Differences in health-related quality of life between HIV-positive and HIV-negative people in Zambia and South Africa: A cross-sectional baseline survey of the HPTN 071 (PopART) trial

Abstract

Background:
The life expectancy of HIV-positive individuals receiving antiretroviral therapy (ART) is approaching that of HIV-negative people. However, little is known about how these populations compare in terms of health-related quality of life (HRQoL). We aimed to compare HRQoL between HIV-positive and HIV-negative people in Zambia and South Africa.

Methods:
As part of the HPTN 071 (PopART) study, data from adults aged 18-44 years were gathered between Nov 28, 2013, and March 31, 2015, in large cross-sectional surveys of random samples of the general population in 21 communities in Zambia and South Africa. HRQoL data were collected with a standardised generic measure of health across five domains. We used β-distributed multivariable models to analyse differences in HRQoL scores between HIV-negative and HIV-positive individuals who were unaware of their status; aware, but not in HIV care; in HIV care, but who had not initiated ART; on ART for less than 5 years; and on ART for 5 years or more. We included controls for sociodemographic variables, herpes simplex virus type-2 status, and recreational drug use.

Findings:
We obtained data for 19 750 respondents in Zambia and 18 941 respondents in South Africa. Laboratory-confirmed HIV status was available for 19 330 respondents in Zambia and 18 004 respondents in South Africa; 4128 (21%) of these 19 330 respondents in Zambia and 4012 (22%) of 18 004 respondents in South Africa had laboratory-confirmed HIV. We obtained complete HRQoL information for 19 637 respondents in Zambia and 18 429 respondents in South Africa. HRQoL scores did not differ significantly between individuals who had initiated ART more than 5 years previously and HIV-negative individuals, neither in Zambia (change in mean score -0·002, 95% CI -0·01 to 0·001; p=0·219) nor in South Africa (0·000, -0·002 to 0·003; p=0·939). However, scores did differ between HIV-positive individuals who had initiated ART less than 5 years previously and HIV-negative individuals in Zambia (-0·006, 95% CI -0·008 to -0·003; p<0·0001). A large proportion of people with clinically confirmed HIV were unaware of being HIV-positive (1768 [43%] of 4128 people in Zambia and 2026 [50%] of 4012 people in South Africa) and reported good HRQoL, with no significant differences from that of HIV-negative people (change in mean HRQoL score -0·001, 95% CI -0·003 to 0·001, p=0·216; and 0·001, -0·001 to 0·001, p=0·997, respectively). In South Africa, HRQoL scores were lower in HIV-positive individuals who were aware of their status but not enrolled in HIV care (change in mean HRQoL -0·004, 95% CI -0·01 to -0·001; p=0·010) and those in HIV care but not on ART (-0·008, -0·01 to -0·004; p=0·001) than in HIV-negative people, but the magnitudes of difference were small.

Interpretation:
ART is successful in helping to reduce inequalities in HRQoL between HIV-positive and HIV-negative individuals in this general population sample. These findings highlight the importance of improving awareness of HIV status and expanding ART to prevent losses in HRQoL that occur with untreated HIV progression. The gains in HRQoL after individuals initiate ART could be substantial when scaled up to the population level.

Funding:
National Institute of Allergy and Infectious Diseases, National Institute on Drug Abuse, National Institute of Mental Health, President's Emergency Plan for AIDS Relief, International Initiative for Impact Evaluation, the Bill & Melinda Gates Foundation.

