Active Tuberculosis Is Associated with Worse Clinical
Outcomes in HIV-Infected African Patients on
Abraham M. Siika1,2*, Constantin T. Yiannoutsos2,3, Kara K. Wools-Kaloustian2,3, Beverly S. Musick2,3,
Ann W. Mwangi2,4, Lameck O. Diero1,2, Sylvester N. Kimaiyo1,2, William M. Tierney2,3,5, Jane E. Carter2,5
1School of Medicine, College of Health Sciences, Moi University, Eldoret, Kenya, 2USAID-Academic Model Providing Access to Healthcare (AMPATH) Partnership, Eldoret,
Kenya, 3Indiana University School of Medicine, Indianapolis, Indiana, United States of America, 4Warren Alpert Medical School of Brown University, Providence, Rhode
Island, United States of America, 5Regenstrief Institute, Inc., Indianapolis, Indiana, United States of America
Objective: This cohort study utilized data from a large HIV treatment program in western Kenya to describe the impact of
active tuberculosis (TB) on clinical outcomes among African patients on antiretroviral therapy (ART).
Design: We included all patients initiating ART between March 2004 and November 2007. Clinical (signs and symptoms),
radiological (chest radiographs) and laboratory (mycobacterial smears, culture and tissue histology) criteria were used to
record the diagnosis of TB disease in the program’s electronic medical record system.
Methods: We assessed the impact of TB disease on mortality, loss to follow-up (LTFU) and incident AIDS-defining events
(ADEs) through Cox models and CD4 cell and weight response to ART by non-linear mixed models.
Results: We studied 21,242 patients initiating ART–5,186 (24%) with TB; 62% female; median age 37 years. There were
proportionately more men in the active TB (46%) than in the non-TB (35%) group. Adjusting for baseline HIV-disease
severity, TB patients were more likely to die (hazard ratio – HR=1.32, 95% CI 1.18–1.47) or have incident ADEs (HR=1.31,
95% CI: 1.19–1.45). They had lower median CD4 cell counts (77 versus 109), weight (52.5 versus 55.0 kg) and higher ADE risk
at baseline (CD4-adjusted odds ratio=1.55, 95% CI: 1.31–1.85). ART adherence was similarly good in both groups. Adjusting
for gender and baseline CD4 cell count, TB patients experienced virtually identical rise in CD4 counts after ART initiation as
those without. However, the overall CD4 count at one year was lower among patients with TB (251 versus 269 cells/ml).
Conclusions: Clinically detected TB disease is associated with greater mortality and morbidity despite salutary response to
ART. Data suggest that identifying HIV patients co-infected with TB earlier in the HIV-disease trajectory may not fully address
TB-related morbidity and mortality.
Citation: Siika AM, Yiannoutsos CT, Wools-Kaloustian KK, Musick BS, Mwangi AW, et al. (2013) Active Tuberculosis Is Associated with Worse Clinical Outcomes in
HIV-Infected African Patients on Antiretroviral Therapy. PLoS ONE 8(1): e53022. doi:10.1371/journal.pone.0053022
Editor: Yolande Richard, Institut National de la Sante ´ et de la Recherche Me ´dicale, France
Received July 24, 2012; Accepted November 22, 2012; Published January 2, 2013
Copyright: ? 2013 Siika 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: The USAID-AMPATH Partnership clinical care programs are supported by USAID PEPFAR’s grant #623-A-00-08-00003-00. The USAID-AMPATH
Partnership program has in the past received support from the Prevention of Maternal To Child Transmission (MTCT - Plus) Initiative, Bill and Melinda Gates
Foundation, Centers for Disease Control and Prevention (CDC), the Kenya National AIDS and STI Control Program (NASCOP) and National AIDS Control Council
(NACC) and is currently funded by the United States Agency for International Development President’s Emergency Program For AIDS Relief (USAID - PEPFAR). Dr.
Yiannoutsos’ and Wools-Kaloustian’s research was partially supported by the National Institutes of Health grant AI-069911. 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.
