Cluster analysis of psychiatric profile, its correlates, and using mental health services among the young people aged 15–34: findings from the first phase of Iranian youth cohort in Ravansar

Khazaie, Habibolah; Najafi, Farid; Hamzeh, Behrooz; Chehri, Azita; Rahimi-Movaghar, Afarin; Amin-Esmaeili, Ma­soumeh; Moradinazar, Mehdi; Zakiei, Ali; Komasi, Saeid; Pasdar, Yahya

doi:10.1007/s00127-018-1580-4

Cited by 11 publications

(13 citation statements)

References 33 publications

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“…About half of American and 40% of European populations met the criteria for psychiatric disorders throughout their lives (3,4). The prevalence of mental health problems in Iran has been reported to range from 10% to 20% (5,6). Depression and anxiety are the most common chronic mental health problems among the general population, which negatively affects the quality of life, performance and productivity of individuals as well as society.…”

Section: Introductionmentioning

confidence: 99%

The Prevalence of Depression and Anxiety and Their Lifestyle Determinants in a Large Sample of Iranian Adults: Results from a Population Based Cross-Sectional Study

Alizadeh

Feizi

Rejali

et al. 2020

Serbian Journal of Experimental and Clinical Research

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Association of lifestyle-related factors and mental health has been less studied in Middle Eastern countries. Th is study aimed to examine the prevalence of two common mental health problems, i.e., depression and anxiety, and their lifestyle determinants in a large sample of Iranian population. This study was conducted within the framework of SEPAHAN population based cross-sectional study (N=4763(. The General Practice Physical Activity Questionnaire (GPPAQ) was used to assess physical activity and the Iranian-validated version of Hospital Anxiety and Depression Scale (HADS) was applied to screen for anxiety and depression. Logistic regression was used as the main statistical method for data analysis by SPSS version 16.0. A P-value <0.05 was considered to be statistically significant. The risk of anxiety and depression was 2.5 (OR=2.56,95% CI: 1.97-3.33) and 2.21(1.83-2.67) times higher in women than men, respectively. With every one-year increase in the age, the risk of anxiety decreased by 2% (OR=0.98,95% CI:0.97-0.99). Individuals with higher education had 56% lower risk of anxiety (OR=0.44,95% CI: 0.36-0.55) and 46% depression (OR=0.54,95% CI: 0.46-0.64) than the undergraduate group, and the risk of depression in the inactive (less than one hour of activity per week) group was 27% higher than the active group (OR=1.27,95% CI: 1.06-1.51). The risk of anxiety in the non-smoker group was 65% (OR=0.35,95% CI: 0.20-0.59) and depression was 64% lower than among smokers (OR=0.34,95% CI:0 .22-0.53). In the ex-smoker group, the risk of anxiety was 60% (OR=0.40,95% CI:0.19-0.85) and depression was 59% lower than for the smoker group (OR=0.41,95% CI: 0.24-0.73). This current study’s results demonstrated significant associations between unhealthy lifestyle factors and increased risk of anxiety and depression. Hence, special attention must be paid to preventive intervention programmes aiming to enhance healthy lifestyle among at-risk populations.

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Section: Introductionmentioning

confidence: 99%

The Prevalence of Depression and Anxiety and Their Lifestyle Determinants in a Large Sample of Iranian Adults: Results from a Population Based Cross-Sectional Study

Alizadeh

Feizi

Rejali

et al. 2020

Serbian Journal of Experimental and Clinical Research

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“…This method could well divide the patients into two groups, according to the severity of aggressive behaviors. Previous studies have also indicated that cluster analysis can well classify general and clinical populations according to mental health (14,22,23). Additionally, the importance and ability of this method have already been proven in partitioning patients with CVDs (24).…”

Section: Discussionmentioning

confidence: 99%

“…Cluster analysis is a beneficial statistical method for partitioning a population with common characteristics and a wide range of diseases and disorders. The clustering of mental health components eases the identification of target groups for timely interventions and helps health policymakers come up with services according to their preferences and needs (14).…”