Full text

www.thelancet.com/lancetgh Published online September 27, 2017 http://dx.doi.org/10.1016/S2214-109X(17)30367-4
1
Articles
Lancet Glob Health 2017
Published Online
September 27, 2017
http://dx.doi.org/10.1016/
S2214-109X(17)30367-4
See Online/Comment
http://dx.doi.org/10.1016/
S2214-109X(17)30384-4
*Members of the HPTN 071
(PopART) Study Team listed at
the end of the paper
Department of Economics,
Stellenbosch University,
Stellenbosch, South Africa
(R Burger PhD); Desmond Tutu
TB Centre, Department of
Paediatrics and Child Health,
Stellenbosch University, Cape
Town, South Africa
(A Harper MSc, N Vanga MPhil,
N Bell-Mandla MPH,
P Bock MRCPUK,
Prof N Beyers PhD); ZAMBART
Project, Ridgeway Campus,
University of Zambia, Lusaka,
Zambia (S Kanema BSc,
L Mwenge MSc); Imperial
College Business School
(Prof P C Smith MSc),
Department of Medicine
(S Fidler PhD), and Department
of Infectious Disease
Epidemiology
(K Hauck PhD , R Thomas PhD),
Imperial College London,
London, UK; Department of
Infectious Disease
Epidemiology, Faculty of
Epidemiology and Population
Health (S Floyd MSc,
Prof R Hayes DSc) and
Department of Clinical
Research, Faculty of Infectious
and Tropical Diseases
(H Ayles PhD), London School
of Hygiene & Tropical
Medicine, London, UK; and
Vaccine and Infectious Disease
Division, Fred Hutchinson
Cancer Research Center,
Seattle, WA, USA
(D Donnell PhD)
Differences in health-related quality of life between
HIV-positive and HIV-negative people in Zambia and
South Africa: a cross-sectional baseline survey of the
HPTN 071 (PopART) trial
Ranjeeta Thomas, Ronelle Burger, Abigail Harper, Sarah Kanema, Lawrence Mwenge, Nosivuyile Vanqa, Nomtha Bell-Mandla, Peter C Smith,
Sian Floyd, Peter Bock, Helen Ayles, Nulda Beyers, Deborah Donnell, Sarah Fidler, Richard Hayes, Katharina Hauck, on behalf of the HPTN 071
(PopART) Study Team*
Summary
Background The life expectancy of HIV-positive individuals receiving antiretroviral therapy (ART) is approaching that
of HIV-negative people. However, little is known about how these populations compare in terms of health-related
quality of life (HRQoL). We aimed to compare HRQoL between HIV-positive and HIV-negative people in Zambia and
South Africa.
Methods As part of the HPTN 071 (PopART) study, data from adults aged 18–44 years were gathered between
Nov 28, 2013, and March 31, 2015, in large cross-sectional surveys of random samples of the general population in
21 communities in Zambia and South Africa. HRQoL data were collected with a standardised generic measure of
health across five domains. We used β-distributed multivariable models to analyse dierences in HRQoL scores
between HIV-negative and HIV-positive individuals who were unaware of their status; aware, but not in HIV care; in
HIV care, but who had not initiated ART; on ART for less than 5 years; and on ART for 5 years or more. We included
controls for sociodemographic variables, herpes simplex virus type-2 status, and recreational drug use.
Findings We obtained data for 19 750 respondents in Zambia and 18 941 respondents in South Africa. Laboratory-
confirmed HIV status was available for 19 330 respondents in Zambia and 18 004 respondents in South Africa;
4128 (21%) of these 19 330 respondents in Zambia and 4012 (22%) of 18 004 respondents in South Africa had
laboratory-confirmed HIV. We obtained complete HRQoL information for 19 637 respondents in Zambia and
18 429 respondents in South Africa. HRQoL scores did not dier significantly between individuals who had initiated
ART more than 5 years previously and HIV-negative individuals, neither in Zambia (change in mean score –0·002,
95% CI –0·01 to 0·001; p=0·219) nor in South Africa (0·000, –0·002 to 0·003; p=0·939). However, scores did dier
between HIV-positive individuals who had initiated ART less than 5 years previously and HIV-negative individuals in
Zambia (–0·006, 95% CI –0·008 to –0·003; p<0·0001). A large proportion of people with clinically confirmed HIV
were unaware of being HIV-positive (1768 [43%] of 4128 people in Zambia and 2026 [50%] of 4012 people in South
Africa) and reported good HRQoL, with no significant dierences from that of HIV-negative people (change in mean
HRQoL score –0·001, 95% CI –0·003 to 0·001, p=0·216; and 0·001, –0·001 to 0·001, p=0·997, respectively). In
South Africa, HRQoL scores were lower in HIV-positive individuals who were aware of their status but not enrolled
in HIV care (change in mean HRQoL –0·004, 95% CI –0·01 to –0·001; p=0·010) and those in HIV care but not on
ART (–0·008, –0·01 to –0·004; p=0·001) than in HIV-negative people, but the magnitudes of dierence were small.
Interpretation ART is successful in helping to reduce inequalities in HRQoL between HIV-positive and HIV-negative
individuals in this general population sample. These findings highlight the importance of improving awareness of
HIV status and expanding ART to prevent losses in HRQoL that occur with untreated HIV progression. The gains in
HRQoL after individuals initiate ART could be substantial when scaled up to the population level.
Funding National Institute of Allergy and Infectious Diseases, National Institute on Drug Abuse, National Institute of
Mental Health, President’s Emergency Plan for AIDS Relief, International Initiative for Impact Evaluation, the Bill &
Melinda Gates Foundation.
Copyright © The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
Introduction
The 2015 UNAIDS Fast-Track targets are a call to action to
protect the health of the roughly 19·8 million people
globally with no access to antiretroviral therapy (ART).
The targets stipulate that by 2020, 90% of people with
HIV know their status, 90% of people who know their
status are on ART, and 90% of people on ART have
suppressed viral loads. However, to reach these ambitious

Articles
2
www.thelancet.com/lancetgh Published online September 27, 2017 http://dx.doi.org/10.1016/S2214-109X(17)30367-4
Correspondence to:
Dr Ranjeeta Thomas,
Department of Infectious Disease
Epidemiology, Imperial College
London, London W2 1PG, UK
ranjeeta.thomas@imperial.ac.
uk
targets, UNAIDS estimates that domestic and
international investment in HIV programmes in low-
income and middle-income countries (LMICs) will need
to increase by about a third, from an estimated
US$19·2 billion available in 2014, to $26·2 billion by 2020.1
It is dicult for policy makers to justify the large
investments needed to step up HIV interventions from
current health budgets when faced with many other
urgent public health priorities.
A potentially large immediate benefit of ART, which has
received little attention in policy debates, is its success in
restoring the health-related quality of life (HRQoL) of
people living with HIV. Studies of clinical cohorts have
shown that most individuals at advanced stages of disease
have improved health outcomes when on ART.2,3 However,
little evidence exists about the HRQoL of HIV-positive
people at various stages of engagement in HIV care, when
benchmarked against the attainable HRQoL of the HIV-
negative population. Evidence about the eectiveness of
ART in reducing the extreme inequalities in population
health caused by HIV in high-burden settings is a crucial
piece of evidence missing from the current debate. Such
evidence would garner support for reducing the funding
gap required to achieve the UNAIDS 2020 Fast-Track
90-90-90 targets.
We did this study to compare the HRQoL of people
living with HIV with that of individuals not infected with
HIV.
Methods
Study population and data
We analysed data from a large cross-sectional random
sample survey of the general population that was done in
Zambia and South Africa as part of the HPTN 071
(PopART) study.4 That study was an ongoing cluster-
randomised trial measuring the eect of a combination
prevention intervention on HIV incidence at population
level, measured in a population cohort of randomly
sampled adults who are being followed up for 36 months.
Full details of the study have been published elsewhere.4
The trial has been implemented in 21 study communities:
nine in the Cape Metro District and Cape Winelands
Research in context
Evidence before this study
We searched MEDLINE, PubMed, and Embase on Feb 9, 2016,
for studies published between Jan 1, 1995, and Dec 31, 2015,
published in English, that compared the health-related quality
of life (HRQoL) of people living with HIV with that of the
general population across all World Bank income groups.
We used the search terms “HIV”, “AIDS”, “quality-of-life”, and
“population”. We excluded studies that focused exclusively on
the health of HIV-positive individuals without comparison
with the health of HIV-negative individuals or the general
population, and studies that evaluated a specific health aspect
(eg, depression) and not quality of life across all dimensions,
that focused on specific populations (eg, pregnant mothers,
diamond miners), or patients with adverse events, particular
comorbidities, or co-infections. We identified five studies:
three from high-income countries and two from South Africa.
One study was published in 2014, and the others were at least
12 years old (one was from 2004, two from 2000, and
one from 1996). HIV-positive patient populations differed
between studies; two studies comprised 2864 and
3258 patients at all stages of disease, two studies focused on
72 and 134 patients at earlier disease stages (exclusion
criterion CD4 cell count <200 per µL or acute or terminal
illness), and one study focused on 123 patients with advanced
disease (exclusion criterion CD4 cell count >200 per µL). All
studies found that HRQoL was lower in HIV-positive individuals
than in the general population. The two studies from South
Africa found that HRQoL was compromised across all
dimensions. The three studies from high-income countries
found that HRQoL was most affected by emotional
functioning. One study found that physical functioning was
worse for patients with AIDS, but not for patients with
asymptomatic disease. Almost all previous studies evaluated
HRQoL in HIV patients who attended a clinic, participated in a
clinical study, or were receiving health care. Because these
individuals sought care, their health could have been
compromised and they were therefore not representative of
the general HIV-positive population.
Added value of this study
This study is one of the most extensive and robust analyses of
differences in HRQoL among HIV-positive and HIV-negative
individuals in a random sample of the general population in
sub-Saharan Africa since the rapid scale up of antiretroviral
therapy (ART). HIV status was determined from blood samples
taken during the survey and confirmed with laboratory testing.
We did a direct comparison of HRQoL between HIV-positive
people and HIV-negative people. Furthermore, our study design
enabled adjustment for confounders that were collected for
both groups in the same way. The data are a random sample of
the general population, thus giving an estimate of the HRQoL
of all people living with HIV, not just the most ill. The study
provides a rare insight into the HRQoL of HIV-positive
individuals at different stages of engagement with HIV care,
even those who were not aware of their status or who were
aware but not in HIV care.
Implications of all the available evidence
Our results can be used to estimate how many quality-adjusted
life-years could be gained with HIV treatment because of
reductions in morbidity. This is crucial information for policy
makers to comprehensively assess the societal worth of HIV
interventions aimed at increasing the number of individuals
receiving treatment.

