Risk of Psychiatric Morbidity in People Living With Human Immunodeficiency Virus: A Population-Based Cohort Study

Tak Kyu Oh; Kyoung-Ho Song; Eunjeong Heo; Hye Yoon Park; In-Ae Song

doi:10.30773/pi.2025.0074

Psychiatry Investig > Volume 23(1); 2026 > Article

Oh, Song, Heo, Park, and Song: Risk of Psychiatric Morbidity in People Living With Human Immunodeficiency Virus: A Population-Based Cohort Study

Original Article

Psychiatry Investigation 2026;23(1):38-47.

Published online: January 7, 2026

DOI: https://doi.org/10.30773/pi.2025.0074

Risk of Psychiatric Morbidity in People Living With Human Immunodeficiency Virus: A Population-Based Cohort Study

Tak Kyu Oh^1,²

, Kyoung-Ho Song^3,⁴

, Eunjeong Heo⁵

, Hye Yoon Park⁶

, In-Ae Song^1,²

¹Department of Anesthesiology and Pain Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea

²Department of Anesthesiology and Pain Medicine, College of Medicine, Seoul National University, Seoul, Republic of Korea

³Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea

⁴Department of Internal Medicine, College of Medicine, Seoul National University, Seoul, Republic of Korea

⁵Department of Pharmacy, Seoul National University Bundang Hospital, Seongnam, Republic of Korea

⁶Department of Psychiatry, Seoul National University Hospital, Seongnam, Republic of Korea

Correspondence: In-Ae Song, MD, PhD Department of Anesthesiology and Pain Medicine, Seoul National University Bundang Hospital, 82 Gumi-ro 173beon-gil, Bundang-gu, Seongnam 13620, Republic of Korea Tel: +82-31-787-7499, Fax: +82-31-787-4063, E-mail: songoficu@outlook.kr

Received February 25, 2025 Revised September 1, 2025 Accepted October 15, 2025

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objective

The incidence of psychiatric illnesses might be higher in individuals people living with human immunodeficiency virus (PLWH) than in individuals without human immunodeficiency virus (HIV). We aimed to investigate whether PLWH had different risks of psychiatric morbidities in comparison to non-HIV-infected individuals.

Methods

This study included all PLWH in South Korea between January 1, 2017 and December 31, 2017. The control group, which had never been diagnosed with HIV, was selected using a 1:10 stratified random sampling technique, considering age and sex. The endpoint of this study was new diagnosis of psychiatric morbidities which were diagnosed from January 1, 2018 to December 31, 2022.

Results

After conducting a 1:5 propensity score (PS) matching process, the final analysis comprised a group of 17,815 PLWH and 78,021 control subjects. In the PS-matched cohort, the incidence rate of psychiatric morbidity in PLWH was 5,677.7 per 100,000 per year, whereas that in the controls was 4,926.3 per 100,000 per year. In Cox regression in the PS-matched cohort, PLWH showed 17% (hazard ratio, 1.17; 95% confidence interval, 1.14-1.21; p<0.001) higher risk of psychiatric morbidity than controls. Specifically, PLWH showed higher risk of depression, mania, bipolar disorder, insomnia disorder, substance use disorder, and schizophrenia compared to controls.

Conclusion

PLWH had a greater likelihood of experiencing psychiatric morbidities compared to those without HIV in South Korea. Our research findings indicate the importance of public health policy in addressing the declining mental health of individuals living with HIV.

Keywords: Acquired immune deficiency syndrome, Human immunodeficiency virus, Mental disorders, Cohort studies

INTRODUCTION

Acquired immune deficiency syndrome (AIDS) was first reported in the 1980s [1]. Since then, numerous fatalities related to human immunodeficiency virus (HIV) have been reported, including those caused by opportunistic infections and acute infection syndrome [2]. According to the Joint United Nations Programme on HIV/AIDS, the global report for 2020 revealed 1.5 million new cases of HIV infection and a staggering 37 [7]. million individuals living with HIV.3 The global death toll from AIDS-related illnesses since the start of the HIV pandemic has been 36.3 million [3]. In contrast, the life expectancy of people living with HIV (PLWH) has significantly increased due to antiretroviral therapy (ART). HIV/AIDS is currently a major global health concern since ART does not provide a cure for individuals with AIDS [4,5].

Psychiatric morbidities such as depression are a significant concern for PLWH [6]. According to a recent study that analyzed multiple data sources, the worldwide occurrence of depression was estimated to be 35% [7]. This is often attributed to social prejudice and stigma [8]. Moreover, PLWH also experience a range of psychiatric conditions, including anxiety disorder, substance use disorder, psychosis, and bipolar disorder, which are evident in clinical presentations [9]. In a recent cohort study in the United Kingdom that used propensity score (PS) matching, the incidence of psychiatric morbidity was higher in PLWH than in individuals without HIV [10]. Nevertheless, there is a lack of information on this matter, and further research is required.

