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Int J High Risk Behav Addict

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Examination of Violence Among Individuals with Police Records in Ilam City Police Stations

Author(s):
Fathola MohamadianFathola MohamadianFathola Mohamadian ORCID1, Sajad SohrabnejadSajad SohrabnejadSajad Sohrabnejad ORCID2, Mohamadreza GachiMohamadreza GachiMohamadreza Gachi ORCID3, Yousef VeisaniYousef VeisaniYousef Veisani ORCID4, Sehat AibodSehat AibodSehat Aibod ORCID3,*
1Department of Psychology, Psychosocial Injuries Research Center, Ilam University of Medical Sciences, Ilam, IR Iran
2Department of Clinical Psychology, School of Medicine, Ilam University of Medical Sciences, Ilam, IR Iran
3Psychosocial Injuries Research Center, Ilam University of Medical Sciences, Ilam, IR Iran
4Non-communicable Diseases Research Center, Ilam University of Medical Sciences, Ilam, IR Iran

International Journal of High Risk Behaviors and Addiction:Vol. 14, issue 3; e161579
Published online:Jul 29, 2025
Article type:Research Article
Received:Mar 30, 2025
Accepted:Jul 19, 2025
How to Cite:Mohamadian F, Sohrabnejad S, Gachi M, Veisani Y, Aibod S. Examination of Violence Among Individuals with Police Records in Ilam City Police Stations.Int J High Risk Behav Addict.2025;14(3):e161579.https://doi.org/10.5812/ijhrba-161579.

Abstract

Background:

Violence is a complex social issue with profound effects on both individuals and communities. Rooted in economic, social, and cultural contexts, it appears in various forms and settings. Police stations, as frontline institutions in handling violence-related incidents, provide a unique opportunity to study this phenomenon.

Objectives:

The present study aimed to assess the prevalence and correlates of violence among individuals visiting police stations in Ilam city.

Patients and Methods:

A descriptive-analytical study was conducted on a randomly selected sample of 400 individuals referring to police stations in Ilam. Data were collected using a researcher-designed questionnaire. Statistical analyses, including chi-square tests and Pearson correlation coefficients, were used to examine associations between variables.

Results:

The analysis revealed significant associations between various demographic and socioeconomic factors and the experience of physical, psychological, and sexual violence. Individuals aged 25 - 34, those working in manual labor (especially construction), with lower educational attainment, and with poor economic status were most affected. Family disputes emerged as the leading context for violent incidents. Other contributing factors included social class, marital status, and place of residence.

Conclusions:

The study indicates that violence among individuals visiting police stations in Ilam is strongly linked to socioeconomic and demographic factors. These findings underscore the need for targeted, context-specific interventions that address underlying causes such as poverty, low education, and social instability. Policymakers and law enforcement agencies can utilize these insights to design more effective violence prevention strategies and improve public safety in the region.

1. Background

Violence is a complex social and behavioral phenomenon with widespread consequences for individuals’ physical, psychological, and social well-being (1, 2). It manifests in various forms — including domestic, street, and institutional violence — and can exacerbate social, cultural, and economic challenges at the community level (1). Understanding its causes and dimensions is thus crucial. Multiple studies have identified a combination of economic, social, psychological, and cultural factors — such as poverty, unemployment, social inequality, and family issues — as contributors to violent behavior (3, 4). These factors are particularly evident among individuals who interact with law enforcement, as they often reflect broader community problems. Police stations serve as vital sources of data regarding social behavior. Reviewing the files of individuals visiting these stations can offer valuable insights into the nature and causes of violence (3, 4).

In Ilam, a city marked by high unemployment and unique cultural features, violence may present differently than in other regions (5). Previous research highlights the role of culture and socioeconomic status in shaping mental health and violent behaviors, especially in the Iranian context (6). Ilam’s distinct cultural landscape — including tribal affiliations and gender norms — makes it essential to consider these factors in studying violence.

2. Objectives

The present study aims to assess the prevalence and types of violence among individuals visiting police stations in Ilam. It explores underlying causes and related socio-cultural and economic variables (e.g., age, gender, SES, and offense type). The findings will support the development of informed policies and targeted interventions to reduce violence and improve community safety.

3. Patients and Methods

This study aimed to examine the status of violence among individuals with violence-related cases at police stations in Ilam city. The research was descriptive-analytical in design.

