Skip to main content
NCBI home page
PLOS Medicine logoLink to PLOS Medicine
. 2021 May 6;18(5):e1003628. doi: 10.1371/journal.pmed.1003628

Digital adherence technology for tuberculosis treatment supervision: A stepped-wedge cluster-randomized trial in Uganda

Adithya Cattamanchi 1,2,*,#, Rebecca Crowder 1,#, Alex Kityamuwesi 2,#, Noah Kiwanuka 3, Maureen Lamunu 2, Catherine Namale 2, Lynn Kunihira Tinka 2, Agnes Sanyu Nakate 2, Joseph Ggita 2, Patricia Turimumahoro 2, Diana Babirye 2, Denis Oyuku 2, Christopher Berger 1, Austin Tucker 4, Devika Patel 5, Amanda Sammann 5, Stavia Turyahabwe 6, David Dowdy 2,4, Achilles Katamba 2,7
Editor: Amitabh Bipin Suthar8
PMCID: PMC8136841  PMID: 33956802

Abstract

Background

Adherence to and completion of tuberculosis (TB) treatment remain problematic in many high-burden countries. 99DOTS is a low-cost digital adherence technology that could increase TB treatment completion.

Methods and findings

We conducted a pragmatic stepped-wedge cluster-randomized trial including all adults treated for drug-susceptible pulmonary TB at 18 health facilities across Uganda over 8 months (1 December 2018–31 July 2019). Facilities were randomized to switch from routine (control period) to 99DOTS-based (intervention period) TB treatment supervision in consecutive months. Patients were allocated to the control or intervention period based on which facility they attended and their treatment start date. Health facility staff and patients were not blinded to the intervention. The primary outcome was TB treatment completion. Due to the pragmatic nature of the trial, the primary analysis was done according to intention-to-treat (ITT) and per protocol (PP) principles. This trial is registered with the Pan African Clinical Trials Registry (PACTR201808609844917). Of 1,913 eligible patients at the 18 health facilities (1,022 and 891 during the control and intervention periods, respectively), 38.0% were women, mean (SD) age was 39.4 (14.4) years, 46.8% were HIV-infected, and most (91.4%) had newly diagnosed TB. In total, 463 (52.0%) patients were enrolled on 99DOTS during the intervention period. In the ITT analysis, the odds of treatment success were similar in the intervention and control periods (adjusted odds ratio [aOR] 1.04, 95% CI 0.68–1.58, p = 0.87). The odds of treatment success did not increase in the intervention period for either men (aOR 1.24, 95% CI 0.73–2.10) or women (aOR 0.67, 95% CI 0.35–1.29), or for either patients with HIV infection (aOR 1.51, 95% CI 0.81–2.85) or without HIV infection (aOR 0.78, 95% CI 0.46–1.32). In the PP analysis, the 99DOTS-based intervention increased the odds of treatment success (aOR 2.89, 95% CI 1.57–5.33, p = 0.001). The odds of completing the intensive phase of treatment and the odds of not being lost to follow-up were similarly improved in PP but not ITT analyses. Study limitations include the likelihood of selection bias in the PP analysis, inability to verify medication dosing in either arm, and incomplete implementation of some components of the intervention.

Conclusions

99DOTS-based treatment supervision did not improve treatment outcomes in the overall study population. However, similar treatment outcomes were achieved during the control and intervention periods, and those patients enrolled on 99DOTS achieved high treatment completion. 99DOTS-based treatment supervision could be a viable alternative to directly observed therapy for a substantial proportion of patients with TB.

Trial registration

Pan-African Clinical Trials Registry (PACTR201808609844917).

Adithya Cattamanchi and co-workers report on a stepped-wedge trial of digital tuberculosis treatment supervision done in Uganda.

Author summary

Why was this study done?

  • A PubMed search for publications prior to 1 January 2019 with the search terms “adherence technology AND tuberculosis” revealed 4 randomized trials and 14 observational studies of digital adherence technologies (DATs) being used to support tuberculosis (TB) treatment, most of which focused primarily on adherence rather than treatment outcome.

  • A systematic review found limited evidence to support the effectiveness of DATs for improving TB treatment outcomes.

What did the researchers do and find?

  • We adapted 99DOTS, a low-cost DAT already widely used in India, with input from local stakeholders, and conducted a pragmatic randomized trial of the resulting 99DOTS-based intervention at 18 health facilities in Uganda.

  • Only about half of patients were initiated on 99DOTS-based treatment supervision during the intervention period.

  • The 99DOTS-based intervention did not increase treatment completion in the full study population.

  • Treatment completion was high (>85%) among the nonrandom sample of patients initiated on 99DOTS-based treatment supervision during the intervention period.

What do these findings mean?

  • 99DOTS should not be used as a universal replacement for directly observed therapy for TB treatment supervision, with the aim of increasing population-level treatment completion.

  • 99DOTS-based treatment supervision could enable a substantial proportion of patients with TB to complete treatment without the inconvenience and additional costs of directly observed therapy.

  • Further research is needed to assess whether overall treatment outcomes can be improved by increasing uptake of 99DOTS or other low-cost DATs, and to identify additional measures needed to support all patients to complete treatment.

Introduction

Ensuring that patients complete treatment for tuberculosis (TB), the leading infectious cause of death worldwide, remains a key challenge to achieving cure in many high-burden countries [1]. Since 1993, a health worker observing the patient when he or she swallows each dose of anti-TB medication (directly observed therapy [DOT]) has been a central aspect of the World Health Organization (WHO)–recommended strategy for TB treatment supervision [2]. However, DOT is time-consuming and costly for patients and health workers, and multiple trials have failed to demonstrate improvement in treatment outcomes [3]. Novel, patient-centered approaches are needed to monitor and promote TB treatment adherence and completion.

Recently, there has been increasing interest in digital adherence technologies (DATs) as an alternative to DOT. DATs enable patients to take TB medicines at home, restoring patient autonomy and dignity, while still enabling health workers to monitor and support adherence [4]. A common DAT platform generates real-time adherence data by placing medications within an electronic pill box. In the context of TB treatment, a cluster-randomized trial in China demonstrated improved adherence with medication reminders delivered via an electronic pill box. However, the trial was not powered to evaluate a difference in TB treatment outcomes. 99DOTS (Everwell Health Solutions, India) is an alternative low-cost DAT that involves patients calling toll-free phone numbers that are ordered in an unpredictable pattern and hidden underneath pills in blister packs [5,6]. The phone numbers are revealed only when patients remove scheduled medication doses from the blister pack, enabling patients to make toll-free calls to self-report medication dosing. Health facility staff can access adherence data for individual patients in real-time through a web dashboard and mobile phone application. Although 99DOTS has been widely scaled up in India, it has not yet been rigorously evaluated in any country. High-quality evidence of impact on treatment outcomes is needed to support WHO policy recommendations [7] and further uptake in other high-TB-burden countries.

The DOT to DAT trial aimed to determine whether a 99DOTS-based treatment supervision strategy improved TB treatment completion in comparison to routine TB treatment supervision. We secondarily assessed the reach of the intervention strategy (proportion initiated on 99DOTS) and short-term treatment outcomes (persistence on treatment through the intensive phase; loss to follow-up). The cluster-randomized design allowed us to study the 99DOTS-based intervention as it would be used in routine care. The stepped-wedge design increased acceptability among the participating health facilities and feasibility of training sites on the intervention. 99DOTS implementation and health economic outcomes are described in the trial protocol [8]; implementation and health economic outcomes will be reported later. Previous publications have described the trial protocol [8], baseline TB treatment outcomes at the trial sites [9], and the human-centered design process used to adapt the 99DOTS platform to better meet the needs of patients and health workers [10,11].

Methods

Study design and participants

We conducted a pragmatic stepped-wedge cluster-randomized trial of a 99DOTS-based TB treatment supervision strategy at 18 health facilities in Uganda with National TB and Leprosy Program (NTLP)–affiliated TB treatment units. The 18 health facilities included 5 regional referral hospitals, 10 general hospitals, and 3 district health centers. Health facilities were eligible for the trial if they treated >10 TB patients/month in 2017, were located within 225 of Kampala but not within Kampala District, and had a TB treatment success rate < 80% in 2017 (S1 Fig).

At each of the 18 health facilities, we included data from all adults (age ≥ 18 years) who initiated treatment for drug-susceptible pulmonary TB between 1 December 2018 and 31 July 2019 and followed patients passively until a TB treatment outcome was assigned. Patients who were transferred to another treatment unit were excluded from the study population, as they would not have had an opportunity to receive the intervention throughout treatment (S1 Fig).

The trial followed a repeated cross-sectional design, with each 1-month period capturing different patients initiated on TB treatment (S2 Fig). All health facilities started with routine TB treatment supervision (control period). Subsequently, 3 health facilities per month were randomly switched to 99DOTS-based TB treatment supervision (intervention period) over a 6-month period. Patients were allocated to the intervention or control period based on the facility they attended and the month in which they started treatment; patients who started treatment when their health facility was in the control period were therefore ineligible to receive 99DOTS-based treatment supervision for the duration of their treatment. The first month of switching to the intervention was considered to be a transition (i.e., buffer) period, during which health facility staff were trained on how to use 99DOTS. Patients who started TB treatment during the buffer period for each health facility were excluded from the primary analysis.

