A Cost Analysis of Implementing Asynchronous Video Directly Observed Therapy for Monitoring Treatment in Patients with Tuberculosis Disease in Uganda

A Cost Analysis of Implementing Asynchronous Video Directly Observed Therapy for Monitoring Treatment in Patients with Tuberculosis Disease in Uganda | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Cost Analysis of Implementing Asynchronous Video Directly Observed Therapy for Monitoring Treatment in Patients with Tuberculosis Disease in Uganda Wilson Tumuhumbise, Rebecca Kuteesa, Damalie Nakkonde, Esther Buregyeya, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7911005/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Video directly observed treatment (VDOT) supports person-centered care and addresses shortcomings of in-person directly observed treatment (DOTS). However, there is limited literature on implementation costs to inform TB program policy in low-resource settings, such as Uganda. Objective This study aimed to assess the costs associated with implementing the VDOT intervention in Uganda, based on a completed randomized trial. Methods We carried out a micro-costing analysis. This employed both bottom-up and top-down approaches to estimate health system costs (in 2024 US dollars) of the VDOT intervention. The analysis covered 71 persons with TB from six health facilities in Kampala, Uganda. Results The estimated overall costs for implementing VDOT in Uganda were $ 16,037.80 among 71 patients with TB. The estimated per-person costs for VDOT were $ 220.93. The largest proportion of the overall cost was associated with smartphones and accessories (29.5%), followed by VDOT platform infrastructure costs (21.7%), program support costs (19.5%), and internet data costs used by patients to send the videos (10.2%). The per-person costs of using VDOT are lower than the potential cost of daily transport that would be associated with facility-based DOT. Conclusion The implementation of VDOT in Kampala was associated with a relatively low cost compared to the projected cost when using the traditional DOT method. This highlights the cost saving benefit to the patients when using remote treatment monitoring with the video technology. A full economic evaluation from the programmatic and societal perspective is recommended to inform the adoption of the VDOT strategy in resource-limited settings. Health Economics & Outcomes Research Video Directly Observed Treatment cost analysis Tuberculosis adherence Implementation Figures Figure 1 Introduction Tuberculosis (TB) remains a major global health concern and the leading infectious killer disease, with over 1.25 million deaths annually ( 1 ). Despite being curable, it remains the leading cause of death among individuals living with HIV/AIDS ( 2 ). In 2023 alone, about 10.8 million people fell ill from the disease globally, of which the majority were from countries in low-resource settings (LRS) ( 1 ). Uganda is among the 30 high TB/HIV burden countries with an incidence rate of 198 per 100,000 people. Treatment adherence remains a significant challenge that leads to poor TB treatment outcomes (such as treatment failure, emergence of drug-resistant TB), and secondary transmission ( 3 , 4 ). TB also places a significant economic burden on the patients and their households. In Uganda, about 53% of the households of patients with TB experience catastrophic costs between 20–40% of their entire household income, estimated at US $ 369 ( 5 , 6 ). The World Health Organization (WHO) defines catastrophic costs as spending 20% or more of the household’s annual income on expenses related to TB care ( 7 , 8 ). The largest proportion of the out-of-pocket costs are associated with travel to the TB clinics for routine visits to fill prescriptions ( 9 ). The financial burden on patients and their families hampers progress towards achieving successful TB treatment outcomes, thus negatively impacting the attainment of the End TB strategy ( 10 , 11 ). The standard TB care and management strategy, known as directly observed therapy (DOT), requires daily observation of a patient as they ingest the TB medication by a health worker or treatment supporter ( 12 ). Although in-person DOT is an effective way to ensure proper adherence to treatment, its feasibility is largely limited because of a shortage of human and financial resources in the public health system ( 13 ). The resource constraints have led to the abandonment of in-person DOT in healthcare settings, rendering it less effective ( 14 ). Digital adherence technologies (DATs) have been recommended by the WHO for addressing the shortcomings of in-person DOT and supporting person-centered TB treatment ( 15 ). These include Short Message Service, real-time medication monitoring systems, ingestible sensors, and video-observed therapy ( 16 – 19 ). Video directly observed therapy (VDOT) is an innovative smartphone-based system that utilizes a mobile application to record TB medication intake videos, thereby substituting the need for frequent face–to-face meetings ( 16 ) as required in the traditional DOT. The feasibility and acceptability of VDOT have been documented in studies done in TB clinics in Uganda and elsewhere ( 20 , 21 ). VDOT has also been shown to be more effective in increasing medication adherence monitoring than usual care in-person DOT in an open-label randomized trial in Uganda ( 16 ). Studies that evaluated the cost of VDOT in the United States have reported a reduction in costs of care for both patients and health departments compared to the traditional in-person DOT ( 22 , 23 ). However, the cost of implementing VDOT in LRS and the associated cost savings have not been well documented. In this study, we utilized data from a recently completed VDOT trial (NCT04134689) in Uganda to conduct a secondary cost analysis. Methods Ethical Considerations Institutional Ethical approvals for this research were obtained from the Makerere University (protocol 756) and the University of Georgia (ID PROJECT00000571) institutional review boards, and the national ethical approval from the Uganda National Council for Science and Technology (HS656ES). Written informed consent was obtained from all the study participants in either English or Luganda, depending on their language preference. Approximately US $ 10 was given to all the research participants per visit as compensation for transportation and the time spent participating in research activities. Study Setting and Participants The study is based on a completed open-label randomized trial known as the “DOT Selfie study”, which was conducted between July 2020 and October 2021 at Lubaga TB clinic and several other public TB clinics in Kampala. The methodology of this study has been reported elsewhere ( 16 , 24 ). Briefly, secondary cost data were extracted for 71 participants who were randomized to the VDOT intervention study group. Eligible patients were aged 18 to 65 years