In this study, we use the term psychiatric morbidity to refer to the occurrence of clinically diagnosed psychiatric disorders identified through the national health insurance database. Psychiatric morbidity was defined as the incident onset of major psychiatric conditions commonly reported among PLWH in prior literature, including mood, anxiety, psychotic, substance use, and sleep disorders [9,10]. Therefore, we aimed to examine whether the incidence of psychiatric morbidity in PLWH differed from individuals without HIV in South Korea. We hypothesized that the risk of psychiatric morbidity was increased in PLWH.

METHODS

Study design and ethical statement

This population-based cohort study followed the recommendations of Strengthening the Reporting of Observational Studies in Epidemiology [11]. As the study protocol utilized publicly accessible data, the Institutional Review Board (IRB) deemed it necessary to hold discussions. The project was assigned IRB number X-2306-837-902 and received clearance on June 20, 2023. The study protocol was approved by the National Health Insurance Service (NHIS; approval number: NHIS-2024-1-021), and permission to access the data was granted. The IRB determined that informed consent was not necessary because anonymized data were retrospectively collected.

Data source

In South Korea, the NHIS is responsible for providing public health insurance. It compiles and manages a wide range of data on prescription drug orders, healthcare operations, and disease diagnoses. Data were organized and categorized using the International Classification of Diseases, 10th edition (ICD-10) codes commonly used in medical research. Enrollment in the NHIS program is mandatory for all residents of South Korea, including foreign nationals who have resided there for over six months. The government provides financial assistance for the treatment or testing covered by NHIS enrollees based on the severity of their conditions. Although most healthcare providers operate privately, government regulations exist regarding prescriptions, treatments, and fees. Unfortunately, there is a lack of information regarding the potential oversight by doctors in terms of prescriptions or diagnoses. In addition to the main diagnosis, we considered all ICD-10 diagnoses. Information on the socioeconomic factors and overall mortality rates can be found in the NHIS database [12].

PLWH in South Korea

The management of HIV/AIDS is the responsibility of the Korea Disease Control and Prevention Agency in South Korea, as it is classified as a Group 3 infectious disease according to the country’s law. HIV-positive patients were screened at every healthcare facility using enzyme-linked immunosorbent assay (ELISA). The Korea National Institute of Health, along with 17 Institutes of Health and Environment across the country, conduct Western blotting tests to verify HIV infection when the ELISA test suggests HIV infection. Upon receiving a positive HIV test result, it is the doctor’s responsibility to inform the public health center immediately. To be associated with a physician, one of the ICD-10 codes (B20-B24) listed in the NHIS database must be selected. PLWH are entitled to state reimbursement for all related medical and treatment expenses as the infection is legally categorized as a Group 3 infectious disease. One aspect of this approach is the prescription of ART.

Study population (PWLH and control)

This study included all PLWH diagnosed with HIV in South Korea between January 1, 2017 and December 31, 2017. The NHIS includes all diagnoses of South Koreans who registered in public health insurance between 2002 and 2017. Therefore, PLWH in 2017 refers to persons diagnosed with HIV in that year, as well as those diagnosed prior to 2017 from 2002. Based on earlier research in a Japanese cohort study [13], four ICD codes were used to identify PLWH: non-specific HIV disease (B24), malignant neoplasms (B21), other specified diseases (B22), other disorders (B23), and HIV diseases resulting in infectious and parasitic diseases (B20).

After extracting all PLWH in 2017 in South Korea using a 1:10 stratified random sampling technique, considering age and sex, we requested the extraction of data for individuals in the control group who had never been diagnosed with HIV from 2002 to 2017 (B20-B24). The NHIS also excluded individuals from the control group in our study if they received a new HIV diagnosis during the follow-up period from 2018 to 2022. Because we followed the deaths of PLWH and controls from 2018 to 2022, we excluded those who died in 2017. In addition, individuals with a history of psychiatric morbidity between 2016 and 2017 were excluded because our focus was on the occurrence of newly developed psychiatric morbidity after 2017.

Study endpoint

The endpoint of this study was newly diagnosed psychiatric morbidities from January 1, 2018 to December 31, 2022. A diagnosis of any of the following mental health conditions was considered an indication of psychiatric morbidity: depression (F32, F33, F34.1), suicide attempt/self-harm (X60-X84, Y870), mania (F30), bipolar disorder (F31), anxiety disorder (F40, F41), insomnia disorder (G47, F51), substance use disorder (F10-F19), and schizophrenia (F20).

These disorders were prespecified based on three criteria: 1) their high prevalence and burden among PLWH, particularly depression [7] and alcohol/substance use disorders [14]; 2) their diagnostic validity in claims data, which ensures reliable ascertainment using ICD-10 codes, especially for mood, anxiety, psychotic, and sleep disorders [15-17]; and 3) their relevance to HIV outcomes such as ART adherence, quality of life, and mortality [9,10]. In contrast, other psychiatric conditions (e.g., posttraumatic stress disorder [PTSD], adjustment disorders, attention-deficit/hyperactivity disorder [ADHD], or personality disorders) were not included because they are less consistently coded in administrative data and have lower population prevalence, which may increase the risk of misclassification [18,19]. Prior registry-based studies on PLWH have also primarily focused on depression, anxiety, psychotic, substance use, and sleep disorders, supporting our outcome selection [10]. Neurocognitive disorder was excluded from the study endpoint due to its classification as a category of conditions marked by reduced cognitive function stemming from medical conditions, which is distinct from psychiatric disorders overall [20].