The trial was approved by institutional review boards at Makerere University School of Public Health and the University of California San Francisco, and by the Uganda National Council for Science and Technology. A waiver of informed consent was granted to access patient demographic and clinical information recorded in TB treatment registers. The trial protocol has been published previously [8], and copies of the trial protocol, statistical analysis plan, and CONSORT checklist are available as S1 Trial Protocol, S1 Statistical Analysis Plan, and S1 CONSORT Checklist, respectively.

Randomization and masking

Before the trial began, the 18 health facilities were first assigned randomly to 1 of 6 blocks of 3 health facilities, and then the order in which each block switched from routine care to the intervention was also assigned randomly using a simple, unrestricted 2-stage process (S2 Fig). The randomization process was carried out at a ceremony attended by stakeholders from all participating health facilities in which facility representatives participated in the randomization by pulling labeled balls out of a bag, determining their allocation [8]. Blinding of the intervention time period to health facility staff and patients was not feasible as they were the targets of the intervention. Investigators and study staff, with the exception of the biostatistician (NK) and data manager (RC), were blinded to treatment outcomes.

Procedures

Staff at each health facility took photos of health facility TB treatment registers and uploaded the photos monthly to a secure server. Research staff then abstracted demographic, clinical, and outcome data for patients initiated on TB treatment from the photos and entered the data into a Research Electronic Data Capture (REDCap) database [8]. Missing data for key variables in treatment registers were obtained via phone call with health facility staff.

During the control period, health facilities continued providing TB treatment supervision in the same manner as before the trial. The community-based DOT model used at the 18 health facilities has been described previously, including the substantial variation in how TB treatment supervision is implemented across the facilities [9]. All facilities request patients to take their TB medicines under direct observation by a treatment supporter. The treatment supporter is most commonly a family member who receives no training or compensation. All health facilities report assessing adherence (most commonly by patient self-report) at drug refill visits scheduled every 2 weeks during the intensive phase and every 4 weeks during the continuation phase. Patients who miss refill visits are not followed up at the majority of health facilities.

During the buffer period for each health facility, research staff conducted a 3-day training on the 99DOTS-based intervention at the facility [8]. Following the training, health facility staff were requested to offer 99DOTS-based TB treatment supervision to all adults initiating treatment for pulmonary TB at their facility. Patients who had previously initiated treatment continued to receive routine care as described above.

During the intervention period, the decision to offer and accept 99DOTS-based supervision was left to treating providers and patients, respectively. The standard 99DOTS platform was adapted using human-centered design methods in collaboration with local stakeholders and end-users prior to the start of the trial, with further adaptation of the envelope design during the first 2 months of the trial (S3 Fig) [10,11]. If enrolled on 99DOTS, patients were given TB medication blister packs placed inside the adapted 99DOTS envelope, received daily automated SMS dosing reminders, and were asked to confirm dosing by making daily toll-free phone calls to the 99DOTS system. Patients heard a rotating series of educational and motivational messages when they called in to report dosing. As in the control period, drug refill visits were scheduled every 2 weeks during the intensive phase and every 4 weeks during the continuation phase.

Outcomes

Treatment outcomes were recorded in health facility TB treatment registers as cured, treatment completed, treatment failed, died, or lost to follow-up as per Uganda NTLP guidelines. The primary outcome was the proportion of patients with treatment success, defined as having a treatment outcome of cured or treatment completed recorded in the TB treatment register. Prespecified secondary outcomes included the proportion of patients completing the intensive phase of TB treatment (defined as completing 60 doses), the proportion of patients not lost to follow-up, and the proportion converted (proportion of bacteriologically positive patients who are smear-negative at 2 months). Conversion was removed as a secondary outcome after the trial began, as the data were not available; many trial sites had switched from smear microscopy to Xpert MTB/RIF as the primary method for TB diagnosis, and most trial sites did not routinely perform 2-month smear examination. In addition, within the intervention period, we assessed the proportion of patients who were enrolled on 99DOTS. Additional secondary outcomes related to the adoption and implementation of 99DOTS will be reported separately [8].

Statistical methods

Power and sample size

With 18 clusters (health facilities), an anticipated harmonic mean of 15 eligible patients per month at each health facility, 6 steps (3 health facilities randomized to the intervention each month), and a type 1 error (alpha) of 0.05, the trial had 89% power to detect a 10% increase in the treatment success proportion, comparing the intervention relative to the control period [8]. The power calculation assumed a pre-implementation treatment success rate of 51% and an intraclass correlation coefficient of 0.001 (both determined using 2017 Uganda NTLP data from participating health facilities).

Statistical analysis

Primary and secondary outcomes were analyzed according to intention-to-treat (ITT) and per protocol (PP) principles. The ITT analysis included all eligible patients initiated on TB treatment during the control and intervention periods and thus aims to provide an unbiased estimate of the effect of the 99DOTS-based intervention in the study population overall. The PP analysis excluded (1) patients who initiated TB treatment while their health facility was in the control period but were later enrolled on 99DOTS at any point during treatment and (2) patients who initiated TB treatment while their health facility was in the intervention period but were not enrolled on 99DOTS during the first month of treatment. Although limited by the potential for selection bias, the PP analysis aims to estimate the effect of actually receiving the assigned intervention.

The main analyses were done using mixed effects logit models to estimate the adjusted odds ratios (aORs) and corresponding 95% confidence intervals, adjusting for clustering of observations at the health facility level as a random effect and time (trial month) and potential confounders (age, sex, HIV status, bacteriologically confirmed versus clinical TB diagnosis, new versus retreatment TB diagnosis, and level of health facility) as fixed effects.

For prespecified subgroup analyses stratified by sex and HIV status, we calculated aORs in the same manner. The intervention effect was compared among subgroups by assessing the significance of a subgroup–period interaction term when included in the non-stratified model. Adjusted proportions of each subgroup with the outcome and proportion differences associated with the intervention were output from the non-stratified model. A prespecified sensitivity analysis was conducted in which patients initiating TB treatment during the buffer period were assigned to the control period if they initiated treatment on or before the first day of 99DOTS training and to the intervention period if they initiated treatment after the first day of 99DOTS training. A post hoc sensitivity analysis included only patients with a phone number listed in the TB treatment register (as a proxy for phone access). For both sensitivity analyses, aORs were calculated in the same manner as for the primary analysis. Stata 15 was used for all analyses [12]. The trial is registered with the Pan African Clinical Trials Registry (PACTR201808609844917).

Results

During the study period from 1 December 2018 to 31 July 2019, 2,790 adults initiated treatment for drug-susceptible pulmonary TB at the 18 health facilities, of whom 566 (20.3%) were ineligible for analysis because they transferred to other health facilities during treatment (S1 Fig). Within the control period, eligible patients were older (mean 39.1 versus 36.1 years, p = 0.001) than ineligible patients. Within the intervention period, eligible patients were older (mean 39.7 versus 35.5 years, p < 0.001), more likely to be HIV-positive (46.2% versus 37.6%, p = 0.01), and more likely to have bacteriologically confirmed TB (52.2% versus 41.6%, p = 0.001) than ineligible patients. There were no differences in the eligible versus ineligible populations by sex (37.0% versus 39.4% female, p = 0.29) or prior TB status (8.6% versus 9.2% retreatment, p = 0.68).

Of the 2,224 eligible patients, 311 who initiated TB treatment while their health facility was in the buffer period were excluded from the primary analysis (Fig 1). The ITT study population (n = 1,913) included the remaining 1,022 (53.4%) patients who started TB treatment while their health facility was allocated to the control period and 891 (46.6%) patients who started TB treatment while their health facility was allocated to the intervention period. The PP study population (n = 1,450) excluded 35 (3.4%) patients who were enrolled on 99DOTS while their health facility was allocated to the control period and 428 (48.0%) patients who were not enrolled on 99DOTS within 1 month of treatment initiation while their health facility was allocated to the intervention period. No patients asked for their data to be excluded, and no data were missing for the primary outcome analysis.

Fig 1. Trial profile.

Fig 1

Open in a new tab

ITT, intention-to-treat; PP, per protocol; TB, tuberculosis.

There were no differences in measured characteristics between patients who initiated treatment during the control and intervention periods, other than a higher proportion of patients with bacteriologically confirmed TB in the intervention period (52.5% versus 48.7%; Table 1). Overall, most (>91%) patients had newly diagnosed TB, mean (SD) age was 39.4 (14.4) years, 38.0% were women, and 46.8% were HIV-infected.

Table 1. Participant baseline characteristics by study population and period.

