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Psychiatry Investig > Volume 23(1); 2026 > Article
Jung, Cho, and Han: Association of Psychiatric Disorder Comorbidities With Global and Orbital Ablative Surgeries: A Real-World Retrospective Cohort Study

Abstract

Objective

To assess the incidence and risk of psychiatric disorders, including depression, anxiety, and trauma-related disorders, in individuals who underwent globe and orbital ablative surgeries compared to a matched control group.

Methods

This retrospective cohort study used Korean National Health Insurance Service data. The cohort included 338,767 individuals, with 16,545 in the surgery group (orbital exenteration, enucleation, or evisceration, 2003-2021) and 322,222 matched controls. After exclusions, 12,965 patients were matched with 251,445 controls via propensity score matching. The cumulative incidence and risk of psychiatric disorders, including depression (F32.x, F34.0, F34.1), anxiety (F40.x, F41.x), and trauma-related disorders (F43.x), over three years post-surgery. Kaplan-Meier analysis assessed cumulative incidence, while Cox proportional hazards regression estimated adjusted hazard ratios (HRs) and 95% confidence intervals (CIs).

Results

A total of 264,410 individuals (mean age, 54.4 years; 63.3% man) were included. The surgery group had significantly higher cumulative incidence of psychiatric disorders (log-rank p<0.001). The incidence rate ratio was 1.63 (95% CI, 1.52-1.75). Risk was greatest in younger individuals (incidence rate ratio, 2.15; 95% CI, 1.75-2.64) and men (1.77 vs. 1.48 in women). Higher comorbidities were associated with lower risk (HR: 0.91 in men, 0.90 in women), as was higher socioeconomic status (HR: 0.76 in men, 0.68 in women).

Conclusion

Globe and orbital ablative surgeries were associated with significantly increased psychiatric risk, particularly in younger men. Higher comorbidities and socioeconomic status appeared to mitigate this risk. Integrated mental health support should be considered in postoperative care.

INTRODUCTION

Orbital exenteration, enucleation, and evisceration are surgical procedures that can have extremely significant psychological effects on patients. These interventions are often necessitated by severe ocular trauma, cancer, or other life-threatening conditions, resulting in significant changes in appearance and visual function. Patients can experience a variety of emotional responses such as depression, anxiety, and loss of self-esteem [1]. Loss of the eye or contents of the orbit can also cause body image disturbances, withdrawal from social life, and impaired interpersonal relationships [2]. Furthermore, people can also find it hard to alteration of depth perception and spatial awareness, which can be hampered in daily activities and independence [3]. Although these surgical procedures have been known to impact the quality of life of the affected patients, the extent of their psychiatric effects remains unclear.
To address this gap, we utilized Korean healthcare big data to analyze the patterns of psychiatric disorders in patients who underwent these procedures. This analysis can contribute to improved postoperative patient management and serve as a significant reference for determining the surgical approach between eye-sacrificing and eye-sparing surgery. Our study aimed to assess the incidence of new-onset psychiatric disorders in individuals, who underwent surgery and those who did not undergo surgery using data from a nationwide healthcare database.

METHODS

Participants and procedures

Data were obtained from the Korean National Health Insurance Service (NHIS) database, a mandatory public health system. The NHIS is a single-insurer system that provides universal coverage for all residents of Korea through two major programs: National Health Insurance (covering approximately 97% of the population) and Medical Aid (covering the remaining 3%). Since 2006, Medical Aid beneficiary data have been incorporated into the unified NHIS database, allowing comprehensive population-level analyses [4,5]. The original cohort included 338,767 individuals: 16,545 in the surgery group (orbital exenteration, enucleation, or evisceration, 2003-2021) and 322,222 controls. The date of surgery served as the reference for matching. We excluded 4,098 individuals with unconfirmed data, 7,309 who died within 6 months, and 18,473 with psychiatric diagnoses (International Classification of Diseases 10 [ICD-10]: depression, anxiety, or trauma-related disorders) within 1 year pre-index. This resulted in 14,013 patients in the surgical group and 294,874 patients in the control group. Propensity score matching (1:20) using logistic regression adjusted for age, gender, socioeconomic status (SES), Charlson Comorbidity Index (CCI) [6], and the presence of diabetes and hypertension comorbidities, followed by exact matching. After excluding 44,477 unmatched cases, the final cohorts included 12,965 surgical and 251,445 control individuals. A flowchart of the study population selection is shown in Supplementary Figure 1.
All the study procedures were approved by the Institutional Review Board of Kangbuk Samsung Hospital and adhered to the Declaration of Helsinki and its future amendments and principles of Good Clinical Practice (approval number: KBSMC 2023-09-015). The requirement for informed consent was waived due to the retrospective nature of the study.

