Abstract
As Antiretroviral Therapy (ART) is scaled up in low- and middle-income countries, it is important to understand Quality of Life (QOL) correlates including disease severity and person characteristics and to determine the extent of between-country differences among those with HIV. QOL and medical data were collected from 1,563 of the 1,571 participants at entry into a randomized clinical trial of ART conducted in the U.S. (n = 203) and 8 resource-limited countries (n = 1,360) in the Caribbean, South America, Asia, and Africa. Participants were interviewed prior to initiation of ART using a modified version of the ACTG SF-21, a health-related QOL measure including 8 subscales: general health perception, physical functioning, role functioning, social functioning, cognitive functioning, pain, mental health, and energy/fatigue. Other measures included demographics, CD4+ lymphocyte count, plasma HIV-1 RNA viral load. Higher quality of life in each of the 8 QOL subscales was associated with higher CD4+ lymphocyte category. General health perception, physical functioning, role functioning, and energy/fatigue varied by plasma HIV-1 RNA viral load categories. Each QOL subscale included significant variation by country. Only the social functioning subscale varied by sex, with men having greater impairments than women, and only the physical functioning subscale varied by age category. This was the first large-scale international ART trial to conduct a standardized assessment of QOL in diverse international settings, thus demonstrating that implementation of the behavioral assessment was feasible. QOL indicators at study entry varied with disease severity, demographics, and country. The relationship of these measures to treatment outcomes can and should be examined in clinical trials of ART in resource-limited settings using similar methodologies.
Keywords: Quality of life (QOL), Highly active antiretroviral therapy (HAART), HIV
Introduction
In resource-rich countries, antiretroviral therapy (ART) is widely available and HIV is now seen as a manageable chronic illness rather than a terminal disease. Accordingly, ART treatment outcomes now also focus on quality of life [1–6] in addition to disease severity and response to antiretroviral therapy.
Quality of life is a broad construct and can be influenced by many factors such as income, housing, social support, and life situation. Health-related quality of life, in particular, encompasses the impact of disease and treatment on a person’s ability to carry out daily activities and affects well-being. It includes physical, social, cognitive, and psychological functioning, as well as subjective sense of health, comfort, and well-being [7].
ART availability is now being scaled up in diverse parts of the world, including low- and middle-income countries, through clinical trials, non-governmental organizations, and government initiatives such as PEPFAR [8]. HIV clinical trials that seek to assess quality of life as a secondary trial outcome require pre-treatment data for HIV-infected individuals in diverse settings. It is well established that quality of life varies with CD4 count and HIV-1 RNA viral load [9–11]. In addition, in various U.S. samples, the AIDS Clinical Trials Group (ACTG) quality of life measures [12, 13] have been shown to vary with person characteristics such as gender [14], race [15], and physical ability [16]. However, the degree to which these associations exist across more diverse settings remains unknown. With the growth of HIV clinical treatment and prevention trials internationally, examining such associations using a standard measure can allow quality of life to be used as an outcome in monitoring HIV treatments.
ACTG A5175, “A Phase IV, Prospective, Randomized, Open Label Evaluation of the Efficacy of Once-Daily Protease Inhibitor and Once-Daily Non-Nucleoside Reverse Transcriptase Inhibitor-Containing Therapy Combinations for Initial Treatment of HIV-Infected Individuals from Resource-Limited Settings (PEARLS) Trial,” enrolled 1,571 patients with HIV from 46 study locations in nine countries. Participants were from Africa (Malawi, South Africa, Zimbabwe), Asia (India, Thailand), South America (Brazil, Peru), Haiti, and the United States. It is one of the first large-scale multi-country HIV treatment trials, comparing the effectiveness of 3 three-drug combinations in treatment-naïve HIV-infected individuals. The diversity of patients and settings allows for a unique opportunity to examine quality of life data from participants when entering the trial and its correlates to disease severity and person characteristics. The analysis examined quality of life indicators from the baseline assessment prior to initiating ART across diverse settings.
Methods
Study Participants
Eligible subjects were men and women ≥18 years with documented HIV-1 infection, CD4+ lymphocytes <300 cells mm−3, Karnofsky performance score ≥70, and ≤7 days of cumulative prior antiretroviral therapy prior to study entry (with the exception of limited prevention of Mother To Child Transmission (pMTCT) therapy). Women of reproductive potential were required to be non-pregnant and, if sexually active, agree to effective contraceptive use. Persons with serious chronic, acute, or recurrent infections must have completed at least 14 days of therapy prior to study entry and be clinically stable. Persons with absolute neutrophil count <750 mm−3, hemoglobin <7.5 g/dL, platelet count <50,000 mm−3, calculated creatinine clearance <60 ml/min, aspartate transaminase (AST), alanine transaminase (ALT) or alkaline phosphatase >fivefold above the upper limit of normal, total bilirubin >2.5-fold above the upper limit of normal, clinical pancreatitis within 3 years, bradycardia (<40 min−1) or a history of untreated active second or third degree heart block were excluded from participation. The study was approved by the institutional review board at each participating institution. Written informed consent was obtained from study participants following the human experimentation guidelines of the U.S. Department of Health and Human Services [17].
Data Collection
At PEARLS study entry, clinical assessments were performed and safety laboratory tests, plasma HIV-1 RNA and CD4+ lymphocyte counts were obtained. Quality of life was measured with the ACTG SF-21, which was adapted from the SF-21 [18] by the ACTG Outcomes Committee with feedback from local community members and site investigators. The ACTG SF-21 was originally adapted from the Medical Outcomes Study HIV Health Survey (MOS-HIV), a measure with well-established reliability and validity [19].
Modifications included removing one item (a visual 0–100 analog scale assessing overall health perceptions), simplifying items, and re-wording items for clarity. The measure consists of 20 items that assessed 8 domains: general health perceptions, physical functioning, role functioning, pain, social functioning, mental health, energy/fatigue, and cognitive functioning. Across all domains, higher scores indicated better quality of life. For example, a low score in the physical functioning domain indicates poor physical functioning, a high score in the energy/fatigue domain indicates high vitality, and a high score in the pain domain indicates less pain. All questions covered the previous 4 weeks. At all sites, the measure was translated and back-translated to ensure accuracy. The measure was administered in a face-to-face interview by study nurses in the local language.