Characteristic Intention-to-treat population Per protocol population Buffer period
(n = 311)
Control period
(n = 1,022)		 Intervention period
(n = 891)		 Control period
(n = 987)		 Intervention period
(n = 463)
Age in years, mean (SD) 39.1 (14.2) 39.7 (14.6) 39.2 (14.3) 38.9 (14.2) 39.4 (15.2)
Female, n (%) 396 (38.8) 330 (37.0) 377 (38.2) 167 (36.1) 97 (31.2)
HIV-positive, n (%) 484 (47.4) 412 (46.2) 469 (47.5) 192 (41.5) 123 (39.6)
    On ART, n (%) 480 (99.2) 412 (100) 465 (99.2) 192 (100) 123 (99.6)
New patient, n (%) 936 (91.6) 812 (91.1) 902 (91.4) 423 (91.4) 283 (91.0)
Bacteriologically confirmed TB, n (%) 498 (48.7) 468 (52.5) 480 (48.6) 278 (60.0) 164 (52.7)
Xpert-positive, n (%) 474 (46.4) 436 (48.9) 456 (46.2) 263 (56.8) 157 (50.5)
Open in a new tab

SD, standard deviation; TB, tuberculosis.

Within the intervention period, 463 of 891 (52.0%) patients were enrolled on 99DOTS within the first month of treatment. The remaining 428 were either never enrolled on 99DOTS (n = 387) or enrolled later on in treatment (n = 41). Patients enrolled on 99DOTS were similar to patients not enrolled on 99DOTS with respect to age (mean 38.9 versus 40.5 years, p = 0.11), sex (36.1% versus 38.1% female, p = 0.53), and history of prior TB (8.6% versus 9.1%, p = 0.80), but were less likely to have HIV infection (41.5% versus 51.4%, p = 0.003) and more likely to have bacteriologically confirmed TB (60.0% versus 44.4%, p < 0.001). The proportion enrolled on 99DOTS also varied across health facilities (median 56.3%, range 29.4%–72.2%, Levene’s test p < 0.001). Reasons for non-enrollment on 99DOTS included lack of regular access to a phone (n = 277, 64.7%), patient preference (n = 33, 7.7%), death/loss to follow-up before enrollment (n = 28, 6.5%), being deemed too ill or elderly by health facility staff (n = 18, 4.2%), work- or school-related reasons (n = 5, 1.2%), and unknown (n = 67, 15.7%).

In the ITT analysis, 72.7% of patients in the intervention period and 70.9% of patients in the control period completed treatment successfully (Table 2). The monthly adjusted proportions of patients with treatment success were similar throughout the study period and overlapped between the control and intervention periods (Fig 2A). The 99DOTS-based intervention did not increase the odds of treatment success (aOR 1.04, 95% CI 0.68–1.58, p = 0.87). Similarly, the adjusted odds of persistence on treatment through the intensive phase (aOR 1.03, 95% CI 0.65–1.63) and not being lost to follow-up (aOR 0.90, 0.52–1.57) were similar in the intervention and control periods (Table 2).

Table 2. Effectiveness of the 99DOTS-based intervention.

Outcome and analysis N Outcome proportion, n/N (%) Effect estimate
Control period Intervention period Adjusted proportion difference†^ (95% CI) Adjusted odds ratio† (95% CI) p-Value‡
Treated successfully*
ITT 1,913 725/1,022 (70.9%) 648/891 (72.7%) 0.65 (−7.25, 8.55) 1.04 (0.68, 1.58) 0.87
PP 1,450 696/987 (70.5%) 401/463 (86.6%) 16.49 (7.66, 25.31) 2.89 (1.57, 5.33) 0.001
Completed intensive phase**
ITT 1,913 806/1,022 (78.9%) 722/891 (81.0%) 0.46 (−6.29, 7.22) 1.03 (0.65, 1.63) 0.89
PP 1,450 774/987 (78.4%) 423/463 (91.4%) 14.26 (7.03, 21.49) 3.47 (1.71, 7.03) 0.001
Not lost to follow-up**
ITT 1,913 892/1,022 (87.3%) 780/891 (87.5%) −1.06 (−6.85, 4.74) 0.90 (0.52, 1.57) 0.72
PP 1,450 857/987 (86.8%) 448/463 (96.8%) 10.83 (4.41, 17.25) 4.92 (1.79, 13.49) 0.002
Open in a new tab

ITT, intention-to-treat; PP, per protocol.

†Adjusted for time (trial month, discrete variable), sex, HIV status, disease class (bacteriologically confirmed versus clinically diagnosed), and TB type (new versus retreatment) as fixed effects and site as a random effect.

^ Proportion difference calculated as intervention minus control.

‡p-Value for adjusted intervention effect.

*Primary outcome.

**Secondary outcome.

Fig 2. Adjusted proportions of treatment success.

Fig 2

Open in a new tab

The data shown are adjusted proportions output by the primary analysis mixed effects logistic regression model for the (A) intention-to-treat analysis and (B) per protocol analysis.

In subgroup analyses, the effect of the 99DOTS-based intervention was strongest in men and HIV-positive patients. Within the ITT study population, the odds of treatment success were increased among men (aOR 1.24, 95% CI 0.73, 2.10) and HIV-infected patients (aOR 1.51, 95% CI 0.81, 2.85), but the aORs and the between-group differences for men and women (p = 0.39) and for HIV-infected and -uninfected patients (p = 0.86) were not statistically significant (Fig 3A).

Fig 3. Effectiveness of 99DOTS among subgroups.

Fig 3

Open in a new tab

The data shown are odds ratios (ORs) from mixed effects logistic regression models for the (A) intention-to-treat analysis and (B) per protocol analysis. All models are adjusted for time (month), disease class (bacteriologically confirmed versus clinically diagnosed), and TB type (new versus retreatment) as fixed effects and site as a random effect. Sex-specific ORs are also adjusted for HIV status as a fixed effect, and HIV-status-specific ORs are also adjusted for sex as a fixed effect.

In the PP analysis, 86.6% of patients in the intervention period and 70.5% of patients in the control period completed treatment successfully (Table 2). The monthly adjusted proportions of patients with treatment success were significantly higher in the intervention period compared to the control period (Fig 2B). The 99DOTS-based intervention increased the odds of treatment success (aOR 2.89, 95% CI 1.57–5.33, p < 0.001), persistence on treatment through the intensive phase (aOR 3.47, 95% CI 1.71–7.03, p = 0.001), and not being lost to follow-up (aOR 4.92, 95% CI 1.79–13.49, p = 0.002) (Table 2). In subgroup analyses (Fig 3B), the odds of treatment success were significantly increased for men (aOR 4.28, 95% CI 1.95–9.36) and HIV-infected patients (aOR 4.60, 95% CI 1.74–12.20). As for the ITT population, the between-group differences for men and women (p = 0.42) and for HIV-infected and -uninfected patients (p = 0.23) were not statistically significant. The same patterns were observed in ITT and PP analyses for the subgroup effects of the 99DOTS-based intervention on persistence and loss to follow-up (Fig 3).

In a prespecified sensitivity analysis, the 311 patients from the buffer period were included in the control population if they initiated treatment on or before the first day of their health facility’s 99DOTS training (n = 118) and in the intervention period if they initiated treatment after the first day of their health facility’s 99DOTS training (n = 193). Similar to the main analyses, there was improvement in treatment success with 99DOTS in the PP analysis (aOR 2.62, 95% CI 1.66–4.12, p < 0.001) but not in the ITT analysis (aOR 1.16, 95% CI 0.83–1.61, p = 0.39) (S1 Table). In a post hoc sensitivity analysis including patients from the control period only if they had a phone number listed in the TB treatment register (as a proxy for access to a phone; n = 473/1022, 46.3%), the intervention effect was similar to that of the PP analysis (aOR 3.70, 95% CI 1.72–9.94, p = 0.001) (S2 Table).

Discussion

In this stepped-wedge cluster-randomized trial of 1,913 patients with drug-susceptible TB in Uganda, there was overall no significant difference in outcomes between the control and intervention periods. Only 52% of patients in the intervention period were enrolled on 99DOTS-based treatment supervision; in the PP analysis, these patients were substantially more likely to complete treatment, including persisting on treatment through the intensive phase and avoiding loss to follow-up during treatment. Thus, while 99DOTS-based treatment supervision does not improve population-level treatment outcomes relative to DOT, it is likely a viable alternative to DOT for the substantial proportion of patients who have access to a phone and are interested in using this technology.

Of the 4 published randomized trials of DATs to support TB treatment [13–16], 2 were conducted in high-burden countries. In a large cluster-randomized trial in China [15], reminders from electronic medication monitors improved adherence (percentage of patient-months on treatment with <20% of doses missed) by 42%, and in an individual-level randomized controlled trial in Kenya [16], a custom digital health platform compatible with routine feature phones reduced unsuccessful treatment outcomes by 3-fold (13.1% versus 4.2%, p < 0.001). Previous observational studies of 99DOTS in India have reported variable acceptance by patients [17], suboptimal accuracy for measuring adherence [18], and worsening of treatment outcomes following its implementation [19]. Our trial confirms that 99DOTS—and likely other DATs—are unlikely on their own to substantially improve population-level TB treatment outcomes when implemented as part of routine care.

