STRETCHing HIV treatment: A replication study of task shifting in South Africa
STRETCHing HIV treatment: A replication study of task shifting in South Africa
Baojiang ChenID 0 1
Morshed Alam 1
0 Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston, School of Public Health in Austin , Austin, Texas , United States of America, 2 Department of Biostatistics, University of Nebraska Medical Center , Omaha, Nebraska , United States of America
1 Editor: Bisola O. Ojikutu, Brigham and Women's Hospital , UNITED STATES
The Streamlining Tasks & Roles to Expand Treatment and Care for HIV (STRETCH) program was developed to increase the reach of antiretroviral therapy (ART) for HIV/AIDS patients in Sub-Saharan Africa by training nurses to prescribe, initiate, and maintain ART. Fairall and colleagues conducted a cluster-randomized trial to determine the effects/impact of STRETCH on patient health outcomes in South Africa between 2008 and 2010. The purpose of our replication study is to evaluate Fairall and colleagues' findings. We conducted push button and pure replication studies and measurement and estimation analyses (MEA). Our MEA validates the original findings: (1) overall, time to death did not differ between intervention (STRETCH) and control (ART) patients; (2) in a subgroup analysis of patients with CD4 counts of 201-350 cells per ?L, the intervention group patients had a 30% lower risk of death than those in the control group, when controlling for baseline characteristics; (3) in a subgroup analysis of patients with CD4 counts of 200 cells per ?L, time to death did not differ between the two groups; and (4) rates of viral suppression one year after enrollment did not differ between the intervention and control groups. This set of results have more caveats in the MEA. Although the intervention did not lead to improvements in the main outcomes, the effectiveness of STRETCH was proven to be similar to standard care while increasing the pool of prescribers, expanding their geographical range, and improving the quality of care for patients. Therefore, our analyses support the implementation of task shifting of antiretroviral therapy from doctors to trained nurses, which enhances confidence in the implementation of the intervention program and policymaking not only in South Africa but also in other developing countries that have similar circumstances.
Data Availability Statement: The authors of this
study used the data from Fairall et al. and do not
have permission to share this data set. Interested
readers can access these datasets by contacting
the original authors of Fairall et al. (2012, Drs.
Bachmann and Fairall). Data requests can be sent
to the corresponding author, Dr. Bachmann, at
. The authors did not have
any special access privileges that others would not
Funding: This research was supported by
International Initiative for Impact Evaluation. The
funders had no role in study design, data collection
The paper Task shifting of antiretroviral treatment from doctors to primary-care nurses in South
Africa (STRETCH): a pragmatic parallel, cluster-randomised trial by Fairall and colleagues [
addresses a critical challenge to widespread treatment of HIV/AIDS in Sub-Saharan Africa.
Although antiretroviral therapy (ART) regimes have proven efficacious in slowing the onset
and symptoms of HIV/AIDS [
], dispensation of ART is hampered by the limited availability
and analysis, decision to publish, or preparation of
of doctors to prescribe the treatment and by the fact that doctors tend to be concentrated in
urban areas [
]. In order to increase the reach of ART, the Streamlining Tasks and Roles to
Expand Treatment and Care for HIV (STRETCH) program was designed to train nurses to
prescribe ART (initiate and maintain on treatment) by introducing an educational outreach
nurse training model [
]. However, information about the efficacy of the STRETCH
program compared to the standard care system?in which only doctors can prescribe ART?is
Fairall and colleagues [
] conducted a cluster-randomized trial to determine the efficacy of
STRETCH on patient health outcomes in South Africa between 2008 and 2010. Two cohort
studies were conducted simultaneously to assess the effect of the intervention (STRETCH)
compared to the standard care system when patients become eligible for ART initiation, and
for individuals already enrolled in treatment programs [
]. Fairall and colleagues? original
hypothesis was that implementation of STRETCH would improve primary outcomes relative
to standard care by expanding ART access. While this was not the case, they do note that
STRETCH was not inferior to standard care. Additionally, the STRETCH program did
improve several other health outcomes and quality of care indicators. Overall, no outcomes
were worse in the STRETCH intervention groups than in the standard care groups [
findings provide support for expanding the pool of ART prescribers beyond doctors to nurses,
thus increasing access to ART among populations not located near doctors, who are typically
more widely available in urban settings.
