Effects of Subjective Socioeconomic Status and Interpersonal and Non-Interpersonal Trauma on Extreme Social Withdrawal: A Path Analysis

Abstract

Background:

Although sociodemographic characteristics related to extreme social withdrawal (ESW) have been investigated, the role of subjective socioeconomic status (SES) and the differential effects of interpersonal and non-interpersonal trauma remain underexplored. This study examined the effects of distal (i.e. subjective SES, interpersonal, and non-interpersonal trauma) and proximal (i.e. a history of psychiatric diagnosis and depressive symptoms) risk factors on ESW.

Methods:

Participants were 98 young adults with ESW and 740 controls, all aged 19−35 years. Measures included sociodemographic characteristics, subjective SES, interpersonal and non-interpersonal trauma exposure, history of psychiatric diagnosis, and depressive symptoms. Hierarchical logistic regression and path analysis were conducted for statistical analyses.

Results:

Hierarchical logistic regression analysis revealed that interpersonal trauma, psychiatric diagnosis, and depression were significantly associated with ESW. Path analysis further demonstrated a significant direct effect of interpersonal trauma on ESW, as well as indirect effects whereby subjective SES, interpersonal trauma, and non-interpersonal trauma influenced ESW through psychiatric diagnoses and depressive symptoms.

Conclusions:

These findings provide a more comprehensive account of the mechanisms underlying ESW and highlight the complex interplay of socioeconomic, traumatic, and psychiatric factors. Interventions should adopt a multifaceted approach that addresses not only behavioral withdrawal but also socioeconomic inequality, trauma exposure, and co-occurring psychiatric symptoms.

Keywords

extreme social withdrawal Hikikomori subjective socioeconomic status interpersonal trauma non-interpersonal trauma depression

Introduction

Extreme social withdrawal (ESW) refers to a profound withdrawal from social interactions, characterized by prolonged isolation at home and a conspicuous absence of engagement in social, educational, and occupational activities outside the home (Kato et al., 2019; Muris & Ollendick, 2023). While originally regarded as a culturally specific phenomenon in Japan under the label of hikikomori (hereafter ESW), ESW is now recognized as a global concern. Epidemiological studies have revealed that ESW transcends geographical boundaries, with cases reported not only in East Asia but also in Australia, Europe, and the United States (Bellini et al., 2024; Kato et al., 2012;Yong & Nomura, 2019). Although prevalence estimates outside Japan remain limited, with 8.0% suggested in a recent meta-analysis (Zhang et al., 2025), ESW has significant ramifications for psychological well-being and daily functioning (Imai et al., 2020; Kondo et al., 2013). In addition, ESW has been linked to heightened vulnerability to suicidal ideation and behavior (Fong & Yip, 2023; Teo et al., 2020; Yong & Nomura, 2019). The escalating prevalence of ESW, particularly in the context of increasing unemployment rates (Nonaka & Sakai, 2021) and the social isolation imposed during the COVID-19 pandemic (Kubo et al., 2022), has intensified scholarly and clinical attention to its social and mental health implications (Orsolini et al., 2022; Wong et al., 2019).

To date, research on ESW has primarily focused on demographic and clinical descriptions (Pupi et al., 2025). However, relatively few studies have adopted a developmental psychopathology framework that considers both distal and proximal risk factors (Muris & Ollendick, 2023), limiting a comprehensive understanding of its etiology. Evidence regarding environmental and socioeconomic correlates of ESW is also limited and often inconsistent. For example, some studies suggest that ESW is more common among individuals from higher socioeconomic status (SES) backgrounds and urban residences (Kato et al., 2012), whereas others have found no significant association with household income (Bellini et al., 2024) or have indicated higher rates of school dropout (Yong & Nomura, 2019). These contradictory findings may be reconciled by considering the psychological burden of individuals with ESW and their perceived responsibility to uphold their SES (Bellini et al., 2024). In particular, given that subjective SES has been shown to be more strongly associated with psychological well-being than objective SES (Tan et al., 2020), the present study sought to examine the role of subjective SES as a potential predictor of ESW.

Traumatic experiences represent another distal risk factor of ESW (Frankova, 2017) that warrants further investigation. Adverse experiences in general, particularly those involving negative interpersonal relationships, are known to exacerbate social withdrawal (Chan & Lo, 2016). Research has documented significantly higher rates of traumatic experiences (Frankova, 2019) and childhood maltreatment (Masuda et al., 2024) among individuals with ESW compared to their non-ESW counterparts. Notably, individuals exposed to interpersonal trauma, such as violence by known or unknown perpetrators, are more likely to avoid trauma-related stimuli and disengage socially than those exposed to non-interpersonal trauma (e.g. a sudden loss, severe accidents, or natural disasters; Forbes et al., 2013). Despite these distinctions, the contribution of non-interpersonal trauma to ESW has received far less attention than that of interpersonal trauma. This gap underscores the need for a more nuanced exploration of both trauma types to better elucidate the developmental pathways of ESW.

