Health-related quality of life in people living with HIV/AIDS in South Sulawesi Province: a cross-sectional study

¹Faculty of Pharmacy, Hasanuddin University, Makassar 90245, South Sulawesi, Indonesia, ²Department of Microbiology, Faculty of Medicine, Hasanuddin University, Makassar 90245, Indonesia, ³Faculty of Pharmacy, Muhammadiyah Sidrap Institute of Health Technology and Science, Sidenreng Rappang 91611, Indonesia, ⁴Department of Pharmacy, Sembilanbelas November University, Kolaka 93561, Indonesia, ⁵Department of Robotics and Artificial Intelligence, Faculty of Advanced Technology and Multidiscipline, Airlangga University, Surabaya 60115, Indonesia, ⁶Faculty of Public Health, Tompotika University, Luwuk 94711, Central Sulawesi, Indonesia, ⁷Department of Health Sciences, University Medical Center Groningen, University of Groningen, 9713 AV Groningen, The Netherlands

^&Corresponding author
Bustanul Arifin, Faculty of Pharmacy, Hasanuddin University, Makassar 90245, South Sulawesi, Indonesia

Introduction    

The Human Immunodeficiency Virus (HIV) attacks the human immune system, leading to progressive weakening of immune function. Without proper treatment, this infection can advance to Acquired Immunodeficiency Syndrome (AIDS), the final stage of HIV infection, marked by severe immune system deterioration and the onset of various opportunistic infections [1]. Individuals infected with HIV are referred to as People Living with HIV (PLHIV), while those who have progressed to AIDS are referred to as People Living with HIV/AIDS (PLWHA) [2,3]. Since its initial detection in Africa in the early 20th century, HIV has become a major global health challenge worldwide. The pandemic has spread extensively worldwide, with varying prevalence rates across different regions. By 2022, it was estimated that 39 million individuals were living with HIV, with approximately 1.3 million new infections reported annually [3]. In 2023, Indonesia reported a total of 515,455 PLHIV [4], with South Sulawesi identified as one of the provinces with a high rate of HIV/AIDS.

Based on preliminary observations from the Health Department of South Sulawesi Province, 7,875 HIV/AIDS cases were reported in 2024. Over the past five years, there has been a sharp rise in new infections, with Makassar City having the highest concentration of PLHIV in the province [5]. Despite improvements in HIV service coverage, access to antiretroviral therapy and HIV-related care remains unequal, particularly in rural and remote areas of South Sulawesi, where healthcare infrastructure and specialist services are limited [6]. The Indonesian government, through the Ministry of Health, has set the goal of achieving the "Three Zero HIV/AIDS 2030" initiative, which aims to eliminate new HIV infections, AIDS-related deaths, and the stigma surrounding PLHIV [7].

Despite the implementation of various strategies to improve access to therapy and reduce HIV transmission, individuals living with HIV/AIDS continue to experience significant physical and psychological health challenges that affect their QoL. QoL refers to an individual's subjective evaluation of their living conditions within a social, cultural and economic framework [8]. In the case of chronic diseases such as HIV/AIDS, HRQoL encompasses not only physical health but also mental, social, spiritual, and environmental dimensions of well-being [9-11]. Social determinants, including HIV-related stigma, discrimination, and social exclusion, are recognized as important factors that adversely affect the mental well-being and overall health-related quality of life of people living with HIV [9].

Although increased access to ARV therapy has been demonstrated to extend the lifespan of individuals living with HIV/AIDS and reduce viral load, adherence to ARV therapy remains a significant challenge. Poor adherence to treatment can adversely affect the overall quality of life of patients [12]. Several tools have been developed to assess the HRQoL of individuals living with HIV/AIDS, including EQ-5D, the WHOQOL-HIV, and MARS [11,13]. However, comprehensive studies in South Sulawesi evaluating the QoL of people living with HIV/AIDS using these instruments are needed. Despite the growing body of literature on HRQoL among PLHIV globally and in other Indonesian provinces, studies focusing specifically on the socioeconomic and clinical determinants of HRQoL in South Sulawesi remain scarce. Most available studies have been conducted in high-income settings or regions with different epidemiological profiles, limiting the applicability of their findings to the context of South Sulawesi. This gap is important because sociocultural dynamics, HIV-related stigma, healthcare accessibility, and regional disparities may substantially shape patient experiences and HRQoL outcomes in this setting.