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District of the Western Cape Province of South Africa and
12 in Zambia, spread across four provinces and six districts
(appendix p 2).
The data used in this paper were taken from the baseline
survey of the population cohort done between Nov 28, 2013,
and March 31, 2015, and the laboratory-confirmed HIV
status of all participants. In each of the 21 trial communities,
a random sample of households was selected and visited
by field sta who enumerated all adults aged 18–44 years.
From this list, one adult from each household was
randomly selected and provided informed consent to
participate in the population cohort. Next, the entire
population cohort survey was administered in the
respondent’s preferred language by trained field workers.
The HRQoL questions were embedded as a section in the
population cohort survey. From each respondent, detailed
information was gathered about HIV testing, self-reported
HIV status, sociodemographics, health, and economic and
behavioural aspects. Respondents self-reported their HIV
status. If they self-reported being HIV-positive, they were
asked whether they were in HIV care, and whether and for
how long they had been on ART. After completion of the
survey, a research nurse oered all respondents an on-the-
spot HIV rapid test with pretest and post-test counselling.
HIV status was confirmed by testing of blood samples
drawn from consenting participants (appendix p 3).
HRQoL information was gathered in South Africa with
the certified translation of the EuroQol five dimensions,
five levels questionnaire (EQ-5D-5L).5 Since no certified
translation of the EQ-5D-5L was available for Zambia, the
study team translated the questionnaire into regional
Zambian dialects. The EQ-5D-5L measures HRQoL in
five separate domains (mobility, self-care, ability to do
daily activities, pain, and anxiety or depression) and each
domain is measured with five levels (no problems, slight,
moderate, severe, or unable to; appendix pp 3–4). Because
the questions are not disease specific, the measured
HRQoL of HIV-positive and HIV-negative people can be
directly compared—an important feature for this study.
EQ-5D has been used previously to study HRQoL in the
general population and in people living with HIV in
LMICs and high-income countries,6,7,8 and it is an
appropriate generic tool for measuring HRQoL in patients
with HIV/AIDS.9
A full ethics review of the trial protocol was done by
the ethics committees of the University of Zambia,
Stellenbosch University, the London School of Hygiene &
Tropical Medicine, Imperial College London, and the US
Centers for Disease Control and Prevention.
Statistical analysis
We used multivariate β regression models to evaluate
the eect of HIV status and ART on HRQoL scores. We
selected complementary log–log link functions over
logit, probit, and log–log alternatives on the basis of the
model that minimised Bayesian information criterion.10
Two defining properties of the HRQoL utility score
guided selection of the regression model. First, it has
truncated support (ranging between 0 and 1). Second, as
in the case of other studies,7 it was negatively skewed
with a spike at the upper end of the scale. Such models
have been widely applied in analysing variables that are
constrained between 0 and 1 and are either positively or
negatively skewed.11–13
β regressions are more robust than other commonly
used approaches in estimating covariate eects on
HRQoL.14 We used the betareg routine in Stata (version 14).
Results are presented as marginal eects, whereby a
negative eect represents the magnitude of reduction in
the score. With HIV-negative individuals as the base case,
the model included people with HIV in five categories:
HIV positive and unaware of status (those reporting
being negative or unaware of their status, but confirmed
Zambia
(n=19 750)
South Africa
(n=18 941)
Age (years) 27 (7·2) 29 (7·4)
HRQoL score 0·88 (0·1) 0·89 (0·03)
Sex
Male 5428/19 733 (28%) 5816/18 612 (31%)
Female 14305/19 733 (73%) 12796/18 612 (69%)
Ethnic group
1 5827/19 750 (30%); Bemba 12 048/18 941 (64%); Xhosa
22453/19 750 (12%); Tonga 4803/18 941 (25%); multiracial
3 1547/19 750 (8%); Lozi 526/18 941 (3%); Afrikaner
41404/19 750 (7%); Chewa 1564/18 941 (8%); other
58519/19 750 (43%); other* ··
Christian 19 479/19 680 (99%) 15 140/18 270 (83%)
Educational level
School education less than
grade 8 (primary school)
5544/19 668 (28%) 1472/18 466 (8%)
School education between
grades 8 and 12
(secondary school)
12 808/19 668 (65%) 15 947/18 466 (86%)
College, university, or other
higher education
1316/19 668 (7%) 1047/18 466 (6%)
HSV-2-positive 8117/19 234 (42%) 8870/17 857 (50%)
Use recreational drugs 480/19 629 (2%) 689/18 432 (4%)
Alcohol consumption† 970/19 732 (5%) 1145/18 770 (6%)
HIV-negative 15 202/19 330 (79%) 13 992/18 004 (79%)
HIV-positive‡ 4128/19 330 (21%) 4012/18 004 (22%)
HIV-positive, unaware of status 1768/4128 (43%) 2026/4012 (50%)
HIV-positive, aware of status,
not in HIV care§
487/4128 (12%) 350/4012 (9%)
HIV-positive, in HIV care, not yet
on antiretroviral therapy§
177/4128 (4%) 173/4012 (4%)
HIV-positive, on antiretroviral
therapy§
1585/4128 (38%) 1236/4012 (31%)
Status unknown 111/4128 (3%) 227/4012 (6%)
Data are mean (SD), n (%), or n/N (%). HRQoL=health-related quality of life. HSV-2=herpes simplex virus type 2.
*All other ethnic groups varied between 0·03% and 6·69%. †Participant drinks five or more drinks of alcohol two or
more times a week. ‡Numbers based on laboratory confirmed test results. §Numbers based on responses by those
self-reporting being HIV-positive in the survey.
Table 1: Sample demographics
See Online for appendix
For the protocol see
https://www.hptn.org/sites/
default/files/2016-05/HPTN%20
Protocol%20071%20V.3.0-%20
16%20Nov%202015%20
Final%20%281%29.compressed.
pdf