Collected covariates

Patient age and sex were assessed. Factors related to socioeconomic status were considered, such as household income and work status, which encompasses self-employment. Five groups were selected to represent different levels of household income, with one group having access to a medical aid program, and a four-quartile ratio. The government classifies individuals living in poverty who cannot afford insurance into groups eligible for medical aid programs.

The evaluation of patients’ comorbidities involved the use of the Charlson Comorbidity Index (CCI) and assessment of their underlying disability. CCI scores were calculated using ICD-10 codes from the NHIS database during hospital admission (Supplementary Table 1). Before accessing the various benefits offered by social welfare programs, all disabilities in South Korea must be recorded in the NHIS database. A recognized disability must be officially identified by a medical professional who assesses the challenges encountered during the execution of everyday tasks. Supplementary Table 2 provides a thorough categorization of disabilities. The patients were categorized into one of six severity levels according to the severity of their symptoms. The severity of the condition was categorized into three groups: severe for grades 1-3 and mild to moderate for grades 4-6.

Statistical analysis

To compare the clinicopathological differences between individuals living with HIV and the control group, we used mean values with standard deviations for continuous variables and numbers with percentages for categorical variables. The t-test and chi-squared test were employed to compare clinicopathological characteristics between the two groups for continuous and categorical variables, respectively.

PS matching was used to reduce the heterogeneity of each covariate between PLWH and controls, as it has been demonstrated to alleviate bias in observational studies [21]. Although the control group was selected using stratified random sampling, PS matching was performed to ensure that both groups were equally adjusted for comorbidities beyond age and sex. Specifically, PS matching was conducted without replacement with a 1:5 ratio and a caliper width of 0.25, utilizing the nearest-neighbor approach. Initially, there were approximately ten times more control subjects than PLWH prior to PS matching. Consequently, a 1:5 PS matching ratio was employed to reduce the number of samples eliminated through further matching processes. We did not employ alternative ratios, such as 1:3 or 1:4, to ensure that the PLWH and control groups, generated at a ratio of 1:10, would minimize the sample size discarded during the PS matching process. We considered that the PS matching process would produce control groups that were marginally less than a 1:5 ratio, contingent upon the configuration of the matching calipers. Logistic regressions were used to calculate PSs in the two groups, and all variables were included in the PS modelling in Table 1. After PS matching, the balance between the two groups was evaluated by calculating the absolute value of the standardized mean difference (ASD). If the ASD value was <0.1, then PS matching was considered suitable.

Considering censoring status in the PLWH and controls, we used the incidence rate per person-year and Cox regression as time to event analysis after PS matching. In the PS-matched cohort, Cox regression analyses were conducted to investigate potential differences in the risk of psychiatric morbidity between PLWH and controls. For this analysis, we defined an event as the diagnosis of any psychiatric morbidity that occurred on or after January 1, 2018. Time was defined as the period from January 1, 2018 to the date of the event. Factors including death or international migration during follow-up were treated as censors. Participants who were not censored and did not experience an event were monitored until December 31, 2022. To investigate this association thoroughly, we conducted Cox regression analyses focusing on psychiatric morbidities. We aimed to determine whether this association varies depending on the specific type of psychiatric morbidity. Additionally, sensitivity analysis was conducted using multivariable Cox regression modeling to compare the results obtained in the PS-matched cohort with those of the entire cohort. All the variables were included in the model after adjusting for multiple factors. We conducted an additional multivariate Cox regression analysis to investigate whether there were any variations in the primary findings among PLWH using ART. Finally, subgroup analyses were conducted based on the participants’ sex and age. Log-log plots were used to verify whether the underlying assumptions of the Cox proportional hazards models were met. Data were presented as hazard ratios (HRs) with 95% confidence intervals (CIs). The multivariate model did not show any correlation between the variables, indicating a low variance inflation factor. Statistical analysis was performed using R software (version 4.0.3; R Foundation for Statistical Computing) with a significance threshold of p<0.05.

RESULTS

Study population

A flowchart depicting the patient selection process is shown in Figure 1. In 2017, South Korea had 22,835 individuals living with HIV and the NHIS extracted data from 239,621 controls. We excluded 1,307 individuals who died in 2017 and 34,839 individuals with previous psychiatric morbidities. Subsequently, 226,310 individuals underwent the initial screening. Following the 1:5 PS matching process, the final analysis included 17,620 individuals living with HIV and 78,021 control subjects. In our study, the only missing data were those excluded from household income level, which we referred to as the unknown group. In the PS-matched cohort, rates of follow-up loss within 5 years due to death or immigration in PLWH and controls were 695 (3.9%) and 2,194 (2.8%), respectively. The clinicopathological characteristics of PLWH and controls are presented in Table 1, both before and after PS matching. The ASDs in the PS-matched cohort were all <0.1, indicating that the two groups were effectively balanced through PS matching. Supplementary Figure 1 illustrates the distribution of the PSs before and after PS matching, which became comparable after the matching process.