* E-mail: firstname.lastname@example.org
The global burden of tuberculosis (TB) and HIV co-infections is
immense. Of the 8.7 million incident cases of TB in 2011, an
estimated 1.13 million (13%) were infected with HIV, of whom
430,000 (38%) died.  The highest rates of HIV co-infection
were reported for TB patients in the African Region where 46% of
those with a HIV test were HIV-positive. In some countries in the
region, this figure was as high as 70%. . Africa is responsible for
79% of the HIV/TB infections globally. [1,2] In Kenya, 106,000
cases of TB were registered in 2010 with 41% being HIV co-
HIV and TB form a lethal combination, each disease fuelling
the other. HIV infection is the most significant known risk factor
for acquisition of TB infection and development of TB disease
[4,5] while TB accelerates HIV disease progression. [6–9] TB is
undoubtedly the leading cause of death in the setting of AIDS,
accounting for about 26% of AIDS-related deaths, 99% of which
occur in developing countries. [10–12] Many TB suspects delay in
seeking care due to HIV-associated stigma. This not only
increases the infectious pool within the community but delays
initiation of effective chemotherapy. While TB disease can occur
at any CD4 cell count, this risk increases as the immune system
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deteriorates. Similarly the likelihood of TB presenting in atypical
and disseminated forms increases the more immune suppressed
the individual becomes. [14,15] Another important relationship
between the two diseases is the difficulty in diagnosing TB in HIV-
infected patients. Alteration in the clinical and radiographic
presentation of TB and reliance on sputum smear microscopy and
chest radiography whose diagnostic accuracy is substantially
impaired in those with HIV co-infection make diagnosis difficult.
[16–19] While these challenges might be surmounted by newer
diagnostic technologies (e.g. Xpert MTB/RIF), these are not
widely available in resource constrained settings such as sub
In sub Saharan Africa, antiretroviral treatment (ART) has
improved survival and markedly reduced the incidence of new
opportunistic infections (OI) including TB. [20,21] Importantly,
recently published randomized clinical trials (ACTG A5221/
STRIDE and CAMELIA) have shown a mortality benefit in
initiating ART early during TB treatment in HIV-infected
patients. [22,23] Whether the benefits of ART are similar between
HIV-infected patients with or without TB, however, still remains
unclear. Simultaneous treatment of TB and HIV is challenging
due to high pill burden, medications side effects, potential drug
interactions and the possibility of the Immune Reconstitution
Inflammatory Syndrome (IRIS). Therefore, treatment outcomes
may be adversely impacted. In Uganda, HIV-infected patients
receiving ART had more subsequent OI and higher death rates if
they were co-infected with TB at the time ART was initiated. 
But in Malawi, patients with TB at ART initiation were found to
have better survival rates and reduced incidence of new OI than
those without TB .
Given the above contradictory findings, we analyzed data from
a large cohort of HIV-infected patients who initiated ART at the
Academic Model Providing Access to Healthcare (AMPATH)
Partnership in western Kenya to determine the long-term
differences in treatment outcomes between HIV-infected patients
with and without TB disease at the time of ART initiation.
Moi University School of Medicine (MUSoM), Moi Teaching
and Referral Hospital (MTRH) and a collaboration of North
American universities led by Indiana University School of
Medicine (IUSM) direct AMPATH.  Established in November
2001 at MTRH in Eldoret, Kenya, AMPATH currently has HIV
treatment, training and research programs in 80 Ministry of
Health facilities spread throughout western Kenya. As of
December 2007, when the data was extracted for this study,
64,008 HIV-infected adults and children had been enrolled. Of
these, 49,172 were actively on follow-up, 23,437 (48%) of whom
were on ART.
TB and HIV Care
HIV and TB care are integrated. All patients attending TB
clinic are tested for HIV under the Provider Initiated Testing and
Counseling (PITC) program  and all HIV-infected patients are
screened for active TB. Diagnosis of TB is based on guidelines
from the Division of Leprosy, Tuberculosis and Lung Diseases of
the Kenya Ministry of Public Health and Sanitation (adopted from
the WHO). [28,29] The TB diagnostic criteria include: 1) clinical
presentation consistent with TB (cough for more than 2 weeks,
unexplained weight loss, night sweats); 2) suggestive radiological
findings (chest radiograph infiltrates consistent with TB, pleural
effusion) and; 3) laboratory mycobacteriology (sputum smears,
culture and tissue histology). During the period of study, TB
treatment was conducted in 2 phases: an initial 2-month intensive
phase with 4 anti-TB medicines (rifampicin, isoniazid, pyrazin-
amide and ethambutol) followed by a 6-month continuation phase
with 2 anti-TB medicines (isoniazid and ethambutol).