3.1. Statistical Population and Sampling

The statistical population included all individuals with violence-related cases registered in the police stations of Ilam city during a three-year study period (from the beginning of 2019 to the end of 2021). Inclusion criteria were all individuals with officially registered case files related to any form of violence (physical, verbal, psychological, or sexual) during this period. Only case files containing sufficient and complete information necessary for analysis, including demographic data and violence-related details, were included. Exclusion criteria involved incomplete or missing case files, cases unrelated to violence, unclear or unconfirmed violence status, and duplicate case files referring to the same incident.

A random sampling method was employed to select 400 case files from the total population. The sampling was conducted using a systematic random sampling approach, whereby every nth case file from the list was selected to ensure the representativeness of the sample. However, it should be noted that although random sampling was used, potential selection biases cannot be entirely ruled out due to factors such as incomplete or missing case files, and variations in reporting or registration practices. Therefore, while the sample provides valuable insights, the generalizability of findings to all individuals visiting police stations in Ilam may have some limitations.

3.2. Sample Size Calculation and Power Analysis

The population was assumed to be large (infinite) for the purpose of sample size calculation. A conservative estimated proportion (p) of 0.5 was used to maximize the required sample size, with a margin of error (E) of 5% (0.05) and a confidence level of 95% (Z = 1.96). Using the formula for sample size calculation for proportions in an infinite population, the sample size was calculated as follows:

Since the selected sample size was 400, it is sufficient and appropriate for this study, providing adequate statistical power for precise estimation of the results. Given the predetermined sample size (n = 400), the statistical power of the analyses conducted in this research is sufficient to detect medium effect sizes (approximately 0.3) with a probability exceeding 80% at a significance level of α = 0.05. This sample size provides adequate power for both parametric and non-parametric tests, including chi-square tests, correlation analyses, and multivariate logistic regression, allowing for precise estimation of effect sizes and reduction of type II error. Furthermore, in regression models with a limited number of independent variables (fewer than 10), the sample size ensures stability and generalizability of the findings. Therefore, the chosen sample size serves as an important criterion in ensuring the reliability and credibility of the statistical analyses in this study.

3.3. Data Collection Tools

A researcher-designed questionnaire was used to collect data on personal, social, and economic characteristics, as well as details of violence related to the cases. The questionnaire was designed to gather precise and comprehensive information regarding the status of violence among individuals visiting the police stations. Given the observational and descriptive nature of the study and the absence of intervention or group assignment, blinding of investigators was not applicable.

3.4. Data Analysis Methods

This study used both quantitative and qualitative methods to assess violence among visitors to police stations in Ilam. Quantitative data were analyzed using means and standard deviations, while qualitative data were examined through absolute and relative frequencies. To explore variable relationships, chi-square tests (for categorical variables), Pearson correlation (for continuous variables), and Kendall’s Tau-c (for ordinal variables) were applied. The analysis aimed to identify key factors associated with different types of violence and support the development of targeted strategies to reduce violence and improve social conditions in the region.

3.5. Ethical Considerations

This study was conducted in full compliance with ethical principles and privacy protection standards. Data were anonymized and extracted from participants’ case files with prior written informed consent from them or their legal representatives. The study adhered to ethical guidelines and was approved by the university’s research ethics committee.

4. Results

Analysis of demographic variables (Table 1) revealed significant associations with experiences of violence. The 25 - 34 age group reported the highest prevalence of physical (32.5%) and psychological (27.5%) violence (P = 0.01). Construction workers showed the highest rates among occupations for physical (30%) and psychological (20%) violence (P = 0.01), likely due to job-related stress. Individuals without a diploma experienced higher levels of physical (35%), psychological (28%), and sexual (21%) violence (P = 0.02). Unmarried individuals were particularly vulnerable, reporting physical (55%), psychological (60%), and sexual (53%) violence (P = 0.03). These findings highlight the critical impact of age, occupation, education, and marital status on the risk of experiencing violence.

Table 1.Relationship Between Physical, Psychological, and Sexual Violence with Demographic Characteristics (Age, Occupation, Education, and Marital Status)
VariablesPhysical Violence (%)Psychological Violence (%)Sexual Violence (%)Significant Relationship (P-Value)
Age (y)
15 - 2422.518.510.00.03
25 - 3432.527.516.50.01
35 - 4420.019.014.00.04
45 - 5412.516.018.50.06
55 and above12.519.019.00.07
Occupation
Construction worker30.020.015.00.01
Employee12.516.510.00.04
Unemployed20.022.017.00.05
Driver15.018.010.50.03
Other occupations22.525.521.50.02
Education
Below diploma35.028.021.00.02
Diploma25.024.020.50.03
Associate degree12.515.014.00.06
Bachelor’s and above27.523.018.00.04
Marital status
Married45.040.032.00.05
Single55.060.053.00.03