Beyond the impact of distal factors, such as SES and trauma, psychiatric disorders and symptoms represent important proximal risk factors of ESW. Prior studies have found significant associations of ESW with major depressive disorder (Teo et al., 2020), social anxiety disorder (Nagata et al., 2013), problematic internet use (Sales-Filho et al., 2023), autism spectrum disorder (Dell’Osso et al., 2023), and psychotic disorders (Stip et al., 2016). Notably, depressive symptoms, even in the absence of a clinical diagnosis, have been linked to increased social distancing (Girard et al., 2014). Depressive symptoms that fluctuate with stress exposure have also been shown to mediate the relationship between childhood maltreatment and ESW status (Masuda et al., 2024). Furthermore, negative affect has been identified as a mediator of the association between subjective SES and health-related outcomes (Kraus et al., 2013; O'Leary et al., 2021). In line with developmental psychopathology frameworks, individuals who are more sensitive to environmental stressors may be particularly vulnerable to proximal mediators such as psychiatric disorders and depressive symptoms, which serve as pathways linking distal risks to ESW (Vidal-Ribas et al., 2024).

In light of these research gaps, the present study had two primary objectives: (a) to examine the impact of distal risk factors (subjective SES and interpersonal and non-interpersonal trauma) and proximal risk factors (psychiatric diagnosis and depressive symptoms) on ESW; and (b) to investigate their direct and indirect associations with ESW, thereby moving beyond the mere description of the demographic characteristics of the ESW population. We hypothesized that (1) subjective SES and interpersonal trauma would exert direct effects on ESW, and (2) subjective SES, interpersonal trauma, and non-interpersonal trauma would also be indirectly associated with ESW through a history of psychiatric diagnosis and depressive symptoms (Figure 1). By integrating both distal and proximal influences, this study aimed to advance a more comprehensive and integrated understanding of the multifaceted nature and underlying mechanisms of ESW.

Figure 1.

Hypothesized structural equation model of the relationships of subjective SES and interpersonal and non-interpersonal trauma with ESW, mediated by depression and a history of psychiatric diagnosis.

Methods

Participants and Procedures

Participants with ESW were recruited through the Social Isolation Youth Link Center, a government-supported program that provides services to socially withdrawn young adults in Seoul, Republic of Korea. Recruitment took place from June to November 2021. The center’s Isolated Youth Support Program defines eligibility as: (a) 19−35 years of age; (b) continuous disengagement from employment, education, or training for at least 6 months; and (c) self-reported feelings of social isolation. These criteria collectively align with the core dimensions of ESW: duration, functional impairment (occupational/educational disengagement), and perceived social isolation (Kato et al., 2020). Due to the limited program resources, 100 individuals were selected from an initial pool of 260 voluntary applicants based on additional internal criteria reflecting greater severity: (a) limited size and density of social networks (i.e. having few friends); (b) low levels of familial support; (c) absence of prior work experience (e.g. no history of previous part-time employment); and (d) diminished career efficacy. The research team was not involved in the recruitment process.

A control group of non-ESW young adults aged 19−35 years from the same region was recruited through an online survey company (n = 740). To ensure comparability, gender quotas were applied so that the control sample’s gender distribution approximated that of the ESW group. At the beginning of the survey, participants completed an initial screening assessing residence in Seoul, absence of ESW, and gender distribution. Absence of ESW was determined using criteria adapted from Muris and Ollendick (2023) and Teo and Gaw (2010), requiring participants to answer “No” to all of the following items: In the past 6 months, (a) “Have you avoided social situations such as going to school/work/training?,” (b) “Have you avoided social relationships such as interacting with friends?,” and (c) “Have you spent most of the day at home almost every day?” Those who did not meet these criteria could not proceed to the main survey. Two attention-check items (e.g. “Please select ‘Strongly disagree’ for this item”; “Please select ‘Very important’ for this item”) were embedded to ensure response validity. According to survey company records, approximately 1,000 respondents were excluded due to ineligibility or poor response quality, yielding a final control sample of 740 participants.