Measures

The surgeries performed in the surgery group included orbital exenteration (S5200), enucleation with tissue implantation (S5220), enucleation (S4900), and evisceration (S4880). Based on the extent of tissue removal, orbital exenteration (S5200), which involves the broader removal of orbital contents, was classified as surgery group 1, whereas enucleation or evisceration of the eyeball (S5220, S4900, and S4880) formed surgery group 2. Sociodemographic factors were age (<20, 20-39, 40-59, 60-79, ≥80 years), gender, and SES (0-5th, 6-10th, 11-15th, and 16-20th). SES was grouped into four levels according to income levels based on 20 quantiles of insurance premiums, with medical aid beneficiaries grouped into the lowest quantile [7]. Clinical variables (CCI score, history of diabetes, and history of hypertension) were also analyzed.
Psychiatric outcomes were defined using ICD-10 codes for depression (F32.x, F34.0, F34.1), anxiety (F40.x, F41.x), and trauma-related disorders (F43.x), recorded from surgery date to three years post-surgery. Pre-existing diagnoses within one year before surgery were excluded. Only psychiatry department diagnoses (code “03”) were considered.

Statistical analysis

Continuous variables were reported as means with standard deviations, and categorical variables as counts with proportions. Baseline characteristics were compared using absolute standardized differences, with values ≤0.1 considered to indicate negligible imbalance between the matched groups. This threshold is commonly applied in propensity score-matched analyses to assess covariate balance and minimize residual confounding [8]. t-tests, chi-square, or Fisher’s exact test were used as appropriate.
The incidence of psychiatric disorders (depression, anxiety, and trauma-related disorders) was calculated as cases per person-year over 3 years for the surgery and control groups and stratified by age and gender. Incidence rate ratios (IRRs) with 95% confidence intervals (CIs) were compared between groups. Kaplan-Meier curves and log-rank tests assessed survival differences. Survival rates and the number at risk were then evaluated at baseline; 1, 2, 3, and 6 months; and 1, 2, and 3 years. Subgroup analyses by gender were performed, and additional stratification was performed according to the surgery type (group 1 vs. group 2).
Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% CIs for all and specific psychiatric disorders, adjusting for key covariates. Interaction effects and proportional hazard (PH) assumptions were evaluated. Stepwise model selection minimized Akaike Information Criterion (AIC). Gender-stratified and surgery-type subgroup analyses were conducted.
All statistical tests were two-tailed, and statistical significance was set at p<0.05. SAS 9.4 (SAS Institute Inc., 2013)/R 4.3.2 (R Core Team, 2023) was used for statistical analyses.

RESULTS

Participants’ characteristics

Baseline characteristics before and after matching are shown in Table 1. Before matching, the surgery group had lower SES and more comorbidities. All standardized mean differences post-matching were less than 0.1. Among matched patients, 280 were in surgery group 1 and 12,685 in group 2.

Effect of surgery on psychiatric comorbidity

Table 2 summarizes psychiatric outcomes. Psychiatric disorders occurred in 808 surgery patients and 10,192 controls (incidence rates [IR]: 7.20 vs. 4.42/1,000 person-years; IRR: 1.63 [95% CI, 1.52-1.75]). The surgery group showed higher IRs for depression (IRR: 1.73 [95% CI, 1.58-1.90]), anxiety (IRR: 1.50 [95% CI, 1.35-1.67]), and trauma-related disorders (IRR: 1.85 [95% CI, 1.45-2.35]). Kaplan-Meier analyses (Figure 1) indicated shorter time to onset in the surgery group (logrank p<0.001). Age-stratified IRRs were highest in the 20-40 group (2.15 [95% CI, 1.75-2.64]), decreasing with age. Gender-stratified IRRs were higher in men (1.77 [95% CI, 1.60-1.95]) than women (1.48 [95% CI, 1.33-1.64]), with survival curves differing significantly by group and gender (Figure 2).
Surgery group 1 showed more trauma-related disorders, while group 2 had broader psychiatric diagnoses (Supplementary Table 1). IRRs were higher in both vs. controls, particularly in group 1 (4.79 vs. 1.59). Kaplan-Meier analyses confirmed higher risk of psychiatric disease onset in both groups, especially group 1 (Supplementary Figure 2). Gender-stratified patterns were stronger in men, especially in group 1 (Supplementary Figure 3).

