Mortality of HIV-Infected Patients Starting Antiretroviral
Therapy in Sub-Saharan Africa: Comparison with HIV-
Martin W. G. Brinkhof1, Andrew Boulle2, Ralf Weigel3, Euge `ne Messou4, Colin Mathers5, Catherine
Orrell6, Franc ¸ois Dabis7, Margaret Pascoe8, Matthias Egger1,9* for the International epidemiological
Databases to Evaluate AIDS (IeDEA)
1Institute of Social and Preventive Medicine (ISPM), University of Bern, Switzerland, 2Infectious Diseases Epidemiology Unit, School of Public Health and Family Medicine,
University of Cape Town, South Africa, 3Lighthouse Clinic, Lilongwe, Malawi, 4Centre de Prise en Charge de Recherches et de Formation, Abidjan, Co ˆte d’Ivoire,
5Information, Evidence and Research Cluster, World Health Organization (WHO), Geneva, Switzerland, 6The Desmond Tutu HIV Centre, Institute of Infectious Disease and
Molecular Medicine, University of Cape Town, South Africa, 7Institut de Sante ´ Publique, d’Epide ´miologie et de De ´veloppement (ISPED), Universite ´ Victor Segalen,
Bordeaux, France, 8Newlands Clinic, Harare, Zimbabwe, 9Department of Social Medicine, University of Bristol, United Kingdom
Background: Mortality in HIV-infected patients who have access to highly active antiretroviral therapy (ART) has declined in
sub-Saharan Africa, but it is unclear how mortality compares to the non-HIV–infected population. We compared mortality
rates observed in HIV-1–infected patients starting ART with non-HIV–related background mortality in four countries in sub-
Methods and Findings: Patients enrolled in antiretroviral treatment programmes in Co ˆte d’Ivoire, Malawi, South Africa, and
Zimbabwe were included. We calculated excess mortality rates and standardised mortality ratios (SMRs) with 95%
confidence intervals (CIs). Expected numbers of deaths were obtained using estimates of age-, sex-, and country-specific,
HIV-unrelated, mortality rates from the Global Burden of Disease project. Among 13,249 eligible patients 1,177 deaths were
recorded during 14,695 person-years of follow-up. The median age was 34 y, 8,831 (67%) patients were female, and 10,811
of 12,720 patients (85%) with information on clinical stage had advanced disease when starting ART. The excess mortality
rate was 17.5 (95% CI 14.5–21.1) per 100 person-years SMR in patients who started ART with a CD4 cell count of less than 25
cells/ml and World Health Organization (WHO) stage III/IV, compared to 1.00 (0.55–1.81) per 100 person-years in patients
who started with 200 cells/ml or above with WHO stage I/II. The corresponding SMRs were 47.1 (39.1–56.6) and 3.44 (1.91–
6.17). Among patients who started ART with 200 cells/ml or above in WHO stage I/II and survived the first year of ART, the
excess mortality rate was 0.27 (0.08–0.94) per 100 person-years and the SMR was 1.14 (0.47–2.77).
Conclusions: Mortality of HIV-infected patients treated with combination ART in sub-Saharan Africa continues to be higher
than in the general population, but for some patients excess mortality is moderate and reaches that of the general
population in the second year of ART. Much of the excess mortality might be prevented by timely initiation of ART.
Please see later in the article for the Editors’ Summary.
Citation: Brinkhof MWG, Boulle A, Weigel R, Messou E, Mathers C, et al. (2009) Mortality of HIV-Infected Patients Starting Antiretroviral Therapy in Sub-Saharan
Africa: Comparison with HIV-Unrelated Mortality. PLoS Med 6(4): e1000066. doi:10.1371/journal.pmed.1000066
Academic Editor: David R. Bangsberg, San Francisco General Hospital, United States of America
Received September 22, 2008; Accepted March 13, 2009; Published April 28, 2009
Copyright: ? 2009 Brinkhof et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by the Office of AIDS Research (OAR) of the National Institutes of Health, the Agence Nationale de Recherches sur le SIDA et
les He ´patites Virales (ANRS), and the National Institute of Allergy and Infectious Diseases (NIAID, grant 1 U01 AI069924-01). The funders had no role in study
design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
Abbreviations: ART, antiretroviral therapy; eHR, excess hazard ratio; IQR, interquartile range; SMR, standardised mortality ratio; WHO, World Health Organization.
* E-mail: email@example.com
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The widespread use since 1996 of highly active antiretroviral
therapy (ART) has substantially improved the prognosis of HIV-
infected patients both in industrialised and low-income settings .
Recent studies from industrialised countries have suggested that all-
cause mortality in patients successfully treated with ART might
approach that of the general population, and that in many patients
mortality rates are comparable to those associated with other chronic
conditions, such as diabetes [2–6]. Such comparisons are important
to gain a better understanding of the treated history of HIV infection,
to monitor and predict the progress of the HIV/AIDS epidemic, and
to plan health services in the era of potent ART.
As a result of scaling up of ART in low- and middle-income
countries, 2.99 million people living with HIV/AIDS were
estimated to be receiving treatment at the end of 2007,
representing 31% of the estimated 9.6 million people in urgent
need of treatment in these countries . In sub-Saharan Africa,
the number of patients on combination ART increased from 1.38
million to 2.12 million from 2006 to 2007. Although the
immunological and virological responses to treatment in re-
source-limited countries can equal that in high-income settings
[1,8–10], mortality of patients starting ART has been substantially
higher than in industrialised countries, particularly in the first few
months of treatment [1,10,11]. To our knowledge, no studies have
compared mortality among HIV-infected people starting ART in
sub-Saharan Africa with the non-HIV–related background
We analysed data from a network of treatment programmes in
sub-Saharan Africa to compare mortality rates observed in HIV-
1–infected patients starting ART with non-HIV–related mortality
in four countries in sub-Saharan Africa.