A total of 97 participants in the ESW group and all 740 control participants completed the online survey, which took approximately 30 minutes. Participants in both groups received a psychological report of their survey results and an online voucher worth approximately USD $10. The incentive amount was consistent with institutional ethical guidelines, offered equally to both groups, and considered insufficient to exert undue influence. Participation was voluntary, and all participants were informed of their right to withdraw at any time without penalty. This study was approved by the Korea University Institutional Review Board (KUIRB-2022-0052-02 and KUIRB-2021-0187-02), and written informed consent was obtained from all participants. The study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (von Elm et al., 2007).

Measurements

Demographic Factors

Participants completed a demographic questionnaire assessing age, gender, sexual orientation, and educational level. Gender categories included male, female, transgender or gender-nonconforming, and other/prefer not to answer. Sexual orientation options included heterosexual, gay or lesbian, bisexual or pansexual, and other/prefer not to answer. Educational level was categorized into three groups: (a) high school graduate or lower (low), (b) currently enrolled in college or college graduate (middle), and (c) currently enrolled in or graduated from a graduate program (high).

Distal Risk Factors of ESW

Subjective SES was evaluated using the MacArthur Scale of Subjective Social Status (Adler et al., 2000). Participants were presented with an image of a ladder with 10 rungs and asked to indicate their perceived position in society regarding financial status, education, and employment.

Interpersonal and non-interpersonal traumatic experiences were measured using the Life Events Checklist for DSM-5 (LEC-5), a 17-item self-report inventory assessing lifetime exposure to 16 types of potentially traumatic events (e.g. physical assault, motor vehicle accident, and natural disaster; Weathers et al., 2013). For each event, the respondents indicated whether they experienced it directly, witnessed it, learned about it, encountered it at work, were unsure about it, or the event did not apply to them. Only direct traumatic experiences were included in the analysis. Following prior research (Cougle et al., 2009; Jaffe et al., 2019), traumatic events were categorized as either interpersonal trauma (i.e. physical assaults, assault with a weapon, sexual abuse, and other unwanted sexual experiences) or non-interpersonal trauma (i.e. other potentially traumatic life events). The number of trauma types individuals have experienced was included in the analysis; recency and severity of trauma and perpetrator identity were not assessed in the present study.

Proximal Risk Factors of ESW

Depression was assessed using the Center for Epidemiologic Studies Depression Scale (CES-D), which includes 20 items covering four domains: depressed affect, somatic symptoms, positive affect, and interpersonal problems (Radloff, 1977). Each item is rated on a 4-point Likert scale ranging from 0 (rarely or none of the time) to 3 (most or almost all the time), with positive affect items reverse-coded. The Korean version of the CES-D (Chon et al., 2001) showed excellent internal reliability in the present sample (McDonald’s ω = 0.93). The total CES-D sum score was used in the analyses.

The history of psychiatric diagnosis was assessed via open-ended responses and dichotomized as yes/no. The distribution of reported psychiatric diagnoses is presented in Supplemental Table S1 in the Supplemental Materials.

Data Analysis

Data were analyzed using jamovi version 2.4.14 (The jamovi project, 2019) and Mplus version 8.0 (Muthén & Muthén, 1998-2015). No missing data were observed; therefore, imputation was not required.

Associations among study variables were examined using Pearson correlations and hierarchical logistic regression. Predictors were entered in three blocks: (1) demographic variables (age, sex, sexual orientation, and education level), (2) distal risk factors (subjective SES and interpersonal/non-interpersonal trauma exposure), and (3) proximal risk factors (history of psychiatric diagnosis and depression). Scaling units were defined as follows: per one rung on the MacArthur ladder for subjective SES, per one additional trauma type for traumatic exposure, and per one point increase in the CES-D sum score for depression. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for all variables.

Path analysis was conducted to examine the direct and indirect impacts of subjective SES and interpersonal and non-interpersonal traumatic events on ESW, as well as their relations with a history of psychiatric diagnosis and depression. Given the categorical nature of the dependent variable, weighted least squares means and variances adjusted (WLSMV) was used as the estimator, which does not assume a normal distribution (DiStefano & Morgan, 2014). Model fit was examined using the comparative fit index (CFI ⩾ 0.95), root mean square error of approximation (RMSEA ⩽ 0.06), and standardized root mean square residual (SRMR ⩽ 0.06; Hu & Bentler, 1999). Indirect effects were assessed for statistical significance using 95% bias-corrected CIs based on 5,000 bootstrapped samples (Fritz & MacKinnon, 2007).