Relationship Between Physical, Psychological, and Sexual Violence with Demographic Characteristics (Age, Occupation, Education, and Marital Status)

Table 2 demonstrates a significant association between experiences of physical, psychological, and sexual violence and socio-economic factors, including place of residence, economic status, and perceived social class. Individuals living in peripheral urban areas reported the highest levels of violence — physical (55%), psychological (50%), and sexual (45%) — compared to those in city centers, who experienced lower rates (P = 0.01 - 0.02). Economic deprivation was strongly linked to violence, with individuals in poor economic conditions showing the highest prevalence: Physical (50%), psychological (45%), and sexual (40%) (P = 0.001). Similarly, those in lower and middle social classes — particularly the lower class — were more affected, reporting physical (37.5%), psychological (40%), and sexual (38.5%) violence at significantly higher rates than individuals in higher social classes (P = 0.03). These findings highlight how socio-economic disparities contribute to increased vulnerability to violence, emphasizing the need for socially equitable prevention and intervention strategies.

Table 2.Relationship Between Physical, Psychological, and Sexual Violence with Social and Economic Characteristics (Place of Residence, Economic Status, and Social Class)
VariablesPhysical Violence (%)Psychological Violence (%)Sexual Violence (%)Significant Relationship (P-Value)
Place of residence
Urban periphery55.050.045.00.01
City center45.040.035.00.02
Economic status
Poor50.045.040.00.001
Average35.030.025.00.04
Good15.018.013.00.05
Social class
Lower37.540.038.50.03
Middle45.042.539.00.04
Upper17.517.518.50.05

Relationship Between Physical, Psychological, and Sexual Violence with Social and Economic Characteristics (Place of Residence, Economic Status, and Social Class)

Data from Table 3 shows significant associations between the nature and severity of violence and contextual factors such as conflict causes, reasons for police visits, and weapons used. Domestic disputes were the leading cause of violence, with the highest rates of physical (62.5%), psychological (58%), and sexual (54%) violence (P = 0.001), while street fights showed lower prevalence (P = 0.05). Crime reporting and involvement in physical altercations were the most common reasons for police visits, associated with 45 - 55% violence rates (P = 0.02 - 0.03). Knives were the most frequently used weapons in physical (37.5%), psychological (32%), and sexual (30.5%) violence (P = 0.03), followed by blunt objects like pipes and sticks, especially in physical (30%) and psychological (28.5%) violence (P = 0.04). These findings highlight the impact of situational and instrumental factors in violent incidents and provide valuable guidance for targeted prevention and intervention efforts.

Table 3.Relationship Between Physical, Psychological, and Sexual Violence with Causes and Instruments of Conflict (Cause of Conflict, Reason for Visit, and Type of Weapons Used)
VariablesPhysical Violence (%)Psychological Violence (%)Sexual Violence (%)Significant Relationship (P-Value)
Cause of conflict
Domestic dispute62.558.054.00.001
Street fight37.530.035.00.05
Reason for visit
Crime report50.045.048.00.02
Physical altercation50.055.052.00.03
Type of weapons used
Knife37.532.030.50.03
Pipe and wood30.028.525.00.04
Fist and kicks32.529.534.00.05

Relationship Between Physical, Psychological, and Sexual Violence with Causes and Instruments of Conflict (Cause of Conflict, Reason for Visit, and Type of Weapons Used)

Table 4 examines the association between experiences of violence and psychological or criminal background variables, including psychiatric consultations and criminal records. Individuals with a history of psychiatric visits reported high levels of physical (45%), psychological (50.5%), and sexual (47%) violence (P = 0.05). Interestingly, those without psychiatric visits showed even higher rates of physical (55%) and sexual (53%) violence (P = 0.03), suggesting possible untreated psychological distress. A significant correlation was also found between criminal history and violence exposure; individuals with criminal records experienced elevated physical (52.5%), psychological (48%), and sexual (50.5%) violence (P = 0.01), whereas those without such history had lower incidences (P = 0.02). These findings highlight the complex interplay between psychological vulnerability, criminal background, and victimization, underscoring the need for integrated psychosocial interventions in violence prevention and rehabilitation.