All HIV-infected patients underwent a standard series of
clinical, laboratory and radiological assessments and were assigned
a WHO stage. ART was initiated in accordance with the national
guidelines for Kenya at the time (CD4 cell count ,200/ml, or
being in WHO stage IV, or being in WHO stage III with CD4 cell
count ,350/ml).  The standard first-line ART regimen used
was stavudine (or zidovudine) in combination with lamivudine and
nevirapine. Efavirenz was substituted for nevirapine for patients on
rifampicin. Co-trimoxazole preventative therapy was given to
patients with CD4 cell counts ,200/ml and to all patients
diagnosed with TB. A nine-month course of isoniazid preventative
therapy (IPT) was administered to patients without prior TB
treatment and no clinical or radiological evidence of TB disease.
For HIV-infected patients on anti-TB medication, timing of ART
initiation was based on baseline CD4 cell count as per National
ART guidelines: within 2 weeks from start of TB treatment for
CD4 count ,50/ml; within 4 weeks for CD4 cell count between
50 and 199/ml; and after completion of intensive phase anti-TB
treatment for CD4 count $200/ml. However, clinicians were at
liberty to initiate ART for an individual patient when they felt it
was appropriate (e.g., the patient can tolerate additional medica-
Due to cost, viral loads are not routinely collected as part of care
in this program. However, a viral load is drawn if a patient has
clinical (worsening WHO stage) or immunologic evidence of ART
failure (.50% drop in CD4 cell count, reduction in CD4 count
below baseline 6 months after ART initiation or non-increment of
CD4 count beyond 100 cells/ml after 1 year of ART). A viral load
result of .10,000 copies/ml is considered definite ART failure
while 1,000–10,000 copies/ml is considered probable failure.
Adherence to ART was measured using the patient’s report of
the number of pills taken within the previous week (‘During the
last 7 days, how many of his/her pills did the patient take?’) Perfect
adherence to ART’ is defined as patient responding ‘All’ every
time and ‘Non-Perfect adherence to ART’ if the patient ever
answers ‘Most’, ‘Half’, ‘Few’, or ‘None. Information on hospital-
ization and death was received passively (as reported by relatives)
and sought actively (from hospital surveillance and outreach team
reports). Patients were considered Lost-To-Follow-Up (LFTU) if
more than 3 months elapsed since their last visit while on ART, or
more than 6 months if not on ART.
All patient data were recorded on standardized structured clinic
encounter forms and later transcribed into the AMPATH Medical
Record System (AMRS), [31,32] a comprehensive electronic
medical information management system using OpenMRS as its
The MUSoM/MTRH Institutional Review and Ethics Com-
mittee (IREC) and the IUSM Institutional Review Board (IRB)
approved the study. All IRBs waived patient informed consent for
this project since the data used had no identifiers.
This retrospective cohort study utilized de-identified data from
records of all ART-naı ¨ve adult, non-pregnant patients .18 years
of age, who initiated ART between March 2004 and November
2007. Our working hypothesis was that HIV-infected patients
initiating ART have worse clinical and immunological outcomes if
they have TB at the time ART is initiated.
TB Disease Adversely Affects ART Outcomes
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Patients were considered to have TB if evidence of TB
treatment was recorded in the AMRS within a 10-month period
beginning 8 months prior to initiation of ART and ending 2
months after. The 8-month period prior to ART initiation covered
the TB treatment duration (2 months intensive phase and 6
months continuation phase) in Kenya during the study period
(meaning patients have TB disease during that time). The 2-month
period after ART initiation addresses cases of indolent TB, which
may become unmasked as a result of IRIS.
While patient care is provided by clinical officers (physician
assistants) and physicians, who can both record diagnoses in the
patient record, we relied on treatment information to confirm
TB diagnosis in the electronic medical record. In this analysis,
all cases of TB are grouped together, irrespective of type of
infection (e.g. milliary) and affected site (e.g. pulmonary,
meningeal and pleural).