Analyses in PS-matched cohort

Table 2 shows the differences in the risk of psychiatric morbidity between PLWH and controls. The mean durations of follow-up in the PS-matched cohort were 4.92 years in PLWH and 4.94 years in controls. In the PS-matched cohort, the incidence rate of psychiatric morbidity in PLWH was 5,677.7 per 100,000 per year, whereas that in the controls was 4,926.3 per 100,000 per year. In the Cox regression analysis, PLWH showed a 17% (HR, 1.17; 95% CI, 1.14-1.21; p<0.001) higher risk of psychiatric morbidity than controls. Table 3 shows the detailed differences in the risk of psychiatric morbidity between PLWH and controls. In Cox regression, PLWH showed higher risk of depression (HR, 1.31; 95% CI, 1.25-1.38; p<0.001), mania (HR, 2.06; 95% CI, 1.23-3.45; p=0.006), bipolar disorder (HR, 1.67; 95% CI, 1.50-1.86; p<0.001), insomnia disorder (HR, 1.42; 95% CI, 1.36-1.47; p<0.001), substance use disorder (HR, 1.31; 95% CI, 1.14-1.49; p<0.001), and schizophrenia (HR, 1.50; 95% CI, 1.18-1.90; p=0.001) than controls, respectively.

Analyses in entire cohort

Table 4 shows the results of the multivariable Cox regression model for diagnosing psychiatric morbidities in the entire cohort. In multivariable model 1, PLWH showed 17% (HR, 1.17; 95% CI, 1.13-1.20; p<0.001) higher risk of psychiatric morbidity than controls. In the multivariable model 1, ART users in PLWH showed a 10% (HR, 1.10; 95% CI, 1.07-1.14; p<0.001) higher risk of psychiatric morbidity than controls. Table 5 shows the results of the subgroup analyses. In the male group, PLWH showed a 16% (HR, 1.16; 95% CI, 1.11-1.19; p<0.001) higher risk of psychiatric morbidity than controls, while PLWH in the female group showed a 12% (HR, 0.88; 95% CI, 0.80-0.97; p=0.010) lower risk of psychiatric morbidity than controls. Moreover, the 21-40-year-old group and 41-60-year-old group in PLWH showed 39% (HR, 1.39; 95% CI, 1.32-1.46; p<0.001) and 7% (HR, 1.07; 95% CI, 1.02-1.12; p=0.004) higher risks of psychiatric morbidity than controls, respectively.

DISCUSSION

This population-based cohort study showed that PLWH in South Korea have a higher risk of psychiatric morbidities than individuals without HIV infection. Specifically, PLWH showed increased risks of depression, mania, bipolar disorder, insomnia disorder, substance use disorder, and schizophrenia compared to individuals without HIV. Moreover, in the subgroup analyses, this association was more evident in males and adults aged 21-40 years. It is crucial for public healthcare policies to consider the declining mental health of PLWH, as indicated by our research.

The incidence of psychiatric morbidity in PLWH was approximately 5.7 per 1000 per year, whereas that in the controls was 4.7 per 1000 per year in this study. It was lower than that reported in the UK [10]. Gooden et al. [10] reported that the incidence of psychiatric morbidity in PLWH was 19.6 per 1,000 person-years, while that in controls was 12.1 per 1,000 person-years among their UK cohort. These differences may be due to differences in the healthcare environment between the UK and South Korea. In South Korea, patients can be supported by national insurance only if they are correctly diagnosed by a psychiatrist and registered using ICD-10 codes, not just psychiatric symptoms. Consequently, psychiatric patients with relatively mild conditions may not have sought medical attention or may have been overlooked in the diagnostic process in South Korea.

Many factors are associated with the development of depression in PLWH. Stigma is a social phenomenon that can have detrimental effects on PLWH [22]. Living with HIV/AIDS can be a challenging journey, often accompanied by the burden of stigma, and PLWH may face various mental health issues and distressing emotions, such as shame and depression [23]. In a systematic review of 64 studies, HIV-related stigma was significantly correlated with increased depression rates, decreased social support, decreased ART adherence, and decreased use of health and social services [24]. In addition, the use of ART may contribute to the higher rates of depression in PLWH. A previous study reported that PLWH often face various physical symptoms such as digestive discomfort, skin rashes, numbness, memory loss, nightmares, and dizziness. These symptoms not only cause physical discomfort but also disrupt different aspects of their social lives [25], which might be closely related to an increased risk of mood disorders such as depression among PLWH.

