Abstract
Background
Oral health-related quality of life (OHRQoL) is a client-centered outcome, yet it is often overlooked in routine HIV care. This study aimed to describe the status of the OHRQoL among people living with HIV (PLWH) in China, examine factors independently associated with OHRQoL, and identify oral-psychological burden profiles.
Methods
A cross-sectional study was conducted with a convenience sample of 449 participants recruited from two designated HIV hospitals in Hunan Province, China, between May and August 2025. Data on sociodemographic and clinical characteristics, oral and psychosocial factors, and OHRQoL were collected. OHRQoL was assessed using the 14-item Oral Health Impact Profile (OHIP-14), which comprises four dimensions—functional limitation, pain and discomfort, disability, and psychosocial impact—with higher scores indicating poorer OHRQoL. OHRQoL and covariates were summarized descriptively, and potential correlates were examined using multivariable regression analysis. Latent profile analysis was then performed using oral health status, depressive symptoms, anxiety symptoms, and HIV-related stigma to identify oral-psychological burden profiles. External comparisons across the retained profiles were conducted using OHRQoL and other sociodemographic and clinical variables.
Results
The mean OHIP total score was 12.27±9.12 (range 0-56). Domain scores were: pain and discomfort (3.74±2.25, range 0-12), functional limitation (2.87±2.45, range 0-12), psychosocial impact (2.62±2.45, range 0-12), and disability (3.03±3.43, range 0-20). Poorer oral health status (β=1.117, 95% CI 0.815-1.419; P<.001) and greater depressive symptoms (β=0.958, 95% CI 0.115-1.801; P=.026) were independently associated with worse OHRQoL. Three oral-psychological burden profiles were identified: low (n=154, 34.3%), moderate (n=191, 42.5%), and high (n=104, 23.2%). External comparisons showed a graded pattern, with the high-burden profile characterized by older age, a higher proportion of women, lower socioeconomic status, poorer antiretroviral therapy adherence, lower social support, and worse OHRQoL.
Conclusion
These findings indicate that PLWH in this study reported measurable oral health impacts on daily life, particularly in pain and discomfort, with oral health status and depressive symptoms as key correlates. Oral and psychological burdens clustered into distinct profiles, highlighting the potential value of considering within-population heterogeneity when addressing oral health and related quality of life.
Keywords: HIV Infections, Quality of Life, Oral Health, Psychological Distress
Background
In 2024, an estimated 40.8 (37.0-45.6) million people were living with HIV worldwide. Among them, 31.6 million (77%), were receiving antiretroviral therapy (ART), and 73% had achieved viral suppression [1], which were far below the global target of “95-95-95” by 2030. As HIV has increasingly become a manageable chronic condition, long-term health and quality of life have become the priority of people living with HIV (PLWH) [2–4]. HIV-related oral manifestations remain common among PLWH, affecting more than 1/3 to 1/2 of this population in some reports [5]. These manifestations may serve as early clinical signs of HIV infection and markers of immune suppression or opportunistic infection [6–8]. Although ART has reduced some HIV-related oral manifestations, it has not fully resolved the broader oral health challenges faced by PLWH [9], including persistent oral symptoms, functional limitations on daily life, unfavorable oral health behaviors, and ongoing barriers to accessing dental care [10–13]. Compared with other unmet healthcare needs, unmet oral health needs among PLWH have been reported to be about twice as common [14, 15], and even in relatively resource-rich settings, 48% of ART-treated participants reported unmet oral health needs and 52% had not visited a dentist in the previous 2 years [16]. Oral health challenges among PLWH may be especially substantial in China. According to the WHO Global Oral Health Status Report, nearly 3.5 billion people worldwide are affected by oral diseases, with three-quarters living in middle-income countries [17]. Moreover, China’s Fourth National Oral Health Epidemiological Survey suggested that oral health problems affect around 90% of the population [18]. For PLWH, this population-level oral disease burden underscores the need for an indicator that goes beyond clinical oral health status and captures the perceived impact of oral conditions on daily life. OHRQoL provides such a patient-reported indicator by reflecting functional, psychological, and social consequences of oral health problems [19]. Therefore, oral health among PLWH cannot be understood solely in terms of oral disease severity, but should also be considered within a broader biopsychosocial context.
Sischo and Broder’s conceptual model positions Oral health-related quality of life (OHRQoL) as a multidimensional patient-reported outcome that reflects how oral conditions are experienced in daily life, rather than as a measure of oral disease severity alone. Within this framework, OHRQoL incorporates functional limitation, pain and discomfort, psychosocial impact, and broader contextual influences. This framework is particularly relevant for PLWH, among whom oral problems may intersect with psychological distress, HIV-related stigma, social support, and barriers to dental care. In addition to individual and disease-related characteristics, oral health status [10], psychological factors (such as depressive and anxiety symptoms, as well as HIV-related stigma) [20, 21] and social support [22] may shape how oral problems are experienced, appraised and managed by PLWH. These considerations suggest that OHRQoL among PLWH is better understood as reflecting the combined influence of oral and psychological burdens rather than oral disease alone.