The International Epidemiological Databases to Evaluate
Analyses were based on cohorts participating in the West
African and Southern African regions of the International
epidemiological Databases to Evaluate AIDS (IeDEA) . The
databases are regularly updated; the November 2007 version was
used for the present analysis. We restricted analyses to five large
treatment programmes in four sub-Saharan African countries,
including two treatment programmes in townships in the greater
Cape Town metropolitan area, Khayelitsha  and Gugulethu
, South Africa; the Lighthouse clinic in Lilongwe, Malawi
; the Centre de Prise en Charge de Recherches et de
Formation (CEPREF)/Agence National de Recherches sur le Sida
(ANRS) 1203 cohort from Abidjan, Co ˆte d’Ivoire ; and the
Connaught Clinic in Harare, Zimbabwe . All patients aged 16
y or older who were ART-naı ¨ve at the start of ART were included.
ART was defined as any combination of three antiretroviral drugs.
Loss to follow-up was assumed in patients who were not known to
have died and who were not seen for at least 1 y before closing the
database for the present analysis. The local Ethics Committees of
all clinics approved participation in IeDEA, which was also
approved by the Cantonal Ethics Committee in Bern, Switzerland.
Estimates of HIV-Free Background Mortality
Country-specific rates of all-cause mortality and HIV-free
mortality by sex and 5-y age groups were obtained from the
World Health Organization (WHO) Global Burden of Disease
project . Beginning with the year 1999, WHO has been
producing annual life tables for all member states. A key use of
these tables is the calculation of healthy life expectancy (HALE),
the basic indicator of population health published each year in the
World Health Report . The methods used to estimate all-
cause and cause-specific mortality have been described in detail
elsewhere [19,20]. Briefly, life tables based on vital registration
data, corrected for under registration of deaths using demographic
techniques, were used to estimate all-cause mortality in South
Africa and Zimbabwe. In Co ˆte d’Ivoire and Malawi data from
other sources, such as census and surveys, were applied to a
modified logit life-table model, using a global standard [20,21].
For all four countries Joint United Nations Programme on
HIVAIDS (UNAIDS) estimates of HIV/AIDS mortality were
used, on the basis of epidemiological models and sentinel
surveillance data on HIV seroprevalence .
Multiple Imputation of Missing Individual Patient Data
Information on the CD4 count, clinical stage at the start of
ART, and vital status at the last contact date was missing in some
patients. Vital status was considered missing if the patient was not
known to have died and the last date of information was less than 2
y after starting ART or before the administrative closure date of
the cohort, whatever came first. We used multiple imputation by
chained equation methods to impute missing information .
Multiple imputation included the outcome, i.e., whether or not a
patient had died. Baseline CD4 cell count, clinical stage of disease,
and survival time after censoring were imputed conditional on
each other as well as on age and sex. All prediction equations
included cohort, log age at start of ART, and sex. To optimise the
imputation procedure we further included available clinical
information on baseline viral load, total lymphocyte count, and
haemoglobin; since females had lower haemoglobin levels the
interaction between haemoglobin and sex was also fitted.
Continuous variables were normalised prior to imputation
modelling if needed, using log-transformation for age at start of
ART and survival time, and square-root transformation for the
baseline CD4. Interval censoring was used for baseline CD4 and
survival time to ensure imputation values within the appropriate
range. To impute survival time we used the complete follow-up
history of all patients and used a log distribution to sample survival
time after censoring in patients for which no death was recorded.
The imputation of log survival time involved left-censoring at the
date of last information, but no right-censoring. In the analysis we
right-censored survival time at 2 y or at the closure date of the
cohort. We created 20 imputed datasets in total. We analyzed
imputed datasets using Poisson regression models (see below) to
examine the association of time on treatment (months 1–3, 4–6, 7–
12, and 13–24 after start of ART) and patient characteristics at
baseline as risk factors of relative survival. Estimates of coefficients
were derived by averaging, and appropriate standard errors were
calculated using the within and between imputation standard
errors of the estimates using Rubin’s rules .
Modelling of Standardised Mortality Ratios and Excess
We quantified mortality of HIV-infected patients on ART
relative to the mortality in the general population using excess
mortality and standardised mortality ratios (SMRs). The excess
mortality risk is derived using an additive model, by subtracting
age- and sex-specific HIV-unrelated mortality rates in the
reference population from mortality in HIV-infected patients.
SMRs are based on a multiplicative model and calculated as the
Mortality on ART in Africa
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ratio of the number of observed deaths over the expected deaths,
using age- and sex-specific rates of HIV-unrelated mortality from
the reference population. The SMR thus quantifies how much
higher mortality is in HIV-infected patients compared to the
reference population, but gives no indication of the excess
mortality in absolute terms. Excess mortality and SMRs with
95% confidence intervals (CIs) were obtained from generalised
linear models with a Poisson error structure, as described by
Dickman and colleagues . The expected number of deaths due
to causes other than HIV d*jfor observation j was calculated by
multiplying the person-time at risk yjby the corresponding sex,
age- (in 5-y age groups), and country-specific rates of HIV-free
mortality. The excess mortality model assumes piecewise constant
hazards lj, implying a Poisson process for the number of deaths dj
in each interval. The generalised linear model with Poisson error
structure for outcome djinvolves offset ln (yj) and the user-defined
link function ln (mj2 d*j), where mj=ljyj. In SMR modelling djis
modelled with offset ln (d*j). Robust standard errors were used to
account for the clustering of data on treatment programme.