In this study, PLWH were at a higher risk of insomnia than individuals without HIV infection. PLWH experience ongoing inflammation in the central nervous system, leading to disturbances in their sleep patterns [26]. ART is known to cause side effects, including insomnia disorder in PLWH [27]. Moreover, comorbid mental conditions such as depression and bipolar disorder among PLWH could increase the risk of insomnia disorder [17]. A recent review reported that the prevalence of insomnia disorders among PLWH varies from 12.5% to 76.5% [28]. Insomnia disorder in PLWH is often an undetected health problem linked to a low quality of life in terms of health [17].

The increased risk of substance use disorders in PLWH is also a critical public health problem. In this study, over 90% of use disorders among PLWH were alcohol use disorders (AUD; ICD-10 code F10). In a systematic review and meta-analysis of 25 previous studies, the prevalence of AUD in PLWH was notably elevated to 29.80% (95% CI, 24.10-35.76) [14]. AUD is a critical problem among PLWH because it can harm the liver, interfere with the body’s immunological system, and alter how ART medications are metabolized [15]. Moreover, PLWH with AUD may experience a decline in their overall quality of life, which affects both their physical and emotional well-being [29]. Consistent with our findings, a study of national data from Denmark discovered an increased risk of schizophrenia in PLWH compared to individuals without HIV [16]. It is widely recognized that individuals with schizophrenia often exhibit high levels of sexual activity, similar to those with HIV infection [30]. Moreover, increased all-cause mortality was observed in PLWH with schizophrenia in British Columbia, Canada [31].

In the subgroup analysis, males and adults aged 21-40 years were at increased risk of psychiatric morbidities among PLWH compared to individuals without HIV in South Korea. According to reports, between 2008 and 2015, men made up 91.6% of PLWH in South Korea [32]. Likewise, the 2017 cohort of our study showed that men comprised a large percentage of PLWH (91%). HIV transmission primarily occurs through sexual conduct among sexual minorities and is associated with a high risk of infection among men [33]. Male sexual minorities among PLWH face an increased risk of psychiatric morbidities due to negative societal perceptions surrounding their sexual orientation and HIV status [34]. Furthermore, there was a notable increase in the likelihood of developing a psychiatric disorder among individuals in the young adult age group (21-40 years), indicating the potential impact of sexual minority status. Therefore, it is of the utmost importance to consider these factors when addressing suicide prevention among individuals living with HIV and developing appropriate policies.

However, the inverse relationship between PLWH and psychiatric morbidity among females in this study should be interpreted carefully. Females constituted fewer than 10% of PLWH in our cohort, raising concerns about statistical instability. The sociocultural context in South Korea, where HIV is predominantly concentrated among men who have sex with men, may also explain this finding. Male PLWH are more likely to face HIV-related stigma linked to sexual minority status, which has been strongly associated with depression and other psychiatric conditions, whereas female PLWH may be less exposed to such stigma. In addition, females are often diagnosed through obstetric or gynecologic services, including antenatal testing, which may provide earlier engagement with care and consistent follow-up, potentially mitigating psychiatric morbidity. Nevertheless, this inverse association may also reflect residual confounding, and further research is needed to clarify sex-specific differences in psychiatric outcomes among PLWH.

Our findings underscore the need for strengthened mental health care in HIV treatment settings. Routine psychiatric screening for depression, anxiety, insomnia, substance use, and suicidality should be incorporated into HIV care protocols, given their elevated incidence in PLWH. Integrating mental health services within HIV clinics may improve early detection and intervention, while reducing stigma-related barriers to care. Particular attention should be directed to vulnerable subgroups, especially young adult men, who demonstrated the greatest risk in our study. Tailored interventions, including culturally sensitive psychosocial support and targeted suicide prevention strategies, are warranted to mitigate the psychiatric burden in PLWH.

This study had some limitations. First, there is a lack of information concerning cluster of differentiation four cell count, a vital marker for assessing HIV activity. Thus, the severity of HIV could not be reflected in this study due to the limitations of the NHIS database. The absence of these clinical markers means that we could not distinguish between individuals with controlled HIV and those with advanced disease. As a result, part of the observed association with psychiatric morbidity may reflect confounding by disease severity rather than HIV status alone. Second, the study did not include information about the lifestyle habits of individuals living with HIV, such as alcohol consumption or a history of tobacco use. Third, despite employing PS matching with multiple variables, the results of this study may have been influenced by unaccounted and residual confounding factors. Fourth, the cohort identified as PLWH in 2017 encompassed cumulative HIV diagnoses from previous years; thus, the specific year of diagnosis was not incorporated into the study. This study may be limited by the inclusion of PLWH, comprising individuals diagnosed at varying times, including those diagnosed in 2017. These groups may exhibit differing risk factors for psychiatric morbidities. Fifth, information on ART was limited to treatment status only; details such as regimen type, treatment duration, and adherence were not available. Because certain ART drugs (e.g., efavirenz) are known to cause neuropsychiatric side effects, and poor adherence can worsen mental health outcomes, our findings cannot account for regimen-specific or adherence-related risks. Sixth, we did not include PTSD, adjustment disorder, ADHD, or personality disorders as outcomes, since these diagnoses are less reliably coded in administrative data. Prior validation studies have shown only moderate predictive validity for PTSD (positive predictive value≈75%-82%) [18] and lower coding reliability for trauma-related disorders compared with psychotic and affective disorders [19]. Their exclusion may underestimate the broader psychiatric burden in PLWH. Seventh, it is important to note that the findings of this study may have limited generalizability due to variations in medical and social environments and systems across different countries. Lastly, we did not include HIV-related neurocognitive disorders in our analysis. This approach does not represent the prevalent view of psychiatric morbidity associated with HIV/AIDS.

