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Lee, Ha, Ko, and Park: Facilitators and Barriers Associated With Mental Health Service Utilization Among Individuals With Alcohol Use Disorder in Korea

Abstract

Objective

The treatment rate for alcohol use disorder (AUD) in Korea is significantly lower than its prevalence rate. Because untreated AUD can have harmful consequences, it is important to identify the factors that contribute to individuals with AUD seeking mental health services.

Methods

We collected nationally representative data from the National Mental Health Survey of Korea 2021 and analyzed responses from 643 individuals with AUD, of which 76.8% were male. Factors related to mental health service utilization among individuals with AUD were classified into three categories: sociodemographic (such as sex, age, marital status, education, and monthly household income), clinical (including symptom severity, psychiatric comorbidity, suicidality, and physical illness), and psychological characteristics (like perceived stigma, loneliness and social isolation, and resilience). We used multiple logistic regression analyses to examine each characteristic separately and combined in a single model to determine the most significant factors.

Results

The three logistic regression models revealed that sex, psychiatric comorbidity, physical illness, and perceived stigma are significantly linked to the utilization of mental health services among individuals with AUD. Results from the comprehensive model indicated that only physical illness and perceived stigma have significant associations with mental health service utilization.

Conclusion

These findings can assist in developing targeted interventions for individuals with AUD.

INTRODUCTION

According to a report by the World Health Organization,1 alcohol consumption and binge drinking were much higher among Koreans aged over 15 years than the global average in 2016. The report indicated that the global population consumed 6.4 liters of alcohol compared to 10.2 liters consumed by Koreans. Likewise, binge drinking prevalence was 18.2% globally and 30.5% in Korea [1]. However, only 2.6% of individuals with alcohol use disorder (AUD) in Korea seek mental health services, despite a lifetime AUD prevalence of 11.6% [2]. A lower rate of mental health service utilization was also observed in a cross-cultural comparison with the U.S., with a significantly higher alcohol dependence prevalence but a significantly lower treatment-seeking rate in the U.S [3].
Untreated AUD can have harmful effects. Studies show that alcohol-related issues can increase the risk of mortality from accidents [4,5] and health conditions like alcoholic liver disease [6] and coronary heart disease [7]. In addition, AUD imposes a burden on both the government and society. For example, in Korea, the socioeconomic cost of alcohol use was KRW 15.806 trillion in 2019, including medical and non-medical care expenses, lost productivity, and potential earnings lost due to premature death [8]. These findings highlight the importance of early intervention for AUD and research on the factors influencing access to mental health services.
Factors influencing the utilization of mental health services among individuals with AUD vary. Typically, demographic factors such as age, sex, marital status, and level of education have been identified as contributing factors [9,10]. In the U.S., women diagnosed with alcohol abuse and dependence were less likely to seek help compared to men [10]. Similarly, in South Korea, women with AUD were less likely to visit mental health professionals than men [11]. Meanwhile, some researchers found that women with 12-month AUDs were more likely to receive mental health treatment overall, but men were more likely to receive treatment specifically for AUD [12]. Apart from sex, other demographic factors have shown different associations with mental health service use across countries. For example, treatment-seekers in the U.S. tend to be older, in committed relationships, and have more years of education [13]. In contrast, Koreans seeking treatment for alcohol dependence are more likely to be single, with no significant differences in age and educational attainment [14]. Additionally, clinical conditions serve as motivators for seeking professional help. Individuals with AUD who have higher severity [13], more symptoms of AUD [15], or other psychiatric disorders [3,14,16] are more likely to seek mental health services. Suicidal behavior is also linked to the use of mental health services; studies have shown that suicide attempt survivors with alcohol dependence are more likely to seek psychiatric treatment [17,18]. Physical illness can also play a role in seeking mental health services, with individuals having more comorbid physical or fatal diseases being more inclined to seek treatment for AUD compared to those with fewer or no physical ailments [11].
Psychological factors may also influence the use of mental health services. Stigma, which includes stereotypes, prejudice, and discrimination toward certain groups [19], has been identified as a major barrier to seeking treatment for those with AUD [20]. The stigma perceived towards individuals with alcohol-related issues is higher when compared to those with other mental disorders [21,22]. It is essential to explore the connection between loneliness and the utilization of mental health services by people with AUD. Loneliness is a risk factor for alcohol-related problems [23] and is prevalent among individuals with substance dependence seeking treatment for substance use [24]. Resilience, as a protective factor against mental disorders [25], is another psychological factor to consider. Research indicates that higher resilience is associated with a reduced likelihood of developing AUD [26], as well as a lower risk of relapse in highly resilient patients compared to those with lower resilience [27]. While there is limited research on the direct link between resilience and mental health service utilization, previous findings suggest that resilience could impact the willingness to seek help for depressive symptoms [28].
While various studies have attempted to identify factors contributing to mental health service use in individuals with AUD, most have focused on isolated relationships instead of comprehensively examining contributors. It is crucial to pinpoint specific determinants to inform interventions that encourage mental health service utilization. Therefore, this study thoroughly investigated contributors identified in previous research, categorizing them into sociodemographic, clinical, and psychological characteristics. These classifications are crucial as interventions may vary based on the type of characteristic. The study aimed to explore the relationship between mental health service use and each category of characteristics, as well as identify factors with the strongest association with mental health service use.