Fairall and colleagues? [
] study has been enormously influential in HIV/AIDS studies,
leading to larger studies in this area and expanded application to other geographic locations
]. Their findings reaffirm that task shifting of ART from doctors to trained nurses
can benefit many HIV-positive patients in South Africa and other developing countries with
similar circumstances, without negative impacts on key health outcomes and while
improving their quality of care. STRETCH can also relieve doctors of a heavy patient burden and
enable them to focus on more severely ill patients. This is essential in South Africa and
other developing countries where shortages of doctors restrict access to ART. For example,
studies in Rwanda, Cameroon and other Sub-Saharan African countries [
] have assessed
the feasibility and effectiveness of task shifting from physicians to nurses due to shortage
of physicians and other human resources for health, and reached similarly positive
Our replication provides influential evidence for policymaking by supporting the results of
prior studies. Validation of the findings can enhance confidence in the implementation of the
intervention program and policymaking not only in South Africa, but also in other
underserved areas with high burden of HIV/AIDS.
Materials and methods
The study by Fairall and colleagues [
] included two datasets: Cohort 1 and Cohort 2 (see
Table A and Table B in S1 File for the variable definitions for the two cohorts). The original
authors provided us with primary outcomes for the two datasets in Stata format, along with
the Stata code used to generate their results. The dataset for Cohort 1 includes patients aged 16
years and older with CD4 counts of 350 cells per ?L who had not yet started ART [
primary outcome for Cohort 1 was the time from enrollment to death. Secondary outcomes
for Cohort 1 were measures of health status and indicators of quality of care. The data set for
Cohort 2 includes patients who were adults, had already received ART for at least 6 months
and were being treated at the time of enrollment. The primary outcome for Cohort 2 was the
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proportion of patients with undetectable viral load one year after enrollment. Secondary
outcomes for Cohort 2 were measures of health status and indicators of quality of care. We
generated findings based on these limited datasets, which included only the complete case data.
Therefore, the results reported here may differ from those in the original study due to missing
variables or discrepancies between the original and current datasets.
We first conducted a push button replication (PBR) study and then followed the statistical
methods used in Fairall and others [
] to conduct the pure replication. We designed our pure
replication to independently test the consistency of the original published results (Our
replication paper is available at
http://www.3ieimpact.org/media/filer_public/2017/11/29/rps13-hivtreatment-south-africa.pdf). The study was restricted to the two primary outcomes analyses,
due to limited access to the original data. The frequency (percentage) for categorical variables
and the median (interquartile range [IQR]) for continuous variables were reported for baseline
characteristics by cohort. In Cohort 1, time from enrollment to death was analyzed with Cox
proportional hazards (PH) models and Huber-White robust adjustment of errors for
intracluster correlation of outcomes. Comparisons of effect between intervention and control groups
were conducted by reporting the number of deaths, person-months at risk and hazard of death
per 100 person-months at risk with 95 percent confidence intervals (CI). All analyses were also
stratified by baseline CD4 count groups (201?350 versus 200 cells per ?L). In Cohort 2,
binomial regression was used to estimate differences in proportions of patients with suppressed
We next conduct a measurement and estimation analysis (MEA) to further evaluate the
robustness of the original findings following the replication process described by Brown,
Cameron and Wood [
]. We first checked the PH assumptions in the Cox PH model using
the Schoenfeld residuals test and cumulative sums of martingale-based residuals methods
] for the analysis of primary outcome in Cohort 1. If the PH assumption were violated
for some predictors, then a stratified Cox model would be used to fit the data. To take the
correlation of the responses in the same cluster into account, in the MEA we utilized two
approaches: (1) the generalized estimating equation (GEE) approach [