In conclusion, PLWH in South Korea have a higher risk of psychiatric morbidity than individuals without HIV infection. Specifically, PLWH have a higher risk of depression, mania, bipolar disorder, insomnia disorder, substance use disorder, and schizophrenia. Furthermore, the subgroup analysis showed stronger connections in males and adults aged 21-40 years. Our research shows that public health policies must address the worsening mental health of PLWH. Our findings also emphasize the need for future research to demonstrate the risk of psychiatric morbidities in PLWH while overcoming the limitations of the study presented above.

Supplementary Materials

The Supplement is available with this article at https://doi.org/10.30773/pi.2025.0074.

Supplementary Table 1.

The ICD-10 codes used by comorbidity to compute the Charlson Comorbidity Index

pi-2025-0074-Supplementary-Table-1.pdf

Supplementary Table 2.

Classification of disabilities in South Korea

pi-2025-0074-Supplementary-Table-2.pdf

Supplementary Figure 1.

Distribution of the PSs before and after PS matching. PS, propensity score.

pi-2025-0074-Supplementary-Fig-1.pdf

Notes

Availability of Data and Material

All data are available upon reasonable request from the corresponding author.

Conflicts of Interest

The authors have no potential conflicts of interest to disclose.

Author Contributions

Conceptualization: all authors. Data curation: Kyoung-Ho Song, Eunjeong Heo. Formal analysis: Tak Kyu Oh. Investigation: Kyoung-Ho Song, Eunjeong Heo, Hye Yoon Park. Methodology: Tak Kyu Oh, In-Ae Song. Project administration: Tak Kyu Oh, In-Ae Song. Resources: Kyoung-Ho Song, Eunjeong Heo, Hye Yoon Park. Software: Kyoung-Ho Song, Eunjeong Heo, Hye Yoon Park. Supervision: Tak Kyu Oh, In-Ae Song. Validation: Tak Kyu Oh, In-Ae Song. Visualization: all authors. Writing—original draft: Tak Kyu Oh. Writing—review & editing: Kyoung-Ho Song, Eunjeong Heo, Hye Yoon Park, In-Ae Song.

Funding Statement

None

Acknowledgments

None

Figure 1.

Diagram illustrating the process of selecting patients. NHIS, National Health Insurance Service; PLWH, people living with human immunodeficiency virus; PS, propensity score.

Table 1.

Clinicopathological characteristics of PLWH and controls before and after PS matching