with confirmed drug-susceptible TB and residing in Kampala during the treatment period. Patients were excluded if they had confirmed multidrug-resistant TB or a documented cognitive, visual, or other disability that would interfere with recording videos. Additionally, those who did not have access to electricity to charge a smartphone or who resided in areas with poor cellular network coverage were excluded. Description of VDOT System A detailed description of the VDOT system has been published elsewhere ( 16 ). Briefly, treatment adherence monitoring was performed using an asynchronous VDOT smartphone application (app) that enabled patients to record videos while swallowing each dose of medication and upload them to a secure cloud server, which was accessed and viewed later by health workers ( 25 ). The app worked on both Android (Google LLC, Menlo Park, CA, USA) and iOS (Apple Inc., Cupertino, CA, USA) smartphones. Date and time-stamped videos were automatically sent by the app through third or fourth-generation (4G) cellular networks or Wi-Fi. The app is secured to prevent videos from being viewed, edited, or deleted by users on the smartphone, thus ensuring confidentiality and fidelity of the videos. After a successful upload, the videos are automatically deleted from the smartphone. Cellular internet data was prepaid weekly at ∼US $ 1.00 and sent directly to the participants’ phones to ensure videos would be sent in a timely manner. Trained research staff logged into an internet-based, password-protected client management system to watch the uploaded videos and documented whether they observed the pills being swallowed. The health workers followed a pre-specified protocol to follow-up with patients if a video was not received within 24 hours. Data Collection and Follow-up A baseline survey was administered to collect information about participants’ socio-demographics, phone ownership, and technology use experience including using a smartphone, internet, taking photos or videos and frequency of use in the last 3 months (Supplementary Appendix 2). Additional information about different cost drivers was collected from study budget lines, receipts, time logs, Telecom Company, server, protocol specifications and cost logs. Study participants returned to the clinic for their routine monthly visit to refill prescriptions and for evaluation at 2, 4, and 6 months per standard of care. Costing Approaches We adapted the VDOT costing tool guide by McGill International, published elsewhere ( 26 ) (Supplementary Appendix 1). This questionnaire aimed to capture all costs associated with the implementation of VDOT. Eight cost drivers were identified and categorized to establish detailed cost components from the health provider’s perspective, and these are; i) smart phone and accessories, ii) prepaid internet data, iii) VDOT platform/infrastructure costs, iv) program support costs (i.e. internet for viewing videos, airtime for phone communication, v) adherence monitoring by healthcare workers on VDOT platform, vi) systems and data management, vii) escalation costs related to unscheduled phone calls or home visits, and those related to VDOT technology malfunction, app upgrades or for phone repairs, and viii) VDOT training for HCWs. At the end of the questionnaire, costs from each category were summed to estimate the total cost per patient associated with the implementation of VDOT. For costs that were only relevant to some participants, we divided them over the full patient population (i.e., prorated) in order to estimate per-patient costs. We calculated the costs using both bottom-up (micro-costing) and top-down approaches (Cunnama 2016). Bottom-up approach focused on detailed activity and input usage data from records (time logs, cost logs, budget lines) to establish unit costs. (e.g., the smartphone and accessories cost category was established from the receipts, the cost of airtime from the time logs, to get the total cost and per-patient cost for phones and accessories). Program Costs To estimate the program costs, we focused on the internet for viewing videos, airtime for phone communication, and patient follow-up as detailed activities. We then extracted the cost of internet packages from the telecom company records and time logs to establish both the patient and total cost of data bundles. For adherence monitoring by HCW on the VDOT platform,we used the fraction of time spent watching videos to confirm adherence per full-time effort of a health worker. We used time logs to establish the average time spent by research staff per patient routine and video reviewing. To establish the cost of escalation related to VDOT technology, we established the number of patients who required phone repairs, app retraining, and maintenance from the cost logs and multiplied by the average amount of costs spent on phone repairs, app retraining per participant, to get the total and per-patient cost of escalation related to VDOT technology. To estimate the cost for VDOT training for HCWs, we assessed the research assistants, their total stipulated time for training, and the hourly wage from the protocol. We then multiplied the total training time and hourly wage to get the cost of training all research assistants. To get the cost of training one research assistant for an individual patient, we established the cost of training one research assistant divided by the number of study participants. The top-bottom approach focused on the overall expenditure (e.g., VDOT platform expenditure), then allocated costs (e.g., software license, configuration costs, to estimate costs per person, and the fixed cost of the platform. To ascertain the cost of the VDOT system and data management, we captured the fraction of time, alongside wages spent on staff providing technical support on the VDOT platform. We established the average wage per hour of the data manager and the total days spent providing the technical service to get the per-patient and total cost of this category. Escalation was related to unscheduled phone calls or home visits. We established the average wage (per hour) for research staff as per the protocol. We then established the average number of phone calls made by the health workers from call logs multiplied by the number of patients that required the phone call and or home visit support to get the per patient cost of escalation related to unscheduled phone calls or home visits. To assess the cost savings in transportation for patients in VDOT, we looked at the average expected videos per patient. We assumed that every video was equivalent to one round trip to the TB clinic. For computation, we established the adjusted projected cost of travel over the duration of treatment by multiplying the actual days of travel to the clinic by the total cost of daily travel. To ascertain the adjusted project cost on VDOT over the duration of treatment, we considered the average per-patient