Significance testing was by Wald tests.
Multivariable models were calculated for excess mortality on the
20 imputed datasets. The interpretation of the excess hazard ratios
(eHRs) from these models is similar to that of the hazard ratio in the
familiar Cox model. For example, an eHR of 0.80 for females
relative to males would indicate that females have a 20% lower risk
of death as compared to males, after controlling for the variation in
background mortality. The following variables were included: age,
sex,ARTregimen, baselineCD4cell count,clinical stage of disease,
and calendar periodof starting ART.Time periodsconsideredwere
months 1–3, 4–6, 7–12, and 13–24 after start of ART. ART
regimen was defined as non-nucleoside reverse transcriptase
inhibitor (NNRTI)-based, protease inhibitor (PI)-based, and other.
Baseline CD4 count was analysed in five categories (0–24, 25–49,
50–99, 100–199, and 200 or more cells/ml). Clinical stage of disease
was defined as less advanced (WHO stage I or stage II) or advanced
(WHO stage III or stage IV). In a sensitivity analysis we excluded
two sites with high loss to follow-up. All analyses were done in Stata
version 10.0 (Stata Corporation), using the ‘‘ice’’ routine for
imputation of missing values.
Treatment Programmes and Patient Characteristics
The combined dataset included 13,249 patients. Table 1
describes the five treatment programmes from four sub-Saharan
African countries. Patient numbers ranged from 857 patients
(Connaught clinic, Zimbabwe) to 4,710 patients (Lighthouse clinic,
Malawi). The majority of patients in each of the treatment
programmes were women, the median age ranged from 32 to 37 y.
The median baseline CD4 cell count ranged from 87 cells/ml in
Khayelitsha, South Africa to 131 cells/ml in Abidjan, Co ˆte
d’Ivoire, and the proportion with advanced clinical stage of disease
(WHO stage III/IV) from 68% (Connaught) to 90% (Khayelitsha).
A total of 1,177 deaths were recorded during 14,695 person-years
of follow-up. Crude estimates of cumulative mortality at 2 y on
ART ranged from 7.4% to 12.3%, and loss to follow-up from
7.1% to 31.7% across programmes.
Information on the CD4 count and clinical stage at the start of
ART was missing for 2,535 patients (19.1%) and 529 patients
(4.0%), respectively. Total follow-up time after imputation
increased to 17,480 y, and the number of deaths to 1,338.
Mortality estimates at 2 y were somewhat higher after imputation
for the Centre de Prise en Charge de Recherches et de Formation
(CEPREF) and Lighthouse cohorts, but similar to the crude
Table 1. Description of treatment programmes included in analyses.
Patients Lost to
Follow-up at 2 y
(95% CI) at 2 y (%)b
Cumulative Mortality (95% CI) at 2-y (%)Crude
Co ˆte d’Ivoire
Number of patients (%) unless otherwise indicated.
aPercent of patients with known clinical stage at baseline.
bEstimated for patients with at least one additional potential year of follow-up until administrative censoring date of the database of their programme.
cOutcomes imputed in patients lost to follow-up.
CEPREF, Centre de Prise en Charge de Recherches et de Formation/Agence National de Recherches sur le Sida (ANRS) 1203 cohort.
Mortality on ART in Africa
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estimates in the other cohorts (Table 1). Patient characteristics at
baseline and the effect of multiple imputation of missing
information on the distribution of CD4 cell count and clinical
stage of disease at baseline are shown in Table 2. At 6 mo, the
median CD4 cell count had increased to 245 cells/ml (interquartile
range [IQR] 167–347), varying between 220 and 272 cells/ml
across programmes. At 12 mo, the median CD4 count was 285
cells/ml (IQR 197–393), ranging from 253 to 307 cells/ml.
All-Cause, HIV-Associated, and HIV-Free Mortality in the
Figure 1 shows estimated all-cause mortality for the year 2004 as
the sum of HIV-associated and HIV-free mortality by 5-y age group
by country. Between age 20 and 50 y (the age range including 90%
of patients starting ART in this study), the estimated relative
contribution of HIV-associated mortality to all-cause mortality was
52% in Co ˆte d’Ivoire, 74% in Malawi, 71% in South Africa, and
84% in Zimbabwe. Across all age groups, estimates of HIV-
unrelated mortality were consistently higher in men than in women.
and 50 y was 1.8 in Co ˆte d’Ivoire, 1.2 in Malawi, 1.6 in South
Africa, and 1.5 in Zimbabwe. The rates of HIV-unrelated mortality
by 5-y age group and sex were used to model the mortality of HIV-
infected patients on ART relative to the mortality in the general
population. These rates are presented in Table S1.