Cox-PH regression analysis

Table 3 presents gender-stratified Cox models. In men, surgery increased risk for total psychiatric disorders (HR: 2.66 [95% CI, 2.07-3.42]), anxiety (1.73 [95% CI, 1.31-2.29]), and trauma-related diseases (3.67 [95% CI, 2.28-5.91]), but not on depression (1.35 [95% CI, 0.32-5.67]). In women, surgery raised risk across all disorders: total psychiatric disorders (2.74 [95% CI, 2.11-3.57]), depression (2.42 [95% CI, 1.79-3.28]), anxiety (2.70 [95% CI, 1.89-3.87]), and trauma-related diseases (2.90 [95% CI, 1.54-5.47]).
Time-stratified AIC-based models showed declining HRs over time: in men, psychiatric risks decreased after 6-12 months; in women, depression and total disorders declined after 6 months, with anxiety and trauma-related disorders after 12 months. Surgery-CCI interactions showed a protective effect with higher comorbidity, except for trauma-related disorders. In men, HRs were 0.91 (95% CI, 0.87-0.95) for total psychiatric disorders, 0.91 (95% CI, 0.86-0.97) for depression, and 0.90 (95% CI, 0.85-0.96) for anxiety; in women, the corresponding HRs were 0.90 (95% CI, 0.85-0.95), 0.89 (95% CI, 0.83-0.95), and 0.91 (95% CI, 0.84-0.97). Surgery-SES interactions were significant for total psychiatric disorders and depression in men and for total psychiatric disorders and anxiety in women, with HRs in the SES 16-20 group at 0.76 (95% CI, 0.59-0.97) and 0.59 (95% CI, 0.43-0.82) for men, and 0.68 (95% CI, 0.52-0.90) and 0.65 (95% CI, 0.45-0.94) for women. IRR analysis showed that the effect of surgery diminished with age (≥20 years), with a significant trend observed only for depression in men.
Stratified analyses (Supplementary Table 2) showed higher risks in both surgery groups vs. controls, with group 1 showing greater impact. In men, group 1 was associated with higher HRs for total and trauma-related disorders; in women, group 2 showed consistent HRs across disorders, while group 1 affected trauma-related disorders only. Risk declined over time in group 2. Higher CCI and SES were associated with lower risk, particularly in group 2. Younger men had the highest depression-related HRs, which decreased with age. Full results are in the Supplementary Material.