Variable	Entire cohort (N=226,310)		ASD	P	PS-matched cohort (N=95,641)		ASD	P
Variable	PLWH (N=17,815)	Control (N=208,495)	ASD	P	PLWH (N=17,620)	Control (N=78,021)	ASD	P
Age (yr)	45.0±13.4	45.7±13.8	0.055	<0.001	44.9±13.4	44.9±13.8	0.039	<0.001
Male sex	16,382 (92.0)	187,928 (90.1)	0.067	<0.001	16,194 (91.9)	71,504 (91.6)	0.017	0.259
Having a job	9,517 (53.4)	143,472 (68.8)	0.309	<0.001	9,517 (54.0)	47,301 (60.6)	0.012	<0.001
Household income level^*				<0.001				<0.001
Medical aid program group	2,268 (12.7)	3,117 (1.5)			2,073 (11.8)	3,107 (4.0)
Q1 (lowest)	3,333 (18.7)	34,732 (16.7)	0.055		3,333 (18.9)	15,157 (19.4)	0.019
Q2	4,242 (23.8)	44,801 (21.5)	0.124		4,242 (24.1)	19,430 (24.9)	0.003
Q3	3,941 (22.1)	56,838 (27.3)	0.228		3,941 (22.4)	19,444 (24.9)	0.023
Q4 (highest)	3,896 (21.9)	65,271 (31.3)	0.337		3,896 (22.1)	20,126 (25.8)	0.009
Unknown	135 (0.8)	3,736 (1.8)	0.119		135 (0.8)	757 (1.0)	0.011
Underlying disability				<0.001				<0.001
Mild to moderate	608 (3.4)	6,492 (3.1)	0.017		599 (3.4)	2,324 (3.0)	0.017
Severe	346 (1.9)	3,301 (1.6)	0.026		344 (2.0)	1,268 (1.6)	0.033
CCI, point	1.1±1.5	0.7±1.3	0.275	<0.001	1.1±1.5	1.0±1.5	0.018	0.170
Myocardial infarction	127 (0.7)	1,161 (0.6)	0.019	0.009	126 (0.7)	511 (0.7)	0.007	0.375
Congestive heart failure	359 (2.0)	2,875 (1.4)	0.045	<0.001	354 (2.0)	1,316 (1.7)	0.003	0.003
Peripheral vascular disease	776 (4.4)	8,241 (4.0)	0.020	0.008	773 (4.4)	3,241 (4.2)	0.012	0.163
Cerebrovascular disease	463 (2.6)	4,638 (2.2)	0.024	0.001	455 (2.6)	1,818 (2.3)	0.013	0.050
Dementia	141 (0.8)	1,479 (0.7)	0.009	0.212	137 (0.8)	521 (0.7)	0.012	0.111
Chronic pulmonary disease	4,352 (24.4)	33,179 (15.9)	0.198	<0.001	4,276 (24.3)	17,041 (21.8)	0.011	<0.001
Rheumatic disease	354 (2.0)	3,056 (1.5)	0.037	<0.001	342 (1.9)	1,456 (1.9)	0.007	0.509
Peptic ulcer disease	2,217 (12.4)	23,146 (11.1)	0.041	<0.001	2,198 (12.5)	9,386 (12.0)	0.013	0.102
Mild liver disease	4,941 (27.7)	25,003 (12.0)	0.352	<0.001	4,747 (26.9)	18,609 (23.9)	0.001	<0.001
DM without chronic complication	2,449 (13.7)	18,565 (8.9)	0.141	<0.001	2,413 (13.7)	9,601 (12.3)	0.013	<0.001
DM with chronic complication	578 (3.2)	5,382 (2.6)	0.037	<0.001	574 (3.3)	2,281 (2.9)	0.014	0.019
Hemiplegia or paraplegia	90 (0.5)	478 (0.2)	0.039	<0.001	88 (0.5)	265 (0.3)	0.015	0.002
Renal disease	208 (1.2)	1,502 (0.7)	0.042	<0.001	203 (1.2)	756 (1.0)	0.014	0.028
Cancer	870 (4.9)	5,793 (2.8)	0.098	<0.001	854 (4.8)	3,265 (4.2)	0.009	<0.001
Moderate or severe liver disease	21 (0.1)	266 (0.1)	0.003	0.727	21 (0.1)	71 (0.1)	0.006	0.276
Metastatic solid tumor	67 (0.5)	478 (0.2)	0.025	<0.001	67 (0.4)	263 (0.3)	0.005	0.378

Data are presented as mean±standard deviation or N (%).

^* five groups were selected to represent different levels of household income, with one group having access to a medical aid program, and a four-quartile ratio.

PLWH, people living with human immunodeficiency virus; PS, propensity score; ASD, absolute value of standardized mean difference; CCI, Charlson Comorbidity Index; DM, diabetes mellitus.

Table 2.

Risk of psychiatric morbidity between PLWH and controls before and after PS matching

Variable	Event	Incidence rate per 100,000 person-years	HR (95% CI)	p
Before PS matching
Psychiatric morbidity
Control	48,077/208,495 (23.1)	4,658.4	1
PLWH	4,955/17,815 (28.0)	5,653.2	1.25 (1.22-1.29)	<0.001
After PS matching
Psychiatric morbidity
Control	18,987/78,021 (24.3)	4,926.3	1
PLWH	4,922/17,620 (27.9)	5,677.7	1.17 (1.14-1.21)	<0.001

Data are presented as N (%). PLWH, people living with human immunodeficiency virus; PS, propensity score; HR, hazard ratio; CI, confidence interval.

Table 3.

Detailed differences in the risk of psychiatric morbidity between PLWH and controls before and after PS matching

Variable	Event	Incidence rate per 100,000 person-years	HR (95% CI)	p
Before PS matching
Depression
Control	17,054/208,495 (8.2)	1,652.4	1
PLWH	2,011/17,815 (11.3)	2,294.4	1.42 (1.36-1.49)	<0.001
Suicide and self-harm
Control	24/208,495 (0.0)	2.3	1
PLWH	3/17,815 (0.0)	3.4	1.51 (0.45-5.00)	0.505
Mania
Control	135/208,495 (0.1)	13.1	1
PLWH	21/17,815 (0.1)	24.0	1.87 (1.18-3.00)	0.007
Bipolar disorder
Control	3,093/208,495 (1.5)	299.7	1
PLWH	478/17,815 (2.7)	545.4	1.86 (1.69-2.05)	<0.001
Anxiety disorder
Control	25,828/208,495 (12.4)	2502.6	1
PLWH	2,196/17,815 (12.3)	2,505.4	1.03 (0.98-1.07)	0.257
Insomnia disorder
Control	25,310/208,495 (12.1)	2,452.4	1
PLWH	3,234/17,815 (18.2)	3,689.7	1.54 (1.49-1.60)	<0.001
Substance use disorder
Control	2,219/208,495 (1.1)	215.0	1
PLWH	282/17,815 (1.6)	321.7	1.53 (1.35-1.73)	<0.001
Schizophrenia
Control	619/208,495 (0.3)	60.0	1
PLWH	94/17,815 (0.5)	107.2	1.83 (1.47-2.27)	<0.001
After PS matching
Depression
Control	6,864/78,021 (8.8)	1,780.9	1
PLWH	1,988/17,620 (11.3)	2,293.2	1.31 (1.25-1.38)	<0.001
Suicide and self-harm
Control	8/78,021 (0.0)	2.1	1
PLWH	3/17,620 (0.0)	3.5	1.69 (0.49-6.36)	0.439
Mania
Control	46/78,021 (0.1)	11.9	1
PLWH	21/17,620 (0.1)	24.2	2.06 (1.23-3.45)	0.006
Bipolar disorder
Control	1,267/78,021 (1.6)	328.7	1
PLWH	469/17,620 (2.7)	541.0	1.67 (1.50-1.86)	<0.001
Anxiety disorder
Control	10,193/78,021 (13.1)	2,644.6	1
PLWH	2,166/17,620 (12.3)	2,498.5	0.96 (0.92-1.01)	0.087
Insomnia disorder
Control	10,182/78,021 (13.1)	2641.8	1
PLWH	3,175/17,620 (18.0)	3,662.5	1.42 (1.36-1.47)	<0.001
Substance use disorder
Control	964/78,021 (1.2)	250.1	1
PLWH	279/17,620 (1.6)	321.8	1.31 (1.14-1.49)	<0.001
Schizophrenia
Control	268/78,021 (0.3)	69.5	1
PLWH	89/17,620 (0.5)	102.7	1.50 (1.18-1.90)	0.001