General Health Perceptions
This three-item subscale asks patients to rate their general health, resistance to illnesses, and health outlook. This subscale has been validated by Davis and Ware [20] and Stewart and Ware [21].
Physical Functioning
This is a subscale with four items inquiring about physical limitations that range from minor to severe, including lifting heavy objects or participating in strenuous sports, walking uphill or climbing a few flights of stairs, and being able to eat, dress, bathe and use the toilet by oneself.
Role Functioning
Using two questions, this subscale asks participants if their health negatively impacts their ability to perform at a job/ school, or to work around the house.
Pain
The two items of this subscale assess intensity of physical pain and degree of interference with daily activities [22].
Social Functioning
This subscale consists of two items that ask participants to what extent their social activities have been limited by their health [23].
Mental Health
The three items in this subscale assess anxiety, depression, and overall psychological well-being [24]. Two questions are reverse coded to control for response set effects.
Energy/Fatigue
This subscale assesses vitality; one item is reverse coded to control for response set effects.
Cognitive Functioning
Consisting of three items, this subscale assesses a participant’s level of difficulty with reasoning/solving problems, being attentive, and remembering.
Data Analysis
Means and standard deviations summarized outcome measurements overall and within subgroups. Differences among categories of CD4+ lymphocyte counts and plasma HIV-1 viral load for each quality of life domain were compared using both the Kruskal–Wallis test for unordered alternatives among subgroups and the Jonckheere–Terpstra test for ordered alternatives among ordinal subgroups. Significance for these tests was assessed at a nominal 0.05 level without adjustment for multiple comparisons.
To compare quality of life domains by country, permutation testing was used to account for the multiplicity issue of 36 pair-wise comparisons among 9 countries. As there is no natural referent country, elucidating how countries differ requires an omnibus (8 degree of freedom) test comparing across all countries to reject equanimity. Alternatively, Monte Carlo samples (size 10,000) of the null distribution (i.e., no differences between the two countries being compared) were assembled by randomly permuting country assignment among participants. The normalized difference (T-statistic) of the observed pair-wise comparison was compared to the simulated null distribution; a 2-sided P value was calculated by the number of times the observed difference was more extreme than the critical value of the largest pair-wise difference from the simulated null distribution.
Results
Demographics
The observed sample consisted of 1,563 of 1,571 participants (8 missing baseline quality of life data) enrolled between May 2005 and August 2007 from the United States and eight countries in resource-limited areas of the world (Table 1). Overall, the sample contained slightly more men (52.7%) than women (47.3%) and was predominately young.
Table 1.
Demographic information for the sample
| Percentage (N = 1,563) | |
|---|---|
| Gender | |
| Male | 52.7 |
| Female | 47.3 |
| Age | |
| <20 | <1 |
| 20–29 | 27 |
| 30–39 | 44 |
| 40–49 | 22 |
| 50–59 | 6 |
| 60+ | 1 |
| Country of origin | |
| Brazil | 14.8 |
| Haiti | 6.4 |
| India | 16.3 |
| Malawi | 14.1 |
| Peru | 8.6 |
| Thailand | 6.4 |
| South Africa | 13.4 |
| United States | 13.4 |
| Zimbabwe | 7.0 |
Plasma HIV-1 RNA Viral Load and CD4+ Lymphocyte Counts
Baseline quality of life was associated with both plasma HIV-1 RNA and CD4+ lymphocyte count categories as shown in Tables 2 and 3. Although viral load and CD4 varied by category at baseline, quality of life in each of the domains was relatively high, indicating low levels of limitations and distress.
Table 2.
Means (standard deviation), Kruskal–Wallis tests, and Jonckheere–Terpstra trend tests of QOL scores (range 0–100) by plasma HIV-1 RNA viral load category
| Overall | ≤4,000 | 4,001–0,000 | 40,001–00,000 | 400,001–749,999 | ≥750,000 | Kruska–-Wallis test | Jonckheere–Terpstra test | |
|---|---|---|---|---|---|---|---|---|
| N = 1,560 | N = 65 | N = 345 | N = 871 | N= 158 | N= 121 | P value | P value | |
| General health perception | 59.96 (24.50) | 58.21 (26.45) | 63.91 (22.95) | 59.61 (23.95) | 59.12 (26.60) | 53.24 (27.22) | 0.002* | 0.367 |
| Physical functioning | 88.21 (19.38) | 86.73 (22.19) | 91.36 (16.98) | 89.01 (17.97) | 84.97 (23.04) | 78.62 (24.71) | <0.001* | 0.004* |
| Role functioning | 84.07 (28.34) | 89.62 (22.92) | 85.79 (27.78) | 84.54 (27.45) | 80.70 (32.04) | 77.27 (32.44) | 0.015* | 0.019* |
| Social functioning | 89.33 (20.57) | 89.23 (21.87) | 91.40 (17.75) | 89.39 (19.69) | 89.03 (23.59) | 83.47 (27.37) | 0.195 | 0.860 |
| Cognitive functioning | 87.26 (17.88) | 86.67 (15.94) | 87.79 (17.95) | 87.22 (17.63) | 86.75 (19.08) | 87.05 (19.04) | 0.609 | 0.494 |
| Pain | 80.60 (22.34) | 79.83 (23.15) | 83.67 (20.26) | 80.13 (21.89) | 78.76 (25.65) | 78.15 (25.38) | 0.134 | 0.600 |
| Mental health | 76.24 (21.36) | 77.03 (19.04) | 77.11 (20.20) | 76.09 (21.66) | 76.16 (21.63) | 74.44 (23.32) | 0.973 | 0.760 |
| Energy/fatigue | 73.51 (24.77) | 76.15 (20.89) | 78.38 (22.30) | 74.02 (23.85) | 67.66 (28.87) | 62.23 (29.17) | <0.001* | 0.044* |
P ≤ 0.05
Table 3.