cost of VDOT as a program, i.e., the summation of all cost drivers of VDOT as a program for each of the 71 participants. Hypothetical, Real-life, and VDOT Scenarios of Costs Associated with Travel To illustrate the cost savings from travel, we evaluated three scenarios that were based on the two-month intensive phase of TB treatment. First, the ‘hypothetical scenario’ represents an ideal situation where there are no missed days of DOT. We assumed that a typical patient with TB who undergoes directly observed treatment at the health facility for five days a week. The patient is expected to incur round trip travel costs to the health facility for 20-week days (excluding weekends) per month without missing any schedule visit. Second, the ‘real-life scenario’ represents a situation that accounts for missed DOT days. We used the information from video submissions as equivalent to in-person hospital visits. For example, we considered the number of videos missed out of the two-month intensive phase of treatment to represent potential DOT face-to-face visits that would have been missed. Since the participants would have missed the visits, the associated travel costs were avoided—resulting in a coincidental savings. Third, the ‘VDOT-aided’ scenario represents a situation where the patient’s treatment adherence is monitored remotely by healthcare workers using digital adherence technology. This approach eliminated the need for daily travel, thereby resulting in a substantial cost saving. The only travel costs that are incurred by the patients are associated with the trips for the initial visit, intensive phase (visit one), and continuation phase (visits two & three). In all these three scenarios, we consider a constant baseline cost for the initial hospital visit for drug initiation and the monthly travel costs (20 days, excluding weekends). Therefore, the ultimate cost savings in transportation for patients in VDOT was the total costs incurred in Hypothetical ideal situation minus the total costs incurred in VDOT aided situation. Results Participants’ Baseline Characteristics Of the 72 participants who had active TB, 50% were male, and the median age was 29.5 (24–42) years. The majority of the participants, 68 (96%), owned a cell phone, of which only 50 (70%) owned smart phones. The study loaned 21 smart phones to the 30% participants who did not own a smart phone. All 72 (100%) participants received the VDOT app except for one participant who was randomized to VDOT but refused to initiate his TB treatment soon after enrollment. The participant was included in the baseline analysis but excluded from any further analyses because he did not have the study outcomes of interest, as shown in Table 1 below. Table 1 Baseline characteristics of VDOT study participants (N = 72) Variables Variables VDOT Arm (%)n = 72 Sex Sex Male 36 (50.0) Female 36 (50.0) Age in years, median (IQR) Age in years, median (IQR) 29.5 (24–42) Age Category Age Category 18–24 18–24 19 (70.4) 25–34 25–34 27 (56.2) 35–44 35–44 10 (33.3) 45–65 45–65 16 (41.0) Highest level of education Highest level of education No education or primary 27 (37.5) Secondary 28 (38.9) Tertiary/University 17 (23.6) Marital status Marital status Currently married 23 (31.9) Previously married 13 (18.1) Never married 36 (50.0) Currently employed Currently employed No 29 (40.3) Yes 43 (59.7) Monthly personal income in USD§, median (IQR) Monthly personal income in USD§, median (IQR) 42.85 (14.29–114.30) Monthly total household income in USD§, median (IQR) Monthly total household income in USD§, median (IQR) 71.43 (28.57–157.14) Currently owned cellphone No 14 (9.4) Yes 58 (80.6) Owned smartphone No 22 (30.6) Yes 50 (69.4) HIV Status Positive 24 (33.3) Negative 48 (66.7) Cost Analysis of VDOT for 71 Participants Our analysis categorized eight main cost drivers as shown in Fig. 1 and Table 2 below. These were further analyzed to establish both per-patient cost and the total costs incurred in the respective cost category. The largest proportion of the total cost was generated by the smartphones and accessories (31.9%), followed by the upfront cost of the VDOT platform (21.8%) and program support costs (19.6%). Others included prepaid internet subscription used by patients to submit their videos (10.2%), escalation costs for follow-up of patients (10%), of which 7.2% were related to unscheduled phone calls or home visits, while 3.8% were related to VDOT technology malfunction, app upgrade, or phone repairs. The lowest costs were related to adherence monitoring by HCWs on the VDOT platform costs (2.6%); VDOT systems and data management costs (2.3%), and the costs related to human resources training in using VDOT (0.2%). Table 2 Cost Analysis of VDOT for 71 Patients in Kampala, Uganda Cost Category Per Patient Cost in UGX Per Patient Cost in USD Per Patient Cost (%) Total Cost in UGX Total Cost in USD Total Cost (%) Smart Phone and Accessories 253,973 70.54 31.9 17,016,191 4726.72 29.5 Prepaid Internet Data 81,750 22.70 10.2 5,886,000 1635 10.2 VDOT Platform / Infrastructure Costs 173,625 48.22 21.8 12,501,120 3472.53 21.7 Program support costs (i.e. internet for viewing videos, airtime for phone communication 156,389 43.44 19.6 11,260,000 3127.78 19.5 Adherence Monitoring by HCWs on VDOT Platform 21,196.4 5.88 2.7 1,483,748 412.15 2.6 Systems and Data Management 18,374 5.10 2.3 5,659,192 1572.00 9.8 Escalation costs related to unscheduled phone calls or home visits 57,397 15.94 7.2 2,382,336 661.76 4.1 Escalation costs related to VDOT technology 30,500 8.47 3.8 610,000 169.44 1.1 VDOT training for HCWs 2,170 0.60 0.3 937,500 260.42 1.6 Grand total 795,374 220.93 100 57,736,087 16037.80 100 One ( 1 ) United States Dollar (USD) ~ 3600 Uganda Shillings (UGX) in 2025 HCW- Health Care Worker Figure 1Per Patient Costs for Implementing VDOT Intervention Projected Cost Savings from Patients’ Travel to the Health Facility over 6 months Illustration of the cost analysis in three scenarios based on the two-month intensive phase of treatment: The Hypothetical Situation with no Missed In-person DOT Days Based on the cost data collected from participants, the average cost of a daily round trip at baseline was UGX 11,450.70 ( $ 3.18). For 71 participants, the cumulative total cost for the initial visit to the clinic was UGX 813,000 ( $ 226) — these costs are the same in all three scenarios. To attend in-person DOT services daily for the first 20 weekdays of the first month, the per-patient average cost of travel would be UGX 229,014.10 ( $ 63.62). Over a six-month treatment period, each participant would require an average of UGX 1,374,084.60 ( $ 381.69) to cover daily travel costs. Including the baseline visit, the total average cost per participant for the entire treatment period would be UGX 1,385,535.30 ( $ 384.88). Therefore, for 71 participants, the total estimated travel cost would be UGX 98,373,000 ( $ 27,325.83), as shown in Table 