Risk Factors for Excess Mortality in HIV-Infected Patients
The adjusted risk of excess mortality steeply declined with time
period on ART. With reference to the first 3 mo, the eHR for the
second year on treatment was 0.10, indicating a risk reduction of
90% (Table 3). Over the 2-y study period females were at 16%
lower risk of excess mortality than males (eHR 0.84). There was
strong evidence for a decline in excess risk with increasing baseline
CD4 count: patients starting with a CD4 count of 200 cells/ml or
more experienced an 81% reduction in risk over 2 y as compared
to patients that started with a CD4 count of less than 25 cells/ml
(eHR 0.19). Similarly, the excess risk was reduced by 72% (eHR
0.28) over the 2 y in patients starting with less advanced disease
(WHO stage I/II) compared to patients starting with advanced
disease (WHO stage III/IV). There was little evidence for an
association between excess mortality and age, treatment regimen,
or calendar period.
We examined effect modifications by adding interaction terms
for variables time after starting ART, baseline CD4, and clinical
stage of disease to the model. There was evidence that the effect of
baseline CD4 count depended on the time period after starting
ART (test of interaction, p,0.001), but not for the other possible
interactions (p.0.48). The association between the baseline CD4
count and excess mortality became weaker with time on treatment
and the interaction was included in estimating excess mortality
Excess mortality declined with time on treatment and increasing
baseline CD4 cell count. It was lower in women as compared to
men, and higher in patients starting ART with advanced stage of
disease (Table 4). Overall excess mortality per 100 person-years
was 6.95 (5.95–8.13), varying between 17.51 (14.50–21.14) and
1.00 (0.55–1.81) for patients starting with worst prognosis (CD4
Table 2. Baseline characteristics and mortality over the first 2 y of ART.
Category SubcategoryOriginal Data Following Multiple Imputationa
n (%)Person-Yearsn Deaths (%)n (%) Person-Yearsn Deaths (%)
Overall— 13,249 (100)14,695 1,177 (100) 13,249 (100)17,480 1,338 (100)
Age (y)16–29 3,436 (26)3,856 276 (23) 3,436 (26)4,564 309 (23)
— 30–39 5,875 (44)6,567 521 (44)5,875 (44)7,789 594 (44)
— 40–49 2,919 (22)3,232 266 (23)2,919 (22) 3,851 308 (23)
$501,019 (8) 1,041114 (10) 1,019 (8)1,276 127 (10)
Sex Female 8,831 (67)10,047701 (60) 8,831 (67) 11,796 789 (59)
— Male 4,418 (33)4,648 476 (40)4,418 (33) 5,684 549 (41)
Initial ART regimenNNRTI-based 11,325 (85)12,616 1,027 (87)11,325 (85) 14,9691162 (87)
— PI-based 94 (1)124 8 (1)94 (1)148 9 (1)
— Unknown or other1,830 (14) 1,955142 (12) 1,830 (14)2,363 167 (12)
Baseline CD4 count
,251,670 (13) 1,753322 (27)1,937 (15) 2,414415 (31)
— 25–491,188 (9) 1,303162 (14)1,457 (11)1,884225 (17)
— 50–992,174 (16)2,571 182 (15)2,725 (21) 3,737275 (20)
— 100–199 3,812 (29)4,408208 (18) 4,645 (35)6,309305 (23)
$2001,870 (14) 1,84578 (7)2,485 (19)3,136118 (9)
2,535 (19) 2,815225 (19)———
Clinical stage of diseaseLess advanced1,909 (14) 2,12553 (5) 2,079 (16)2,800 65 (5)
—Advanced 10,811 (82) 11,8521,097 (93) 11,170 (84)14,680 1,273 (95)
529 (4) 71827 (2)———
aMultiple imputation was used to impute missing values of baseline CD4 count and clinical stage, and to impute outcomes in patients lost to follow-up (see Table 1).
Abbreviations: NNRTI, non-nucleoside reverse transcriptase inhibitor; PI, protease inhibitor.
Mortality on ART in Africa
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cell count ,25 cells/ml and advanced stage of disease) and best
prognosis (CD4 cell count $200 cells/ml and less advanced stage
of disease), respectively. In the second year on ART excess
mortality in the patients group with best prognosis was 0.27 (0.08–
0.94) per 100 person-years. Figure 2 shows the distribution of
estimated excess mortality rates over the first 2 y of ART, taking
into account baseline CD4 count, clinical stage, age, and sex. 34%
of patients were exposed to excess mortality rates between four
and six additional deaths per 100 person-years, 25% to rates below
four per 100 person-years, and 41% to rates above six per 100
person-years. Table S2 gives excess mortality rates over 2 y by
baseline CD4 count, clinical stage, and by sex and age group.
SMRs, overall and stratified by time period on ART, baseline
CD4 cell count, and clinical stage of disease are shown in Table 5.
The overall SMR over the first 2 y was 18.7 (17.7–19.8), declining
from 130.0 (110.9–152.4) to 4.0 (3.3–5.0) over months 1–3 to
months 13–24, respectively. Over the first 3 mo, SMRs varied
between 552.7 (400.1–763.5) for patients starting ART with worst
prognosis to 30.2 (15.7–58.0) among patients starting with best
prognosis. In the second year on ART, SMRs for these two
patients groups were 11.5 (7.95–16.7) and 1.14 (0.47–2.77),
respectively. Over the full first 2 y and depending on CD4 count
and clinical stage of disease, SMRs varied between 47.1 (39.1–
Figure 1. All-cause mortality for the year 2004 as the total of
HIV-related (red area) and HIV-unrelated mortality (white area)
by 5-y age group in Co ˆte d’Ivoire, Malawi, South Africa, and
Zimbabwe. Age groups are indicated by the lower age, e.g., age group
15 indicating age 15–19 y, age group 20 indicating age 20–24 y, and so
forth. Data from the Global Burden of Disease Study [17,20].
