GeneXpert Performance Evaluation for Linkage to Tuberculosis Care: The XPEL-TB Trial
Phase: NA
Conditions: Tuberculosis, Pulmonary, Rifampicin Resistant Tuberculosis
Sponsor: University of California, San Francisco

Protocol
Protocol for: Cattamanchi A, Reza TF, Nalugwa T, et al. Multicomponent strategy with decentralized molecular 
testing for tuberculosis. N Engl J Med 2021;385:2441-50. DOI: 10.1056/NEJMoa2105470
This trial protocol has been provided by the authors to give readers additional information about the work.

This supplement contains the following items: 
    1.  Original protocol, final protocol, summary of changes. 
    2.  Original statistical analysis plan, final statistical analysis plan, summary of changes. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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GeneXpert Performance Evaluation for Linkage to Tuberculosis 
Care:  
The XPEL TB Trial 
 
 
ClinicalTrials.gov Identifier:   
NCT03044158  
 
Pan African Clinical Trials Registry:   
PACTR201610001763265 
 
Sponsored by: 
NIH/NHLBI 
 
Unique Protocol ID: 
R01HL130192 
 
Principal Investigator: 
Adithya Cattamanchi MD, MAS 
 
Draft or Version Number:  
Version 1.0 
 
Day Month Year 
May 1, 2018 
 
 
 

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Statement of Compliance 
The study will be carried out in accordance with Good Clinical Practice (GCP) as 
required by the following: 
• 
U.S. Code of Federal Regulations applicable to clinical studies (45 CFR 46) 
• 
ICH GCP E6 
• 
Completion of Human Subjects Protection Training 
• 
NIH Clinical Terms of Award 
Refer to:  http://www.hhs.gov/ohrp/humansubjects/guidance/45cfr46.htm#46  
http://www.fda.gov/cder/guidance/959fnl.pdf  
http://grants.nih.gov/grants/guide/notice-files/NOT-OD-01-061.html  
http://cme.cancer.gov/c01/  
 
 
 

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Signature Page 
The signature below constitutes the approval of this protocol and the attachments, and 
provides the necessary assurances that this trial will be conducted according to all 
stipulations of the protocol, including all statements regarding confidentiality, and 
according to local legal and regulatory requirements and applicable US federal 
regulations and ICH guidelines. 
 
 
Uganda Study Site Principal Investigator:* 
 
Signed:________________________________ 
Date:_____________________  
      Achilles Katamba, MBChB, PhD 
 
 
 
 
 
Overall Study Principal Investigator:* 
 
Signed:________________________________ 
Date:_____________________  
      Adithya Cattamanchi, MD, MAS  
 
 
 
 

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Table of Contents 
 
Overview ..........................................................................................................................................6 
Key Roles .........................................................................................................................................7 
Background ......................................................................................................................................8 
Background information .............................................................................................................. 8 
Theoretical basis for intervention strategy .................................................................................. 9 
Table 2. Key clinic-level barriers targeted by intervention strategy. ..................................... 11 
Objectives ..................................................................................................................................... 12 
Methods/Design ............................................................................................................................ 13 
Overview .................................................................................................................................... 13 
Target Setting and Study Population ........................................................................................ 13 
Eligibility criteria for the cluster-randomized trial .................................................................. 13 
Eligibility criteria for mixed methods and health economic sub-studies ............................... 14 
Recruitment ............................................................................................................................... 14 
Health centers........................................................................................................................ 14 
Participants for cluster-randomized trial ................................................................................ 15 
Participants for mixed methods and health economic sub-studies ...................................... 15 
Intervention and Control (Wait-list) Arms .................................................................................. 15 
Figure 2. Comparison of TB diagnostic evaluation in the intervention and control arms ..... 16 
On-Site Molecular Testing ..................................................................................................... 16 
Process Redesign for Same-Day TB Diagnosis and Treatment .......................................... 17 
Performance Feedback ......................................................................................................... 17 
Randomization ....................................................................................................................... 18 
Procedures ................................................................................................................................ 18 
Health center training ............................................................................................................ 18 
Patient-level data collection................................................................................................... 19 
Patient surveys ...................................................................................................................... 19 
Provider surveys .................................................................................................................... 20 
Health system costing ........................................................................................................... 20 
Focus group discussions ....................................................................................................... 20 
Patient vital status assessment ............................................................................................. 20 
Provider interviews ................................................................................................................ 21 
Figure 3. Site visit activities ................................................................................................... 21 
Outcomes .................................................................................................................................. 22 
Definitions .............................................................................................................................. 22 
I. Primary Outcome (Effectiveness) ...................................................................................... 23 
II. Secondary Outcomes (Effectiveness) ............................................................................... 23 
III. Implementation Outcomes ............................................................................................... 24 
IV. Cost-effectiveness/Modeling Outcomes .......................................................................... 25 
Data Management ..................................................................................................................... 26 
Statistical Analysis..................................................................................................................... 26 
Aim 1 ...................................................................................................................................... 26 
Aim 2 ...................................................................................................................................... 27 
Aim 3 ...................................................................................................................................... 27 
Sample Size Considerations ..................................................................................................... 28 
Aim 1 ...................................................................................................................................... 28 
Aim 2 ...................................................................................................................................... 29 
Aim 3 ...................................................................................................................................... 29 
Ethical Considerations .................................................................................................................. 30 

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Potential risks to participants .................................................................................................... 30 
Protection against risk ............................................................................................................... 30 
Data protection ...................................................................................................................... 31 
Potential benefits of the study to participants and society ....................................................... 31 
Dissemination ............................................................................................................................ 31 
Trial governance........................................................................................................................ 31 
Abbreviations ................................................................................................................................ 32 
Competing interests ...................................................................................................................... 33 
Trial Steering Committee .............................................................................................................. 33 
References .................................................................................................................................... 34 
 
 
 

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Overview 
Title 
GeneXpert Performance Evaluation for Linkage to Tuberculosis 
Care: The XPEL TB Trial 
Target Population 
Adults undergoing evaluation for pulmonary TB (N=6500-7500) 
Sites 
20 TB microscopy centers (10 intervention, 10 control) in Uganda 
Study Design 
Clustered-randomized trial with nested mixed methods and 
economic/transmission modeling studies 
Study Duration 
2 years 
Objectives 
Aim 1: To compare patient outcomes at health centers 
randomized to intervention vs. standard-of-care TB diagnostic 
evaluation strategies. 
• 
Intervention: onsite molecular testing for TB + process 
redesign to facilitate same-day TB diagnosis and treatment 
+ performance feedback 
• 
Standard-of-care: onsite ZN or LED fluorescence 
microscopy + hub-based GeneXpert testing per existing 
protocols  
Aim 2: To identify processes and contextual factors that 
influence the effectiveness and fidelity of the intervention TB 
diagnostic evaluation strategy. 
 
Aim 3: To compare the costs and epidemiological impact of 
intervention vs. standard-of-care TB diagnostic evaluation 
strategies. 
 
 
 

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Key Roles 
Principal Investigator  
Adithya Cattamanchi, MD, MAS 
Associate Professor of Medicine, University of California San Francisco 
San Francisco General Hospital 
1001 Potrero Ave, Room 5K1 
San Francisco, CA 94110 
Phone: +1-415-206-5489 
Fax: +1-415-695-1551 
adithya.cattamanchi@ucsf.edu 
 
Uganda Principal Investigator 
Achilles Katamba, MBChB, MSc, PhD 
Senior Lecturer, Makerere University College of Health Sciences 
Upper Mulago Hill Road 
Kampala, Uganda 
Phone: +256-(0)414-530-692 
Fax: +256-(0)414-540-524 
akatamba@yahoo.com  axk95@case.edu 
 
NHLBI Program Officer 
Elisabet Caler, PhD 
AIDS Program Lung Team Leader 
Lis.caler@nih.gov 
 
 

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Background 
Background information 
 
Prompt diagnosis and treatment of TB patients is essential to making progress towards TB 
elimination. However, at least 4.3 million of the estimated 10.4 million new cases in 2015 were 
not detected and reported to the World Health Organization (WHO).1 There are three 
overarching reasons for this large gap: TB patients are not being notified to public health 
authorities, not seeking care, or not being diagnosed and treated even after seeking care. The 
last reason represents a clear health system failure that is pervasive in high burden countries – 
a recent systematic review of published studies found that up to 38% of sputum smear-positive 
patients in Africa and 28% in Asia are lost to follow-up prior to treatment initiation.2 Patients with 
smear-negative TB are even less likely to complete the diagnostic cascade of care and be 
linked to treatment.  
A principal reason for these failures in linkage to care is the inadequacy of the current approach 
to TB diagnosis at community health centers. It is well known that sputum smear microscopy, 
the most common test for TB at community health centers worldwide, has important limitations 
that contribute to delays in TB diagnosis and treatment. First, smear microcopy has sub-optimal 
sensitivity, identifying only about 50% of patients who actually have TB.3 Second, the typical 
process of sputum collection and smear examination is burdensome for patients and staff. 
Guidelines usually require staff to ask patients to submit a sputum specimen on the day of 
presentation, return the following morning to submit a second specimen, and return a third time 
for treatment initiation (if smear-positive) or to consider further workup (if smear-negative). In 
high burden countries, clinicians fail to refer up to half of patients reporting TB symptoms for 
sputum smear examination.4, 5 In addition, the direct and indirect costs of this standard multi-day 
diagnostic evaluation process consume up to 3 months of household income for already poor 
patients.6-10 It is therefore not surprising that a substantial proportion do not return after their 
initial health center visit to submit additional sputum specimens, collect results or initiate 
treatment if smear-positive.5, 11-13  
To address these limitations, there has been substantial donor investment in scale-up of Xpert 
MTB/RIF (Xpert)14, a semi-automated molecular assay endorsed by the WHO in 2010 and by 
the US FDA in 2013. Xpert identifies 90% of TB cases, has a 2-hour turn-around time, and can 
be performed with minimal training and human resource requirements.15 Although this 
represents an important advance, modeling analyses have found that Xpert is unlikely to bend 
the TB incidence curve significantly, primarily because it cannot be deployed at community 
health centers in high burden countries.16 Indeed, because of high device costs and 
infrastructure requirements, the vast majority of Xpert devices are being placed at district or 
higher-level facilities14, which are accessed by <15% of the population.17 Many countries have 
therefore adopted a hub-and-spoke model in which several community health centers (spokes) 
are linked to an Xpert testing site (hub). However, a cluster-randomized trial has found that 
Xpert implementation using a hub-and-spoke model did not impact mortality; the major reason 
was failure to link patients with confirmed TB to treatment.18  

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The next generation of molecular diagnostics has strong potential to close gaps in the TB 
diagnostic cascade of care. In particular, GeneXpert Omni is a portable, single-cartridge version 
of the GeneXpert platform that is already WHO- and FDA-approved, and widely used in Uganda 
and other high TB burden countries. Because it only has a single test module (instead of 4 or 
more), it is lower cost (subsidized price $5000 vs. $10000 or more for previous GeneXpert 
platforms) and has lower power requirements enabling it to be battery-operated.19 These 
features increase the likelihood that onsite Xpert testing can eventually be scaled up as a 
replacement for sputum smear at community health centers. 
Our overall objective is to assess the effectiveness, implementation and costs of a streamlined 
TB diagnostic evaluation strategy based around rapid, onsite molecular testing. The intervention 
strategy was developed based on theory-informed assessment of barriers to TB diagnostic 
evaluation at community health centers in Uganda and a process of engagement with local 
stakeholders. It includes: 1) Point-of-care molecular testing using GeneXpert as a replacement 
for sputum smear microscopy; 2) Re-structuring of clinic-level procedures to facilitate same-day 
TB diagnosis and treatment; and 3) Quarterly feedback of TB evaluation metrics to health center 
staff. Our central hypothesis is that the intervention strategy will have high uptake and increase 
the number of patients diagnosed with and treated for active pulmonary TB. To test this 
hypothesis, we will conduct a pragmatic cluster-randomized trial at community health centers 
that provide TB microscopy services in Uganda in partnership with the National TB Program 
(NTP). We utilize an effectiveness-implementation hybrid design in which, concurrent with the 
clinical trial, we will conduct nested mixed methods, health economic and modeling studies to 
assess 1) whether the intervention strategy modifies targeted barriers to TB diagnostic 
evaluation; 2) fidelity of implementation of the intervention components (i.e, the degree to which 
intervention components were implemented as intended vs. adapted across sites); and 3) cost-
effectiveness and public health impact. We will use the RE-AIM framework to evaluate how the 
intervention strategy affects processes and outcomes important to patients and TB programs, 
and to determine its scale-up potential. RE-AIM encompasses 5 dimensions common to 
successful multi-level interventions: depth of reach into a target population; effectiveness; 
factors that promote adoption; resources needed for implementation; and factors that ensure 
maintenance over time.20 Thus, our focus is on evaluating the impact of the proposed multi-level 
intervention, rather than evaluating the impact of its individual components. The process of 
guideline implementation and technology uptake is complex, and it is unlikely that desired 
outcomes can be achieved with a simple intervention. However, if the desired outcomes are 
achieved, the proposed mixed methods studies will identify which barriers were modified, 
providing some insight into which intervention components are critical. In addition, future studies 
can focus on testing simplified versions of the multi-level intervention that remove one or more 
components. 
 
Theoretical basis for intervention strategy 

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The intervention strategy was designed based on a mixed methods study conducted at six 
health centers in Uganda to better understand reasons for gaps along the TB diagnosis cascade 
of care and to inform intervention design. The process was guided by the Theory of Planned 
Behavior as the conceptual framework and the PRECEDE model to select intervention 
components (Figure 1). In a systematic review of guideline implementation studies, the Theory 
of Planned Behavior was the most likely theory to predict guideline adherence.21 This theory 
asserts that intention is the best predictor of behavior and that three factors mediate the 
strength of intention: 1) attitudes – expected value of behavioral performance; 2) subjective 
norms – what others think about the behavior; and 3) self-efficacy – perception of ability to 
overcome barriers to behavioral performance.22 The PRECEDE model was chosen based on its 
strong empirical base and applicability to guideline adherence. The model is based on three 
factors relevant to health behavior change: 1) predisposing factors – prior motives that either 
support or inhibit behavior; 2) reinforcing factors – rewards or punishments following a behavior or 
anticipated as a consequence of it; and 3) enabling factors – objective characteristics of an 
individual or environment that facilitate behavior.23 A meta-analysis of 50 randomized controlled 
trials of continuing medical education demonstrated that the studies employing a combination of 
interventions representing PRECEDE categories were the most likely to influence patient 
outcomes.24  
The intervention strategy was designed in consultation with the Uganda NTP to target 
modifiable clinic-level barriers identified in the formative assessment as well as key theoretical 
constructs. Key barriers to TB evaluation identified through semi-structured interviews with 22 
staff at the six health centers and organized within PRECEDE model categories are shown in 
Table 2.25 The intervention strategy includes: 1) onsite molecular testing to reduce laboratory 
workload and increase the sensitivity of TB diagnostic testing; 2) re-structuring clinic-level 
procedures to facilitate same-day TB diagnosis and treatment, thereby increasing provider self-
efficacy/motivation and enabling linkage of confirmed TB patients to treatment; and 3) 
performance feedback to increase communication between staff and with NTP supervisors, 
thereby reinforcing uptake of onsite molecular testing and adherence to TB evaluation 
guidelines. Re-assessing these barriers as part of Aim 2 will help understand whether the XPEL 
TB intervention worked as intended and help explain why it was or was not effective in 
improving TB diagnostic evaluation outcomes. 
ISTC, International Standards for TB Care 
REINFORCING FACTORS 
Performance feedback* 
PREDISPOSING AND ENABLING FACTORS 
1. Onsite molecular testing with GeneXpert/Xpert Ultra 
2. Process redesign to facilitate same-day diagnosis and treatment 
Provider Factors: 
 • Attitudes 
 • Social Norms 
 • Self-efficacy 
 
Patient Factors 
Health System Factors 
Intention to Follow 
ISTC 
ISTC Adherence 
Case Detection and 
Treatment 
Figure 1. Theory-informed barrier assessment and intervention design 

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Table 2. Key clinic-level barriers targeted by intervention strategy. 
PRECEDE framework 
Recurring themes highlighting barriers targeted by the 
intervention strategy 
Predisposing factors 
(Knowledge, attitudes, beliefs, 
intention) 
• 
Time and resource constraints → low self-efficacy 
• 
Low motivation of staff 
• 
Low sensitivity of sputum smear microscopy 
• 
Poor patient perception of care at government health centers 
Enabling Factors 
(Factors that if addressed make 
it easier to initiate the desired 
behavior) 
• 
Failure of patients to return after initial visit (due to time and 
costs) 
• 
Inability to track and follow-up patients → low-self-efficacy 
“When they have a cough for more than 2 weeks they are sent to the 
lab. But the problem is they get the first sample and sometimes, 
actually most times they don’t bring the second sample.” 
Reinforcing Factors 
(Factors that if addressed make 
it easier to continue the desired 
behavior) 
• 
Lack of communication and coordination among staff 
• 
Insufficient oversight from NTP 
 “…Actually at times we have met but we don’t meet [regularly], only 
when we realize there is a problem that’s when we communicate and 
say why is this happening, then we try to rectify.” 
 
 

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Objectives 
Aim 1: To compare patient outcomes at health centers randomized to intervention vs. 
standard-of-care TB diagnostic evaluation strategies. We will randomize 20 community 
health centers to continue standard TB evaluation (routine microscopy plus referral of patients 
for Xpert testing per existing processes of care) or to implement the intervention strategy (1. 
Onsite molecular testing; 2. Re-structuring clinic-level procedures to facilitate same-day TB 
diagnosis and treatment; and 3. Performance feedback). We will compare reach and 
effectiveness based on the numbers and proportions of patients (N=6500) who complete TB 
testing, are found to have TB, and have treatment initiated within one week of specimen 
provision. 
Aim 2: To identify processes and contextual factors that influence the effectiveness and 
fidelity of the intervention TB diagnostic evaluation strategy. We will use quantitative 
process metrics to assess the adoption and maintenance over time of the core components of 
the intervention strategy. We will also collect quantitative and qualitative data to describe the 
fidelity of implementation of each component and faithfulness to our conceptual model. 
Aim 3: To compare the costs and epidemiological impact of intervention vs. standard-of-
care TB diagnostic evaluation strategies We will model the incremental costs and cost-
effectiveness of intervention relative to standard-of-care TB diagnostic evaluation from the 
health system and patient perspective. We will then construct an epidemic model of the 
population-level impact of the intervention strategy on TB incidence and mortality. 
 
 

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Methods/Design 
Overview 
The study proposes to conduct a pragmatic, parallel cluster-randomized trial with nested mixed 
methods, health economic and modeling studies to evaluate the effectiveness, implementation 
and costs of the intervention strategy relative to standard TB diagnostic evaluation at community 
health centers that are part of Xpert referral networks in Uganda. The effectiveness of the 
intervention strategy will be assessed using routine data collected as part of mandatory 
reporting to the Uganda NTLP on consecutive patients who present to participating health 
centers during the 2-year trial period and meet eligibility criteria. Selected patients and providers 
who provide informed consent will also be surveyed, interviewed, and/or participate in focus 
groups to identify reasons for its success or failure across study sites (Aim 2), and to collect 
relevant cost data for cost-effectiveness analyses (Aim 3). 
Target Setting and Study Population  
The target setting is community health centers with TB microscopy units (i.e., the lowest level of 
the health system where TB diagnostic services are provided by the Uganda NTLP). The unit of 
randomization will be community health centers (N=20). The unit of analysis will be patients 
undergoing TB diagnostic evaluation during the 2-year trial period (estimated total 6500 over 2-
year trial period). 
Eligibility criteria for the cluster-randomized trial 
A. Site-level Inclusion Criteria 
1. Use standard (multi-day) sputum smear microscopy as the primary method of TB 
diagnosis 
2. Participate in NTP-sponsored external quality assurance (EQA) for sputum 
smear microscopy 
3. Send samples to a district or regional hospital/health center for Xpert testing 
B. Site-level Exclusion Criteria 
1. Do not agree to be randomized to standard-of-care vs. intervention arms 
2. Perform sputum smear examination on <150 patients per year (based on 2015 
data) 
3. Diagnose <15 smear-positive TB cases per year (based on 2015 data) 
C. Patient-level Inclusion Criteria 
1. Initiate evaluation for active pulmonary TB at a study health center 
D. Patient-level Exclusion Criteria 
1. Have sputum collected for monitoring of response to anti-TB therapy 
2. Have sputum collected as part of active, community-based case finding (e.g., 
contact tracing, community outreach campaign) 
3. Referred to a study health center for TB treatment after a diagnosis is 
established elsewhere 
4. Started on TB treatment for extra-pulmonary TB only 
 

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In addition, patients age <18 years and those with a documented prior history of TB treatment 
(e.g., reason for Xpert testing or TB treatment marked as treatment failure, relapse, treatment 
after loss to follow-up, etc.) will be excluded from the primary analysis of all outcomes. Patients 
identified as RIF resistant by Xpert testing will be excluded from the primary analysis of all 
outcomes except for the comparison of the number and proportion of patients with RIF 
resistance between arms. 
Eligibility criteria for mixed methods and health economic sub-studies 
Patient Surveys: Same eligibility criteria as for the CRT. 
Provider Survey: Providers at each study site who are (a) aged ≥18 years; (b) employed by the 
peripheral health center; and (c) involved in the conduct or supervision of health center work 
related to diagnosis and management of TB will be included.  
Focus Group Discussion and Interviews: Same inclusion criteria as for the provider survey. 
Recruitment 
Health centers 
Potential trial sites were identified from a list of Uganda NTLP-affiliated TB microscopy centers 
that refer samples to Xpert testing hubs. Study staff reviewed 2014 TB testing and treatment 
data reported to the Uganda NTLP to identify health centers that meet eligibility criteria (based 
on numbers of patients tested for TB and found to be smear-positive), focusing on those within 
150 km of Kampala for study feasibility purposes. Study staff then obtained permission from 
District Health Officials and Health Center Directors for visiting health centers to confirm 
eligibility and assess interest in study participation. At the site assessment visit, study staff 
counted from the 2015 TB laboratory register the number of patients tested by microscopy and 
the number of smear-positive patients (excluding patients tested for treatment monitoring or as 
part of community-based active case finding activities). Health centers confirmed to be eligible 
and that expressed interest in participating in the study were reviewed with Uganda NTLP and 
NTRL Directors. Following their approval, research staff met with the District Health Officer 
(DHO) to inform him or her about the project and requested the participation of potential trial 
sites in the District. The DHO was asked to sign a Memorandum of Understanding (MoU) that 
describes key study procedures and expectations of participating sites. If the DHO signed the 
MoU, study staff scheduled an enrollment visit with potential trial sites in the District by calling 
the facility 3-4 days prior to the anticipated visit date. Project staff then contacted the Program 
Manager of the Uganda NTLP to arrange for an NTLP representative to participate in the 
enrollment site visit. During the site visit, project staff followed a standard script to present the 
project to health center staff, outlined the project aims, and described project procedures. 
Project staff presented a copy of the MoU signed by the DHO and endorsed by the Chief 
Administrative Officer and NTLP Program Manager to the health center in-charge. Of note, the 
final list of 20 sites to be randomized was determined in consultation with the trial statistician (to 
minimize potential for site-level variation that could impact power). 

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Participants for cluster-randomized trial 
This is a pragmatic trial that will study outcomes of routine care. Thus, rather than recruiting 
participants, the intervention and control arms will be compared using data available in routine 
TB laboratory and treatment registers for consecutive patients who present to participating 
health centers during the two-year trial period and meet eligibility criteria (estimated N=6500 
patients). 
Participants for mixed methods and health economic sub-studies 
Patient Cost and Satisfaction Surveys: The patient cost and satisfaction surveys will be 
administered during the baseline (pre-randomization) period (N=20/site; 400 total) and in the 
second year after randomization (N=40/site, 800 total). Health center staff will assist with 
identification and recruitment of patients who are interested in participating in the surveys (from 
among patients providing sputum samples or receiving results). Participation will be voluntary. 
Research staff will then contact interested patients by phone at the end of their health center 
visit to review the verbal consent script, answer questions the patient may have, and administer 
the survey to consenting patients. 
Provider Survey: The provider survey will be administered during the baseline (pre-
randomization) period and in the second year after randomization. All eligible providers at each 
study site (N=5-10/site; 100-200 total) will be invited to participate in the survey. Participation 
will be voluntary and written informed consent will be obtained. 
Provider Focus Group Discussions and Interviews:  Focus group discussions will occur during 
the second year after randomization and semi-structured interviews after the trial is completed. 
All eligible providers at intervention sites (N=5-10/site; 50-100 total) will be invited to participate 
in focus group discussions. Providers (N=20-30) at low- and high-performing sites will be invited 
to participate in semi-structured interviews. Participation in focus group discussions and 
interviews will be voluntary and written informed consent will be obtained. 
Intervention and Control (Wait-list) Arms 
With the current standard-of-care (i.e., control arm), patients make up to 4 health center visits to 
complete TB diagnostic evaluation (Figure 2). At the first visit, sputum is collected for smear 
microscopy. All patients are required to return for a second visit; patients are initiated on TB 
treatment if the first sputum is smear-positive or are asked to provide a second sputum 
specimen and return for a third visit to collect results if the first sputum is smear-negative. 
Patients who return for a third visit are initiated on TB treatment if the second sputum is smear-
positive or are asked to provide a third sputum specimen that is sent out for Xpert testing. 
Patients are asked to return for a fourth visit to collect Xpert results. Patients who return for a 
fourth visit are initiated on TB treatment if Xpert results are positive (or referred to an MDR TB 
treatment center if Xpert testing indicates rifampin resistance). Of note, HIV-infected patients 
have sputum collected for Xpert testing at their first visit and all patients could be started on TB 
treatment empirically at any visit.  
The intervention strategy seeks to facilitate diagnosis and treatment of TB at the initial health 
center visit (or referral of patients to MDR TB treatment center if rifampin resistance is detected 

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by Xpert). As further described below, it includes the following components: 1) onsite molecular 
testing as a replacement for microscopy; 2) process re-design; and 3) feedback of TB diagnostic 
evaluation quality indicators to health center staff.  
Figure 2. Comparison of TB diagnostic evaluation in the intervention and control arms 
 
*Consider empiric treatment or additional testing if test results are negative or invalid 
On-Site Molecular Testing 
GeneXpert Omni (Cepheid, Sunnyvale, CA) is a point-of-care molecular diagnostic system 
designed for lower level health facilities. It is small (1.0 kg weight), portable, and consumes less 
power than previous GeneXpert systems. The standard configuration includes a single test 
cartridge module, a mobile device, a country-specific AC/DC power cord, a supplemental 
battery, and a user guide. The rechargeable battery allows for up to 4 hours of operation with 
voltage surge protection for unexpected power supply interruptions. Additionally, a supplemental 
battery can be charged for up to 12 hours of use in the event of sustained power loss. Data is 
stored in real-time via cloud-based connectivity to Cepheid Cloud Control using Wi-Fi or cellular 
networks. Training materials will be provided by Cepheid, with an anticipated training time of 
 
Standard-of-Care (CONTROL ARM) 
 INTERVENTION ARM 
 
All patients 
Patients with HIV 
All patients 
VISIT 1 
 
 
 
 
 
 
VISIT 2 
 
 
 
 
 
 
VISIT 3 
 
 
 
VISIT 4 
 
 
 
 
 
Treat if Xpert positive* 
Send sputum for Xpert 
testing 
Treat if Xpert positive* 
Perform Xpert testing 
Collect sputum 
Collect sputum 
Prepare/examine smear* 
Smear-positive: Treat 
Treat if Xpert positive* 
Smear-negative:   
Collect sputum and 
 send for Xpert testing*  
Smear-positive: Treat 
Smear-negative:   
Collect sputum and 
prepare/examine smear* 

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one half-day. Additional device-integrated videos and self-instructions are available for training 
reinforcement.19  
Due to delays in release of GeneXpert Omni, the trial will begin with intervention sites using a 
modified conventional GeneXpert instrument (GeneXpert I). GeneXpert I is a one-module 
device that includes a 4-hour battery back and a specially-fitted dust cover. The device can be 
operated by a touchscreen tablet instead of a laptop computer. Sites randomized to the 
intervention arm will have a solar panel installed temporarily to power the GeneXpert I device. 
Once GeneXpert Omni is available (expected in early 2019), it will replace the GeneXpert I 
instrument, and the solar power will be removed. After the trial is completed, a GeneXpert Omni 
or GeneXpert I device will be provided to control health centers and health center staff will be 
trained on the use and maintenance of the device. 
Of note, Xpert testing in both the control and intervention arms will occur using the same 
cartridge, Xpert MTB/RIF or Xpert MTB/RIF Ultra, depending on which ever is in use in Uganda 
at the time of the trial.  
Process Redesign for Same-Day TB Diagnosis and Treatment 
Research and Uganda NTLP staff will engage health center staff in a discussion of how to re-
organize clinical, laboratory and pharmacy services to enable same-day TB diagnosis and 
treatment. Health center staff will be asked to identify site-specific solutions to: 
1. Rapid screening for TB at registration desk and/or waiting area 
2. Immediate referral of patients identified during screening as needing further testing to 
the lab for sputum collection 
3. On-demand testing of sputum specimens and immediate reporting of results to 
clinicians and patients 
4. Availability of TB pharmacy services throughout the clinic day 
Agreed upon changes to existing procedures and staff responsibilities will be recorded and 
reviewed with the health center in-charge, lab director and TB pharmacist. 
Performance Feedback 
Performance feedback is a strategy employing regular monitoring and feedback to allow health 
care workers to critically analyze performance and identify areas for improvement. A systematic 
review of audit and feedback interventions identified 5 factors associated with greater impact: 
low baseline performance, feedback coming from a supervisor or colleague, feedback provided 
multiple times, feedback delivered in both verbal and written form, and feedback including 
explicit targets and an action plan.26 Thus, performance feedback will involve delivery of a 
monthly Report Card which displays 1) TB diagnostic evaluation quality indicators for the current 
month and for the previous 6 months; 2) performance data averaged across all intervention 
health centers; and 3) performance data for the top- performing intervention health center. The 
Report Card will include a section that asks staff to write down their interpretation of the 
performance data, identify barriers to performance improvement, and specify plans to improve 
performance. The Report Card will be introduced to health center staff at the post-randomization 
site visit and will then be sent electronically every month to the health center in- charge or TB 