Results

Sociodemographic and Psychological Characteristics

In total, 838 participants were included in the analysis (n = 98 in the ESW group and n = 740 in the control group). Table 1 shows the sociodemographic and psychological characteristics of both groups. Chi-squared tests and independent samples t-tests revealed significant group differences in terms of mean age (t = −3.70, p < .001) and sexual orientation (χ² = 53.57, p < .001), with the ESW group being older on average and reporting a higher proportion of bisexual/pansexual and other/prefer not to answer than the control group. Regarding socioeconomic factors, the ESW group showed significantly lower educational level (χ² = 70.52, p < .001) and subjective SES (t = 7.25, p < .001) compared to controls. Clinically, participants in the ESW group were significantly more likely to have psychiatric diagnoses (χ² = 169.49, p < .001) and higher exposure to both interpersonal (t = −10.58, p < .001) and non-interpersonal (t = −6.27, p < .001) trauma. In addition, the severity of depression was significantly higher in the ESW group than in the control group (t = −16.41, p < .001). However, no significant group difference was observed for gender (χ² = 6.94, p = .074).

Table 1.

Sociodemographic and Clinical Characteristics of the Participants.

	ESW(n = 98)	Control(n = 740)	Statistics
Variable	ESW(n = 98)	Control(n = 740)	t (χ²)	p
Age (years)	28.1 ± 3.92	26.4 ± 4.33	−3.70	<.001
Gender
Male	31 (31.6%)	256 (34.6%)	(6.94)	.074
Female	64 (65.3%)	479 (64.7%)
TGNC	0 (0%)	1 (0.1%)
Other/Prefer not to answer	3 (3.1%)	4 (0.5%)
Sexual orientation
Heterosexual	71 (72.5%)	662 (89.5%)	(53.57)	<.001
Gay/Lesbian	1 (1.0%)	18 (2.4%)
Bisexual/Pansexual	8 (8.2%)	41 (5.5%)
Other/Prefer not to answer	18 (18.4%)	19 (2.6%)
Education level
Low	39 (39.8%)	70 (9.5%)	(70.52)	<.001
Middle	53 (54.1%)	613 (82.8%)
High	6 (6.1%)	57 (7.7%)
Subjective SES	4.10 ± 1.96	5.39 ± 1.60	7.25	<.001
With psychiatric diagnoses	67 (68.4%)	96 (13.0%)	(169.49)	<.001
Number of trauma types exposed to
Interpersonal trauma	1.53 ± 1.28	0.54 ± 0.80	−10.58	<.001
Non-interpersonal trauma	2.44 ± 1.93	1.36 ± 1.55	−6.27	<.001
Depression	35.08 ± 22.12	11.78 ± 11.55	−16.41	<.001

Note. ESW = extreme social withdrawal; SES = socioeconomic status; TGNC = transgender or gender-nonconforming.

Impacts of Distal and Proximal Risk Factors on the Development of ESW

Pearson correlations showed that ESW status was positively correlated with interpersonal trauma (r = .34, p < .001), non-interpersonal trauma (r = .21, p < .001), history of psychiatric diagnosis (r = .45, p < .001), and depression (r = .49, p < .001), and negatively correlated with subjective SES (r = −.24, p < .001). The full correlation matrix is presented in Table 2.

Table 2.

Bivariate Correlations Between Study Variables.

Variable	1	2	3	4	5	6
1. ESW status	-
2. Interpersonal trauma	.34^***	-
3. Non-interpersonal trauma	.21^***	.51^***	-
4. History of psychiatric diagnosis	.45^***	.28^***	.25^***	-
5. Subjective SES	−.24^***	−.23^***	−.22^***	−.20^***	-
6. Depression	.49^***	.34^***	.33^***	.40^***	−.29^***	-

Note. ESW = extreme social withdrawal; SES = socioeconomic status.

***

p < .001.

Hierarchical logistic regression models examining predictors of ESW are presented in Table 3. Multicollinearity checks indicated variance inflation factor (VIF) values ranging from 1.038 to 1.224, suggesting negligible collinearity.

Table 3.

Hierarchical Logistic Regression Analysis of ESW.