Means (standard deviation), Kruskal–Wallis tests, and Jonckheere–Terpstra trend tests of QOL scores (range 0–100) by CD4+ lymphocyte count category
| Overall | <50 | 50–99 | 100–199 | 200–249 | 250–299 | Kruskal–Wallis test | Jonckheere–Terpstra test | |
|---|---|---|---|---|---|---|---|---|
| N = 1,563 | N= 198 | N = 229 | N = 526 | N = 336 | N = 274 | P value | P value | |
| General health perception | 59.97 (24.49) | 48.27 (26.02) | 54.00 (24.71) | 62.20 (23.44) | 63.37 (22.66) | 64.98 (23.87) | <0.001* | <0.001* |
| Physical functioning | 88.24 (19.37) | 77.21 (25.76) | 84.06 (20.87) | 90.45 (17.41) | 91.21 (15.88) | 91.83 (16.54) | <0.001* | <0.001* |
| Role functioning | 84.10 (28.32) | 71.59 (34.69) | 79.37 (30.75) | 86.57 (26.40) | 86.53 (25.96) | 89.42 (24.16) | <0.001* | <0.001* |
| Social functioning | 89.34 (20.55) | 76.71 (29.78) | 88.69 (19.85) | 91.11 (19.03) | 91.20 (18.00) | 93.35 (14.51) | <0.001* | <0.001* |
| Cognitive functioning | 87.29 (17.87) | 83.20 (20.36) | 85.74 (18.41) | 87.76 (17.53) | 88.93 (15.84) | 88.61 (18.05) | 0.002* | 0.005* |
| Pain | 80.62 (22.32) | 71.83 (27.35) | 76.95 (22.68) | 82.65 (20.36) | 83.00 (20.96) | 83.25 (21.36) | <0.001* | <0.001* |
| Mental health | 76.24 (21.35) | 70.20 (24.08) | 73.60 (21.62) | 77.13 (20.63) | 78.58 (19.91) | 78.25 (21.23) | <0.001* | 0.001* |
| Energy/fatigue | 73.55 (24.77) | 60.30 (29.50) | 69.52 (24.16) | 76.56 (22.94) | 76.88 (22.99) | 76.64 (23.50) | <0.001* | <0.001* |
P ≤ 0.05
Four quality of life domains varied significantly across viral load categories: general health perception (P = 0.002), physical functioning (P < 0.001), role functioning (P = 0.015), and energy/fatigue (P < 0.001). More specifically, a trend of decreasing quality of life was observed for increasing plasma HIV-1 RNA viral load category for three quality of life domains: physical functioning (P = 0.004), role functioning (P = 0.019), and energy/fatigue (P = 0.044).
All quality of life domains varied significantly across CD4+ lymphocyte count categories (all P ≤ 002). Trend tests revealed significantly increasing quality of life scores across increasing CD4+ lymphocyte count categories for each of the quality of life domains (all P ≤ 0.005).
Associations of Quality of Life Domains to Sex and Age
Only the social functioning subscale varied by sex, with men having greater impairments than women (P < 0.002), and only the physical functioning subscale varied by age category (P < 0.002), with physical functioning decreasing as age increased (data not shown).
Quality of Life Domains by Country
Quality of life domains varied significantly by country; rankings of quality of life domains by country and significant differences between countries are shown in Table 4. For general health perceptions, India’s and South Africa’s mean scores were significantly higher than 6 other countries. Haiti’s mean general health perception was significantly lower than the 3 top-ranked countries, and Malawi’s mean was significantly lower than all countries in the top 5. In the domain of physical functioning, Peru’s and India’s mean scores were highest, while Brazil and the U.S. ranked significantly lower than all 5 of the top-ranked countries. For role functioning, Zimbabwe’s, Thailand’s, and India’s mean scores were highest, while Peru had significantly lower role functioning scores than all other countries. For pain, Zimbabwe’s mean score was highest (least pain) while Peru, India, South Africa, Haiti, and Thailand also scored significantly higher (less pain) than the countries with the lowest-ranked mean scores. In the domain of social functioning, India, Zimbabwe, and South Africa ranked significantly higher than the four lowest-rated countries, while the U.S. ranked significantly lower than every other country. In terms of mental health, Zimbabwe, South Africa, India, Thailand, and Malawi ranked significantly higher than the four countries with the lowest-ranked mean scores. For energy, Zimbabwe’s and Thailand’s means were highest; the U.S. had significantly lower energy scores than every other country. Finally, for cognitive functioning, India, South Africa, and Zimbabwe ranked significantly higher than the four lowest-ranked countries. In addition, Haiti and Peru had significantly higher scores than the lowest-ranked country, the U.S.
Table 4.