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focal person. Health workers will be asked to review the Report Card at monthly staff meetings 
to devise a performance improvement plan. This process will continue monthly, with each new 
Report Card being used to evaluate the success of plans developed in the previous month and 
determine the need for new actions. Report Cards will be collected by study staff at quarterly 
site visits to assess use and completion.  
Randomization 
Health centers will be grouped into two strata using baseline data for the primary outcome (see 
Outcomes below), and randomized within strata to help reduce between-cluster variation and 
improve balance between arms at baseline.26 Restriction, a common approach in cluster-
randomized trials with a small number of clusters, may be used to help achieve baseline 
balance of important site- and patient-level covariates.27 We will consider restricting on factors 
likely to be associated with the primary outcome including health center region (four quadrants 
of Uganda), health center size (based on volume of patients tested for TB), HIV prevalence 
among TB patients, and patient cost and satisfaction with care. The restriction factor will be 
calculated and the validity of the restriction assessed using the validity matrix.28 The Trial 
Statistician (KF) will be responsible for the randomization. Randomization will be unveiled to 
health center directors (or their designated representative) at a meeting chaired by the Uganda 
NTLP Director.  
Procedures 
The following procedures will occur at site visits to each participating health center. Site visits 
will occur approximately every 2-4 months throughout the study. 
Health center training 
Training will occur at the first site visit after health center enrollment and after health center 
randomization. 
Following enrollment, we will conduct TB guideline and registry training at the first pre-
randomization site visit. Project staff, an NTLP/NTRL representative and the District TB Officer 
will conduct the training jointly using a standardized slide set that emphasizes Uganda NTLP 
guidelines/algorithms for TB diagnosis and treatment. In addition, project staff will assess the 
completeness of data recording in TB registers, and provide training as needed to improve 
completeness. Last, project staff will visit all GeneXpert testing hubs that receive samples from 
project sites, and provide refresher training on GeneXpert device maintenance and operation. 
Following randomization, we will conduct a 2-day site visit to each intervention and control 
health center.  
At intervention health centers, we will engage staff in a discussion of the site-specific laboratory, 
clinical, and pharmacy workflow reorientation required as part of the intervention TB diagnostic 
evaluation strategy, with an emphasis on prompt identification of patients suspected of TB, 
immediate referral to the laboratory for sputum collection, on-demand molecular testing of 
sputum, communication of results to clinicians, and same-day treatment initiation. A GeneXpert 
I device and solar panel will be installed (with both replaced by GeneXpert Omni once it 

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becomes available in 2019), and laboratory staff will be trained on sputum collection, sputum 
testing using GeneXpert, recording of results, and device maintenance. Laboratory and clinical 
staff will be trained on interpretation of results. In addition, all staff will be introduced to 
performance feedback Report Cards and expectations for reviewing and acting on the Report 
Cards. A staff member will be identified to record any change made in the “Comments” section 
of the Report Card. Lab and drug inventories will be reviewed to ensure there is an adequate 
supply of sputum collection cups, glass slides, staining reagents and TB drugs. Completeness 
of TB laboratory and treatment registers will be assessed, and re-training provided as needed. 
At control health centers, we will re-emphasize messages related to TB guidelines presented 
during the initial pre-trial site visit, including guidelines and procedures related to referral of 
sputum samples for Xpert testing. Laboratory staff will receive refresher training on sputum 
collection and sputum smear microscopy, including proficiency testing using panel slides. Lab 
and drug inventories will be reviewed to ensure there is an adequate supply of sputum collection 
cups, glass slides, staining reagents and TB drugs. Completeness of TB laboratory and 
treatment registers will be assessed, and re-training provided as needed.  
Patient-level data collection  
Patient demographic and clinical (HIV and TB testing and treatment) data will be collected at all 
sites. At the first pre-trial site visit, project staff will photograph each page of the following data 
sources from January 2016 until the visit date: 1) NTLP Presumptive TB, Laboratory and 
Treatment registers and 2) Xpert laboratory requisition forms. Project staff will also train two 
health center staff (one primary, one backup) identified by the health center in-charge to take 
photos of these data sources every two weeks for the duration of the project using a camera-
enabled smartphone, and to upload the photos to a central secure server through REDCap 
mobile. Health center staff will be trained to delete photos from the phone after upload 
confirmation. In addition to these data sources, study staff will 1) Review pre-ART and ART 
registers during scheduled site visits to verify ART status for any HIV-positive patient missing 
ART information; 2) review additional data sources (e.g., clinic registration logbook) as needed 
at scheduled site visits to attempt to track down key information missing from primary data 
sources; and 3) call MDR TB treatment centers to determine referral, additional testing and 
treatment status and outcomes for patients with RIF resistance identified through Xpert testing. 
Patient surveys 
We will administer cost and satisfaction with care surveys to 20 patients at each health center 
(N=400 total) in the pre-randomization period and to 40 patients at each health center (N=800) 
at least 6 months post-randomization. The two surveys will involve the same patients when 
feasible, but administered to different patients if a patient does not wish to complete both. 
The cost survey will collect data on direct and indirect costs (time to complete visit, lost wages, 
etc.) of TB diagnostic evaluation. Cost data collection will be based on the Tool to Estimate 
Patient Costs developed by the TB Coalition for Technical Assistance, which we have already 
adapted and used in Uganda. 
The satisfaction with care survey will assess 18 items taken from the previously validated 
Patient Satisfaction Questionnaire (PSQ)29 and adapted to the Ugandan context.30 The items 

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are constructed with a five-point Likert scale with categories ranging from “strongly disagree” to 
“strongly agree”, and include both positively and negatively worded questions to minimize the 
potential bias that occurs from clustering of responses to one side of the scale. The 
questionnaire measures general satisfaction as well as four dimensions of care known to impact 
satisfaction: 1) Accessibility, availability, and convenience of health services (3 items); 2) 
Provider interpersonal skills (5 items); 3) Provider technical competence with respect to patient 
education, examination and counseling (4 items); and 4) Health facility environment, specifically 
with respect to the cleanliness and space in the waiting area (2 items). 
Provider surveys 
We will administer a survey to all health workers involved in providing TB diagnostic and/or 
treatment services at each study site in the pre-randomization period and again at least 12 
months after randomization. The survey will collect data on four key constructs of the Theory of 
Planned Behavior (TPB) as related to adherence to TB evaluation guidelines (Figure 1): 
intention, beliefs/attitude, normative beliefs/subjective norms and self-efficacy/perceived 
behavioral control. Based on prior experience, we anticipate 5-10 health workers will be 
involved in TB diagnostic/treatment services at each health center (N=100-200 health workers 
total pre- and post-randomization). 
Health system costing 
Health system cost data will be collected from all 20 sites at least 12 months after 
randomization. Cost data will be collected through detailed budgetary analysis, interviews of key 
staff members, review of logbooks and/or timesheets to record proportions of staff time devoted 
to various activities, and direct observation (e.g., time-motion studies of at least 10 patients and 
3 staff members per clinic). 
Focus group discussions 
Study staff will conduct focus group discussions (one at each intervention health center) with all 
eligible providers at least 12 months after randomization. The focus group discussions will 
assess fidelity and barriers to uptake of each intervention component. Focus group discussions 
will be conducted by trained and experienced staff, audio-recorded, and professionally 
transcribed. 
Patient vital status assessment 
Study staff will call all participants included in the study 6 months after their initial health center 
visit to assess vital status. Staff will call phone numbers recorded in the lab register, Xpert 
referral form and/or TB treatment register. For participants who cannot be reached, staff will 
make up to two additional phone calls on successive days. If the participant or a family member 
cannot be reached by telephone, study staff will work with a community health worker from the 
health center at which the participant underwent TB testing to conduct a home visit (using the 
address recorded in the lab register, Xpert referral form and/or TB treatment register). In 
addition, staff will review TB treatment register data to ascertain vital status for participants who 
cannot be reached by phone but are known to have initiated TB treatment.  

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Provider interviews 
Study staff will conduct in depth semi-structured interviews with 20-30 (2-3 per site) eligible 
providers to understand reasons for variability in uptake and effectiveness of the intervention 
strategy after completion of the trial. Health workers will be sampled purposively (i.e., from low- 
and high-performing sites). Interviews will be conducted by trained and experienced staff, audio-
recorded, and professionally transcribed. 
 
Figure 3. Site visit activities 
 
 
 
 
 
Y1 Months 1-3 
Y1 Months 1-12 
Y2 Months 1-3 
Y2 & Y3 Months 1-12 
Y4 Months 1-3 
* At intervention heatlh centers, GeneXpert I will be replaced with GeneXpert Omni once Omni is 
available at a regularly scheduled post-randomization visit. Lab staff will receive training on use of 
GeneXpert Omni and the solar panel used to power the GeneXpert I instrument will be removed. 
 
 
 
Post-Enrollment  
Visit (1x) 
Training:  
• TB guidelines & 
registry 
completion 
• Photograph data 
sources 
• Xpert hub 
refresher 
training 
 
Data collection: 
• Patient-level 
data (Jan 2016-
visit date) 
 
Pre-Randomization 
Visits (every 2-3 
months) 
 
Surveys: 
• Patient cost & 
satisfaction  
• Provider  
Data collection:  
• Review pre-ART 
& ART registers, 
other data 
sources for 
missing data 
• Contact MDR 
facilities 
 
1st Post-
Randomization 
Visit 
Training: 
• Intervention 
sites: Workflow 
staff discussion, 
GeneXpert I  
installation & 
training, Report 
Card introduced 
• Control sites: 
TB guidelines & 
registry refresher 
training 
 
Subsequent Post-
Randomization 
Visits (every 2-3 
months)* 
Surveys: 
• Patient 
satisfaction 
• Patient cost  
• Provider survey 
• Provider focus 
groups 
 
Data collection: 
• Health systems 
costing  
• Resolve missing 
patient-level data 
queries 
 
Post-Trial 
Visits 
• Provider semi-
structured 
interviews 
 

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Outcomes 
Definitions 
• 
Number enrolled: Number of eligible patients identified over a defined enrolment period 
through review of the NTLP Presumptive TB register, NTLP Laboratory register, 
GeneXpert laboratory requisition forms, GxAlert database and NTLP Treatment register 
at each study site (includes patients treated for TB without undergoing any sputum 
testing). 
• 
Date enrolled: Earliest date recorded in the NTLP Presumptive TB register, NTLP 
Laboratory register, GeneXpert laboratory requisition form, GxAlert database, or NTLP 
Treatment register. Records will be prospectively reviewed using name, sex, and age to 
identify patients presenting multiple times over the study period. Visits occurring within 
six months of the initial presentation will be considered as part of the same episode. 
• 
Number referred for testing: Number of eligible patients identified through review of 
the NTLP Presumptive TB register, NTLP Laboratory register, GeneXpert laboratory 
requisition forms, and GxAlert database at each study site. 
• 
Number tested: Number of eligible patients with any smear or Xpert result entered into 
the NTLP Presumptive TB register, NTLP Laboratory register, GeneXpert laboratory 
requisition forms, NTLP treatment register, or GxAlert database at each study site. 
• 
Number completing testing: Number of eligible patients with one valid Xpert result plus 
number of eligible patients with a valid smear result entered into the NTLP Presumptive 
TB register, NTLP Laboratory register, GeneXpert laboratory requisition forms, GxAlert 
database, or NTLP treatment register. Definitions of valid results are as follows:  
o A positive Xpert result with a semi-quantitative result of high, medium, low, or 
very low for all patients; a positive Xpert Ultra result with a semi-quantitative 
result of trace for HIV-positive patients; or second positive Xpert Ultra result if the 
initial result is trace-positive for HIV-negative patients; 
o A negative Xpert or Xpert Ultra result for all patients;31 
o One positive or two negative smear results for HIV-negative/HIV status unknown 
patients. 
 
• 
Number diagnosed: Number of eligible patients with microbiologically-confirmed TB via 
a positive smear and/or Xpert test result entered into the NTLP Presumptive TB register, 
NTLP Laboratory register, GeneXpert laboratory requisition forms, NTLP treatment 
register, and/or GxAlert database within 6 months of date enrolled. 
• 
Number with suspected RIF resistant TB: Number of eligible patients with RIF 
resistance identified by Xpert testing entered into the GxAlert database, GeneXpert 
laboratory requisition forms, and/or NTLP Laboratory registers within 6 months of date 
enrolled. 
• 
Number with confirmed RIF resistant TB: Number of eligible patients with RIF 
resistance identified by Xpert testing entered into the GxAlert database, GeneXpert 
laboratory requisition forms, and/or NTLP Laboratory registers, and RIF resistance 
confirmed by culture-based DST or a second molecular assay as determined by review 
of Lab Register at MDR treatment center within 6 months of date enrolled. 

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• 
Number treated: Number of eligible patients entered into the NTLP Treatment register 
as having started Category I or II regimen, or started on MDR treatment at the MDR 
treatment center as as determined by review of Treatment Register at MDR treatment 
center within 6 months of date enrolled. 
• 
Number completing treatment: Number treated and with a treatment outcome of cured 
or completed entered into the NTLP Treatment register or indicated by MDR treatment 
center staff through direct follow up. 
• 
Number died: Number treated and with a treatment outcome of died entered into the 
NTLP Treatment register within 6 months of date enrolled, or number eligible with 
treatment outcome entered as died on direct follow-up form. 
• 
Time-to-diagnosis: Number of days from date enrolled to earliest date of positive smear 
or Xpert result recorded in NTLP Laboratory register, GeneXpert laboratory requisition 
form, NTLP treatment register, or GxAlert database within 6 months of date enrolled. 
• 
Time-to-treatment: Number of days from date enrolled to treatment start date entered 
into NTLP Treatment register if treatment start date is within 6 months of date enrolled.  
I. Primary Outcome (Effectiveness)  
Outcome 
Numerator 
Denominator 
Proportion treated for 
microbiologically-confirmed TB 
within two weeks of referral for 
sputum-based testing 
Number diagnosed and time-to-
treatment within 14 days  
Number referred for TB testing  
 
II. Secondary Outcomes (Effectiveness)  
A. Testing 
Outcome 
Numerator 
Denominator 
Number referred for TB testing 
Number referred for testing 
None 
Proportion completing testing 
Number completing testing 
Number referred for testing 
B. Diagnosis 
Outcome 
Numerator 
Denominator 
Number diagnosed with 
microbiologically-confirmed TB* 
Number diagnosed 
None 
Proportion diagnosed with 
microbiologically-confirmed TB* 
Number diagnosed 
Number referred for testing 
Number suspected/diagnosed with 
RIF-resistant TB* 
Number with suspected/confirmed 
RIF resistant TB 
None 
Proportion suspected/diagnosed 
with RIF-resistant TB* 
Number with suspected/confirmed 
RIF resistant TB 
Number referred for testing 
Time to microbiologically-
confirmed TB 
Time-to-diagnosis if 
microbiologically-confirmed TB 
None 
C. Treatment 
Outcome 
Numerator 
Denominator 
Number treated for TB* 
Number treated 
None 

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Proportion treated for TB* 
Number treated 
Number enrolled 
Number treated for 
microbiologically-confirmed TB* 
Number diagnosed AND treated 
None 
Proportion treated for 
microbiologically-confirmed TB* 
Number diagnosed AND treated 
Number referred for testing 
Proportion with microbiologically-
confirmed TB treated* 
Number diagnosed AND treated 
Number diagnosed 
Time-to-treatment of 
microbiologically-confirmed TB 
Time-to-treatment if 
microbiologically-confirmed TB and 
treated 
None 
D. Follow-up  
Outcome 
Numerator 
Denominator 
Number with microbiologically-
confirmed TB completing treatment 
Number diagnosed AND 
completing treatment 
None 
Proportion with microbiologically-
confirmed TB completing treatment 
Number diagnosed AND 
completing treatment 
Number diagnosed AND 
treated 
Number who died within 6 months  
Number died 
None 
Proportion who died within 6 
months** 
Number died 
Number enrolled 
* Outcome will be assessed within 1 day and within 14 days of initial sputum submission. One 
day was chosen because the intervention focuses on same-day diagnosis and treatment. 14 
days was chosen because the diagnostic process could take 7-10 days in the control arm 
depending on the frequency of sample transport to Xpert testing sites. 
**Treatment outcomes will be assessed at 7, 9, 12, and 24-month post-treatment initiation 
intervals determined by drug regimen. 
III. Implementation Outcomes 
Comparison across arms 
Outcome 
Definition 
Patient costs 
 
a) Total costs 
a) Sum of all patient-reported costs on cost 
questionnaire 
b) Total direct costs 
b) Total costs paid by the patient (i.e., excluding 
lost wages or other lost opportunities to earn 
income) 
c) Total indirect costs 
c) Patient-reported lost wages or other lost 
opportunities to earn income 
Patient satisfaction with care survey: 
 
a) General satisfaction with care 
a) Sum of score on 3 question items (Q16, 17 
and 18) 
b) Convenience of services 
b) Sum of score on 3 question items (Q1, 2 and 
3) 
c) Health facility environment 
c) Sum of score on 2 question items (Q4 and 5) 
d) Provider interpersonal skills 
d) Sum of score on 5 question items (Q7-11) 
e) Provider technical competence 
e) Sum of score on 4 question items (Q12-15) 
Provider survey: 
 
a) Intention 
a) Score on 1 question item (Q4) 

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b) Attitudes/beliefs 
b) Sum of score on 3 question items (Q5a, 6a, 
and 6b) 
c) Social norms/expectations 
c) Sum of score on 2 question items (Q5b, 6c 
and 6d) 
d) Self-efficacy/behavioral control 
d) Sum of score on 5 question items (Q5c, 6e 
and 6f) 
 
Intervention arm only 
Outcome 
Numerator 
Denominator 
Process metrics: 
 
 
a) GeneXpert device non-
operation days 
a) GeneXpert device non-
operation days1 
a) Total number of lab 
operation days1 
b) Proportion tested by 
GeneXpert: 
b) Number with GeneXpert 
result date  
b) Number tested by 
GeneXpert 2 
1. On same day as initial 
sputum submission 
1. Same as initial sputum 
submission date2 
 
2. By next day after 
initial sputum 
submission 
2. By next day after initial 
sputum submission date2 
 
c) Proportion with 
indeterminate results 
c) Number with coded error, 
invalid or no test result2 
c) Number of initial GeneXpert 
tests done2 
d) Proportion with coded 
error result 
d) Number with coded error 
result2 
d) Number of initial GeneXpert 
tests done2 
e) Proportion with invalid 
result 
e) Number with invalid result2 
e) Number of initial GeneXpert 
tests done2 
f) 
Proportion with “no test” 
result 
g) Proportion with 
GeneXpert test repeated 
if initial result 
indeterminate 
h) Proportion treated on 
same-day if Xpert-positive 
i) 
Proportion of report cards 
reviewed at staff meetings 
f) 
Number with “no test” result2 
 
g) Number with second 
GeneXpert result2 
 
h) # treated on same day as 
initial health center visit3 
i) 
Number of Report Cards with 
Comments section filled4 
f) 
Number of initial GeneXpert 
tests done2 
g) Number with initial 
indeterminate GeneXpert 
result2 
 
h) # Xpert-positive2 
 
i) 
Total number of report cards 
issues to health centers3 
Variation in and barriers to 
intervention strategy uptake 
Themes emerging from focus group discussions, semi-structured 
interviews with health center staff 
Source of data: 1 Health center laboratory log; 2 GeneXpert Software; 3 Treatment register; 4 Performance 
feedback report card 
IV. Cost-effectiveness/Modeling Outcomes 
Outcome 
Definition 
Source 
Incremental cost per DALY 
averted 
(Cost in intervention – cost in 
control)/(DALYs averted by 
intervention – DALYs averted in 
control) from societal perspective 
Markov-based decision models 
Incremental health system cost 
per DALY averted 
As above, but from health 
system/provider perspective 
Incremental patient cost per 
As above, but from patient 

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DALY averted 
perspective 
Projected reduction in TB 
incidence over 10 years 
(Projected incidence in control 
model – projected incidence in 
intervention model)/(Projected 
incidence in control model), 
cumulative over 10 years 
Transmission models 
Projected reduction in TB 
mortality over 10 years 
(Projected mortality in control 
model – projected mortality in 
intervention model)/(Projected 
mortality in control model), 
cumulative over 10 years 
 
Data Management 
Dr. Fielding (statistician) will oversee data management in conjunction with UCSF- and Uganda-
based study coordinators using the NIH-recommended Research Electronic Data Capture 
(REDCap) software, password-protected and accessible only to research staff. All data 
collection forms will be entered into standardized REDCap forms, with validation of data using 
range and consistency checks. Quality control procedures will include review of all study data 
collection forms for completeness and accuracy prior to data capture. Study staff will begin data 
capture by compiling photos from each individual health center for each data collection period. 
Using patient name, age, and sex, the staff will create one unique REDCap database entry (i.e. 
record) per patient by tracking patients across data sources in the following order: 1) 
Presumptive TB Register; 2) Laboratory Register; 3) Xpert Referral form; and 4) Treatment 
Register. If the patient cannot be located in a subsequent data source, the staff will check the 
next, until all data sources are consulted. If a patient does not meet inclusion criteria for analysis 
due to examination type (outreach, transferred in, follow up, or other), the staff will not collect 
data beyond the patient’s name, sex, age, and exam type from the Laboratory Register. Once 
patients have been matched across all data sources, the staff will run a series of reports in 
REDCap to verify the completeness and accuracy of key variables impacting patient eligibility 
and/or study outcomes: age, sex, HIV status, examination type, smear and Xpert test results, 
outcomes dates (sputum collection, test results, start and end of treatment, treatment outcome), 
and treatment status. Study staff will review the original data photographs to verify the 
information is missing, and they will compile a list of follow up items for each health center. 
Research staff will phone health center staff after each 2-week period of data is extracted to 
resolve missing information and clarify any discrepancies or uncertainties in matching. Once the 
health center reviews the missing and/or inaccurate data, the study staff will update REDCap. 
The UCSF study coordinator will visit Uganda 2-3 times a year and review a random sample of 
forms and primary data sources for quality assurance. 
  
Statistical Analysis 
Aim 1  
A detailed Statistical Analysis Plan will be developed. Briefly, we will calculate and compare all 
effectiveness outcomes for the two trial arms using an intention-to-treat analysis. We will use 

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descriptive statistics and 95% confidence intervals to summarize the yield, efficiency, and speed 
outcomes for the two intervention arms by site. To assess the intervention effect on outcomes, 
we will use methods appropriate for randomization of a small number of clusters and accounting 
for the stratified design.26 A cluster-level analysis, giving each cluster equal weight, will be 
conducted to calculate unadjusted ratio and difference effect measures, and their associated 
95% confidence intervals. Adjusted effect estimates will also be calculated taking into account 
any imbalance of important factors at baseline by study arm. Pre-specified subgroup analyses 
(e.g., gender and HIV status) will be conducted for the primary outcome. A detailed statistical 
analysis plan will document methods used for the analysis of primary and secondary outcomes, 
and document pre-specified sub-group analyses. 
Aim 2  
1) Descriptive analyses – We will report process metrics on a monthly basis to assess adoption 
and maintenance of each intervention component overall, within key patient sub-groups, and at 
individual sites. We will report median and change between pre- and post-randomization 
assessments in A) patient costs; B) patient satisfaction with care; and C) provider TPB 
construct scores at control and intervention sites. 
2) Comparative analyses – To identify patient-, provider-, and/or clinic-level factors 
independently associated with adoption and maintenance of intervention components, we will 
develop linear or logistic regression models, taking into account the clustered design (for 
example, robust standard errors). To compare by study arm the change from baseline (pre-
randomization) to post-intervention in A) patient cost; B) patient satisfaction with care; and C) 
TPB construct scores, we will use a cluster-level analysis, similar to methods described for the 
primary outcome under Aim 1. The analysis will take into account the stratified design and an 
adjusted analysis will also be conducted taking into account other baseline imbalances by study 
arm. 
3) Qualitative analyses – De-identified focus group or interview transcripts will be uploaded to 
the qualitative data analysis software Dedoose (SocioCultural Research Consultants, USA). 
Thematic interpretation32-34 will include collaborative development of a coding framework and 
detailed coding of transcripts using Dedoose. Coded transcripts will be sorted to identify 
thematic groupings. The thematic groupings will be reviewed to identify emergent themes within 
each domain of the coding framework and quotes that best represent each domain. Thematic 
interpretation will focus on individual, social and structural factors associated with successful or 
unsuccessful adoption and/or maintenance of the intervention components at different sites. 
Aim 3  
Cost-effectiveness -- Our primary outcome will be the incremental cost-effectiveness of the 
intervention strategy from a societal perspective, measured as the cost per disability-adjusted 
life year (DALY) averted relative to standard TB evaluation. Secondary outcomes will include 1) 
the incremental cost of introducing and maintaining the intervention strategy (a measure of 
affordability for health systems) and 2) the incremental patient cost per diagnostic evaluation 
and per treatment initiated (a measure of affordability and access for patients). For all 
outcomes, we will report stratified results for key populations including women and people living 
with HIV. To assess cost-effectiveness, we will use the primary effectiveness data from Aim 1 

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and relevant literature estimates (e.g., clinical outcomes among those initiating treatment) to 
construct a Markov model including states for undiagnosed TB but seeking care, undiagnosed 
TB not seeking care (e.g., because initial diagnostic evaluation was too expensive), treated TB, 
self-resolved TB, and death. States will be subdivided according to smear and Xpert status 
(smear-positive, smear-negative/Xpert-positive, and smear/Xpert-negative) and HIV status 
(positive and negative, on/off ART, with CD4 strata).  Data to inform transition probabilities and 
health utilities will come from study data where feasible (including specific questions of patients 
to ascertain probabilities such as future care-seeking if diagnoses are missed), and the literature 
where unavailable. We will follow international conventions for all procedures including 
economic costing, discounting, and reporting. We will conduct one-way sensitivity analyses 
across all model parameters, multi-way sensitivity analyses for those parameters found to be 
most influential, and a probabilistic uncertainty analysis in which all parameters are varied 
simultaneously using Latin Hypercube Sampling. 
Population-Level Epidemiological Impact – We will construct a compartmental epidemic model 
of TB in Uganda to evaluate the potential impact of scaling up the intervention strategy across a 
representative district in Uganda. Using our team’s prior models of TB diagnostics in India35 and 
sub-Saharan Africa as a starting point, we will construct a population model that includes 
structure both for TB natural history (e.g., latent, subclinical, pre-diagnostic, diagnosis-seeking, 
treated)36 and steps in the diagnostic cascade (e.g., pursuing diagnosis, diagnosed but not 
treated, treated).35, 37 We will include structure for both HIV and MDR TB, and will link this 
model’s structure to that of the Markov model constructed for cost-effectiveness analysis above 
(for purposes of explicitly estimating the importance of transmission to considerations of cost-
effectiveness).  We will fit the model to epidemiological data from a selected representative 
district in Uganda according to its TB incidence, prevalence, and mortality, as well as additional 
factors including prevalence of HIV and of MDR-TB. This model will project TB incidence and 
mortality over a primary time-frame of 10 years under two alternative scenarios: 1) standard TB 
diagnostic evaluation and 2) streamlined TB diagnostic evaluation using the intervention 
strategy. In a secondary analysis, we will incorporate economic data as described above, 
comparing cost-effectiveness measured under a dynamic (epidemic-economic) framework to 
that of the Markov model. The dynamic transmission model has the advantage of incorporating 
transmission dynamics at the population level, but requires more assumptions (e.g., 
homogeneous mixing in the source population). By comparing results from Markov and 
transmission modeling, we will be able to assess: 1) the relative contribution of population-level 
transmission to the overall effectiveness and cost-effectiveness of different TB diagnostic 
evaluation strategies over time and 2) the relative influence of given model parameters on cost- 
effectiveness under a cohort-based versus transmission-based evaluation model. 
 
Sample Size Considerations 
Aim 1 
The study is based on the health clinic being the unit of randomization and aims to demonstrate 
the superiority of the intervention arm. The sample size calculation uses formulae appropriate 

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for cluster-randomized trials with a parallel design and stratified and/or restricted randomization, 
including the addition of one extra cluster per arm to allow for the loss of degrees of freedom 
due to stratification to obtain conservative estimates.26 The outcome is the proportion of patients 
treated for microbiologically-confirmed TB within two weeks of referral for sputum-based testing. 
A type I error of 5% and power of 90% is assumed. Pre-randomization data collected from 
February to July 2017 from the 20 selected trial sites in Uganda suggests the average 
proportion of patients referred for TB evaluation who initiate treatment for active TB within two 
weeks is 8.6%, and the coefficient of variation (k) between clusters is 0.27, with a harmonic 
mean of 325 patients enrolled at each health center cluster during the 18-month enrolment 
period. Based on these assumptions and assuming a type I error of 5%, we will have 90% 
power to detect a 6% or greater absolute increase in the outcome proportion in the intervention 
arm with a trial duration of 18 months (see Table). Table 1 also shows detectable absolute 
effect sizes with varying power (80%, 85%, 90%) during various trial time intervals and 
respective average cluster size estimates.   
Average cluster size 
Effect size (10 clusters per arm, k=0.27) 
80% Power 
85% Power 
90% Power 
215 (12 months) 
5.3% 
5.8% 
6.4% 
270 (15 months) 
5.1% 
5.5% 
6.2% 
325 (18 months) 
4.9% 
5.4% 
6.0% 
375 (21 months) 
4.8% 
5.3% 
5.8% 
430 (24 months) 
4.7% 
5.2% 
5.7% 
Aim 2 
The sample size for Aim 2 analyses is either fixed (process metrics; provider surveys and focus 
groups) by parameters of the clinical trial or based on feasibility considerations (patient surveys 
and provider in depth interviews). For quantitative analyses, the sample size is sufficiently large 
(data on 3400 patients for process metrics analyses, 800 patient surveys, and 100-200 provider 
surveys) to enable multivariable analysis to identify factors associated with intervention adoption 
and maintenance. 
Aim 3 
As Aim 3 is primarily a modeling aim, sample size calculations are not applicable. Sample size 
considerations for the cost data (i.e., number of direct observations through time and motion 
studies, number of patients completing the cost questionnaires) are based on feasibility 
considerations and desire to estimate model parameters to sufficient levels of precision, as 
described above for Aim 2. 
 