Variable	Model 1		Model 2		Model 3
Variable	Β (SE)	OR [95% CI]	Β (SE)	OR [95% CI]	Β (SE)	OR [95% CI]
Intercept	−4.57 (.85)^***	0.01 [0.002, 0.06]	−2.97 (1.04)^**	0.05 [0.01, 0.39]	−6.04 (1.36)^***	0.002 [1.65e⁻⁴, 0.03]
Age	.14 (.03)^***	1.15 [1.09, 1.23]	.10 (.03)^**	1.10 [1.03, 1.18]	.12 (.04)^**	1.12 [1.04, 1.22]
Gender (Ref: Male)
Female	.12 (.26)	1.13 [0.68, 1.87]	−.13 (.28)	0.87 [0.51, 1.50]	−.09 (.32)	0.91 [0.48, 1.72]
TGNC	−10.28 (535.41)	3.42e⁻⁵ [NE]	−10.96 (535.41)	1.75e⁻⁵ [NE]	−13.13 (882.74)	1.98e⁻⁶ [NE]
Other/Prefer not to answer	.88 (1.03)	2.41 [0.32, 18.11]	1.10 (1.04)	3.01 [0.39, 23.27]	.85 (1.40)	2.34 [0.15, 36.09]
Education level (Ref: Low)
Middle	−2.15 (.28)^***	0.12 [0.07, 0.20]	−1.61 (.31)^***	0.20 [0.11, 0.37]	−1.45 (.37)^**	0.23 [0.11, 0.49]
High	−2.03 (.50)^***	0.13 [0.05, 0.35]	−1.33 (.53)^*	0.27 [0.09, 0.75]	−.72 (.60)	0.49 [0.15, 1.56]
Sexual orientation (Ref: Heterosexual)
Gay/Lesbian	−.97 (1.08)	0.38 [0.05, 3.16]	−1.84 (1.16)	0.16 [0.02, 1.56]	−3.38 (1.60)^*	0.03 [0.001, 0.77]
Bisexual/Pansexual	.56 (.45)	1.76 [0.72, 4.29]	.31 (.48)	1.36 [0.53, 3.49]	−.54 (.66)	0.58 [0.16, 2.11]
Other/Prefer not to answer	1.98 (.42)^***	7.25 [3.17, 16.59]	1.40 (.46)^**	4.07 [1.64, 10.12]	1.30 (.58)^*	3.68 [1.18, 11.51]
Subjective SES			−.24 (.08)^**	0.79 [0.67, 0.92]	−.07 (.09)	0.94 [0.78, 1.12]
Trauma
Interpersonal			.70 (.14)^***	2.01 [1.51, 2.66]	.64 (.17)***	1.90 [1.37, 2.64]
Non-interpersonal			−.01 (.08)	0.99 [0.85, 1.17]	−.20 (.11)	0.82 [0.67, 1.01]
Psychiatric diagnosis (Ref: No)
Yes					1.83 (.31)^***	6.25 [3.41, 11.46]
Depression					.06 (.01)^***	1.06 [1.05, 1.08]
Nagelkerke’s Pseudo R²	.24^**		.33^***		.55^***

Note. CI = confidence internal; ESW = extreme social withdrawal; NE = not estimable; OR = odds ratio; SES = socioeconomic status; TGNC = transgender and gender-nonconforming.

p < .05, ^**p < .01, ^***p < .001.

Among demographic variables, after adjusting for distal and proximal risk factors, age significantly predicted ESW, such that each additional year of age was associated with a 12% increase in the odds of ESW (Model 3, OR = 1.12, 95% CI [1.04, 1.22], p = .004), whereas gender showed no significant association. In Model 3, having a middle level of education emerged as a significant protective factor, with individuals showing 77% lower odds of ESW compared to those with low education (Model 3, OR = 0.23, 95% CI [0.11, 0.49], p < .001). Conversely, high education was not a significant predictor (Model 3, OR = 0.49, 95% CI [0.15, 1.56], p = .225). For sexual orientation, gay/lesbian status was associated with a 97% reduction in odds of ESW (Model 3, OR = 0.03, 95% CI [0.001, 0.77], p = .034); however, this result should be interpreted with caution as only one ESW participant identified as gay or lesbian. In contrast, individuals in the “Other/Prefer not to answer” category had more than threefold higher odds of ESW (Model 3, OR = 3.68, 95% CI [1.18, 11.51], p = .025).

Regarding distal risk factors, lower subjective SES was associated with a 21% increase in odds of ESW for each 1-rung decrease on the MacArthur ladder in Model 2 (OR = 0.79, 95% CI [0.67, 0.92], p = .003); however, this effect became nonsignificant after proximal risk factors were added in Model 3 (OR = 0.94, 95% CI [0.78, 1.12], p = .476). Interpersonal trauma was a strong predictor: each additional type of interpersonal trauma increased the odds of ESW by 90% (Model 3, OR = 1.90, 95% CI [1.37, 2.64], p < .001). In contrast, non-interpersonal trauma did not reach significance (Model 3, OR = 0.82, 95% CI [0.67, 1.01], p = .057).