Due to the pragmatic nature of our trial, health workers and patients made all decisions about the use of 99DOTS during the intervention period, and only about half of all eligible patients were enrolled on 99DOTS. The substantially better treatment outcomes among these patients in the PP analysis should not be interpreted as improving outcomes in the overall population, primarily due to the possibility of selection bias. Patients enrolled on 99DOTS likely represent a nonrandom sample of patients initiating treatment with higher interest in engaging in care and therefore higher likelihood of completing treatment even without 99DOTS. Although our post hoc analysis excluding patients without listed phone numbers showed similar findings to the PP analysis, it may not adequately control for this bias—the reason for non-enrollment on 99DOTS was unrelated to phone access for approximately one-third of patients in the intervention period. Despite these caveats, it is noteworthy that 99DOTS-based treatment supervision empowered half of all patients to take TB medicines at the time and place of their choosing, resulting in >85% treatment completion while mitigating the potential inconvenience, stigma, and costs commonly reported as barriers associated with DOT [20–22]. Further research is needed on whether overall treatment outcomes can be improved by increasing the uptake of low-cost DATs such as 99DOTS (e.g., by providing patients with phones or offering a choice of different DAT platforms) and strengthening the capacity of health workers to provide enhanced adherence support using real-time dosing information.

A major strength of our study is that it comprises a robust yet real-world evaluation of the impact of 99DOTS on TB treatment outcomes. The trial was conducted in TB treatment units similar to those found in other high-burden countries, and there were minimal exclusion criteria. The short training on the 99DOTS-based intervention [8] with subsequent patient management directed by routine health workers reflects how 99DOTS would eventually be scaled up. We therefore anticipate that these findings would be generalizable to public sector TB treatment units in other high-TB-burden countries.

Our trial also had several potential limitations. The highly pragmatic nature of the trial constrained our ability to control certain aspects of the design. For example, we were unable to collect adherence data for patients treated under routine care and did not verify the accuracy of patients calling to self-report medication dosing as a surrogate for actual dosing. However, the trial reflects treatment outcomes as they are routinely reported by national TB programs, and followed a prespecified protocol and analysis plan. The nature of the 99DOTS-based intervention precluded masking of clinicians, but study personnel (with the exception of the trial statistician and data manager) were blinded to aggregated treatment outcomes. As discussed above, the PP analyses should be interpreted with caution given the probability of selection bias in the PP population. Last, the 99DOTS-based intervention was not fully implemented as planned. In particular, aspects related to patient monitoring and support, including weekly interactive voice response check-ins and health worker task lists to facilitate patient follow-up, were not fully developed or implemented during the trial period, which could have reduced potential effectiveness.

In conclusion, our trial provides randomized evidence that 99DOTS-based treatment supervision does not improve population-level treatment outcomes. However, the high levels of treatment completion among those who used 99DOTS suggest that this technology may be a viable alternative to DOT for many patients. Given that 99DOTS likely reduces costs and burden to patients, these findings support a more patient-centered approach to TB treatment supervision that replaces universal DOT with offering 99DOTS-based treatment supervision as an alternative treatment supervision modality for those patients who are willing and able to use it.

Supporting information

S1 CONSORT Checklist

(PDF)

Click here for additional data file. (57.8KB, pdf)
S1 Data. Raw de-identified data used to conduct this analysis.

Each row corresponds to a patient. The dataset includes 13 columns: intervention group—randomization block (1–6); health facility—health facility where the patient initiated treatment (1–18); health center—type of facility (health center, hospital); trial month—month during which the patient initiated treatment (0–7); study period (control, buffer, intervention); sex; age; HIV status; disease class (bacteriologically confirmed pulmonary TB, clinically diagnosed pulmonary TB); retreatment—patient type (new, retreatment); treated successfully—primary outcome; persisting on treatment—secondary outcome; not lost to follow-up—secondary outcome.

(XLSX)

Click here for additional data file. (124.1KB, xlsx)
S1 Fig. Health facilities included in the DOT to DAT trial.

(TIF)

Click here for additional data file. (528.6KB, tif)
S2 Fig. Stepped-wedge trial design and patient enrollment.

The target population includes all adults initiating treatment for drug-susceptible pulmonary TB. The eligible population excludes patients in the target population who were transferred out to another health facility during their treatment. Patients who initiated treatment during the buffer period were excluded from the study population.

(TIF)

Click here for additional data file. (804.1KB, tif)
S3 Fig. Adapted 99DOTS platform.

The original 99DOTS envelope (top) had 2 sides. We redesigned the original envelope using human-centered design to reduce stigma, encourage appropriate dosing, and facilitate communication between patients and health workers. Prototype 1 (middle) was used from January to June 2019. Prototype 2 (bottom) was used from July 2019 through the end of the trial. In addition to the changes shown here, the ring tone heard when patients called toll-free numbers to self-report dosing was replaced with a rotating series of educational or motivational messages recorded by local health workers.

(TIF)

Click here for additional data file. (1.5MB, tif)
S1 Statistical Analysis Plan

(DOCX)

Click here for additional data file. (135.9KB, docx)
S1 Table. Prespecified sensitivity analysis of the effectiveness of the 99DOTS-based intervention when including the buffer period.

ITT, intention-to-treat; PP, per protocol. *Primary outcome. **Secondary outcome. †Adjusted for time (trial month, discrete variable), sex, HIV status, disease class (bacteriologically confirmed versus clinically diagnosed), and TB type (new versus retreatment) as fixed effects and site as a random effect. ^Proportion difference calculated as proportion in intervention period minus that in the control period. p-Value for adjusted intervention effect.

(DOCX)

Click here for additional data file. (15.7KB, docx)
S2 Table. Post hoc sensitivity analysis of the effectiveness of the 99DOTS-based intervention when excluding patients without a listed phone number.

*Primary outcome. **Secondary outcome. ‡Adjusted for time (trial month, discrete variable), sex, HIV status, disease class (bacteriologically confirmed versus clinically diagnosed), and TB type (new versus retreatment) as fixed effects and site as a random effect. ^Proportion difference calculated as intervention minus control. p-Value for adjusted intervention effect.

(DOCX)

Click here for additional data file. (15KB, docx)
S1 Trial Protocol

(DOC)

Click here for additional data file. (308KB, doc)

Acknowledgments

We thank the administration, staff, and patients at participating health facilities. The DOT to DAT trial was implemented in collaboration with the Uganda NTLP and was made possible by the commitment of district TB focal persons and health facility staff.