 

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Ethical Considerations 
Potential risks to participants 
There are minimal risks to participants in this study. The primary risk to patients undergoing TB 
diagnostic evaluation during the study period is the potential for loss of confidentiality and 
stigma should their personal health information, including HIV or TB status, be disclosed. 
Patients participating in surveys also have the potential of sensitive information regarding their 
income being disclosed. Finally, the primary research risks for health workers related to the 
surveys and qualitative studies are punitive actions by the employer in response to the 
information they provide for research. 
Protection against risk 
The trial will be submitted for approval to the Research Ethics Committees of the University of 
California San Francisco and Makerere University College of Health Sciences, and to the 
Uganda National Council for Science and Technology. It is registered with the U.S. National 
Institutes of Health’s ClinicalTrials.gov and the Pan African Clinical Trials Registry (PACTR) as 
a Phase 4 clinical trial. All study staff will be required to have completed Good Clinical Practice 
(GCP) training. 
To minimize the potential for loss of confidentiality, all patient-identifiable data will be stored in 
locked or password protected areas accessible only to study personnel. Patient names will be 
used to match patient records across NTLP Laboratory and Treatment registers, but will not be 
included in the password-protected, electronic study database. Primary data collection forms 
used when matching records across data sources will be destroyed once entry of data into the 
electronic study database is completed. 
To minimize risks to autonomy for patients and providers who participate in surveys, research 
staff will be carefully trained in how to administer the consent form to the individuals in the 
different target populations, with attention given to the background and principles of research 
ethics. 
The Directors of participating health centers will be asked to sign a Memorandum of 
Understanding agreeing that their health center participate in the trial and agreeing not to 
introduce any new TB evaluation interventions during the time period of the trial without 
informing trial staff. Individual patients evaluated for TB at participating health centers will not be 
consented because the trial meets the requirements to qualify for waiver of informed consent 
under U.S. Department of Health and Human Services (DHHS) Regulation 46.116 (d): 1) No 
data or samples will be collected specifically for research purposes; 2) Patients will receive the 
same or higher quality of care; 3) It is not practical for health workers to obtain informed consent 
during the process of delivering routine clinical care; and 4) All pertinent TB testing results will 
be communicated to patients via routine or enhanced processes of care. Verbal informed 
consent using a script will be obtained from patients who participate in surveys. Written 
informed consent will be obtained from providers who participate in surveys, direct observation 
(i.e., time-motion) studies and/or focus group discussions/interviews. The consent forms, which 

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will be approved by institutional review boards (IRB) in the U.S. and Uganda, will be translated 
from English into the local languages, and back-translated into English as required by IRBs to 
be sure that no significant language or concepts are lost in translation. 
Data protection 
All patient-identifiable data will be stored in locked or password-protected cabinets or databases 
accessible only to study personnel. Patient names will be used to match patient records across 
NTLP Laboratory and Treatment registers, but will not be included in the password-protected, 
electronic study database. Primary data collection forms and images used when matching 
records across data sources will be destroyed once entry of data into the electronic study 
database is completed. 
Potential benefits of the study to participants and society 
Patients undergoing TB diagnostic evaluation at participating health centers may benefit from 
the study through potential enhanced diagnosis and treatment of TB, either because of training 
(control health centers) or training plus other interventions (intervention health centers). Earlier 
diagnosis and treatment of TB may lead to improved patient outcomes and reduced disease 
transmission in patients’ communities. 
Patients and providers who participate in surveys, in depth interviews or focus group 
discussions will receive sodas and/or lunch to compensate for their time. Otherwise, they will 
not directly benefit from participating in these research activities. 
Potential benefits to society include identification of strategies to improve TB case finding and to 
decrease TB incidence and prevalence in low-income, high-burden countries. If successful, the 
proposed intervention could potentially be scaled up to improve TB care in similar settings. 
Dissemination 
The trial results will be communicated to stakeholders through dissemination meetings and to 
participating health centers using language-appropriate information sheets. Investigators will 
present results at relevant conferences, and submit manuscript(s) to peer-reviewed journals. 
Public access to the participant-level dataset of main trial results and statistical code will be 
made available. 
Trial governance 
Because of the low-risk nature of the research, the Principal Investigator will be responsible for 
monitoring the data, assuring protocol compliance, and conducting safety reviews on a quarterly 
basis. An independent Trial Steering Committee (TSC) will meet approximately every 6 months 
and as needed. Prior to each meeting, the Principal Investigator will submit a progress report, 
including recommendations on whether the project should continue unchanged, require 
modification/amendment, or close to enrollment to an independent Trial Steering Committee 
(TSC). All major modifications (e.g., study design, sample size, study termination or 
suspension), will be approved by the TSC and ethics committees. 
 

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Abbreviations 
AFB 
 
 
 
Acid-Fast Bacilli 
ART 
 
 
 
Anti-retroviral therapy 
CFR 
 
 
 
Code of Federal Regulations  
CRT 
 
 
 
Cluster-randomized trial 
DALY  
 
 
Disability-adjusted life year 
DHHS  
 
 
Department of Health & Human Services 
DMC  
 
 
Data Monitoring Committee 
EQA 
 
 
 
External quality assurance 
FDA 
 
 
 
U.S. Food and Drug Administration 
GCP 
 
 
 
Good Clinical Practice 
ICH 
 
 
 
International Council for Harmonisation of Technical  
Requirements for Pharmaceuticals for Human Use 
IRB  
 
 
 
Institutional review board 
ISTC  
 
 
International Standards for TB Care 
KAP 
 
 
 
Knowledge, attitudes, and practices 
LED 
 
 
 
Light-emitting diode 
MDR-TB 
 
 
Multi-drug-resistant tuberculosis 
MOU  
 
 
Memorandum of Understanding 
MUREC 
 
 
Makerere University Research Ethics Committee 
NIH 
 
 
 
National Institutes of Health 
NHLBI  
 
 
National Heart, Lung, and Blood Institute 
NTLP  
 
 
National Tuberculosis and Leprosy Program 
NTP 
 
 
 
National TB Program 
NTRL  
 
 
National TB Reference Laboratory 
PRECEDE 
Predisposing, Reinforcing, and Enabling Constructs in 
Educational/Environmental Diagnosis and Evaluation 
PSQ 
Patient Satisfaction Questionnaire 
RE-AIM 
 
 
Reach Effectiveness Adoption Implementation Maintenance 
REDCap 
 
 
Research Electronic Data Capture 
TB 
 
 
 
Tuberculosis 
TPB 
 
 
 
Theory of Planned Behavior 
TSC 
 
 
 
Trial Steering Committee 
UCSF   
 
 
University of California San Francisco 
UCSF CHR  
 
 
UCSF Committee on Human Research 
UNCST  
 
 
Uganda National Council for Science and Technology 
WHO  
 
 
World Health Organization 
XPEL TB 
 
 
GeneXpert Performance Evaluation for Linkage to Tuberculosis 
Care 
ZN 
 
 
 
Ziehl–Neelsen 
 
 
 

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Competing interests 
The investigators declare that they have no competing interests. 
Trial Steering Committee 
Jerry Friedland (Yale University) 
Noah Kiwanuka (Makerere University) 
Madhukar Pai (McGill University) 
Andrew Ramsay (WHO/TDR) 
Grant Theron (Stellenbosch University) 
 
 

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Shete PB, Haguma P, Miller CR, Ochom E, Ayakaka I, Davis JL, Dowdy DW, Hopewell 
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Salje H, Andrews JR, Deo S, Satyanarayana S, Sun AY, Pai M, Dowdy DW. The 
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Sun AY, Denkinger CM, Dowdy DW. The impact of novel tests for tuberculosis depends 
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GeneXpert Performance Evaluation for Linkage to Tuberculosis 
Care:  
The XPEL TB Trial 
 
 
ClinicalTrials.gov Identifier:   
NCT03044158  
 
Pan African Clinical Trials Registry:   
PACTR201610001763265 
 
Sponsored by: 
NIH/NHLBI 
 
Unique Protocol ID: 
R01HL130192 
 
Principal Investigator: 
Adithya Cattamanchi MD, MAS 
 
Draft or Version Number:  
Version 2.0 
 
Day Month Year 
September 11, 2019 
 
 
 

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Statement of Compliance 
The study will be carried out in accordance with Good Clinical Practice (GCP) as 
required by the following: 
• 
U.S. Code of Federal Regulations applicable to clinical studies (45 CFR 46) 
• 
ICH GCP E6 
• 
Completion of Human Subjects Protection Training 
• 
NIH Clinical Terms of Award 
Refer to:  http://www.hhs.gov/ohrp/humansubjects/guidance/45cfr46.htm#46  
http://www.fda.gov/cder/guidance/959fnl.pdf  
http://grants.nih.gov/grants/guide/notice-files/NOT-OD-01-061.html  
http://cme.cancer.gov/c01/  
 
 
 

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Signature Page 
The signature below constitutes the approval of this protocol and the attachments, and 
provides the necessary assurances that this trial will be conducted according to all 
stipulations of the protocol, including all statements regarding confidentiality, and 
according to local legal and regulatory requirements and applicable US federal 
regulations and ICH guidelines. 
 
 
Uganda Study Site Principal Investigator:* 
 
Signed:________________________________ 
Date:_____________________  
      Achilles Katamba, MBChB, PhD 
 
 
 
 
 
Overall Study Principal Investigator:* 
 
Signed:________________________________ 
Date:_____________________  
      Adithya Cattamanchi, MD, MAS  
 
 
 
 

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Table of Contents 
 
Overview ....................................................................................................................................................... 6 
Key Roles ..................................................................................................................................................... 7 
Table 1. Timeline of Activities ................................................................................................................... 8 
Background .................................................................................................................................................. 9 
Background information ......................................................................................................................... 9 
Theoretical basis for intervention strategy ........................................................................................ 11 
Table 2. Key clinic-level barriers targeted by intervention strategy. ................................ 12 
Objectives ................................................................................................................................................... 13 
Methods/Design ........................................................................................................................................ 14 
Overview ................................................................................................................................................. 14 
Target Setting and Study Population ................................................................................................. 14 
Eligibility criteria for the cluster-randomized trial ................................................................. 14 
Eligibility criteria for mixed methods and health economic sub-studies ........................ 15 
Recruitment ............................................................................................................................................ 15 
Health centers .................................................................................................................................. 15 
Participants for cluster-randomized trial.................................................................................. 16 
Participants for mixed methods and health economic sub-studies ................................. 16 
Intervention and Control (Wait-list) Arms .......................................................................................... 16 
Figure 2. Comparison of TB diagnostic evaluation in the intervention and control 
arms .................................................................................................................................................... 17 
On-Site Molecular Testing ............................................................................................................ 17 
Process Redesign for Same-Day TB Diagnosis and Treatment ........................................ 18 
Performance Feedback ................................................................................................................. 18 
Randomization ................................................................................................................................. 19 
Procedures ............................................................................................................................................. 19 
Health center training .................................................................................................................... 19 
Patient-level data collection ......................................................................................................... 20 
Symptom Screening ....................................................................................................................... 20 
Patient surveys ................................................................................................................................ 21 
Provider surveys ............................................................................................................................. 21 
Health system costing ................................................................................................................... 21 
Focus group discussions ............................................................................................................. 21 

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Assessment of patient’s vital status, diagnostic and treatment status, HIV status, and 
ART status ........................................................................................................................................ 22 
Provider interviews ........................................................................................................................ 22 
Figure 3. Site visit activities ......................................................................................................... 22 
Outcomes ............................................................................................................................................... 23 
Definitions ......................................................................................................................................... 23 
I. Primary Outcome (Effectiveness) ........................................................................................... 24 
II. Secondary Outcomes (Effectiveness) .................................................................................. 24 
III. Implementation Outcomes...................................................................................................... 25 
IV. Cost-effectiveness/Modeling Outcomes ............................................................................ 27 
Data Management ................................................................................................................................ 27 
Statistical Analysis ................................................................................................................................ 28 
Aim 1 .................................................................................................................................................. 28 
Aim 2 .................................................................................................................................................. 28 
Aim 3 .................................................................................................................................................. 29 
Sample Size Considerations ............................................................................................................... 30 
Aim 1 .................................................................................................................................................. 30 
Revised detectable effect size estimates: ..................................................................................... 30 
Table 3: Detectable effect sizes .................................................................................................. 31 
Aim 2 .................................................................................................................................................. 31 
Aim 3 .................................................................................................................................................. 31 
Ethical Considerations.............................................................................................................................. 32 
Potential risks to participants .............................................................................................................. 32 
Protection against risk .......................................................................................................................... 32 
Data protection ................................................................................................................................ 33 
Potential benefits of the study to participants and society.............................................................. 33 
Dissemination ........................................................................................................................................ 33 
Trial governance.................................................................................................................................... 33 
Abbreviations ............................................................................................................................................. 34 
Competing interests .................................................................................................................................. 35 
Trial Steering Committee ......................................................................................................................... 35 
References ................................................................................................................................................. 36 
 
 
 

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Overview 
Title 
GeneXpert Performance Evaluation for Linkage to Tuberculosis 
Care: The XPEL TB Trial 
Target Population 
Adults undergoing evaluation for pulmonary TB (N=11,283) 
Sites 
20 TB microscopy centers (10 intervention, 10 control) in Uganda 
Study Design 
Clustered-randomized trial with nested mixed methods and 
economic/transmission modeling studies 
Study Duration 
18 months 
Objectives 
Aim 1: To compare patient outcomes at health centers 
randomized to intervention vs. standard-of-care TB 
diagnostic evaluation strategies. 
• 
Intervention: onsite molecular testing for TB + process 
redesign to facilitate same-day TB diagnosis and 
treatment + performance feedback 
• 
Standard-of-care: onsite ZN or LED fluorescence 
microscopy + hub-based GeneXpert testing per existing 
protocols  
Aim 2: To identify processes and contextual factors that 
influence the effectiveness and fidelity of the intervention TB 
diagnostic evaluation strategy. 
 
Aim 3: To compare the costs and epidemiological impact of 
intervention vs. standard-of-care TB diagnostic evaluation 
strategies. 
 
 
 

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Key Roles 
Principal Investigator  
Adithya Cattamanchi, MD, MAS 
Associate Professor of Medicine, University of California San Francisco 
San Francisco General Hospital 
1001 Potrero Ave, Room 5K1 
San Francisco, CA 94110 
Phone: +1-415-206-5489 
Fax: +1-415-695-1551 
adithya.cattamanchi@ucsf.edu 
 
Uganda Principal Investigator 
Achilles Katamba, MBChB, MSc, PhD 
Senior Lecturer, Makerere University College of Health Sciences 
Upper Mulago Hill Road 
Kampala, Uganda 
Phone: +256-(0)414-530-692 
Fax: +256-(0)414-540-524 
axk95@case.edu 
 
NHLBI Program Officer 
Elisabet Caler, PhD 
AIDS Program Lung Team Leader 
Lis.caler@nih.gov 
 
 

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Table 1. Timeline of Activities 
2018 
2019 
2020 
2021 
Q1 Q2 Q3 Q4 
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 
Randomization and site training 
Randomization unveiling ceremony 
Post randomization site visits (to introduce intervention or 
conduct guideline refresher training)  
Assess patient-level outcomes  
Collect TB diagnosis and treatment initiation data 
Collect 6-month vital status data 
Assess intervention implementation (intervention sites) 
   Collect process metric data 
   Conduct health worker interviews/focus groups 
Assess whether targeted barriers are modified 
   Provider survey (all involved in TB evaluation) 
   Patient cost & satisfaction survey 
Assess cost-effectiveness and impact 
   Conduct health system costing studies 
Data cleaning, analysis and manuscript preparation 
 
 

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Background 
Background information 
 
Prompt diagnosis and treatment of TB patients is essential to making progress towards TB 
elimination. However, at least 4.3 million of the estimated 10.4 million new cases in 2015 were 
not detected and reported to the World Health Organization (WHO).1 There are three overarching 
reasons for this large gap: TB patients are not being notified to public health authorities, not 
seeking care, or not being diagnosed and treated even after seeking care. The last reason 
represents a clear health system failure that is pervasive in high burden countries – a recent 
systematic review of published studies found that up to 38% of sputum smear-positive patients in 
Africa and 28% in Asia are lost to follow-up prior to treatment initiation.2 Patients with smear-
negative TB are even less likely to complete the diagnostic cascade of care and be linked to 
treatment.  
A principal reason for these failures in linkage to care is the inadequacy of the current approach 
to TB diagnosis at community health centers. It is well known that sputum smear microscopy, the 
most common test for TB at community health centers worldwide, has important limitations that 
contribute to delays in TB diagnosis and treatment. First, smear microcopy has sub-optimal 
sensitivity, identifying only about 50% of patients who actually have TB.3 Second, the typical 
process of sputum collection and smear examination is burdensome for patients and staff. 
Guidelines usually require staff to ask patients to submit a sputum specimen on the day of 
presentation, return the following morning to submit a second specimen, and return a third time 
for treatment initiation (if smear-positive) or to consider further workup (if smear-negative). In high 
burden countries, clinicians fail to refer up to half of patients reporting TB symptoms for sputum 
smear examination.4, 5 In addition, the direct and indirect costs of this standard multi-day 
diagnostic evaluation process consume up to 3 months of household income for already poor 
patients.6-10 It is therefore not surprising that a substantial proportion do not return after their initial 
health center visit to submit additional sputum specimens, collect results or initiate treatment if 
smear-positive.5, 11-13  
To address these limitations, there has been substantial donor investment in scale-up of Xpert 
MTB/RIF (Xpert)14, a semi-automated molecular assay endorsed by the WHO in 2010 and by the 
US FDA in 2013. Xpert identifies 90% of TB cases, has a 2-hour turn-around time, and can be 
performed with minimal training and human resource requirements.15 Although this represents an 
important advance, modeling analyses have found that Xpert is unlikely to bend the TB incidence 
curve significantly, primarily because it cannot be deployed at community health centers in high 
burden countries.16 Indeed, because of high device costs and infrastructure requirements, the 
vast majority of Xpert devices are being placed at district or higher-level facilities14, which are 
accessed by <15% of the population.17 Many countries have therefore adopted a hub-and-spoke 
model in which several community health centers (spokes) are linked to an Xpert testing site 
(hub). However, a cluster-randomized trial has found that Xpert implementation using a hub-and-
spoke model did not impact mortality; the major reason was failure to link patients with confirmed 
TB to treatment.18  

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The next generation of molecular diagnostics has strong potential to close gaps in the TB 
diagnostic cascade of care. In particular, GeneXpert Omni is a portable, single-cartridge version 
of the GeneXpert platform that is already WHO- and FDA-approved, and widely used in Uganda 
and other high TB burden countries. Because it only has a single test module (instead of 4 or 
more), it is lower cost (subsidized price $5000 vs. $10000 or more for previous GeneXpert 
platforms) and has lower power requirements enabling it to be battery-operated.19 These features 
increase the likelihood that onsite Xpert testing can eventually be scaled up as a replacement for 
sputum smear at community health centers. 
Our overall objective is to assess the effectiveness, implementation and costs of a streamlined 
TB diagnostic evaluation strategy based around rapid, onsite molecular testing. The intervention 
strategy was developed based on theory-informed assessment of barriers to TB diagnostic 
evaluation at community health centers in Uganda and a process of engagement with local 
stakeholders. It includes: 1) Point-of-care molecular testing using GeneXpert as a replacement 
for sputum smear microscopy; 2) Re-structuring of clinic-level procedures to facilitate same-day 
TB diagnosis and treatment; and 3) Quarterly feedback of TB evaluation metrics to health center 
staff. Our central hypothesis is that the intervention strategy will have high uptake and increase 
the number of patients diagnosed with and treated for active pulmonary TB. To test this 
hypothesis, we will conduct a pragmatic cluster-randomized trial at community health centers that 
provide TB microscopy services in Uganda in partnership with the National TB Program (NTP). 
We utilize an effectiveness-implementation hybrid design in which, concurrent with the clinical 
trial, we will conduct nested mixed methods, health economic and modeling studies to assess 1) 
whether the intervention strategy modifies targeted barriers to TB diagnostic evaluation; 2) fidelity 
of implementation of the intervention components (i.e, the degree to which intervention 
components were implemented as intended vs. adapted across sites); and 3) cost-effectiveness 
and public health impact. We will use the RE-AIM framework to evaluate how the intervention 
strategy affects processes and outcomes important to patients and TB programs, and to 
determine its scale-up potential. RE-AIM encompasses 5 dimensions common to successful 
multi-level interventions: depth of reach into a target population; effectiveness; factors that 
promote adoption; resources needed for implementation; and factors that ensure maintenance 
over time.20 Thus, our focus is on evaluating the impact of the proposed multi-level intervention, 
rather than evaluating the impact of its individual components. The process of guideline 
implementation and technology uptake is complex, and it is unlikely that desired outcomes can 
be achieved with a simple intervention. However, if the desired outcomes are achieved, the 
proposed mixed methods studies will identify which barriers were modified, providing some insight 
into which intervention components are critical. In addition, future studies can focus on testing 
simplified versions of the multi-level intervention that remove one or more components. 
 

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Theoretical basis for intervention strategy 
The intervention strategy was designed based on a mixed methods study conducted at six health 
centers in Uganda to better understand reasons for gaps along the TB diagnosis cascade of care 
and to inform intervention design. The process was guided by the Theory of Planned Behavior as 
the conceptual framework and the PRECEDE model to select intervention components (Figure 
1). In a systematic review of guideline implementation studies, the Theory of Planned Behavior 
was the most likely theory to predict guideline adherence.21 This theory asserts that intention is 
the best predictor of behavior and that three factors mediate the strength of intention: 1) attitudes 
– expected value of behavioral performance; 2) subjective norms – what others think about the 
behavior; and 3) self-efficacy – perception of ability to overcome barriers to behavioral 
performance.22 The PRECEDE model was chosen based on its strong empirical base and 
applicability to guideline adherence. The model is based on three factors relevant to health behavior 
change: 1) predisposing factors – prior motives that either support or inhibit behavior; 2) reinforcing 
factors – rewards or punishments following a behavior or anticipated as a consequence of it; and 
3) enabling factors – objective characteristics of an individual or environment that facilitate 
behavior.23 A meta-analysis of 50 randomized controlled trials of continuing medical education 
demonstrated that the studies employing a combination of interventions representing PRECEDE 
categories were the most likely to influence patient outcomes.24  
The intervention strategy was designed in consultation with the Uganda NTP to target modifiable 
clinic-level barriers identified in the formative assessment as well as key theoretical constructs. 
Key barriers to TB evaluation identified through semi-structured interviews with 22 staff at the six 
health centers and organized within PRECEDE model categories are shown in Table 2.25 The 
intervention strategy includes: 1) onsite molecular testing to reduce laboratory workload and 
increase the sensitivity of TB diagnostic testing; 2) re-structuring clinic-level procedures to 
facilitate same-day TB diagnosis and treatment, thereby increasing provider self-
efficacy/motivation and enabling linkage of confirmed TB patients to treatment; and 3) 
performance feedback to increase communication between staff and with NTP supervisors, 
thereby reinforcing uptake of onsite molecular testing and adherence to TB evaluation guidelines. 
Re-assessing these barriers as part of Aim 2 will help understand whether the XPEL TB 
intervention worked as intended and help explain why it was or was not effective in improving TB 
diagnostic evaluation outcomes. 
ISTC, International Standards for TB Care 
REINFORCING FACTORS 
Performance feedback* 
PREDISPOSING AND ENABLING FACTORS 
1. Onsite molecular testing with GeneXpert/Xpert Ultra 
2. Process redesign to facilitate same-day diagnosis and treatment 
Provider Factors: 
 • Attitudes 
 • Social Norms 
 • Self-efficacy 
 
Patient Factors 
Health System Factors 
Intention to Follow 
ISTC 
ISTC Adherence 
Case Detection and 
Treatment 
Figure 1. Theory-informed barrier assessment and intervention design 

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Table 2. Key clinic-level barriers targeted by intervention strategy. 
PRECEDE framework 
Recurring themes highlighting barriers targeted by the 
intervention strategy 
Predisposing factors 
(Knowledge, attitudes, beliefs, 
intention) 
• 
Time and resource constraints → low self-efficacy 
• 
Low motivation of staff 
• 
Low sensitivity of sputum smear microscopy 
• 
Poor patient perception of care at government health centers 
Enabling Factors 
(Factors that if addressed 
make it easier to initiate the 
desired behavior) 
• 
Failure of patients to return after initial visit (due to time and 
costs) 
• 
Inability to track and follow-up patients → low-self-efficacy 
“When they have a cough for more than 2 weeks they are sent to 
the lab. But the problem is they get the first sample and sometimes, 
actually most times they don’t bring the second sample.” 
Reinforcing Factors 
(Factors that if addressed 
make it easier to continue the 
desired behavior) 
• 
Lack of communication and coordination among staff 
• 
Insufficient oversight from NTP 
 “…Actually at times we have met but we don’t meet [regularly], 
only when we realize there is a problem that’s when we 
communicate and say why is this happening, then we try to rectify.” 
 
 

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Objectives 
Aim 1: To compare patient outcomes at health centers randomized to intervention vs. 
standard-of-care TB diagnostic evaluation strategies. We will randomize 20 community health 
centers to continue standard TB evaluation (routine microscopy plus referral of patients for Xpert 
testing per existing processes of care) or to implement the intervention strategy (1. Onsite 
molecular testing; 2. Re-structuring clinic-level procedures to facilitate same-day TB diagnosis 
and treatment; and 3. Performance feedback). We will compare reach and effectiveness based 
on the numbers and proportions of patients (N=11,283) who complete TB testing, are found to 
have TB, and have treatment initiated within one week of specimen provision. 
Aim 2: To identify processes and contextual factors that influence the effectiveness and 
fidelity of the intervention TB diagnostic evaluation strategy. We will use quantitative process 
metrics to assess the adoption and maintenance over time of the core components of the 
intervention strategy. We will also collect quantitative and qualitative data to describe the fidelity 
of implementation of each component and faithfulness to our conceptual model. 
Aim 3: To compare the costs and epidemiological impact of intervention vs. standard-of-
care TB diagnostic evaluation strategies We will model the incremental costs and cost-
effectiveness of intervention relative to standard-of-care TB diagnostic evaluation from the health 
system and patient perspective. We will then construct an epidemic model of the population-level 
impact of the intervention strategy on TB incidence and mortality. 
 
 

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Methods/Design 
Overview 
The study proposes to conduct a pragmatic, parallel cluster-randomized trial with nested mixed 
methods, health economic and modeling studies to evaluate the effectiveness, implementation 
and costs of the intervention strategy relative to standard TB diagnostic evaluation at community 
health centers that are part of Xpert referral networks in Uganda. The effectiveness of the 
intervention strategy will be assessed using routine data collected as part of mandatory reporting 
to the Uganda NTLP on consecutive patients who present to participating health centers during 
the 18-month trial period and meet eligibility criteria. Selected patients and providers who provide 
informed consent will also be surveyed, interviewed, and/or participate in focus groups to identify 
reasons for its success or failure across study sites (Aim 2), and to collect relevant cost data for 
cost-effectiveness analyses (Aim 3). 
Target Setting and Study Population  
The target setting is community health centers with TB microscopy units (i.e., the lowest level of 
the health system where TB diagnostic services are provided by the Uganda NTLP). The unit of 
randomization will be community health centers (N=20). The unit of analysis will be patients 
undergoing TB diagnostic evaluation during the 18-month trial period (estimated total 11,283 over 
18-month trial period). 
Eligibility criteria for the cluster-randomized trial 
A. Site-level Inclusion Criteria 
1. Use standard (multi-day) sputum smear microscopy as the primary method of TB 
diagnosis 
2. Participate in NTP-sponsored external quality assurance (EQA) for sputum smear 
microscopy 
3. Send samples to a district or regional hospital/health center for Xpert testing 
B. Site-level Exclusion Criteria 
1. Do not agree to be randomized to standard-of-care vs. intervention arms 
2. Perform sputum smear examination on <150 patients per year (based on 2015 
data) 
3. Diagnose <15 smear-positive TB cases per year (based on 2015 data) 
C. Patient-level Inclusion Criteria 
1. Initiate evaluation for active pulmonary TB at a study health center 
D. Patient-level Exclusion Criteria 
1. Have sputum collected for monitoring of response to anti-TB therapy 
2. Have sputum collected as part of active, community-based case finding (e.g., 
contact tracing, community outreach campaign) 
3. Referred to a study health center for TB treatment after a diagnosis is 
established elsewhere 
4. Started on TB treatment for extra-pulmonary TB only 
 

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In addition, patients age <18 years and those with a documented prior history of TB treatment 
(e.g., reason for Xpert testing or TB treatment marked as treatment failure, relapse, treatment 
after loss to follow-up, etc.) will be excluded from the primary analysis of all outcomes. Patients 
identified as RIF resistant by Xpert testing will be excluded from the primary analysis of all 
outcomes except for the comparison of the number and proportion of patients with RIF resistance 
between arms. 
Eligibility criteria for mixed methods and health economic sub-studies 
Patient Surveys: Same eligibility criteria as for the CRT. 
Provider Survey: Providers at each study site who are (a) aged ≥18 years; (b) employed by the 
peripheral health center; and (c) involved in the conduct or supervision of health center work 
related to diagnosis and management of TB will be included.  
Focus Group Discussion and Interviews: Same inclusion criteria as for the provider survey. 
Recruitment 
Health centers 
Potential trial sites were identified from a list of Uganda NTLP-affiliated TB microscopy centers 
that refer samples to Xpert testing hubs. Study staff reviewed 2014 TB testing and treatment data 
reported to the Uganda NTLP to identify health centers that meet eligibility criteria (based on 
numbers of patients tested for TB and found to be smear-positive), focusing on those within 150 
km of Kampala for study feasibility purposes. Study staff then obtained permission from District 
Health Officials and Health Center Directors for visiting health centers to confirm eligibility and 
assess interest in study participation. At the site assessment visit, study staff counted from the 
2015 TB laboratory register the number of patients tested by microscopy and the number of 
smear-positive patients (excluding patients tested for treatment monitoring or as part of 
community-based active case finding activities). Health centers confirmed to be eligible and that 
expressed interest in participating in the study were reviewed with Uganda NTLP and NTRL 
Directors. Following their approval, research staff met with the District Health Officer (DHO) to 
inform him or her about the project and requested the participation of potential trial sites in the 
District. The DHO was asked to sign a Memorandum of Understanding (MoU) that describes key 
study procedures and expectations of participating sites. If the DHO signed the MoU, study staff 
scheduled an enrollment visit with potential trial sites in the District by calling the facility 3-4 days 
prior to the anticipated visit date. Project staff then contacted the Program Manager of the Uganda 
NTLP to arrange for an NTLP representative to participate in the enrollment site visit. During the 
site visit, project staff followed a standard script to present the project to health center staff, 
outlined the project aims, and described project procedures. Project staff presented a copy of the 
MoU signed by the DHO and endorsed by the Chief Administrative Officer and NTLP Program 
Manager to the health center in-charge. Of note, the final list of 20 sites to be randomized was 
determined in consultation with the trial statistician (to minimize potential for site-level variation 
that could impact power). 