This study seeks to address the existing gap in research by assessing the HRQoL of PLHIV/PLWHA in South Sulawesi and identifying factors that influence their HRQoL, such as sociodemographic factors, clinical conditions, and adherence to antiretroviral (ARV) treatment. The findings are anticipated to offer valuable insights that can contribute to the formulation of more effective policies and intervention strategies aimed at enhancing the quality of life of PLHIV/PLWHA in Indonesia, particularly in South Sulawesi Province.

Methods     

Study design and setting: this research employed a cross-sectional study design and observational approach that examined data from a population at a specific point in time. This design was used to assess the prevalence of health conditions, their determinants, and the characteristics of the population under particular circumstances. This study aimed to assess the HRQoL of PLHIV/PLWHA in South Sulawesi Province, with an emphasis on the physical, psychological, social, and environmental aspects of HRQoL. As a cross-sectional study, this design captures associations between variables at a single point in time and does not allow for the establishment of temporal relationships or causal conclusions to be drawn. Longitudinal studies are required to determine the direction of the observed associations.

Participants and sampling method: the participants were individuals living with HIV/AIDS who met specific inclusion criteria. The criteria included being at least 18 years of age and having a confirmed HIV-positive diagnosis through laboratory tests. Additionally, the participants were required to be currently receiving antiretroviral (ARV) therapy to ensure stable health. Participants also needed to voluntarily agree to participate in the study and provide informed consent as evidence of their willingness to participate. Participants were excluded if they (1) had documented cognitive impairments or psychiatric conditions affecting response validity, (2) had terminal illnesses precluding meaningful participation, (3) were below 18 years of age, or (4) did not provide informed consent.

Sample size estimation: a sample size of 367 was determined using the Raosoft calculator based on 7,875 HIV/AIDS patients, a 95% confidence level, and a 5% margin of error [14]. The sample size was adjusted for the unintentional error/loss rate [13] using the following formula:

In this study, the sample size was calculated using the formula, where n = 367 and d = 5%. This calculation resulted in a required sample size of 385.35 patients (n = 367 + n1 = 18.35). Therefore, the minimum sample size required was 386 participants. The final sample of 403 participants exceeded this minimum, providing adequate statistical power for the planned analyses, including multivariate ordinal regression with five independent variables.

Data collection and instruments: data collection for this study used a purposive sampling technique, wherein participants were selected based on specific criteria relevant to the research objectives [15]. Data collection for the study was carried out using two methods: direct (face-to-face) and indirect (online via Google Forms), depending on the technical conditions and the availability of participants at each healthcare facility. The initial phase involved coordination between the researchers and HIV/AIDS program managers at various health facilities, including the Jumpandang Baru Community Health Center, Dr. Wahidin Sudirohusodo General Hospital, and Labuang Baji Regional Hospital in Makassar City, as well as additional facilities in Parepare City and Sidenreng Rappang Regency. Healthcare facilities were selected based on: (1) their designation as official HIV treatment centers by the South Sulawesi Provincial Health Office; (2) geographic distribution across Makassar City, Parepare City, and Sidenreng Rappang Regency to capture variations across urban and semi-urban settings; and (3) agreement and coordination with HIV/AIDS program managers at each site. This coordination aimed to provide an overview of the study objectives and technical procedures. Following this, HIV/AIDS program officers at each facility assisted in identifying eligible PLHIV/PLWHA patients who met the inclusion and exclusion criteria of the study. Potential participants were informed about the study and asked for their consent to participate in it. For those participating online, a link to the Google Form, which contained the information sheet and research instruments, was distributed by the healthcare facility staff. In contrast, for in-person participants, the researchers provided a direct explanation, confirmed their willingness to participate, and obtained informed consent. Participants then independently completed the questionnaire, either through a Google Form containing the EQ-5D-5L and MARS-5 instruments or through a printed version with the assistance of a researcher during in-person visits. The Google Form was designed to ensure participant anonymity by not recording any personal information. Data obtained via Google Forms were automatically stored in the digital system, whereas data collected from in-person responses were manually coded and entered by researchers. The process was documented with the participants' explicit consent.