Table 3. eHRs for death according to different time periods
after starting ART, demographic and clinical characteristics at
baseline, and calendar period of starting ART.
and Starting PeriodeHR (95% CI)
Months 4–6 0.36 (0.33–0.40)—
Months 7–120.18 (0.15–0.22)—
Months 13–24 0.10 (0.086–0.12)—
Age (y)— 0.34
30–39 1.06 (0.80–1.41)—
$50 1.27 (0.96–1.67)—
Female 0.84 (0.71–0.99)—
Initial regimen— 0.86
Two NRTIs + one NNRTI
Two NRTIs + one PI
Unknown or other combination0.94 (0.73–1.21)—
Baseline CD4 (cells/ml)—
50–99 0.42 (0.32–0.56)—
WHO stage I/II 0.28 (0.17–0.46)—
WHO stage III/IV1—
Calendar period of starting ART— 0.24
2005 or later1.16 (0.91–1.49)—
eHRs from multivariable Poisson regression models, comparing mortality
among HIV-infected patients, taking into account the background mortality
among non-HIV–infected individuals in the general populations of the four
countries included in the study. Models were adjusted for all variables listed in
the table. p-Values are from Wald tests. eHRs for demographic and clinical
characteristics at baseline, and calendar period of starting ART, are based on
mortality over the first two years of treatment.
Abbreviations: NNRTI, non-nucleoside reverse transcriptase inhibitor; PI,
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56.6) and 3.4 (1.9–6.2). Table S3 gives SMRs over 2 y by baseline
CD4 count, clinical stage, and by sex and age group.
When restricting the analysis to the three treatment pro-
grammes with rates of loss to follow-up below 10% (Khayelitsha,
Gugulethu, Connaught), estimates of excess mortality and SMRs
were somewhat lower, but the variation with time period, baseline
CD4, and clinical stage was similar to that observed using all data
(Tables S4 and S5). For example, in the second year on ART
excess mortality in the patients group with the best prognosis was
0.15 (0.015–1.50) per 100 person-years and the SMR was 0.76
In this collaborative study of five treatment programmes in four
countries in sub-Saharan Africa, the mortality of HIV-infected
patients starting ART could be compared with that estimated for
the corresponding non-HIV–infected general populations. In these
countries, a large proportion of deaths among young and middle-
aged adults are HIV-related. We found that mortality during the
first 2 y of ART was more than 18 times higher than in the general
population not infected by HIV. However, there was large
variability between prognostic groups and over time: in patients
with very low CD4 counts and advanced clinical disease, mortality
was increased over 300 times in the first 3 mo of treatment,
whereas in the second year of ART, patients who started with high
CD4 counts and less advanced disease had mortality rates that
were comparable to those estimated for non-HIV–infected
We used excess mortality rates as well as SMRs and thus took
the background mortality in the general population into account.
The calculation of expected numbers of deaths was restricted to
people not infected with HIV, which is crucial when the
prevalence of the exposure (HIV infection) in the general
population is high and SMRs are large . The mortality of
over 13,000 patients was analyzed, including women and men,
teenagers and middle-aged people, and patients with severe and
less pronounced immunodeficiency. Our results should therefore
be applicable to many other patients receiving ART in sub-
Saharan Africa. We used estimates of non-HIV–related mortality
from the WHO Global Burden of Disease project . Beginning
with the year 1999, WHO has been producing annual life tables
for all member states. A key use of these tables is the calculation of
healthy life expectancy, the basic indicator of population health
published each year in the World Health Report .
One limitation of our study is that the reference rates for HIV-
unrelated mortality are unlikely to be completely accurate for the
source populations from which the HIV-infected patients
originate, and that errors in the calculation of expected number
of deaths are not reflected in the confidence limits of SMRs and
excess mortality rates . The five ART programmes included in
this study are public sector scale-up programmes, which serve
Figure 2. Distribution of excess mortality over the first 2 y of
ART in patients starting ART in five treatment programmes in
Table 4. Excess mortality per 100 person-years by time period on ART, baseline CD4 count, and clinical stage of disease.
CD4 Count (Cells/ml) Clinical Stage
Time after Starting ART (mo)
1–34–6 7–1213–24 Overall (1–24)
,25Advanced 63.79 (44.67–91.10)15.87 (8.62–29.22)7.99 (4.87–13.09) 4.94 (3.39–7.21)17.5 (14.5–21.1)
— Less advanced18.25 (9.15–36.41)4.54 (1.91–10.79) 2.29 (0.96–5.43)1.41 (0.75–2.67)4.87 (2.64–9.00)
25–49Advanced 38.32 (25.15–58.38)14.36 (8.49–24.28)7.87 (3.98–15.57)2.70 (1.18–6.16) 12.1 (9.09–16.0)
— Less advanced 10.96 (5.34–22.50) 4.11 (1.81–9.35)2.25 (0.83–6.11) 0.77 (0.26–2.28)3.36 (1.74–6.49)
50–99Advanced 21.86 (11.39–41.97) 8.80 (4.46–17.36)4.55 (2.22–9.33)3.18 (1.65–6.12)7.38 (4.98–10.95)
— Less advanced 6.25 (2.49–15.70)2.52 (0.98–6.44) 1.30 (0.45–3.77)0.91 (0.36–2.29) 2.05 (0.98–4.31)
100–199Advanced 13.73 (7.20–26.19)7.52 (4.65–12.18) 2.71 (1.54–4.79)1.81 (0.94–3.50) 4.83 (3.56–6.56)
— Less advanced 3.93 (1.57–9.84)2.15 (0.95–4.87) 0.78 (0.34–1.80)0.52 (0.23–1.17)1.35 (0.70–2.59)
$200Advanced 10.20 (7.55–13.77)3.50 (1.90–6.45) 3.12 (0.19–5.01) 0.96 (0.37–2.49)3.59 (2.82–4.56)
— Less advanced 2.92 (1.53–5.58)1.00 (0.43–2.35) 0.89 (0.48–1.66)0.27 (0.08–0.94) 1.00 (0.55–1.81)
OverallOverall21.20 (19.21–23.38)7.58 (6.48–8.86) 3.79 (2.93–4.90)2.15 (1.79–2.58) 6.95 (5.95–8.13)
Results from Poisson model that included all variables listed and allowed for interaction between baseline CD4 cell count and time after starting ART.