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Participants for cluster-randomized trial 
This is a pragmatic trial that will study outcomes of routine care. Thus, rather than recruiting 
participants, the intervention and control arms will be compared using data available in routine TB 
laboratory and treatment registers for consecutive patients who present to participating health 
centers during the two-year trial period and meet eligibility criteria (estimated N=11,283 patients). 
Participants for mixed methods and health economic sub-studies 
Patient Cost and Satisfaction Surveys: The patient cost and satisfaction surveys will be 
administered during the baseline (pre-randomization) period (N=20/site; 400 total) and in the 
second year after randomization (N=40/site, 800 total). Health center staff will assist with 
identification and recruitment of patients who are interested in participating in the surveys (from 
among patients providing sputum samples or receiving results). Participation will be voluntary. 
Research staff will then contact interested patients by phone at the end of their health center visit 
to review the verbal consent script, answer questions the patient may have, and administer the 
survey to consenting patients. 
Provider Survey: The provider survey will be administered during the baseline (pre-randomization) 
period and in the second year after randomization. All eligible providers at each study site (N=5-
10/site; 100-200 total) will be invited to participate in the survey. Participation will be voluntary 
and written informed consent will be obtained. 
Provider Focus Group Discussions and Interviews:  Focus group discussions will occur during the 
second year after randomization and semi-structured interviews after the trial is completed. All 
eligible providers at intervention sites (N=5-10/site; 50-100 total) will be invited to participate in 
focus group discussions. Providers (N=20-30) at low- and high-performing sites will be invited to 
participate in semi-structured interviews. Participation in focus group discussions and interviews 
will be voluntary and written informed consent will be obtained. 
Intervention and Control (Wait-list) Arms 
With the current standard-of-care (i.e., control arm), patients make up to 4 health center visits to 
complete TB diagnostic evaluation (Figure 2). At the first visit, sputum is collected for smear 
microscopy. All patients are required to return for a second visit; patients are initiated on TB 
treatment if the first sputum is smear-positive or are asked to provide a second sputum specimen 
and return for a third visit to collect results if the first sputum is smear-negative. Patients who 
return for a third visit are initiated on TB treatment if the second sputum is smear-positive or are 
asked to provide a third sputum specimen that is sent out for Xpert testing. Patients are asked to 
return for a fourth visit to collect Xpert results. Patients who return for a fourth visit are initiated on 
TB treatment if Xpert results are positive (or referred to an MDR TB treatment center if Xpert 
testing indicates rifampin resistance). Of note, HIV-infected patients have sputum collected for 
Xpert testing at their first visit and all patients could be started on TB treatment empirically at any 
visit.  
The intervention strategy seeks to facilitate diagnosis and treatment of TB at the initial health 
center visit (or referral of patients to MDR TB treatment center if rifampin resistance is detected 
by Xpert). As further described below, it includes the following components: 1) onsite molecular 

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testing as a replacement for microscopy; 2) process re-design; and 3) feedback of TB diagnostic 
evaluation quality indicators to health center staff.  
Figure 2. Comparison of TB diagnostic evaluation in the intervention and control arms 
 
*Consider empiric treatment or additional testing if test results are negative or invalid 
On-Site Molecular Testing 
GeneXpert Omni (Cepheid, Sunnyvale, CA) is a point-of-care molecular diagnostic system 
designed for lower level health facilities. It is small (1.0 kg weight), portable, and consumes less 
power than previous GeneXpert systems. The standard configuration includes a single test 
cartridge module, a mobile device, a country-specific AC/DC power cord, a supplemental battery, 
and a user guide. The rechargeable battery allows for up to 4 hours of operation with voltage 
surge protection for unexpected power supply interruptions. Additionally, a supplemental battery 
can be charged for up to 12 hours of use in the event of sustained power loss. Data is stored in 
real-time via cloud-based connectivity to Cepheid Cloud Control using Wi-Fi or cellular networks. 
Training materials will be provided by Cepheid, with an anticipated training time of one half-day. 
Additional device-integrated videos and self-instructions are available for training reinforcement.19  
 
Standard-of-Care (CONTROL ARM) 
 INTERVENTION ARM 
 
All patients 
Patients with HIV 
All patients 
VISIT 1 
 
 
 
 
 
 
VISIT 2 
 
 
 
 
 
 
VISIT 3 
 
 
 
VISIT 4 
 
 
 
 
 
Treat if Xpert positive* 
Send sputum for Xpert 
testing 
Treat if Xpert positive* 
Perform Xpert testing 
Collect sputum 
Collect sputum 
Prepare/examine smear* 
Smear-positive: Treat 
Treat if Xpert positive* 
Smear-negative:   
Collect sputum and 
 send for Xpert testing*  
Smear-positive: Treat 
Smear-negative:   
Collect sputum and 
prepare/examine smear* 

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Due to delays in release of GeneXpert Omni, the trial will begin with intervention sites using a 
modified conventional GeneXpert instrument (GeneXpert I). GeneXpert I is a one-module device 
with a specially-fitted dust cover that can be powered using a UPS/battery back and can be 
operated by a touchscreen tablet instead of a laptop computer. Sites randomized to the 
intervention arm will be given a solar panel charger to charge the UPS/battery pack. Once 
GeneXpert Omni is available, it will replace the GeneXpert I instrument. After the trial is 
completed, a GeneXpert Omni or GeneXpert I device will be provided to control health centers 
and health center staff will be trained on the use and maintenance of the device. 
Of note, Xpert testing in both the control and intervention arms will occur using Xpert MTB/RIF 
Ultra, which is already being scaled-up in Uganda.  
Process Redesign for Same-Day TB Diagnosis and Treatment 
Research and Uganda NTLP staff will engage health center staff in a discussion of how to re-
organize clinical, laboratory and pharmacy services to enable same-day TB diagnosis and 
treatment. Health center staff will be asked to identify site-specific solutions to: 
1. Rapid screening for TB at registration desk and/or waiting area 
2. Immediate referral of patients identified during screening as needing further testing to 
the lab for sputum collection 
3. On-demand testing of sputum specimens and immediate reporting of results to 
clinicians and patients 
4. Availability of TB pharmacy services throughout the clinic day 
Agreed upon changes to existing procedures and staff responsibilities will be recorded and 
reviewed with the health center in-charge, lab director and TB pharmacist. 
Performance Feedback 
Performance feedback is a strategy employing regular monitoring and feedback to allow health 
care workers to critically analyze performance and identify areas for improvement. A systematic 
review of audit and feedback interventions identified 5 factors associated with greater impact: low 
baseline performance, feedback coming from a supervisor or colleague, feedback provided 
multiple times, feedback delivered in both verbal and written form, and feedback including explicit 
targets and an action plan.26 Thus, performance feedback will involve delivery of a monthly 
Report Card which displays 1) TB diagnostic evaluation quality indicators for the current month 
and for the previous 6 months; 2) performance data averaged across all intervention health 
centers; and 3) performance data for the top- performing intervention health center. The Report 
Card will include a section that asks staff to write down their interpretation of the performance 
data, identify barriers to performance improvement, and specify plans to improve performance. 
The Report Card will be introduced to health center staff at the post-randomization site visit and 
will then be sent electronically every month to the health center in- charge or TB focal person. 
Health workers will be asked to review the Report Card at monthly staff meetings to devise a 
performance improvement plan. This process will continue monthly, with each new Report Card 
being used to evaluate the success of plans developed in the previous month and determine the 

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need for new actions. Report Cards will be collected by study staff at quarterly site visits to assess 
use and completion.  
Randomization 
Health centers will be grouped into two strata using baseline data for the primary outcome (see 
Outcomes below), and randomized within strata to help reduce between-cluster variation and 
improve balance between arms at baseline.26 Restriction, a common approach in cluster-
randomized trials with a small number of clusters, may be used to help achieve baseline balance 
of important site- and patient-level covariates.27 We will consider restricting on factors likely to be 
associated with the primary outcome including health center region (four quadrants of Uganda), 
health center size (based on volume of patients tested for TB), HIV prevalence among TB 
patients, and patient cost and satisfaction with care. The restriction factor will be calculated and 
the validity of the restriction assessed using the validity matrix.28 The Trial Statistician (KF) will be 
responsible for the randomization. Randomization will be unveiled to health center directors (or 
their designated representative) at a meeting chaired by the Uganda NTLP Director.  
Procedures 
The following procedures will occur at site visits to each participating health center. Site visits 
will occur approximately every 2-4 months throughout the study. 
Health center training 
Training will occur at the first site visit after health center enrollment and after health center 
randomization. 
Following enrollment, we will conduct TB guideline and registry training at the first pre-
randomization site visit. Project staff, an NTLP/NTRL representative and the District TB Officer 
will conduct the training jointly using a standardized slide set that emphasizes Uganda NTLP 
guidelines/algorithms for TB diagnosis and treatment. In addition, project staff will assess the 
completeness of data recording in TB registers, and provide training as needed to improve 
completeness. Last, project staff will visit all GeneXpert testing hubs that receive samples from 
project sites, and provide refresher training on GeneXpert device maintenance and operation. 
Following randomization, we will conduct a 2-day site visit to each intervention and control health 
center.  
At intervention health centers, we will engage staff in a discussion of the site-specific laboratory, 
clinical, and pharmacy workflow reorientation required as part of the intervention TB diagnostic 
evaluation strategy, with an emphasis on prompt identification of patients suspected of TB, 
immediate referral to the laboratory for sputum collection, on-demand molecular testing of 
sputum, communication of results to clinicians, and same-day treatment initiation. A GeneXpert I 
device and solar panel will be installed (with both replaced by GeneXpert Omni once it becomes 
available in 2019), and laboratory staff will be trained on sputum collection, sputum testing using 
GeneXpert, recording of results, and device maintenance. Laboratory and clinical staff will be 
trained on interpretation of results. In addition, all staff will be introduced to performance feedback 
Report Cards and expectations for reviewing and acting on the Report Cards. A staff member will 

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be identified to record any change made in the “Comments” section of the Report Card. Lab and 
drug inventories will be reviewed to ensure there is an adequate supply of sputum collection cups, 
glass slides, staining reagents and TB drugs. Completeness of TB laboratory and treatment 
registers will be assessed, and re-training provided as needed. 
At control health centers, we will re-emphasize messages related to TB guidelines presented 
during the initial pre-trial site visit, including guidelines and procedures related to referral of 
sputum samples for Xpert testing. Laboratory staff will receive refresher training on sputum 
collection and sputum smear microscopy, including proficiency testing using panel slides. Lab 
and drug inventories will be reviewed to ensure there is an adequate supply of sputum collection 
cups, glass slides, staining reagents and TB drugs. Completeness of TB laboratory and treatment 
registers will be assessed, and re-training provided as needed.  
Patient-level data collection  
Patient demographic and clinical (HIV and TB testing and treatment) data will be collected at all 
sites. At the first pre-trial site visit, project staff will photograph each page of the following data 
sources from January 2016 until the visit date: 1) NTLP Presumptive TB, Laboratory and 
Treatment registers and 2) Xpert laboratory requisition forms. Project staff will also train two health 
center staff (one primary, one backup) identified by the health center in-charge to take photos of 
these data sources every two weeks for the duration of the project (between October 1, 2018 and 
March 31, 2020) using a camera-enabled smartphone, and to upload the photos to a central 
secure server through REDCap mobile. Health center staff will be trained to delete photos from 
the phone after upload confirmation. In addition to these data sources, study staff will 1) Review 
pre-ART and ART registers during scheduled site visits to verify ART status for any HIV-positive 
patient missing ART information; 2) review additional data sources (e.g., clinic registration 
logbook) as needed at scheduled site visits to attempt to track down key information missing from 
primary data sources; and 3) call MDR TB treatment centers to determine referral, additional 
testing and treatment status and outcomes for patients with RIF resistance identified through 
Xpert testing. 
Symptom Screening 
A questionnaire adapted from the routine TB case finding guide will be administered to document 
TB symptoms reported at each patient’s initial visit to the health clinic. This information will be 
used to assess the proportion of patients referred for TB testing who were eligible for testing 
based on reported symptoms. Surveys will be administered by phone two weeks after the patient’s 
initial clinic visit to avoid influencing the primary outcome. Surveys will also be administered in-
person to patients who provide a sputum sample for TB testing. In-person surveys will take place 
during quarterly site visits. All patients who are at least 18 years old, meet the main study inclusion 
criteria, and enrolled over a specified three-month period will be eligible for participation in the 
symptom screening activity. Due to logistical feasibility, however, it is anticipated that only patients 
with a documented valid phone number and patients selected for in-person interviews during 
quarterly site visits will be included in this activity. Study staff will prioritize clinics with a smaller 
proportion of patients with phone numbers available for in-person interviews.  

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Patient surveys 
We will administer cost and satisfaction with care surveys to 20 patients at each health center 
(N=400 total) in the pre-randomization period and to 40 patients at each health center (N=800) at 
least 6 months post-randomization (April-September 2019). The two surveys will involve the same 
patients when feasible, but administered to different patients if a patient does not wish to complete 
both. 
The cost survey will collect data on direct and indirect costs (time to complete visit, lost wages, 
etc.) of TB diagnostic evaluation. Cost data collection will be based on the Tool to Estimate Patient 
Costs developed by the TB Coalition for Technical Assistance, which we have already adapted 
and used in Uganda. 
The satisfaction with care survey will assess 18 items taken from the previously validated Patient 
Satisfaction Questionnaire (PSQ)29 and adapted to the Ugandan context.30 The items are 
constructed with a five-point Likert scale with categories ranging from “strongly disagree” to 
“strongly agree”, and include both positively and negatively worded questions to minimize the 
potential bias that occurs from clustering of responses to one side of the scale. The questionnaire 
measures general satisfaction as well as four dimensions of care known to impact satisfaction: 1) 
Accessibility, availability, and convenience of health services (3 items); 2) Provider interpersonal 
skills (5 items); 3) Provider technical competence with respect to patient education, examination 
and counseling (4 items); and 4) Health facility environment, specifically with respect to the 
cleanliness and space in the waiting area (2 items). 
Provider surveys 
We will administer a survey to all health workers involved in providing TB diagnostic and/or 
treatment services at each study site in the pre-randomization period and again at quarterly site 
visits beginning at visits that occur at least 12 months after randomization (October 2019 – March 
2020). The survey will collect data on four key constructs of the Theory of Planned Behavior (TPB) 
as related to adherence to TB evaluation guidelines (Figure 1): intention, beliefs/attitude, 
normative beliefs/subjective norms and self-efficacy/perceived behavioral control. Based on prior 
experience, we anticipate 5-10 health workers will be involved in TB diagnostic/treatment services 
at each health center (N=100-200 health workers total pre- and post-randomization). 
Health system costing 
Health system cost data will be collected from all 20 sites at least 12 months after randomization 
(October 2019 – March 2020). Cost data will be collected through detailed budgetary analysis, 
interviews of key staff members, review of logbooks and/or timesheets to record proportions of 
staff time devoted to various activities, and direct observation (e.g., time-motion studies of at least 
10 patients and 3 staff members per clinic). 
Focus group discussions 
Study staff will conduct focus group discussions (one at each intervention health center) with all 
eligible providers at least 12 months after randomization. The focus group discussions will assess 
fidelity and barriers to uptake of each intervention component. Focus group discussions will be 
conducted by trained and experienced staff, audio-recorded, and professionally transcribed. 

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Assessment of patient’s vital status, diagnostic and treatment status, HIV status, and 
ART status  
Study staff will call all eligible patients included in the study 6 months after their initial health 
center visit to assess vital status, diagnostic and treatment status, HIV status, and ART status ( 
if the patient is HIV-infected). Staff will call phone numbers recorded in the lab register, Xpert 
referral form and/or TB treatment register. For participants who cannot be reached, staff will 
make up to two additional phone calls on successive days. If the participant or a family member 
cannot be reached by telephone, study staff will work with a community health worker from the 
health center at which the participant underwent TB testing to conduct a home visit (using the 
address recorded in the lab register, Xpert referral form and/or TB treatment register). In 
addition to tracing efforts, staff will review TB treatment register data to ascertain vital status for 
participants who cannot be reached by phone but are known to have initiated TB treatment.  
Provider interviews 
Study staff will conduct in depth semi-structured interviews with 20-30 (2-3 per site) eligible 
providers to understand reasons for variability in uptake and effectiveness of the intervention 
strategy after completion of the trial. Health workers will be sampled purposively (i.e., from low- 
and high-performing sites). Interviews will be conducted by trained and experienced staff, audio-
recorded, and professionally transcribed. 
 
Figure 3. Site visit activities 
 
 
 
 
 
2017 
2018 
Aug – Sep 2018 
Oct 2018 – Mar 2020 Apr 2020 – Oct 2020 
* At intervention health centers, GeneXpert I will be replaced with GeneXpert Omni once Omni is 
available at a regularly scheduled post-randomization visit. Lab staff will receive training on use of 
GeneXpert Omni and the solar panel used to power the GeneXpert I instrument will be removed. 
 
Post-Enrollment  
Visit (1x) 
Training:  
• TB guidelines & 
registry 
completion 
• Photograph data 
sources 
• Xpert hub 
refresher 
training 
 
Data collection: 
• Patient-level 
data (Jan 2016-
visit date) 
 
Pre-Randomization 
Visits (every 2-3 
months) 
 
Surveys: 
• Patient cost & 
satisfaction  
• Provider  
Data collection:  
• Review pre-ART 
& ART registers, 
other data 
sources for 
missing data 
• Contact MDR 
facilities 
 
1st Post-
Randomization 
Visit 
Training: 
• Intervention 
sites: Workflow 
staff discussion, 
GeneXpert I  
installation & 
training, Report 
Card introduced 
• Control sites: 
TB guidelines & 
registry refresher 
training 
 
Subsequent Post-
Randomization Visits 
(every 2-3 months)* 
Surveys: 
• Patient satisfaction 
• Patient cost  
• Provider survey 
• Vital status 
assessment  
• Symptom screening 
(3 months only) 
 
Data collection: 
• Health systems 
costing  
• Resolve missing 
patient-level data 
queries 
 
Post-Trial 
Visits 
• Provider semi-
structured 
interviews 
• Vital status 
assessment 
 

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Outcomes 
Definitions 
• 
Number enrolled: Number of eligible patients identified over a defined enrolment period 
through review of the NTLP Presumptive TB register, NTLP Laboratory register, 
GeneXpert laboratory requisition forms, GxAlert database and NTLP Treatment register 
at each study site (includes patients treated for TB without undergoing any sputum 
testing). 
• 
Date enrolled: Earliest date recorded in the NTLP Presumptive TB register, NTLP 
Laboratory register, GeneXpert laboratory requisition form, GxAlert database, or NTLP 
Treatment register. Records will be prospectively reviewed using name, sex, and age to 
identify patients presenting multiple times over the study period. Visits occurring within six 
months of the initial presentation will be considered as part of the same episode. 
• 
Number referred for testing: Number of eligible patients identified through review of the 
NTLP Presumptive TB register, NTLP Laboratory register, GeneXpert laboratory 
requisition forms, and GxAlert database at each study site. 
• 
Number tested: Number of eligible patients with any smear or Xpert result entered into 
the NTLP Presumptive TB register, NTLP Laboratory register, GeneXpert laboratory 
requisition forms, NTLP treatment register, or GxAlert database at each study site. 
• 
Number completing testing: Number of eligible patients with one valid Xpert result plus 
number of eligible patients with a valid smear result entered into the NTLP Presumptive 
TB register, NTLP Laboratory register, GeneXpert laboratory requisition forms, GxAlert 
database, or NTLP treatment register. Definitions of valid results are as follows:  
o A positive Xpert result with a semi-quantitative result of high, medium, low, or very 
low for all patients; a positive Xpert Ultra result with a semi-quantitative result of 
trace for HIV-positive patients; or second positive Xpert Ultra result if the initial 
result is trace-positive for HIV-negative patients; 
o A negative Xpert or Xpert Ultra result for all patients;31 
o One positive or two negative smear results for HIV-negative/HIV status unknown 
patients. 
 
• 
Number diagnosed: Number of eligible patients with microbiologically-confirmed TB via 
a positive smear and/or Xpert test result entered into the NTLP Presumptive TB register, 
NTLP Laboratory register, GeneXpert laboratory requisition forms, NTLP treatment 
register, and/or GxAlert database within 6 months of date enrolled. 
• 
Number with suspected RIF resistant TB: Number of eligible patients with RIF 
resistance identified by Xpert testing entered into the GxAlert database, GeneXpert 
laboratory requisition forms, and/or NTLP Laboratory registers within 6 months of date 
enrolled. 
• 
Number with confirmed RIF resistant TB: Number of eligible patients with RIF 
resistance identified by Xpert testing entered into the GxAlert database, GeneXpert 
laboratory requisition forms, and/or NTLP Laboratory registers, and RIF resistance 
confirmed by culture-based DST or a second molecular assay as determined by review of 
Lab Register at MDR treatment center within 6 months of date enrolled. 

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• 
Number treated: Number of eligible patients entered into the NTLP Treatment register as 
having started Category I or II regimen, or started on MDR treatment at the MDR treatment 
center as determined by review of Treatment Register at MDR treatment center within 6 
months of date enrolled. 
• 
Number completing treatment: Number treated and with a treatment outcome of cured 
or completed entered into the NTLP Treatment register or indicated by MDR treatment 
center staff through direct follow up. 
• 
Number died: Number treated and with a treatment outcome of died entered into the 
NTLP Treatment register within 6 months of date enrolled, or number eligible with outcome 
entered as died on direct follow-up form. 
• 
Time-to-diagnosis: Number of days from date enrolled to earliest date of positive smear 
or Xpert result recorded in NTLP Laboratory register, GeneXpert laboratory requisition 
form, NTLP treatment register, or GxAlert database within 6 months of date enrolled. 
• 
Time-to-treatment: Number of days from date enrolled to treatment start date entered 
into NTLP Treatment register if treatment start date is within 6 months of date enrolled.  
I. Primary Outcome (Effectiveness)  
Outcome 
Numerator 
Denominator 
Number treated for 
microbiologically-confirmed TB 
within two weeks of referral for 
sputum-based testing 
Number diagnosed and time-to-
treatment within 14 days  
None  
 
II. Secondary Outcomes (Effectiveness)  
 
A. Testing 
Outcome 
 
Numerator 
Denominator 
Number referred for TB 
testing 
 
Number referred for 
testing 
None 
Proportion completing 
testing 
 
Number completing 
testing 
Number referred for 
testing 
 
B. Diagnosis 
Outcome 
 
Numerator 
Denominator 
Number diagnosed with 
microbiologically-
confirmed TB* 
 
Number diagnosed 
None 
Proportion diagnosed 
with microbiologically-
confirmed TB* 
 
Number diagnosed 
Number referred for 
testing 
Number 
suspected/diagnosed 
with RIF-resistant TB* 
 
Number with 
suspected/confirmed 
RIF resistant TB 
None 
Proportion 
suspected/diagnosed 
with RIF-resistant TB* 
 
Number with 
suspected/confirmed 
RIF resistant TB 
Number referred for 
testing 

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Time to 
microbiologically-
confirmed TB 
 
Time-to-diagnosis if 
microbiologically-
confirmed TB 
None 
 
C. Treatment 
Outcome 
 
Numerator 
Denominator 
Number treated for TB* 
 
Number treated 
None 
Proportion treated for 
TB* 
 
Number treated 
Number enrolled 
Number treated for 
microbiologically-
confirmed TB* 
 
Number diagnosed AND 
treated 
None 
Proportion treated for 
microbiologically-
confirmed TB* 
 
Number diagnosed AND 
treated 
Number referred for 
testing 
Proportion with 
microbiologically-
confirmed TB treated* 
 
Number diagnosed AND 
treated 
Number diagnosed 
Time-to-treatment of 
microbiologically-
confirmed TB 
 
Time-to-treatment if 
microbiologically-
confirmed TB and 
treated 
None 
 
D. Follow-up  
Outcome 
 
Numerator 
Denominator 
Number with 
microbiologically-
confirmed TB 
completing treatment 
 
Number diagnosed AND 
completing treatment 
None 
Proportion with 
microbiologically-
confirmed TB 
completing treatment 
 
Number diagnosed AND 
completing treatment 
Number diagnosed 
AND treated 
Number who died within 
6 months  
 
Number died 
None 
Proportion who died 
within 6 months** 
 
Number died 
Number enrolled 
* Outcome will be assessed within 1 day and within 14 days of initial sputum submission. One 
day was chosen because the intervention focuses on same-day diagnosis and treatment. 14 days 
was chosen because the diagnostic process could take 7-10 days in the control arm depending 
on the frequency of sample transport to Xpert testing sites. 
**Treatment outcomes will be assessed at 7, 9, 12, and 24-month post-treatment initiation 
intervals determined by drug regimen. 
III. Implementation Outcomes 
Comparison across arms 
Outcome 
Definition 
Patient costs 
 
a) Total costs 
a) Sum of all patient-reported costs on cost 
questionnaire 

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b) Total direct costs 
b) Total costs paid by the patient (i.e., excluding 
lost wages or other lost opportunities to earn 
income) 
c) Total indirect costs 
c) Patient-reported lost wages or other lost 
opportunities to earn income 
Patient satisfaction with care survey: 
 
a) General satisfaction with care 
a) Sum of score on 3 question items (Q16, 17 
and 18) 
b) Convenience of services 
b) Sum of score on 3 question items (Q1, 2 and 
3) 
c) Health facility environment 
c) Sum of score on 2 question items (Q4 and 5) 
d) Provider interpersonal skills 
d) Sum of score on 5 question items (Q7-11) 
e) Provider technical competence 
e) Sum of score on 4 question items (Q12-15) 
Provider survey: 
 
a) Intention 
a) Score on 1 question item (Q4) 
b) Attitudes/beliefs 
b) Sum of score on 3 question items (Q5a, 6a, 
and 6b) 
c) Social norms/expectations 
c) Sum of score on 2 question items (Q5b, 6c 
and 6d) 
d) Self-efficacy/behavioral control 
d) Sum of score on 5 question items (Q5c, 6e 
and 6f) 
 
Intervention arm only 
Outcome 
Numerator 
Denominator 
Process metrics: 
 
 
a) GeneXpert device non-
operation days 
a) GeneXpert device non-
operation days1 
a) Total number of lab 
operation days1 
b) Proportion tested by 
GeneXpert: 
b) Number with GeneXpert 
result date  
b) Number tested by 
GeneXpert 2 
1. On same day as 
initial sputum 
submission 
1. Same as initial sputum 
submission date2 
 
2. By next day after 
initial sputum 
submission 
2. By next day after initial 
sputum submission 
date2 
 
c) Proportion with 
indeterminate results 
c) Number with coded error, 
invalid or no test result2 
c) Number of initial GeneXpert 
tests done2 
d) Proportion with coded 
error result 
d) Number with coded error 
result2 
d) Number of initial GeneXpert 
tests done2 
e) Proportion with invalid 
result 
e) Number with invalid result2 
e) Number of initial GeneXpert 
tests done2 
f) 
Proportion with “no test” 
result 
g) Proportion with 
GeneXpert test repeated 
if initial result 
indeterminate 
f) 
Number with “no test” result2 
 
g) Number with second 
GeneXpert result2 
 
h) # treated on same day as 
initial health center visit3 
f) 
Number of initial GeneXpert 
tests done2 
g) Number with initial 
indeterminate GeneXpert 
result2 
 
h) # Xpert-positive2 
 

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h) Proportion treated on 
same-day if Xpert-
positive 
i) 
Proportion of report 
cards reviewed at staff 
meetings 
i) 
Number of Report Cards 
with Comments section 
filled4 
i) 
Total number of report 
cards issues to health 
centers3 
Variation in and barriers to 
intervention strategy uptake 
Themes emerging from focus group discussions, semi-structured 
interviews with health center staff 
Source of data: 1 Health center laboratory log; 2 GeneXpert Software; 3 Treatment register; 4 Performance 
feedback report card 
IV. Cost-effectiveness/Modeling Outcomes 
Outcome 
Definition 
Source 
Incremental cost per DALY 
averted 
(Cost in intervention – cost in 
control)/(DALYs averted by 
intervention – DALYs averted in 
control) from societal 
perspective 
Markov-based decision models 
Incremental health system cost 
per DALY averted 
As above, but from health 
system/provider perspective 
Incremental patient cost per 
DALY averted 
As above, but from patient 
perspective 
Projected reduction in TB 
incidence over 10 years 
(Projected incidence in control 
model – projected incidence in 
intervention model)/(Projected 
incidence in control model), 
cumulative over 10 years 
Transmission models 
Projected reduction in TB 
mortality over 10 years 
(Projected mortality in control 
model – projected mortality in 
intervention model)/(Projected 
mortality in control model), 
cumulative over 10 years 
 
Data Management 
Dr. Fielding (statistician) will oversee data management in conjunction with UCSF- and Uganda-
based study coordinators using the NIH-recommended Research Electronic Data Capture 
(REDCap) software, password-protected and accessible only to research staff. All data collection 
forms will be entered into standardized REDCap forms, with validation of data using range and 
consistency checks. Quality control procedures will include review of all study data collection 
forms for completeness and accuracy prior to data capture. Study staff will begin data capture by 
compiling photos from each individual health center for each data collection period. Using patient 
name, age, and sex, the staff will create one unique REDCap database entry (i.e. record) per 
patient by tracking patients across data sources in the following order: 1) Presumptive TB 
Register; 2) Laboratory Register; 3) Xpert Referral form; and 4) Treatment Register. If the patient 
cannot be located in a subsequent data source, the staff will check the next, until all data sources 
are consulted. If a patient does not meet inclusion criteria for analysis due to examination type 
(outreach, transferred in, follow up, or other), the staff will not collect data beyond the patient’s 

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name, sex, age, and exam type from the Laboratory Register. Once patients have been matched 
across all data sources, the staff will run a series of reports in REDCap to verify the completeness 
and accuracy of key variables impacting patient eligibility and/or study outcomes: age, sex, HIV 
status, examination type, smear and Xpert test results, outcomes dates (sputum collection, test 
results, start and end of treatment, treatment outcome), and treatment status. Study staff will 
review the original data photographs to verify the information is missing, and they will compile a 
list of follow up items for each health center. Research staff will phone health center staff after 
each 2-week period of data is extracted to resolve missing information and clarify any 
discrepancies or uncertainties in matching. Once the health center reviews the missing and/or 
inaccurate data, the study staff will update REDCap. The UCSF study coordinator will visit 
Uganda 2-3 times a year and review a random sample of forms and primary data sources for 
quality assurance. 
  