] using the working
correlation matrix; and (2) the frailty model [
]. For the Cohort 2 study, to take the
correlation of the responses (i.e. viral suppression one year after enrollment) in the same cluster
into account, in the MEA we utilized two approaches: (1) the GEE approach ; and (2) the
generalized linear mixed-effects model (GLMM) [
]. All the MEA analyses were conducted
using R. This alternative coding language may have introduced slight differences from the
The push button replication result
The PBR results are reported in the Supporting Information. Table C in S1 File is the PBR
result for Table 2 in the original paper of Fairall et al. [
], and Table D in S1 File is the PBR
result for Table 4 in the original paper of Fairall et al. [
]. In Table C in S1 File, there are minor
differences for the number of subjects in the subgroup analysis from the original results. We
obtain n = 2,258 and 6,994 for the subgroups with baseline CD4 count 201?350 cells per ?L
and CD4 count <= 200 cells per ?L, respectively, whereas the original results reported 2,283
and 6,969. The other replicated results are classified as comparable.
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PLOS ONE | https://doi.org/10.1371/journal.pone.0206677
Person- Hazard of
months at death per 100
risk person- months
at risk (95%
Person- Hazard of
months at death per 100
at risk (95%
Hazard ratio Unadjusted/
(95% CI) Crude
The pure replication result
Fig 1. KM curves stratified by treatment groups in cohort 1.
Cohort 1. We also reproduced the Kaplan-Meier failure curve of time to death (Fig 1) and for
CD4 subgroups for Cohort 1 (Fig 2). Table 3 reports the original and pure replication results
for the primary outcome in Cohort 2.
Overall, our replication analysis conclusions are consistent with the original results, which
indicate that time to death did not differ between the two groups when controlling for baseline
characteristics (p = 0.400). In subgroup analysis with CD4 counts of 201?350 cells per ?L, the
intervention group patients had a 30 percent lower risk of death than those in the control
group when controlling for baseline characteristics (p = 0.019). In subgroup analysis with CD4
counts of 200 cells per ?L, time to death did not differ between the two groups when
controlling for baseline characteristics (p = 0.568). Table 3 results indicate that viral suppression rates
one year after enrollment did not differ between intervention and control patients.
Measurement and estimation analysis results
Fig 2. KM curve stratified by treatment and CD4 subgroups in cohort 1.
the same, although there were minor differences in the estimates. In the adjusted analysis, the
GEE results (HR = 0.73, 95% CI: 0.56?0.94, p = 0.016) showed the same conclusion as in the
original publication (HR = 0.70, 95% CI: 0.52?0.95, p = 0.020), although there were minor
differences in the estimates. The frailty model analysis (HR = 0.72, 95% CI: 0.50?1.04, p = 0.079)
showed a loss of significance from the original results.
In the subgroup analysis with baseline CD4 count 200 cells per ?L, the GEE and frailty
analyses both showed the same conclusion as in the original publication, although there were
minor differences in the estimates.
Intracluster correlation coefficient
Suppressed viral load
dstimate (95% CI)
Hazard ratio (95%
Adjusted hazard ratio (95% Adjusted
Odds ratio (95% CI)
We also applied the GEE and GLMMs to account for the cluster effects for the primary
outcome in Cohort 2. We obtained the same conclusion as in the original result. See Table 5. For
more details of the whole replication study, please refer our replication paper series at http://
We conducted the MEA by assessing the validity of model assumptions and proposed other
advanced methods to assess the robustness of the conclusions reached by Fairall and colleagues
Since the adjusted analyses control for potential confounders, we are more confident
interpreting the adjusted analysis results than the unadjusted results. It may not be surprising that
the frailty model or GLMM analysis showed a different conclusion from the original or GEE
results, as the results from the two methods have different interpretations. The estimate from
the GEE analysis has a marginal or population average interpretation, while the estimate from
the frailty or GLMM analysis has a subject-specific inference. The GEE results are more
meaningful to a policymaker, as they reflect population average inferences. The frailty or GLMM
model results might be more meaningful for a patient.
