2. Statistical Analysis 3.1. Study Design Due to the nature of mental disorders as a dichoto- In this cross-sectional study, 286 high school students mous response to variable and auxiliary data at the unit were recruited in 2015, which is about 10% of the sample level, the logistic linear mixed model under hierarchical size in Soltanian’s study in 2005 (15). They were selected by Bayesian approach was used to estimate the prevalence of the multistage random sampling method from 8 counties mental disorders. The linear mixed model logistic by hier- of the Bushehr province. The number of students in each archical Bayesian approach was considered as follows: ∼iid county was determined as quotas under sex and grade yij jpij Bernolli (pij ); i = 1; : : : ; m; j = 1;:::;Ni (1) level. In this study, the Iranian version of the GHQ-28 was Where, yij and pij represent a dichotomous response th also used to estimate the mental health status. GHQ-28 is a variable and probability of mental disorders for j individ- th self-rating questionnaire based on 4 dimensions of psycho- ual at i area, respectively. Additionally, m and Ni is the logical symptoms including: anxiety, depression, somatic number of small areas and sample size, respectively. symptoms, and social dysfunction. The 28-items GHQ is θij = logit (pij ) (2) scored on a four point Likert-type scale according to a 0 - T ∼iid 2 = xij β + vi; vi N 0; σv 1 - 2 - 3 system. Minimum and maximum score of GHQ-28 th is 0 and 84, respectively. In this study, the cut-point was < Where, xij is auxiliary variable at unit level for j indi- th 23 and ≥ 23 for health and mental, respectively. Reliabil- vidual at i area, and vi is the random effect of the small ity of GHQ-28 was determined 0.87 by Coronbach’s alpha. area. 2 Avicenna J Neuro Psych Physio. 2016; 3(3):e45127. Soltanian AR et al. −2 f (β) / 1; σv ∼ Gamma (a; b) ; a ≥ 0; b > 0 (3) Table 1. Demographic Characteristics of High School Students 2 Where, β and σ v are mutually independent. The pa- Characteristic No. (%) rameters were estimated by hierarchical Bayesian meth- Sex ods after determining the auxiliary variables associated Boy 88 (48.34) with pij with respect to equation and initial values a = b = Girl 93 (51.66) 0.001. Markov Chain Monte Carlo algorithm (MCMC) and Mother education Gibbs sampling method was used to determine posterior Illiterate 7 (3.85) distribution (19). To evaluate the reliability of the small Primary 29 (15.93) Secondary 47 (25.82) area estimation, the indicator as below was used, where pi High School 71 (39.01) and P˙ i are true estimations and small area estimations of mental disorders prevalence, respectively (20). The clus- Academic 22 (12.09) ter analysis was used for clustering the counties. Statisti- Non-response 7 (3.85) cal software SPSS version 16 was used to determine the aux- Father education Illiterate 4 (2.20) iliary variables associated with mental disorders and clus- Primary 9 (4.94) ter analysis. WinBUGS 4.1 was used to run the MCMC algo- Secondary 44 (24.18) rithm. High School 79 (43.41) − Pm Academic 43 (23.63) i=1 pi− pi ASE = (4) Non-response 5 (2.75) m Mother job Housewife 62 (34.07) 4. Results Employed 115 (63.19) Non-response 5 (2.75) In this study, 182 high school students were evaluated.
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