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as positive from the laboratory tests); HIV positive and
aware of status, but not in HIV care; HIV positive and in
HIV care, but not yet on ART; HIV positive and on ART
initiated within the last 5 years; and HIV-positive people
who initiated ART 5 or more years previously. The model
included the adjustment variables age, sex, education,
religion, ethnic group, herpes simplex virus type 2 status,
and use of recreational drugs. We also included trial
cluster dummy variables to capture community-level
unobservable dierences. We ran models separately for
Zambia and South Africa. The appendix provides results
for alternative specifications.
We analysed the five domains of HRQoL to determine
which domains contributed to the observed eects on
HRQoL. We used seemingly unrelated ordered probit
regressions to take into account that an individual’s
responses in each of the five domains might be correlated.
For example, individuals reporting problems with
mobility might also be more likely to report problems
completing daily activities. This approach is a
generalisation of the standard ordered probit regression
model allowing for the error terms of each individual’s
responses in the five domains to be correlated. In this
case, we had five ordered probit equations (one for each
domain) with error terms correlated across the
five models. Negative marginal eects show the reduction
in the probability of reporting no problems in the specific
domain of health. We did the analysis with the cmp
routine in Stata (version 14).
We used the results of the HRQoL score regressions to
quantify the average quality-adjusted life-years (QALYs)
that might be gained from treatment. For example,
assuming each untreated HIV-positive individual has
10 remaining years of life, irrespective of current age or
disease stage, and those on ART have remaining years of
life according to life tables by country, age, and sex, we
Zambia South Africa
HIV-negative
(n=15 145)*
HIV-positive
(n=4102)*
p value for
difference†
HIV-negative
(n=13 648)*
HIV-positive
(n=3898)*
p value for
difference†
Mobility ·· ·· p<0·0001 ·· ·· p=0·25
No problems walking around 14 727 (97%) 3905 (95%) ·· 13 435 (98%) 3847 (99%) ··
Slight or moderate problems
walking around
389 (3%) 169 (4%) ·· 199 (2%) 48 (1%) ··
Severe problems or unable
to walk around
29 (<1%) 28 (<1%) ·· 14 (<1%) 3 (<1%) ··
Self-care ·· ·· p<0·0001 ·· ·· p=0·18
No problems washing
and dressing myself
14 810 (98%) 3932 (96%) ·· 13 407 (98%) 3842 (99%) ··
Slight or moderate problems
washing and dressing myself
320 (2%) 156 (4%) ·· 235 (2%) 53 (1%) ··
Severe problems or unable
to wash or dress myself
15 (<1%) 14 (<1%) ·· 6 (<1%) 3 (<1%) ··
Daily activities ·· ·· p<0·0001 ·· ·· p=0·38
No problems doing my usual
activities
14 608 (97%) 3860 (94%) 13 337 (98%) 3801 (98%) ··
Slight or moderate problems
doing my usual activities
516 (3%) 226 (6%) ·· 301 (2%) 91 (2%) ··
Severe problems or unable to
do my usual activities
21 (<1%) 16 (<1%) ·· 10 (<1%) 6 (<1%) ··
Pain ·· ·· p<0·0001 ·· ·· p=0·12
No pain or discomfort 13 201 (87%) 3425 (83%) ·· 13 068 (96%) 3710 (95%) ··
Slight or moderate pain
or discomfort
1850 (12%) 640 (16%) ·· 568 (4%) 181 (5%) ··
Severe or extreme pain
or discomfort
94 (<1%) 37 (1%) ·· 12 (<1%) 7 (<1%) ··
Anxiety or depression ·· ·· p<0·0001 ·· ·· p=0·02
Not anxious or depressed 13 873 (92%) 3642 (89%) ·· 13 069 (96%) 3699 (95%) ··
Slightly or moderately
anxious or depressed
1186 (8%) 424 (10%) ·· 540 (4%) 188 (5%) ··
Anxious or depressed 86 (<1%) 36 (1%) ·· 39 (<1%) 11 (<1%) ··
HRQoL score 0·88 (0·04) 0·88 (0·06) 0·89 (0·3) 0·89 (0·4)
Data are n (%), n/N (%), or mean (SD), unless otherwise stated. HRQoL=health-related quality of life. *Numbers based on complete responses to the five dimensions of
HRQoL and laboratory-confirmed HIV status.†p value (Wilcoxon rank-sum test) for the difference between HIV-negative and HIV-positive groups.
Table 2: Five health domain classifications for Zambia and South Africa