Means, ranks, and significant differences (T statistic) by country and QOL domain
| Country | Domain | Mean | Rank | Sig. differences | Mean difference (95% CI) |
|---|---|---|---|---|---|
| India | General health perceptions | 68.40 | 1 | 4 Thailand | 7.90 (2.98, 12.81)* |
| 5 Peru | 8.51 (4.11, 12.91)** | ||||
| 6 Zimbabwe | 12.49 (7.37, 17.61)*** | ||||
| 7 USA | 12.55 (7.92, 17.18)*** | ||||
| 8 Haiti | 15.98 (10.62, 21.35)*** | ||||
| 9 Malawi | 18.89 (14.6, 23.19)*** | ||||
| Social functioning | 97.25 | 1 | 4 Thailand | 5.48 (2.22, 8.73)* | |
| 5 Haiti | 8.03 (3.98, 12.09)** | ||||
| 6 Peru | 8.37 (4.95, 11.78)*** | ||||
| 7 Brazil | 10.15 (6.77, 13.52)*** | ||||
| 8 Malawi | 11.5 (8.54, 14.45)*** | ||||
| 9 USA | 21.61 (17.56, 25.66)*** | ||||
| Cognitive functioning | 93.46 | 1 | 5 Peru | 5.65 (2.95, 8.35)** | |
| 6 Thailand | 8.73 (6.12, 11.34)*** | ||||
| 7 Brazil | 10.03 (6.66, 13.4)*** | ||||
| 8 Malawi | 10.34 (7.45, 13.24)*** | ||||
| 9 USA | 11.99 (8.73, 15.24)*** | ||||
| Physical functioning | 93.53 | 2 | 6 Malawi | 4.78 (1.84, 7.72)* | |
| 7 South Africa | 5.14 (2.17, 8.10)* | ||||
| 8 Brazil | 10.74 (7.38, 14.10)*** | ||||
| 9 USA | 16.28 (12.27, 20.29)*** | ||||
| Role functioning | 92.35 | 3 | 6 Malawi | 8.03 (3.89, 12.18)** | |
| 7 Brazil | 9.13 (4.83, 13.42)** | ||||
| 8 USA | 16.73 (11.9, 21.55)*** | ||||
| 9 Peru | 33.58 (25.91, 41.26)*** | ||||
| Pain | 85.45 | 3 | 7 Brazil | 8.53 (4.49, 12.58)** | |
| 8 Malawi | 10.45 (6.6, 14.29)*** | ||||
| 9 USA | 14.28 (10.1, 18.46)*** | ||||
| Mental health | 81.62 | 3 | 6 Peru | 11.67 (8.05, 15.29)*** | |
| 7 Brazil | 12.13 (8.05, 16.2)*** | ||||
| 8 Haiti | 14.15 (9.96, 18.34)*** | ||||
| 9 USA | 14.48 (10.61, 18.35)*** | ||||
| Energy | 76.63 | 4 | 9 USA | 16.78 (12.28, 21.27)*** | |
| South Africa | General health perceptions | 68.33 | 2 | 4 Thailand | 7.83 (3.08, 12.59)* |
| 5 Peru | 8.45 (4.23, 12.66)** | ||||
| 6 Zimbabwe | 12.42 (7.46, 17.39)*** | ||||
| 7 USA | 12.48 (8.03, 16.94)*** | ||||
| 8 Haiti | 15.92 (10.7, 21.13)*** | ||||
| 9 Malawi | 18.83 (14.72, 22.93)*** | ||||
| Mental health | 85.37 | 2 | 5 Malawi | 7.09 (3.68, 10.5)** | |
| 6 Peru | 15.41 (11.67, 19.16)*** | ||||
| 7 Brazil | 15.87 (11.69, 20.05)*** | ||||
| 8 Haiti | 17.90 (13.61, 22.19)*** | ||||
| 9 USA | 18.22 (14.24, 22.2)*** | ||||
| Cognitive functioning | 91.87 | 2 | 6 Thailand | 7.14 (4.13, 10.15)*** | |
| 7 Brazil | 8.44 (4.75, 12.13)*** | ||||
| 8 Malawi | 8.75 (5.49, 12.02)*** | ||||
| 9 USA | 10.40 (6.81, 13.98)*** | ||||
| India | General health perceptions | 68.40 | 1 | 4 Thailand | 7.90 (2.98, 12.81)* |
| 5 Peru | 8.51 (4.11, 12.91)** | ||||
| 6 Zimbabwe | 12.49 (7.37, 17.61)*** | ||||
| 7 USA | 12.55 (7.92, 17.18)*** | ||||
| 8 Haiti | 15.98 (10.62, 21.35)*** | ||||
| 9 Malawi | 18.89 (14.6, 23.19)*** | ||||
| Social functioning | 97.25 | 1 | 4 Thailand | 5.48 (2.22, 8.73)* | |
| 5 Haiti | 8.03 (3.98, 12.09)** | ||||
| 6 Peru | 8.37 (4.95, 11.78)*** | ||||
| 7 Brazil | 10.15 (6.77, 13.52)*** | ||||
| 8 Malawi | 11.5 (8.54, 14.45)*** | ||||
| 9 USA | 21.61 (17.56, 25.66)*** | ||||
| Cognitive functioning | 93.46 | 1 | 5 Peru | 5.65 (2.95, 8.35)** | |
| 6 Thailand | 8.73 (6.12, 11.34)*** | ||||
| 7 Brazil | 10.03 (6.66, 13.4)*** | ||||
| 8 Malawi | 10.34 (7.45, 13.24)*** | ||||
| 9 USA | 11.99 (8.73, 15.24)*** | ||||
| Physical functioning | 93.53 | 2 | 6 Malawi | 4.78 (1.84, 7.72)* | |
| 7 South Africa | 5.14 (2.17, 8.10)* | ||||
| 8 Brazil | 10.74 (7.38, 14.10)*** | ||||
| 9 USA | 16.28 (12.27, 20.29)*** | ||||
| Role functioning | 92.35 | 3 | 6 Malawi | 8.03 (3.89, 12.18)** | |
| 7 Brazil | 9.13 (4.83, 13.42)** | ||||
| 8 USA | 16.73 (11.9, 21.55)*** | ||||
| 9 Peru | 33.58 (25.91, 41.26)*** | ||||
| Pain | 85.45 | 3 | 7 Brazil | 8.53 (4.49, 12.58)** | |
| 8 Malawi | 10.45 (6.6, 14.29)*** | ||||
| 9 USA | 14.28 (10.1, 18.46)*** | ||||
| Mental health | 81.62 | 3 | 6 Peru | 11.67 (8.05, 15.29)*** | |
| 7 Brazil | 12.13 (8.05, 16.2)*** | ||||
| 8 Haiti | 14.15 (9.96, 18.34)*** | ||||
| 9 USA | 14.48 (10.61, 18.35)*** | ||||
| Energy | 76.63 | 4 | 9 USA | 16.78 (12.28, 21.27)*** | |
| South Africa | General health perceptions | 68.33 | 2 | 4 Thailand | 7.83 (3.08, 12.59)* |
| 5 Peru | 8.45 (4.23, 12.66)** | ||||
| 6 Zimbabwe | 12.42 (7.46, 17.39)*** | ||||
| 7 USA | 12.48 (8.03, 16.94)*** | ||||
| 8 Haiti | 15.92 (10.7, 21.13)*** | ||||
| 9 Malawi | 18.83 (14.72, 22.93)*** | ||||
| Mental health | 85.37 | 2 | 5 Malawi | 7.09 (3.68, 10.5)** | |
| 6 Peru | 15.41 (11.67, 19.16)*** | ||||
| 7 Brazil | 15.87 (11.69, 20.05)*** | ||||
| 8 Haiti | 17.90 (13.61, 22.19)*** | ||||
| 9 USA | 18.22 (14.24, 22.2)*** | ||||