Abbreviations

aOR

adjusted odds ratio

DAT

digital adherence technology

DOT

directly observed therapy

ITT

intention-to-treat

NTLP

National TB and Leprosy Program

PP

per protocol

TB

tuberculosis

WHO

World Health Organization

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

This Project is supported by the Stop TB Partnership’s TB REACH initiative, grant number STBP/TBREACH/GSA/W6-37 (AC, AKa), which is funded by the Government of Canada, the Bill & Melinda Gates Foundation, and the United States Agency for International Development. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.World Health Organization. Global tuberculosis report 2017. Geneva: World Health Organization; 2017. [cited 2021 Apr 30]. Available from: http://www.who.int/tb/publications/global_report/en/. [Google Scholar]
  • 2.Bhandari R. I nternational standards for tuberculosis care (ISTC) and patients’ charter: new advances in tuberculosis care. Health Prospect. 2011;10:43–5. [Google Scholar]
  • 3.Karumbi J, Garner P. Directly observed therapy for treating tuberculosis. Cochrane Database Syst Rev. 2015(5):Cd003343. 10.1002/14651858.CD003343.pub4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Metcalfe JZ, O’Donnell MR, Bangsberg DR. Moving beyond directly observed therapy for tuberculosis. PLoS Med. 2015;12(9):e1001877. 10.1371/journal.pmed.1001877 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Everwell Health Solutions. 99DOTS: low-cost monitoring and improving medication adherence. Bangalore: Everwell Health Solutions; 2021. [cited 2021 Apr 30]. Available from: https://www.99dots.org. [Google Scholar]
  • 6.Cross A, Gupta N, Liu B, Nair V, Kumar A, Kuttan R, et al. 99DOTS: a low-cost approach to monitoring and improving medication adherence. In: ICTD ‘19: Proceedings of the Tenth International Conference on Information and Communication Technologies and Development. Ahmedabad (India): Association for Computing Machinery; 2019.
  • 7.World Health Organization. Guidelines for treatment of drug-susceptible tuberculosis and patient care (2017 update). Geneva: World Health Organization; 2017. [cited 2021 Apr 30]. Available from: https://www.who.int/tb/publications/2017/dstb_guidance_2017/en/. [Google Scholar]
  • 8.Crowder R, Kityamuwesi A, Kiwanuka N, Lamunu M, Namale C, Tinka LK, et al. Study protocol and implementation details for a pragmatic, stepped-wedge cluster randomised trial of a digital adherence technology to facilitate tuberculosis treatment completion. BMJ Open. 2020;10(11):e039895. 10.1136/bmjopen-2020-039895 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Berger CA, Kityamuwesi A, Crowder R, Lamunu M, Kunihira Tinka L, Ggita J, et al. Variation in tuberculosis treatment outcomes and treatment supervision practices in Uganda. J Clin Tuberc Other Mycobact Dis. 2020;21:100184. 10.1016/j.jctube.2020.100184 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Berger CA, Patel D, Kityamuwesi A, Ggita J, Tinka LK, Turimumahoro P, et al. Opportunities to improve digital adherence technologies and TB care using human-centered design. Int J Tuberc Lung Dis. 2020;24(10):1112–5. 10.5588/ijtld.20.0184 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Patel D, Berger CA, Kityamuwesi A, Ggita J, Kunihira Tinka L, Turimumahoro P, et al. Iterative adaptation of a tuberculosis digital medication adherence technology to meet user needs: qualitative study of patients and health care providers using human-centered design methods. JMIR Form Res. 2020;4(12):e19270. 10.2196/19270 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.StataCorp. Stata statistical software. Release 15. College Station (TX): StataCorp; 2017. [Google Scholar]
  • 13.Story A, Aldridge RW, Smith CM, Garber E, Hall J, Ferenando G, et al. Smartphone-enabled video-observed versus directly observed treatment for tuberculosis: a multicentre, analyst-blinded, randomised, controlled superiority trial. Lancet. 2019;393(10177):1216–24. 10.1016/S0140-6736(18)32993-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Browne SH, Umlauf A, Tucker AJ, Low J, Moser K, Gonzalez Garcia J, et al. Wirelessly observed therapy compared to directly observed therapy to confirm and support tuberculosis treatment adherence: a randomized controlled trial. PLoS Med. 2019;16(10):e1002891. 10.1371/journal.pmed.1002891 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Liu X, Lewis JJ, Zhang H, Lu W, Zhang S, Zheng G, et al. effectiveness of electronic reminders to improve medication adherence in tuberculosis patients: a cluster-randomised trial. PLoS Med. 2015;12(9):e1001876. 10.1371/journal.pmed.1001876 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Yoeli E, Rathauser J, Bhanot SP, Kimenye MK, Mailu E, Masini E, et al. Digital health support in treatment for tuberculosis. N Engl J Med. 2019;381(10):986–7. 10.1056/NEJMc1806550 [DOI] [PubMed] [Google Scholar]
  • 17.Thomas BE, Kumar JV, Onongaya C, Bhatt SN, Galivanche A, Periyasamy M, et al. Explaining differences in the acceptability of 99DOTS, a cell phone-based strategy for monitoring adherence to tuberculosis medications: qualitative study of patients and health care providers. JMIR Mhealth Uhealth. 2020;8(7):e16634. 10.2196/16634 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Thomas BE, Kumar JV, Chiranjeevi M, Shah D, Khandewale A, Thiruvengadam K, et al. Evaluation of the accuracy of 99DOTS, a novel cellphone-based strategy for monitoring adherence to tuberculosis medications: comparison of digital adherence data with urine isoniazid testing. Clin Infect Dis. 2020;71(9):e513–6. 10.1093/cid/ciaa333 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Thekkur P, Kumar AN, Chinnakali P, Selvaraju S, Bairy R, Singh AR, et al. Outcomes and implementation challenges of using daily treatment regimens with an innovative adherence support tool among HIV-infected tuberculosis patients in Karnataka, India: a mixed-methods study. Glob Health Action. 2019;12(1):1568826. 10.1080/16549716.2019.1568826 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Katamba A, Neuhauser DB, Smyth KA, Adatu F, Katabira E, Whalen CC. Patients perceived stigma associated with community-based directly observed therapy of tuberculosis in Uganda. East Afr Med J. 2005;82(7):337–42. [PubMed] [Google Scholar]
  • 21.Noyes J, Popay J. Directly observed therapy and tuberculosis: how can a systematic review of qualitative research contribute to improving services? A qualitative meta-synthesis. J Adv Nurs. 2007;57(3):227–43. 10.1111/j.1365-2648.2006.04092.x [DOI] [PubMed] [Google Scholar]
  • 22.McLaren ZM, Milliken AA, Meyer AJ, Sharp AR. Does directly observed therapy improve tuberculosis treatment? More evidence is needed to guide tuberculosis policy. BMC Infect Dis. 2016;16(1):537. 10.1186/s12879-016-1862-y [DOI] [PMC free article] [PubMed] [Google Scholar]

Decision Letter 0

Thomas J McBride

9 Jan 2021

Dear Dr Cattamanchi,

Thank you for submitting your manuscript entitled "Digital adherence technology for tuberculosis treatment supervision: a stepped-wedge cluster randomized trial" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff as well as by an academic editor with relevant expertise and I am writing to let you know that we would like to send your submission out for external peer review.

However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

Please re-submit your manuscript within two working days, i.e. by .

Login to Editorial Manager here: https://www.editorialmanager.com/pmedicine

Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed all checks it will be sent out for review.

Feel free to email us at plosmedicine@plos.org if you have any queries relating to your submission.

Kind regards,

Thomas J McBride, PhD

Senior Editor

PLOS Medicine

Decision Letter 1

Richard Turner

6 Feb 2021

Dear Dr. Cattamanchi,

Thank you very much for submitting your manuscript "Digital adherence technology for tuberculosis treatment supervision: a stepped-wedge cluster randomized trial" (PMEDICINE-D-20-06186R1) for consideration at PLOS Medicine.

Your paper was evaluated by an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to invite you to submit a revised version that addresses the reviewers' and editors' comments fully. You will appreciate that we cannot make a decision about publication until we have seen the revised manuscript and your response, and we expect to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

We hope to receive your revised manuscript by Mar 01 2021 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

Please use the following link to submit the revised manuscript:

https://www.editorialmanager.com/pmedicine/

Your article can be found in the "Submissions Needing Revision" folder.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/plosmedicine/s/submission-guidelines#loc-methods.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

Please let me know if you have any questions, and we look forward to receiving your revised manuscript in due course.

Sincerely,

Richard Turner, PhD

Senior Editor, PLOS Medicine

rturner@plos.org

-----------------------------------------------------------

Requests from the editors:

Please add a new final sentence to the "Methods and findings" subsection of your abstract, which should begin "Study limitations include ..." or similar and quote 2-3 of the study's main limitations.

Do you mean "waiver of informed consent" in the Methods section?

Please remove trademarks, e.g., in the Methods section.

Throughout the text, please ensure that reference call-outs fall before punctuation, e.g., "... blister packs [5,6]." (noting the absence of spaces in the square brackets).

Please remove the information on study funding from the "Acknowledgments" section at the end of the main text. Funding information should appear in the article metadata, via entries in the submission form.

In the reference list, please ensure that journal names are abbreviated consistently, e.g., "PLoS Med.".

Are references 4 & 18 duplicates?

Please update reference 8.

Please spell out the institutional author name for reference 13.

Please adapt the attached CONSORT checklist so that individual items are referred to by section (e.g., "Methods") and paragraph number rather than by line or page numbers, as the latter generally change in the event of publication.

Please name the checklist "S1_CONSORT_Checklist" or similar, and refer to it by this label in your Methods section. Please also refer to the attached trial protocol document in the same way.

Comments from the reviewers:

***Reviewer #1:

This is an important and well-conducted stepped-wedge cluster randomized trial assessing the impact of implementing a digital adherence technology (DAT) on TB treatment outcomes in a low-income country. With ongoing critique of DOT models in TB care, interest in DATs—as a replacement for DOT—has been growing; however, there is a paucity of evidence regarding the impact of these technologies on TB treatment outcomes and longer-term outcomes, particularly in low- and middle-income country settings. As such, this study is a potentially important contribution.

While the study is well-conducted, I have considerable concerns about the interpretation and framing of the study findings, particularly with regard to the per protocol analysis, which seems to drive the entire Discussion section of the paper. While the authors appropriately interpret the findings of the intent to treat analysis—that 99DOTS-based treatment supervision did not improve overall treatment outcomes in the patient population—the very substantial limitations of the per protocol analysis raise questions about their interpretation that "99DOTS improved treatment outcomes among patients who received the intervention." I worry that, given the considerable limitations of the per protocol analysis, this finding could be very misleading, especially given the lack of transparent discussion of these limitations—which I believe are significant enough that they should be emphasized in the abstract, author summary, and discussion sections, so as to signal to the reader that the study findings have not ultimately answered the question of whether this intervention improves outcomes. I outline the reasons for my concerns further below.