Measures and variables: the EQ-5D-5L is a generic HRQoL instrument [16] that has been used as a generic tool to assess five dimensions: mobility, self-care, usual activities, pain or discomfort, and anxiety or depression. Each dimension has five severity levels: no problems, mild problems, moderate problems, severe problems, and extreme problems, which reflect the degree of difficulty experienced in a particular area. Participants indicated the severity level for each dimension, represented by a number ranging from 1 to 5 [16]. The combination of these five numbers creates a unique "health code" for each individual. For instance, the code "12345" would denote Mobility: No problems (1), Self-care: Mild problems (2), usual activities: moderate problems (3), Pain/discomfort: Severe problems (4), and Anxiety/depression: Extreme problems (5). To derive index values that reflect the utility or quality of life from these health codes, a specific "value set" is applied that provides weights for each possible combination of dimensions and severity levels. In the absence of a dedicated value set for a given population or country, a crosswalk method may be employed to map the EQ-5D-5L to the EQ-5D-3L [17].

The Indonesian EQ-5D-5L value set, developed by Purba et al. (2017) using a Time Trade-Off methodology with a nationally representative sample, was applied to derive index scores [17]. The Bahasa Indonesia version of the EQ-5D-5L has demonstrated good construct validity and reliability in the Indonesian population [16]. The MARS-5 has been validated in multiple international settings and has demonstrated acceptable internal consistency (Cronbach's alpha ? 0.70) in adherence research. The adherence domain comprises five items from the MARS-5 questionnaire that measure medication adherence using self-reported data. The questionnaire included five questions related to forgetting, altering doses, stopping, skipping, and reducing medication intake. These questions assess both unintentional (one item) and intentional nonadherence (four items). Participants' adherence to ARV use was evaluated using a Likert scale. The total adherence score was categorized into two groups: high adherence (score ≥ 25) and low adherence (score < 25), with high adherence indicating full adherence and low adherence indicating nonadherence [18].

Statistical analysis: all 403 participants included in the final analysis provided complete responses to all the questionnaire items, resulting in 100% data completeness. For clinical biomarkers (CD4 count and viral load), participants who were unaware of their recent test results were categorized as "Unknown" (CD4: n=280, 69.5%; viral load: n=185, 45.9%). These represent valid response categories rather than missing data. No data imputations were performed. Data collected from the quality-of-life questionnaire (EQ-5D-5L) were organized using Microsoft Excel for efficient tabulation. Subsequently, the data were subjected to statistical analysis using SPSS (statistical product and service solutions), employing the Mann-Whitney U Kruskal-Wallis test and multivariate analysis. Given the exploratory nature of this study and the use of multivariate ordinal regression as the primary analytical approach, formal correction for multiple comparisons was not applied to univariate comparisons. Therefore, significant findings from univariate analyses should be interpreted with caution, given the potential for inflated Type I error rates.