However, to our knowledge, evidence from China on OHRQoL among PLWH remains absent. Previous studies among Chinese PLWH usually focused more on oral health status, oral symptoms, oral hygiene behaviors, or general health-related quality of life, whereas OHRQoL as a distinct oral health-specific patient-reported outcome has received limited attention. Moreover, most existing studies have used variable-centred approaches, such as multivariable regression, whereas person-centred analyses examining heterogeneity across individuals remain scarce [11, 23, 24]. While such analyses are useful for identifying independent correlates, they may obscure meaningful heterogeneity in how oral and psychological burdens cluster within PLWH. A person-centred approach such as latent profile analysis (LPA) may therefore provide additional insight by identifying distinct oral-psychological burden profiles related to OHRQoL. Guided by this multidimensional understanding of OHRQoL, a variable-centred approach can identify factors associated with poorer OHRQoL at the sample level, whereas a person-centred approach can examine whether oral and psychosocial burdens cluster within distinct subgroups of PLWH. Combining these two approaches may therefore provide complementary insights into both average-level associations and within-population heterogeneity.
Therefore, this study aimed to describe the status of OHRQoL among PLWH, examine factors independently associated with OHRQoL, and identify oral-psychological burden profiles using LPA.
Methods
Study design and setting
Our study utilized a cross-sectional design, and convenience sampling was used to select consecutive outpatients in the only two designated HIV hospitals located in Changsha and Hengyang, in Hunan Province, China.
Participants
Participants were recruited between May and August 2025. Individuals were eligible if they were aged 18 years or older, had a confirmed HIV antibody-positive report based on Western blot testing, and provided written informed consent. To capture oral health challenges among PLWH regardless of treatment stage, both ART-naive individuals and those participants receiving ART were eligible for inclusion. Exclusion criteria included insufficient physical strength or expressive ability to answer questions, and concurrent participation in other intervention research projects that might affect oral health status.
Using a medium effect size (f²=0.15), a significance level of 0.05, α power of 0.95, and 20 predictors, the minimum required sample size for the multivariable linear regression analysis was estimated to be 222. Although there is no strict minimum sample size for LPA, it is widely recommended that studies involving LPA include at least 300 participants to ensure stable and reliable estimation of latent classes, especially when multiple indicators are analyzed [25]. The final sample exceeded this requirement and was adequate for the planned analyses.
Measures
Sociodemographic and clinical characteristics
Data on sociodemographic and clinical characteristics were collected using a structured questionnaire. Sociodemographic variables included age, sex, education, monthly income, and reimbursability of dental expenses (whether the expenses from the participant’s most recent dental visit could be reimbursed through health insurance or other medical coverage). Clinical variables included years since HIV diagnosis, self-reported CD4 counts, comorbidity, and ART adherence (only for participants receiving ART). Comorbidity was coded as a binary variable, with 1 indicating the presence of any comorbid condition and 0 indicating none. Suboptimal ART adherence was defined as reporting any missed ART dose in the past 30 days, consistent with prior studies using 30-day self-reported adherence measures [26, 27].
Oral health-related quality of life
OHRQoL was assessed using the Chinese version of the 14-item Oral Health Impact Profile (OHIP-14), in alignment with Sischo and Broder’s conceptual model, which views OHRQoL as a multidimensional patient-reported construct reflecting the functional, psychological, and social impacts of oral conditions. The OHIP-14 operationalizes this framework by assessing the frequency of adverse oral impacts over the past year across four domains: functional limitation (range 0–12), pain and discomfort (range 0–12), disability (range 0–20), and psychosocial impact (range 0–12) [28]. Each item is rated on a 5-point Likert scale from 0 (“never”) to 4 (“very often”), yielding a total score ranging from 0 to 56, with higher scores indicating poorer OHRQoL.
Oral health status
Oral health status was assessed using the Chinese version of the Brief Oral Health Status Examination (BOHSE) [29]. In this study, BOHSE was used as a structured oral health status indicator to examine whether oral health burden was associated with OHRQoL measured by the OHIP-14, rather than as a direct measure of quality of life. The BOHSE is recommended for use by non-dental health professionals and has been widely used to assess oral health across diverse populations [30]. It consists of 10 items, each item is scored on a 3-point scale: 0 (“healthy”), 1 (“changes”), and 2 (“unhealthy”), with higher scores indicating worse oral health status.
Depressive symptoms
Depressive symptoms were measured by the Patient Health Questionnaire-2 (PHQ-2) [31]. The PHQ-2 score ranges from 0 to 6, with a score higher than 3 indicating a tendency toward depression. It has shown good reliability and validity in its application to PLWH in China [32].
Anxiety symptoms
Anxiety symptoms were assessed using the 2-item Generalized Anxiety Disorder scale (GAD-2) [33]. The scale includes two items, yielding a total score from 0 to 6, a total score of 3 or above is commonly used as the screening threshold for clinically relevant anxiety symptoms. The GAD-2 has been applied in Chinese HIV-related research and has shown acceptable applicability in this population [34].
Self-perceived HIV-related stigma
We used the12-item short version of the HIV Stigma Scale (HSS-12) [35] to measure self-perceived HIV-related stigma, excluding other forms such as ageism. This scale has 4 dimensions (concerns with public attitudes, disclosure concerns, negative self-image, and personalized stigma), with a higher total score indicating a higher level of perceived stigma.
Social support
Social support was assessed using the Social Support Rating Scale (SSRS) developed by Shuiyuan Xiao for use in Chinese populations [36]. The SSRS consists of 10 items covering three dimensions: objective support, subjective support, and support utilization. Total scores range from 0 to 65, with higher scores indicating greater social support; scores < 20 indicate low social support, scores of 20–30 indicate moderate social support, and scores > 30 indicate high social support.