Finally, proximal risk factors were strongly associated with ESW. A history of psychiatric diagnosis was linked to more than a sixfold increase in the odds of ESW (Model 3, OR = 6.25, 95% CI [3.41, 11.46], p < .001). In addition, higher depressive symptom severity increased the risk: each one-point increase in the CES-D sum score was associated with 6% higher odds of ESW (Model 3, OR = 1.06, 95% CI [1.05, 1.08], p < .001). The final hierarchical regression model accounted for 54.8% of the variance in ESW (Nagelkerke pseudo R² = 0.55; χ² (14) = 275.5, p < .001).

Structural Relationships of Distal and Proximal Risk Factors With ESW

The results of the path analysis examining the direct and indirect effects of distal and proximal risk factors on ESW status are presented in Figure 2 and Table 4. The hypothesized model includes subjective SES and interpersonal and non-interpersonal trauma as independent variables, with a history of psychiatric diagnosis and depressive symptoms as mediators. The hypothesized model indicated excellent fit (χ²(1) = 2.07, p = .150, RMSEA = 0.036, 90% CI, 0.000−0.107, CFI = 0.999, SRMR = 0.008). Path coefficients revealed significant direct effects of interpersonal trauma (β = .12, 95% CI [0.02, 0.21], p = .017), a history of psychiatric diagnosis (β = .56, 95% CI [0.45, 0.66], p < .001), and depression (β = .22, 95% CI [0.13, 0.31], p < .001) on ESW status. By contrast, the direct effect of subjective SES on ESW was not significant (β = −.10, 95% CI [−0.21, 0.01], p = .053). Further, indirect path analyses showed that both psychiatric diagnoses and depressive symptoms significantly mediated the pathways from subjective SES and interpersonal and non-interpersonal trauma to ESW (all ps < 0.05).

Figure 2.

Path model depicting the relationships of subjective SES and interpersonal and non-interpersonal trauma with ESW, mediated by depression and a history of psychiatric disorders.

Table 4.

Standardized Effect Values for the Path Model of ESW.

Model paths	β	95% CI	p
Subjective SES → ESW
Total effect	−.25	[−0.36, −0.13]	<.001
Direct	−.10	[−0.21, 0.01]	.053
Total indirect effect	−.15	[−0.22, −0.09]	<.001
Specific indirect effects
Subjective SES → Depression → ESW	−.03	[−0.06, −0.02]	.001
Subjective SES → Psychiatric diagnosis → ESW	−.10	[−0.16, −0.05]	<.001
Subjective SES → Psychiatric diagnosis → Depression → ESW	−.01	[−0.03, −0.01]	.008
Interpersonal trauma → ESW
Total effect	.29	[0.19, 0.38]	<.001
Direct	.12	[0.02, 0.21]	.017
Total indirect effect	.17	[0.11, 0.24]	<.001
Specific indirect effects
Interpersonal trauma → Depression → ESW	.03	[0.01, 0.06]	.044
Interpersonal trauma → Psychiatric diagnosis → ESW	.13	[0.07, 0.20]	<.001
Interpersonal trauma → Psychiatric diagnosis → Depression → ESW	.02	[0.01, 0.03]	.001
Non-interpersonal trauma → ESW
Total effect	.12	[0.06, 0.18]	<.001
Total indirect effect	.12	[0.06, 0.18]	<.001
Specific indirect effects
Non-interpersonal trauma → Depression → ESW	.03	[0.02, 0.05]	.001
Non-interpersonal trauma → Psychiatric diagnosis → ESW	.08	[0.03, 0.13]	.003
Non-interpersonal trauma → Psychiatric diagnosis → Depression → ESW	.01	[0.004, 0.02]	.016

Note. ESW = extreme social withdrawal; SES = socioeconomic status.

Discussion

ESW is associated with adverse outcomes across the lifespan, including persistent unemployment and poor mental well-being (Koyama et al., 2010). These individual difficulties, in turn, impose substantial social and economic burdens on families and communities. The present study investigated multifaceted risk factors for ESW by examining both distal (e.g. subjective SES, trauma) and proximal (e.g. psychiatric diagnoses, depressive symptoms) influences. Our analyses identified interpersonal trauma, a history of psychiatric diagnosis, and depressive symptoms as significant predictors of ESW. By contrast, subjective SES and non-interpersonal trauma did not exert significant direct effects on ESW. Path analysis further confirmed a significant direct relationship between interpersonal trauma and ESW, while demonstrating that the effects of subjective SES and non-interpersonal trauma on ESW are primarily indirect, operating through psychiatric diagnoses and depressive symptoms. These findings underscore the complex interplay of multiple risk factors in the development of ESW and highlight the need for multifaceted, practical interventions. Specifically, prevention and treatment strategies should not only target the behavioral aspects of ESW but also aim to enhance perceived socioeconomic conditions and integrate trauma-informed, clinically tailored therapies.