Table 4.Relationship Between Physical, Psychological, and Sexual Violence with Psychological and Criminal Factors (Experience of Visiting a Psychiatrist, and Criminal Record)
VariablesPhysical Violence (%)Psychological Violence (%)Sexual Violence (%)Significant Relationship (P-Value)
Experience of visiting a psychiatrist
Yes45.050.547.00.05
No55.049.553.00.03
Criminal record
Yes52.548.050.50.01
No47.552.049.50.02

Relationship Between Physical, Psychological, and Sexual Violence with Psychological and Criminal Factors (Experience of Visiting a Psychiatrist, and Criminal Record)

Table 5 shows chi-Square analysis results examining associations between socio-demographic and conflict-related variables and physical, psychological, and sexual violence. Significant relationships (P < 0.05) were found for age, occupation, education, marital status, economic status, social class, and cause of conflict with different types of violence. Younger age, lower education, poor economic conditions, and involvement in family disputes were linked to higher violence exposure. High-stress occupations, like construction work, also correlated with increased violence risk. Place of residence showed no significant effect. These findings highlight the importance of considering demographic, socioeconomic, and contextual factors in designing targeted violence prevention and intervention programs.

Table 5.Chi-Square Test for Examining the Relationship Between Demographic and Social Variables and Types of Physical, Psychological, and Sexual Violence
VariablesPhysical Violence (χ2)Psychological Violence (χ2)Sexual Violence (χ2)P-Value
Age12.459.325.720.014
Occupation15.2212.368.210.003
Education level9.817.566.020.044
Marital status7.566.154.920.023
Place of residence5.323.752.600.069
Economic status14.5810.328.010.003
Social class10.118.295.560.038
Cause of conflict18.7514.5011.200.001

Chi-Square Test for Examining the Relationship Between Demographic and Social Variables and Types of Physical, Psychological, and Sexual Violence

Table 6 uses Pearson correlation to examine relationships between demographic variables and experiences of physical, psychological, and sexual violence. Age showed significant positive correlations with physical (R = 0.42) and psychological (R = 0.35) violence, indicating increased exposure with age. Education level was significantly negatively correlated with physical (R = -0.36), psychological (R = -0.29), and sexual (R = -0.24) violence, suggesting higher education reduces violence exposure. Monthly income also had significant negative correlations with all three types of violence, highlighting the protective effect of financial stability. The number of family members showed a weak, non-significant positive correlation (P = 0.055). These results emphasize the influence of age, education, and economic status on vulnerability to violence, while family size has minimal impact.

Table 6.Pearson Correlation Coefficient for Examining the Relationship between Demographic Variables and Types of Physical, Psychological, and Sexual Violence
VariablesPhysical Violence (R)Psychological Violence (R)Sexual Violence (R)P-Value
Age0.420.350.200.005
Education level-0.36-0.29-0.240.012
Monthly income-0.29-0.20-0.150.039
Number of family members0.250.150.120.055

Pearson Correlation Coefficient for Examining the Relationship between Demographic Variables and Types of Physical, Psychological, and Sexual Violence

Kendall’s Tau-c test results reveal significant positive correlations between several factors and types of violence (Table 7). The cause of conflict strongly correlates with all types of violence (P = 0.002), with clearer and more intense causes linked to higher physical, psychological, and sexual violence. Reasons for physician visits also positively relate to violence levels (P = 0.010), increasing with visit frequency. Weapon type shows a weaker but significant correlation (P = 0.042). Experience with psychiatric visits is also significantly associated with all types of violence (P = 0.015). These findings highlight the key roles of these factors in influencing violence levels.

Table 7.Kendall’s Tau-c Test for Examining the Relationship Between Demographic Variables and Types of Physical, Psychological, and Sexual Violence
VariablesPhysical Violence (τ)Psychological Violence (τ)Sexual Violence (τ)P-Value
Cause of conflict0.410.380.290.002
Reason for visit0.350.300.250.010
Type of weapon used0.220.190.160.042
Experience of visiting a psychiatrist0.310.280.220.015

Kendall’s Tau-c Test for Examining the Relationship Between Demographic Variables and Types of Physical, Psychological, and Sexual Violence

Table 8 presents the multivariate logistic regression results identifying independent predictors of self-injurious behavior. Major depressive disorder (OR = 2.78; 95% CI: 1.45 - 5.31; P = 0.002), generalized anxiety disorder (OR = 2.46; 95% CI: 1.19 - 5.10; P = 0.015), substance use disorders (OR = 1.89; 95% CI: 1.01 - 3.54; P = 0.047), and borderline personality disorder (OR = 4.91; 95% CI: 1.19 - 20.34; P = 0.028) significantly increased the risk of self-injury. Additionally, living in high-violence areas (OR = 2.22; 95% CI: 1.20 - 4.10; P = 0.011), exposure to domestic violence (OR = 2.63; 95% CI: 1.35 - 5.12; P = 0.004), and history of street fights (OR = 2.15; 95% CI: 1.02 - 4.54; P = 0.043) were also associated with higher self-injury risk. These findings highlight the complex interplay between psychological vulnerabilities and environmental stressors, underscoring the need for integrated prevention strategies.