Major feedback:

1. Interpretation of the per protocol analysis findings: Per protocol analyses have the potential to lead to erroneous findings of intervention effect because—particularly when the analysis excludes a large number of patients that were in the intent to treat analysis—it breaks the random assignment that a randomized trial is supposed to achieve. As such, the correct interpretation of these findings is not necessarily that outcomes improved only among those who received the intervention, but rather that—given the exclusion of a large number of the original patients included in the trial—it remains unclear whether the intervention caused the improved outcomes, or whether the patients left behind in the per protocol analysis are those who were likely to have better outcomes anyways even without the intervention (i.e., introduction of selection bias). In the Discussion section, the authors allude very briefly to this limitation of their study before minimizing it when they state: "It is possible that the patients who were enrolled on 99OTS would have been more likely to complete the treatment even under routine care."

I worry that this is a much bigger problem than they suggest, for the following reasons:

(a) Per protocol findings may be more likely to reveal a real effect if there is at least some concordance in the directionality of the finding in the ITT analysis (even if the ITT findings were not significant). However, the ITT findings here don't have the same direction (the odds ratio nicely straddles 1) as the PP analysis findings.

(b) PP findings may be more likely to reveal real effect if it excludes a small proportion of patients; however, this PP analysis excluded nearly half (48%) of study participant in the intervention arm, while only about 3% were excluded from the control group, which could introduce a high level of bias into the findings, as the PP intervention group essentially may represent a non-random sample of the intervention group.

(c) Further the discretion exercised by both healthcare workers and patients in deciding which patients should be enrolled in 99DOTS (from the healthcare workers' side) and whether to engage with 99DOTS (from the patients' side) suggest a possibility that patients who enrolled in 99DOTS may have had higher baseline interest in engaging in care and may have been likely to have better outcomes even without the intervention. As such, when the PP analysis restricts to this subset of patients in the intervention group, it is hard to know whether the effect seen is a result of the intervention or of selection bias in the PP analysis. This is of course not the fault of the authors or the study design - but it does speak to the importance of placing precedence on the findings from the ITT analysis (which showed no effect), and of interpreting the PP analysis cautiously, rather than suggesting it shows a clear treatment effect. It's not clear if the PP analysis has much validity given the loss of half of the intervention sample.

(d) In addition, 6.5% of those who didn't enroll in 99DOTS died or were lost to follow-up before they could enroll and another 4.2% were deemed too ill or elderly to enroll - presumably they weren't included in the PP intervention group. The 6.5% all clearly had bad outcomes while the 4.2% were highly likely to have bad outcomes - and, from what I can tell, early deaths or severely ill individuals were not excluded from the PP control arm. This is a very clear source of bias in favor of seeing a positive effect from the 99DOTS/intervention group. In fact, just the exclusion of this ~10% of patients from the intervention period group could account for much of the ~10% difference in outcomes seen between the control and intervention periods in the PP analysis. Again, highlights the concerns about the PP analysis and the importance of highlighting these limitations and prioritizing the finding from the ITT analysis.

(e) Finally, the authors do a post-hoc analysis in which they compare the individuals in the intervention period/group in the PP analysis to those in the control/period group who had phone numbers listed on their treatment cards, in a reasonable attempt to control for the bias in the PP intervention group. However, their description of patients who engaged with 99DOTS suggests that it may not match well with this subset of patients in the control period. For example, 65% of patients who didn't enroll in 99DOTS in the intervention period didn't have regular access to a phone - which suggests one-third of patients actually did have regular access to a phone but still chose not to enroll in 99DOTS, again raising concerns about those who enrolled with 99DOTS being more engaged with their TB care even without the intervention. As such the intervention period/group in the PP analysis excludes many patients who actually had regular access phones. Also, in the modified PP control group, just having a phone number listed on the treatment card does not mean that these patients will have regular access to their phones - which is another reason to why this modified PP control group probably does not actually address the problem of bias in the PP analysis.

2. Importance of IIT findings versus PP: Everything described in major critique #1 above does not undermine the trial study design per se - it just highlights that the primary valid finding is the finding from the ITT analysis, and the one from the PP is interesting but at such high risk of bias that using it as the focus of the paper could be misleading in suggesting 99DOTS has a treatment effect when it may or may not. The framing is critical. If the discussion is framed around the ITT findings, the reader walks away viewing this as an intervention with no clear benefit to the overall population of TB patients, but that may still be worth investigating further. Such a framing, which is more appropriate in my mind, supports further research rather than a "scaling up" of 99DOTS as advocated by the authors. The current discussion framed around the PP findings has readers walking away thinking that this intervention has clear benefit to those who use it, when this is far from clear given the substantial limitations and bias in the PP analysis - and therefore the current discussion is misleading. The ITT findings are clearly the scientifically sound findings.

In addition to reframing the discussion, a robust discussion of the limitations and biases in the PP analysis should be included in the Discussion section.

Minor feedback:

1. On page 5, the authors state that "The most common DAT platform…within an electronic pillbox." Electronic pillboxes certainly are being used at scale in China, but there are many other DATs in wide use - including video DOT in high-income settings. In fact, the most commonly used DAT is possibly 99DOTS, given its very large-scale rollout to hundreds of thousands of patients in India, Myanmar, and elsewhere. As such, revise this to say "A common DAT platform…"

2. On page 18, "To our knowledge, these data represent the first to show the impact of a low-cost DAT on TB treatment outcomes." - This isn't a correct statement, as the authors note in the very same paragraph. The Yoeli et al. trial of a two-way SMS platform in Kenya that the authors discussed showed an decline in unfavorable treatment outcomes (and therefore, conversely, an improvement in favorable outcomes). Also, if the authors take major critique #1 above seriously, then this also would not be a true statement b/c it is not clear that this study shows a real impact on treatment outcomes, if they prioritize the ITT findings.

3. There is some observational literature on TB treatment outcomes with 99DOTS in India - see Thekkur et al. Global Health Action 2019;12(1):1568826. The study finds poorer treatment outcomes at sites that rolled out 99DOTS, even after some adjustment for differences in patient characteristics. This is of course an observational study with its own risk of bias - however, there is similar risk of bias in the current PP analysis given the large proportion of patients excluded in the intervention period. It would be helpful for the authors to discuss their study findings in the context of this prior research on 99DOTS. There is also research on 99DOTS accuracy for measuring adherence and patient engagement with the technology that might be helpful to contextualize their findings.

*** Reviewer #2:

Improved tuberculosis (TB) treatment outcomes were observed in a controlled trial of over 2200 participants in Uganda in 2018-2019 when patients contacted their healthcare giver on a freephone service after taking medications, as against in person direct observation of treatment. The article includes novel findings from the study of 99DOTS, a low-tech digital adherence technology that has been implemented at scale in India and elsewhere ahead of completion of trials. There is thus a lot of interest in the finding of this RCT (esp. during lockdowns and also for the delivery of other treatments, like ART). Moreover the adaptations to the basic technology that were introduced by the investigators of the Uganda trial (educational messages to callers and more neutral packaging) appear to be helpful and could be integrated in the basic package of 99DOTS. In my opinion this work can be published subject to some minor revisions to improve understanding and the authors should be allowed a reasonable excess over the word limit to explain certain key issues of their interesting research and its implications adequately:

page 2, line 1: treatment completion for new & relapse cases is on target globally (at around 85%). Maybe you can state that in many countries (including Uganda) adherence and treatment completion remain problematic and that loss to follow up is one of the eminent barriers to improving cure in TB patients

page 6, last sentence: it would be critical to add some details from the article referenced about the extent of local DOT implementation around the time that the trial was conducted. The reader needs to understand how much the SOC in Uganda matches what should be happening on paper

page 11, bottom lines: "However, treatment success even among patients using 99DOTS was below the 90% target specified in the END TB Strategy." isn't this partly explained by your inclusion of centres that had a treatment success <80% in previous years (the mean was only 51% treatment success as per Methods in p8)? the national reported rates of 74% in new & relapse cases in 2018 may mean that quite a few centres could reach the target values with the support of aids like 99DOTS (https://worldhealthorg.shinyapps.io/tb_profiles/?_inputs_&entity_type=%22country%22&lan=%22EN%22&iso2=%22UG%22) ? can you comment some more in the Discussion about this?

page 12, para 2: here or in the last para on this page can you add one or two sentences to explain why the stepped-wedge design is expected to provide a better approach to the study of DAT when compared to other more standard methods? if you believe there are downsides please add a note in the limitations para further down. Was there risk of "contamination" between participants in the intervention and control groups when they returned to the clinics to collect medication during follow up in centres randomised to the intervention in the first months?

page 12, midway: "Our trial extends upon these prior findings and lends further support to the increasing calls to abandon DOT and make the TB treatment experience more patient-centered" can you temper this commens with the relative disadvantages of 99DOTS as an approach when compared with direct physical interaction with a healthcare worker? in your opinion, and based on the findings, which patient risk factors and at which stage in their treatment would you think this DAT could be particularly helpful (e.g. would you posit that the improved effect in HIV-positive and in men has application beyond your study population)? do you anticipate an intervention model whereby all TB patients get 100% 99DOTS as the primary standard of care?

page 13, conclusion para: do you think that the adaptations explained in supplementary figure 2 contributed to the effects observed and do you advise similar customization in the future implementation of the technology?