DISCUSSION

This large-scale nationwide insurance claims database study aimed to assess the relationship between orbital ablative surgery and the development of psychiatric disorders such as depression, anxiety, and trauma-related disorders. The surgery group had significantly higher IRRs for all three conditions, with stronger effects in younger men (20-40 years). Patients with pre-existing comorbidities and high SES had lower HRs than those without. Wider resections were associated with higher psychiatric risk. This study examined the psychological impact of eye removal alongside social factors like SES and comorbidities, highlighting the need for integrated care strategies.
The increased incidence of psychiatric disorders, particularly trauma-related conditions, aligns with previous findings on post-surgical mental health risks [9,10]. Prior research has shown elevated depression and anxiety after major operations, reflecting the psychological burden of invasive procedures. Given that ocular removal often follows trauma [11], the high prevalence of trauma-related disorders may result from both initial injury and surgery. Surgery itself can function as a traumatic stressor [12], potentially triggering psychiatric symptoms in vulnerable individuals. Our findings underscore the complex interaction between physical trauma, surgical stress, and mental health outcomes. Psychiatric disorders were more common in surgery group 1, which underwent more extensive resections, suggesting that surgical extent influences psychiatric risk. Similar patterns have been noted in other surgical populations, where more invasive procedures are linked to worse mental health outcomes due to extended recovery periods, functional limitations, and aesthetic concerns [13]. Patients undergoing enucleation frequently report difficulties in maintaining social connections, hobbies, and occupational roles [14,15]. Nearly half experience challenges in sports and recreational activities they once enjoyed [14], contributing to poorer mental health outcomes, particularly in those receiving more extensive surgeries. Psychiatric risks declined over time. In men, HRs for psychiatric disorders, depression, and trauma-related disorders fell by 6 months, while anxiety declined after 1 year. In women, total disorders and depression decreased at 6 months; anxiety and trauma-related disorders declined more gradually. This suggests initial distress may reflect acute trauma reactions, while prolonged anxiety relates to identity challenges and long-term social adaptation—highlighting the need for extended psychological support.
Further analysis, stratified by gender and age, revealed distinct psychiatric risk patterns. Although depression, anxiety, and trauma-related disorders are more prevalent in women [16], men exhibited higher post-surgery incidence rates, particularly in the surgery group 1. Individuals aged 20-40 years had the highest incidence rates, which could be attributed to their career instability and financial insecurity. Job disruptions at this stage may have a greater impact as financial hardship is a known risk factor for mental health issues, including psychological distress and suicidal ideation [17,18]. Prior studies have suggested that job instability has a stronger psychological effect on men [19,20], which aligns with our findings that global and orbital ablative surgeries disproportionately affect younger men. Sudden income losses and employment disruptions have been linked to immediate psychological distress among younger individuals [21,22]. Lower SES was associated with higher psychiatric risk, reinforcing financial stability as a protective factor [23,24]. Limited economic resources may restrict access to rehabilitation, assistive devices, mental health care, and post-surgical distress [13]. In addition to functional impairments, the cosmetic impact of eye loss may contribute significantly to psychiatric distress. Facial appearance plays a crucial role in social interactions and self-identity, and disfigurement has been strongly associated with increased depression and anxiety, regardless of prosthetic rehabilitation [2,25]. Studies have indicated that visible facial deformities can lead to social avoidance, low self-esteem, and heightened emotional distress [26,27]. Younger individuals who are more sensitive to self-image and societal perceptions may be particularly affected [28,29]. The psychosocial burden of facial disfigurement has been shown to affect social functioning and emotional well-being, with a strong link to psychiatric disorders [27,30]. Additionally, lower education levels were correlated with higher depression levels, possibly because of reduced mental health awareness and care access [31,32]. These findings highlight the need for a multidimensional approach that integrates financial, social, and psychological support into postsurgical care. Addressing economic instability, social stigma, and functional rehabilitation may help to mitigate psychiatric distress, particularly in younger individuals and those undergoing extensive resections.
In our HR interaction analysis, only men aged 20-40 years had a significantly increased depression risk post-surgery. This effect was absent in women and other disorders, suggesting age- and gender-specific vulnerability. Several factors may contribute to this pattern. Young and middle-aged men often face strong gendered expectations and psychological pressures related to work and social roles. In Korean society, cultural norms emphasizing resilience and emotional restraint may, in part, discourage help-seeking, thereby increasing psychological strain [33,34]. However, gender differences in healthcare utilization, diagnostic patterns, and possible biological vulnerability may also underlie these findings [35-37]. Additionally, global and orbital ablative surgeries affect physical appearance, challenging masculinity and self-worth [2]. In contrast, women tended to use social support more effectively, aiding psychological recovery [38]. The greater impact in the 20-40 year age group may reflect heightened career and social pressures, where physical changes can affect professional and personal identity [39]. Career instability and self-perceived concerns may contribute to increased distress in younger man patients. Depression rather than anxiety- or trauma-related symptoms was the predominant psychological response, aligning with the long-term adaptation process to global and orbital ablative surgeries. Although anxiety and post-traumatic stress disorder like symptoms are common after acute trauma, prolonged social and occupational limitations are more likely to lead to depression [1,40]. In addition, men are more prone to internalizing distress, which may cause anxiety to progress into depression over time [41].
This study has several limitations. First, its retrospective design and reliance on claims data limited assessment of individual factors such as psychiatric history and social support. Because psychiatric diagnoses were identified through ICD-10 codes in insurance claims, potential underdiagnosis or misclassification cannot be excluded. Some patients with subclinical depression or anxiety may not have sought psychiatric care, leading to an underestimation of the true incidence of postoperative psychiatric disorders. Second, unmeasured confounders like resilience and informal support may have influenced outcomes. Third, using insurance premium quantiles for SES may have reduced precision. Fourth, the small size of surgery group 1 precluded direct matching, limiting subgroup comparisons. Fifth, although several interaction terms (time, comorbidity index, SES, and age group) were included in the Cox models, formal interaction testing between surgery and gender was not performed. Therefore, subgroup differences by gender should be interpreted with caution. Lastly, psychiatric outcomes were only tracked for three years, leaving long-term effects unknown. Future studies should include longer follow-up and broader psychosocial assessments.
In conclusion, this research identified a higher risk of psychiatric disorders among young men and those who received major resection. Financial instability, career disturbance, and appearance concern probably contributed to these findings. Using a large-scale population-based dataset with propensity score matching, this study minimized selection bias while providing insights into the roles of gender, age, SES, and surgical extent in psychiatric outcomes. These findings underscore the importance of comprehensive mental health care in ophthalmologic care, with a focus on early intervention and interdisciplinary treatment to enhance postoperative well-being.

Supplementary Materials

The Supplement is available with this article at https://doi.org/10.30773/pi.2025.0280.
Supplementary Material
pi-2025-0280-Supplementary-Material.pdf
Supplementary Table 1.
Psychiatric disorder in the surgery group compared with the matched healthy control group
pi-2025-0280-Supplementary-Table-1.pdf
Supplementary Table 2.
Cox proportional hazard regression analysis for psychiatric disorder within 3 years in the surgery group compared with the matched healthy control group
pi-2025-0280-Supplementary-Table-2.pdf
Supplementary Figure 1.
Flowchart.
pi-2025-0280-Supplementary-Fig-1.pdf
Supplementary Figure 2.
Kaplan-Meier curves illustrating the hazards for total psychiatric disorders (A), depression (B), anxiety (C), and trauma-related disease (D) over time, comparing surgery group 1, surgery group 2, and the healthy control group.
pi-2025-0280-Supplementary-Fig-2.pdf
Supplementary Figure 3.
Kaplan-Meier curves illustrating the hazards for total psychiatric disorders (A), depression (B), anxiety (C), and trauma-related diseases (D) over time, comparing surgery group 1, surgery group 2, and the healthy control group, with separate analyses for males and females. M, male; F, female.
pi-2025-0280-Supplementary-Fig-3.pdf

Notes

Availability of Data and Material

Data necessary to interpret, replicate, and build upon the methods or findings reported in this article are available upon request from the corresponding author J.H. Data are not publicly available owing to ethical restrictions that protect patient privacy and consent.