The primary clinical outcomes we assessed were mortality,
AIDS-defining events (ADEs), first-line ART regimen failure,
weight changes and immune system reconstitution (CD4 cell
response) following ART initiation in both the TB and non-TB
patients. ADEs were defined as any condition(s) listed as part of the
WHO Stage IV criteria.  They were considered to have been
present at baseline if they were diagnosed within 3 months prior to
or up to 2 months after ART initiation. On the other hand, an
ADE was considered to be incident if it occurred more than 2
months after initiation of ART. As with TB, this window was
utilized to account for pre-existing conditions (present and likely to
be active before ART initiation) and indolent infections that
become manifest following immune improvement after ART
For purposes of this analysis, patients were categorized into 3
groups based on their CD4 cell count at ART initiation: 1)
CD4,50 cells/ml; 2) CD4 50–199 cells/ml; and 3) CD4$200
cells/ml. Responses to ART were compared between patients with
TB and those without. Descriptive statistics were generated for all
factors analyzed. While p-values ,0.05 are considered statistically
significant, due to the size of the cohort and the small p-values
generated by even clinically meaningless differences, emphasis is
placed on point estimates and 95% confidence intervals in
describing differences between patient subgroups.
The effect of TB at the time of ART initiation on the
occurrence of death, subsequent ADE, and LTFU rates was
investigated through Cox Regression (Proportional Hazards)
models, which were adjusted for baseline CD4 count category
(defined above). For the mortality analysis, patients who
remained alive until the end of follow-up or who were LTFU
were censored on the date of their last visit. For the analysis of
LTFU rates, subjects who remained on observation were
censored at their last visit date, and those who were known
to have died were censored on their date of death. For incident
ADE, subjects who did not develop a new OI prior to
November 2007 or who were lost or died without a known new
infection, were censored at the last date they were known to be
alive. These analyses were performed with the SAS system
version 9.2 (SAS Institute, Cary, NC).
The impact of TB on CD4 cell count changes after ART
initiation was assessed by using a piece-wise linear model of the
square-root CD4 counts recorded at baseline (start of ART) and
during follow-up.  Specifically, the early period after initiation
of ART was modeled with a linear segment with a steep slope,
while the later period was modeled according to a line segment
with a less steep slope. Average (population-level) effects of active
TB were tested as well as interactions of CD4 change and TB by
use of generalized equation models (GEE). We searched through a
number of candidate time points (weeks 1–26 after ART initiation)
to estimate the temporal location of the changepoint. Interaction
effects between TB group and time were also included in both
phases. A negative interaction effect is associated with widening of
TB-associated lags in CD4 counts over time, while a positive
interaction implies a convergence (catching up) of TB subjects with
their non-TB counterparts.
Weight changes post ART initiation were assessed by non-linear
mixed models. We used a model of exponential weight increase,
which prescribes that weight rises to a maximum (asymptote). The
use of this asymptotic weight increase protects against the
possibility that weight will be predicted by the statistical model
as increasing past a maximum point, a biologically implausible
situation. The model (in its most simple form) of the weight Yijfor
subject i at visit j is given by the following equation:
The first part of the model Q1is the estimate of the weight at
ART start, while the sum of the first and second part Q1+Q2is the
estimate of the final (‘‘asymptotic’’) weight (when the exponential
term becomes zero). The term Q3in the exponential component
(1-e[2exp(Q3)]) governs how quickly the final weight (asymptote) is
reached. We also included subject-specific (random) effects for the
baseline weight. We adjusted our analyses for gender and baseline
CD4 grouping as factors possibly associated with weight at
baseline or with weight changes after ART initiation by including
them as factors in the Q1 and Q2 parts respectively. We also
assessed the impact of gender and baseline CD4 count on the
speed of weight gain by including them as factors in Q3. These
analyses were performed with the R language nlme package.
Between March 2004 and November 2007, 21,242 patients
(62% females) fulfilling the study inclusion criteria initiated ART.
Of these patients, 5,186 (24%) had active TB at ART initiation as
defined in Methods.
At the time of ART initiation, patients with TB as compared
with those without TB: were of similar age; had a higher
proportion of men; were more likely to be treated at an urban
clinic; were significantly more immunosuppressed (had lower CD4
cell counts); had lower weight and BMI; and had a higher
proportion of patients with one or more ADE. Adherence levels
were comparable in both groups. Summaries of these results are
given in Table 1.