3 below. Table 3 Cost Saving Scenarios using Participant Travel Cost Data (n = 71) Hypothetical (Ideal) facility based DOT (no missed doses) Scenario Hypothetical (Ideal) facility based DOT (no missed doses) Scenario Hypothetical (Ideal) facility based DOT (no missed doses) Scenario Hypothetical (Ideal) facility based DOT (no missed doses) Scenario Hypothetical (Ideal) facility based DOT (no missed doses) Scenario Treatment Period Cumulative cost for N = 71 (UGX) Cumulative cost for N = 71 (USD) Mean (SD) per patient cost (UGX) Mean (SD) per patient cost (USD) Baseline (Cost of daily round trip per participant) 813,000 226 11450.7 (8883) 3.18 (2.5) Monthly travel cost 16,260,000 4516.67 229,014.1 (177,660.6) 63.615(49.4) Travel cost for six months 97,560,000 27100.02 1,374,084.6 (1,065,963.9) 381.69 (296.1) Total cumulative ideal costs over six months treatment period 98,373,000 27,325.83 1,385,535.3 (1074,846.9) 384.88 (298.6) Real-world facility based DOT/ (859 missed doses) Scenario Real-world facility based DOT/ (859 missed doses) Scenario Real-world facility based DOT/ (859 missed doses) Scenario Real-world facility based DOT/ (859 missed doses) Scenario Real-world facility based DOT/ (859 missed doses) Scenario Baseline 813,000 226 11450.7 (8883) 3.18 (2.5) Travel cost at visit one (intensive phase— 321 videos were missed) 28,883,000 8023 406802.0 (316174) 113 (87.8) Travel cost at visit two (Continuation phase— 236 videos were missed) 30,218,000 8394 425605.6 (339041.3) 118.2 (94.2) Travel cost at visit three (Continuation phase— 302 videos were missed) 29,534,000 8204 415971.8 (352288.1) 115.5 (97.8) Total cumulative real-world costs over six months treatment period 89,448,000 24847 1,259,830.1 (1,016,386.4) 350 (282) VDOT-aided Scenario VDOT-aided Scenario VDOT-aided Scenario VDOT-aided Scenario VDOT-aided Scenario Baseline (Cost of daily round trip per participant) 813,000 226 11450.7 (8883) 3.18 (2.5) Monthly travel cost at each visit 813,000 226 11450.7 (8883) 3.18 (2.47) Cumulative Travel cost over six months treatment period 3,252,000 904 45,802.8 (35,532.1) 12.72 (9.9) Cost savings in travel for patients in VDOT Cost savings in travel for patients in VDOT Cost savings in travel for patients in VDOT Cost savings in travel for patients in VDOT Cost savings in travel for patients in VDOT Cost saving Cost saving Cost saving Total amount (UGX) Total amount (USD) Individual travel cost saving (Cumulative per-patient travel cost incurred in the hypothetical ideal situation - Cumulative per-patient travel cost incurred in the VDOT aided scenario) Individual travel cost saving (Cumulative per-patient travel cost incurred in the hypothetical ideal situation - Cumulative per-patient travel cost incurred in the VDOT aided scenario) Individual travel cost saving (Cumulative per-patient travel cost incurred in the hypothetical ideal situation - Cumulative per-patient travel cost incurred in the VDOT aided scenario) 1,339,732.5 372.14 Real beneficial cost saving (Total costs incurred in Hypothetical ideal situation - Total costs incurred in VDOT aided situation) Real beneficial cost saving (Total costs incurred in Hypothetical ideal situation - Total costs incurred in VDOT aided situation) Real beneficial cost saving (Total costs incurred in Hypothetical ideal situation - Total costs incurred in VDOT aided situation) 95,121,000 26,422.5 Coincidental cost saving (total missed doses * Average daily round trip cost) Coincidental cost saving (total missed doses * Average daily round trip cost) Coincidental cost saving (total missed doses * Average daily round trip cost) 9,836,151.3 2732.26 The Real-Life Scenario Accounting for Missed In-Person DOT Days In this scenario, we not only accounted for weekends but also for missed days of observation. A total of 859 videos were missed. During the intensive phase (visit one), each participant would incur an average travel cost of UGX 406,802 ( $ 113), in the continuation phase, UGX 425,605.60 ( $ 118.20) at visit two, and UGX 415,971.80 ( $ 115.50) at visit three. In total, each participant would incur an average of UGX 1,259,830.10 ( $ 350) for the entire treatment period. For all 71 participants, the total estimated travel cost would be UGX 89,448,000 ( $ 24,847). VDOT Aided Scenario In this scenario, we considered costs for the round-trip travel for the baseline visit UGX 11,450.70 ( $ 3.18) and the three subsequent medication refill visits, as opposed to the 40 days of travel required in a traditional in-person DOT setup. Therefore, each participant would incur a total average of UGX 45,802.80 ( $ 12.72) over the treatment period. For all 71 participants, the total cost was UGX 3,252,000 ( $ 904). Discussion We performed a cost analysis for the implementation of the VDOT for monitoring treatment among patients with TB to inform future adoption and potential scale up of digital adherence technologies in Uganda and similar settings. To our knowledge, this is the first study to assess the costs of a VDOT intervention among patients with drug-susceptible TB in Uganda. Our findings show that VDOT can offer a cost-saving alternative for monitoring TB treatment in this setting. The overall per-person cost of $ 220.93 over the 6 months of treatment was estimated for implementing VDOT in Uganda. Our findings report a slightly lower per-patient cost compared to a multi-country study that reported $ 304 in Moldova (n = 173), $ 1,154 in Haiti (n = 87), $ 661 in the Philippines (n = 110) ( 27 ). This is largely attributed to a relatively smaller sample considered and leveraging on participants who used their own devices, which could have reduced the costs if a study provided smartphones to all the participants. Our findings are consistent with the conclusion from a U.S based study that showed that the VDOT approach contributes to the reduction in costs of care for both patients and health departments compared to the traditional approach of direct DOT ( 22 ) and also in tandem with a study by Rosu and colleagues that reported that VDOT would reduce direct and indirect costs incurred by patients with multidrug resistant (MDR) TB by more than 90% compared to in-person DOT ( 28 ). In our study, the largest total cost drivers were the smartphones and accessories (29.5%) and the patient costs (31.9%). This is in tandem with other global findings ( 27 ), which highlighted smartphones as a major contributor to VDOT costs. Therefore, given that VDOT is dependent on smartphones, implementers should ensure that participants have compatible devices or consider the loaning options for those who don’t have them ( 29 ). This model was also used in a study by Drabarek and colleagues in Vietnam that loaned smartphones to participants who did not own smart phones( 30 ). Loaned smartphones increase the upfront costs of VDOT in low-resource settings ( 28 ); however, it foster a sustainable model of reusability that can be scaled up, given