Conflicts of Interest

The authors have no potential conflicts of interest to disclose.

Author Contributions

Conceptualization: Sung Joon Cho, Jisang Han. Data curation: Sung Joon Cho, Jisang Han. Formal analysis: all authors. Funding acquisition: Jisang Han. Investigation: all authors. Methodology: all authors. Project administration: Jisang Han. Resources: Sung Joon Cho. Supervision: Sung Joon Cho, Jisang Han. Validation: all authors. Visualization: all authors. Writing—original draft: Sra Jung. Writing—review & editing: Sung Joon Cho, Jisang Han.

Funding Statement

This research was supported by a grant from the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT), Republic of Korea (Grant No. 2019R1C1C1007917). The sponsor or funding organization had no role in the design or conduct of this research.

Acknowledgments

None

Figure 1.
Kaplan-Meier curves showing how surgery impacts the hazards for total psychiatric disorders (A), depression (B), anxiety (C), and trauma-related disease (D) over time.
pi-2025-0280f1.jpg
Figure 2.
Kaplan-Meier curves showing how surgery impacts the hazards for total psychiatric disorders (A), depression (B), anxiety (C), and trauma-related disease (D) over time, with separate analyses conducted for males and females. M, male; F, female.
pi-2025-0280f2.jpg
Table 1.
Baseline sociodemographic and clinical characteristics of study participants before and after propensity score matching
Variable Before matching
After matching
Total (N=308,887) Surgery group (N=14,013) Healthy control group (N=294,874) ASD* Total (N=264,410) Surgery group (N=12,965) Healthy control group (N=251,445) ASD*
Age at index date (yr) 55.48±17.96 54.75±20.27 55.52±17.84 0.043 54.39±17.47 56.19±18.40 54.30±17.42 <0.001
 <20 11,691 (3.78) 907 (6.47) 10,784 (3.66) 9,427 (3.57) 516 (3.98) 8,911 (3.54)
 20-39 47,270 (15.30) 2,045 (14.59) 45,225 (15.34) 44,339 (16.77) 1,876 (14.47) 42,463 (16.89)
 40-59 104,463 (33.82) 4,469 (31.89) 99,994 (33.91) 93,489 (35.36) 4,259 (32.85) 89,230 (35.49)
 60-79 127,750 (41.36) 5,518 (39.38) 122,232 (41.45) 106,691 (40.35) 5,380 (41.50) 101,311 (40.29)
 ≥80 17,713 (5.73) 1,074 (7.66) 16,639 (5.64) 10,464 (3.96) 934 (7.20) 9,530 (3.79)
Gender 0.003 <0.001
 Man 188,799 (61.12) 8,608 (61.43) 180,191 (61.11) 167,307 (63.28) 8,069 (62.24) 159,238 (63.33)
 Woman 120,088 (38.88) 5,405 (38.57) 114,683 (38.89) 97,103 (36.72) 4,896 (37.76) 92,207 (36.67)
Socioeconomic status 0.105 <0.001
 0-5 (low) 74,873 (24.24) 3,955 (28.22) 70,918 (24.05) 64,086 (24.24) 3,644 (28.11) 60,442 (24.04)
 6-10 57,053 (18.47) 2,611 (18.63) 54,442 (18.46) 46,990 (17.77) 2,413 (18.61) 44,577 (17.73)
 11-15 73,777 (23.88) 3,277 (23.39) 70,500 (23.91) 62,395 (23.60) 2,994 (23.09) 59,401 (23.62)
 16-20 (high) 103,184 (33.41) 4,170 (29.76) 99,014 (33.58) 90,939 (34.39) 3,914 (30.19) 87,025 (34.61)
Charlson Comorbidity Index 1.18±1.78 2.13±2.39 1.14±1.74 0.571 0.91±1.40 1.87±2.06 0.86±1.34 <0.001
Comorbidity
 History of diabetes 53,341 (17.27) 4,724 (33.71) 48,617 (16.49) 0.172 35,929 (13.59) 4,225 (32.59) 31,704 (12.61) <0.001
 History of hypertension 94,843 (30.70) 5,468 (39.02) 89,375 (30.31) 0.087 72,554 (27.44) 5,088 (39.24) 67,466 (26.83) <0.001

Data was reported as mean±standard deviation for continuous variables and N (%) for categorical variables.

* p-value was computed by t-test for continuous variables and chi-squared test or Fisher’s exact test for categorical variables, as appropriate.

ASD, absolute standardized difference.