Survival, LTFU, Incident ADE and First-line ART Failure
As shown in Table 2, after adjusting for CD4 count at ART
initiation, patients with TB disease had a 32% increase in adjusted
mortality rate, were 31% more likely to experience a new ADE
(both statistically significant) and 7% more likely to be lost to
follow-up (not statistically significant). The rates of first-line
regimen failure were comparable in the two groups. A pictorial
presentation of these results is shown in Figure 1 and in Figures S1
TB Disease Adversely Affects ART Outcomes
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CD4 Cell Response after ART Initiation
During the early phase, patients belonging to the two lowest
baseline CD4 count groups experienced proportionally larger
CD4 increases compared to patients in the highest group. This
pattern continued past the sixth week post-ART initiation with
these two groups continuing to experience higher relative CD4
increases compared to the highest baseline CD4 group. However,
in absolute terms, even after one year, the differences in CD4
counts were large in all three groups (Table 3).
Overall CD4 levels at six weeks (200 versus 221 cells/ml) and
one year (251 versus 269 cells/ml) were lower in the active TB
group versus those without (Table 3). Once considered within each
CD4 count category however (,50, 50–199 and $200 cells/ml)
the six-week and one-year levels are virtually identical in the two
groups. This may mean that the lower overall level seen in the
active TB group at six weeks and one year is the result of the
disproportionate representation of active TB patients in the lower
Weight Gain after ART Initiation
Effect of TB and gender on weight gain.
active-TB patients weighed on average 3.3 kg less than patients
without TB after adjusting for gender and CD4 count at baseline
(treatment initiation). However, active-TB patients gained on
average 2.3 kg more than non-TB infected patients after ART
initiation. Thus, even though ART (and anti-TB treatment)
restores almost 70% of the weight gap between patients with active
TB and those without (in both genders), those with TB continued
to slightly (albeit significantly in the statistical sense) lag in weight
behind without TB disease. The overall mean weight at one year
after ART initiation was 60.4 kg (95% confidence interval – CI:
60.3–60.6) in the active TB group and 61.2 (60.8–61.5) in the
Effect of initial CD4 cell count on weight gain.
with CD4 50–199 cells/ml were on average 4.1 kg heavier than
patients with CD4 count ,50 cells/ml at the time of ART
initiation. Patients with CD4$200 cells/ml weighed 7.0 kg more
than patients with CD4,50 cells/ml. However, patients with CD4
50–199 cells/ml gained 3.9 kg less than individuals with
CD4,50 cell/ml, effectively erasing the lag in weight between
Table 1. Baseline social, demographic and clinical characteristics of patients on ART in an ambulatory HIV care program in western
Kenya, by TB status.
patients on ART N=5,186
on ART N=16,056
Absolute or % Difference (95% CI
Median (IQR)37.0 (31.4, 43.4)37.7 (31.6, 44.5) 0.70 (0.414, 1.000)
Male Gender 2,406 (46.4%) 5,600 (34.9%)0.115 (0.010, 0.131)
Employed Outside Home1,188 (24.1%) 3,258 (21.6%)0.025 (0.011, 0.039)
Post-Primary (.8 years) Education
2,035 (43.9%)5,677 (40.4%)0.035 (0.018, 0.051)
Urban2,724 (52.5%)7,676 (47.8%) 0.047 (0.032, 0.063)
CD4 count (cells/ml) at ART start
Median (IQR) 77 (29, 146) 109 (47, 175)32.5 (25.85, 33.51)
CD4 count (cells/ml) at ART start
0–491,330 (37.1%) 3,292 (26.0%)0.111 (0.081, 0.141)2
50–1991,810 (50.5%) 7,284 (57.5%)
20.070 (20.096, 20.044)
$200446 (12.4%) 2,097 (16.6%)
20.041 (20.076, 20.007)
Weight (Kg) at ART start
Median (IQR) 52.5 (46, 59)55 (48.5, 62)2.5 (2.374, 3.031)
Median (IQR) 18.7 (16.9, 20.7)19.8 (17.8, 22.1)1.1 (1.123, 1.341)
ADE3(overall)736 (14.2%) 1,317 (8.2%)
CD4 (cells/ml) at ART start
N=3,586N=12,6730.06 (0.049, 0.070)
0–49244 (18.4%)415 (12.6%)1.275 (1.027, 1.581)
50–199 237 (13.1%)439 (6.1%)1.100 (0.886, 1.364)
$20055 (12.3%) 194 (9.3%)0.503 (0.359, 0.698)
Perfect Adherence to ART
3,852 (79.6%)11,590 (78.4%)0.012 (20.001, 0.025)
1Difference between proportions (categorical variables) or means (continuous variables) of TB versus non-TB group.