Statistical Analysis 
Aim 1  
A detailed Statistical Analysis Plan will be developed. Briefly, we will calculate and compare all 
effectiveness outcomes for the two trial arms using an intention-to-treat analysis. We will use 
descriptive statistics and 95% confidence intervals to summarize the yield, efficiency, and speed 
outcomes for the two arms by site. To assess the intervention effect on outcomes, we will use 
methods appropriate for randomization of a small number of clusters and accounting for the 
stratified design.26 A cluster-level analysis, giving each cluster equal weight, will be conducted to 
calculate unadjusted ratio and difference effect measures, and their associated 95% confidence 
intervals. Adjusted effect estimates will also be calculated taking into account any imbalance of 
important factors at baseline by study arm. Pre-specified subgroup analyses (e.g., gender and 
HIV status) will be conducted for the primary outcome. A detailed statistical analysis plan will 
document methods used for the analysis of primary and secondary outcomes, and document pre-
specified sub-group analyses. 
Aim 2  
1) Descriptive analyses – We will report process metrics on a monthly basis to assess adoption 
and maintenance of each intervention component overall, within key patient sub-groups, and at 
individual sites. We will report median and change between pre- and post-randomization 
assessments in A) patient costs; B) patient satisfaction with care; and C) provider TPB construct 
scores at control and intervention sites. 
2) Comparative analyses – To identify patient-, provider-, and/or clinic-level factors independently 
associated with adoption and maintenance of intervention components, we will develop linear or 
logistic regression models, taking into account the clustered design (for example, robust standard 
errors). To compare by study arm the change from baseline (pre-randomization) to post-
intervention in A) patient cost; B) patient satisfaction with care; and C) TPB construct scores, we 
will use a cluster-level analysis, similar to methods described for the primary outcome under Aim 
1. The analysis will take into account the stratified design and an adjusted analysis will also be 
conducted taking into account other baseline imbalances by study arm. 

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3) Qualitative analyses – De-identified focus group or interview transcripts will be uploaded to the 
qualitative data analysis software Dedoose (SocioCultural Research Consultants, USA). 
Thematic interpretation32-34 will include collaborative development of a coding framework and 
detailed coding of transcripts using Dedoose. Coded transcripts will be sorted to identify thematic 
groupings. The thematic groupings will be reviewed to identify emergent themes within each 
domain of the coding framework and quotes that best represent each domain. Thematic 
interpretation will focus on individual, social and structural factors associated with successful or 
unsuccessful adoption and/or maintenance of the intervention components at different sites. 
Aim 3  
Cost-effectiveness -- Our primary outcome will be the incremental cost-effectiveness of the 
intervention strategy from a societal perspective, measured as the cost per disability-adjusted life 
year (DALY) averted relative to standard TB evaluation. Secondary outcomes will include 1) the 
incremental cost of introducing and maintaining the intervention strategy (a measure of 
affordability for health systems) and 2) the incremental patient cost per diagnostic evaluation and 
per treatment initiated (a measure of affordability and access for patients). For all outcomes, we 
will report stratified results for key populations including women and people living with HIV. To 
assess cost-effectiveness, we will use the primary effectiveness data from Aim 1 and relevant 
literature estimates (e.g., clinical outcomes among those initiating treatment) to construct a 
Markov model including states for undiagnosed TB but seeking care, undiagnosed TB not seeking 
care (e.g., because initial diagnostic evaluation was too expensive), treated TB, self-resolved TB, 
and death. States will be subdivided according to smear and Xpert status (smear-positive, smear-
negative/Xpert-positive, and smear/Xpert-negative) and HIV status (positive and negative, on/off 
ART, with CD4 strata).  Data to inform transition probabilities and health utilities will come from 
study data where feasible (including specific questions of patients to ascertain probabilities such 
as future care-seeking if diagnoses are missed), and the literature where unavailable. We will 
follow international conventions for all procedures including economic costing, discounting, and 
reporting. We will conduct one-way sensitivity analyses across all model parameters, multi-way 
sensitivity analyses for those parameters found to be most influential, and a probabilistic 
uncertainty analysis in which all parameters are varied simultaneously using Latin Hypercube 
Sampling. 
Population-Level Epidemiological Impact – We will construct a compartmental epidemic model of 
TB in Uganda to evaluate the potential impact of scaling up the intervention strategy across a 
representative district in Uganda. Using our team’s prior models of TB diagnostics in India35 and 
sub-Saharan Africa as a starting point, we will construct a population model that includes structure 
both for TB natural history (e.g., latent, subclinical, pre-diagnostic, diagnosis-seeking, treated)36 
and steps in the diagnostic cascade (e.g., pursuing diagnosis, diagnosed but not treated, 
treated).35, 37 We will include structure for both HIV and MDR TB, and will link this model’s structure 
to that of the Markov model constructed for cost-effectiveness analysis above (for purposes of 
explicitly estimating the importance of transmission to considerations of cost-effectiveness).  We 
will fit the model to epidemiological data from a selected representative district in Uganda 
according to its TB incidence, prevalence, and mortality, as well as additional factors including 
prevalence of HIV and of MDR-TB. This model will project TB incidence and mortality over a 
primary time-frame of 10 years under two alternative scenarios: 1) standard TB diagnostic 

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evaluation and 2) streamlined TB diagnostic evaluation using the intervention strategy. In a 
secondary analysis, we will incorporate economic data as described above, comparing cost-
effectiveness measured under a dynamic (epidemic-economic) framework to that of the Markov 
model. The dynamic transmission model has the advantage of incorporating transmission 
dynamics at the population level, but requires more assumptions (e.g., homogeneous mixing in 
the source population). By comparing results from Markov and transmission modeling, we will be 
able to assess: 1) the relative contribution of population-level transmission to the overall 
effectiveness and cost-effectiveness of different TB diagnostic evaluation strategies over time and 
2) the relative influence of given model parameters on cost- effectiveness under a cohort-based 
versus transmission-based evaluation model. 
 
Sample Size Considerations 
Aim 1 
Original detectable effect size estimates: The study is based on the health clinic being the unit of 
randomization and aims to demonstrate the superiority of the intervention arm. The sample size 
calculation uses formulae appropriate for cluster-randomized trials with a parallel design and 
stratified and/or restricted randomization, including the addition of one extra cluster per arm to 
allow for the loss of degrees of freedom due to stratification to obtain conservative estimates.26 
The original detectable effect size estimates were based on the outcome being the proportion of 
patients treated for microbiologically-confirmed TB within two weeks of referral for sputum-based 
testing. A type I error of 5% and power of 90% was assumed. Pre-randomization data collected 
from January to December 2017 from the 20 selected trial sites in Uganda suggests the average 
proportion of patients referred for TB evaluation who initiate treatment for active TB within two 
weeks is 6.7%, and the coefficient of variation (k) between clusters is 0.36, with a harmonic mean 
of 268 patients enrolled at each health center cluster during the 18-month enrolment period. 
Based on these assumptions and assuming a type I error of 5%, we will have 90% power to detect 
a 6% or greater absolute increase in the outcome proportion in the intervention arm with a trial 
duration of 18 months (see Table). Table 1 also shows detectable absolute effect sizes with 
varying power (80%, 85%, 90%) during various trial time intervals and respective average cluster 
size estimates.   
Average cluster size 
Effect size (10 clusters per arm, k=0.36) 
80% Power 
85% Power 
90% Power 
178 (12 months) 
5.9% 
6.5% 
7.4% 
223 (15 months) 
5.7% 
6.3% 
7.1% 
268 (18 months) 
5.5% 
6.1% 
6.9% 
312 (21 months) 
5.4% 
6.0% 
6.7% 
357 (24 months) 
5.3% 
5.9% 
6.6% 
 
Revised detectable effect size estimates: The revised detectable effect size estimates are for the 
outcome being the number of patients treated for microbiologically-confirmed TB within two weeks 
of referral for sputum-based testing. We first used 10 months of pre-intervention period data 
across all 20 clinics to assess 1) the geometric mean number of patients diagnosed and treated 

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for TB within 14 days (new primary outcome), the natural log (ln) of the geometric mean and 3) 
the standard deviation of the natural log at control sites, intervention sites and overall.. For the 
pre-intervention period, the intervention clinics have a lower geometric mean number of clinic 
attendees who were diagnosed with TB and started on TB treatment within 14 days compared 
with the control arm: 
 
Geometric mean 
Mean of ln outcome 
SD of ln outcome 
Control 
14.70 
2.69 
0.23 
Intervention 
6.49 
1.87 
0.32 
 
We then estimated the detectable effect size (expressed as a geometric mean ratio). The 
calculations assume the same parameters as for the original primary outcome (10 clinics/arm, 18-
month trial duration to achieve geometric mean of 286 patients/cluster in control and intervention 
arms). Table 1 below shows the detectable effect size assuming within-arm SD of ln outcome of 
0.2 to 0.3 and power of 80-90%. The detectable effect sizes shown below are conservative 
(calculations do not take into account the baseline differences between arms in the new primary 
outcome and are based on 10-months rather than full 24-months of pre-intervention data). Final 
calculations will be repeated once baseline data is fully entered and available for analysis. 
Table 3: Detectable effect sizes 
SD of ln outcome 
Geometric Mean Ratio of the new primary outcome  
(intervention vs control) 
80% power 
90% power 
0.2 
1.30 
1.36 
0.3 
1.49 
1.58 
 
We believe a GMR of 1.30-1.58 is a reasonable detectable effect size. Xpert MTB/RIF is twice as 
sensitive as smear microscopy (double the number of confirmed TB cases) and we expect onsite 
testing to reduce pre-treatment loss to follow-up by at least half (from 30% to <15%).  
 
Aim 2 
The sample size for Aim 2 analyses is either fixed (process metrics; provider surveys and focus 
groups) by parameters of the clinical trial or based on feasibility considerations (patient surveys 
and provider in depth interviews). For quantitative analyses, the sample size is sufficiently large 
(data on 3400 patients for process metrics analyses, 800 patient surveys, and 100-200 provider 
surveys) to enable multivariable analysis to identify factors associated with intervention adoption 
and maintenance. 
Aim 3 
As Aim 3 is primarily a modeling aim, sample size calculations are not applicable. Sample size 
considerations for the cost data (i.e., number of direct observations through time and motion 
studies, number of patients completing the cost questionnaires) are based on feasibility 
considerations and desire to estimate model parameters to sufficient levels of precision, as 
described above for Aim 2. 

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Ethical Considerations 
Potential risks to participants 
There are minimal risks to participants in this study. The primary risk to patients undergoing TB 
diagnostic evaluation during the study period is the potential for loss of confidentiality and stigma 
should their personal health information, including HIV or TB status, be disclosed. Patients 
participating in surveys also have the potential of sensitive information regarding their income 
being disclosed. Finally, the primary research risks for health workers related to the surveys and 
qualitative studies are punitive actions by the employer in response to the information they provide 
for research. 
Protection against risk 
The trial will be submitted for approval to the Research Ethics Committees of the University of 
California San Francisco and Makerere University College of Health Sciences, and to the Uganda 
National Council for Science and Technology. It is registered with the U.S. National Institutes of 
Health’s ClinicalTrials.gov and the Pan African Clinical Trials Registry (PACTR) as a Phase 4 
clinical trial. All study staff will be required to have completed Good Clinical Practice (GCP) 
training. 
To minimize the potential for loss of confidentiality, all patient-identifiable data will be stored in 
locked or password protected areas accessible only to study personnel. Patient names will be 
used to match patient records across NTLP Laboratory and Treatment registers, but will not be 
included in the password-protected, electronic study database. Primary data collection forms used 
when matching records across data sources will be destroyed once entry of data into the 
electronic study database is completed. 
To minimize risks to autonomy for patients and providers who participate in surveys, research 
staff will be carefully trained in how to administer the consent form to the individuals in the different 
target populations, with attention given to the background and principles of research ethics. 
The Directors of participating health centers will be asked to sign a Memorandum of 
Understanding agreeing that their health center participate in the trial and agreeing not to 
introduce any new TB evaluation interventions during the time period of the trial without informing 
trial staff. Individual patients evaluated for TB at participating health centers will not be consented 
because the trial meets the requirements to qualify for waiver of informed consent under U.S. 
Department of Health and Human Services (DHHS) Regulation 46.116 (d): 1) No data or samples 
will be collected specifically for research purposes; 2) Patients will receive the same or higher 
quality of care; 3) It is not practical for health workers to obtain informed consent during the 
process of delivering routine clinical care; and 4) All pertinent TB testing results will be 
communicated to patients via routine or enhanced processes of care. Verbal informed consent 
using a script will be obtained from patients who participate in surveys. Written informed consent 
will be obtained from providers who participate in surveys, direct observation (i.e., time-motion) 
studies and/or focus group discussions/interviews. The consent forms, which will be approved by 
institutional review boards (IRB) in the U.S. and Uganda, will be translated from English into the 

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local languages, and back-translated into English as required by IRBs to be sure that no significant 
language or concepts are lost in translation. 
Data protection 
All patient-identifiable data will be stored in locked or password-protected cabinets or databases 
accessible only to study personnel. Patient names will be used to match patient records across 
NTLP Laboratory and Treatment registers but will not be included in the password-protected, 
electronic study database. Primary data collection forms and images used when matching records 
across data sources will be destroyed once entry of data into the electronic study database is 
completed. 
Potential benefits of the study to participants and society 
Patients undergoing TB diagnostic evaluation at participating health centers may benefit from the 
study through potential enhanced diagnosis and treatment of TB, either because of training 
(control health centers) or training plus other interventions (intervention health centers). Earlier 
diagnosis and treatment of TB may lead to improved patient outcomes and reduced disease 
transmission in patients’ communities. 
Patients and providers who participate in surveys, in depth interviews or focus group discussions 
will receive sodas and/or lunch to compensate for their time. Otherwise, they will not directly 
benefit from participating in these research activities. 
Potential benefits to society include identification of strategies to improve TB case finding and to 
decrease TB incidence and prevalence in low-income, high-burden countries. If successful, the 
proposed intervention could potentially be scaled up to improve TB care in similar settings. 
Dissemination 
The trial results will be communicated to stakeholders through dissemination meetings and to 
participating health centers using language-appropriate information sheets. Investigators will 
present results at relevant conferences, and submit manuscript(s) to peer-reviewed journals. 
Public access to the participant-level dataset of main trial results and statistical code will be made 
available. 
Trial governance 
Because of the low-risk nature of the research, the Principal Investigator will be responsible for 
monitoring the data, assuring protocol compliance, and conducting safety reviews on a quarterly 
basis. An independent Trial Steering Committee (TSC) will meet approximately every 6 months 
and as needed. Prior to each meeting, the Principal Investigator will submit a progress report, 
including recommendations on whether the project should continue unchanged, require 
modification/amendment, or close to enrollment to an independent Trial Steering Committee 
(TSC). All major modifications (e.g., study design, sample size, study termination or suspension), 
will be approved by the TSC and ethics committees. 
 
 

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Abbreviations 
AFB 
 
 
 
Acid-Fast Bacilli 
ART 
 
 
 
Anti-retroviral therapy 
CFR 
 
 
 
Code of Federal Regulations  
CRT 
 
 
 
Cluster-randomized trial 
DALY  
 
 
Disability-adjusted life year 
DHHS  
 
 
Department of Health & Human Services 
DMC  
 
 
Data Monitoring Committee 
EQA 
 
 
 
External quality assurance 
FDA 
 
 
 
U.S. Food and Drug Administration 
GCP 
 
 
 
Good Clinical Practice 
ICH 
 
 
 
International Council for Harmonisation of Technical  
Requirements for Pharmaceuticals for Human Use 
IRB  
 
 
 
Institutional review board 
ISTC  
 
 
International Standards for TB Care 
KAP 
 
 
 
Knowledge, attitudes, and practices 
LED 
 
 
 
Light-emitting diode 
MDR-TB 
 
 
Multi-drug-resistant tuberculosis 
MOU  
 
 
Memorandum of Understanding 
MUREC 
 
 
Makerere University Research Ethics Committee 
NIH 
 
 
 
National Institutes of Health 
NHLBI  
 
 
National Heart, Lung, and Blood Institute 
NTLP  
 
 
National Tuberculosis and Leprosy Program 
NTP 
 
 
 
National TB Program 
NTRL  
 
 
National TB Reference Laboratory 
PRECEDE 
Predisposing, Reinforcing, and Enabling Constructs in 
Educational/Environmental Diagnosis and Evaluation 
PSQ 
Patient Satisfaction Questionnaire 
RE-AIM 
 
 
Reach Effectiveness Adoption Implementation Maintenance 
REDCap 
 
 
Research Electronic Data Capture 
TB 
 
 
 
Tuberculosis 
TPB 
 
 
 
Theory of Planned Behavior 
TSC 
 
 
 
Trial Steering Committee 
UCSF   
 
 
University of California San Francisco 
UCSF CHR  
 
 
UCSF Committee on Human Research 
UNCST  
 
 
Uganda National Council for Science and Technology 
WHO  
 
 
World Health Organization 
XPEL TB 
 
 
GeneXpert Performance Evaluation for Linkage to Tuberculosis 
Care 
ZN 
 
 
 
Ziehl–Neelsen 
 
 
 

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Competing interests 
The investigators declare that they have no competing interests. 
Trial Steering Committee 
Jerry Friedland (Yale University) 
Noah Kiwanuka (Makerere University) 
Madhukar Pai (McGill University) 
Andrew Ramsay (Division of Infection and Global Health, St Andrews University Medical 
School, Scotland) 
Grant Theron (Stellenbosch University) 
 
 

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Table of Amendments 
 
XPEL TB Trial Protocol 
 
Change 
Date of change 
Overview: We increased the sample size from 6500-7500 to 11,283   
September 11, 2019 
Overview: We reduced the study duration from 2 years to 18 months   
September 11, 2019 
Timeline: We added a timeline of activities   
September 11, 2019 
Symptom Screening: In response to the increase in patients referred for TB 
testing in the intervention arm, we documented TB symptoms reported at 
each patent’s initial visit to the health clinic for a quarter to assess the 
proportion of patients referred for TB testing who are eligible for testing 
based on reported symptoms      
September 11, 2019 
Outcomes: We updated the primary outcome from a proportion to a count   
September 11, 2019 
Sample Size Considerations: We added text on the revised detectable effect 
size estimates given the change in the primary outcome from a proportion to 
a count  
September 11, 2019 
Trial Steering Committee: We updated affiliations for one of the trial 
steering committee members  
September 11, 2019 
 
 
 

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XPEL study 
Statistical Analysis Plan (version 1.0) 
11 Oct 2019 
Statistical Analysis Plan: XPEL TB 
 
Full Title 
GeneXpert Performance Evaluation for Linkage to Tuberculosis Care: 
The XPEL TB Trial 
Acronym 
XPEL TB 
Document History 
Version No. 
Version Date 
Description of Change 
1.0 
2019-10-11 
Initial release 
 
 
 
 
 
 
 
 
 
 
 
 
Trial Registration 
ClinicalTrials.gov (NCT03044158)  
Pan African Clinical Trials Registry (PACTR201610001763265) 
Principal Investigators 
Adithya Cattamanchi, Achilles Katamba 
SAP Authors 
Katherine Fielding, Katherine Farr, Tania Reza 
 
 
 

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XPEL study 
Statistical Analysis Plan (version 1.0) 
11 Oct 2019 
1. Introduction 
 
1.1 Aim 
 
To evaluate the performance of GeneXpert in improving tuberculosis diagnosis and treatment initiation rates 
using a multi-faceted strategy to streamline care (XPEL TB) relative to the prevailing standard-of-care in 
Uganda.  
2. Background and Objectives  
 
2.1 Specific Objectives 
 
1. To compare patient outcomes at health centers randomized to intervention vs. standard-of-care TB 
diagnostic evaluation strategies. 
a. Intervention: Onsite molecular testing for TB with GeneXpert device + process redesign to 
facilitate same-day TB diagnosis and treatment + performance feedback 
b. Standard-of-care: Onsite ZN or LED fluorescence microscopy + hub-based GeneXpert testing 
per existing protocols  
2. To identify processes and contextual factors that influence the effectiveness and fidelity of the 
intervention TB diagnostic evaluation strategy. 
3. To compare the costs and epidemiological impact of intervention vs. standard-of-care TB diagnostic 
evaluation strategies. 
2.2 Summary of aims 
 
The study proposes to conduct a pragmatic, parallel cluster-randomized trial (CRT) with nested mixed 
methods, health economic and modelling studies to evaluate the effectiveness, implementation and costs 
of the intervention strategy relative to standard TB diagnostic evaluation at community health centers that 
are part of Xpert referral networks in Uganda. The effectiveness of the intervention strategy will be 
assessed using routine data collected as part of mandatory reporting to the Uganda NTLP on consecutive 
patients who present to participating health centers during the 18-month enrolment period and meet 
eligibility criteria. Selected patients and providers who provide informed consent will also be surveyed 
and/or interviewed to identify reasons for its success or failure across study sites (Aim 2), and to collect 
relevant cost data for cost-effectiveness analyses (Aim 3). 
2.3 General comments 
 
This statistical analysis plan covers all effectiveness, implementation, and cost-effectiveness/modelling 
outcomes. Trial results reporting will follow the CONSORT 2010 statement extension to cluster randomized 
trials. 
This document summarizes analyses for the primary outcome of the number of patients treated for 
microbiologically-confirmed TB within 14 days of referral for sputum-based testing; and secondary outcomes 
for testing (number and proportion of patients completing TB testing), diagnosis (number and proportion 
diagnosed with microbiologically-confirmed TB, number and proportion diagnosed with RIF-resistant TB,  
time to microbiologically-confirmed TB), treatment (number and proportion treated for TB, time to treatment 
of microbiologically-confirmed TB), and follow-up (number and proportion with microbiologically-confirmed 
TB completing treatment, number and proportion who died within 6 months). These endpoints will be 
evaluated through review of TB registers and GeneXpert testing logs for all patients initiating TB evaluation 
at participating health centers.  
 

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XPEL study 
Statistical Analysis Plan (version 1.0) 
11 Oct 2019 
This Statistical Analysis Plan is written in support of and is predominantly consistent with the full trial protocol; 
however, this analysis plan takes precedence. The analysis plan covers the primary and secondary study 
objectives.  
 
3. Trial Summary 
 
3.1 Trial design 
 
The study is a parallel cluster-randomized trial (CRT) with two arms of equal number of clusters. The type 
of cluster used is institutional health units; in this case, health centers that provide TB microscopy services 
in Uganda in partnership with the National TB Program. Entire health centers will be the unit of 
randomization rather than individual participants to reduce contamination. The unit of analysis will be 
patients undergoing TB diagnostic evaluation during the 18-month enrolment period. The trial includes 
nested mixed methods and economic analyses to assess the implementation and cost-effectiveness of the 
intervention. 
 
3.2 Study population 
 
Eligibility Criteria 
 
A. 
Site-level Inclusion Criteria 
1. 
Use standard (multi-day) sputum smear microscopy as the primary method of TB 
diagnosis 
2. 
Participate in NTP-sponsored external quality assurance (EQA) for sputum smear 
microscopy 
3. 
Send samples to a district or regional hospital/health center for Xpert testing 
B. 
Site-level Exclusion Criteria 
1. 
Do not agree to be randomized to standard-of-care vs. intervention arms 
2. 
Perform sputum smear examination on <150 patients per year (based on 2015 data) 
3. 
Diagnose <15 smear-positive TB cases per year (based on 2015 data) 
C. 
Patient-level Inclusion Criteria 
1. 
Initiating TB evaluation at a study health center 
D. 
Patient-level Exclusion Criteria 
1. 
Have sputum collected for monitoring of response to anti-TB therapy (i.e., recorded as 
Examination type=Follow Up in NTLP Laboratory Register); 
2. 
Have sputum collected as part of active, community-based case finding (e.g., contact 
tracing, community outreach campaign) (i.e., noted in the remarks in NTLP Laboratory 
Register); 
3. 
Referred to a study health center for TB treatment after a diagnosis is established 
elsewhere (i.e, noted in the remarks in NTLP Laboratory Register); 
4. 
Started on TB treatment for extra-pulmonary TB only (i.e., recorded as Disease 
Class=EP (Extra Pulmonary TB) in NTLP Treatment Register). 
 
The patient population contributing to the primary analysis of all outcomes will not include patients age <18 
years and those with a documented prior history of TB treatment (e.g., reason for Xpert testing or TB 
treatment marked as treatment failure, relapse, treatment after loss to follow-up, etc.). In addition, patients 
identified as RIF resistant by Xpert testing will not contribute to primary and secondary analysis except for 
the number and proportion suspected or diagnosed with RIF-resistant TB. 
Settings and Locations  
20 microscopy centers met eligibility criteria based on an initial review of NTLP data. 5 (25%) health 
centers are level four health facilities (i.e. those serving counties with a target population of 100,000 
people), 14 (70%) are level three (i.e. those serving sub-counties with a target population of 20,000 
people), and one (5%) is a hospital. 18 (90%) health facilities are government-owned, one (St. Francis 
Njeru) is owned by an NGO, and one (Bishop Asili) is a private not for profit facility. The administrative 

Page | 4 
 
 
XPEL study 
Statistical Analysis Plan (version 1.0) 
11 Oct 2019 
authority is the Ministry of Health for 18 (90%) health centers, the Uganda Catholic Medical Bureau for St. 
Francis Njeru, and the local government for Bukulula. 12 (60%) of these health centers are located in the 
Central region of Uganda, and eight (40%) in the Eastern region. Four health centers are located in the 
Mayuge district, three in Kayunga, three in Iganga, and one in each of the districts of Buikwe, Kalungu, 
Kamuli, Luwero, Lwengo, Mityana, Mpigi, Mubende, Nakasongola, and Wakiso.6 District populations in 
2014 ranged from 181,799 (Nakasongola) to 1,997,418 (Wakiso), with a median population of 465,099 
(IQR 358,288-490,789).7 Five (25%) health centers are in subcounties classified in the 2014 census as 
urban, while 15 (75%) are rural.8 Health centers range in distance to central Kampala from 16 to 151 
kilometers, with a median of 110 km (IQR 76-128). The range of distance from each health center to their 
primary Xpert hub is 1 to 50 km, median of 16 km (IQR 13-24).  
 
3.3 Interventions 
 
Intervention arm: Health centers randomized to the intervention arm will received the following intervention 
components: 1) onsite molecular testing for TB with GeneXpert as a replacement for sputum smear 
microscopy; 2) process redesign to facilitate same-day TB diagnosis and treatment; and 3) performance 
feedback of TB diagnostic evaluation quality indicators to health center staff. 
 
Control arm: Health centers randomized to the control arm will follow the standard-of-care TB evaluation 
strategies, which are onsite ZN or LED fluorescence microscopy and hub-based GeneXpert testing for 
selected patients in accordance with Uganda NTLP guidelines. 
 