Table 2.
Psychiatric disorder in the surgery group compared with the matched healthy control group
Variables Surgery group
Healthy control group
IRR (95% CI)
No. of events IR (95% CI) No. of events IR (95% CI)
Total psychiatric disorders 808 7.20 (6.72-7.71) 10,192 4.42 (4.33-4.51) 1.63 (1.52-1.75)
 Depression 480 4.19 (3.82-4.57) 5,659 2.42 (2.35-2.48) 1.73 (1.58-1.90)
  Man 271 3.80 (3.37-4.27) 2,875 1.96 (1.89-2.03) 1.94 (1.71-2.19)
  Woman 209 4.82 (4.19-5.50) 2,784 3.19 (3.07-3.31) 1.51 (1.31-1.74)
 Anxiety 381 3.30 (2.98-3.65) 5,167 2.20 (2.14-2.26) 1.50 (1.35-1.67)
  Man 204 2.84 (2.47-3.25) 2,667 1.82 (1.75-1.89) 1.56 (1.35-1.80)
  Woman 177 4.06 (3.49-4.69) 2,500 2.86 (2.74-2.97) 1.42 (1.22-1.66)
 Trauma-related disease 72 0.61 (0.48-0.76) 795 0.33 (0.31-0.36) 1.85 (1.45-2.35)
  Man 48 0.66 (0.49-0.86) 459 0.31 (0.28-0.34) 2.13 (1.58-2.87)
  Woman 24 0.54 (0.35-0.78) 336 0.38 (0.34-0.42) 1.42 (0.94-2.15)
Age at index date (yr)
 <20 10 1.62 (0.81-2.85) 153 1.26 (1.07-1.47) 1.29 (0.67-2.46)
 20-39 101 4.73 (3.87-5.72) 1,080 2.20 (2.07-2.33) 2.15 (1.75-2.64)
 40-59 246 6.06 (5.33-6.84) 3,077 3.57 (3.45-3.70) 1.70 (1.49-1.93)
 60-79 391 9.82 (8.88-10.83) 5,339 6.81 (6.63-6.99) 1.44 (1.30-1.60)
 ≥80 60 14.08 (10.81-17.95) 543 11.30 (10.38-12.28) 1.25 (0.95-1.63)
Gender
 Man 449 6.41 (5.84-7.03) 5,262 3.63 (3.53-3.73) 1.77 (1.60-1.95)
 Woman 359 8.51 (7.66-9.42) 4,930 5.76 (5.60-5.92) 1.48 (1.33-1.64)

IR, incidence rate of psychiatric disorder (/1,000 person-years); IRR, incidence rate ratio; CI, confidence interval.

Table 3.
Cox proportional hazard regression analysis for psychiatric disorder within 3 years in the surgery group compared with the matched healthy control group
Variable Total psychiatric disorders
Depression
Anxiety
Trauma-related disease
HR (95% CI) p* HR (95% CI) p* HR (95% CI) p* HR (95% CI) p*
Man
 Group
  Healthy control group 1 (Reference) 1 (Reference) 1 (Reference) 1 (Reference)
  Surgery group 2.66 (2.07-3.42) <0.001 1.35 (0.32-5.67) 0.685 1.73 (1.31-2.29) <0.001 3.67 (2.28-5.91) <0.001
 Interaction with time (day)
  Surgery group: time (184-365) 0.75 (0.56-1.00) 0.049 0.53 (0.36-0.78) 0.001 1.15 (0.77-1.74) 0.497 0.38 (0.15-0.98) 0.046
  Surgery group: time (366-1,095) 0.64 (0.51-0.79) <0.001 0.48 (0.36-0.64) <0.001 0.76 (0.56-1.05) 0.095 0.35 (0.19-0.68) 0.002
 Interaction with CCI
  Surgery group: CCI 0.91 (0.87-0.95) <0.001 0.91 (0.86-0.97) 0.003 0.90 (0.85-0.96) 0.001 - -
 Interaction with SES (quantile)
  Surgery group: SES 6 to 15 0.87 (0.68-1.10) 0.236 0.73 (0.54-0.99) 0.042 - - - -
  Surgery group: SES 16 to 20 0.76 (0.59-0.97) 0.027 0.59 (0.43-0.82) 0.002 - - - -
 Interaction with age at index date (yr)
  Surgery group: age at index (20-39) - - 4.46 (1.05-18.92) 0.043 - - - -
  Surgery group: age at index (40-59) - - 3.56 (0.86-14.82) 0.081 - - - -
  Surgery group: age at index (60-79) - - 2.96 (0.71-12.33) 0.136 - - - -
  Surgery group: age at index (≥80) - - 2.35 (0.51-10.86) 0.274 - - - -
Woman
 Group
  Healthy control group 1 (Reference) 1 (Reference) 1 (Reference) 1 (Reference)
  Surgery group 2.74 (2.11-3.57) <0.001 2.42 (1.79-3.28) <0.001 2.70 (1.89-3.87) <0.001 2.90 (1.54-5.47) 0.001
 Interaction with time (day)
  Surgery group: time (184-365) 0.55 (0.39-0.76) <0.001 0.59 (0.38-0.92) 0.019 0.64 (0.40-1.01) 0.057 0.67 (0.22-2.03) 0.474
  Surgery group: time (366-1,095) 0.58 (0.46-0.73) <0.001 0.58 (0.42-0.81) 0.001 0.56 (0.40 - 0.79) 0.001 0.27 (0.10-0.69) 0.006
 Interaction with CCI
  Surgery group: CCI 0.90 (0.85-0.95) <0.001 0.89 (0.83-0.95) 0.001 0.91 (0.84-0.97) 0.006 - -
 Interaction with SES (quantile)
  Surgery group: SES 6 to 15 0.80 (0.62-1.04) 0.091 - - 0.68 (0.47-0.98) 0.038 - -
  Surgery group: SES 16 to 20 0.69 (0.52-0.90) 0.007 - - 0.65 (0.45-0.94) 0.023 - -
 Interaction with age at index date (yr)
  Surgery group: age at index (20-39) - - - - - - - -
  Surgery group: age at index (40-59) - - - - - - - -
  Surgery group: age at index (60-79) - - - - - - - -
  Surgery group: age at index (≥80) - - - - - - - -