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5
can combine the remaining years of life with the predicted
HRQoL scores for each country to generate the value of
remaining years of life, taking into account the extension
of life and HRQoL.
Role of the funding source
The funders of the study had no role in the study design,
data collection, data analysis, data interpretation, or
writing of the report. RT and KH had full access to the
data in the study. RT had final responsibility for the
decision to submit for publication.
Results
The full sample included responses from 19 750 (83%) of
23 676 randomly selected individuals in Zambia and
18 941 (88%) of 21 568 randomly selected individuals in
South Africa. HIV status from laboratory-tested blood
samples was available for 19 330 (98%) participants in
Zambia and 18 004 (95%) participants in South Africa.
4128 (21%) of these 19 330 respondents in Zambia and
4012 (22%) of 18 004 respondents in South Africa had
laboratory-confirmed HIV. 19 637 (99%) participants in
Zambia and 18 429 (97%) participants in South Africa had
complete EQ-5D-5L information.
Prevalence of HIV in the trial communities was similar
in both countries (table 1). A large proportion of HIV-
positive participants were unaware of their status (table 1).
Of HIV-positive participants aware of their HIV status,
more reported being on ART in Zambia than in South
Africa (table 1). Both countries had lower proportions of
male respondents than female respondents (table 1). The
unadjusted results show that HIV-positive people in
Zambia reported lower levels of HRQoL than HIV-
negative people, particularly in the domain of pain,
which had a 4 percentage-point dierence between the
two groups (table 2). Except for a significant dierence in
the domain of anxiety or depression, there was no
dierence in HRQoL between HIV-positive and HIV-
negative individuals in South Africa. Mean HRQoL score
in HIV-positive and HIV-negative people was 0·88 in
Zambia and 0·89 in South Africa (table 2).
Regression results show that, in Zambia, individuals
who initiated ART less than 5 years previously reported
significantly lower HRQoL scores than HIV-negative
individuals (table 3). However, the dierence is small and
unlikely to be clinically meaningful. We recorded no
additional dierences in HRQoL between HIV-negative
and HIV-positive individuals (table 3). Results for South
Africa show that HRQoL did not dier between HIV-
positive individuals on ART and HIV-negative individuals
(table 3). Compared with HIV-negative individuals, small
reductions in HRQoL were reported by HIV-positive
individuals who were aware of their status but not
enrolled in HIV care and those in HIV-care but not yet on
ART (table 3). Although significant, these magnitudes are
again unlikely to represent meaningful reductions
(table 3).
When we analysed the five domains of HRQoL, results
for Zambia showed that HIV-positive individuals who
had initiated ART less than 5 years previously were less
likely than HIV-negative individuals to report no
problems across all five domains (table 4). In both
Zambia
(18 910 observations)
South Africa
(16 805 observations)
HIV-negative (base) 1 (ref) 1 (ref)
HIV-positive, unaware of status –0·001
(–0·003 to 0·001); p=0·216
0·001
(–0·001 to 0·001); p=0·997
HIV-positive, aware of status, not in care –0·002
(–0·01 to 0·001); p=0·223
–0·004
(–0·01 to –0·001); p=0·010
HIV-positive, in care, never taken ART 0·001
(–0·01 to 0·07); p=0·695
–0·008
(–0·01 to –0·004); p=0·0001
HIV-positive, initiated ART less than 5 years
ago
–0·006
(–0·008 to –0·003);
p<0·0001
–0·001
(–0·003 to 0·000); p=0·140
HIV-positive, initiated ART 5 years or more
ago
–0·002
(–0·01 to 0·001); p=0·219
0·000
(–0·002 to 0·003); p=0·939
Age 18–25 years (base) 1 (ref) 1 (ref)
Age 25–34 years –0·003
(–0·004 to –0·001);
p<0·0001
0·00
(0·001 to 0·001); p=0·513
Age >35 years –0·01
(–0·009 to –0·006);
p<0·0001
–0·002
(–0·003 to –0·001); p=0·0002
Women (base) 1 (ref) 1 (ref)
Men 0·001
(0·000 to 0·002); p=0·151
0·001
(0·001 to 0·002); p=0·001
Bemba (base Zambia), Xhosa (base South
Africa)
1 (ref) 1 (ref)
Tonga (Zambia), multiracial (South Africa) 0
(–0·002 to 0·002); p=0·827
0
(–0·001 to 0·001); p=0·0991
Lozi (Zambia), Afrikaner (South Africa) 0·002
(–0·001 to 0·004); p=0·149
–0·001
(–0·004 to 0·002); p=0·0446
Chewa (Zambia) 0
(–0·002 to 0·002); p=0·901
··
Other –0·001
(–0·002 to 0·001); p=0·370
0
(–0·001 to 0·002); p=0·0618
Other religion (base) 1 (ref) 1 (ref)
Christian 0·001
(–0·004 to 0·006); p=0·727
0·001
(0·000 to 0·002); p=0·037
School education less than grade 8
(primary school, base)
·· ··
School education between grade 8 and 12
(secondary school)
0·002
(0·000 to 0·003); p=0·013
0·003
(0·002 to 0·01); p<0·0001
College, university, or other higher education 0·002
(–0·001 to 0·004); p=0·112
0·004
(0·002 to 0·006); p=0·0007
HSV-2-negative (base) 1 (ref) 1 (ref)
HSV-2-positive –0·001
(–0·002 to 0·000); p=0·088
0·001
(–0·000 to 0·002); p=0·102
Does not use recreational drugs (base) 1 (ref) 1 (ref)
Uses recreational drugs –0·01
(–0·01 to –0·002);
p=0·0009
–0·002
(–0·004 to 0·000); p=0·067
Community fixed effects Yes Ye s
Data are change in mean health-related quality of life score (95% CI), unless otherwise stated. For all factor variables,
each category is compared with the base category. ART=antiretroviral treatment. HSV-2=herpes simplex virus type 2.
Table 3: Multivariable analysis of factors associated with health-related quality of life