*** Reviewer #3:

This study reported a stepped wedge clustered randomized trial aiming to evaluate the effectiveness of 99DOTS in improving treatment outcome among drug susceptible tuberculosis (TB) patients among 18 health facilities in rural Uganda. This is an important study to fill the gap of the lack of evidence of applying the new digital technologies in improving TB treatment adherence in low and middle income countries. The study reported a non-significant result on the primary outcome, i.e., treatment success rate, between patients under 99DOTS intervention and those in routine care based on the intention to treat (ITT) analysis, but significant improvements in the intervention group compared with control regarding primary, secondary outcomes and in sub-group analyses. The manuscript was well written and clearly presented, but needs substantial work to address the following methodological concerns regarding patient inclusion, analyses and discussion.

1. Per protocol analysis was not mentioned in the protocol. This must be an ad-hoc analysis and have to be stated clearly the rationale of the PP analysis, and how the criteria of per protocol analysis was established.

2. There are other alterations among the secondary outcomes, comparing what was reported on page 17 of the protocol. In the manuscript, the nominator of one of the secondary outcomes, the proportion of persistence, has been changed from patients completed at least 60 doses of treatment to patients who completed the intensive phases. There were no reports on the proportion converted as in the protocol.

3. What are the adherence rates of patients on 99DOTS program? Did non-adherence happen not only in the beginning but also in other periods during the treatment, e.g., when patients become better by the end of the treatment? Within the PP analysis, why the study excluded patients who did not enrol on 99DOTS within the first month, but did include patients who stopped using or dropped out 99DOTS in subsequent months?

4. On Page 7 of the manuscript, the authors stated that patients who started TB treatment during the buffer period, i.e., the first month of switching from control to intervention, were excluded from primary analysis. However, in S1 Figure, the flow chart, the authors did not state the number of patients excluded for this reason. This should appear in the same line as the 566 transferred-out patients.

Table S4 indicated that patients who initiated their treatment during the buffer period were not excluded from analysis (probably included for the control period), because the total number of patients, if including those initiating treatment during the buffer period, are the same (n=2224) as before. This seems to contradict the methods section.

If these patients were included in the control period, this will dilute the difference between the two groups.

5. There have been a large number of patients who did not enrol in 99DOTS program in the first month (47%, 428/ 891). What were reasons the authors may identify from the process evaluation? These patients apparently had much worse outcomes compared with the rest of their peers in the intervention group/period. This is important message that needs to be discussed.

6. In Table S5, there is a large number of patients who did not register a telephone number in the records. Were these patients provided with a cell phone? On page 19, the authors reported that 99DOTS was not used for a substantial proportion of the patients. What is the rate of patients using the 99DOTS? There are a number of related outcomes in the category of Implementation outcomes. This needs to be either reported or quoted from your other publications. This a crucial to understand the effectiveness of 99DOTS. It seems that the author indicated that the acceptability/ usability of 99DOTS were low. So the program is only effective among patients who use it.

I would suggest the authors would focus on the possible reasons regarding the difference between the ITT and PP analyses in the discussion section.

*** Reviewer #4:

This is a well-conducted stepped-wedge cluster RCT on digital adherence technology for tuberculosis treatment supervision. The study design, dataset, statististical methods and analyses, and presentation (tables and figures) and interpretation of the results are mostly adequate and of a good standard. However, there are still some issues needing attention.

1) Sample size calculation. With the information on Page 10, one is unable to reproduce the sample size and power. More details of the sample size calculation (parameters) are needed. How many clusters in the calculation? is it 18 or 6? How many steps? what's the cluster size? what's justificaiton of the assumption of 10% increase in the treatment success proportion? Where is the ICC of 0.001 from and any justification? Stepped-wedge design is complex so that sample size calculation needs to be very clear and detailed.

2) The ITT vs PP analysis is the key of the paper. However, the definiton of the PP analysis "The PP analysis excluded patients who initiated treatment during the control period but were nonetheless enrolled on 99DOTS prior to treatment completion" is a bit difficult to understand. Can authors please explain what exactly this means?

*** Reviewer #5:

Thank you for the opportunity to review this interesting submission. I have a number of comments and suggestions, but I do believe that this paper presents information important to the discipline (improving TB treatment/health outcomes through the use of innovative, patient-centered technologies) and is meritorious of publication. The paper correctly points out that, while there are many studies and publications addressing the use of digital adherence technologies to improve medication adherence, there are relatively fewer studies and publications (i) involving TB, (ii) conducted in highly pragmatic settings with a view toward generalizability with respect to other high burden regions, and, most importantly, (iii) focused on treatment/health outcomes as a primary endpoint. Especially given the other aspects of this study that have been published (user-centered design aspects) and are indicated as to be published (cost-effectiveness aspects), this study/paper represents the centerpiece of what is probably the most comprehensive evaluation to date of the use of digital adherence technologies as an enabler/enhancer of TB care. Again, I think it merits publication.

My specific comments and questions to the authors are set forth below:

1. My most important question/comment related to the primary end point and the distinction drawn between ITT and PP populations. I understand the distinction and the figures did an OK (not great job) of showing the differences in these patient populations. However, given the extremely important differences in outcomes (no significant effect in ITT and quite significant effect in PP), I think that the authors should be clearer about which is the more relevant group/more significant finding and why. It would seem that one could argue that the PP population/finding is more important in that more patients in this group seem to have received the actual intervention in the intervention arm. However, the PP group also excludes a number of "typical" patients so maybe it is an unrepresentative population. I would like to see the ITT/PP distinction and significance made clearer if possible. The authors stater on page 28 that "in conclusion, our trial provides evidence from a randomized trial that 99DOTS-based treatment supervision is effective for adults with pulmonary TB who are enrolled on the platform." Why isn't that the true bottom line here, rather than looking to an ITT population where the results seem more affected by "reach" than "efficacy" of the intervention?

2. Related to point #1 above, I found the abstract to be underwhelming (compared to the well-stated conclusions and insights on pages 19-20). I wonder if some improvements might be possible/in order.

3. On page 6 of the paper, there is a reference to $4-6/patient at scale. Have the authors validated that pricing in Uganda or is this based on 99DOTS or other information? I am not sure the parenthetical adds value (maybe low-cost alone is sufficient) and may be provocative if not substantiated as real in the Uganda context.

4. I very much like the secondary end points related to persistence through intensive phase and also loss to follow up. Given the potential impact of these aspects on both transmission and recurrence, I applaud the study design and inclusion of these additional end points.

5. I wonder if it is worth noting in the discussion and conclusions that Uganda seems to have primarily community as opposed to facility-based DOT. That is noted earlier in the paper, but it might be worth noting that the conclusions supporting use of 99DOTS vis a vis DOT are in that (community-based rather than facility-based) context.

6. I thought the description of the 99DOTS intervention was clear. I wonder if it might be worth adjusting language slightly to highlight that the "hidden number" is a feature designed to increase confidence that the patient has "engaged" with his/her medication and therefore the dosing signal is more accurate. Not a major point.

7. I did have to get out a calculator to determine how the populations in the ITT and PP groups were calculated. I wonder if a figure showing how these groups were derived and the various exclusions applied in getting to the PP group might be helpful.

8. Again, just to reinforce the point -- the paragraph on page 15 is a very strong statement of the effectiveness of 99DOTS (in treatment outcomes, in persistence through IP phase, and in LTFU -- 3X more likely to complete treatment) for the PP group. If that is really the more relevant group, these findings are really important. But is it -- or is the ITT group the more relevant. Unclear.

9. The Kenya study referenced on page 18 had, I believe, a health outcomes impact/end point. Are the authors comfortable with the characterization on page 18?

10. On page 19, the authors state: " Even though overall treatment outcomes did not improve . . . " and then goes on to talk about other, ancillary benefits of 99DOTS (empowerment, etc.) Is that statement correct and/or appropriate? Elsewhere in the paper the authors make it clear that 99DOTS did in fact improve outcomes for patients enrolled thereon. Should this statement on 19 be revised? IF the ITT result was more about reach than effectiveness (do the data give any insights on this?), then that is not clear -- especially in statements like the one referenced.

I hope these comments are helpful, and thanks to the authors for their efforts on behalf of TB patients globally.

***

Any attachments provided with reviews can be seen via the following link:

[LINK]

Attachment

Submitted filename: 99DOTS Uganda trial - PLOS Med 2021 review.docx

Click here for additional data file. (17.5KB, docx)

Decision Letter 2

Richard Turner

6 Apr 2021

Dear Dr. Cattamanchi,

Thank you very much for re-submitting your manuscript "Digital adherence technology for tuberculosis treatment supervision: a stepped-wedge cluster randomized trial" (PMEDICINE-D-20-06186R2) for consideration at PLOS Medicine.