Existing research has consistently shown that subjective SES influences mental health outcomes, with lower perceived SES linked to elevated risk for depression (Kwong et al., 2020; Madigan & Daly, 2023), anxiety disorders (McLaughlin et al., 2012), and substance use disorders (Scott et al., 2014) across both adolescents (Quon & McGrath, 2014) and adults (Honjo et al., 2014). Our findings extend this literature by showing that low perceived SES was associated with greater depression severity and a higher risk of psychiatric disorders among individuals with ESW. Moreover, low subjective SES was associated with heightened risk of ESW, even after accounting for education, suggesting unique contributions of both objective and subjective SES. However, the direct effect of subjective SES was no longer significant once psychiatric diagnoses and depression were considered, indicating that its influence operates through these proximal mechanisms rather than exerting an independent effect. This result aligns with prior evidence that negative affect, including depression and hopelessness, mediates the relationship between subjective SES and adverse health-related outcomes (Operario et al., 2004). Similarly, perception of low social rank, combined with depressive symptoms and perceived inequality, may increase vulnerability to social withdrawal (Kraus et al., 2013; Park et al., 2025). While earlier studies have primarily implicated objective SES indicators such as unemployment rate and household income (Nonaka & Sakai, 2021), this study is among the first to identify subjective SES as a relevant factor for ESW. Distinguishing between subjective and objective SES provides a more nuanced understanding of the social and psychological mechanisms underlying ESW, with important implications for the development of targeted intervention.

Both interpersonal (e.g. sexual or physical assault) and non-interpersonal (e.g. life-threatening accident or natural disaster) trauma are well-established risk factors for a range of psychiatric conditions, such as posttraumatic stress disorder (McLaughlin et al., 2013), anxiety disorders (Fernandes & Osório, 2015), and substance use disorders (Tucci et al., 2010), along with increased levels of depression (Mandelli et al., 2015) and suicidality (Panagioti et al., 2012). Prior research has shown that individuals with ESW report higher exposure to interpersonal trauma than controls (Frankova, 2017). Extending this evidence, our findings demonstrate that both interpersonal and non-interpersonal trauma contribute to ESW indirectly through psychiatric diagnoses and depressive symptoms. These findings highlight the importance of early intervention addressing trauma exposure to disrupt the developmental pathway toward ESW.

Notably, interpersonal trauma exerted a direct effect on ESW, whereas the effect of non-interpersonal trauma was primarily driven indirectly by the mediating effects of psychiatric diagnosis and depression. This aligns with existing literature demonstrating that individuals exposed to interpersonal trauma are more likely to socially isolate themselves and avoid social situations in an effort to avoid trauma-related stressors (Forbes et al., 2013; Littleton et al., 2007). Although such avoidance strategies may provide temporary relief from distress, their persistent use can foster entrenched withdrawal and maladaptive psychiatric outcomes (Masi et al., 2023). Recent studies further reinforce this perspective, identifying adverse interpersonal experiences such as childhood maltreatment (Malagón-Amor et al., 2020), peer victimization (Barzeva et al., 2020), bullying (Kanai et al., 2024), and even teacher-related adverse childhood experiences (Wakuta et al., 2023) as risk factors for ESW. These findings highlight the importance of incorporating trauma-focused interventions alongside behavioral strategies that encourage gradual social re-engagement, while addressing the considerable barriers to help-seeking faced by individuals with ESW. Future research should also account for trauma characteristics, such as severity, recency, and perpetrator identity, to inform more personalized, trauma-informed treatment approaches.

Psychiatric diagnoses and depressive symptoms emerged as strong predictors of ESW and as key mediators of the effects of distal risk factors. The high comorbidity of ESW and psychiatric disorders has been well-documented (Bellini et al., 2024; Kato et al., 2019; Katsuki et al., 2020). The relationship is likely bidirectional: the isolation and dysfunction associated with ESW may precipitate psychiatric disorders (Kato et al., 2024), while pre-existing mental health conditions, especially depression, may increase risk for ESW. However, depressive symptom severity was associated with only a small rise in risk, warranting cautious interpretation. Nonetheless, longitudinal evidence supports this pattern, showing that depressive symptoms in adulthood are associated with persistent social isolation extending from childhood into adulthood (Lay-Yee et al., 2023). These findings underscore the importance of comprehensive psychiatric assessments in individuals with ESW and suggest that effective treatment should be multidimensional, targeting both ESW and co-occurring mental health conditions.