3.4 Outcomes 
 
Definitions used to assess study outcomes 
 
• 
Number enrolled: Number of eligible patients identified over a defined enrolment period through 
review of the NTLP Presumptive TB register, NTLP Laboratory register, GeneXpert laboratory 
requisition forms, GxAlert database and NTLP Treatment register at each study site (includes 
patients treated for TB without undergoing any sputum testing). 
• 
Date enrolled: Earliest date recorded in the NTLP Presumptive TB register, NTLP Laboratory 
register, GeneXpert laboratory requisition form, GxAlert database, or NTLP Treatment register. 
Records will be prospectively reviewed using name, sex, and age to identify patients presenting 
multiple times over the study period. Visits occurring within six months of the initial presentation will 
be considered as part of the same episode. 
• 
Number referred for testing: Number of eligible patients identified through review of the NTLP 
Presumptive TB register, NTLP Laboratory register, GeneXpert laboratory requisition forms, and 
GxAlert database at each study site. 
• 
Number tested: Number of eligible patients with any smear or Xpert result entered into the NTLP 
Presumptive TB register, NTLP Laboratory register, GeneXpert laboratory requisition forms, NTLP 
treatment register, or GxAlert database at each study site. 
• 
Number completing testing: Number of eligible patients with one valid Xpert result plus number of 
eligible patients with a valid smear result entered into the NTLP Presumptive TB register, NTLP 
Laboratory register, GeneXpert laboratory requisition forms, GxAlert database, or NTLP treatment 
register. Definitions of valid results are as follows: 
o A positive Xpert result with a semi-quantitative result of high, medium, low, or very low for all 
eligible patients; a positive Xpert Ultra result with a semi-quantitative result of trace for eligible 
HIV-positive patients; or second positive Xpert Ultra result if the initial result is trace-positive 
for eligible HIV-negative patients;9  
o A negative Xpert result for all eligible patients; 
o One positive or two negative smear results for eligible HIV-negative/HIV status unknown 
patients. 

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XPEL study 
Statistical Analysis Plan (version 1.0) 
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• 
Number diagnosed: Number of eligible patients with microbiologically-confirmed TB via a positive 
smear and/or valid Xpert test result entered into the NTLP Presumptive TB register, NTLP Laboratory 
register, GeneXpert laboratory requisition forms, NTLP treatment register, and/or GxAlert database 
within 6 months of date enrolled. 
• 
Number with suspected RIF resistant TB: Number of eligible patients with RIF resistance identified 
by Xpert testing entered into the GxAlert database, GeneXpert laboratory requisition forms, and/or 
NTLP Laboratory registers within 6 months of date enrolled. 
• 
Number with confirmed RIF resistant TB: Number of eligible patients with RIF resistance identified 
by Xpert testing entered into the GxAlert database, GeneXpert laboratory requisition forms, and/or 
NTLP Laboratory registers, and RIF resistance confirmed by culture-based DST or a second 
molecular assay as determined by review of Lab Register at MDR treatment center within 6 months 
of date enrolled. 
• 
Number treated: Number of eligible patients entered into the NTLP Treatment register as having 
started Category I or II regimen, or started on MDR treatment at the MDR treatment center as 
determined by review of Treatment Register at MDR treatment center within 6 months of date 
enrolled. 
• 
Number completing treatment: Number treated and with a treatment outcome of cured or completed 
entered into the NTLP Treatment register or indicated by MDR treatment center staff through direct 
follow up. 
• 
Number died: Number treated and with a treatment outcome of died entered into the NTLP Treatment 
register within 6 months of date enrolled, or number eligible with treatment outcome entered as died 
on direct follow-up form. 
• 
Time-to-diagnosis: Number of days from date enrolled to earliest date of positive smear or valid 
Xpert result recorded in NTLP Presumptive TB register, NTLP Laboratory register, GeneXpert 
laboratory requisition form, NTLP treatment register, or GxAlert database within 6 months of date 
enrolled. 
• 
Time-to-treatment: Number of days from date enrolled to treatment start date entered into NTLP 
Treatment register if treatment start date is within 6 months of date enrolled.  
Primary Outcome  
Outcome 
Numerator 
Denominator 
Number treated for microbiologically-
confirmed TB within 14 days of 
referral for sputum-based testing 
Number diagnosed  
and time-to-treatment within 14 days 
None 
 
 
Secondary Outcomes  
A. Testing 
Outcome 
Numerator 
Denominator 
Number referred for TB testing 
Number referred for testing 
None 
Proportion completing testing 
Number completing testing 
Number referred for testing 
B. Diagnosis 
Outcome 
Numerator 
Denominator 
Number diagnosed with 
microbiologically-confirmed TB* 
Number diagnosed 
None 
Proportion diagnosed with 
microbiologically-confirmed TB* 
Number diagnosed 
Number referred for testing 

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Number suspected/diagnosed with 
RIF-resistant TB* 
Number with suspected/confirmed 
RIF resistant TB 
None 
Proportion suspected/diagnosed with 
RIF-resistant TB* 
Number with suspected/confirmed 
RIF resistant TB 
Number referred for testing 
Time to microbiologically-confirmed 
TB** 
Time-to-diagnosis if 
microbiologically-confirmed TB 
None 
C. Treatment 
Outcome 
Numerator 
Denominator 
Number treated for TB* 
Number treated 
 
None 
Proportion treated for TB* 
Number treated 
Number enrolled 
Number treated for microbiologically-
confirmed TB* 
Number diagnosed AND treated 
None 
Proportion treated for 
microbiologically-confirmed TB* 
Number diagnosed AND treated 
Number referred for testing 
Proportion with microbiologically-
confirmed TB treated* 
Number diagnosed AND treated 
Number diagnosed 
Time-to-treatment of 
microbiologically-confirmed TB** 
Time-to-treatment if 
microbiologically-confirmed TB and 
treated 
None 
D. Follow-up  
Outcome 
Numerator 
Denominator 
Number with microbiologically-
confirmed TB completing treatment 
Number diagnosed AND completing 
treatment 
None 
Proportion with microbiologically-
confirmed TB completing treatment 
Number diagnosed AND completing 
treatment 
Number diagnosed AND treated 
Number who died within 6 months  
Number died 
None 
Proportion who died within 6 
months*** 
Number died 
Number enrolled 
 
*Outcome will be assessed within 1 day and within 14 days of initial sputum submission. One day was chosen because the 
intervention focuses on same-day diagnosis and treatment. 14 days was chosen because the diagnostic process could take 7-10 
days in the control arm depending on the frequency of sample transport to Xpert testing sites. 
**For the control arm, missing Xpert result dates will be recorded as the next scheduled date of sputum transportation from the 
health center to the GeneXpert facility (results from the previous batch of testing are returned when the next batch of samples are 
picked up). For the intervention arm, missing Xpert result dates will be recorded as the day following sample collection. 
***Treatment outcomes will be assessed at 7, 9, 12, and 24-month post-treatment initiation intervals determined by drug regimen.  
 
3.5 Sample Size 
 
Sample size 
 
Based on pre-randomization data collected from January to December 2017, we estimate 5360 patients will 
be enrolled across 20 study sites over the 18-month enrolment period. Other study intervals ranging from 12 
to 24 months are considered.  
 
Justification 
 
Original detectable effect size estimates: The study is based on the health center being the unit of 
randomization and aims to demonstrate the superiority of the intervention arm. The sample size calculation 
uses formulae appropriate for cluster-randomized trials with a parallel design and stratified and/or restricted 
randomization, including the addition of one extra cluster per arm to allow for the loss of degrees of freedom 

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Statistical Analysis Plan (version 1.0) 
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due to stratification to obtain conservative estimates.4 The original detectable effect size estimates were 
based on the outcome being the proportion of patients treated for microbiologically-confirmed TB within two 
weeks of referral for sputum-based testing. A type I error of 5% and power of 90% was assumed. Pre-
randomization data collected from January to December 2017 from the 20 selected trial sites in Uganda 
suggests the average proportion of patients referred for TB evaluation who initiate treatment for active TB 
within two weeks is 6.7%, and the coefficient of variation (k) between clusters is 0.36, with a harmonic mean 
of 268 patients enrolled at each health center cluster during the 18-month enrolment period. Based on these 
assumptions and assuming a type I error of 5%, we will have 90% power to detect a 6% or greater absolute 
increase in the outcome proportion in the intervention arm (see Table 1). Table 1 also shows detectable 
absolute effect sizes with varying power (80%, 85%, 90%) during various trial time intervals and respective 
average cluster size estimates.   
 
The minimum detectable effect size is based on published literature, our preliminary data demonstrating the 
increased sensitivity of Xpert Ultra vs. smear microscopy (95% vs. 40%), and reduced pre-treatment loss to 
follow-up expected in the intervention vs. control arms due to rapid, onsite molecular testing (7% vs. 30%). 
When applying these parameters to a population of 1000 individuals with TB prevalence of 10%, we would 
expect 95 patients to be diagnosed with TB and 88 to start treatment within 14 days in the intervention arm, 
and 40 patients to be diagnosed with TB and 28 to start treatment within 14 days in the control arm. Thus, 
the expected difference in the primary outcome proportion would be 8.8% vs. 2.8%, or 6%.5,6 
 
Table 1. Absolute difference in the proportion of clinic attendees initiating TB treatment within 2 
weeks of initial sputum submission, among those referred for TB testing 
Average cluster size 
Effect size (10 clusters per arm, k=0.36) 
80% Power 
85% Power 
90% Power 
178 (12 months) 
5.9% 
6.5% 
7.4% 
223 (15 months) 
5.7% 
6.3% 
7.1% 
268 (18 months) 
5.5% 
6.1% 
6.9% 
312 (21 months) 
5.4% 
6.0% 
6.7% 
357 (24 months) 
5.3% 
5.9% 
6.6% 
 
Figure 1. Effect size by cluster size. Number of clusters per arm is 10, k=0.36.    
 
 
 
 
Revised detectable effect size estimates: The revised detectable effect size estimates are for the outcome 
being the number of patients treated for microbiologically-confirmed TB within two weeks of referral for 
5.2%
5.4%
5.6%
5.8%
6.0%
6.2%
6.4%
6.6%
6.8%
7.0%
7.2%
7.4%
178
223
268
312
357
Effect Size
Cluster Size
80% Power
85% Power
90% Power

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XPEL study 
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sputum-based testing. We first used 10 months of pre-intervention period data across all 20 clinics to assess 
1) the geometric mean number of patients diagnosed and treated for TB within 14 days (new primary 
outcome), the natural log (ln) of the geometric mean and 3) the standard deviation of the natural log at control 
sites, intervention sites and overall.. For the pre-intervention period, the intervention clinics have a lower 
geometric mean number of clinic attendees who were diagnosed with TB and started on TB treatment within 
14 days compared with the control arm: 
 
Geometric mean 
Mean of ln outcome 
SD of ln outcome 
Control 
14.70 
2.69 
0.23 
Intervention 
6.49 
1.87 
0.32 
 
We then estimated the detectable effect size (expressed as a geometric mean ratio). The calculations 
assume the same parameters as for the original primary outcome (10 clinics/arm, 18-month trial duration to 
achieve geometric mean of 286 patients/cluster in control and intervention arms). Table 1 below shows the 
detectable effect size assuming within-arm SD of ln outcome of 0.2 to 0.3 and power of 80-90%. The 
detectable effect sizes shown below are conservative (calculations do not take into account the baseline 
differences between arms in the new primary outcome and are based on 10-months rather than full 24-
months of pre-intervention data). Final calculations will be repeated once baseline data is fully entered and 
available for analysis. 
Table 3: Detectable effect sizes 
SD of ln outcome 
Geometric Mean Ratio of the new primary outcome  
(intervention vs control) 
80% power 
90% power 
0.2 
1.30 
1.36 
0.3 
1.49 
1.58 
 
We believe a GMR of 1.30-1.58 is a reasonable detectable effect size. Xpert MTB/RIF is twice as sensitive 
as smear microscopy (double the number of confirmed TB cases) and we expect onsite testing to reduce 
pre-treatment loss to follow-up by at least half (from 30% to <15%).  
3.6 Blinding 
 
The trial will be open-label for participants and researchers, as blinding of the assigned intervention is not 
feasible given intervention implementation at the health center level. Staff, however, will be blinded to the 
intervention allocations through the use of codes to identify clusters rather than health center names. 
Where possible, the investigators and study staff will be masked to ongoing aggregated data by study. The 
trial statistician and data manager will have access to the aggregate data. Some site-level data will be used 
for performance feedback to sites in the intervention arm; this data will not be a comparison to baseline 
sites across study arms. 
 
4. Randomization 
 
4.1 Sequence Generation 
 
A random allocation sequence will be generated by STATA 14 (StataCorp LP, College Station, TX). 
Randomisation of clusters to the intervention or control arm was conducted using stratification and 
restriction. Stratified randomisation was used to help reduce between-cluster variability and achieve 
balance at baseline. Using data from 2017 on the percentage of adult clinic attendees being investigated 
for TB who started TB treatment within 14 days, the clusters were stratified into two equal-sized groups – 
“low” and “high” percentages of this outcome based on the median. Restricted randomisation was used to 
help ensure balance for key factors. Variables we restricted on were; year cluster enrolled in project (2017 
vs. 2018); sputum transport frequency to Xpert hub; distance to Xpert hub; number of patients evaluated for 

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Statistical Analysis Plan (version 1.0) 
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TB; percentage starting TB treatment within 14d; urban vs. rural health clinics; HIV prevalence; number of 
facilities per district. For these restriction criteria the overall proportion of unacceptable allocations was 
calculated and the the validity assessed using the validity matrix.1 From a total of 63,504 possible 
randomisations based on two equal-sized strata, applying the restriction criteria resulted in 11,392 
randomisations; one of these was chosen at random. 
 
4.2 Allocation concealment mechanism 
 
Allocation concealment will be used to prevent selection bias by concealing from the research staff the 
allocation sequence until assignment has occurred.   
 
4.3 Implementation  
 
The trial statistician will generate the random allocation assignments, and clusters will be enrolled by local 
study staff. Eligible individual patients will be included in clusters through complete enumeration. District 
health officers provide administrative authority and consent to health center participation prior to 
randomization. Consent is documented in a signed Memorandum of Understanding (MOU) between the 
district and the National Tuberculosis and Leprosy Control Program (NTLP), endorsed by the Chief 
Administrative Officer, the District Health Officer, and the Program Manager for NTLP. A copy of the 
agreement is provided to the facility in-charge at each health center.  
 
Public ceremony for randomization will be held in Kampala, Uganda. Facility in-charges, district health 
officers, and representatives from NTLP will attend.  
 
4.4 Statistical Methods 
 
 
4.4.1 General analyses principles 
 
We will calculate and compare all effectiveness outcomes for the two trial arms using prespecified analyses 
following the intention-to-treat principle. Statistical analysis methods to determine intervention effect on 
outcomes will be used that are appropriate for CRTs with a small number of clusters. Analysis of cluster-
level summaries is based on a total of 20 clusters (10 per arm), each comprised of one health center, and 
each being given equal weight. Effect estimates will be reported with 95% confidence intervals, and an 
associated P-value.  
 
4.4.2 Baseline data 
 
Baseline variables will be cross-tabulated to check for appropriate balance between study arms and to 
characterize the study population using summary statistics both at cluster and individual levels (Table 1, 2). 
 
Cluster (i.e. health center) level data will be collapsed across study arms and compared using mean, 
median and range with standard deviations and/or interquartile ranges as appropriate. The basis of this 
analysis will be data on demographics, health center and laboratory infrastructure, organizational hierarchy, 
Xpert referral network, and TB testing and treatment collected during site assessment, recruitment, and 
guidelines training.  
 
Individual level data for all patients eligible for analysis will also be collapsed across study arms and 
compared using mean, median and range with standard deviations and/or interquartile ranges as 
appropriate. These individual factors include age, sex, HIV status, and ART status.  
 
We will also summarize data obtained during the pre-randomization period for the cohort of health centers 
and patients who are being enrolled in the study and randomized to a trial arm.  
 
Descriptive data between control and intervention arms will be presented in tabular format in lieu of formal 
statistical comparison or significance tests, and will be reviewed to confirm comparability and identify any 
substantial imbalances. Adjusted effect estimates will be calculated by study arm to account for imbalance 

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XPEL study 
Statistical Analysis Plan (version 1.0) 
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of covariates which are risk factors for the outcome of interest. Losses in degrees of freedom due to 
cluster-level covariate adjustments will be accounted for in all calculations.  
 
4.4.3 Primary analysis of primary outcome 
 
Analysis will be conducted at the cluster (clinic)-level. The outcome is the count of the number treated for 
microbiologically-confirmed TB within two weeks of referral for sputum-based testing at the clinic-level. 
Initially Poisson regression will be used, with an offset at the clinic level of the total number of months the 
clinic contributes individuals to the study (starting from 22 October 2018). Evidence for over-dispersion will 
be assessed and if over-dispersion is detected then negative binomial regression will be used, using the 
same offset. Either model will adjust for randomisation strata and the count of microbiologically-confirmed 
TB cases that initiated on treatment within 14 days in the 12 month period before 22 October 2018.  The 
exact functional form of this covariate will be identified through fractional polynomials or by using loge 
transformation. Further adjustment for clinic-level factors that show imbalance will be explored, though the 
number of adjustment factors will be limited as all are at the clinic-level. See tables 4 and 5a. 
 
Xpert Ultra cartridges will be used in our intervention (see Outcomes Definitions Section 3.4). Two 
sensitivity analyses will be performed reclassifying 1) all trace-positive results as negative and 2) trace-
positive results in HIV negative patients as true positive (even if not confirmed by a second positive test) to 
determine the impact of Ultra on the primary outcome.  
 
No sub-group analyses for strata defined at the individual-level (eg., sex and HIV status) will be conducted 
as the primary outcome analyses is conducted at the cluster-level. 
 
 
4.4.4 Analysis of secondary outcomes 
 
Secondary outcomes are reported as either counts, proportions, or time to event, and are derived from data 
collected across routine data sources.  
 
All analyses will adjust for randomisation strata. Intervention effect estimates will be reported for analyses 
(1) adjusting for randomisation strata only and (2) adjusting for randomisation strata and individual-level 
(proportions and time to event only) and clinic-level factors considered imbalanced, as outlined below.  
See table 5b. 
 
 
4.4.4.1 Count outcomes: 
 
The analytic approach will be the same as described for the primary outcome. . The outcome is the count of 
the respective outcome at the clinic-level. Analysis will be conducted at the cluster (clinic)-level. Either 
Poisson or negative binomial regression will be used, depending on over-dispersion. Adjustment will be 
made for the clinic-level count of the outcome using data in the 12 month period before 22 October 2018.  
The exact functional form of this covariate will be identified through fractional polynomials or by using loge 
transformation.  
 
4.4.4.2 Proportions 
 
The analysis will give each cluster equal weight. The overall risk for each cluster will be calculated and 
shown, by strata and arm. A log transformation (where necessary) will be applied to the risk for each 
cluster. If a cluster has no events, 0.5 event will be added to all clusters in order that the log transformation 
can be conducted. The mean and standard deviation of these log risks will be used to obtain the geometric 
mean (GM) and associated 95% CI for each arm of the study.  
The risk ratio is estimated using linear regression of the log risks/rates/means on stratum and arm.  
An approximate standard error for the difference in log (GM) between arms is obtained based on the 
residual mean square from a two-way analysis of variance (ANOVA) of the cluster log risk on stratum, 
study arm and the interaction between stratum and study arm. The 95% CI is calculated from this standard 
error, using a t-statistic with 16 degrees of freedom.  

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Logistic regression will be used to adjust for any baseline imbalance at the individual level, adopting a two-
stage approach. The regression model will include terms for the adjustment factors (individual level) and 
strata, but not study arm. For each cluster the fitted model will be used to obtain the ratio of observed to 
expected (O/E) events, and a log transformation will be applied to this ratio, where appropriate.  
Linear regression of the log (O/E) on stratum and arm (2-way ANOVA) will be used to estimate the risk 
ratio. The variance for the ratio of mean O/E is calculated from the residual mean square from an ANOVA 
of log(O/E) on stratum, arm and the interaction between stratum and arm. The 95% CI is calculated from 
this variance, using a t-statistic with 16 degrees of freedom. Limited adjustment for individual level factors is 
only possible; age, sex, HIV status, and ART status. Adjustment will only be done if data completeness for 
the variable is more than 80% in both trial arms.  
 
If imbalance is observed for the equivalent cluster-level outcome in the 12 month period before 22 October 
2018, adjustment at the second stage described above will be conducted, with appropriate adjustment for 
the degrees of freedom for the intervention effect. The exact functional form of this cluster-level covariate 
will be identified through fractional polynomials or by using loge transformation.  
 
 
4.4.4.1 Time to event 
 
The analysis will give each cluster equal weight. The arithmetic and geometric mean time to event 
(amongst all those experiencing the effect) will be calculated for each cluster and shown, by strata and arm. 
A log transformation of the individual-level time to event will likely be necessary as the times to event may 
be positively skewed, resulting in geometric means. Clusters where no individuals contribute time to event 
will be excluded from the analysis. 
 
ANOVA will be used to calculate either the difference in means between arms or geometric mean ratio, 
depending on whether a log transformation was used. A two-stage approach, similar to as described 
above, will be used to adjust for confounders at the individual level. Limited adjustment for individual level 
factors is only possible; age, sex, HIV status, and ART status. Adjustment will only be done if data 
completeness for the variable is more than 80% in both trial arms. If imbalance is observed for the 
equivalent cluster-level outcome in the 12 month period before 22 October 2018, adjustment at the second 
stage described above will be conducted, with appropriate adjustment for the degrees of freedom for the 
intervention effect. The exact functional form of this cluster-level covariate will be identified through 
fractional polynomials or by using loge transformation.  
 
 
4.4.5 Sub-group analyses for secondary outcomes 
 
Pre-specified sub-group analyses for a subset of secondary outcomes will be conducted to assess whether 
the effect of the intervention differs between sub-groups. The sub-groups are (1) HIV status at enrolment 
(self-reported, laboratory test resulted as noted on primary data sources, or through pre-ART and ART 
register data extraction), (2) and sex. The effect of the intervention will be estimated for each sub-group, 
and P-values for effect modification, appropriate for individual-level covariates, will be reported.2 These 
sub-group analyses will be conducted for the following secondary outcomes: proportion completing testing; 
proportion diagnosed with microbiologically-confirmed TB; proportion treated for microbiologically-
confirmed TB within two weeks of referral for sputum-based testing; and the proportion who died within 6 
months. See table 5c. 
 
 
 
 
 
 
 
 
 

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XPEL study 
Statistical Analysis Plan (version 1.0) 
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Tables 
 
Table 1. Summary of baseline study variables 
 
Intervention 
Control 
Year of enrolment into project 
2017 or 2018 
 
 
Distance to Xpert hub 
Median (IQR) 
 
 
Sputum transport frequency to Xpert 
hub 
Once per week or ≥2 times per week 
 
 
Total number referred for TB testing* 
# 
 
 
Average number referred for TB 
testing, across clusters* 
Harmonic mean 
 
 
Region 
Central or Eastern, % (n/N) 
 
 
District 
% per district 
 
 
Geographical area 
Urban or rural, % (n/N) 
 
 
Population of sub-county 
# 
 
 
Patients undergoing TB testing who are 
diagnosed with and treated for TB 
within 14 days* 
(< 6.2%  or  ≥ 6.2%) 
 
 
HIV prevalence among TB patients* 
% (n/N) 
 
 
 
*  in the 12 month period before 22 October 2018 
 
Table 2. Summary of demographic variables among those referred for TB testing* (collapsed over 
cluster) 
 
Intervention 
Control 
Total number referred for 
TB testing 
# 
 
 
Average number referred 
for TB testing, across 
clusters 
Harmonic mean 
 
 
Missing data for sex 
% (n/Ntotal) 
 
 
Female 
% (n/Nnon-miss) 
 
 
Missing data for age 
% (n/ Ntotal) 
 
 
Age 
Median (IQR), Nnon-
miss 
 
 
HIV status known: 
% (n/Ntotal) 
 
 
HIV-positive 
% (n/Nnon-miss) 
 
 
ART status known: 
% (n/Ntotal HIV+) 
 
 
ART prevalence 
% (n/Nnon-miss HIV+) 
 
 
 
*  in the intervention period after 22 October 2018 
 
 

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Statistical Analysis Plan (version 1.0) 
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Table 3: Summary of numbers referred for TB testing* in intervention and control arms (collapsed 
over cluster) using data from record review and facility follow up.   
 
Intervention 
Control 
Xpert result 
Negative 
Positive 
Unknown 
 
 
Semi-Quantitative result 
High 
Medium 
Low 
Very Low 
Trace 
 
 
RIF result 
Negative 
Positive 
Indeterminate/Invalid/No Result/Error 
Unknown/No Result 
 
 
MDR status 
Yes, confirmed positive by DST 
No, confirmed negative by DST 
Unknown 
 
 
Smear result 
Negative 
Positive 
Unknown 
 
 
Treatment status 
Yes, on treatment 
No, not on treatment 
Unknown 
 
 
Treatment category  
Category 1- 2RHZE/6EH 
Category 1- 2RHZE/4RH 
Category 2-2SRHZE/1RHZE/5RHE 
Category 3-2RHE/4RH 
Other 
 
 
Treatment outcome 
Cured 
Completed 
Failure (smear positive) 
Died 
Transferred out 
Lost to Follow up 
 
 
 
*  in the period after 22 October 2018 
 
 

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Statistical Analysis Plan (version 1.0) 
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Table 4: Primary outcome, number treated for microbiologically-confirmed TB within two weeks of 
initial sputum submission, summary of number diagnosed within 14 days, number referred for TB 
testing, and proportion, by control and intervention arm 
 
Control arm 
Intervention arm 
Cluster 
# diagnosed and 
treated within 14 
days 
total number of 
months the clinic 
contributes 
individuals to the 
study 
# diagnosed and 
treated within 14 
days 
total number of 
months the clinic 
contributes 
individuals to the 
study 
1 
 
 
 
 
2 
 
 
 
 
3 
 
 
 
 
4 
 
 
 
 
5 
 
 
 
 
6 
 
 
 
 
7 
 
 
 
 
8 
 
 
 
 
9 
 
 
 
 
10 
 
 
 
 
Geometric 
Mean 
 
 
 
 
 
 

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Table 5a: Primary outcome: number treated within 14 days, unadjusted and adjusted risk ratio (95% 
confidence intervals) and associated P-values 
Primary 
outcome 
I: 
# 
C: 
# 
Unadjusted 
RR*  
(95% CI) 
P-value 
Adjusted 
RR** 
(95% CI) 
P-value 
Primary 
analysis 
 
 
 
 
 
 
Sensitivity 
analysis 1 
 
 
 
 
 
 
Sensitivity 
analysis 2 
 
 
 
 
 
 
I Intervention ; C control 
* adjusted for randomisation strata; ** adjusted for randomisation strata and count of microbiologically-confirmed TB 
cases that initiated on treatment within 14 days in the 12 month period before 22 October 2018 
Table 5b: Secondary outcomes: unadjusted and adjusted risk ratio (95% confidence intervals) and 
associated P-values 
Secondary outcomes 
 
I:  
C: 
RR  
(95% CI) 
P-value 
Adjusted 
RR  
(95% CI) 
P-value 
A. Testing  
 
 
 
 
 
 
 
Number referred for TB 
testing 
GM 
 
 
 
 
 
 
Proportion completing 
testing 
% (n/N) 
 
 
 
 
 
 
B. Diagnosis 
 
 
 
 
 
 
 
Number diagnosed with 
microbiologically-confirmed 
TB (by day 1) 
GM 
 
 
 
 
 
 
Number diagnosed with 
microbiologically-confirmed 
TB (by day 14) 
GM 
 
 
 
 
 
 
Proportion diagnosed with 
microbiologically-confirmed 
TB (by day 1) 
% (n/N) 
 
 
 
 
 
 
Proportion diagnosed with 
microbiologically-confirmed 
TB (by day 14) 
% (n/N) 
 
 
 
 
 
 
Number 
suspected/diagnosed with 
RIF-resistant TB (by day 1) 
GM 
 
 
 
 
 
 
Number 
suspected/diagnosed with 
RIF-resistant TB (by day 14) 
GM 
 
 
 
 
 
 

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XPEL study 
Statistical Analysis Plan (version 1.0) 
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Proportion 
suspected/diagnosed with 
RIF-resistant TB (by day 1) 
% (n/N) 
 
 
 
 
 
 
Proportion 
suspected/diagnosed with 
RIF-resistant TB (by day 14) 
% (n/N) 
 
 
 
 
 
 
Time to microbiologically-
confirmed TB 
GM 
 
 
 
 
 
 
C. Treatment 
 
 
 
 
 
 
 
Number treated for TB (by 
day 1) 
GM 
 
 
 
 
 
 
Number treated for TB (by 
day 14) 
GM 
 
 
 
 
 
 
Proportion treated for TB (by 
day 1) 
% (n/N) 
 
 
 
 
 
 
Proportion treated for TB (by 
day 14) 
% (n/N) 
 
 
 
 
 
 
Number treated for 
microbiologically-confirmed 
TB (by day 1) 
GM 
 
 
 
 
 
 
Number treated for 
microbiologically-confirmed 
TB (by day 14) 
GM 
 
 
 
 
 
 
Proportion treated for 
microbiologically-confirmed 
TB (by day 1) 
% (n/N) 
 
 
 
 
 
 
Proportion treated for 
microbiologically-confirmed 
TB (by day 14) 
% (n/N) 
 
 
 
 
 
 
Proportion with 
microbiologically-confirmed 
TB treated (by day 1) 
% (n/N) 
 
 
 
 
 
 
Proportion with 
microbiologically-confirmed 
TB treated (by day 1) 
% (n/N) 
 
 
 
 
 