Bias: selection bias was minimized through purposive sampling based on the inclusion criteria. Information bias was addressed using validated instruments (EQ-5D-5L and MARS-5 scores). Although purposive sampling may introduce selection bias, participants were recruited from multiple healthcare facilities across different areas in South Sulawesi to improve the sample diversity and representativeness. However, self-administered questionnaires may introduce recall and social desirability bias. Additionally, the use of online data collection via Google Forms may have introduced selection bias toward participants with greater Internet access and digital literacy. Although the 100% data completeness rate achieved mitigates concerns about item non-response, social desirability bias in self-reported adherence cannot be excluded from consideration.

Ethics statement: this study was approved by the Ethics Committee of Pharmacy and Health Research, Faculty of Pharmacy, Hasanuddin University (ethics number 2514/UN4.1.23/KP.06.05/2025). All procedures involving human participants adhered to institutional and national ethical standards. Written informed consent was obtained from all participants, and data confidentiality was ensured. The questionnaires were anonymous, and consent forms were stored separately. Ethical approval was obtained from the Ethics Committees of Wahidin Hospital (DP.04.03/D.XIX.2/8948/2025) and Labuang Baji Hospital (800.2/275/LB-01.3/VI/2025). All procedures involving human participants adhered to institutional and national ethical standards. Written informed consent was obtained from all participants, with data confidentiality maintained

Results     

Participant characteristics: among the 403 participants included in the final analysis, 80.4% (N = 324) were male, 86.1% (N = 347) were in the adult age group (18-44 years), and 47.6% (N = 192) had a tertiary education. Over four-fifths of the participants (84.6%, N = 341) reported no symptoms, 69.5% (N = 280) were unaware of their CD4 count, more than half (52.9%, N = 213) had an undetectable viral load, and 53.8% (N = 217) contracted the infection through same-sex intercourse (men who have sex with men) (Table 1).

Descriptive data: a total of 22.8% of participants reported experiencing problems of varying severity, ranging from mild (level 2) to severe/extreme (level 5), with the majority of these problems occurring in the depression and anxiety dimension, which was the highest among the five EQ-5D dimensions analyzed (Table 2, Table 2.1). Male participants reported a higher prevalence of problems in all dimensions than female participants did. Unmarried participants and those with incomes below the South Sulawesi Regional Minimum Wage (UMR) reported a higher prevalence of problems across all dimensions than married participants or those with incomes at or above the minimum wage level.

Participants aged 45-60 years reported a higher prevalence of problems across nearly all dimensions, except anxiety, than those aged 18-44 years. Participants with lower educational levels reported a higher prevalence of problems across most dimensions, except for depression and anxiety. Laborers reported a higher prevalence of problems across all dimensions, except for pain/discomfort, than other occupational groups. Participants who consumed coffee reported a higher prevalence of problems in all dimensions than those who did.

Participants with symptomatic HIV status reported higher levels of problems across all dimensions than asymptomatic participants. Participants unaware of their CD4 count and viral load reported a higher prevalence of pain/discomfort and depression/anxiety, although these differences were not statistically significant. Participants infected through same-sex sexual intercourse reported the highest prevalence of problems across all dimensions compared to other transmission routes. Participants on antiretroviral (ARV) therapy for less than six months reported higher levels of problems than those on treatment for more than six months. Adherent participants reported a higher prevalence of problems across all dimensions than non-adherent participants, although the number of non-adherent participants was very small. Participants receiving care at secondary or advanced healthcare facilities, receiving medications within their home area, and visiting healthcare facilities with a companion reported a higher prevalence of problems than their counterparts.

Outcome data: the average EQ-5D index score among PLHIV/PLWHA in South Sulawesi was 0.958 (95% CI: 0.95-0.97) (Figure 1). The anxiety dimension showed the highest prevalence of problems, with a statistically significant association (p <0.001). The EQ-5D index score was compared with the Time Trade-Off (TTO) value for the general population in Indonesia [17]. Female participants had higher EQ-5D index scores (0.984; 95% CI: 0.97-0.99) than male participants, with a statistically significant difference (p <0.05). Married participants had higher EQ-5D index scores than unmarried participants (p <0.05). Participants with higher education levels and those employed as civil servants reported higher EQ-5D index scores than the other groups did. Participants with incomes at or above the UMR reported significantly higher EQ-5D scores than those with incomes below the UMR (p < 0.001).