Data collection
Participants were invited to take part in our study when they visited the outpatient clinic of the hospital for healthcare services. Two research assistants who had received formal training conducted a brief face-to-face screening after obtaining oral permission and then explained the purpose, content, procedures, voluntary nature, and confidentiality of this study. Participants were informed that refusal to participate or withdrawal from the study would not affect their routine medical care. After signing a written informed consent form, participants could fill out a paper-pencil based questionnaire or an electronic version of the questionnaire, which took about 20 min. Oral health status was assessed by the trained research assistants using the Brief Oral Health Status Examination (BOHSE) through a standardized oral examination. Before data collection, both research assistants received standardized training from a dentist on BOHSE items, scoring criteria, and oral examination procedures. The training included demonstration, practice scoring, and review of practice ratings by the dentist. Formal data collection began only after the dentist reviewed the practice ratings and confirmed that the research assistants had a shared understanding of the BOHSE scoring criteria. During data collection, uncertain cases were discussed with the principal investigator to maintain consistency. After completing the study procedures, each participant received a modest oral care kit, including mouthwash, dental floss, a toothbrush, and toothpaste, valued at approximately 20 Chinese yuan (US $2.85), as a token of appreciation for their time rather than as a condition for participation.
Statistical analysis
All statistical analyses were conducted in R (version 2025.05.1 + 513). Participant characteristics and study variables were summarized descriptively. Continuous variables were described using means and standard deviations or medians and interquartile ranges, as appropriate, and categorical variables were summarized using frequencies and percentages.
Multivariable linear regression was used to identify factors independently associated with OHRQoL, with the OHIP-14 total score entered as the dependent variable. Candidate variables included oral health burden, depressive symptoms, anxiety symptoms, HIV-related stigma, self-reported CD4 counts, years since HIV diagnosis, comorbidity, age, sex, income, education level, reimbursability of dental expenses, and social support. ART adherence was not included in the main multivariable regression model because this variable was only applicable to participants receiving ART. Because the OHIP-14 total score showed non-normality but was retained as a continuous outcome, HC3 robust standard errors were used in the final model. Complete-case analysis was used for the multivariable regression model. Multicollinearity was assessed using variance inflation factors (VIFs), with adjusted generalized variance inflation factors (adjusted GVIFs) used for categorical variables. Regression coefficients, 95% confidence intervals and p-values were reported.
Latent profile analysis was performed to identify subgroups of PLWH with similar patterns of oral and psychological burden. The profiling indicators were BOHSE score, PHQ-2 score, GAD-2 score, and HIV-related stigma score. These indicators were selected because they corresponded to the conceptual framing of OHRQoL as a multidimensional patient-reported outcome embedded in both oral and psychosocial contexts. BOHSE represented oral health status, whereas PHQ-2, GAD-2, and HIV-related stigma represented psychological and HIV-related psychosocial burden. Before model estimation, all profiling indicators were standardized to z scores to reduce the influence of different scale ranges. All 449 participants had complete data on the four profiling indicators and were included in the LPA. Models with one to five profiles were estimated in R using the tidyLPA package. The model was specified with equal variances across profiles and zero covariances among indicators. Model selection considered statistical fit, classification quality, profile size, and interpretability. Lower AIC, BIC, and sample-adjusted BIC values indicated better fit, higher entropy indicated better classification quality, and the BLRT was used to compare adjacent models. Solutions with very small profiles were considered less stable and less interpretable. The default optimization settings of tidyLPA were used; random starts were not manually specified, and no convergence warnings were observed during model estimation. After profile identification, OHRQoL and other external variables not included in the profiling model were compared across profiles using the Kruskal-Wallis test for continuous variables and the chi-square test for categorical variables. Statistical significance was set at P<.05.
Ethical considerations
Ethical approval was received from the Institutional Review Board for Behavioral and Nursing Research of Central South University, Xiangya School of Nursing (Project No. E202504).
Results
Internal consistency of study instruments
In the present study, the instruments showed acceptable to excellent internal consistency. Cronbach’s α was 0.931 for the OHIP-14, 0.784 for the BOHSE, 0.933 for the PHQ-2, 0.925 for the GAD-2, 0.880 for the HSS-12, and 0.697 for the SSRS.
Participant characteristics
Initially, 539 PLWH were approached, and 482 provided informed consent and completed the questionnaire. After excluding questionnaires with logical inconsistencies (n = 6), or missing key variables (n = 24), and duplicate submissions (n = 3), a total of 449 participants were included in the final analysis. The mean age was 42.87 ± 16.25 years, and 36.1% (162/449) were aged ≥ 50 years. Most participants were male (79.7%, 358/449). In terms of socioeconomic characteristics, 32.5% (146/449) reported a monthly personal income of ≤ 3,000 Chinese yuan, and 73.5% (330/449) reported not getting reimbursement for their most recent dental visit. The mean duration since HIV diagnosis was 6.43 ± 4.97 years, 41.2% (185/449) reported at least one comorbidity. Of the 449 participants, 39 were ART-naive. Among participants receiving ART (n = 410), 37.1% (152/410) had suboptimal adherence in the past 30 days. Detailed participant characteristics were presented in Table 1.
Table 1.