Table 8.Logistic Regression Analysis: Predicting the Probability of Self-injury Based on Psychiatric Disorders and Community-Level Violence
Independent VariablesCrude OR (95% CI)Adjusted OR* (95% CI)Adjusted P-Value
Major depressive disorder3.52 (2.00 - 6.21)2.78 (1.45 - 5.31)0.002
Generalized anxiety disorder3.18 (1.65 - 6.12)2.46 (1.19 - 5.10)0.015
Substance use disorders2.21 (1.28 - 3.83)1.89 (1.01 - 3.54)0.047
Borderline personality disorder6.58 (1.79 - 24.16)4.91 (1.19 - 20.34)0.028
High violence in the residence area2.49 (1.48 - 4.17)2.22 (1.20 - 4.10)0.011
Victim of domestic violence3.05 (1.72 - 5.40)2.63 (1.35 - 5.12)0.004
History of street fights2.72 (1.41 - 5.25)2.15 (1.02 - 4.54)0.043
Living in a high-violence neighborhood2.13 (1.20 - 3.78)1.76 (0.92 - 3.37)0.088

Logistic Regression Analysis: Predicting the Probability of Self-injury Based on Psychiatric Disorders and Community-Level Violence

5. Discussion

This study revealed significant associations between violence and various demographic, economic, and psychological factors among individuals visiting police stations in Ilam. Age, occupation, education level, and marital status were identified as key contributors, with young adults and individuals in high-stress jobs (e.g., construction workers) being more vulnerable to physical violence (3, 7-9). Lower educational attainment and poor marital status were also linked to higher exposure, likely due to limited access to resources and psychological resilience (10, 11). Economic hardship emerged as a critical factor, aligning with prior research on poverty and inequality as violence drivers (12-15). Those in marginalized neighborhoods and lower social classes reported higher violence levels, emphasizing the role of structural disparities (16, 17). Our findings support the need for multi-sectoral interventions involving health, social, and governmental agencies (18).

Family disputes were the leading cause of physical violence, consistent with earlier findings (19, 20). Knives and blunt objects were commonly used, indicating the need for preventive strategies targeting household violence and weapon accessibility (21). A meaningful relationship was observed between psychiatric history, criminal records, and exposure to violence. Psychological disorders such as depression and anxiety may predispose individuals to violence, while prior offenses increase recurrence risk (22-28). These findings underline the importance of integrating mental health services into violence prevention programs. The study also connected psychiatric and environmental factors with self-injurious behaviors, echoing past research linking mental health disorders and adverse environments with self-harm (29-32). Effective prevention requires a dual focus on treating psychiatric conditions and improving social environments (33, 34).

In summary, violence is shaped by interconnected individual and societal factors. Tailored prevention strategies targeting vulnerable groups — especially youth, the unemployed, and those with mental health issues — are essential for reducing violence in Ilam.

5.1. Conclusions

This study reveals that violence among individuals visiting police stations in Ilam is influenced by a complex interplay of demographic, socioeconomic, psychological, and environmental factors. Young adults, those with lower education, and economically vulnerable groups are at the highest risk, with family conflicts being the predominant setting for violence. Psychiatric disorders and community violence exposure significantly increase the risk of self-injury. These findings call for integrated public health interventions combining mental health support, community safety, and socioeconomic empowerment tailored to high-risk groups, forming a foundation for effective, context-specific prevention policies.

5.2. Limitations

This study has several limitations. Its cross-sectional design restricts causal inference. Self-reported data may be subject to recall or response bias. Moreover, findings are limited to Ilam city and may not generalize to other contexts. Future studies should adopt longitudinal and qualitative methods to provide deeper insights.

5.3. Recommendations

Implementing evidence-based interventions tailored to the socio-cultural and psychological characteristics of Ilam province — such as specialized vocational training, provision of psychological support services, and designing community-engaged violence prevention programs — can play a significant role in enhancing social stability (35). Additionally, it is recommended that more comprehensive and large-scale studies be conducted across different regions to utilize their findings for the more effective development and implementation of preventive and therapeutic policies.

Acknowledgments

Footnotes

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