Articles
6
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countries, HIV-positive individuals on ART for at least
5 years had a similar HRQoL to HIV-negative individuals
across all five domains (table 4). In South Africa,
individuals in HIV care who had never taken ART were
less likely than HIV-negative individuals to report no
problems with mobility, self-care, and daily activities
(table 4). In both countries, individuals aware of their
HIV-positive status but not in HIV care were significantly
less likely to report no anxiety or depression than were
HIV-negative individuals (table 4).
We estimate that, on average, each HIV-positive
individual on ART would gain 26·24 QALYs in
South Africa and 26·20 QALYs in Zambia, compared
with an untreated individual. If we project these data to
the UNAIDS 2016 estimates of 3·64 million individuals
not yet on ART in South Africa, treating 90% of these
individuals would equate to a gain of roughly 86 million
QALYs as a direct benefit. Similar estimates for Zambia
would mean 10·4 million QALYs could be gained
from reaching 90% of the 0·44 million HIV-positive
individuals not yet on ART.
Discussion
To our knowledge, this is the first and largest study to
evaluate the dierences in HRQoL between HIV-positive
and HIV-negative individuals since the expansion of
ART in LMICs with high HIV burden. Unlike most
previous studies, which compared the HRQoL of HIV
patients at clinics (who are often at advanced disease
stages) with the HRQoL of the general population, this
study is the first to evaluate HRQoL by awareness of
infection and ART status in a random sample from the
general population, using laboratory-confirmed HIV
status. We estimated several multivariable models with
dierent categorisations of HIV status. We did analyses
separately for Zambia and South Africa. Although a
multicountry analysis provides valuable added insight,
the two countries have very dierent population and
health-system characteristics; therefore, we refrained
from a direct comparison of results between countries.
38% of HIV-positive individuals in Zambia and 31% in
South Africa were receiving ART, and receipt of
treatment raised their HRQoL to that of HIV-negative
individuals. The only exception was individuals in
Zambia who had initiated ART less than 5 years
previously, who reported a lower HRQoL score than
HIV-negative individuals; however, the dierence was
very small. Roughly 4% of HIV-positive people in both
countries were in care and had not started ART. In
South Africa, these individuals had lower HRQoL than
HIV-negative individuals. This finding was due to the
dimensions of mobility, self-care, and problems in doing
daily activities, but dierences in scores were small
when compared with HIV-negative people. 12% of HIV-
positive people in Zambia and 9% of those in South Africa
were aware of their status but not linked to care. In both
countries, these individuals were more likely to report
Zambia (n=18 964 observations) South Africa (n=16 886 observations)
Mobility Self-care Daily activities Pain Anxiety
or depression
Mobility Self-care Daily activities Pain Anxiety
HIV-positive, unaware
of status
–0·01
(–0·02 to 0·00);
p=0·102
–0·01
(–0·01 to 0·00);
p=0·180
–0·003
(–0·01 to 0·01);
p=0·508
0·001
(–0·02 to 0·02);
p=0·957
–0·004
(–0·02 to 0·01);
p=0·604
0·001
(–0·00 to 0·01);
p=0·820
0·001
(–0·00 to 0·01);
p=0·614
0·001
(–0·01 to 0·01);
p=0·797
0·01
(–0·00 to 0·02);
p=053
0·01
(–0·00 to 0·01);
p=0·165
Aware of HIV-positive
status, not in care
0·001
(–0·01 to 0·01);
p=0·909
0·003
(–0·01 to 0·01);
p=0·612
–0·011
(–0·03 to 0·01);
p=0·188
–0·024
(–0·06 to 0·01);
p=0·121
–0·03
(–0·06 to –0·002);
p=0·037
0
(–0·01 to 0·01);
p=0·921
–0·01
(–0·03 to 0·00);
p=0·127
–0·02
(–0·04 to 0·003);
p=0·068
–0·015
(–0·04 to 0·01);
p=0·151
–0·03
(–0·06 to –0·005);
p=0·016
Aware of HIV-positive
status, in care, never
taken ART
–0·004
(–0·03 to 0·02);
p=0·719
–0·03
(–0·06 to –0·00);
p=0·033
–0·02
(–0·05 to 0·01);
p=0·274
0·03
(–0·01 to 0·07);
p=0·170
0·02
(–0·02 to 0·05);
p=0·345
–0·04
(–0·07 to –0·01);
p=0·015
–0·03
(–0·05 to –0·003);
p=0·034
–0·06†
(–0·10 to –0·02);
p=0·002
–0·03
(–0·07 to 0·003);
p=0·070
–0·02
(–0·05 to 0·01);
p=0·204
Initiated ART less than
5 years ago
–0·02
(–0·03 to –0·01);
p=0·002
–0·02
(–0·03 to –0·01);
p=0·002
–0·02
(–0·03 to –0·01);
p=0·004
–0·04
(–0·06 to –0·01);
p=0·002
–0·03
(–0·05 to –0·01);
p=0·001
–0·01
(–0·02 to 0·001);
p=0·080
–0·01
(–0·02 to 0·003);
p=0·173
–0·02
(–0·03 to –0·00);
p=0·018
–0·01
(–0·03 to 0·002);
p=0·073
–0·02
(–0·03 to 0·002);
p=0·051
Initiated ART at least
5 years ago
–0·01
(–0·03 to 0·00);
p=0·125
–0·002
(–0·01 to 0·01);
p=0·697
–0·015
(–0·03 to 0·00);
p=0·085
–0·01
(–0·04 to 0·02);
p=0·503
–0·01
(–0·03 to 0·01);
p=0·438
–0·002
(–0·01 to 0·01);
p=0·667
0·002
(–0·01 to 0·01);
p=0·749
–0·01
(–0·02 to 0·01);
p=0·387
–0·002
(–0·02 to 0·02);
p=0·766
0·01
(–0·01 to 0·02);
p=0·471
Data are marginal effects (95% CI). HIV-negative is the base category. A negative marginal effect shows the reduction in probability of reporting “no problems”. Models include the covariates age, gender, education, ethnic group, religion, uses recreational
drugs, and herpes simplex virus type 2 status. ART=antiretroviral therapy.
Table 4: Multivariable analysis of dimensions of health-related quality of life in Zambia and South Africa