Participants who did not consume coffee had higher EQ-5D index scores than those who consumed coffee. Asymptomatic participants had significantly higher EQ-5D index scores than symptomatic participants (p <0.001). Differences in EQ-5D index scores according to CD4 count, viral load, sexual orientation, adherence status, healthcare facility level, and ARV service location were observed but did not reach statistical significance in several comparisons [6]. Participants receiving ARV therapy for six months or more had significantly higher EQ-5D index scores than those on therapy for less than six months (p <0.001).

Main results: the results of the multivariate ordinal regression analysis are shown in Table 3. Income (coefficient = 0.27; p = 0.005), duration of ARV therapy (coefficient = 0.063; p <0.001), HIV status (coefficient = -0.039; p = 0.003), and source of HIV infection (coefficient = -0.12; p = 0.014) were significantly associated with the EQ-5D index scores. Sex showed a positive coefficient (0.22) but did not reach statistical significance (p = 0.075).

Discussion     

The overall EQ-5D index score of 0.958 indicates a generally high health-related quality of life (HRQoL) among PLHIV/PLWHA in South Sulawesi. Despite this high overall score, anxiety/depression emerged as the most affected domain, consistent with findings from Ghana and other settings showing persistent mental health challenges among PLHIV, even when general HRQoL appears to be favorable [19].

The higher EQ-5D scores observed among female participants contrast with the findings from Ethiopia, where women living with HIV were more likely to experience a poorer HRQoL [20]. This discrepancy may reflect regional differences in healthcare access, sociocultural norms, and support systems, underscoring the importance of contextual factors in shaping HRQoL outcomes in this population group. Married participants demonstrated better HRQoL than unmarried participants, supporting the evidence that social support plays a critical role in buffering psychological distress and enhancing well-being among PLHIV [13]. Similarly, higher education, employment as civil servants, and higher income levels were associated with better HRQoL, reinforcing the role of socioeconomic stability in improving health outcomes [13,21].

Participants who abstained from coffee reported better HRQoL, aligning with evidence that regular coffee consumption may negatively affect social functioning, an important component of overall quality of life [22]. Clinically, asymptomatic HIV status and longer duration of ARV therapy were strongly associated with higher EQ-5D scores, highlighting the benefits of early diagnosis, sustained treatment, and viral suppression [13,23,24].

Although CD4 count, viral load, sexual orientation, and adherence status showed variations in EQ-5D scores, many of these differences were not statistically significant, likely due to the small subgroup sizes and unequal data distribution, as reported previously [25-27]. The unexpectedly higher EQ-5D scores among non-adherent participants should be interpreted cautiously, given the very small proportion of non-adherent respondents and potential reporting bias [12]. Multivariate analysis confirmed that income, duration of ARV therapy, HIV clinical status, and source of infection were key determinants of HRQoL. These findings are consistent with international evidence emphasizing the combined influence of socioeconomic and clinical factors on the QoL of PLHIV [28-310].

This study is the first in South Sulawesi to comprehensively assess HRQoL among PLHIV/PLWHA using validated instruments. However, its limitations include an uneven sample distribution across sites and potential information bias from self-administered questionnaires. Future studies should aim for more balanced sampling and longitudinal designs to strengthen causal inferences and generalizability. These findings have direct implications for policymakers at both the provincial and national levels. Targeted income support programs, expanded access to ART in remote regions, and mental health integration within HIV care services could substantially improve the HRQoL of PLHIV in South Sulawesi. Furthermore, interventions addressing HIV-related stigma, particularly among men who have sex with, are warranted to reduce the disproportionate psychological burden observed in this subgroup [28,29].