Based on the GEE result for Cohort 1, shown in Table 4, the MEA generated the same
conclusion as the original analysis: for the primary analysis and subgroup analysis with baseline
CD4 count 200 cells per ?L, time to death did not differ between intervention and control
patients. In the subgroup analysis with baseline CD4 count 201?350 cells per ?L, the
intervention group patients had a 30 percent lower risk of death than those in the control group when
controlling for baseline characteristics (Table 4). For Cohort 2 analysis, all methods yielded the
same conclusions: rates of viral suppression one year after enrollment did not differ between
the intervention and control groups.
This replication study focuses on the two primary outcomes in Cohorts 1 and 2. Though
the original paper also analyzed secondary health outcomes and quality of care indicators,
our replication study cannot evaluate findings for these outcomes due to limited data access.
Another limitation of this study is that we cannot evaluate how the missing data will affect the
conclusions. Fairall et al. [
] discussed the issue of incomplete data, ?We were missing data for
weight and CD4 cell count in both cohorts, and for viral load after 12 months of ART in cohort
], but they have not addressed the missing data issue. Due to limited data, we also cannot
address this important issue.
Although there are some minor differences between results of our analyses and the original
paper, our replication study findings primarily validate the original findings. The minor
differences may be due to discrepancies between the datasets or methods used in our analysis and in
the original analysis. Overall, time to death did not differ between intervention and control
patients, and rates of viral suppression one year after enrollment did not differ between the
intervention and control groups. In subgroup analysis with CD4 counts of 201?350 cells
per ?L, the intervention group patients had a 30 percent lower risk of death than those in the
control group when controlling for baseline characteristics. In subgroup analysis with CD4
counts of 200 cells per ?L, time to death did not differ between the two groups. Although the
intervention did not lead to improved well-being for all the main outcomes, it was proven safe
to use, and it increased the pool of prescribers and their geographical range, which increased
the quality of care of these patients [
The original authors have used a draft version of this replication study in a summary of all
research on the intervention that they provided to the Government of South Africa?s National
Department of Health [
]. They informed us that these replication results will be included in
documentation around a further possible scale-up of the STRETCH intervention within South
Africa in the near future. Our replication study enhances the confidence in implementation of
task shifting of ART from doctors to trained nurses in developing countries similar to South
Africa. Implementing the STRETCH program will benefit many HIV-positive patients in
South Africa and other developing countries with similar circumstances without negatively
influencing key health outcomes and while improving their quality of care. It can also relieve
doctors from a heavy patient burden and enable them to focus on more severely ill patients.
This is essential in South Africa and elsewhere where shortages of doctors restrict access to
S1 File. Variable information and PBR results for cohorts 1 and 2.
First, we would like to thank 3ie for providing us with the support to conduct this replication
study (Benjamin Wood, Eric Djimeu and Scott Neilitz). We give special thanks to the original
authors?Drs. Bachmann, Fairall, Lombard and colleagues?for providing the data sets and
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code for the push button replications. Finally, we thank the Editor and the two referees for
their valuable comments to enhance the quality of this manuscript.
Conceptualization: Baojiang Chen.
Formal analysis: Baojiang Chen, Morshed Alam.
Investigation: Baojiang Chen.
Methodology: Baojiang Chen.
Project administration: Baojiang Chen.
Resources: Baojiang Chen.
Supervision: Baojiang Chen.
Validation: Baojiang Chen.
Writing ? original draft: Baojiang Chen.
Writing ? review & editing: Baojiang Chen.
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