that different phones have different qualities, which may result in incompatibility, which affects usability ( 31 ). The smartphone loaning system has been reported to minimize the heterogeneity in quality and incompatibility of personal phone devices across patients ( 32 ). It is important to note that the cost of smartphones is gradually decreasing globally; thus the prospects of phone ownership in low income settings like Uganda will be high over time ( 33 ). The costs related to the VDOT platform (21.7%) were another high cost driver. This is consistent with literature on expenditure of digital adherence technologies as essential one-time costs (Nsengiyumva et al. 2024). These VDOT platform costs are unavoidable should one decide to implement VDOT, for they are the lever on which the whole process is hinged. These costs are largely dependent on the existence of prior infrastructure, the ability to leverage shared cloud-based resources, and the degree of platform customization and tailoring ( 22 , 34 ). However, costs can be reduced if the same platform is customized to accommodate as many participants as possible, which is dependent on the study design and the period of treatment follow-up ( 27 ). Prepaid internet subscriptions were among the high cost drivers in this cost evaluation. This was because an internet subscription is required by both patients and healthcare providers for the VDOT program implementation. This is consistent with what has been reported in other settings ( 34 ) and our previous study that reported internet costs as a potential barrier to the uptake of VDOT in Uganda ( 35 , 36 ). In the future, the National TB scale-up programs should explore public-private partnerships with telecommunications companies. These partnerships could, in turn, allocate corporate social responsibility resources to subsidize internet data costs. However, with the new advances in internet technology like 5G and 6G that enhance high data communication rates, low latency, and reliability ( 37 ), access to the internet is projected to expand in LRS like Uganda. On the other hand, developers should think of standalone applications that do not require the internet for patients to upload videos ( 38 ). Our cost analysis shows that there is substantial cost savings in transportation associated with VDOT. For example, a patient who uses VDOT could save thirty times more ( $ 12.72 vs $ 384.88) in transportation costs than if the standard in-person facility based DOT is used. This comparison assumes that a participant doesn’t miss any clinic visits for the whole treatment period. When we further compared the real life scenario where a participant may miss going to the facility for in-person DOT (considering that a missed video submission is a missed hospital visit), we found that a VDOT patient would on average still incur a total of $ 12.72 compared to $ 350 that he would have incurred in the real life scenario of in-person DOT during the entire six-month treatment period. This implies that VDOT is less costly, as confirmed by a study by Lam and colleagues that reported a significant cost saving while using VDOT ( 39 ). Therefore, considering both travel costs and per-patient costs associated with the implementation of the VDOT in this setting (e.g., internet costs, smartphone accessories, airtime) on the side of the user and platform/infrastructure costs, system/data management, and training on the side of the facility, VDOT is associated with lower implementation costs. Our cost analysis has some strengths and limitations. This study is the first to conduct a preliminary cost evaluation of VDOT implemented in the local Uganda context; therefore, the results could inform the next steps of scale-up. We conducted a partial economic evaluation that does not account for the real cost of usual care and a full scale of societal costs; therefore, a cost-effectiveness analysis study is recommended. The secondary cost analysis is based on a limited sample size from the pilot VDOT trial conducted in an urban setting; therefore, the findings may not be generalizable to rural settings. We recommend future research that explores the costs of VDOT in both urban and rural populations, as there might be context specific differences in cost drivers. Conclusion The implementation of VDOT for monitoring medication adherence among patients with drug susceptible TB was cost saving compared to a hypothetical scenario of using in-person facility-based DOT in an urban Ugandan setting. We recommend further research to evaluate the cost-effectiveness of VDOT. Declarations Ethics approval and consent to participate Institutional Ethical approvals for this research were obtained from the Makerere University (protocol 756) and the University of Georgia (ID PROJECT00000571) institutional review boards, and the national ethical approval from the Uganda National Council for Science and Technology (HS656ES). Written informed consent was obtained from all the study participants in either English or Luganda, depending on their language preference. Approximately US $ 10 was given to all the research participants per visit as compensation for transportation and the time spent participating in research activities. Consent for publication Not applicable Declaration of Interest The authors declare that they have no conflict of interest. Funding This study is funded by the United States National Institutes of Health, Grant #1 R21 TW011365. The funders do not have any role in study design; in the collection, management, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication. The funders did not have any authority over the study activities. Authors' contributions Conceptualization: JNS, EB. Data curation: RK, DN. Formal analysis: RK, WT. Funding acquisition: JNS. Methodology: JNS, WT, RK, DN. Supervision: JNS, EB, SZ. Literature Review: RK, DN, WT. Writing original draft: RK, WT, DN. Writing – review & editing: WT, RK, DN, EB, SZ, AM, JNS. All authors read and approved the final manuscript. Acknowledgements We acknowledge Drs. Christopher Whalen, Noah Kiwanuka, and Robert Kakaire for their scientific input during the implementation of the study. We thank the VDOT research assistants, Mitchell Geno, Daphne Kyaine, and Gloria Nassanga, who collected the data and mobilized the study participants for interviews at the Lubaga study site in Uganda. Lastly, we acknowledge Everwell Health Solutions for providing the VDOT mobile technology service and the initial training of the research team. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on a reasonable request. References WHO Tuberculosis 2024 [cited 2025 7th Feb ]. Available from: https://www.who.int/news-room/fact-sheets/detail/tuberculosis Chen X, Du L, Wu R, Xu J, Ji H, Zhang Y et al (2021) Tuberculosis-related stigma and its determinants in Dalian, Northeast China: a cross-sectional study. BMC Public Health 21:1–10 Appiah MA, Arthur JA, Gborgblorvor D, Asampong E, Kye-Duodu G, Kamau EM et al (2023) Barriers to tuberculosis treatment adherence in high-burden tuberculosis settings in Ashanti region, Ghana: a qualitative study from patient’s perspective. BMC Public Health 23(1):1317 Grigoryan Z, McPherson R, Harutyunyan T, Truzyan N, Sahakyan S (2022) Factors influencing treatment adherence among drug-sensitive tuberculosis (DS-TB) patients in Armenia: a qualitative study. Patient Prefer Adherence. :2399–2408 Kizito S, Nabayinda J, Nabunya P, Ssewamala FM (2024) HIV and tuberculosis in Uganda: are we neglecting poverty? Lancet 404(10455):844–845 WHO_Uganda Uganda Government reiterates commitment to end TB by 2030 2023 [cited 2025 15th July]. Available from: https://www.afro.who.int/countries/uganda/news/uganda-government-reiterates-commitment-end-tb-2030 Wingfield T, Boccia D, Tovar M, Gavino A, Zevallos K, Montoya R et al (2014) Defining catastrophic costs and comparing their importance for adverse tuberculosis outcome with multi-drug resistance: a prospective cohort study, Peru. PLoS Med 11(7):e1001675 WHO (2022) Global Tuberculosis Report 2022. World Health Organization McAllister SM, Lestari BW, Sullivan T, Hadisoemarto PF, Afifah N, Apip RA et al (2020) Out-of-pocket costs for patients diagnosed with tuberculosis in different healthcare settings in Bandung, Indonesia. Am J Trop Med Hyg 103(3):1057 Tanimura T, Jaramillo E, Weil D, Raviglione M, Lönnroth K (2014) Financial burden for tuberculosis patients in low-and middle-income countries: a systematic review. Eur Respir J 43(6):1763–1775 WHO Eliminating financial and economic barriers to tuberculosis diagnosis 2018 [cited 2025 7th February]. Available from: https://www.who.int/news/item/17-12-2018-eliminating-financial-and-economic-barriers-to-tuberculosis-diagnosis-and-care Nahid P, Dorman SE, Alipanah N, Barry PM, Brozek JL, Cattamanchi A et al (2016) Official American thoracic society/centers for disease control and prevention/infectious diseases society of America clinical practice guidelines: treatment of drug-susceptible tuberculosis. Clin Infect Dis 63(7):e147–e95 Salehitali S, Noorian K, Hafizi M, Dehkordi AH (2019) Quality of life and its effective factors in tuberculosis patients receiving directly observed treatment short-course (DOTS). J Clin Tuberculosis Other Mycobact Dis 15:100093 Wynne A, Richter S, Banura L, Kipp W (2014) Challenges in tuberculosis care in Western Uganda: health care worker and patient perspectives. Int J Afr Nurs Sci 1:6–10 Leung CL, Alacapa J (2024) Digital Adherence Technologies and Differentiated Care for Tuberculosis Treatment and Their Acceptability Among Persons With Tuberculosis, Health Care Workers, and Key Informants in the Philippines. Qualitative Interview Study 11:e54117 Sekandi JN, Buregyeya E, Zalwango S, Nakkonde D, Kaggwa P, Quach THT et al (2025) Effectiveness of a Mobile Health Intervention (DOT Selfie) in Increasing Treatment Adherence Monitoring and Support for Patients With Tuberculosis in Uganda: Randomized Controlled Trial. JMIR mHealth uHealth 13(1):e57991 Musiimenta A, Tumuhimbise W, Mugaba AT, Muzoora C, Armstrong-Hough M, Bangsberg D et al (2019) Digital monitoring technologies could enhance tuberculosis medication adherence in Uganda: Mixed methods study. J Clin tuberculosis other Mycobact Dis 17:100119 Haberer JE, Musiimenta A, Atukunda EC, Musinguzi N, Wyatt MA, Ware NC et al (2016) Short message service (SMS) reminders and real-time adherence monitoring improve antiretroviral therapy adherence in rural Uganda. Aids 30(8):1295–1299 Thompson RR, Kityamuwesi A, Kuan A, Oyuku D, Tucker A, Ferguson O et al (2022) Cost and cost-effectiveness of a digital adherence technology for tuberculosis treatment support in Uganda. Value Health 25(6):924–930 Sekandi JN, Buregyeya E, Zalwango S, Dobbin KK, Atuyambe L, Nakkonde D et al (2020) Video directly observed therapy for supporting and monitoring adherence to tuberculosis treatment in Uganda: a pilot cohort study. ERJ open Res. ;6(1) Krueger K, Ruby D, Cooley P, Montoya B, Exarchos A, Djojonegoro B et al (2010) Videophone utilization as an alternative to directly observed therapy for tuberculosis. Int J Tuberc Lung Dis 14(6):779–781 Beeler Asay GR, Lam CK, Stewart B, Mangan JM, Romo L, Marks SM et al (2020) Cost of tuberculosis therapy directly observed on video for health departments and patients in New York City; San Francisco, California; and Rhode Island (2017–2018). Am J Public Health 110(11):1696–1703 Garfein RS, Liu L, Cepeda J, Graves S, San Miguel S, Antonio A et al (eds) (2024) Asynchronous Video Directly Observed Therapy to Monitor Short-Course Latent Tuberculosis Infection Treatment: Results of a Randomized Controlled Trial. Open Forum Infectious Diseases; : Oxford University Press US Sekandi JN, Onuoha NA, Buregyeya E, Zalwango S, Kaggwa PE, Nakkonde D et al (2021) Using a mobile health intervention (DOT selfie) with transfer of social bundle incentives to increase treatment adherence in tuberculosis patients in Uganda: protocol for a randomized controlled trial. JMIR Res protocols 10(1):e18029 Garfein RS, Liu L, Cuevas-Mota J, Collins K, Muñoz F, Catanzaro DG et al (2018) Tuberculosis treatment monitoring by video directly observed therapy in 5 health districts, California, USA. Emerg Infect Dis 24(10):1806 StopTB_Partnership Costing Tool -Wave 6 Digital Adherence Technology Projects 2020 [cited 2025 6th July ]. Available from: https://stoptb.org/assets/documents/global/awards/tbreach/DAT%20costing%20tool_V2.pdf Nsengiyumva NP, Khan A, Gler MMTS, Tonquin ML, Marcelo D, Andrews MC et al (2024) Costs of Digital Adherence Technologies for Tuberculosis Treatment Support, 2018–2021. Emerg Infect Dis 30(1):79 Rosu L, Madan J, Bronson G, Nidoi J, Tefera MG, Malaisamy M et al (2023) Cost of digital technologies and family-observed DOT for a shorter MDR-TB regimen: a modelling study in Ethiopia, India and Uganda. BMC Health Serv Res 23(1):1275 Kafie C, Mohamed MS, Zary M, Chilala CI, Bahukudumbi S, Gore G et al (2024) Cost and cost-effectiveness of digital technologies for support of tuberculosis treatment adherence: a systematic review. BMJ global health. ;9(10) Drabarek D, Trinh-Hoang D, Yapa M, Dang TT, Vu HD, Nguyen TA et al (2025) Examining the challenges in sustaining user engagement with a mobile app to enhance multidrug-resistant tuberculosis (MDR-TB) care in Vietnam and its implications for implementing person-centred mHealth interventions. PLOS Global Public Health 5(4):e0004454 Tumuhimbise W, Theuring S, Kaggwa F, Atukunda EC, Rubaihayo J, Atwine D et al (2024) Enhancing the implementation and integration of mHealth interventions in resource-limited settings: a scoping review. Implement Sci 19(1):72 Bommakanti KK, Smith LL, Liu L, Do D, Cuevas-Mota J, Collins K et al (2020) Requiring smartphone ownership for mHealth interventions: who could be left out? BMC Public Health 20(1):81 Subbaraman R, de Mondesert L, Musiimenta