METHODS

Ethics

This study was conducted in accordance with the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of the National Center for Mental Health (No. 116271-2022-17). Since the National Mental Health Survey of Korea (NMHSK) 2021 contains only de-identified data, the need for informed consent was waived.

Data source and study sample

Data for this study were obtained from the NMHSK 2021, which aimed to investigate the prevalence of mental disorders and their risk factors in the Korean general population. A nationally representative sample of non-institutionalized residents aged 18-79 was selected for the study using a stratified multi-stage sampling method based on Population and Housing Census data provided by Statistics Korea. A total of 5,511 respondents took part in face-to-face interviews for the survey. More information on the study design and sampling procedure can be found elsewhere [2].
From the total respondents, we focused on 643 individuals (76.8% male) who met the diagnostic criteria for lifetime AUD, including alcohol abuse and/or dependence, as defined by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) [29].

Measures

We categorized the potential contributing factors influencing mental health service use among individuals with AUD into three groups. The first group consists of sociodemographic characteristics such as sex, age, marital status, education, and monthly household income. The second group comprises clinical characteristics like AUD severity, psychiatric comorbidities, suicidality, and physical illness. Lastly, psychological characteristics include perceived stigma, loneliness and social isolation, and resilience.

Clinical characteristics

The NMHSK 2021 utilized the Korean version of the Composite International Diagnostic Interview (K-CIDI) [30] 2.1 to assess mental disorders. K-CIDI 2.1 is specifically designed to diagnose common mental and substance disorders according to both DSM-IV and the International Classification of Diseases, Tenth Revision [31]. Trained lay interviewers conducted fully structured interviews as part of the diagnostic process. Research has indicated that K-CIDI 2.1 exhibits appropriate psychometric properties [30]. While capable of diagnosing a range of mental disorders, certain conditions such as schizophrenia and bipolar disorder were not included in the NMHSK 2021 study. Diagnoses in the study encompassed major depressive disorder, dysthymia, panic disorder, agoraphobia, social anxiety disorder, generalized anxiety disorder, specific phobia, obsessive-compulsive disorder, post-traumatic stress disorder, AUD, and nicotine use disorder.
Diagnoses from K-CIDI 2.1 were used to determine if psychiatric comorbidities were present. These comorbid psychiatric disorders were defined as having one or more mental disorders, except for nicotine use disorder with AUD, during an individual’s lifetime. Additionally, K-CIDI 2.1 assessed the severity of AUD. Changes in diagnoses from the fourth to fifth editions of the DSM occurred. In the fourth edition, alcohol abuse and dependence were separate diagnoses, which required meeting one of four and three of seven diagnostic criteria, respectively. However, in the fifth edition, these diagnoses were combined under AUD, for which the threshold is at least 2 out of 11 symptom criteria. DSM-5 outlined the severity of AUD as mild with two to three symptoms, moderate with four to five symptoms, and severe with six or more symptoms. Since K-CIDI 2.1 follows DSM-IV, we categorized one to three symptoms of AUD as mild, four to five as moderate, and six or more as severe.
Suicidality was assessed by inquiring if respondents had ever had suicidal thoughts, plans, or attempts in their lifetime. If they confirmed at least one of these experiences, they were classified as having suicidal tendencies. The presence of physical illness was determined by whether the respondent had been diagnosed with one or more of the following conditions: hypertension, hyperlipidemia, stroke, myocardial infarction, angina pectoris, diabetes mellitus, or cancer.