Surgery group, S5200, S5220, S4900, S4880.

* p-value was calculated by Cox proportional hazard regression.

HR, hazard ratio; CI, confidence interval; CCI, Charlson Comorbidity Index; SES, socioeconomic status; -, not available.

REFERENCES

1. Ackuaku-Dogbe EM, Biritwum RB, Briamah ZI. Psycho-social challenges of patients following orbital exenteration. East Afr Med J 2012;89:385-389.
pmid
2. Ye J, Lou L, Jin K, Xu Y, Ye X, Moss T, et al. Vision-related quality of life and appearance concerns are associated with anxiety and depression after eye enucleation: a cross-sectional study. PLoS One 2015;10:e0136460
crossref pmid pmc
3. Jordan DR, Klapper SR. Enucleation, evisceration, secondary orbital implantation. In: Black E, Nesi F, Calvano C, Gladstone G, Levine M, editor. Smith and Nesi’s ophthalmic plastic and reconstructive surgery. New York: Springer, 2012, p. 1105-1130.

4. Lee YH, Han K, Ko SH, Ko KS, Lee KU, Taskforce Team of Diabetes Fact Sheet of the Korean Diabetes Association. Data analytic process of a nationwide population-based study using national health information database established by National Health Insurance Service. Diabetes Metab J 2016;40:79-82.
crossref pmid pmc pdf
5. Seong SC, Kim YY, Park SK, Khang YH, Kim HC, Park JH, et al. Cohort profile: the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) in Korea. BMJ Open 2017;7:e016640
crossref pmid pmc
6. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40:373-383.
crossref pmid
7. Lee SY, Lee SR, Choi EK, Han KD, Oh S, Lip GYH. Impact of socioeconomic status on emergency department visits in patients with atrial fibrillation: a nationwide population-based cohort study. J Am Heart Assoc 2022;11:e027192
crossref pmid pmc
8. Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med 2009;28:3083-3107.
crossref pmid pmc
9. McKenzie LH, Simpson J, Stewart M. A systematic review of pre-operative predictors of post-operative depression and anxiety in individuals who have undergone coronary artery bypass graft surgery. Psychol Health Med 2010;15:74-93.
crossref pmid
10. Strøm J, Bjerrum MB, Nielsen CV, Thisted CN, Nielsen TL, Laursen M, et al. Anxiety and depression in spine surgery-a systematic integrative review. Spine J 2018;18:1272-1285.
crossref pmid
11. Hartley C, Grundon RA. Diseases and surgery of the globe and orbit. In: Gilger BC, editor. Equine ophthalmology. Hoboken: John Wiley & Sons, Inc., 2022, p. 133-186.

12. Dobson GP. Addressing the global burden of trauma in major surgery. Front Surg 2015;2:43
crossref pmid pmc
13. Balakrishnan N, Agrawal S, Bhargava R, Jain V, Pushker N, Meel R, et al. Psychosocial factors among patients undergoing orbital exenteration. Clin Exp Optom 2023;106:626-632.
crossref pmid
14. Coday MP, Warner MA, Jahrling KV, Rubin PA. Acquired monocular vision: functional consequences from the patient’s perspective. Ophthalmic Plast Reconstr Surg 2002;18:56-63.
pmid
15. Ahn JM, Lee SY, Yoon JS. Health-related quality of life and emotional status of anophthalmic patients in Korea. Am J Ophthalmol 2010;149:1005-1011.e1.
crossref pmid
16. Afifi M. Gender differences in mental health. Singapore Med J 2007;48:385-391.
pmid
17. Butterworth P, Rodgers B, Windsor TD. Financial hardship, socio-economic position and depression: results from the PATH Through Life Survey. Soc Sci Med 2009;69:229-237.
crossref pmid
18. Kiely KM, Leach LS, Olesen SC, Butterworth P. How financial hardship is associated with the onset of mental health problems over time. Soc Psychiatry Psychiatr Epidemiol 2015;50:909-918.
crossref pmid pdf
19. Choi M, Lim J, Chang SS, Hwang M, Kim CS, Ki M. Financial hardship and suicide ideation: age and gender difference in a Korean panel study. J Affect Disord 2021;294:889-896.
crossref pmid
20. Elbogen EB, Lanier M, Montgomery AE, Strickland S, Wagner HR, Tsai J. Financial strain and suicide attempts in a nationally representative sample of US adults. Am J Epidemiol 2020;189:1266-1274.
crossref pmid pdf
21. Rudenstine S, McNeal K, Schulder T, Ettman CK, Hernandez M, Gvozdieva K, et al. Depression and anxiety during the COVID-19 pandemic in an urban, low-income public university sample. J Trauma Stress 2021;34:12-22.
crossref pmid pmc pdf
22. Tan Z. Socioeconomic factors that affect low-income college students’ mental health [dissertation]. Fullerton; California State University; 2024.