Mortality on ART in Africa
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disadvantaged urban populations. Data from the 1970s and early
1980s suggest that adult mortality is lower in urban Africa than in
rural Africa . The generally lower mortality rates observed in
urban settings may, however, conceal pockets of poverty and high
mortality among urban dwellers . Nevertheless, the use of
national rates may have lead to estimates of the expected number
of HIV-unrelated deaths that are too high, and SMRs and excess
mortality rates that are too low. Given that reliable local mortality
data are not available, we believe that the data from the Global
Burden of Disease project are the best reference data available. Of
note, the estimates used in this study for South Africa are in line
with those from other analyses. For example, a recent modelling
study of the demographic impact of HIV/AIDS in South Africa by
the University of Cape Town and the South African Medical
Research Council estimated that in 2006, 71% of deaths in the
15–49 y age group were due to HIV infection . Similarly, a
study of AIDS-related mortality in rural KwaZulu-Natal estimated
that 127 of 186 deaths (68%) were attributable to AIDS in 2004
. A demographic surveillance study using verbal autopsy in the
Agincourt subdistrict, rural South Africa, also found that HIV and
tuberculosis were the leading causes of death in people aged 15–49
Our study has other limitations. Complete ascertainment of risk
factors and deaths and complete follow-up of patients is difficult to
achieve in treatment programmes in low-income countries
[31,32]. Loss to follow-up was particularly high in one programme
in Malawi, however, this is probably due to a higher rate of
transfer out of patients in this programme. At present we cannot
distinguish between loss to follow-up and transfer to another
programme; this will be remedied in the next update of the
database. We used multiple imputation to deal with missing
baseline CD4 cell counts and loss to follow-up. This method
assumes that missing values can accurately be predicted using the
available data. In other words, the probability of missing no longer
depends on the missing value after taking the available data into
account (‘‘missing at random’’ in Rubin’s terminology ). The
plausibility of this assumption is unverifiable, but it is clear that
mortality is increased in patients lost to follow-up [34–36], and
unlikely that this can fully be captured by the clinical stage and
CD4 cell count at baseline. Of note, sensitivity analyses excluding
the sites with high rates of loss to follow-up from Malawi and Co ˆte
d’Ivoire gave similar results.
Follow-up was limited to 2 y in the present analyses, reflecting
the relatively recent scale up of ART in sub-Saharan Africa, and it
is possible that mortality will increase again in HIV-infected
patients with longer duration of treatment. The short follow-up
also meant that life expectancy of patients starting ART could not
be examined. The ART Cohort Collaboration of HIV cohorts in
Europe and North America recently estimated that life expectancy
at age 35 y among patients on ART not infected through injecting
drug use was 33 y . These questions will be addressed in future
analyses of the IeDEA databases. Finally, our analysis did not
consider differences between the HIV-infected and non-HIV–
infected populations other than gender and age. In industrialised
countries, there are important differences in the prevalence of risk
factors, for example smoking, between infected and noninfected
populations. In sub-Saharan Africa, where the epidemic is
generalised and transmission by heterosexual contacts, differences
in lifestyle factors are unlikely to be a major source of bias.
How do these SMRs compare with other population groups at
increased risk of death due to unhealthy lifestyles, occupational
exposures, or chronic conditions other than HIV infection? Few
data are available for sub-Saharan Africa. White South African
gold miners, compared to the white male population, had an SMR
of 1.3, because of excess mortality due to lung cancer, chronic
obstructive lung disease, and liver cirrhosis . Among male
British doctors born in the 1920s, the probability of dying from
any cause in middle age was three times higher in smokers than
lifelong nonsmokers . Similarly, an analysis of the National
Alcohol Survey in the US showed that regular, heavy drinkers had
mortality rates from all causes that were 2.2 times higher than
those observed in lifetime abstainers . The mortality of people
with a body mass index (BMI) over 35 kg/m2is increased by factor
1.5 to 2.5, compared to those with a BMI between 20 and 25 kg/
m2, and a similar increase in all-cause mortality is found in
physically inactive people compared to physically active individ-
uals . In a population-based study in Turin, Northern Italy,
women with type 1 diabetes had an SMR for all causes of 3.4 and
men an SMR of 2.0 . The SMRs found in these patients and
populations exposed to risk factors are thus quite comparable to
those found in some of the patient groups included in our analysis.
Excess mortality was greater among men than among women.
A recent analysis from the ART in Lower Income Countries
(ART-LINC) collaboration found that although women are more
Table 5. SMRs by time period on ART, baseline CD4 count, and clinical stage of disease.