 
Time-to-treatment of 
microbiologically-confirmed 
TB 
GM 
 
 
 
 
 
 
D. Follow-up 
 
 
 
 
 
 
 
Number with 
microbiologically-confirmed 
TB completing treatment 
GM 
 
 
 
 
 
 
Proportion with 
microbiologically-confirmed 
TB completing treatment 
% (n/N) 
 
 
 
 
 
 
Number who died within 6 
months  
GM 
 
 
 
 
 
 
Proportion who died within 6 
months 
% (n/N) 
 
 
 
 
 
 
 
GM geometric mean 
 
 

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XPEL study 
Statistical Analysis Plan (version 1.0) 
11 Oct 2019 
 
Table 5c: Sub-group analyses for selected secondary outcomes: stratum-specific unadjusted and 
adjusted risk ratios (95% confidence intervals) and associated P-values for interaction 
Secondary 
outcomes 
 
I:  
C: 
RR  
(95% CI) 
P-value 
Adjusted 
RR  
(95% CI) 
P-value 
P-value for 
interaction 
A. Proportion completing testing 
HIV-positive 
% (n/N)  
 
 
 
 
 
 
HIV-negative 
% (n/N)  
 
 
 
 
 
 
Male 
% (n/N)  
 
 
 
 
 
 
Female 
% (n/N)  
 
 
 
 
 
 
B. Proportion diagnosed with microbiologically-confirmed TB (by day 14) 
HIV-positive 
% (n/N)  
 
 
 
 
 
 
HIV-negative 
% (n/N)  
 
 
 
 
 
 
Male 
% (n/N)  
 
 
 
 
 
 
Female 
% (n/N)  
 
 
 
 
 
 
C. Proportion treated for microbiologically-confirmed TB (by day 14) 
HIV-positive 
% (n/N)  
 
 
 
 
 
 
HIV-negative 
% (n/N)  
 
 
 
 
 
 
Male 
% (n/N)  
 
 
 
 
 
 
Female 
% (n/N)  
 
 
 
 
 
 
D. Proportion who died within 6 months 
HIV-positive 
% (n/N)  
 
 
 
 
 
 
HIV-negative 
% (n/N)  
 
 
 
 
 
 
Male 
% (n/N)  
 
 
 
 
 
 
Female 
% (n/N)  
 
 
 
 
 
 
 
 
 

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XPEL study 
Statistical Analysis Plan (version 1.0) 
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References 
1. 
Xpert 
Omni 
Performance 
Evaluation 
(XPEL 
TB). 
2017; 
https://clinicaltrials.gov/ct2/show/NCT03044158. 
2. 
Xpert Omni Performance Evaluation for Linkage to Tuberculosis Care: The XPEL TB Trial. 2016; 
http://www.pactr.org/ATMWeb/appmanager/atm/atmregistry?dar=true&tNo=PACTR2016100017632
65. 
3. 
Campbell MK, Piaggio G, Elbourne DR, Altman DG, Group C. Consort 2010 statement: extension to 
cluster randomised trials. BMJ. 2012;345:e5661. 
4. 
Hayes RJ, Moulton LH. Cluster Randomized Trials. Boca Raton, Florida, USA: CRC Press; 2009. 
5. 
Farr K, Nalugwa T, Shete P, et al. Late Breaking Abstract - The tuberculosis diagnostic evaluation 
cascade of care at microscopy centers linked to Xpert MTB/RIF hubs in Uganda. European 
Respiratory Journal Sep 2017, 50 (suppl 61) OA1943; DOI: 10.1183/1393003.congress-
2017.OA1943.  
6.  
Ministry of Health. Health Facilities Inventory. Kampala, Uganda 2012. 
7. 
Uganda Bureau of Statistics. Census 2014 Results. 2014; http://www.ubos.org/2014-census/census-
2014-final-results/. 
8. 
Uganda 
National 
Population 
and 
Housing 
Census 
2014 
Provisional 
Results. 
2014; 
http://ugandadata.org/redbin/RpWebEngine.exe/Portal?&BASE=cen2014. 
9. 
World Health Organization. WHO Meeting Report of a Technical Expert Consultation: Non-inferiority 
analysis 
of 
Xpert 
MTB/RIF 
Ultra 
compared 
to 
Xpert 
MTB/RIF 
2017; 
http://apps.who.int/iris/bitstream/10665/254792/1/WHO-HTM-TB-2017.04-eng.pdf. 
10. 
Moulton LH. Covariate-based constrained randomization of group-randomized trials. Clinical trials. 
2004;1(3):297-305. 
11. 
Sismanidis C, Moulton LH, Ayles H, et al. Restricted randomization of ZAMSTAR: a 2 x 2 factorial 
cluster randomized trial. Clinical trials. 2008;5(4):316-327. 
12. 
Cheung YB, Jeffries D, Thomson A, Milligan P. A simple approach to test for interaction between 
intervention and an individual-level variable in community randomized trials. Trop Med Int Health. 
2008;13(2):247-255. 
13. 
 Analysis of TB prevalence and drug resistance surveys. TB surveillance and surveys: a training 
workshop 
for 
consultants. 
2011; 
http://www.who.int/tb/advisory_bodies/impact_measurement_taskforce/meetings/survmay11_analys
is_clustering_missing_data_sismanidis.pdf 
 
 
 

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XPEL study 
Statistical Analysis Plan (version 1.0) 
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Figure X. Comparison of TB diagnostic evaluation in the intervention and control arms 
 
*Consider empiric treatment or additional testing if test results are negative or invalid 
 
 
 
Standard-of-Care (CONTROL ARM) 
 INTERVENTION ARM 
 
All patients 
Patients with HIV 
All patients 
VISIT 1 
 
 
 
 
 
 
VISIT 2 
 
 
 
 
 
 
VISIT 3 
 
 
 
VISIT 4 
 
 
 
 
 
Treat if Xpert 
positive* 
Send sputum for 
Xpert testing 
Treat if Xpert positive* 
Perform Xpert testing 
Collect sputum 
Collect sputum 
Prepare/examine smear* 
Smear-positive: Treat 
Treat if Xpert positive* 
Smear-negative:   
Collect sputum and 
 send for Xpert testing*  
Smear-positive: Treat 
Smear-negative:   
Collect sputum and 
prepare/examine smear* 

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XPEL study 
Statistical Analysis Plan (version 1.0) 
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CONSORT diagram 
CONSORT diagram will be constructed show individual and cluster-level data at baseline and follow-up and 
based on the CONSORT extension to CRTs. 
 
 
 
 
 
40 Health centers assessed for eligibility
20 Health centers randomized
10 health centers allocated to control 
arm
Median cluster size:
XX patients (IQR)
XX health centers lost to follow up
XX Health centers analyzed (XX
patients)
XX Patients excluded from analysis
10 Health centers allocated to 
intervention arm
Median cluster size:
XX patients (IQR)
XX health centers lost to follow up
XX Health centers analyzed (XX
patients)
XX Patients excluded from analysis
20 Excluded
10 Not meeting inclusion criteria
10 Dropped out before randomization
ALLOCATION 
FOLLOW-UP 
ANALYSIS 
ENROLLMENT 

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XPEL study 
Statistical Analysis Plan (version 1.0) 
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1. 
Moulton LH. Covariate-based constrained randomization of group-randomized trials. Clinical trials. 
2004;1(3):297-305. 
2. 
Cheung YB, Jeffries D, Thomson A, Milligan P. A simple approach to test for interaction between 
intervention and an individual-level variable in community randomized trials. Trop Med Int Health. 
2008;13(2):247-255. 
 

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XPEL study 
Statistical Analysis Plan (version 1.5) 
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Statistical Analysis Plan: XPEL TB 
 
Full Title 
GeneXpert Performance Evaluation for Linkage to Tuberculosis Care: 
The XPEL TB Trial 
Acronym 
XPEL TB 
Document History 
Version No. 
Version Date 
Description of Change 
1.0 
2019-10-11 
Initial release 
1.1 
2019-11-21 
Updated based on Investigators’ inputs 
1.2 
2020-01-10 
Version circulated to TSC 
1.3 
2020-03-04 
Final version 
1.4 
2020-8-06 
Updated appendix to include outcome 
details omitted from final version 
1.5 
2021-07-12 
Updated the denominator for proportion 
outcomes to improve clarity for 
reporting purposes  
Trial Registration 
ClinicalTrials.gov (NCT03044158)  
Pan African Clinical Trials Registry (PACTR201610001763265) 
Principal Investigators 
Adithya Cattamanchi, Achilles Katamba 
SAP Authors 
Katherine Fielding, Katherine Farr, Tania Reza 
 
 
 

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XPEL study 
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1. Introduction 
 
1.1 Aim 
 
To assess the effectiveness, implementation and costs of a streamlined TB diagnostic evaluation strategy 
based around rapid, onsite molecular testing 
2. Background and Objectives  
 
2.1 Specific Objectives 
 
1. To compare patient outcomes at health centers randomized to intervention vs. standard-of-care TB 
diagnostic evaluation strategies. 
a. Intervention: Onsite molecular testing for TB with GeneXpert device + process redesign to 
facilitate same-day TB diagnosis and treatment + performance feedback 
b. Standard-of-care: Onsite ZN or LED fluorescence microscopy + hub-based GeneXpert testing 
per existing protocols  
2. To identify processes and contextual factors that influence the effectiveness and fidelity of the 
intervention TB diagnostic evaluation strategy. 
3. To compare the costs and epidemiological impact of intervention vs. standard-of-care TB diagnostic 
evaluation strategies. 
2.2 Summary of aims 
 
The study proposes to conduct a pragmatic, parallel cluster-randomized trial (CRT) with nested mixed 
methods, health economic and modelling studies to evaluate the effectiveness, implementation and costs 
of the intervention strategy relative to standard TB diagnostic evaluation at community health centers that 
are part of Xpert referral networks in Uganda. The effectiveness of the intervention strategy will be 
assessed using routine data collected as part of mandatory reporting to the Uganda NTLP on consecutive 
patients who present to participating health centers during the 18-month enrolment period and meet 
eligibility criteria. Selected patients and providers who provide informed consent will also be surveyed 
and/or interviewed to identify reasons for its success or failure across study sites (Aim 2), and to collect 
relevant cost data for analyses of cost-effectiveness and epidemiologic impact (Aim 3). 
2.3 General comments 
 
Trial reporting will follow the Standards for Reporting Implementation Studies (STaRI) checklist to evaluate 
the trial’s coprimary aims: clinical effectiveness and implementation of onsite Xpert testing.1 In addition, 
reporting will include elements from the CONSORT 2010 extension checklist for cluster randomized trials, 
such as the randomization process and a participant flow diagram.2 
This Statistical Analysis Plan summarizes analyses for all effectiveness outcomes, including the primary 
outcome of the number of patients treated for microbiologically-confirmed TB within 14 days of referral for 
sputum-based testing. Secondary outcomes for testing (number and proportion of patients completing TB 
testing), diagnosis (number and proportion diagnosed with microbiologically-confirmed TB, number and 
proportion diagnosed with RIF-resistant TB,  time to microbiologically-confirmed TB), treatment (number and 
proportion treated for TB, time to treatment of microbiologically-confirmed TB), and follow-up (number and 
proportion with microbiologically-confirmed TB completing treatment, number and proportion who died within 
6 months) will also be assessed. These endpoints will be evaluated through review of TB registers and 
GeneXpert machine data for all patients initiating TB evaluation at participating health centers.  
This Statistical Analysis Plan is written in support of and is predominantly consistent with the full trial protocol; 
however, this analysis plan takes precedence.   
 

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XPEL study 
Statistical Analysis Plan (version 1.5) 
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3. Trial Summary 
 
3.1 Trial design 
 
The study is a parallel cluster-randomized trial (CRT) with two arms of equal number of clusters. The type 
of cluster used is institutional health units; in this case, health centers that provide TB microscopy services 
in Uganda in partnership with the National TB Program. Entire health centers will be the unit of 
randomization rather than individual participants to reduce contamination. The unit of analysis will be 
patients undergoing TB diagnostic evaluation during the 18-month enrolment period. The trial includes 
nested mixed methods and economic analyses to assess the implementation and cost-effectiveness of the 
intervention.  
 
3.2 Study population 
 
Eligibility Criteria 
 
A. Site-level Inclusion Criteria 
a. Use standard (multi-day) sputum smear microscopy as the primary method of TB diagnosis 
b. Participate in NTP-sponsored external quality assurance (EQA) for sputum smear 
microscopy  
c. Send samples to a district or regional hospital/health center for Xpert testing 
B. Site-level Exclusion Criteria 
a. Do not agree to be randomized to standard-of-care vs. intervention arms 
b. Perform sputum smear examination on <150 patients per year (based on 2015 data) 
c. Diagnose <15 smear-positive TB cases per year (based on 2015 data) 
C. Patient-level Inclusion Criteria  
a. Initiating TB evaluation at a study health center  
D. Patient-level Exclusion Criteria 
a. Have sputum collected for monitoring of response to anti-TB therapy (i.e., recorded as a 
follow-up patient in routine data source);  
b. Have sputum collected as part of active, community-based case finding (e.g., contact 
tracing, community outreach campaign) (i.e., noted in the remarks in the NTLP Laboratory 
Register);  
c. Referred to a study health center for TB treatment after a diagnosis is established elsewhere 
(i.e., noted in the remarks in the NTLP Laboratory Register)  
d. Started on TB treatment for extra-pulmonary TB only (i.e., recorded as a Disease Class=EP 
(Extra Pulmonary TB) in the NTLP Treatment Register) 
 
Settings and Locations  
Twenty microscopy centers met eligibility criteria based on an initial review of NTLP data. 5 (25%) health 
centers are level four health facilities (i.e. those serving counties with a target population of 100,000 
people), 14 (70%) are level three (i.e. those serving sub-counties with a target population of 20,000 
people), and one (5%) is a hospital. 18 (90%) health facilities are government-owned, one (St. Francis 
Njeru) is owned by an NGO, and one (Bishop Asili) is a private not for profit facility. The administrative 
authority is the Ministry of Health for 18 (90%) health centers, the Uganda Catholic Medical Bureau for St. 
Francis Njeru, and the local government for Bukulula. 12 (60%) of these health centers are located in the 
Central region of Uganda, and eight (40%) in the Eastern region. Four health centers are located in the 
Mayuge district, three in Kayunga, three in Iganga, and one in each of the districts of Buikwe, Kalungu, 
Kamuli, Luwero, Lwengo, Mityana, Mpigi, Mubende, Nakasongola, and Wakiso.3 District populations in 
2014 ranged from 181,799 (Nakasongola) to 1,997,418 (Wakiso), with a median population of 465,099 
(IQR 358,288-490,789).4 Five (25%) health centers are in subcounties classified in the 2014 census as 
urban, while 15 (75%) are rural.5 Health centers range in distance to central Kampala from 16 to 151 
kilometers, with a median of 110 km (IQR 76-128). The range of distance from each health center to their 
primary Xpert hub is 1 to 50 km, median of 16 km (IQR 13-24).  
 

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3.3 Interventions 
 
Intervention arm: Health centers randomized to the intervention arm will received the following intervention 
components: 1) onsite molecular testing for TB with GeneXpert as a replacement for sputum smear 
microscopy; 2) process redesign to facilitate same-day TB diagnosis and treatment; and 3) performance 
feedback of TB diagnostic evaluation quality indicators to health center staff. 
 
Control arm: Health centers randomized to the control arm will follow the standard-of-care TB evaluation 
strategies, which are onsite ZN or LED fluorescence microscopy and hub-based GeneXpert testing for 
selected patients in accordance with Uganda NTLP guidelines. 
 
3.4 Outcomes 
 
Definitions used to assess study outcomes 
 
• 
Eligible patient: clinic attendees satisfying our inclusion and exclusion criteria (as listed above) 
AND excluding patients aged <18 years or those with a documented prior history of TB treatment 
(e.g., reason for Xpert testing or TB treatment marked as treatment failure, relapse, treatment after 
loss to follow-up, etc.). In addition, patients identified as RIF resistant by Xpert testing will not 
contribute to the primary and secondary analysis except for the number and proportion with RIF 
resistance detected by Xpert or RIF resistance confirmed by culture.   
 
• 
Number enrolled (i.e., number evaluated for TB): Number of eligible patients identified over a 
defined enrolment period through review of the NTLP Presumptive TB register, NTLP Laboratory 
register, GeneXpert laboratory requisition forms and NTLP Treatment register at each study site 
(includes patients treated for TB without undergoing any sputum testing).  
 
• 
Date enrolled: Earliest date recorded in the NTLP Presumptive TB register, NTLP Laboratory 
register, GeneXpert laboratory requisition form, NTLP Treatment register or machine data. Records 
will be prospectively reviewed using name, sex, and age to identify patients presenting multiple 
times over the study period. Visits occurring within six months of enrollment will be considered as 
part of the same episode. 
 
• 
Number referred for testing: Number of eligible patients identified through review of the NTLP 
Presumptive TB register, NTLP Laboratory register and GeneXpert laboratory requisition forms at 
each study site. Patients indicated as new in the NTLP Treatment Register will also be considered 
testing referrals.  
 
• 
Number tested: Number of eligible patients with any smear or Xpert result entered into the NTLP 
Presumptive TB register, NTLP Laboratory register, GeneXpert laboratory requisition forms, or 
NTLP treatment register at each study site, or with a test result recorded in the onsite GeneXpert 
machine. Results must be dated within six months of enrolment. If result date is missing and cannot 
be found in the GeneXpert machine data, it will be assumed that the patient was tested within six 
months of enrolment.  
 
• 
Number completing testing: Number of eligible patients with a valid Xpert result plus number of 
eligible patients with a valid smear result entered into the NTLP Presumptive TB register, NTLP 
Laboratory register, GeneXpert laboratory requisition forms or NTLP treatment register, or with a test 
result recorded in the GeneXpert machine data. Testing must be completed within six months of 
enrolment. If the test result is missing and cannot be found in the GeneXpert machine data, it will be 
assumed that the patient was tested within six months of enrolment. Definitions of valid results are as 
follows: 
o A positive Xpert (Ultra) result with a semi-quantitative result of high, medium, low, or very low 
for all eligible patients; a positive Xpert Ultra result with a semi-quantitative result of trace for 
eligible HIV-positive patients; or a second positive Xpert Ultra result (including trace) if the 
initial result is trace-positive for eligible HIV-negative patients;6 

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XPEL study 
Statistical Analysis Plan (version 1.5) 
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o A negative Xpert result for all eligible patients; 
o One positive OR two negative smear results for eligible HIV-negative/HIV status unknown 
patients. 
 
• 
Microbiologically-confirmed TB: A positive smear and/or positive Xpert test result (as defined 
above) entered into the NTLP Presumptive TB register, NTLP Laboratory register, GeneXpert 
laboratory requisition forms, NTLP treatment register, and/or GeneXpert machine data (intervention 
sites only) within six months of enrolment. Number diagnosed: Number of eligible patients with 
microbiologically-confirmed TB via a positive smear and/or valid Xpert test result entered into the 
NTLP Presumptive TB register, NTLP Laboratory register, GeneXpert laboratory requisition form, 
NTLP treatment register, and/or GeneXpert machine data  within six months of enrolment.   
• 
Number with RIF resistance detected by Xpert: Number of eligible patients with RIF resistance 
identified by Xpert testing entered into the GeneXpert laboratory requisition forms and/or NTLP 
Laboratory registers within six months of enrolment . 
• 
Number with RIF resistance confirmed by culture: Number of eligible patients with: (1) RIF 
resistance detected by Xpert testing entered into the GeneXpert laboratory requisition forms and/or 
NTLP Laboratory registers, and (2) RIF resistance confirmed by culture-based DST or a second 
molecular assay as determined by review of Lab Register at MDR treatment center within six months 
of enrolment . 
• 
Number treated: Number of eligible patients entered into the NTLP Treatment register as having 
started Category I or II regimen. 
• 
Number completing treatment: Number treated and with a treatment outcome of cured or completed 
entered into the NTLP Treatment register. 
• 
Number died, among those initiated on TB treatment: Number treated and with a treatment 
outcome of died entered into the NTLP Treatment register within six months of enrolment , or number 
treated and confirmed dead by next of kin with a date of death within six months of enrolment. 
• 
Number died, among those with confirmed TB: Number diagnosed and with a treatment outcome 
of died entered into the NTLP Treatment Register within six months of enrolment, or number 
diagnosed and confirmed dead by next of kin with a date of death within six months of enrolment.  
• 
Number died, among all those eligible: Number treated and with a treatment outcome of died 
entered into the NTLP Treatment register within six months of enrolment, or number traced with a 
death recorded 
• 
Time-to-diagnosis: Number of days from date enrolled to earliest date of positive smear or valid 
Xpert result recorded in NTLP Presumptive TB register, NTLP Laboratory register, GeneXpert 
laboratory requisition form, or NTLP treatment register within six months of enrolment . For the control 
arm, attempts will be made to abstract the missing Xpert result dates from machine data from 
GeneXpert machines at testing hubs. For patients with microbiologically confirmed TB who are 
missing the smear or Xpert result date, the date of treatment initiation will be used as the date of 
diagnosis. 
• 
Time-to-treatment: Number of days from date enrolled to treatment start date entered into NTLP 
Treatment register if treatment start date is within six months of enrolment.  
 
 
 

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Primary Outcome  
Outcome 
Numerator 
Denominator 
Number treated for microbiologically-
confirmed TB within 14 days of date 
enrolled. 
Number diagnosed  
and time-to-treatment within 14 days 
None 
 
 
Secondary Outcomes  
A. Testing 
Outcome 
Numerator 
Denominator 
Number referred for TB testing 
Number referred for testing 
None 
Proportion completing testing 
Number completing testing 
Number enrolled  
B. Diagnosis 
Outcome 
Numerator 
Denominator 
Number diagnosed with 
microbiologically-confirmed TB* 
Number diagnosed 
None 
Proportion diagnosed with 
microbiologically-confirmed TB* 
Number diagnosed 
Number enrolled 
Number with RIF resistance detected 
by Xpert/number with RIF resistance 
confirmed by culture* 
Number with RIF resistance detected 
by Xpert/number with RIF resistance 
confirmed by culture 
None 
Proportion with RIF resistance 
detected by Xpert/proportion with RIF 
resistance confirmed by culture* 
Number with RIF resistance detected 
by Xpert/number with RIF resistance 
confirmed by culture 
Number enrolled 
Time to microbiologically-confirmed 
TB** 
Time-to-diagnosis if microbiologically-
confirmed TB 
None 
C. Treatment 
Outcome 
Numerator 
Denominator 
Number treated for TB* 
Number treated 
 
None 
Proportion treated for TB* 
Number treated 
Number enrolled 
Number treated for microbiologically-
confirmed TB* 
Number diagnosed AND treated 
None 
Proportion treated for 
microbiologically-confirmed TB* 
Number diagnosed AND treated 
Number enrolled 
Proportion with microbiologically-
confirmed TB treated* 
Number diagnosed AND treated 
Number diagnosed  
Time-to-treatment of 
microbiologically-confirmed TB 
Time-to-treatment if microbiologically-
confirmed TB and treated 
None 
D. Follow-up  
Outcome 
Numerator 
Denominator 
Number with microbiologically-
confirmed TB completing treatment 
Number diagnosed AND completing 
treatment 
None 
Proportion with microbiologically-
confirmed TB completing treatment 
Number diagnosed AND completing 
treatment 
Number diagnosed AND treated 
Number who died within 6 months  
Number died 
None 

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XPEL study 
Statistical Analysis Plan (version 1.5) 
12 Jul 2021 
Proportion who died within 6 
months*** 
Number died 
Number enrolled 
Number who died within 6 months 
Number diagnosed AND died 
None 
Proportion who died within 6 
months*** 
Number diagnosed AND died 
Number diagnosed  
Number who died within 6 months 
Number treated AND died 
None 
Proportion who died within 6 
months*** 
Number treated AND died 
Number treated 
 
*Outcome will be assessed within 1 day and within 14 days of enrolment. One day was chosen because the intervention focuses 
on same-day diagnosis and treatment. 14 days was chosen because the diagnostic process could take 7-10 days in the control 
arm depending on the frequency of sample transport to Xpert testing sites. 
**For the control arm, missing Xpert result dates will be recorded as the next scheduled date of sputum transportation from the 
health center to the GeneXpert facility (results from the previous batch of testing are returned when the next batch of samples are 
picked up). For the intervention arm, missing Xpert result dates will be recorded as the day following sample collection. 
***Vital status will be assessed between 4-9 months from enrolment  
 
3.5 Sample Size 
 
Sample size 
 
Based on pre-randomization data collected from January to December 2017, we estimate 5360 patients will 
be enrolled across 20 study sites over the 18-month enrolment period. Other study intervals ranging from 12 
to 24 months are considered.  
 
Justification 
 
Original detectable effect size estimates: The study is based on the health center being the unit of 
randomization and aims to demonstrate the superiority of the intervention arm. The sample size calculation 
uses formulae appropriate for cluster-randomized trials with a parallel design and stratified and/or restricted 
randomization, including the addition of one extra cluster per arm to allow for the loss of degrees of freedom 
due to stratification to obtain conservative estimates.7 The original detectable effect size estimates were 
based on the outcome being the proportion of patients treated for microbiologically-confirmed TB within two 
weeks of referral for sputum-based testing. A type I error of 5% and power of 90% was assumed. Pre-
randomization data collected from January to December 2017 from the 20 selected trial sites in Uganda 
suggested that the average proportion of patients referred for TB evaluation who initiate treatment for active 
TB within two weeks would be 6.7%, and the coefficient of variation (k) between clusters is 0.36, with a 
harmonic mean of 268 patients enrolled at each health center cluster during the 18-month enrolment period. 
Based on these assumptions and assuming a type I error of 5%, we will have 90% power to detect a 6% or 
greater absolute increase in the outcome proportion in the intervention arm (see Table 1). Table 1 also shows 
detectable absolute effect sizes with varying power (80%, 85%, 90%) during various trial time intervals and 
respective average cluster size estimates.   
 
The minimum detectable effect size is based on published literature, our preliminary data demonstrating the 
increased sensitivity of Xpert Ultra vs. smear microscopy (95% vs. 40%), and reduced pre-treatment loss to 
follow-up expected in the intervention vs. control arms due to rapid, onsite molecular testing (7% vs. 30%). 
When applying these parameters to a population of 1000 individuals with TB prevalence of 10%, we would 
expect 95 patients to be diagnosed with TB and 88 to start treatment within 14 days in the intervention arm, 
and 40 patients to be diagnosed with TB and 28 to start treatment within 14 days in the control arm. Thus, 
the expected difference in the primary outcome proportion would be 8.8% vs. 2.8%, or 6%.8  
 
Table 1. Absolute difference in the proportion of clinic attendees initiating TB treatment within 14 
days of initial sputum submission, among those referred for TB testing 
Average cluster size 
Effect size (10 clusters per arm, k=0.36) 
80% Power 
85% Power 
90% Power 

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XPEL study 
Statistical Analysis Plan (version 1.5) 
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178 (12 months) 
5.9% 
6.5% 
7.4% 
223 (15 months) 
5.7% 
6.3% 
7.1% 
268 (18 months) 
5.5% 
6.1% 
6.9% 
312 (21 months) 
5.4% 
6.0% 
6.7% 
357 (24 months) 
5.3% 
5.9% 
6.6% 
 
Figure 1. Effect size by cluster size. Number of clusters per arm is 10, k=0.36.    
 
 
 
 
The primary outcome was changed to a count of the number of adults diagnosed and treated with TB at the 
cluster-level.  The new primary outcome better reflects the intended effect of the multi-component intervention 
to drive more people who present to health facilities through the entire TB diagnostic evaluation cascade of 
care, including being screened for TB, tested for TB (if screen-positive), diagnosed with TB (if tested) and 
initiated on treatment (if diagnosed). Process re-design is expected to help improve the number screened 
and tested. Onsite molecular testing is expected to increase the numbers diagnosed (increased sensitivity) 
and treated (faster turn-around time and reduced workload). Performance feedback is expected to maintain 
improvement along each step of the cascade. Thus, the intervention is expected to increase both the number 
of patients entering and completing the cascade of care.  
Revised detectable effect size estimates: The revised detectable effect size estimates are for the outcome 
being the number of patients treated for microbiologically-confirmed TB within two weeks of referral for 
sputum-based testing. We first used 10 months of pre-intervention period data across all 20 clinics to assess 
1) the geometric mean number of patients diagnosed and treated for TB within 14 days (new primary 
outcome), the natural log (ln) of the geometric mean and 3) the standard deviation of the natural log at control 
sites, intervention sites and overall. For the pre-intervention period, the intervention clinics have a lower 
geometric mean number of clinic attendees who were diagnosed with TB and started on TB treatment within 
14 days compared with the control arm (table 2). 
Table 2: summary data from the baseline period 
 
 
Geometric mean 
Mean of ln outcome 
SD of ln outcome 
Control 
14.70 
2.69 
0.23 
5.2%
5.4%
5.6%
5.8%
6.0%
6.2%
6.4%
6.6%
6.8%
7.0%
7.2%
7.4%
178
223
268
312
357
Effect Size
Cluster Size
80% Power
85% Power
90% Power

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Statistical Analysis Plan (version 1.5) 
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Intervention 
6.49 
1.87 
0.32 
 
We then estimated the detectable effect size (expressed as a geometric mean ratio). The calculations 
assume the same parameters as for the original primary outcome (10 clinics/arm, 18-month trial duration to 
achieve geometric mean of 268 patients/cluster in control and intervention arms). Table 3 below shows the 
detectable effect size assuming within-arm SD of ln outcome of 0.2 to 0.3 and power of 80-90%. The 
detectable effect sizes shown below are conservative (calculations do not take into account the baseline 
differences between arms in the new primary outcome and are based on 10-months rather than full 24-
months of pre-intervention data). Final calculations will be repeated once baseline data is fully entered and 
available for analysis. 
Table 3: Detectable effect sizes 
SD of ln outcome 
Geometric Mean Ratio of the new primary outcome  
(intervention vs control) 
80% power 
90% power 
0.2 
1.30 
1.36 
0.3 
1.49 
1.58 
 
We believe a GMR of 1.30-1.58 is a reasonable detectable effect size. Xpert MTB/RIF is twice as sensitive 
as smear microscopy (double the number of confirmed TB cases) and we expect onsite testing to reduce 
pre-treatment loss to follow-up by at least half (from 30% to <15%).  
3.6 Blinding 
 
The trial will be open-label for participants and researchers, as blinding of the assigned intervention is not 
feasible given intervention implementation at the health center level. Staff, however, will be blinded to the 
intervention allocations through the use of codes to identify clusters rather than health center names. 
Where possible, the investigators and study staff will be masked to ongoing aggregated data by study. The 
trial statistician and data manager will have access to the aggregate data. Some site-level data will be used 
for performance feedback to sites in the intervention arm; this data will not be a comparison to baseline 
sites across study arms. 
 