Psychological characteristics

To assess perceived stigma, the NMHSK 2021 utilized a Korean-translated and modified version of the Devaluation-Discrimination Measure [32], which assesses the public’s beliefs regarding devaluation and discrimination towards individuals with mental disorders [33]. The measure included statements focusing on AUD (e.g., “Most people would accept a former AUD patient as a close friend”). It consisted of 12 items rated on a six-point Likert scale, ranging from 0 (strongly agree) to 5 (strongly disagree). The total score was calculated by averaging all item scores, with a higher score indicating a higher level of stigma. The scale demonstrated good internal consistency in this study (Cronbach’s α=0.81).
The Loneliness and Social Isolation Scale [34] was used to assess both loneliness and social isolation. It comprises six items rated on a scale from 0 (rarely) to 3 (very much), inquiring about social connections and time spent communicating with family or friends. The scale demonstrated adequate reliability and validity [34], with acceptable internal consistency in the present study (Cronbach’s α=0.77). The total score was calculated by summing the scores of each item, and a higher score indicated an increased level of loneliness and social isolation for the respondent.
Resilience was evaluated using the Connor-Davidson Resilience Scale (CD-RISC) [35]. The Korean version of CD-RISC was validated among individuals in high-stress work environments and at risk of psychological trauma, demonstrating suitable psychometric properties [36]. Originally a 25-item, 5-factor model [35], questions have been raised regarding its factor structure. Campbell-Sills and Stein [37] established the construct validity of the CD-RISC with a 10-item single-factor model in a large sample, which NMHSK 2021 adopted. This scale, rated on a five-point Likert scale from 0 (not true at all) to 4 (true nearly all the time), calculates the total score by summing item scores, with higher scores reflecting greater resilience. The internal consistency in the present study was excellent (Cronbach’s α=0.94).

Mental health service use

Individuals’ mental health service use was defined as seeking help from a professional for mental health issues at any point in their life. The professionals consulted included: 1) psychiatrists, 2) professionals in mental health specialty settings such as psychologists, nurses, and social workers, and 3) non-psychiatric physicians. This data was gathered by asking participants if they had ever sought help from a professional and to specify the type of professional they had consulted.

Statistical analysis

The analysis was conducted in three steps. Initially, descriptive statistics were utilized to compare patterns among respondents who had sought mental health services and those who had not. Chi-square tests under the Rao-Scott correction were conducted to evaluate the differences in categorical variables (such as sociodemographic and clinical characteristics), while t-tests were performed for continuous variables (including psychological characteristics). Subsequently, three separate multiple logistic regression analyses were executed to explore the relationship between each characteristic category and the use of mental health services. In the final step, a single model multiple logistic regression analysis was performed, incorporating all characteristics to determine the most influential contributing factors while controlling for each other.
To assess the variance in mental health service use explained by characteristic categories, Nagelkerke’s R2 was reported. Odds ratios (ORs) were utilized to explore the associations between variables and the probability of using mental health services. We estimated 95% confidence intervals (CIs) to evaluate the statistical significance of the ORs, with a p-value <0.05 indicating significance. The analyses were performed using a complex sample design, and Statistical Package for the Social Sciences (SPSS; IBM Corp., Armonk, NY, USA) version 26 was used for all analyses.

RESULTS

Only 9.8% of respondents with AUD used mental health services during their lifetime. The characteristics of respondents who received and those who did not receive mental health service are summarized in Table 1. Compared with mental health service users, respondents who had not utilized mental health services were more likely to be males (78.5%) and to have never been diagnosed with other psychiatric disorders (79.6%) or physical illness (69.1%). They were also less likely to have experienced suicidal ideation or behavior (80.4%). Respondents who had never used mental health services reported higher perceived stigma (Wald F=4.986, p<0.05), lower loneliness and social isolation (Wald F=10.291, p<0.01), and higher resilience (Wald F=23.024, p<0.001) than mental health service users.

Association between mental health service use and each characteristic category

The results of the logistic regression analysis for the three characteristic categories are displayed in Table 2. Among the sociodemographic characteristics, sex was the sole significant variable. Males were less inclined to use mental health services compared to females (OR=0.386; 95% CI, 0.198-0.752). The model showed Nagelkerke’s R2=0.059, explaining 5.9% of the variance attributed to sociodemographic characteristics. In terms of clinical characteristics, comorbid psychiatric disorders and physical illnesses showed significant associations with mental health service utilization. Having other psychiatric disorders along with AUD (OR=19.101; 95% CI, 8.930-40.855) or a history of physical illness diagnosis (OR=2.006; 95% CI, 1.019-3.950) heightened the likelihood of seeking mental health services. Nagelkerke’s R2 was estimated to be 0.348, with clinical characteristics accounting for 34.8% of the variance.
Perceived stigma was the only psychological characteristic significantly linked to mental health service utilization. The likelihood of using these services decreases as perceived stigma levels increase (OR=0.264; 95% CI, 0.101-0.691). Nagelkerke’s R2 indicated that 14.4% of the variance was explained by psychological characteristics.