CD4 Count (Cells/ml) Clinical Stage
Time Period (mo)
1–34–67–12 13–24 Overall (1–24)
,25 Advanced552.7 (400.1–763.5)142.7 (85.3–238.7) 37.2 (22.3–62.0)11.5 (7.95–16.7) 47.1 (39.1–56.6)
Less advanced186.3 (99.3–349.2)48.1 (22.7–102.0) 12.5 (5.52–28.4) 3.88 (2.10–7.17) 15.8 (8.99–27.9)
25–49 Advanced333.1 (233.3–475.5) 130.4 (79.9–212.6)37.2 (20.1–68.9)7.01 (3.51–14.0) 31.4 (26.1–37.7)
Less advanced112.3 (59.9–210.4) 43.9 (20.7–93.1)12.5 (5.17–30.3)2.36 (0.95–5.85) 10.6 (6.08–18.4)
50–99 Advanced192.2 (108.5–340.5)80.4 (44.5–145.1) 22.6 (11.7–43.6) 8.04 (4.73–13.7)19.6 (15.1–25.5)
Less advanced64.8 (28.9–145.0)27.1 (11.9–61.7) 7.61 (2.95–19.6) 2.71 (1.24–5.90)6.59 (3.58–12.1)
100–199 Advanced 123.0 (70.6–214.4)70.6 (46.7–106.8)14.5 (8.67–24.1)5.34 (3.46–8.23)13.6 (11.5–16.1)
Less advanced41.5 (18.7–91.9) 23.8 (11.8–48.1) 4.87 (2.29–10.4) 1.80 (0.98–3.31)4.57 (2.67–7.84)
$200 Advanced 89.5 (62.1–129.0)34.3 (18.4–63.8)16.1 (11.2–23.1) 3.39 (1.79–6.40)10.2 (7.63–13.7)
Less advanced30.2 (15.7–58.0)11.5 (4.98–26.8)5.43 (3.13–9.43)1.14 (0.47–2.77) 3.44 (1.91–6.17)
OverallOverall 130.0 (110.9–152.4)49.6 (42.2–58.3)13.4 (10.4–17.3) 4.05 (3.25–5.04)18.7 (17.7–19.8)
Mortality on ART in Africa
PLoS Medicine | www.plosmedicine.org7 April 2009 | Volume 6 | Issue 4 | e1000066
vulnerable than men to becoming infected with HIV, they were
equally or more likely than men to start ART . Women were
younger and started treatment at a less advanced clinical stage,
which could partly explain their lower excess mortality. Gender
inequities in health may affect men as well as women: traditional
masculine roles cast men as taking risks, being unconcerned about
their health, and not needing help or healthcare . Conven-
tional views of gender inequality might have made it easier for
women than men in some settings to become engaged with HIV
diagnosis and treatment services [43,45,46]. Clearly, continued
efforts are needed to empower women and secure their rights to
treatment and care for HIV infection. However, more attention
needs to be paid to HIV-infected men.
Although some HIV-infected patients starting ART in sub-
Saharan Africa experienced mortality rates that were comparable
with those experienced by other patients with a chronic condition,
early mortality in adults starting ART continues to be high in sub-
Saharan Africa . Many patients start treatment late, with a
history of AIDS defining illnesses and low CD4 cell counts.
Leading causes of death include tuberculosis, acute sepsis,
cryptococcal meningitis, malignancies, and wasting syndrome
. Of note, the Starting Antiretrovirals at three Points in
Tuberculosis (SAPIT) trial recently showed that mortality among
patients co-infected with tuberculosis and HIV can be reduced by
55% if ART is provided with TB treatment . Although our
study cannot determine the CD4 cell count when ART should be
started in order to minimise mortality, much of the excess
mortality observed in our study would probably be preventable
with timely initiation of ART. Further expansion of public health
strategies to increase access to ART in sub-Saharan Africa is
therefore urgently needed. In collaboration with the Global
Burden of Disease project, the IeDEA network will continue to
monitor mortality of HIV-infected patients starting ART and
compare their mortality to that of the general population not
infected by HIV.
population in Co ˆte d’Ivoire, Malawi, Zimbabwe, and South
Africa, 2004. Data from the Global Burden of Disease study
Found at: doi:10.1371/journal.pmed.1000049.s001 (0.05 MB
Age- and sex-specific HIV-unrelated mortality per 100
24 by baseline CD4 count and clinical stage of disease, and by sex
and age group.
Found at: doi:10.1371/journal.pmed.1000049.s002 (0.06 MB
Excess mortality per 100 person-years for months 1–
clinical stage of disease, and by sex and age group.
Found at: doi:10.1371/journal.pmed.1000049.s003 (0.06 MB
SMRs for months 1–24 by baseline CD4 count and
on ART, baseline CD4 count, and clinical stage of disease in the
three ART programmes with low rates of loss to follow-up
(Connaught, Gugulethu, Khayelitsha).