Sociodemographic and clinical characteristics of the participants (N = 449)
| Characteristic | N (%) |
|---|---|
| Age | |
| <50 years | 287 (63.9) |
| ≥50 years | 162 (36.1) |
| Sex | |
| Male | 358 (79.7) |
| Female | 91 (20.3) |
| Education level | |
| Primary school or below | 69 (15.4) |
| Junior high school | 98 (21.8) |
| Senior high school/technical secondary school | 66 (14.7) |
| Junior college | 80 (17.8) |
| Bachelor’s degree or above | 136 (30.3) |
| Average monthly personal income (Chinese Yuan) | |
| ≤3000 | 146 (32.5) |
| 3001–5000 | 126 (28.1) |
| 5001–7000 | 78 (17.4) |
| 7001–9000 | 35 (7.8) |
| 9001–11,000 | 34 (7.6) |
| >11,000 | 30 (6.7) |
| Reimbursability of dental expenses | |
| No | 330 (73.5) |
| Yes | 119 (26.5) |
| Self-reported CD4 counts (cells/µL) | |
| <200 | 95 (21.2) |
| 200–350 | 90 (20.0) |
| 351–500 | 91 (20.3) |
| >500 | 113 (25.2) |
| Unknown* | 60 (13.4) |
| Suboptimal ART adherence in the past 30 days among participants receiving ART** | |
| No | 258 (57.5) |
| Yes | 152 (33.9) |
| Any comorbidity | |
| No | 264 (58.8) |
| Yes | 185 (41.2) |
* “Unknown” indicates participants who did not report a CD4 count
** Only 410 ART -experienced participants
OHRQoL status
The mean BOHSE score was 6.51 ± 3.65, and the mean OHIP-14 total score was 12.27 ± 9.12, indicating that participants reported oral health impacts on daily life. Among the OHIP-14 domains, pain and discomfort had the highest mean score. The mean scores across OHIP-14 domains (based on raw scores) were as follows: pain and discomfort (3.74 ± 2.25, range 0–12), functional limitation (2.87 ± 2.45, range 0–12), psychosocial impact (2.62 ± 2.45, range 0–12), and disability (3.03 ± 3.43, range 0–20) (Table 2).
Table 2.
Descriptive statistics for continuous study variables and OHIP-14 domain scores (N = 449)
| Variables | Mean ± SD |
|---|---|
| Age, years | 42.87 ± 16.25 |
| Years since HIV diagnosis, years | 6.43 ± 4.97 |
| BOHSE score | 6.51 ± 3.65 |
| PHQ-2 score, | 1.96 ± 1.70 |
| GAD-2 score | 3.86 ± 1.65 |
| HIV-related stigma score | 35.18 ± 8.03 |
| Social support score | 27.16 ± 7.78 |
| OHIP-14 score | |
| Total score (range 0–56) | 12.27 ± 9.12 |
| Functional limitation (range 0–12) | 2.87 ± 2.45 |
| Pain and discomfort (range 0–12) | 3.74 ± 2.25 |
| Disability (range 0–20) | 3.03 ± 3.43 |
| Psychosocial impact (range 0–12) | 2.62 ± 2.45 |
Factors independently associated with OHRQoL
In the adjusted model, poorer oral health status and greater depressive symptom burden were independently associated with worse OHRQoL. Multicollinearity diagnostics did not indicate substantial multicollinearity, with all adjusted GVIF values below 5 and the highest adjusted GVIF being 1.680. BOHSE score (β = 1.117, 95% CI 0.815 to 1.419; P<.001) and PHQ-2 score (β = 0.958, 95% CI 0.115 to 1.801; P=.026) were positively associated with OHIP-14 total score. GAD-2 score, HIV-related stigma, self-reported CD4 counts, years since HIV diagnosis, comorbidity, age, sex, reimbursability of dental expenses, and social support were not significantly associated with OHRQoL. Compared with participants with average monthly personal income ≤ 3000 Chinese yuan, participants with monthly income > 11,000 Chinese yuan had lower OHIP-14 scores (β=-4.115, 95% CI -7.311 to -0.919; P=.012). In addition, participants with a junior college education had higher OHIP-14 scores than those with primary school education or below (β = 4.840, 95% CI 0.980 to 8.700; P=.014). However, the overall effect of education did not reach conventional statistical significance (F = 1.980, P=.097), and adjusted pairwise comparisons showed no statistically significant difference between junior college and primary school education or below (adjusted P=.072) or between junior college and bachelor’s degree or above (adjusted P=.197). Full regression results were shown in Table 3.
Table 3.