Section: Introductionmentioning

confidence: 99%

Aggregation Partitioning and Study of Demographic, Medical, and Psychological Predictors Related to Derived Clusters in Cardiac Rehabilitation Patients: A Cross-Sectional Study

Saeidi

Momeni

Kakabaraee

2019

Middle East J Rehabil Health Stud

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Background: Classification of high-risk behaviors such as aggression and identifying social, medical, and psychological factors related to it can help the emergence and development of the strategies to prevent these destructive behaviors. Objectives: Thus, the present study was done with two objectives: (i) cluster analysis of the aggression components and partitioning cardiac rehabilitation (CR) patients and (ii) determining the demographic, medical, and psychological correlations of each cluster. Methods: The sample of this cross-sectional study was 167 CR patients in western Iran examined from June to December 2017. Demographic and risk factors checklist, Beck anxiety inventory (BAI), Beck depression inventory (BDI), and Buss-Perry aggression questionnaire (BPAQ) were used for data collection. The data were analyzed using hierarchical and k-means cluster analysis, Cramer-V test, analysis of variance (ANOVA), and analysis of binary logistic regression. Results:The mean age of the participants (66.5% male) was 59.14 ± 9.03. The model proposed two clusters: (i) patients with mild aggression and (ii) patients with severe aggression. Occupation (P = 0.048), marital status (P = 0.048), anxiety (P = 0.006), and depression (P = 0.001) were the most essential predictors of the unhealthy cluster. Our model could explain 30.7% to 41% of the variance of the unhealthy cluster. Conclusions: Cluster analysis divided patients into two groups with mild and severe aggression. Marital status and occupation are the most important demographic correlates, and depression and anxiety are the most important psychological predictors of the cluster with high aggression. The results of the present study can provide a map of the focus of attention on harm reduction interventions by health professionals.

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“…In this prospective study, the participants are followed up on for at least 3–5 years using phones and face-to-face meetings. [ 26 ]…”

Section: Methodsmentioning

confidence: 99%

Cluster analysis of risky behaviors among the youth in Western Iran

Khazaie

Najafi

Hamzeh

et al. 2021

Indian Journal of Psychiatry

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Background and Aims: The objectives of the study were (i) cluster analysis of risky behaviors; (ii) determining correlates; and (iii) comparing clusters with regard to the attitude toward mental health help seeking. Methods: The current cross-sectional study is a part of the first phase of the Persian Youth Cohort. From October 2014 to January 2017, 2990 individuals from Ravansar City in western Iran completed structured interviews and national and international standard questionnaires. The obtained data were analyzed using two-stage cluster analysis, multinomial logistic regression analysis, and Chi-square test. Results: This model provided three distinct clusters: (i) low-risk group with mild distress, (ii) high-risk group with high distress, and (iii) violent group with medium distress. Some sociodemographic and lifetime psychiatric disorders were the correlates of unhealthy clusters ( P < 0.05). Compared to the reference cluster, a higher number of members in unhealthy clusters were suffering from medium to severe disability. Nevertheless, the participants in these clusters were less inclined to mental health help seeking. Conclusions: More than half of the youth were suffering from suicidal and violent behaviors. Since high-risk participants are less inclined to mental health help seeking, the health policymakers can successfully utilize the results in planning general health programs.

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Cluster analysis of psychiatric profile, its correlates, and using mental health services among the young people aged 15–34: findings from the first phase of Iranian youth cohort in Ravansar

Cited by 11 publications

References 33 publications

The Prevalence of Depression and Anxiety and Their Lifestyle Determinants in a Large Sample of Iranian Adults: Results from a Population Based Cross-Sectional Study

The Prevalence of Depression and Anxiety and Their Lifestyle Determinants in a Large Sample of Iranian Adults: Results from a Population Based Cross-Sectional Study

Aggregation Partitioning and Study of Demographic, Medical, and Psychological Predictors Related to Derived Clusters in Cardiac Rehabilitation Patients: A Cross-Sectional Study

Cluster analysis of risky behaviors among the youth in Western Iran

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