A, Pai M, Mayer KH, Thomas BE et al (2018) Digital adherence technologies for the management of tuberculosis therapy: mapping the landscape and research priorities. BMJ global health 3(5):e001018 WHO (2020) Quick guide to video-supported treatment of tuberculosis, World Health Organization. Quick guide to video-supported treatment of tuberculosis Sekandi JN, Kasiita V, Onuoha NA, Zalwango S, Nakkonde D, Kaawa-Mafigiri D et al (2021) Stakeholders’ perceptions of benefits of and barriers to using video-observed treatment for monitoring patients with tuberculosis in Uganda: exploratory qualitative study. JMIR mHealth uHealth 9(10):e27131 Sekandi JN, Kasiita V (2021) Stakeholders' Perceptions of Benefits of and Barriers to Using Video-Observed Treatment for Monitoring Patients With Tuberculosis in Uganda. Exploratory Qualitative Study 9(10):e27131 Gkagkas G, Vergados DJ, Michalas A, Dossis M (2024) The Advantage of the 5G Network for Enhancing the Internet of Things and the Evolution of the 6G Network. Sensors 24(8):2455 Tumuhimbise W, Musiimenta A (2021) A review of mobile health interventions for public private mix in tuberculosis care. Internet interventions 25:100417 Lam C, Fluegge K, Macaraig M, Burzynski J (2019) Cost savings associated with video directly observed therapy for treatment of tuberculosis. Int J Tuberc Lung Dis 23(11):1149–1154 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Despite being curable, it remains the leading cause of death among individuals living with HIV/AIDS (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In 2023 alone, about 10.8\u0026nbsp;million people fell ill from the disease globally, of which the majority were from countries in low-resource settings (LRS) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Uganda is among the 30 high TB/HIV burden countries with an incidence rate of 198 per 100,000 people. Treatment adherence remains a significant challenge that leads to poor TB treatment outcomes (such as treatment failure, emergence of drug-resistant TB), and secondary transmission (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTB also places a significant economic burden on the patients and their households. In Uganda, about 53% of the households of patients with TB experience catastrophic costs between 20\u0026ndash;40% of their entire household income, estimated at US\u003cspan\u003e$\u003c/span\u003e369 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The World Health Organization (WHO) defines catastrophic costs as spending 20% or more of the household\u0026rsquo;s annual income on expenses related to TB care (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The largest proportion of the out-of-pocket costs are associated with travel to the TB clinics for routine visits to fill prescriptions (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The financial burden on patients and their families hampers progress towards achieving successful TB treatment outcomes, thus negatively impacting the attainment of the End TB strategy (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The standard TB care and management strategy, known as directly observed therapy (DOT), requires daily observation of a patient as they ingest the TB medication by a health worker or treatment supporter (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Although in-person DOT is an effective way to ensure proper adherence to treatment, its feasibility is largely limited because of a shortage of human and financial resources in the public health system (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The resource constraints have led to the abandonment of in-person DOT in healthcare settings, rendering it less effective (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDigital adherence technologies (DATs) have been recommended by the WHO for addressing the shortcomings of in-person DOT and supporting person-centered TB treatment (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). These include Short Message Service, real-time medication monitoring systems, ingestible sensors, and video-observed therapy (\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Video directly observed therapy (VDOT) is an innovative smartphone-based system that utilizes a mobile application to record TB medication intake videos, thereby substituting the need for frequent face\u0026ndash;to-face meetings (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) as required in the traditional DOT. The feasibility and acceptability of VDOT have been documented in studies done in TB clinics in Uganda and elsewhere (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). VDOT has also been shown to be more effective in increasing medication adherence monitoring than usual care in-person DOT in an open-label randomized trial in Uganda (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStudies that evaluated the cost of VDOT in the United States have reported a reduction in costs of care for both patients and health departments compared to the traditional in-person DOT (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). However, the cost of implementing VDOT in LRS and the associated cost savings have not been well documented. In this study, we utilized data from a recently completed VDOT trial (NCT04134689) in Uganda to conduct a secondary cost analysis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eEthical Considerations\u003c/h2\u003e\u003cp\u003e Institutional Ethical approvals for this research were obtained from the Makerere University (protocol 756) and the University of Georgia (ID PROJECT00000571) institutional review boards, and the national ethical approval from the Uganda National Council for Science and Technology (HS656ES). Written informed consent was obtained from all the study participants in either English or Luganda, depending on their language preference. Approximately US \u003cspan\u003e$\u003c/span\u003e10 was given to all the research participants per visit as compensation for transportation and the time spent participating in research activities.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Setting and Participants\u003c/h3\u003e\n\u003cp\u003eThe study is based on a completed open-label randomized trial known as the \u0026ldquo;DOT Selfie study\u0026rdquo;, which was conducted between July 2020 and October 2021 at Lubaga TB clinic and several other public TB clinics in Kampala. The methodology of this study has been reported elsewhere (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Briefly, secondary cost data were extracted for 71 participants who were randomized to the VDOT intervention study group. Eligible patients were aged 18 to 65 years with confirmed drug-susceptible TB and residing in Kampala during the treatment period. Patients were excluded if they had confirmed multidrug-resistant TB or a documented cognitive, visual, or other disability that would interfere with recording videos. Additionally, those who did not have access to electricity to charge a smartphone or who resided in areas with poor cellular network coverage were excluded.