These findings can be interpreted using two theoretical frameworks. The Wilson and Cleary HRQoL Model links biological factors, symptom status, and functional outcomes to overall QoL, consistent with our finding that HIV clinical status and ART duration independently mediate HRQoL. The WHO Social Determinants of Health Framework explains how upstream socioeconomic factors (income, education, and employment) affect health outcomes through differential vulnerability, supporting income as an independent predictor of HRQoL in this population.

Strengths and limitations: this study represents the first comprehensive evaluation of HRQoL among PLHIV/PLWHA in South Sulawesi utilizing validated instruments. A key strength of this study is the use of standardized, validated tools (EQ-5D-5L and MARS-5) and the achievement of 100% data completeness for the participants analyzed, which bolsters the reliability and validity of the results. However, the study has limitations, such as an uneven distribution of participants across sites, with most being recruited from urban healthcare facilities, and the potential for information bias due to the use of self-administered questionnaires, which could lead to recall and social desirability biases. This cross-sectional design limits the ability to establish temporal relationships and causal inferences between socioeconomic, clinical, and behavioral factors and HRQoL outcomes. Future studies should prioritize the inclusion of participants from rural and peri-urban healthcare facilities to improve the representativeness of the findings and enhance external validity across the diverse geography of South Sulawesi. This study could not establish temporal or causal relationships because the data were collected at a single point in time.

Conclusion     

The quality of life of PLHIV/PLWHA in South Sulawesi Province is influenced by various participant characteristics, including gender, income, HIV status, duration of antiretroviral (ARV) therapy, and mode of HIV transmission. The findings showed that male participants and those with lower incomes had poorer quality of life. Furthermore, participants with symptomatic HIV had lower EQ-5D scores than those without. Differences in HIV transmission modes also impacted QoL, with those infected through same-sex relationships reporting more problems. These findings highlight the importance of economic factors and health conditions in designing interventions to improve the QoL of PLWHA. Furthermore, the duration of ARV use was also shown to have a significant impact on the quality of life of PLWHA.

Competing interests     

The authors declare no competing interests.

Authors' contributions     

Dian Permadani and Bustanul Arifin conceptualized and designed the study. Dian Permadani coordinated data collection and drafted the initial manuscript. Muhammad Nasrum Massi contributed to the microbiological and clinical interpretation of the data. Raudhatul Jannah N and Akhmad Ardiansyah assisted with the data acquisition, curation, and preliminary analysis. Sylmina Dalily Alkaff contributed to data analysis and methodological refinement. Muhammad Syahrir provided public health perspectives and supported data interpretation. Bustanul Arifin critically reviewed and revised the manuscript for intellectual content. All authors have read and approved the final version of this manuscript.

Acknowledgments     

We extend our appreciation to Prof. Yusnita Rifai, M. Pharm., Ph.D. Apt. and Muh. Akbar Bahar, M.Pharm.Sc., Ph.D., Apt. for serving as the thesis examiners.

Tables and figure     

Table 1: distribution of participants across various subgroups reporting problems on EQ-5D dimensions categorized by sociodemographic characteristics in PLHIV/PLWHA using the Indonesian version of the EQ-5D instrument

Table 2: distribution of participants across various subgroups reporting problems on EQ-5D dimensions categorized by clinical conditions in PLHIV/PLWHA using the Indonesian version of the EQ-5D instrument

Table 2.1: distribution of participants across various subgroups reporting problems on EQ-5D dimensions categorized by clinical conditions in PLHIV/PLWHA using the Indonesian version of the EQ-5D instrument

Table 3: relationship between sociodemographic characteristics, clinical conditions, and EQ-5D index scores using a multivariate ordinal regression model

Figure 1: mean (95% CI) EQ-5D index scores based on sociodemographic characteristics and clinical conditions in people living with HIV/AIDS (PLWHA) in South Sulawesi province using the Indonesian EQ-5D-5L rates

References