Multivariable linear regression of factors associated with OHIP-14 total score (N = 389)
| Variable | β | 95% CI | p-value |
|---|---|---|---|
| Age, years | 0.040 | -0.047 to 0.127 | 0.376 |
| Sex | |||
| Male | Ref. | ||
| Female | 0.675 | -1.625 to 2.974 | 0.566 |
| Average monthly personal income (Chinese Yuan) | |||
| ≤3000 | Ref. | ||
| 3001–5000 | 0.606 | -1.631 to 2.842 | 0.596 |
| 5001–7000 | -0.453 | -3.154 to 2.249 | 0.743 |
| 7001–9000 | -0.751 | -3.862 to 2.361 | 0.637 |
| 9001–11,000 | 1.644 | -1.971 to 5.260 | 0.373 |
| > 11,000 | -4.115 | -7.311 to -0.919 | 0.012 |
| Education level | |||
| Primary school or below | Ref. | ||
| Junior high school | 0.243 | -2.552 to 3.038 | 0.865 |
| Senior high school/technical secondary school | 2.530 | -0.791 to 5.851 | 0.136 |
| Junior college | 4.840 | 0.980 to 8.700 | 0.014 |
| Bachelor’s degree or above | 3.079 | -0.779 to 6.936 | 0.119 |
| Reimbursability of dental expenses | |||
| No | Ref. | ||
| Yes | 1.266 | -0.971 to 3.502 | 0.268 |
| Self-reported CD4 counts | 0.669 | -0.089 to 1.428 | 0.103 |
| Years since HIV diagnosis | 0.009 | -0.189 to 0.207 | 0.883 |
| Any comorbidity | |||
| No | Ref. | ||
| Yes | 0.479 | -1.395 to 2.353 | 0.579 |
| BOHSE score | 1.117 | 0.815 to 1.419 | < 0.001 |
| PHQ-2 score | 0.958 | 0.115 to 1.801 | 0.026 |
| GAD-2 score | 0.663 | -0.208 to 1.534 | 0.137 |
| HIV-related stigma score | 0.001 | -0.105 to 0.104 | 0.994 |
| Social support score | -0.015 | -0.123 to 0.094 | 0.792 |
Latent profile analysis
Fit indices for the 1- to 5-class models were presented in Table 4. Although the information criteria generally decreased as the number of profiles increased, the 3-class solution showed high entropy (0.940) and adequate class sizes, indicating clear classification. The 4-class solution did not significantly improve fit based on the BLRT (P=.446), while the 5-class solution produced very small profiles (3.6% and 1.8%). Therefore, the 3-class solution was retained as the most interpretable and stable model.
Table 4.
Fit indices for latent profile models of oral-psychological burden related to OHRQoL (N = 449)
| Model | AIC | BIC | aBIC | Entropy | BLRT p-value | Class proportions% |
|---|---|---|---|---|---|---|
| 1-Class | 5108.823 | 5141.679 | 5116.290 | NA | NA | 100.0 |
| 2-Class | 4736.945 | 4790.336 | 4749.079 | 0.864 | 0.010 | 73.5 / 26.5 |
| 3-Class | 4520.930 | 4594.856 | 4537.731 | 0.940 | 0.010 | 34.3 / 23.2 / 42.5 |
| 4-Class | 4525.563 | 4620.024 | 4547.031 | 0.829 | 0.446 | 34.1 / 7.6 / 23.8 / 34.5 |
| 5-Class | 4514.345 | 4629.342 | 4540.481 | 0.889 | 0.010 | 3.6 / 1.8 / 23.8 / 40.1 / 30.7 |
AIC Akaike Information Criterion, BIC Bayesian Information Criterion, aBIC Sample-Adjusted Bayesian Information Criterion; Entropy, information entropy, BLRT bootstrap likelihood ratio test. Model selection was therefore based on information criteria, entropy, class proportions, and clinical interpretability
As shown in Fig. 1, the retained 3-class solution identified three distinct oral-psychological burden profiles related to OHRQoL. These profiles were labeled as low oral and psychological burden (n = 154), high oral and psychological burden (n = 104), and moderate oral and psychological burden (n = 191). The low oral and psychological burden profile was characterized by below-average scores across BOHSE, PHQ-2, GAD-2, and HIV-related stigma, whereas the high oral and psychological burden profile showed above-average scores on all four indicators. The moderate oral and psychological burden profile showed values close to the sample mean across indicators. Overall, these findings suggest that oral and psychological burdens tended to cluster together rather than forming clearly separated oral-dominant and psychological-dominant subgroups.
Fig. 1.
Profile plot for the 3-class solution of oral-psychological burden among PLWH
External comparisons across latent profiles
External comparisons across the retained latent profiles revealed significant differences in several sociodemographic and clinical characteristics (Table 5). Compared with the low oral and psychological burden profile, the high oral and psychological burden profile was older (46.0 ± 17.5 vs. 39.4 ± 14.1 years, P=.017), included a markedly higher proportion of women (36.5% vs. 13.0%, P<.001), and showed a less favorable socioeconomic pattern, with significant differences in monthly income (P=.002), education level (P<.001), and reimbursability of dental expenses (P=.019). The high oral and psychological burden profile also had higher comorbidity prevalence (51.9% vs. 34.4%, P=.020), poorer ART adherence among participants receiving ART (51.7% vs. 31.1%, P=.005), lower social support (24.7 ± 7.3 vs. 29.5 ± 8.2, P<.001), and worse OHRQoL (OHIP-14 total score: 17.5 ± 9.9 vs. 8.1 ± 6.4, P<.001). By contrast, self-reported CD4 counts and years since HIV diagnosis did not differ significantly across profiles (P=.430 and P=.366, respectively). Overall, the three profiles showed a graded pattern, with the high oral and psychological burden group exhibiting the greatest external vulnerability, the low oral and psychological burden group the least, and the moderate oral and psychological burden group generally falling in between.
Table 5.