Found at: doi:10.1371/journal.pmed.1000049.s004 (0.04 MB
Excess mortality per 100 person-years by time period
and clinical stage of disease in the three ART programmes with
low rates of loss to follow-up (Connaught, Gugulethu, Khayelit-
Found at: doi:10.1371/journal.pmed.1000049.s005 (0.04 MB
SMRs by time period on ART, baseline CD4 count,
We are grateful to all patients, doctors, and study nurses who were involved
in the participating cohort studies. The views expressed in this paper are
solely the responsibility of the named authors and do not necessarily reflect
the decisions or stated policy of the World Health Organization or its
ICMJE criteria for authorship read and met: MWGB AB RW EM CM
CO FD MP ME. Agree with the manuscript’s results and conclusions:
MWGB AB RW EM CM CO FD MP ME. Designed the experiments/the
study: MWGB EM FD ME. Analyzed the data: MWGB CM. Collected
data/did experiments for the study: AB RW EM CM CO MP. Enrolled
patients: RW EM CO. Contributed to the writing of the paper: MWGB
AB RW EM CO FD MP ME. Responsible for the data contributed by one
of the participating cohorts (PI for the cohort study): AB. Contributed
conceptually to multiple iterations of this analysis: AB. Responsible for the
data contributed by participating cohorts: RW EM CO MP. Analyzed
African mortality data to prepare the estimates and projections of the
mortality levels in HIV-negative African population: CM.
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Mortality on ART in Africa
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Background. Acquired immunodeficiency syndrome (AIDS)
has killed more than 25 million people since 1981 and more
than 30 million people (22 million in sub-Saharan Africa
alone) are now infected with the human immunodeficiency
virus (HIV), which causes AIDS. HIV destroys immune system
cells (including CD4 cells, a type of lymphocyte), leaving
infected individuals susceptible to other infections. Early in
the AIDS epidemic, most HIV-positive people died within ten
years of infection. Then, in 1996, highly active antiretroviral
therapy (ART)—combinations of powerful antiretroviral
drugs—was developed and the life expectancy of HIV-
infected people living in affluent countries improved
dramatically. Now, in industrialized countries, all-cause
mortality (death from any cause) among HIV-infected
patients treated successfully with ART is similar to that of
the general population and the mortality rate (the number of
deaths in a population per year) among patients with HIV/
AIDS is comparable to that among patients with diabetes
and other chronic conditions.
Why Was This Study Done? Unfortunately, combination
ART is costly, so although HIV/AIDS quickly became a chronic
disease in industrialized countries, AIDS deaths continued
unabated among the millions of HIV-infected people living in
low- and middle-income
governments, international agencies and funding bodies
began to implement plans to increase ART coverage in
developing countries. By the end of 2007, nearly three million
people living with HIV/AIDS in these countries were receiving
ART—nearly a third of the people who urgently need ART. In
sub-Saharan Africa more than 2 million people now receive
ART and mortality in HIV-infected patients who have access to
ART is declining. However, no-one knows how mortality
among HIV-infected people starting ART compares with non-
HIV related mortality in sub-Saharan Africa. This information is
needed to ensure that appropriate health services (including
access to ART) are provided in this region. In this study, the
researchers compare mortality rates among HIV-infected
patients starting ART with non-HIV related mortality in the
general population of four sub-Saharan countries.
countries. Then,in 2003,
What Did the Researchers Do and Find? The researchers
obtained estimates of the number of HIV-unrelated deaths
and information about patients during their first two years
on ART at five antiretroviral treatment programs in the Co ˆte
d’Ivoire, Malawi, South Africa, and Zimbabwe from the World
Health Organization Global Burden of Disease (GBD) project
and the International epidemiological Databases to Evaluate
AIDS (IeDEA) initiative, respectively. They then calculated the
excess mortality rates among the HIV-infected patients (the
death rates in HIV-infected patients minus the national HIV-
unrelated death rates) and the standardized mortality rate
(SMR; the number of deaths among HIV-infected patients
divided by the number of HIV-unrelated deaths in the
general population). The excess mortality rate among HIV-
infected people who started ART when they had a low CD4
cell count and clinically advanced disease was 17.5 per 100
person-years of follow-up. For HIV-infected people who
started ART with a high CD4 cell count and early disease, the
excess mortality rate was 1.0 per 100 person-years. The SMRs
over two years of ART for these two groups of HIV-infected
patients were 47.1 and 3.4, respectively. Finally, patients who
started ART with a high CD4 cell count and early disease who
survived the first year of ART had an excess mortality of only
0.27 per 100 person-years and an SMR over two years follow-
up of only 1.14.
What Do These Findings Mean? These findings indicate
that mortality among HIV-infected people during the first
two years of ART is higher than in the general population in
these four sub-Saharan countries. However, for patients who
start ART when they have a high CD4 count and clinically
early disease, the excess mortality is moderate and similar to
that associated with diabetes. Because the researchers
compared the death rates among HIV-infected patients
with estimates of national death rates rather than with
estimates of death rates for the areas where the ART
programs were located, these findings may not be
completely accurate. Nevertheless, these findings support
further expansion of strategies that increase access to ART in
sub-Saharan Africa and suggest the excess mortality among
HIV-infected patients in this region might be largely
prevented by starting ART before an individual’s HIV
infection has progressed to advanced stages.
Additional Information. Please access these Web sites via
the online version of this summary at http://dx.doi.org/10.
N Information is available from the US National Institute of
Allergy and Infectious Diseases on HIV infection and AIDS
N HIV InSite has comprehensive information on all aspects of
N Information is available from Avert, an international AIDS
charity on many aspects of HIV/AIDS including HIV and
AIDS in Africa, providing AIDS drug treatment for millions,
and on the stages of HIV infection
N The World Health Organization provides information about
universal access to HIV treatment and about the Global
Burden of Disease project (in several languages)
N More information about the International epidemiological
Databases to evaluate AIDS initiative is available on the
IeDEA Web site
Mortality on ART in Africa
PLoS Medicine | www.plosmedicine.org 10 April 2009 | Volume 6 | Issue 4 | e1000066