| Cognitive functioning | 91.87 | 2 | 6 Thailand | 7.14 (4.13, 10.15)*** | |
| 7 Brazil | 8.44 (4.75, 12.13)*** | ||||
| 8 Malawi | 8.75 (5.49, 12.02)*** | ||||
| 9 USA | 10.40 (6.81, 13.98)*** | ||||
| Social functioning | 94.92 | 3 | 6 Peru | 6.03 (2.38, 9.69)* | |
| 7 Brazil | 7.81 (4.2, 11.43)** | ||||
| 8 Malawi | 9.16 (5.93, 12.39)*** | ||||
| 9 USA | 19.28 (15.02, 23.53)*** | ||||
| Energy | 76.95 | 3 | 9 USA | 17.10 (12.22, 21.98)*** | |
| Role functioning | 88.33 | 4 | 8 USA | 12.71 (7.38, 18.04)*** | |
| 9 Peru | 29.56 (21.57, 37.56)*** | ||||
| Pain | 85.24 | 4 | 7 Brazil | 8.33 (4.02, 12.64)** | |
| 8 Malawi | 10.24 (6.11, 14.36)*** | ||||
| 9 USA | 14.07 (9.63, 18.51)*** | ||||
| Physical functioning | 88.39 | 7 | 9 USA | 11.14 (6.65, 15.63)*** | |
| Brazil | General health perceptions | 61.83 | 3 | 8 Haiti | 9.42 (3.61, 15.22)* |
| 9 Malawi | 12.33 (7.49, 17.16)*** | ||||
| Role functioning | 83.23 | 7 | 9 Peru | 24.46 (16.28, 32.64)*** | |
| Social functioning | 87.11 | 7 | 9 USA | 11.47 (6.4, 16.53)** | |
| Energy | 71.77 | 7 | 9 USA | 11.92 (6.4, 17.45)** | |
| Pain | 76.91 | 7 | |||
| Mental health | 69.49 | 7 | |||
| Cognitive functioning | 83.43 | 7 | |||
| Physical functioning | 82.79 | 8 | |||
| Thailand | Role functioning | 92.50 | 2 | 6 Malawi | 8.18 (3.4, 12.97)* |
| 7 Brazil | 9.27 (4.36, 14.19)* | ||||
| 8 USA | 16.88 (11.49, 22.26)*** | ||||
| 9 Peru | 33.73 (25.69, 41.77)*** | ||||
| Energy | 81.00 | 2 | 7 Brazil | 9.23 (3.7, 14.75)* | |
| 8 Malawi | 9.91 (5.04, 14.78)** | ||||
| 9 USA | 21.15 (15.88, 26.41)*** | ||||
| Physical functioning | 92.63 | 3 | 8 Brazil | 9.83 (6.07, 13.59)*** | |
| 9 USA | 15.38 (11.02, 19.73)*** | ||||
| General healthperceptions | 60.50 | 4 | 9 Malawi | 10.99 (5.71, 16.28)* | |
| Social functioning | 91.78 | 4 | 9 USA | 16.13 (11.14, 21.12)*** | |
| Mental health | 79.20 | 4 | 6 Peru | 9.25 (4.48, 14.02)** | |
| 7 Brazil | 9.71 (4.59, 14.82)** | ||||
| 8 Haiti | 11.73 (6.52, 16.94)*** | ||||
| 9 USA | 12.06 (7.1, 17.01)*** | ||||
| Pain | 79.89 | 6 | 9 USA | 8.72 (3.96, 13.48)* | |
| Cognitive functioning | 84.73 | 6 | |||
| Peru | Physical functioning | 93.94 | 1 | 7 South Africa | 5.54 (2.18, 8.91)* |
| 8 Brazil | 11.14 (7.43, 14.86)*** | ||||
| 9 USA | 16.69 (12.37, 21.00)*** | ||||
| Pain | 85.66 | 2 | 7 Brazil | 8.74 (4.2, 13.28)** | |
| 8 Malawi | 10.66 (6.29, 15.02)*** | ||||
| 9 USA | 14.49 (9.83, 19.15)*** | ||||
| General health perceptions | 59.89 | 5 | 9 Malawi | 10.38 (5.57, 15.2)** | |
| Cognitive functioning | 87.81 | 5 | 9 USA | 6.33 (2.53, 10.14)* | |
| Social functioning | 88.89 | 6 | 9 USA | 13.25 (8.15, 18.34)*** | |
| Energy | 75.22 | 6 | 9 USA | 15.37 (10.11, 20.63)*** | |
| Mental health | 69.95 | 6 | |||
| Role functioning | 58.77 | 9 | |||
| Zimbabwe | Role function ing | 96.14 | 1 | 4 South Africa | 7.80 (3.81, 11.8)** |
| 5 Haiti | 11.14 (5.24, 17.03)* | ||||
| 6 Malawi | 11.82 (7.62, 16.02)*** | ||||
| 7 Brazil | 12.91 (8.56, 17.26)*** | ||||
| 8 USA | 20.51 (15.64, 25.39)*** | ||||
| 9 Peru | 37.37 (29.66, 45.07)*** | ||||
| Pain | 88.99 | 1 | 6 Thailand | 9.10 (4.56, 13.64)** | |
| 7 Brazil | 12.08 (7.42, 16.74)*** | ||||
| 8 Malawi | 13.99 (9.5, 18.48)*** | ||||
| 9 USA | 17.82 (13.05, 22.6)*** | ||||
| Mental health | 86.21 | 1 | 5 Malawi | 7.94 (4.34, 11.53)*** | |
| 6 Peru | 16.26 (12.35, 20.17)*** | ||||
| 7 Brazil | 16.72 (12.39, 21.05)*** | ||||
| 8 Haiti | 18.75 (14.31, 23.19)*** | ||||
| 9 USA | 19.07 (14.93, 23.21)*** | ||||
| Energy | 83.18 | 1 | 4 India | 6.55 (2.69, 10.42)* | |
| 6 Peru | 7.96 (3.23, 12.68)* | ||||
| 7 Brazil | 11.41 (6.39, 16.42)*** | ||||
| 8 Malawi | 12.09 (7.8, 16.38)*** | ||||
| 9 USA | 23.33 (18.6, 28.06)*** | ||||
| Social functioning | 95.96 | 2 | 6 Peru | 7.07 (3.03, 11.11)* | |
| 7 Brazil | 8.85 (4.84, 12.86)** | ||||
| 8 Malawi | 10.20 (6.54, 13.87)*** | ||||
| 9 USA | 20.32 (15.72, 24.91)*** | ||||
| Cognitive functioning | 91.58 | 3 | 6 Thailand | 6.84 (3.56, 10.12)** | |
| 7 Brazil | 8.14 (4.23, 12.05)** | ||||
| 8 Malawi | 8.45 (4.94, 11.97)*** | ||||
| 9 USA | 10.10 (6.29, 13.91)*** | ||||
| Physical functioning | 92.61 | 4 | 8 Brazil | 9.82 (6.03, 13.61)*** | |
| 9 USA | 15.36 (10.99, 19.74)*** | ||||
| General health perceptions | 55.91 | 6 | |||
| Haiti | Cognitive functioning | 88.87 | 4 | 9 USA | 7.39 (3.27, 11.51)* |
| Physical functioning | 91.00 | 5 | 8 Brazil | 8.21 (4.18, 12.23)** | |
| 9 USA | 13.75 (9.17, 18.33)*** | ||||
| Role functioning | 85.00 | 5 | 9 Peru | 26.23 (17.13, 35.33)*** | |
| Pain | 83.22 | 5 | 8 Malawi | 8.22 (3.11, 13.34)* | |
| 9 USA | 12.06 (6.68, 17.43)*** | ||||
| Social functioning | 89.22 | 5 | 9 USA | 13.58 (8.04, 19.12)*** | |