Limitations

This study had several limitations. First, its cross-sectional design and reliance on self-report measures limit causal inferences regarding relationships among the socioeconomic, environmental, and psychological variables contributing to ESW. Retrospective reporting of trauma may also introduce a recall bias. Longitudinal cohort studies with prospective trauma assessments are needed to clarify developmental pathways. Second, participants were recruited from the Social Isolation Youth Link Center in Seoul, which restricts the generalizability of the findings. Future studies should include more diverse populations across cultural contexts. Third, ESW recruitment was based on the center’s internal criteria rather than a standardized instrument. Although these criteria capture the core characteristics of ESW, including restricted social networks, limited work experiences, and feelings of isolation, future research should employ validated measures, such as the 25-item Hikikomori Questionnaire (Teo et al., 2018), to ensure construct validity and enhance comparability across studies. Finally, the study did not assess potential protective factors such as social support or adaptive coping skills. Future research should adopt a more comprehensive framework incorporating both vulnerability and resilience factors of ESW to better inform tailored interventions and efficient resource allocation for individuals with ESW.

Conclusions

This study advances understanding of ESW by comprehensively examining both distal and proximal risk factors. The analyses identified subjective SES, interpersonal and non-interpersonal trauma, psychiatric diagnoses, and depressive symptoms as significant correlates of ESW. Notably, interpersonal trauma demonstrated a direct effect on ESW, whereas the effects of subjective SES and non-interpersonal trauma were predominantly indirect, operating through psychiatric diagnoses and depressive symptoms. A particularly novel contribution lies in highlighting subjective SES as a determinant of ESW that is distinct from traditional objective SES indicators, even though proximal risk factors primarily mediated its ultimate influence. Taken together, these findings emphasize the importance of adopting a multifaceted, evidence-based approach that simultaneously addresses perceived socioeconomic position, trauma exposure, and psychiatric comorbidities. Integrating trauma-informed care with clinically tailored therapeutic strategies may be essential for mitigating the long-term psychosocial and clinical consequences of ESW.

Supplemental Material

sj-docx-1-isp-10.1177_00207640251393374 – Supplemental material for Effects of Subjective Socioeconomic Status and Interpersonal and Non-Interpersonal Trauma on Extreme Social Withdrawal: A Path Analysis

Supplemental material, sj-docx-1-isp-10.1177_00207640251393374 for Effects of Subjective Socioeconomic Status and Interpersonal and Non-Interpersonal Trauma on Extreme Social Withdrawal: A Path Analysis by Hayoung Bae, Minkyung Yim, Jae Oh Lee, Haeun Kim and Ji-Won Hur in International Journal of Social Psychiatry

Footnotes

Acknowledgements

We thank the Social Isolation Youth Link Center for recruiting individuals with ESW, conducting the survey, and collecting the data.

ORCID iDs

Hayoung Bae

Minkyung Yim

Jae Oh Lee

Haeun Kim

Ji-Won Hur

Ethical Considerations

This study was approved by the Korea University Institutional Review Board (KUIRB-2022-0052-02 and KUIRB-2021-0187-02).

Consent to Participate

All participants provided written informed consent prior to enrollment in the study.

Author Contributions

HB was involved in the conceptualization, formal analysis, visualization, writing – original draft, and writing – review & editing. MY was involved in the conceptualization, formal analysis, investigation, methodology, project administration, and writing – original draft; JOL was involved in the conceptualization, data curation, formal analysis, investigation, project administration, and writing – original draft. HK was involved in the data curation and project administration. JWH was involved in the funding acquisition, project administration, resources, supervision, and writing – review & editing.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Information and Communications Technology (ICT) and Future Planning for Convergent Research in the Development Program for R&D Convergence over Science and Technology Liberal Arts (NRF-2022M3C1B6080866) and the Korea University Center for Human Rights & Gender Equity and Diversity Council. The funders had no role in the design or conduct of the study; collection, analysis, and interpretation of data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The datasets generated and analyzed during the current study are not publicly available due to ethical considerations as the participants did not provide written consent for their data to be publicly shared, but de-identified participant data are available from the corresponding author on reasonable request.

Supplemental Material

Supplemental material for this article is available online.

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