2Difference in the proportion of subjects with CD4,50 cells/ml in the TB versus the non-TB group.
3Patients presenting with an AIDS-defining event (other than extra-pulmonary TB) 3 months prior and 2 months after ART initiation (some CD4 data are missing).
TB Disease Adversely Affects ART Outcomes
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the two groups that was present at the start of ART. Although
patients with CD4$200 cells/ml gained 5.5 kg less than patients
with CD4,50 cells/ml, differences persisted between the highest
CD4 group and the lower two strata.
HIV-infected patients with active TB had higher rates of
mortality and experienced more incident ADEs despite the
observed robust response to ART. There are a number of possible
reasons why this is the case. Patients with TB were more likely to
have lower CD4 counts at the time they started ART, compared
with patients without TB. In addition, TB was associated with a
higher incidence of ADEs at the start of ART in all three CD4
groups. TB was also associated with, overall, lower average weight
among both men and women starting ART, a strong predictor of
survival. [37,38] Thus, the combination of low CD4 count and
weight along with the higher incidence of ADEs was likely the
main cause of the higher observed mortality.
We found TB disease to be associated with lower CD4 cell
counts at ART initiation. While both TB and non-TB patients
experienced almost similar CD4 increases after ART initiation,
overall CD4 counts were still lower in the TB group, even after a
year from ART initiation.
Weight was lower in the TB group within all baseline CD4
strata as compared to the non-TB group. These effects are also
reflected in the way that patients regained weight after initiating
ART. Our study findings indicate that patients with TB gain
weight rapidly but lag slightly behind non-TB patients even after a
year of ART. This observation is particularly evident in the group
of patients with CD4 cell counts at or above 200/ml at baseline.
The clinical meaning of about one-kilogram residual difference in
weight (,2% of total weight) between the TB and non-TB groups
is not clear. Wheeler and colleagues analyzed results from four
trials and found that even a mild weight loss of 5% over a short
period to be associated with increased mortality and OIs. 
However, it is unknown whether a ,2% lag in weight gain would
be associated with increased rates of adverse clinical outcomes.
Figure 1. Kaplan-Meier curve of mortality (left) and new AIDS Defining Events (right) in the TB group (dashed line) versus the non-
TB group (solid line).
Table 2. Estimated hazard ratios for death, Loss To Follow-Up, incident AIDS Defining Event (ADE) and first-line ART failure for
patients with TB/HIV initiating ART compared to non-TB co-infected patients.
Unadjusted Hazard RatioAdjusted1Hazard Ratio
Time from start of ART to …(95% CI)(95% CI)
Death1.456 (1.304–1.626)1.319 (1.180–1.474)
Loss-to-follow-up1.124 (1.045–1.210) 1.068 (0.991–1.150)
1.395 (1.262–1.541)1.313 (1.187–1.453)
Start of second-line ART3
1.048 (0.841–1.305)0.989 (0.793–1.234)
1Adjusted according to CD4 count at ART initiation.
2Including extra-pulmonary TB.
3Surrogate marker for 1stLine ART Failure.
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Contrary to our hypothesis that the increased pill load and likely
increased side-effects of anti-TB and ART co-administration
would adversely impact ART adherence, we found that self-
reported drug adherence was similar between HIV-infected
patients with TB disease and those without. The similar levels of
adherence in the two study groups likely explain the strong
response of weight and CD4 counts to ART observed in all
patients as well as in the similar risk of first-line regimen failure in
the two groups. These results are encouraging indeed given the
reported higher rates of treatment failure among patients with low
CD4 counts at the start of therapy .
Last, but not least, we found that disproportionately more males
than females were affected by TB. Although the reasons for the
higher prevalence of TB among men are complex, it may be due
to our previously published finding that men tend to seek care later
in the course of HIV infection compared to women and thus have
generally lower CD4 counts and more advanced WHO stage than
Strengths and Limitations of the Study
The main strength of this study is the very large cohort of
subjects studied in a resource-constrained setting representative of
the realities of HIV and TB care and treatment in sub-Saharan
Africa, where the epicenter of the two epidemics is located. This
study involved well over 20,000 patients starting therapy and close
to 40,000 CD4 cell count and 250,000 weight observations.