Association between mental health service use and the overall characteristics

The right-hand column of Table 2 displays the results of multiple logistic regression regarding overall characteristics as predictors. Among these variables, physical illness and perceived stigma showed significant associations with mental health service utilization. History of physical illness was linked to increased likelihood of mental health service utilization (OR=13.075; 95% CI, 1.938-88.233). Meanwhile, greater perceived stigma was associated with reduced likelihood of using mental health services (OR=0.194; 95% CI, 0.042-0.889). All characteristics together explained 47.9% of the variance (Nagelkerke’s R2=0.479).

DISCUSSION

This study aimed to identify the factors contributing to mental health service use among individuals with AUD using nationally representative data collected in Korea. We explored the relationship between mental health service use and sociodemographic, clinical, and psychological characteristics, investigating the most influential contributors among all characteristics.
Our findings showed that sex was the sole significant sociodemographic factor associated with mental health service utilization among individuals with AUD. Specifically, men were less inclined to seek help compared to females. This contradicts a prior study where Korean females were at a higher risk of not receiving treatment [11]. The discrepancy may be due to the different data sources used in the two studies. Rim et al. [11] analyzed administrative data from the National Health Insurance in Korea, comprising diagnosis and treatment information for health insurance subscribers and recipients. In contrast, we relied on self-reported experiences of mental health service use. Participants in our study who sought mental health services may not have been included in administrative data if those services were not covered by health insurance. The National Health Insurance in Korea only covers medical treatment by psychiatrists and physicians, excluding out-of-hospital services by mental health professionals. Thus, Korean females might be more inclined to seek out-of-hospital services, such as community addiction management centers, for addressing mental health issues.
Meanwhile, respondents with AUD who had comorbid psychiatric disorders or were diagnosed with a physical illness were more likely to utilize mental health services. These findings align with previous studies [3,11,14,16]. Individuals with AUD may seek help from mental health professionals to address other psychiatric disorders rather than the alcohol-related issue itself or may be referred to psychiatrists when seeking treatment for physical conditions. Previous research has shown that the initial contact for alcohol-related problems may not be determined by AUD but by mood disorders such as major depressive disorder, dysthymia, and bipolar disorder [38]. While past studies indicated that individuals with more severe AUD symptoms are more inclined to seek assistance [13,15], our research did not support this. One possible explanation for these conflicting results could be cultural variations in diagnostic criteria determining AUD severity. In Americans, higher AUD severity can be determined by “withdrawal” symptoms [39,40], whereas in Thailand, greater alcohol dependence severity was indicated by “time spent drinking” symptoms [41]. Therefore, the inconsistent results in this study may be due to the cultural difference from previous studies.
Although previous studies have suggested a close relationship between suicide attempts and the use of mental health services [17,18,42], our study did not find any such connection. A prior study indicated that the high lethality of suicide attempts is the only significant risk factor for referral to aftercare among Korean suicide attempt survivors who visit emergency departments [43]. Furthermore, individuals who have attempted suicide and consumed alcohol tend to exhibit less severe lethality and intention to die compared to those who have not consumed alcohol [43]. Consequently, the presence of both AUD and suicidality does not appear to lead to increased use of mental health services. Nevertheless, AUD is likely a contributing factor to suicidality.
In the current study, perceived stigma emerged as the sole significant factor linked to mental health service utilization. Past research has consistently shown that perceived stigma acts as a barrier to mental health service access for individuals with AUD, in line with our findings [20,44]. Corrigan [45] outlined the process by which the stigmatization of mental illness leads to treatment avoidance. Public stereotypes, prejudice, and discrimination can negatively impact the lives of those labeled as mentally ill in areas such as employment, housing, and healthcare benefits. To avoid such repercussions, individuals with mental disorders may be hesitant to accept the “mentally ill” label, resulting in a reluctance to seek mental health services [45].