Comparisons of external variables across the 3 retained latent profiles
| Variable | Low oral and psychological burden (n = 154) | Moderate oral and psychological burden (n = 191) | High oral and psychological burden (n = 104) | Test statistic | p-value |
|---|---|---|---|---|---|
| Age, mean ± SD | 39.4 ± 14.1 | 43.9 ± 16.8 | 46.0 ± 17.5 | H = 8.14 | 0.017 |
| Sex, n (%) | χ²=23.15 | < 0.001 | |||
| Male | 134 (87.0) | 158 (82.7) | 66 (63.5) | ||
| Female | 20 (13.0) | 33 (17.3) | 38 (36.5) | ||
| Average monthly personal income (Chinese Yuan), n (%) | χ²=27.69 | 0.002 | |||
| ≤ 3000 | 45 (29.2) | 67 (35.1) | 34 (32.7) | ||
| 3001–5000 | 29 (18.8) | 57 (29.8) | 40 (38.5) | ||
| 5001–7000 | 31 (20.1) | 30 (15.6) | 17 (16.3) | ||
| 7001–9000 | 20 (13.0) | 10 (5.2) | 5 (4.8) | ||
| 9001–11,000 | 12 (7.8) | 17 (8.9) | 5 (4.8) | ||
| >11,000 | 17 (11.0) | 10 (5.2) | 3 (2.9) | ||
| Education level, n (%) | χ²=34.78 | < 0.001 | |||
| Primary school or below | 8 (5.2) | 38 (19.9) | 23 (22.1) | ||
| Junior high school | 28 (18.2) | 41 (21.5) | 29 (27.9) | ||
| Senior high school/technical secondary school | 31 (20.1) | 20 (10.5) | 14 (13.5) | ||
| Junior college | 25 (16.2) | 36 (18.8) | 19 (18.3) | ||
| Bachelor’s degree or above | 61 (39.6) | 56 (29.3) | 19 (18.3) | ||
| Reimbursability of dental expenses, n (%) | χ²=7.97 | 0.019 | |||
| No | 102 (66.2) | 143 (74.9) | 85 (81.7) | ||
| Yes | 50 (33.8) | 48 (25.1) | 19 (18.3) | ||
| Self-reported CD4 counts, n (%) | χ²=8.04 | 0.430 | |||
| <200 cells/µL | 26 (16.9) | 45 (23.6) | 24 (23.1) | ||
| 200–350 cells/µL | 32 (20.8) | 38 (19.9) | 20 (19.2) | ||
| 351–500 cells/µL | 35 (22.7) | 40 (20.9) | 16 (15.4) | ||
| >500 cells/µL | 41 (26.6) | 48 (25.1) | 24 (23.1) | ||
| Unknown* | 20 (13.0) | 20 (10.5) | 20 (19.2) | ||
| Years since HIV diagnosis | 6.5 ± 4.5 | 6.5 ± 5.1 | 6.2 ± 5.4 | H = 2.01 | 0.366 |
| Any comorbidity, n (%) | χ²=7.87 | 0.020 | |||
| No | 101 (65.6) | 113 (59.2) | 50 (48.1) | ||
| Yes | 53 (34.4) | 78 (40.8) | 54 (51.9) | ||
| Suboptimal ART adherence in the past 30 days among participants receiving ART**, n (%) | χ²=10.65 | 0.005 | |||
| No | 102 (68.9) | 114 (65.1) | 42 (48.3) | ||
| Yes | 44 (31.1) | 61 (34.9) | 45 (51.7) | ||
| Social support score, mean ± SD | 29.5 ± 8.2 | 26.6 ± 7.2 | 24.7 ± 7.3 | H = 23.77 | < 0.001 |
| OHIP-14 total score, mean ± SD | 8.1 ± 6.4 | 12.8 ± 9.0 | 17.5 ± 9.9 | H = 65.14 | < 0.001 |
* “Unknown” indicates participants who did not report a CD4 count
** Only 410 ART -experienced participants
Discussion
This study examined OHRQoL among PLWH using both variable-centred and person-centred approaches, providing a more integrated understanding of oral health vulnerability in this population. Consistent with the multidimensional framing of OHRQoL, our findings suggest that OHRQoL in this population was related to both oral health status and psychological burden. Specifically, the regression analysis identified poorer oral health status and greater depressive symptoms as independent correlates of worse OHRQoL, while the latent profile analysis showed that oral and psychological burdens tended to cluster across individuals. By linking average-level associations with within-population heterogeneity, these findings support interpreting OHRQoL among PLWH as a patient-reported outcome embedded in both oral and psychosocial contexts, rather than as a reflection of oral disease severity alone.
Interpretation of OHRQoL level
The mean OHIP-14 score in the present study (12.27 ± 9.12) is close to that reported in a Brazilian sample of PLWH (12.29 ± 13.94) [37], but higher than values reported in studies from Malaysia (8.8 ± 7.92) [10], Portugal (5.83 ± 7.79) [38], and the United States (5.8 ± 8.8 and 5.9) [39, 40]. This pattern suggests a relatively high burden of oral health impacts in the present sample, while recognizing that differences in healthcare systems, participant characteristics, disease severity, and study methodology may partly account for variation in OHIP-14 scores across studies. Conversely, the mean OHIP-14 score in the present study is lower than that reported in PLWH with greater clinical or social vulnerability. For example, an Australian subgroup of PLWH with xerostomia (18.6 ± 13.0) [41] and an institutionalized HIV+/AIDS population in northern Mexico (28.4 ± 12.35) [42] both demonstrated higher OHIP-14 score. Several factors may help explain the relatively high burden of oral health impacts observed in this study. Prior studies suggest that poorer OHRQoL among PLWH is more closely related to the presence of oral symptoms and their interference with daily life than to score magnitude alone [10, 24, 43]. In our sample, participants were recruited from outpatient clinics rather than inpatient or acute care settings. Therefore, the observed OHIP-14 scores may reflect oral health impacts experienced in daily life rather than acute or crisis-driven oral conditions. In this context, persistent oral problems may still interfere with daily life and be associated with poorer OHRQoL. Previous studies in China suggest that PLWH may face limited oral health awareness and barriers to dental care, including cost, stigma, and disclosure-related concerns [44, 45]. Such factors may be associated with delayed care-seeking and the persistence of oral problems, and may be linked to the long-term oral health burden experienced in daily life.