However, the background within which this study was carried out
is also responsible for a number of shortfalls, which are related to
weaknesses inherent in routine clinical data collected in this
setting. Diagnosis of TB is problematic in an area where most
clinics do not have TB culture capability. This limitation also
impacted our ability to differentiate infection with mycobacterial
from non-mycobacterial acid fast bacilli. All adherence measure-
ments were based on self reports, which we know do not capture
true adherence status for patients. Also, the program does not
conduct routine plasma viral load testing for monitoring of ART.
This means that there may have been many more patients with 1st
line failure than we reported.
Another weakness of this study is the differential levels of death
and LTFU in the TB and non-TB groups. The likely bias
generated from these factors may have resulted in an overestima-
tion of the CD4 and weight response to ART among TB patients
and an underestimation of CD4 and weight differences between
the TB and non-TB groups, particularly during the first weeks
after ART initiation when the hazard of death is highest. Such
biases may also account for some of the convergence of CD4 and
weight trajectories in the two groups seen in the longitudinal CD4
and weight analyses. Thus, the association of TB with worse long-
term outcomes may be even more serious than the data suggest,
particularly since LTFU rates were higher in the TB group. Given
our previous data on LTFU in our clinical setting, [41,42] we
postulate that a greater proportion of deaths occurred in patients
who where LTFU as compared to those retained in care.
Therefore, the impact of TB on mortality may be even higher
than observed. For these reasons, although significant biases exist
in our data, their nature and likely direction suggest that TB may
have serious short and long-term impacts on HIV-infected
individuals. The presence of these biases is thus unlikely to
materially affect the main conclusions of this study.
Conclusion and Recommendations
HIV-infected patients with active TB initiating ART have
higher mortality and incident ADE rates compared to those
without TB. While HIV/TB co-infected patients are generally
more immunosuppressed, have higher rates of ADEs and weigh
less at ART initiation the differences in initial CD4 cell count do
not fully account for the worse outcomes observed in the TB
patients, particularly given the robust response to ART experi-
enced by all patients. These data suggest, therefore, that TB, on its
own, is associated with additional risk for poor clinical outcomes
beyond what would be explained through the association of TB
with suppressed immune function (evidenced by the higher rates of
low CD4 counts observed among TB patients starting ART).
Thus, even though interventions to enhance early identification
and comprehensive treatment of TB in HIV-infected patients may
have salutary effects on survival, because they will increase the
number of TB patients who initiate ART at higher CD4 counts,
this will only partially address the higher mortality associated with
TB/HIV co-infection. Initiation of ART at higher CD4 counts as
well as a concerted TB prophylaxis effort in HIV-infected
individuals will be necessary to reduce TB-associated mortality
in this patient population.
death in the TB group (dashed line) versus the non-TB group (solid
Kaplan-Meier curve of hazard (instantaneous risk) of
Table 3. Results from the piece-wise linear model for CD4 response after ART initiation.
BaselineSix weeks after ARTOne year after ART
CD4 count (95% CI1) CD4 count (95% CI)CD4 count (95% CI)
TB group (overall)
Non-TB group (overall)
1Confidence intervals were derived based on the delta method of approximation, using estimates of the variance of the parameters produced by the GEE model. Note
that the overall differences in CD4 counts present in the TB versus non-TB groups, are not evident among the three CD4 groupings suggesting that differences in CD4
response are a function of higher rates of baseline immunosuppression among TB patients rather than an independent TB-associated effect.
TB Disease Adversely Affects ART Outcomes
PLOS ONE | www.plosone.org6January 2013 | Volume 8 | Issue 1 | e53022
first-line ART regimen failure in the TB group (dashed line) versus
the non-TB group (solid line).
Kaplan-Meier curve of time from ART initiation to
We would like to thank all of the staff from AMPATH, Moi Teaching and
Referral Hospital, Moi University School of Medicine, Indiana University
School of Medicine and Warren Alpert Medical School at Brown that
participated in the development, institution and rollout of the USAID-
AMPATH Partnership HIV treatment and care program. We are also very
grateful to the AMPATH Research Network and AMPATH Data Team
for data management and statistical analysis support.
Conceived and designed the experiments: AMS CTY KKW-K JEC LOD
WMT SNK. Performed the experiments: AMS KKW-K LOD SNK JEC.
Analyzed the data: AMS CTY BSM AWM WMT. Contributed reagents/
materials/analysis tools: CTY. Wrote the paper: AMS CTY KKW-K
AWM BSM LOD SNK WMT JEC.
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