4. Randomization 
 
4.1 Sequence Generation 
 
A random allocation sequence will be generated by STATA 14 (StataCorp LP, College Station, TX). 
Randomisation of clusters to the intervention or control arm was conducted using stratification and 
restriction. Stratified randomisation was used to help reduce between-cluster variability and achieve 
balance at baseline. Using data from 2017 on the percentage of adult clinic attendees being investigated 
for TB who started TB treatment within 14 days, the clusters were stratified into two equal-sized groups – 
“low” and “high” percentages of this outcome based on the median. Restricted randomisation was used to 
help ensure balance for key factors. Variables we restricted on were; year cluster enrolled in project (2017 
vs. 2018); sputum transport frequency to Xpert hub; distance to Xpert hub; number of patients evaluated for 
TB; percentage starting TB treatment within 14 days; urban vs. rural health clinics; HIV prevalence; number 
of facilities per district. For these restriction criteria the overall proportion of unacceptable allocations was 
calculated and the the validity assessed using the validity matrix.9 From a total of 63,504 possible 
randomisations based on two equal-sized strata, applying the restriction criteria resulted in 11,392 
randomisations; one of these was chosen at random. 
 
4.2 Allocation concealment mechanism 
 

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Allocation concealment was used to prevent selection bias by concealing from the research staff the 
allocation sequence until assignment had occurred.   
 
4.3 Implementation  
 
The trial statistician generated the random allocation assignments, and clusters were enrolled by local 
study staff. Eligible individual patients will be included in clusters through complete enumeration. District 
health officers provide administrative authority and consent to health center participation prior to 
randomization. Consent is documented in a signed Memorandum of Understanding (MOU) between the 
district and the National Tuberculosis and Leprosy Control Program (NTLP), endorsed by the Chief 
Administrative Officer, the District Health Officer, and the Program Manager for NTLP. A copy of the 
agreement is provided to the facility in-charge at each health center.  
 
A public ceremony for randomization was held in Kampala, Uganda. Facility in-charges, district health 
officers, and representatives from NTLP attended.  
 
4.4 Statistical Methods 
 
 
4.4.1 General analyses principles 
 
We will calculate and compare all effectiveness outcomes for the two trial arms using prespecified analyses 
following the intention-to-treat principle. Statistical analysis methods to determine intervention effect on 
outcomes will be used that are appropriate for CRTs with a small number of clusters. Analysis of cluster-
level summaries is based on a total of 20 clusters (10 per arm), each comprised of one health center, and 
each being given equal weight. Effect estimates will be reported with 95% confidence intervals, and an 
associated P-value.  
 
4.4.2 Baseline data 
 
Baseline variables (from the 12 month period before 22 October 2018 and socio-demographic data from 12 
month period after 22 October 2018) will be cross-tabulated to check for appropriate balance between 
study arms and to characterize the study population using summary statistics both at cluster and individual 
levels (Annex Table 1, 2). 
 
Cluster (i.e. health center) level data will be collapsed across study arms and compared using mean, 
median and range with standard deviations and/or interquartile ranges as appropriate. The basis of this 
analysis will be data on demographics, health center and laboratory infrastructure, organizational hierarchy, 
Xpert referral network, and TB testing and treatment collected during site assessment, recruitment, and 
guidelines training.  
 
Individual level data for all patients eligible for analysis will also be collapsed across study arms and 
compared using mean, median and range with standard deviations and/or interquartile ranges as 
appropriate. These individual factors include age, sex, HIV status, and ART status.  
 
We will summarize data obtained during the pre-randomization period for the cohort of health centers and 
patients who are being enrolled in the study and randomized to a trial arm (Annex table 1). 
 
We will also summarise of numbers referred for TB testing in intervention and control arms (collapsed over 
cluster) in the 12 month period after 22 October 2018 (Annex Table 3).    
 
Descriptive data between control and intervention arms will be presented in tabular format in lieu of formal 
statistical comparison or significance tests, and will be reviewed to confirm comparability and identify any 
substantial imbalances. Adjusted effect estimates will be calculated by study arm to account for imbalance 
of covariates which are risk factors for the outcome of interest. Losses in degrees of freedom due to 
cluster-level covariate adjustments will be accounted for in all calculations.  
 

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4.4.3 Primary analysis of primary outcome 
 
Analysis will be conducted at the cluster (clinic)-level. The outcome is the count of the number treated for 
microbiologically-confirmed TB within two weeks of referral for sputum-based testing at the clinic-level. 
Initially Poisson regression will be used, with an offset at the clinic level of the total number of days the 
clinic contributes individuals to the study (starting from 22 October 2018). Evidence for over-dispersion will 
be assessed and if over-dispersion is detected then negative binomial regression will be used, using the 
same offset. Either model will adjust for randomisation strata and the count of microbiologically-confirmed 
TB cases that initiated on treatment within 14 days in the 12 month period before 22 October 2018.  The 
exact functional form of this covariate will be identified through fractional polynomials. Further adjustment 
for clinic-level factors that show imbalance will be explored, though the number of adjustment factors will be 
limited as all are at the clinic-level. See Annex tables 4a and 4b, and 5a. 
 
Xpert Ultra cartridges will be used in our intervention (see Outcomes Definitions Section 3.4). Two 
sensitivity analyses will be performed reclassifying 1) all trace-positive results as negative and 2) trace-
positive results in HIV negative patients as true positive (even if not confirmed by a second positive test) to 
determine the impact of Ultra on the primary outcome. We expect to see trace-positive results in between 
10-20% of all positive Xpert Ultra results, irrespective of study arm. Therefore, sensitivity analyses will only 
be done if the ratio of trace-positive results to all positive Xpert Ultra results is similar in both trial arms.    
 
No sub-group analyses for strata defined at the individual-level (e.g., sex and HIV status) will be conducted 
as the primary outcome analyses are conducted at the cluster-level. 
 
 
4.4.4 Analysis of secondary outcomes 
 
Secondary outcomes are reported as either counts, proportions, or time to event (continuous outcome), 
and are derived from data collected across routine data sources.  
 
All analyses will adjust for randomisation strata. Intervention effect estimates will be reported for analyses 
(1) adjusting for randomisation strata only and (2) adjusting for randomisation strata and individual-level 
(proportions and time to event only) and clinic-level factors considered imbalanced, as outlined below.  
See Annex table 5b. 
 
 
4.4.4.1 Count outcomes: 
 
The analytic approach will be the same as described for the primary outcome The outcome is the count of 
the respective outcome at the clinic-level. Analysis will be conducted at the cluster (clinic)-level. Either 
Poisson or negative binomial regression will be used, depending on over-dispersion. Adjustment will be 
made for the clinic-level count of the outcome using data in the 12 month period before 22 October 2018.  
The exact functional form of this covariate will be identified through fractional polynomials.  
 
4.4.4.2 Proportions 
 
The analysis will give each cluster equal weight. The overall risk for each cluster will be calculated and 
shown, by strata and arm. A log transformation (where necessary) will be applied to the risk for each 
cluster. If a cluster has no events, 0.5 event will be added to all clusters in order that the log transformation 
can be conducted. The mean and standard deviation of these log risks will be used to obtain the geometric 
mean (GM) and associated 95% CI for each arm of the study. The risk ratio is estimated using linear 
regression of the log risks on stratum and arm. An approximate standard error for the difference in log (GM) 
between arms is obtained based on the residual mean square from a two-way analysis of variance 
(ANOVA) of the cluster log risk on stratum, study arm and the interaction between stratum and study arm. 
The 95% CI is calculated from this standard error, using a t-statistic with 16 degrees of freedom. This will 
be the primary analysis. 
 

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Limited adjustment for individual level factors is possible; age, sex, HIV status, and ART status. 
Adjustment, using a complete case analysis, will only be done if data completeness for the variable is more 
than 80% in both trial arms and imbalance is observed (not assessed through a P-value).Logistic 
regression will be used to adjust for any baseline imbalance at the individual level, adopting a two-stage 
approach. The regression model will include terms for the adjustment factors (individual level) and strata, 
but not study arm. For each cluster the fitted model will be used to obtain the ratio of observed to expected 
(O/E) events, and a log transformation will be applied to this ratio, where appropriate.  
 
Linear regression of the log (O/E) on stratum and arm (2-way ANOVA) will be used to estimate the risk 
ratio. The variance for the ratio of mean O/E is calculated from the residual mean square from an ANOVA 
of log(O/E) on stratum, arm and the interaction between stratum and arm. The 95% CI is calculated from 
this variance, using a t-statistic with 16 degrees of freedom  
 
If imbalance is observed for the equivalent cluster-level outcome in the 12 month period before 22 October 
2018, adjustment at the second stage described above will be conducted, with appropriate adjustment for 
the degrees of freedom for the intervention effect. The exact functional form of this cluster-level covariate 
will be identified through fractional polynomials.  
 
 
4.4.4.1 Time to event (continuous outcome) 
 
The analysis will give each cluster equal weight. The arithmetic and geometric mean for the continuous 
outcome of time to event (amongst all those experiencing the event) will be calculated for each cluster and 
shown, by strata and arm. A log transformation of the individual-level time to event will likely be necessary 
as the times to event may be positively skewed, resulting in geometric means. Clusters where no 
individuals contribute time to event outcome data will be excluded from the analysis. 
ANOVA will be used to calculate either the difference in means between arms or geometric mean ratio, 
depending on whether a log transformation was used. This will be the primary analysis. 
 
A two-stage approach, similar to as described above, will be used to adjust for confounders at the individual 
level. Limited adjustment for individual level factors is only possible; age, sex, HIV status, and ART status. 
Adjustment will only be done if data completeness for the variable is more than 80% in both trial arms and 
imbalance is observed (not assessed through a P-value). If imbalance is observed for the equivalent 
cluster-level outcome in the 12 month period before 22 October 2018, adjustment at the second stage 
described above will be conducted, with appropriate adjustment for the degrees of freedom for the 
intervention effect. The exact functional form of this cluster-level covariate will be identified through 
fractional polynomials.  
 
 
4.4.5 Sub-group analyses for secondary outcomes 
 
Pre-specified sub-group analyses for a subset of secondary outcomes will be conducted to assess whether 
the effect of the intervention differs between sub-groups. The sub-groups are (1) HIV status at enrolment 
(self-reported, laboratory test resulted as noted on primary data sources, or through pre-ART and ART 
register data extraction) defined as positive or negative, and (2) sex (male or female). The effect of the 
intervention will be estimated for each sub-group, and P-values for effect modification, appropriate for 
individual-level covariates, will be reported.10 These sub-group analyses will be conducted for the following 
secondary outcomes: proportion completing testing; proportion of patients referred for TB testing who are 
diagnosed with microbiologically-confirmed TB within 14 days; proportion of patients with microbiologically-
confirmed TB who are treated within 14 days of referral for sputum-based testing; and the proportion who 
died within 6 months. See Annex table 5c. Results from subgroup analyses will also be summarised as 
forest plots. 
 
 
 
 

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Annex tables 
Table 1. Summary of baseline study variables at the cluster-level or using data for the 12 month period 
before 22 Oct 2018 
 
Intervention 
Control 
Year of enrolment into project 
2017 or 2018 
 
 
Distance to Xpert hub 
Median (IQR) 
 
 
Sputum transport frequency to Xpert 
hub 
Once per week or ≥2 times per week 
 
 
Total number referred for TB testing* 
# 
 
 
Average number referred for TB 
testing, across clusters* 
Harmonic mean 
 
 
Region 
Central or Eastern, % (n/N) 
 
 
District 
% per district 
 
 
Geographical area 
Urban or rural, % (n/N) 
 
 
Population of sub-county 
# 
 
 
Patients undergoing TB testing who are 
diagnosed with and treated for TB 
within 14 days* 
(< 6.2% or ≥ 6.2%) 
 
 
HIV prevalence among TB patients* 
% (n/N) 
 
 
*  in the 12 month period before 22 October 2018 
 
Table 2. Summary of demographic variables among those referred for TB testing* (collapsed over 
cluster) 
 
Intervention 
Control 
Total number referred for 
TB testing 
# 
 
 
Average number referred 
for TB testing, across 
clusters 
Harmonic mean 
 
 
Missing data for sex 
% (n/Ntotal) 
 
 
Female 
% (n/Nnon-miss) 
 
 
Missing data for age 
% (n/ Ntotal) 
 
 
Age 
Median (IQR), Nnon-
miss 
 
 
HIV status known: 
% (n/Ntotal) 
 
 
HIV-positive 
% (n/Nnon-miss) 
 
 
ART status known: 
% (n/Ntotal HIV+) 
 
 
ART prevalence 
% (n/Nnon-miss HIV+) 
 
 
*  in the intervention period after 22 October 2018 
 
 

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Table 3: Summary of numbers referred for TB testing* in intervention and control arms (collapsed 
over cluster) using data from record review and facility follow up.   
 
Intervention 
Control 
Xpert result 
Negative 
Positive 
Unknown 
 
 
Semi-Quantitative result 
High 
Medium 
Low 
Very Low 
Trace 
 
 
RIF result 
Negative 
Positive 
Indeterminate/Invalid/No Result/Error 
Unknown/No Result 
 
 
MDR status 
Yes, confirmed positive by DST 
No, confirmed negative by DST 
Unknown 
 
 
Smear result 
Negative 
Positive 
Unknown 
 
 
Treatment status 
Yes, on treatment 
No, not on treatment 
Unknown 
 
 
Treatment category  
Category 1- 2RHZE/6EH 
Category 1- 2RHZE/4RH 
Category 2-2SRHZE/1RHZE/5RHE 
Category 3-2RHE/4RH 
Other 
 
 
Treatment outcome 
Cured 
Completed 
Failure (smear positive) 
Died 
Transferred out 
Lost to Follow up 
 
 
*  in the period after 22 October 2018 
 
 

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Table 4a: Primary outcome - summary of number treated for microbiologically-confirmed TB within 
14 days of referral for sputum-based testing and total number of months clinic contributes individuals 
to the study, by control and intervention arm 
 
Control arm 
Intervention arm 
Cluster 
# diagnosed and 
treated within 14 
days 
total number of 
months the clinic 
contributes 
individuals to the 
study 
# diagnosed and 
treated within 14 
days 
total number of 
months the clinic 
contributes 
individuals to the 
study 
1 
 
 
 
 
2 
 
 
 
 
3 
 
 
 
 
4 
 
 
 
 
5 
 
 
 
 
6 
 
 
 
 
7 
 
 
 
 
8 
 
 
 
 
9 
 
 
 
 
10 
 
 
 
 
Geometric 
Mean 
 
 
 
 
 
 
 

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Table 4b: Primary outcome, summary of number treated for microbiologically-confirmed TB within 
14 days of referral for sputum-based testing and total number of months clinic contributes individuals 
to the study, by control and intervention arm for the 12 month period before 22 October 2018 
 
Control arm 
Intervention arm 
Cluster 
# diagnosed and 
treated within 14 
days 
total number of 
months the clinic 
contributes 
individuals to the 
study 
# diagnosed and 
treated within 14 
days 
total number of 
months the clinic 
contributes 
individuals to the 
study 
1 
 
 
 
 
2 
 
 
 
 
3 
 
 
 
 
4 
 
 
 
 
5 
 
 
 
 
6 
 
 
 
 
7 
 
 
 
 
8 
 
 
 
 
9 
 
 
 
 
10 
 
 
 
 
Geometric 
Mean 
 
 
 
 
 
 

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Table 5a: Primary outcome: summary of number treated for microbiologically-confirmed TB within 
14 days of referral for sputum-based testing and, adjusted risk ratio (95% confidence intervals), and 
associated P-values 
Primary 
outcome 
I: 
#, GM 
C: 
#. GM 
Adjusted 
RR* 
(95% CI) 
P-value 
Primary 
analysis 
 
 
 
 
Sensitivity 
analysis 1 
 
 
 
 
Sensitivity 
analysis 2 
 
 
 
 
I Intervention ; C control, GM geometric mean 
*  adjusted for randomisation strata and count of microbiologically-confirmed TB cases that initiated on treatment 
within 14 days in the 12 month period before 22 October 2018. The RR and 95% CI, adjusting for randomisation strata 
only is XX (95% CI yy to zz). 
Sensitivity analyses are: reclassifying 1) all trace-positive results as negative and 2) trace-positive results in HIV 
negative patients as true positive (even if not confirmed by a second positive test) to determine the impact of Ultra on 
the primary outcome.  
 
Table 5b: Secondary outcomes: unadjusted and adjusted risk ratio (95% confidence intervals) and 
associated P-values 
Secondary outcomes 
 
I:  
C: 
RR  
(95% CI) 
P-value 
Adjusted 
RR  
(95% CI) 
P-value 
A. Testing  
 
 
 
 
 
 
 
Number referred for TB 
testing 
# 
GM 
 
 
 
 
 
 
Proportion completing 
testing 
% (n/N) 
GM % 
 
 
 
 
 
 
B. Diagnosis 
 
 
 
 
 
 
 
Number diagnosed with 
microbiologically-confirmed 
TB (by day 1) 
# 
GM 
 
 
 
 
 
 
Number diagnosed with 
microbiologically-confirmed 
TB (by day 14) 
# 
GM 
 
 
 
 
 
 
Proportion diagnosed with 
microbiologically-confirmed 
TB (by day 1) 
% (n/N) 
GM % 
 
 
 
 
 
 
Proportion diagnosed with 
microbiologically-confirmed 
TB (by day 14) 
% (n/N) 
GM % 
 
 
 
 
 
 

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Number with RIF resistance 
detected by Xpert/ number 
with RIF resistance 
confirmed by culture (by day 
1) 
# 
GM 
 
 
 
 
 
 
Number with RIF resistance 
detected by Xpert/ number 
with RIF resistance 
confirmed by culture (by day 
14) 
# 
GM 
 
 
 
 
 
 
Proportion with RIF 
resistance detected by 
Xpert/proportion with RIF 
resistance confirmed by 
culture (by day 1) 
% (n/N) 
GM % 
 
 
 
 
 
 
Proportion with RIF 
resistance detected by 
Xpert/proportion with RIF 
resistance confirmed by 
culture (by day 14) 
% (n/N) 
GM % 
 
 
 
 
 
 
Time to microbiologically-
confirmed TB 
# 
GM 
 
 
 
 
 
 
C. Treatment 
 
 
 
 
 
 
 
Number treated for TB (by 
day 1) 
# 
GM 
 
 
 
 
 
 
Number treated for TB (by 
day 14) 
# 
GM 
 
 
 
 
 
 
Proportion treated for TB (by 
day 1) 
% (n/N) 
GM % 
 
 
 
 
 
 
Proportion treated for TB (by 
day 14) 
% (n/N) 
GM % 
 
 
 
 
 
 
Number treated for 
microbiologically-confirmed 
TB (by day 1) 
# 
GM 
 
 
 
 
 
 
Number treated for 
microbiologically-confirmed 
TB (by day 14) 
# 
GM 
 
 
 
 
 
 
Proportion treated for 
microbiologically-confirmed 
TB (by day 1) 
% (n/N) 
GM % 
 
 
 
 
 
 
Proportion treated for 
microbiologically-confirmed 
TB (by day 14) 
% (n/N) 
GM % 
 
 
 
 
 
 
Proportion with 
microbiologically-confirmed 
TB treated (by day 1) 
% (n/N) 
GM % 
 
 
 
 
 
 
Proportion with 
microbiologically-confirmed 
TB treated (by day 1) 
% (n/N) 
GM % 
 
 
 
 
 
 
Time-to-treatment of 
microbiologically-confirmed 
TB 
GM 
 
 
 
 
 
 
D. Follow-up 
 
 
 
 
 
 
 

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Number with 
microbiologically-confirmed 
TB completing treatment 
# 
GM 
 
 
 
 
 
 
Proportion with 
microbiologically-confirmed 
TB completing treatment 
% (n/N) 
GM % 
 
 
 
 
 
 
Number who died within 6 
months  
# 
GM 
 
 
 
 
 
 
Proportion who died within 6 
months 
% (n/N) 
GM % 
 
 
 
 
 
 
GM geometric mean; GM % geometric mean of the cluster-level proportions 
 
Table 5c: Sub-group analyses for selected secondary outcomes: stratum-specific unadjusted and 
adjusted risk ratios (95% confidence intervals) and associated P-values for interaction 
Secondary 
outcomes 
 
I:  
C: 
RR  
(95% CI) 
P-value 
Adjusted 
RR  
(95% CI) 
P-value 
P-value for 
interaction 
A. Proportion completing testing 
HIV-positive 
% (n/N)  
 
 
 
 
 
 
HIV-negative 
% (n/N)  
 
 
 
 
 
 
Male 
% (n/N)  
 
 
 
 
 
 
Female 
% (n/N)  
 
 
 
 
 
 
B. Proportion referred for TB testing who are diagnosed with microbiologically-confirmed TB (by day 14) 
HIV-positive 
% (n/N)  
 
 
 
 
 
 
HIV-negative 
% (n/N)  
 
 
 
 
 
 
Male 
% (n/N)  
 
 
 
 
 
 
Female 
% (n/N)  
 
 
 
 
 
 
C. Proportion  of patients with microbiologically-confirmed TB who are treated within 14 days of referral for sputum-
based testing 
HIV-positive 
% (n/N)  
 
 
 
 
 
 
HIV-negative 
% (n/N)  
 
 
 
 
 
 
Male 
% (n/N)  
 
 
 
 
 
 
Female 
% (n/N)  
 
 
 
 
 
 
D. Proportion who died within 6 months 
HIV-positive 
% (n/N)  
 
 
 
 
 
 
HIV-negative 
% (n/N)  
 
 
 
 
 
 
Male 
% (n/N)  
 
 
 
 
 
 
Female 
% (n/N)  
 
 
 
 
 
 
 
 
 
 

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Figure 1: Comparison of TB diagnostic evaluation in the intervention and control arms 
 
*Consider empiric treatment or additional testing if test results are negative or invalid 
 
 
 
Standard-of-Care (CONTROL ARM) 
 INTERVENTION ARM 
 
All patients 
Patients with HIV 
All patients 
VISIT 1 
 
 
 
 
 
 
VISIT 2 
 
 
 
 
 
 
VISIT 3 
 
 
 
VISIT 4 
 
 
 
 
 
Treat if Xpert 
positive* 
Send sputum for 
Xpert testing 
Treat if Xpert positive* 
Perform Xpert testing 
Collect sputum 
Collect sputum 
Prepare/examine smear* 
Smear-positive: Treat 
Treat if Xpert positive* 
Smear-negative:   
Collect sputum and 
 send for Xpert testing*  
Smear-positive: Treat 
Smear-negative:   
Collect sputum and 
prepare/examine smear* 

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Figure 2: CONSORT diagram 
CONSORT diagram will be constructed show individual and cluster-level data at baseline and follow-up and 
based on the CONSORT extension to CRTs. 
 
 
 
 
 
40 Health centers assessed for eligibility
20 Health centers randomized
10 health centers allocated to control 
arm
Harmonic mean across health centers 
for number enrolled:
XX patients
XX health centers lost to follow up
XX Health centers analyzed (XX
patients)
XX Patients excluded from analysis
10 Health centers allocated to 
intervention arm
Harmonic mean across health centers 
for number enrolled:
XX patients
XX health centers lost to follow up
XX Health centers analyzed (XX
patients)
XX Patients excluded from analysis
20 Excluded
10 Not meeting inclusion criteria
10 Dropped out before randomization
ALLOCATION 
FOLLOW-UP 
ANALYSIS 
ENROLLMENT 

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Post-hoc analyses  
The following outcomes were not specified in the SAP but were included in the trial manuscript to ensure 1) 
each proportion outcome had a corresponding count outcome (or vice versa) and 2) every relevant step of 
the TB diagnostic evaluation cascade was represented (for those with microbiologically-confirmed TB). 
 
Overall 
• 
Number completing testing  
 
Patients with microbiologically-confirmed TB  
• 
Proportion diagnosed*  
 
Subgroup analyses 
• 
Number completing testing  
• 
Number diagnosed with microbiologically-confirmed TB* 
• 
Number treated for microbiologically-confirmed TB* 
• 
Number treated for TB*  
• 
Proportion with same-day diagnosis of microbiologically-confirmed TB  
• 
Proportion with same-day treatment for microbiologically-confirmed TB  
• 
Proportion treated for TB* 
 
*Outcomes assessed within 1 day and within 14 days of initial sputum submission  
 
 

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References 
1. 
Pinnock H, Barwick M, Carpenter CR, et al. Standards for Reporting Implementation Studies (StaRI): 
explanation and elaboration document. BMJ Open. 2017;7(4):e013318. 
2. 
Campbell MK, Piaggio G, Elbourne DR, Altman DG, Group C. Consort 2010 statement: extension to 
cluster randomised trials. BMJ. 2012;345:e5661. 
3. 
Ministry of Health (Republic of Uganda). Health Facilities Inventory. Department of Clinical Services. 
Kampala, Uganda 2012:140. 
4. 
Uganda Bureau of Statistics. The National Population and Housing Census 2014 - Main Report. 
Kampala, Uganda 2016. 
5. 
Uganda Bureau of Statistics. National Population and Housing Census 2014 - Provisional Results. 
Kampala, Uganda 2014. 
6. 
World Health Organization. Non-inferiority analysis of Xpert MTB/RIF Ultra compared to Xpert 
MTB/RIF. Geneva: World Health Organization; 2017. 
7. 
Hayes RJ, Moulton, L.H. Cluster Randomized Trials. Boca Raton: CRC Press; 2009. 
8. 
Farr K, Nalugwa, T., Shete, P., Cattamanchi, A., Nantale, M., Ojok, C., Oyuku, D., Nabwire, S., 
Fielding, K., Joloba, M., Mugabe, F., Dowdy, D., Moore, D., Davis, J.L., Katamba, A. . Late Breaking 
Abstract - The tuberculosis diagnostic evaluation cascade of care at microscopy centers linked to 
Xpert MTB/RIF hubs in Uganda. European Respiratory Journal. 2017;50. 
9. 
Moulton LH. Covariate-based constrained randomization of group-randomized trials. Clin Trials. 
2004;1(3):297-305. 
10. 
Cheung YB, Jeffries D, Thomson A, Milligan P. A simple approach to test for interaction between 
intervention and an individual-level variable in community randomized trials. Trop Med Int Health. 
2008;13(2):247-255. 
 

Table of Amendments 
 
XPEL TB SAP 
 
Change 
Date of change 
Section 1.1: We updated the aims section to reflect the aims listed in the 
protocol   
November 21, 2019 
Section 2.3: We added that trial reporting will follow the STaRI checklist 
based on feedback from one of the co-investigators. 
November 21, 2019 
Section 2.3: We specified that the SAP summarizes analyses for 
effectiveness outcomes only.   
November 21, 2019  
Section 3.2: We removed the exclusion criteria from this section and moved 
this text to “Outcomes” (Section 3.4).   
November 21, 2019 
Section 3.4: We added definitions for “eligible patent”, “number died, 
among those with confirmed TB”, and “number died, among all those 
eligible”.  
November 21, 2019 
Section 3.4: We removed reference to GxAlert database (not used during the 
trial)  
November 21, 2019  
Section 3.4: We specified GeneXpert machine data can be used as a data 
source for the trial  
November 21, 2019 
(intervention) 
 
March 4, 2020 (intervention 
and control) 
Section 3.4: We specified that test results must be available within 6 months 
of enrollment to be considered in the trial analysis  
November 21, 2019  
Section 3.4: For the new mortality outcomes we added, we indicated in the 
footnotes of Table 1 that we will assess vital status between 4-9 months from 
enrollment.  
November 21, 2019 
Section 3.5: We included a justification for changing the primary outcome 
from a proportion to a count 
November 21, 2019  
Section 3.5: We corrected a typo about our geometric mean (updated to 
reflect what we had intended geometric mean: 268 patients/cluster).   
November 21, 2019  
Section 4.4: We indicated we would summarize the number of patients 
referred for TB testing (collapsed by cluster) in the 12-month period after the 
trial start date. 
November 21, 2019  
Section 4.4: We indicated we would only perform sensitivity analyses on 
trace positive results if the ratio of trace-positive results to all positive Xpert 
Ultra results was similar in both trial arms.  
January 10, 2020  
Section 4.4: We indicated when adjustment for individual-level 
characteristics will be done (if data completeness >80%).  
November 21, 2019  
Section 4.4: We clarified the language about for which variables subgroup 
analyses will be performed. We also indicated results from subgroup 
analyses will be summarized as forest plots. 
November 21, 2019  
Appendix (Post-hoc Analyses): We added an appendix listing the post-hoc 
analyses that we conducted and included in the trial manuscript 
August 6, 2020  
Section 3.4: To improve clarity for reporting purposes, we updated the 
denominator for proportion outcomes to be “number enrolled” (i.e., the 
number evaluated for TB, which is our full study population).  
July 12, 2021