| Energy | 75.60 | 5 | 9 USA | 15.75 (10.29, 21.2)*** | |
| Mental health | 67.47 | 8 | |||
| General health perceptions | 52.42 | 8 | |||
| Malawi | Mental health | 78.27 | 5 | 6 Peru | 8.32 (4.35, 12.29)** |
| 7 Brazil | 8.78 (4.39, 13.16)** | ||||
| 8 Haiti | 10.81 (6.31, 15.3)*** | ||||
| 9 USA | 11.13 (6.93, 15.33)*** | ||||
| Physical functioning | 88.75 | 6 | 9 USA | 11.50 (7.03, 15.97)*** | |
| Role functioning | 84.32 | 6 | 9 Peru | 25.55 (17.45, 33.65)*** | |
| Pain | 75.00 | 8 | |||
| Social functioning | 85.76 | 8 | 9 USA | 10.11 (5.32, 14.91)** | |
| Energy | 71.09 | 8 | 9 USA | 11.24 (6.37, 16.11)*** | |
| Cognitive functioning | 83.12 | 8 | |||
| General health perceptions | 49.51 | 9 | |||
| USA | General health perceptions | 55.85 | 7 | ||
| Role functioning | 75.63 | 8 | 9 Peru | 16.86 (8.38, 25.33)** | |
| Physical functioning | 77.25 | 9 | |||
| Pain | 71.17 | 9 | |||
| Social functioning | 75.64 | 9 | |||
| Mental health | 67.14 | 9 | |||
| Energy | 59.85 | 9 | |||
| Cognitive functioning | 81.48 | 9 |
≤0.05,
≤0.01,
≤0.0001
Discussion
In the first study evaluating established quality of life measures among clinical trial participants from diverse international settings, we found marked variability in the measure across countries. For all measures of quality of life, higher CD4+ lymphocyte count category was associated with higher quality of life performance scores. For several quality of life domains (general health perception, physical functioning, role functioning, and energy/fatigue), higher viral load was associated with lower scores. The more consistent association with CD4 than viral load is expected because CD4 is an overall indicator of immune function and more of an indicator of disease progression, whereas viral load is an indicator of the amount of virus in the system, not an indicator of the effects of the virus [25, 26]. These data provide preliminary evidence for the utility of this quality of life assessment across diverse settings.
Comparisons of the quality of life subscales by country, however, provided a complex set of findings, which differed across the quality of life domains. There was no single country consistently showing the highest quality of life subscale scores across domains, though, with one exception, India was consistently in the top 3. U.S. quality of life scores were not significantly higher than other countries, and for many domains, were ranked lowest. This may be partially attributable to selection bias regarding disease stage. Twenty-two percent (22%) of U.S. subjects had CD4+ lymphocyte counts <50 compared to an average of 13% overall and 19% of U.S. subjects had been diagnosed with AIDS compared to 8% overall. Treatment-naïve subjects consisted mostly of late presenters in the U.S., whereas at other sites, the pool of treatment-naïve potential subjects was more diverse in terms of disease progression. Within-country studies of the quality of life measure may further identify contributors to the various domains of quality of life within a given culture. In addition, future research may wish to address the clinical significance and magnitude of differences by country or across quality of life subscales.
There are several limitations of the present study. First, this analysis was cross-sectional. Longitudinal follow-up data from the PEARLS trial will allow for determining the timing of CD4 and viral load changes with respect to improvements or decrements in quality of life as measured by the ACTG SF. Second, individuals with Karnofsky scores <70 were excluded, thereby limiting generalizability of the study. Additionally, the study sample was not a representative sample of HIV-infected persons in participating countries, thereby also limiting the generalizability of the between-country variations in quality of life. Third, the process of adapting the ACTG-SF measure of quality of life occurred through consensus meetings across site investigators and ACTG behavioral scientists, but pilot testing and an iterative process of scale development was not possible in the context of a clinical trial. Finally, the ACT-SF measure has not been formally validated in many of the local languages used in this study. However, the reliability and validity of MOS-HIV, the measure upon which the ACTC-SF was based, has been assessed and found to be comparable to the original measure in French, German, Italian, Dutch, UK English translations [27], Thai [28], Chinese [29], and most recently Greek [30], implying that the measure remains reliable and valid across a variety of languages. Regardless, future studies should address measure validation across languages.