Meanwhile, loneliness and social isolation, and resilience did not exhibit a significant association with mental health service utilization in our study. Although previous research has not directly explored the connection between these factors, there have been indirect implications [24,28]. However, our findings indicated that mental health service users displayed lower resilience and higher levels of loneliness and social isolation compared to those who had never received mental health services, suggesting the presence of a potential third variable. A qualitative study proposed that stigma might deter individuals from seeking help, leading to reduced resilience [46], indicating a negative relationship between stigma and resilience. Since stigma and resilience were analyzed together in one model, their effects may have offset each other (suppression effect). Additionally, there may be circumstances under which an association between loneliness and social isolation, and mental health service use could manifest. For instance, individuals experiencing depression, high levels of loneliness and social isolation might exhibit more help-seeking behavior if they have frequent interactions with family members or perceive an external locus of control [47]. This finding underscores the role of moderators in the relationship between loneliness, social isolation, and mental health service utilization among individuals with AUD.
Our findings emphasize the importance of interventions to increase mental health service utilization among individuals with AUD and identify the specific targeted factors. Reducing stigma towards individuals with AUD can be an effective approach. For instance, a school-based program providing knowledge about alcohol misuse, dispelling myths, and teaching how to support friends seeking help has been proven to reduce stigmatizing attitudes in adolescents. This decrease in stigma led to a greater intention among adolescents to support peers in seeking help from family or professionals [48]. Moreover, therapeutic patient education focused on boosting selfefficacy in patients with AUD helped reduce internalized stigma compared to standard treatment [49]. The utilization of mental health services was also linked to a diagnosis of physical illness. Therefore, promoting service usage among individuals with AUD could be enhanced if non-psychiatric physicians sensitively identified alcohol-related issues and actively sought psychiatric consultation or referrals for treatment.
This study has limitations. First, we examined the associations between mental health service use and characteristic categories cross-sectionally. Therefore, future research should replicate these results using longitudinal data to verify causal relationships. Second, mental health service use in this study encompassed services for a wide range of psychiatric problems, not just AUD. Despite using large-scale data, the number of respondents with AUD seeking mental health services specifically for alcohol-related issues was insufficient for statistical analysis. Obtaining additional information on the type, duration, intensity, and fidelity of services used by respondents was challenging. The data collection on mental health service use was limited to a few questions asking if participants used the service and who the provider was. Therefore, future research should utilize objective resources like medical records and include individuals with AUD from various settings to enhance the understanding of factors contributing to mental health service use in AUD.
This study examined facilitators and barriers to mental health service use among individuals with AUD, finding that a history of physical illness and perceived stigma were key contributors. Specifically, individuals with AUD who had a lifetime history of physical illness were more likely to use mental health services compared to those without such a diagnosis. Moreover, a high level of perceived stigma among individuals with AUD was linked to a decreased likelihood of using mental health services. These findings shed light on mental health service utilization patterns among Koreans with AUD, offering insights for developing evidence-based interventions and policies to promote the use of mental health services.