Data are presented as N (%). PLWH, people living with human immunodeficiency virus; PS, propensity score; HR, hazard ratio; CI, confidence interval.

Table 4.

Mutivariable Cox model for psychiatric morbidity in entire cohort

Variable	HR (95% CI)	p
PLWH (N=17,815) (vs. control; N=208,495), model 1	1.17 (1.13-1.20)	<0.001
ART user in PLWH (N=17,748) (vs. control; N=208,495), model 2	1.10 (1.07-1.14)	<0.001
Other covariates in model 1
Age	1.02 (1.02-1.02)	<0.001
Male sex	0.71 (0.69-0.72)	<0.001
Having a job	0.94 (0.92-0.96)	<0.001
Household income level
Medical aid program group	1.29 (1.23-1.36)	<0.001
Q1	1
Q2	0.95 (0.93-0.98)	0.002
Q3	0.94 (0.92-0.97)	<0.001
Q4	0.95 (0.93-0.97)	<0.001
Unknown	0.96 (0.89-1.03)	0.268
Underlying disability
Mild to moderate	1.20 (1.15-1.25)	<0.001
Severe	1.25 (1.18-1.32)	<0.001
CCI, point	1.10 (1.09-1.11)	<0.001
Myocardial infarction	0.97 (0.89-1.07)	0.585
Congestive heart failure	1.06 (1.00-1.13)	0.041
Peripheral vascular disease	1.28 (1.24-1.33)	<0.001
Cerebrovascular disease	1.18 (1.13-1.24)	<0.001
Dementia	1.32 (1.23-1.42)	<0.001
Chronic pulmonary disease	1.33 (1.30-1.36)	<0.001
Rheumatic disease	1.14 (1.08-1.21)	<0.001
Peptic ulcer disease	1.32 (1.29-1.36)	<0.001
Mild liver disease	1.20 (1.17-1.23)	<0.001
DM without chronic complication	1.10 (1.07-1.13)	<0.001
DM with chronic complication	1.04 (1.00-1.09)	0.070
Hemiplegia or paraplegia	0.90 (0.79-1.03)	0.133
Renal disease	1.12 (1.07-1.21)	0.003
Cancer	1.12 (1.07-1.17)	<0.001
Moderate or severe liver disease	1.03 (0.86-1.25)	0.733
Metastatic solid tumor	1.06 (0.92-1.22)	0.453

HR, hazard ratio; CI, confidence interval; PLWH, people living with human immunodeficiency virus; ART, antiretroviral therapy; CCI, Charlson Comorbidity Index; DM, diabetes mellitus.

Table 5.

Results of the subgroup analyses

Variable	HR (95% CI)	p
Sex
Male (N=204,310)
PLWH (N=16,382) (vs. control; N=187,928)	1.16 (1.11-1.19)	<0.001
Female (N=22,000)
PLWH (N=1,433) (vs. control; N=20,567)	0.88 (0.80-0.97)	0.010
Age (yr)
0-20 (N=1,583)
PLWH (N=106) (vs. control; N=1,477)	1.49 (0.96-2.31)	0.072
21-40 (N=83,429)
PLWH (N=6,862) (vs. control; N=76,567)	1.39 (1.32-1.46)	<0.001
41-60 (N=107,925)
PLWH (N=8,506) (vs. control; N=99,419)	1.07 (1.02-1.12)	0.004
≥61 (N=33,373)
PLWH (N=2,341) (vs. control; N=31,032)	0.86 (0.80-0.92)	<0.001

PLWH, people living with human immunodeficiency virus; HR, hazard ratio; CI, confidence interval.

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