Oral health status and depressive symptoms as independent correlates
At the variable-centred level, poorer oral health status remains the strongest independent correlate of worse OHRQoL, underscoring that the burden captured by OHRQoL was still fundamentally rooted in underlying oral conditions. This finding is consistent with previous studies that show oral symptoms, oral lesions and oral health problems are closely linked to poorer OHRQoL in this population [10, 11, 46, 47]. Depressive symptoms also retain an independent association with OHRQoL after adjustment, indicating that OHRQoL is related not only to oral conditions themselves, but also to the psychological context in which those conditions are experienced. This is plausible in light of broader HIV research showing that depressive symptoms are strongly associated with poorer health-related quality of life [48, 49], and with oral health studies suggesting that psychological distress and poorer mental health may amplify the lived impact of oral problems in PLWH [24, 50].
However, anxiety symptoms, HIV-related stigma, and social support are not independently associated with OHRQoL in the adjusted model. Multicollinearity diagnostics did not indicate substantial multicollinearity. However, these psychosocial variables may share conceptual or statistical overlap with depressive symptoms, which could have attenuated their independent associations in the adjusted model. In particular, HIV-related stigma deserves attention. Although it is not retained as an independent correlate in the regression model, it remained one of the profiling indicators in the latent profile analysis and helps distinguish between burden profiles. This pattern suggests that stigma may be more relevant to how oral and psychological vulnerabilities cluster within individuals than to average-level variation in OHRQoL alone. Internalized stigma may also discourage timely care-seeking and result in avoidance or delay in the use of health services, which may in turn shape how oral problems are addressed in daily life [11]. Limited social support may further reinforce this process by reducing emotional and practical resources for coping and service use [51, 52]. As for anxiety, its non-significant association in the fully adjusted model may reflect overlap with depressive symptoms, given their interrelatedness in PLWH and the stronger link between quality of life and depression than anxiety alone [53].
Previous studies have generally reported that lower educational attainment is associated with poorer OHRQoL among PLWH [23, 54]. In the present study, junior college education showed a positive coefficient compared with primary school education or below; however, the overall effect of education did not reach conventional statistical significance, and adjusted pairwise comparisons between education groups were not significant after correction for multiple comparisons. Therefore, this finding should be interpreted as a conditional and exploratory association rather than a robust educational gradient. In the Chinese education system, junior college represents a heterogeneous post-secondary category below the bachelor’s degree level, which may not correspond directly to a stable socioeconomic advantage. Studies among Chinese PLWH have reported limited oral health knowledge and self-care behaviors, and suggested that perceived need, oral health attitudes, financial barriers, and care-seeking behavior may affect oral health service use [44]; another study among rural Chinese HIV/AIDS patients reported limited exposure to oral health education, weak oral health awareness, poor oral hygiene habits, an association between education and caries, and improvement in oral health knowledge and behaviors after education-based intervention [55]. Differences in oral health awareness, expectations, reporting behavior, and residual socioeconomic or care-related confounding may therefore partly explain this result, although it should be interpreted with caution.
Heterogeneity in oral-psychological burden profiles
The LPA findings complement the regression results by descriptively showing that oral and psychological burdens are not distributed uniformly across participants, but clustered into low, moderate and high burden profiles. Notably, the retained three-class solution suggests a graded distribution of burden rather than clearly separated oral-dominant and psychological-dominant subgroups. This pattern may indicate that, within this PLWH sample, oral and psychological burdens tended to co-occur rather than form sharply distinct oral-dominant or psychological-dominant configurations. From an interpretive perspective, this finding suggests that oral health-related vulnerability among PLWH may involve co-occurring oral and psychosocial disadvantages, rather than isolated problems within a single domain.
The external comparisons further support the practical relevance of the retained profiles, the low-, moderate and high oral and psychological burden groups also differed in OHRQoL and a range of external sociodemographic and clinical characteristics. The high oral and psychological burden profile is characterized by older age, a higher proportion of women, and shows a less favorable socioeconomic pattern. Such clustering of clinical and psychological disadvantage is consistent with previous studies showing that poorer quality of life among PLWH is often associated with co-occurring social and psychological vulnerabilities rather than isolated factors [56, 57]. The intermediate position of the moderate oral and psychological burden group further suggests a graded distribution of co-occurring oral and psychosocial burden, which may be useful for describing heterogeneity within this population.
Notably, self-reported CD4 counts and years since HIV diagnosis did not differ significantly across profiles. This finding is noteworthy and may reflect limitations in the sensitivity of these indicators in capturing day-to-day health burden. CD4 count, while clinically important, may not fully correspond to individuals’ perceived health status or functional well-being, as some individuals may remain clinically stable despite relatively low CD4 levels. In addition, years since HIV diagnosis may have limited discriminative value in the current treatment context. With the widespread implementation of early ART initiation and “treat-all” strategies, in which treatment is recommended regardless of CD4 count and initiated as early as possible after diagnosis [58, 59], many individuals achieve clinical stabilization within a relatively short period. Effective ART further reduces morbidity and preserves immune function [60]. As a result, both newly diagnosed and long-term treated individuals may present with comparable health status, reducing the ability of diagnosis duration and CD4 count to distinguish differences in everyday health burden.