Articles
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7
being anxious or depressed than people without HIV. A
high proportion of HIV-positive individuals were
unaware of their status (43% in Zambia, 50% in
South Africa). In both countries, these individuals
reported the same HRQoL as HIV-negative individuals,
possibly representing the asymptomatic nature of HIV
infection in its earlier stages.
Modelling estimates for KwaZulu-Natal suggest that it
would take an average of 4·9 years for 50% of HIV
seroconverters to be linked to care.15 Our findings
support the observation that, at any one time, most HIV-
positive people do not receive care and are not even
aware of their status, but report good health. Overall, our
estimates of dierences are small and possibly not
clinically relevant at the individual level. However, when
scaled up to population level, they constitute a substantial
loss in QALYs. Our calculations suggest that nearly
100 million QALYs could be gained across the two
countries if 90% of currently untreated individuals are
on ART, but most of these gains are due to extension in
length of life. Other research has shown that early
mortality rates among adults accessing ART are high in
the first year of ART in sub-Saharan Africa,16 and that
many people enter care at an advanced stage of disease
and with clinically significant comorbidities.17 Our
findings call for strategies to avoid losses in HRQoL that
occur before individuals receive ART, by aiming at early
diagnosis, timely initiation of ART, and improvement of
adherence. Delays in health-systems initiation of ART
must be minimised, especially in patients who present
with advanced immunodeficiency.
Previous studies from high-income countries6,18–20 and
LMICs21,22 found that average HRQoL of HIV-positive
individuals was overall lower than that of HIV-negative
individuals. However, evidence is contradictory as to
whether HIV-positive individuals with asymptomatic
disease or viral suppression have the same20 or lower6
HRQoL than HIV-negative people. We found smaller
magnitudes of dierences in HRQoL, by contrast with
previous studies that compared clinical cohorts with the
general population. In our sample from the general
population, almost 60% of HIV-positive people belonged
to one of two groups—either unaware of their status and
potentially still in good health, or stable on ART for over
5 years and therefore also in relatively good health.
Therefore, comparison of our findings with previous
studies is problematic. Additionally, all but one of these
studies was done before access to testing and treatment
was accelerated. Most previous studies also sampled
patients enrolled in HIV care, who were likely to be at a
more advanced stage of disease and not representative of
the overall population of people living with HIV.18,20–22
The main strengths of this study are that data were
gathered recently, covered a large sample of the general
population, comprised both HIV-negative and HIV-
positive people from two countries, and enabled
adjustment for several confounders that were collected
for both groups in the same way. This approach allowed
us to provide a rare insight into the HRQoL of HIV-
positive individuals at dierent stages of engagement
with HIV care, including those who were not aware of
their status or who were aware but not in HIV care. As
the largest survey of HRQoL in these countries, our
survey findings provide an important resource of quality-
of-life estimates for future studies.
Our study has limitations. Blood samples from
respondents were tested for their HIV status, but no
information about disease stage was available. Therefore,
we could not dierentiate HRQoL by confirmed disease
stage. However, evidence shows that in sub-Saharan
Africa, mean CD4 cell count at ART initiation has
remained at about 152 per µL in the past decade.17 The
group of individuals on ART in our study is thus likely to
have been in more advanced clinical stages of HIV at
treatment initiation, with associated lower HRQoL. Our
results suggest that, with ART, average HRQoL scores
recover to levels in the general population, a finding
corroborated by clinical studies.3 We relied on self-reports
of ART initiation, which might have been aected by
recall bias. Men were under-represented in the sample
because the survey was done during the day and fewer
men were available at home for interviews. This
imbalance might have biased results if there were
systematic dierences in reported HRQoL between sexes.
Results from previous studies have suggested that women
might report lower HRQoL than men at similar disease
stages, but these studies used a dierent instrument.23,24
Although we adjusted for a large number of possible
confounders, some could have been unobserved and
could have aected results if they diered systematically
by HIV status. We had to use the health state valuations
for Zimbabwe because valuations were not available for
South Africa or Zambia. Stigma has been shown to
substantially aect mental health of HIV-positive
individuals,25 but this influence could be captured by the
anxiety or depression dimension of the EQ-5D-5L.
The unique design of our study allowed us to identify
the success of ART in reducing inequalities between the
HRQoL of HIV-infected individuals and the HIV-negative
population. But our findings are also a call to step up
eorts to extend these benefits to the millions of people
not yet on ART. Improved access to ART is considered the
main reason for the marked increase in overall life
expectancy in sub-Saharan Africa over the last decade.26–28
Additionally, ART can reduce rates of sexual transmission
of HIV,29 and substantial reductions in incidence, with
associated savings in future treatment costs, have been
predicted.30–35 However, the beneficial eect of ART on the
HRQoL of HIV-positive individuals is often not the focus
of attention. This noteworthy and direct benefit of
treatment could provide important additional support to
international advocacy eorts for the UNAIDS Fast-Track
targets. Policy makers should remember the purpose of
medical treatment is to add years to life, and life to years.