23. Grundy E, Sloggett A. Health inequalities in the older population: the role of personal capital, social resources and socio-economic circumstances. Soc Sci Med 2003;56:935-947.
crossref pmid
24. Lorant V, Kunst AE, Huisman M, Costa G, Mackenbach J, EU Working Group on Socio-Economic Inequalities in Health. Socio-economic inequalities in suicide: a European comparative study. Br J Psychiatry 2005;187:49-54.
crossref pmid
25. Clarke A, Rumsey N, Collin JR, Wyn-Williams M. Psychosocial distress associated with disfiguring eye conditions. Eye (Lond) 2003;17:35-40.
crossref pmid pdf
26. McBain HB, Ezra DG, Rose GE, Newman SP, Appearance Research Collaboration (ARC). The psychosocial impact of living with an ocular prosthesis. Orbit 2014;33:39-44.
crossref pmid
27. Islam S, Ahmed M, Walton GM, Dinan TG, Hoffman GR. The association between depression and anxiety disorders following facial trauma--a comparative study. Injury 2010;41:92-96.
crossref pmid
28. Wang Y. Behind South Korean cosmetic surgery: its historical causes and its intertwined relationship with Korean pop culture [dissertation] Newark, University of Delaware. 2015.

29. Watts J. China’s cosmetic surgery craze. Lancet 2004;363:958
crossref pmid
30. Macgregor FC. Facial disfigurement: problems and management of social interaction and implications for mental health. Aesthetic Plast Surg 1990;14:249-257.
crossref pmid pdf
31. Parslow RA, Jorm AF. Who uses mental health services in Australia? An analysis of data from the National Survey of Mental Health and Wellbeing. Aust N Z J Psychiatry 2000;34:997-1008.
crossref pmid pdf
32. Wang PS, Berglund P, Kessler RC. Recent care of common mental disorders in the United States: prevalence and conformance with evidence-based recommendations. J Gen Intern Med 2000;15:284-292.
crossref pmid pmc
33. Tsuya NO, Bumpass LL, Choe MK. Gender, employment, and housework in Japan, South Korea, and the United States. Review of Population and Social Policy 2000;9:195-220.

34. Simon RW. Gender, multiple roles, role meaning, and mental health. J Health Soc Behav 1995;36:182-194.
crossref pmid
35. Mohammadi S, Seyedmirzaei H, Salehi MA, Jahanshahi A, Zakavi SS, Dehghani Firouzabadi F, et al. Brain-based sex differences in depression: a systematic review of neuroimaging studies. Brain Imaging Behav 2023;17:541-569.
crossref pmid pmc pdf
36. Kang HJ, Park Y, Yoo KH, Kim KT, Kim ES, Kim JW, et al. Sex differences in the genetic architecture of depression. Sci Rep 2020;10:9927
crossref pmid pmc pdf
37. Noh JW, Kim KB, Park H, Kwon YD. Gender differences in outpatient utilization: a pooled analysis of data from the Korea Health Panel. J Womens Health (Larchmt) 2017;26:178-185.
crossref pmid
38. Reevy GM, Maslach C. Use of social support: gender and personality differences. Sex Roles 2001;44:437-459.
crossref pdf
39. Mossakowski KN. Unfulfilled expectations and symptoms of depression among young adults. Soc Sci Med 2011;73:729-736.
crossref pmid
40. Ginzburg K, Ein-Dor T, Solomon Z. Comorbidity of posttraumatic stress disorder, anxiety and depression: a 20-year longitudinal study of war veterans. J Affect Disord 2010;123:249-257.
crossref pmid
41. Rich AR, Kirkpatrick-Smith J, Bonner RL, Jans F. Gender differences in the psychosocial correlates of suicidal ideation among adolescents. Suicide Life Threat Behav 1992;22:364-373.
crossref pmid


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