Notes

Availability of Data and Material

The data that support the findings of this study are openly available in the Mental Health Survey of Korea repository at https://mhs.ncmh.go.kr/.

Conflicts of Interest

Subin Park, a contributing editor of the Psychiatry Investigation, was not involved in the editorial evaluation or decision to publish this article. All remaining authors have declared no conflicts of interest.

Author Contributions

Conceptualization: Subin Park. Data curation: Eun Sol Lee, Yujeong Ha, Young-Mi Ko. Formal analysis: Eun Sol Lee. Funding acquisition: Subin Park. Investigation: Eun Sol Lee, Yujeong Ha, Young-Mi Ko. Methodology: Eun Sol Lee, Yujeong Ha, Young-Mi Ko. Project administration: Subin Park. Supervision: Subin Park. Writing—original draft: all authors. Writing—review & editing: Subin Park, Eun Sol Lee.

Funding Statement

This work was supported by an intramural research grant from the National Center for Mental Health, Ministry of Health & Welfare, Republic of Korea [R2023-A].

ACKNOWLEDGEMENTS

None

Table 1.
Characteristics of respondents with lifetime alcohol use disorder
Mental health service use
Total Statistics2/F)
Yes No
Sociodemographic characteristics
 Sex 7.660**
  Male 42 (60.6) 457 (78.5) 499 (76.8)
  Female 21 (39.4) 123 (21.5) 144 (23.2)
 Age (yr) 1.671
  18-39 15 (28.8) 153 (29.8) 168 (29.7)
  40-59 21 (35.8) 272 (46.4) 293 (45.4)
  60-79 27 (35.4) 155 (23.8) 182 (24.9)
 Marital status 1.794
  Married or cohabitating 36 (53.1) 380 (64.4) 416 (63.3)
  Not living with partner 13 (18.7) 80 (10.6) 93 (11.4)
  Never married 14 (28.2) 120 (25.0) 134 (25.3)
 Education 0.953
  ≤Secondary school 13 (16.7) 82 (12.9) 95 (13.3)
  High school 29 (48.5) 260 (43.0) 289 (43.6)
  ≥College/university 21 (34.8) 238 (44.1) 259 (43.2)
 Monthly household income (USD) 0.711
  <1,500 13 (15.5) 72 (10.5) 85 (10.9)
  <3,760 32 (47.9) 301 (47.1) 333 (47.2)
  ≥3,760 17 (36.6) 202 (42.4) 219 (41.9)
Clinical characteristics
 Severity 2.879
  Severe 32 (53.5) 215 (37.3) 247 (38.9)
  Moderate 18 (26.5) 178 (30.1) 196 (29.7)
  Mild 13 (20.0) 187 (32.6) 200 (31.4)
 Pyschiatric comorbidity 136.589***
  Yes 51 (85.3) 116 (20.4) 167 (26.7)
  No 12 (14.7) 464 (79.6) 476 (73.3)
 Suicidality 31.306***
  Yes 37 (55.0) 112 (19.6) 149 (23.1)
  No 26 (45.0) 468 (80.4) 494 (76.9)
 Physical illness 4.014*
  Yes 33 (45.2) 184 (30.9) 217 (32.3)
  No 30 (54.8) 396 (69.1) 426 (67.7)
Psychological characteristics
 Perceived stigma 2.11±0.67 2.56±0.71 2.53±0.71 4.986*
 Loneliness and social isolation 8.21±3.35 6.45±3.49 6.62±3.52 10.291**
 Resilience 19.40±6.40 23.93±7.36 23.48±7.40 23.024***

Values are presented as unweighted number (weighted %) or mean±standard deviation.

* p<0.05;

** p<0.01;

*** p<0.001;

separated, divorced, or widowed;

chi-square tests under the Rao-Scott correction were conducted for categorical variables, while Wald F values from t-test were provided for continuous variables

Table 2.
Multiple logistic regression analysis results by mental health service use
Individual model
Comprehensive model
OR 95% CI p OR 95% CI p
Sociodemographic characteristics
 Sex 0.005 0.778
  Male 0.386** 0.198-0.752 0.812 0.185-3.566
  Female 1.000 1.000
 Age (yr) 0.106 0.338
  18-39 0.302 0.087-1.048 0.129 0.008-2.068
  40-59 0.452 0.194-1.054 0.654 0.115-3.721
  60-79 1.000 1.000
 Marital status 0.285 0.078
  Married or cohabitating 0.539 0.169-1.714 1.880 0.227-15.597
  Not living with partner 0.897 0.261-3.080 6.191 1.075-35.675
  Never married 1.000 1.000
 Education 0.921 0.906
  ≤Secondary school 0.855 0.332-2.197 0.969 0.032-29.752
  High school 1.005 0.486-2.078 0.670 0.101-4.447
  ≥College/university 1.000 1.000
 Monthly household income (USD) 0.900 0.450
  <1,500 1.232 0.467-3.248 7.270 0.324-163.190
  <3,760 1.059 0.505-2.223 2.709 0.278-26.421
  ≥3,760 1.000 1.000
Clinical characteristics
 Severity 0.727 0.169
  Severe 1.290 0.569-2.923 0.991 0.151-6.518
  Moderate 0.996 0.351-2.823 0.163 0.013-2.004
  Mild 1.000 1.000
 Psychiatric comorbidity 19.101*** 8.930-40.855 0.000 7.428 0.839-65.732 0.071
 Suicidality 1.501 0.727-3.101 0.271 4.497 0.597-33.890 0.141
 Physical illness 2.006* 1.019-3.950 0.044 13.075** 1.938-88.233 0.009
Psychological characteristics
 Perceived stigma 0.264** 0.101-0.691 0.007 0.194* 0.042-0.889 0.035
 Loneliness and social isolation 1.107 0.913-1.342 0.298 1.048 0.843-1.303 0.667
 Resilience 0.931 0.835-1.038 0.197 0.910 0.789-1.049 0.190

The relationship between mental health service utilization and various characteristic categories was analyzed using individual models, while the overall association between mental health service utilization and characteristics was examined through a comprehensive model. Mental health service use reference=no.

* p<0.05;

** p<0.01;

*** p<0.001;

referent group.

OR, odds ratio; CI, confidence interval

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