Articles
8
www.thelancet.com/lancetgh Published online September 27, 2017 http://dx.doi.org/10.1016/S2214-109X(17)30367-4
Contributors
RT and KH both developed the research idea. RT developed and led on
the statistical analysis and contributed to writing the Article. KH took
the lead on writing and revising the Article. All other authors
commented on the Article and approved the final version.
HPTN 071 (PopART) Study Team
James Hargreaves (London School of Hygiene & Tropical Medicine,
London, UK), Deborah Watson-Jones (London School of Hygiene &
Tropical Medicine, London, UK), Peter Godfrey-Faussett (London
School of Hygiene & Tropical Medicine, London, UK), Anne Cori
(Imperial College London, London, UK), Mike Pickles (Rady Faculty of
Health Sciences, University of Manitoba, MB, Canada), Nomtha Mandla
(Desmond Tutu TB Centre, Stellenbosch University, Stellenbosch,
South Africa), Blia Yang (Desmond Tutu TB Centre, Stellenbosch
University, Stellenbosch, South Africa), Anelet James (Desmond Tutu
TB Centre, Stellenbosch University, Stellenbosch, South Africa),
Redwaan Vermaak (Desmond Tutu TB Centre, Stellenbosch University,
Stellenbosch, South Africa), Nozizwe Makola (Desmond Tutu TB
Centre, Stellenbosch University, Stellenbosch, South Africa),
Graeme Hoddinott (Desmond Tutu TB Centre, Stellenbosch University,
Stellenbosch, South Africa), Vikesh Naidoo (Desmond Tutu TB Centre,
Stellenbosch University, Stellenbosch, South Africa), Virginia Bond
(London School of Hygiene & Tropical Medicine, London, UK, and
Zambart, University of Zambia School of Medicine, Lusaka, Zambia),
Musonda Simwinga (Zambart, University of Zambia School of
Medicine, Lusaka, Zambia), Alwyn Mwinga (Zambart, University of
Zambia School of Medicine, Lusaka, Zambia), Barry Koslo (Zambart,
University of Zambia School of Medicine, Lusaka, Zambia),
Mohammed Limbada (Zambart, University of Zambia School of
Medicine, Lusaka, Zambia), Justin Bwalya (Zambart, University of
Zambia School of Medicine, Lusaka, Zambia), Chepela Ngulube
(Zambart, University of Zambia School of Medicine, Lusaka, Zambia),
Christophe Fraser (Nueld Department of Medicine, Oxford University,
Oxford, UK), Susan Eshleman (Department of Pathology,
Johns Hopkins University School of Medicine, Baltimore, MD, USA),
Yaw Agyei (Department of Pathology, Johns Hopkins University School
of Medicine, Baltimore, MD, USA), Vanessa Cummings (Department of
Pathology, Johns Hopkins University School of Medicine, Baltimore,
MD, USA), Denni Catalano (Department of Pathology, Johns Hopkins
University School of Medicine, Baltimore, MD, USA), Lynda Emel
(Vaccine and Infectious Disease Division, Fred Hutchinson Cancer
Research Center, Seattle, WA, USA), Lisa Bunts (Vaccine and Infectious
Disease Division, Fred Hutchinson Cancer Research Center, Seattle,
WA, USA), Heather Noble (Vaccine and Infectious Disease Division,
Fred Hutchinson Cancer Research Center, Seattle, WA, USA),
David Burns (Division of AIDS, National Institute of Allergy and
Infectious Diseases, National Institutes of Health, Bethesda, MD, USA),
Alain Kouda (Division of AIDS, National Institute of Allergy and
Infectious Diseases, National Institutes of Health, Bethesda, MD, USA),
Niru Sista (FHI 360, Durham, NC, USA), Ayana Moore (FHI 360,
Durham, NC, USA), Rhonda White (FHI 360, Durham, NC, USA),
Tanette Headen (FHI 360, Durham, NC, USA), Eric Miller (FHI 360,
Durham, NC, USA), Kathy Hinson (FHI 360, Durham, NC, USA),
Sten Vermund (Yale University, New Haven, CT, USA), Mark Barnes
(Ropes & Gray, Boston, MA, USA), Lyn Horn (Desmond Tutu TB
Centre, Stellenbosch University, Stellenbosch, South Africa),
Albert Mwango (Zambart, University of Zambia School of Medicine,
Lusaka, Zambia), Megan Baldwin (Vaccine and Infectious Disease
Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA),
Shauna Wolf (Department of Pathology, Johns Hopkins University
School of Medicine, Baltimore, MD, USA), and Erin Hughes (Vaccine
and Infectious Disease Division, Fred Hutchinson Cancer Research
Center, Seattle, WA, USA).
Declaration of interests
RH, RT, HA, SFi, KH, SF, AH, SK, NV, PB, NB, and NB-M report grants
from National Institutes of Health (NIH), the President’s Emergency
Plan for AIDS Relief (PEPFAR), and the International Initiative for
Impact Evaluation (3ie), during the conduct of the study. DD reports
grants from NIH/National Institute of Allergy and Infectious Diseases
and PEPFAR during the conduct of the study. SFi reports grants from
UK Medical Research Council, Viiv, and GlaxoSmithKline, outside of the
submitted work. LM reports grants from 3ie and the Bill & Melinda
Gates Foundation during the conduct of the study. In addition,
RH receives royalties for a textbook on Cluster Randomised Trials.
RT reports personal fees from the International Decision Support
Initiative, outside of the submitted work; KH reports personal fees from
International Decision Support Initiative and personal fees from KPMG,
outside of the submitted work. HA reports personal fees from Gilead
and the Global Fund for AIDS, Tuberculosis and Malaria, outside of the
submitted work. PCS reports personal fees from the International
Decision Support Initiative, WHO, Inter-American Development Bank,
World Bank, European Commission, Finnish Ministry of Social Aairs
and Health, and Health Foundation, outside of the submitted work. RB
reports grants from South African National Research Fund Research
Career Advancement fellowship during the conduct of the study.
Acknowledgments
We are grateful to all members of the HPTN 071 (PopART) Study
Team and to the study participants and their communities for their
contributions to this research. HPTN 071 is sponsored by the National
Institute of Allergy and Infectious Diseases (NIAID) under
Cooperative Agreements UM1-AI068619, UM1-AI068617, and
UM1-AI068613, with funding from PEPFAR. Additional funding is
provided by 3ie with support from the Bill & Melinda Gates
Foundation, as well as by NIAID, the National Institute on Drug Abuse
(NIDA), and the National Institute of Mental Health (NIMH), all part
of NIH. The content is solely the responsibility of the authors and does
not necessarily represent the ocial views of the NIAID, NIMH,
NIDA, PEPFAR, 3ie, or the Bill & Melinda Gates Foundation. KH was
also partly funded by the National Institute for Health Research Health
Protection Research Unit in Modelling Methodology at Imperial
College London in partnership with Public Health England, and by the
MRC Centre for Outbreak Analysis and Modelling (funding
reference MR/K010174/1B).
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