I have discussed the paper with editorial colleagues and our academic editor, and it was also seen again by three reviewers. I am pleased to tell you that, provided the remaining editorial and production issues are fully dealt with, we expect to be able to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We hope to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

Please let me know if you have any questions, and we look forward to receiving the revised manuscript shortly   

Kind regards,

Richard Turner, PhD

Senior Editor, PLOS Medicine

rturner@plos.org

------------------------------------------------------------

Requests from Editors:

Please quote the study setting (Uganda) in your title.

Please quote aggregate demographic details for participant groups in the abstract.

We ask you to de-emphasize the per-protocol findings in your abstract and main text. In the abstract, we suggest quoting the ITT findings, followed immediately by a summary of the subgroup findings by sex and HIV status (noting that these are non-significant), with the per-protocol findings summarized briefly thereafter.

Please avoid claims such as "the first" (e.g., in your author summary) and where needed add "to our knowledge" or similar.

Please trim the author summary so that each subsection contains 3-4 short points.

Similarly, in the main text please present the subgroup analyses before the per-protocol findings.

Throughout the text, please use the style "the 4 randomized trials" (though numbers should be spelt out at the start of sentences).

Comments from Reviewers:

*** Reviewer #1:

I have reviewed the changes made to this manuscript, and the authors have done a very nice job of addressing my main concern, which was the interpretation and claims made regarding the findings of the PP analysis.

The authors have shifted the main take-away of their study to suggest that 99DOTS may be a viable alternative to DOT for some patients, while also affirming that the intervention did not improve outcomes in the overall patient population. While they did not use a non-inferiority trial design, I think that this is a reasonable conclusion given comparable outcomes during the control and intervention periods, as well as the generally good outcomes among the non-random subset of patients who used 99DOTS. Of course, this conclusion may just reflect the many shortcomings of DOT as a model of care, rather than any actual benefits of 99DOTS.

My only final piece of feedback is that, given that the authors suggest that 99DOTS may be a viable alternative to DOT for some patients in this setting, they should provide a better description of what the "control" condition of DOT was that they were actually comparing against. "Community DOT" can mean many different things, and the authors note that there is considerable variability in implementation of community DOT. With that said, it would still be critical for readers to know:

(1) In general (acknowledging that there may be variability) who most commonly serve as treatment supporters? Are they patients' family members—such that it is relatively convenient/easy for patients to be observed? Are they other people living in the community? Are they local healthcare workers?

(2) Are treatment supporters paid or compensated by the government?

(3) Are there any other costs of DOT in this context that would not also be there under 99DOTS-based monitoring? (Authors do not need to provide the exact costs but just very briefly—in a few words—note other aspects of care where costs may be incurred that might be different with DOT as compared to 99DOTS).

Two or three sentences in the Methods better describing what community DOT means in this context—even while recognizing that the model may be variable—is important for readers to be able to assess the authors' final claim that 99DOTS may help some patients avoid the "inconvenience and additional costs of directly observed therapy."

The authors don't provide enough basic information for the reader to assess whether this could potentially be true. For example, if "treatment supporters" are simply unpaid family members, this would essentially be free for the government and therefore cheaper than 99DOTS, which involves extensive printing of paper envelopes, personnel time to pack medication blister packs in these envelopes and review the dashboard, tech support costs, lost time for patients to call phone numbers on a daily basis, and other costs. On the other hand, if treatment supporters are usually paid non-family members then it would be clear that 99DOTS may potentially be cheaper to the government and more convenient for some patients, as the authors claim.

This clarity would be very helpful to readers. A major problem with the TB digital adherence technologies (DATs) literature is that the "control" condition can be so variable across contexts and is often poorly defined in papers that are trying to assess whether these improve outcomes.

Otherwise, the authors have done an excellent job of addressing my concerns and those of other reviewers and I have no other concerns. Thank you for involving me in the peer review of this manuscript.

*** Reviewer #3:

I felt the authors have adequately addressed questions, and made substantial revision of the manuscript.

*** Reviewer #4:

Thanks authors for their effort to improve the manuscript. I am satisfied with the response and revision. No further issues needing attention.

***

Any attachments provided with reviews can be seen via the following link:

[LINK]

Decision Letter 3

Richard Turner

14 Apr 2021

Dear Dr Cattamanchi, 

On behalf of my colleagues and the Academic Editor, Dr Suthar, I am pleased to inform you that we have agreed to publish your manuscript "Digital adherence technology for tuberculosis treatment supervision: a stepped-wedge cluster randomized trial in Uganda" (PMEDICINE-D-20-06186R3) in PLOS Medicine.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

In the author submission form, the data statement and competing interest statements, for example, appear to be duplicated - this will need to be rectified prior to final acceptance. One iteration of the competing interest statement quotes a "DS", presumably an error as no author has these initials.

In the meantime, please log into Editorial Manager at http://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process. 

PRESS

We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate with medicinepress@plos.org. If you have not yet opted out of the early version process, we ask that you notify us immediately of any press plans so that we may do so on your behalf.

We also ask that you take this opportunity to read our Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visit http://www.plos.org/about/media-inquiries/embargo-policy/.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper. 

Sincerely, 

Richard Turner, PhD 

Senior Editor, PLOS Medicine

rturner@plos.org

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 CONSORT Checklist

    (PDF)

    Click here for additional data file. (57.8KB, pdf)
    S1 Data. Raw de-identified data used to conduct this analysis.

    Each row corresponds to a patient. The dataset includes 13 columns: intervention group—randomization block (1–6); health facility—health facility where the patient initiated treatment (1–18); health center—type of facility (health center, hospital); trial month—month during which the patient initiated treatment (0–7); study period (control, buffer, intervention); sex; age; HIV status; disease class (bacteriologically confirmed pulmonary TB, clinically diagnosed pulmonary TB); retreatment—patient type (new, retreatment); treated successfully—primary outcome; persisting on treatment—secondary outcome; not lost to follow-up—secondary outcome.

    (XLSX)

    Click here for additional data file. (124.1KB, xlsx)
    S1 Fig. Health facilities included in the DOT to DAT trial.

    (TIF)

    Click here for additional data file. (528.6KB, tif)
    S2 Fig. Stepped-wedge trial design and patient enrollment.

    The target population includes all adults initiating treatment for drug-susceptible pulmonary TB. The eligible population excludes patients in the target population who were transferred out to another health facility during their treatment. Patients who initiated treatment during the buffer period were excluded from the study population.

    (TIF)

    Click here for additional data file. (804.1KB, tif)
    S3 Fig. Adapted 99DOTS platform.

    The original 99DOTS envelope (top) had 2 sides. We redesigned the original envelope using human-centered design to reduce stigma, encourage appropriate dosing, and facilitate communication between patients and health workers. Prototype 1 (middle) was used from January to June 2019. Prototype 2 (bottom) was used from July 2019 through the end of the trial. In addition to the changes shown here, the ring tone heard when patients called toll-free numbers to self-report dosing was replaced with a rotating series of educational or motivational messages recorded by local health workers.

    (TIF)

    Click here for additional data file. (1.5MB, tif)
    S1 Statistical Analysis Plan

    (DOCX)

    Click here for additional data file. (135.9KB, docx)
    S1 Table. Prespecified sensitivity analysis of the effectiveness of the 99DOTS-based intervention when including the buffer period.

    ITT, intention-to-treat; PP, per protocol. *Primary outcome. **Secondary outcome. †Adjusted for time (trial month, discrete variable), sex, HIV status, disease class (bacteriologically confirmed versus clinically diagnosed), and TB type (new versus retreatment) as fixed effects and site as a random effect. ^Proportion difference calculated as proportion in intervention period minus that in the control period. p-Value for adjusted intervention effect.

    (DOCX)

    Click here for additional data file. (15.7KB, docx)
    S2 Table. Post hoc sensitivity analysis of the effectiveness of the 99DOTS-based intervention when excluding patients without a listed phone number.

    *Primary outcome. **Secondary outcome. ‡Adjusted for time (trial month, discrete variable), sex, HIV status, disease class (bacteriologically confirmed versus clinically diagnosed), and TB type (new versus retreatment) as fixed effects and site as a random effect. ^Proportion difference calculated as intervention minus control. p-Value for adjusted intervention effect.

    (DOCX)

    Click here for additional data file. (15KB, docx)
    S1 Trial Protocol

    (DOC)

    Click here for additional data file. (308KB, doc)
    Attachment

    Submitted filename: 99DOTS Uganda trial - PLOS Med 2021 review.docx

    Click here for additional data file. (17.5KB, docx)
    Attachment

    Submitted filename: PLOS Med Response to Reviewers_2021.2.24.docx

    Click here for additional data file. (69KB, docx)
    Attachment

    Submitted filename: PLOS Med Response to Reviewers_2021.4.8.docx

    Click here for additional data file. (24.8KB, docx)

    Data Availability Statement

    All relevant data are within the manuscript and its Supporting Information files.

    Articles from PLoS Medicine are provided here courtesy of PLOS

    RESOURCES