Implications for future research and clinical practice
These findings suggest that oral health problems in PLWH deserve attention. Even in the context of contemporary HIV care, participants reported meaningful oral health impacts on daily life in this study, indicating that oral health continues to be a meaningful part of long-term well-being. At the same time, oral health should not be understood solely in terms of clinical status. Rather, it should be considered in terms of OHRQoL, which reflects not only oral conditions but also psychological burden and social context. From this perspective, addressing OHRQoL among PLWH may require attention beyond routine dental management, including consideration of depressive symptoms, social support, and access to dental care. Future research could further examine how oral and psychological factors interact over time and assess the feasibility and potential value of integrated approaches that combine oral care with attention to depressive symptoms and social support. At a broader level, these findings suggest the potential relevance of improving access to dental care and considering oral health assessment within HIV clinical services.
Limitations
There are several limitations to the current study. First, this is a cross-sectional study, and causal relationships cannot be drawn from these findings, nor can temporal relationships be established. Second, participants are recruited from a specific setting, which may limit the generalizability of the findings to PLWH in other geographical areas. However, the composition of our sample is broadly consistent with the contemporary HIV care context in China. In particular, 36.1% of participants in our sample are aged ≥ 50 years, which is close to the proportion reported nationally (34.66%) [61]. Participants are also predominantly male, and a substantial proportion have socioeconomic or care-related disadvantage, including low income and non-reimbursable dental expenses, suggesting that the study captured a clinically relevant population facing both oral health and broader social vulnerability. Third, several measures relied on self-report and may therefore have been affected by recall bias or social desirability bias, particularly for psychologically sensitive and HIV-related variables. Self-reported CD4 counts may also be subject to recall error and may not reflect participants’ current immunological status. In addition, OHIP-14 captures perceived oral health impacts rather than clinical disease severity, and BOHSE may not capture detailed dental diagnoses or condition-specific severity, such as dental caries, periodontal disease severity, xerostomia, oral lesions, or HIV-related oral manifestations. Although research assistants received standardized BOHSE training from a dentist before data collection, inter-rater reliability for BOHSE scoring was not formally assessed, which may have introduced measurement variability. Fourth, the latent profiles identified in this study should be considered exploratory and require replication in other samples. Finally, the LPA indicators captured only select dimensions of oral and psychological burden; other potentially relevant factors, such as broader care experiences, service accessibility and structural barriers, are not included and warrant further investigation.
Conclusions
PLWH in this study reported measurable oral health impacts on daily life, particularly in the domain of pain and discomfort. Poorer oral health status and greater depressive symptom burden were independently associated with worse OHRQoL. Beyond these average-level associations, latent profile analysis identified low, moderate, and high oral-psychological burden profiles, with the high oral and psychological burden profile showing the poorest OHRQoL and being characterized by greater socioeconomic and care-related disadvantages. These findings suggest that OHRQoL among PLWH reflects both oral and psychological burden and highlight the potential value of considering oral-psychological burden profiles when assessing oral health and related quality of life among PLWH.
Acknowledgements
The authors would like to acknowledge the contribution of all the nursing staff of the various hospitals that participated in the research study.
Abbreviations
- ART
Antiretroviral therapy
- aBIC
Sample-adjusted Bayesian information criterion
- AIC
Akaike information criterion
- BLRT
Bootstrap likelihood ratio test
- BOHSE
Brief Oral Health Status Examination
- CI
Confidence interval
- GAD-2
Generalized Anxiety Disorder-2
- HIV
Human immunodeficiency virus
- HSS-12
12-item HIV Stigma Scale
- LPA
Latent profile analysis
- OHIP-14
14-item Oral Health Impact Profile
- OHRQoL
Oral health-related quality of life
- PHQ-2
Patient Health Questionnaire-2
- PLWH
People living with HIV
- SSRS
Social Support Rating Scale
Authors’ contributions
Study conception and design: YZ, XL and GC. Participant recruitment and data collection: YZ, XY, MC, and DH. Data screening, data cleaning, and data integration: YZ and XY. Statistical analysis and initial manuscript drafting: YZ. Interpretation of findings and critical revision of the manuscript: LAG, XL, and GC. All authors reviewed and approved the final version of the manuscript.
Funding
This research is funded by the China Medical Board (grant number 22--465), Hainan Provincial Department of Science and Technology (grant number ZDYF2024SHFZ042), the National Nature Science Foundation of China (grant number 72574244), and the Hospital-level Youth Innovation Fund of Changsha First Hospital (Grant No. Y2024-03).
Data availability
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
Declarations
Ethics approval and consent to participate
This study was approved by the Institutional Review Board for Behavioral and Nursing Research of Central South University, Xiangya School of Nursing (Project No. E202504). All procedures performed in this study involving human participants were conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants prior to participation.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Guiying Cao and Xianhong Li contributed equally to this work and share co-corresponding authorship.
Contributor Information
Guiying Cao, Email: 343769964@qq.com.
Xianhong Li, Email: xianhong_li@csu.edu.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.