This report provides initial evidence for the ability of the ACTG SF-21 scale to be used in multi-national studies, as well as potential for use as a baseline to compare to current and future cohorts. Because of the associations of subscales to CD4 and viral load, further investigations of the measure may provide supporting evidence that provision of ART not only reduces the medical effects of HIV but also improves functioning and quality of life.
Acknowledgments
The authors thank the PEARLS study participants who volunteered their time and efforts. We thank Bristol Myers Squibb for providing atazanavir and efavirenz (with consent of Merck); Gilead for providing emtricitabine, tenofovir, emtricitabine/ tenofovir and financial support; GlaxoSmithKline for providing lamivudine, zidovudine and lamivudine/zidovudine; and Boehringer Ingelheim Pharmaceuticals, Inc. for providing nevirapine. The authors acknowledge the contributions of the following PEARLS investigators: Edith Swann, Ph.D., HIV Research Branch, TRP, DAIDS, NIAD, NIH, Bethesda; Ronald L. Barnett, Ph.D., ACTG Operations Centre, Social and Scientific Systems, Inc. Silver Spring; Barbara Brizz, B.S.N., M.H.S.Ed., ACTG Operations Center, Social and Scientific Systems, Inc. Silver Spring; Yvette Delph, M.D., ACTG Operations Center, Social and Scientific Systems, Inc. Silver Spring; Nikki Gettinger, M.P.H., ACTG Operations Center, Social and Scientific Systems Inc. Silver Spring; Ann Walawander, M.A., Frontier Science and Technology Research Foundation, Amherst, NY; Apsara Nair, M.S., Frontier Science and Technology Research Foundation, Amherst, NY; Ronald T. Mitsuyasu, M.D., UCLA CARE Center, Los Angeles; Susan A. Fiscus, Ph.D., Department of Microbiology and Immunology, University of North Carolina, School of Medicine, Chapel Hill, NC; Adriana Andrade, M.D., M.P.H., Division of Infectious Diseases, John Hopkins University, Baltimore; David W. Haas, M.D., Infectious Diseases, Vanderbilt University, Nashville; Farida Amod, MB CHB, FCPath, FCP, Department of Medicine, Nelson R Mandela School of Medicine, Durban; Vladimir Berthaud, M.D., Infectious Disease, Vanderbilt University Medical Centre, Nashville; Robert C. Bollinger, M.D., Division of Infectious Diseases, John Hopkins University, Baltimore; Yvonne Bryson, M.D., Pediatric Infectious Disease Dept., UCLA School of Medicine, Los Angeles; David Celentano, Sc.D., M.H.S., Department of Epidemiology, Johns Hopkins School of Hygiene and Public Health, Baltimore; David Chilongozi, C.O., M.P.H., UNC HIVNET, UNC Project, Lilongwe, Malawi; Myron Cohen, M.D., University of North Carolina, Chapel Hill; Ann C. Collier, M.D., University of Washington, ACTU, Harborview Medical Centre, Seattle; Judith Silverstein Currier, M.D., M.Sc, University of Carolina, Los Angeles; Susan Cu-Uvin, M.D., The Miriam Hospital, Brown University, Immunology Centre, Providence; Joseph Eron, M.D., Division of Infectious Diseases, Dept. of Medicine, University of N Carolina; Charles Flexner, M.D., Johns Hopkins University Hospital, Baltimore; Joel E. Gallant, M.D., M.P.H., Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore; Roy M. Gulick, M.D., M.P.H., The Cornell Clinical Trials Unit, New York; Scott M. Hammer, M.D., Division of Infectious Diseases, Columbia Presbyterian Medical Centre, NY; Irving Hoffman, P.A., M.P.H., University of North Carolina, Chapel Hill; Peter Kazembe, MBCHB FRCP(C), Baylor College of Medicine-Abbott Fund Children’s Clinical Centre of Excellence, Lilongwe, Malawi; Newton Kumwenda, M.P.H., Ph.D., Johns Hopkins Project, Malawi; Javier R. Lama, M.D., M.P.H., Investigaciones Medicas en Salud (INMENSA), Lima, Peru; Jody Lawrence, M.D., University of California, San Francisco, Adult AIDS Clinical Trials Unit; Chiedza Maponga, Pharm. D., DaTIS, Medical University of Zimbabwe, Zimbabwe; Francis Martinson, M.D., UNC Project, Lilongwe; Kenneth Mayer, M.D., Division of Infectious Diseases, Brown University School of Medicine, Memorial Hospital of Rhode Island, Pawtucket; Karin Nielsen, M.D., UCLA School of Medicine, Los Angeles; Richard B. Pendame M.D., M.P.H., Malawi; Bharat Ramratnam, M.D. Laboratory of Retrovirology, Division of Infectious Diseases, Brown University Medical School, Providence; Ian Sanne, University of Witwatersrand, Johannesburg, South Africa; Patrice Severe, M.D. Internal Medical, Infectious Diseases, Institute de Laboratories et de Recherches, Haiti; Thira Sirisanthana, M.D., Research Institute for Health Sciences, Chiang Mai University, Thailand; Suniti Solomon, M.D., YRG Centre for AIDS Research and Education, India; Steve Tabet, M.D., University of Washington, Harborview Medical Centre, Seattle; Taha Taha, M.D., Johns Hopkins University, School of Hygiene and Public Health, Baltimore; Charles van der Horst, M.D., Department of Medicine, University of N. Carolina, Chapel Hill; Christine Wanke, M.D., Tufts University School of Medicine, Boston; Joan Gormley, B.S.N., The Miriam Hospital, Immunology Centre, Providence; Cheryl J. Marcus, R.N., B.S.N., University of N. Carolina, Chapel Hill; Beverly Putnam, R.N., M.S.N., University of Colorado Health Sciences, Denver; Smanga Ntshele, Community Advisory Board Member, Durban; Edde Loeliger, M.D., Clinical Development and Medical Affairs, Greenford, Middlesex; Keith A. Pappa, Pharm, D., GlaxoSmithKline, Infectious Diseases Medicine, Triangle Park, NC; Nancy Webb, M.S., Frontier Science and Technology Research Foundation, Inc., Amherst; David L. Shugarts, M.A., University of Colorado Health Sciences, Denver; Mark A. Winters, M.S., Stanford University Medical Center, Division of Infectious Disease, Stanford.; Renard S. Descallar, Joseph Steele, and Howard Jaffe, M.D., Gilead Sciences, Foster City, CA.
The project described was supported by Award Number U01AI068636 from the National Institute of Allergy and Infectious Diseases and supported by National Institute of Mental Health (NIMH), National Institute of Dental and Craniofacial Research (NIDCR).
The project was also supported in part by the General Clinical Research Center Units funded by the National Center for Research Resources.
Also supported in part by the AIDS Clinical Trials Group (ACTG), funded by the National Institute of Allergy and Infectious Diseases, National Institutes of Health: grants AI68636, AI68634, AI69450 and the following grants to individual Clinical Trials Units (CTU): YRG CARE Medical Ctr., VHS CRS (Site 11701) CTU Grant # U01A1069432; Instituto de Pesquisa Clinica Evandro Chagas-Fiocruz CRS (Site 12101) CTU Grant # U01AI069476; College of Med. JHU CRS (Site 30301) CTU Grant # 1U01AI069518-01; Durban Adult HIV CRS (Site 11201) CTU Grant # 1U01AI069426-01; UNC Project, Lilongwe (Site 12001) CTU Grant # 5 U01 AI069518-04; UZ-Parienyatwa CRS (Site 30313) CTU Grant # 1U01AI069436-01; Clinical HIV Research Unit University of Witwatersrand (Site 11101) CTU Grant # AI069463; Chiang Mai Univ. ACTG CRS (Site 11501) CTU Grant # 5 U01 AI069399-04; Hospital Conceicao, Porto Alegre (Site 12201) CTU Grant # 5U01AI069401; Les Centres GHESKIO (Site 30022) CTU Grant # U01 AI069421-05; National AIDS Research Institute (Site 11601); NARI Clinic at Gadikhana Dr. Kotnis Municipal Disp (Site 11602); NARI Clinic at NIV CRS (Site 11603) CTU Grant # 5U01AI069417-04; Asociacion Civil Impacta Salud y Educacion—Miraflores, CRS (Site 11301); Investigaciones Medicas en Salud—INMENSA. Lince CRS (Site 11302) CTU Grant # 5U01AI069438; UT Southwestern Medical Center at Dallas (Site 3751) CTU Grant # 3U01AI046376-05S4; University of Cincinnati (Site 2401) CTU Grant # 1U01AI069513-01; UC Davis School of Medicine (Site 3851, 3852) CTU Grant # AI38858-09S1; University of Colorado Hospital (Site 6101) CTU Grant # AI69450; RR025780; The Ohio State University (Site 2301) CTU Grant # AI069474; Northwestern University CRS (Site 2701); Rush University Medical Center (Site 2702) CTU Grant # AI069471; University of Minnesota (Site 1501) CTU Grant # AI27661; Washington University (Site 2101) CTU Grant # U01AI069495; Duke University Medical Center (Site 1601) CTU Grant # 5U01 AI069 484-02; Beth Israel Medical Center (Site 2851) CTU Grant # AI46370; The Miriam Hospital (Site 2951) CTU Grant # AI069472; University of Southern California (Site 1201) CTU Grant # AI069428; UCLA CARE Center (Site 601); Harbor-UCLA (Site 603) CTU Grant # AI069424; Grant # MOI-RR00865; GCRC Grant # RR00424; University of North Carolina (Site 3201, 3206) CTU Grant # AI069423-03; CFAR Grant # AI050410; CTSA Grant # UL 1RR 025747; Vanderbilt Therapeutics (Site 3652) CTU Grant # AI-069439; Hosp. of the Univ. of Pennsylvania (Site 6201) CTU Grant # 001-AI069467-04; CFAR Grant # P30-AI045008-11; Columbia University—HIV Prevention and Treatment CRS (Site 30329) CTU Grant # AI069470; CTSA Grant # UL1RR024156; New York University/NYC HHC at Bellevue Hospital Center (Site 401) CTU Grant # AI-27665 and AI069532; The University of Texas Medical Branch, Galveston (Site 6301) CTU Grant # AI032782; University of Rochester (Site 1101); AIDS Care (Site 1108) CTU Grant # U01AI069511-02; Cook County Hospital Core Center (Site 2705) CTU Grant # AI25915; University of Hawaii at Manoa (Site 5201) CTU Grant # AI34853; Cornell CRS (Site 7804) CTU Grant # AI-69419; Grant # RR024996.
Data analysis was performed by the ACTG Statistical Data Management Center with the support of grant U01AI68634.
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Allergy and Infectious Diseases or the National Institutes of Health.
Contributor Information
Steven A. Safren, Email: ssafren@partners.org, Massachusetts General Hospital/Harvard Medical School, 1 Bowdoin Square, 7th Floor, Boston, MA 02445, USA.
Ellen S. Hendriksen, Massachusetts General Hospital/Harvard Medical School, 1 Bowdoin Square, 7th Floor, Boston, MA 02445, USA
Laura Smeaton, Harvard School of Public Health, Boston, MA, USA.
David D. Celentano, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
Mina C. Hosseinipour, University of North Carolina School of Medicine, Chapel Hill, NC, USA
Ronald Barnett, National Institutes of Health, Bethesda, MD, USA.
Juan Guanira, Investigaciones Medicas en Salud (INMENSA), Lima, Peru.
Timothy Flanigan, Brown University Medical School/Rhode Island and The Miriam Hospitals, Providence, RI, USA.
N. Kumarasamy, YRG CARE, Chennai, India
Karin Klingman, National Institutes of Health, Bethesda, MD, USA.
Thomas Campbell, University of Colorado Denver School of Medicine, Denver, CO, USA.
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