\u003c/p\u003e\n\u003ch3\u003eDescription of VDOT System\u003c/h3\u003e\n\u003cp\u003eA detailed description of the VDOT system has been published elsewhere (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Briefly, treatment adherence monitoring was performed using an asynchronous VDOT smartphone application (app) that enabled patients to record videos while swallowing each dose of medication and upload them to a secure cloud server, which was accessed and viewed later by health workers (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The app worked on both Android (Google LLC, Menlo Park, CA, USA) and iOS (Apple Inc., Cupertino, CA, USA) smartphones. Date and time-stamped videos were automatically sent by the app through third or fourth-generation (4G) cellular networks or Wi-Fi. The app is secured to prevent videos from being viewed, edited, or deleted by users on the smartphone, thus ensuring confidentiality and fidelity of the videos. After a successful upload, the videos are automatically deleted from the smartphone. Cellular internet data was prepaid weekly at \u0026sim;US\u003cspan\u003e$\u003c/span\u003e1.00 and sent directly to the participants\u0026rsquo; phones to ensure videos would be sent in a timely manner. Trained research staff logged into an internet-based, password-protected client management system to watch the uploaded videos and documented whether they observed the pills being swallowed. The health workers followed a pre-specified protocol to follow-up with patients if a video was not received within 24 hours.\u003c/p\u003e\n\u003ch3\u003eData Collection and Follow-up\u003c/h3\u003e\n\u003cp\u003eA baseline survey was administered to collect information about participants\u0026rsquo; socio-demographics, phone ownership, and technology use experience including using a smartphone, internet, taking photos or videos and frequency of use in the last 3 months (Supplementary Appendix 2). Additional information about different cost drivers was collected from study budget lines, receipts, time logs, Telecom Company, server, protocol specifications and cost logs. Study participants returned to the clinic for their routine monthly visit to refill prescriptions and for evaluation at 2, 4, and 6 months per standard of care.\u003c/p\u003e\n\u003ch3\u003eCosting Approaches\u003c/h3\u003e\n\u003cp\u003eWe adapted the VDOT costing tool guide by McGill International, published elsewhere (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) (Supplementary Appendix 1). This questionnaire aimed to capture all costs associated with the implementation of VDOT. Eight cost drivers were identified and categorized to establish detailed cost components from the health provider\u0026rsquo;s perspective, and these are; i) smart phone and accessories, ii) prepaid internet data, iii) VDOT platform/infrastructure costs, iv) program support costs (i.e. internet for viewing videos, airtime for phone communication, v) adherence monitoring by healthcare workers on VDOT platform, vi) systems and data management, vii) escalation costs related to unscheduled phone calls or home visits, and those related to VDOT technology malfunction, app upgrades or for phone repairs, and viii) VDOT training for HCWs.\u003c/p\u003e\u003cp\u003eAt the end of the questionnaire, costs from each category were summed to estimate the total cost per patient associated with the implementation of VDOT. For costs that were only relevant to some participants, we divided them over the full patient population (i.e., prorated) in order to estimate per-patient costs.\u003c/p\u003e\u003cp\u003eWe calculated the costs using both bottom-up (micro-costing) and top-down approaches (Cunnama 2016). Bottom-up approach focused on detailed activity and input usage data from records (time logs, cost logs, budget lines) to establish unit costs. (e.g., the smartphone and accessories cost category was established from the receipts, the cost of airtime from the time logs, to get the total cost and per-patient cost for phones and accessories).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eProgram Costs\u003c/h2\u003e\u003cp\u003eTo estimate the program costs, we focused on the internet for viewing videos, airtime for phone communication, and patient follow-up as detailed activities. We then extracted the cost of internet packages from the telecom company records and time logs to establish both the patient and total cost of data bundles. For adherence monitoring by HCW on the VDOT platform,we used the fraction of time spent watching videos to confirm adherence per full-time effort of a health worker. We used time logs to establish the average time spent by research staff per patient routine and video reviewing. To establish the cost of escalation related to VDOT technology, we established the number of patients who required phone repairs, app retraining, and maintenance from the cost logs and multiplied by the average amount of costs spent on phone repairs, app retraining per participant, to get the total and per-patient cost of escalation related to VDOT technology. To estimate the cost for VDOT training for HCWs, we assessed the research assistants, their total stipulated time for training, and the hourly wage from the protocol. We then multiplied the total training time and hourly wage to get the cost of training all research assistants. To get the cost of training one research assistant for an individual patient, we established the cost of training one research assistant divided by the number of study participants.\u003c/p\u003e\u003cp\u003eThe top-bottom approach focused on the overall expenditure (e.g., VDOT platform expenditure), then allocated costs (e.g., software license, configuration costs, to estimate costs per person, and the fixed cost of the platform. To ascertain the cost of the VDOT system and data management, we captured the fraction of time, alongside wages spent on staff providing technical support on the VDOT platform. We established the average wage per hour of the data manager and the total days spent providing the technical service to get the per-patient and total cost of this category. Escalation was related to unscheduled phone calls or home visits. We established the average wage (per hour) for research staff as per the protocol. We then established the average number of phone calls made by the health workers from call logs multiplied by the number of patients that required the phone call and or home visit support to get the per patient cost of escalation related to unscheduled phone calls or home visits.\u003c/p\u003e\u003cp\u003eTo assess the cost savings in transportation for patients in VDOT, we looked at the average expected videos per patient. We assumed that every video was equivalent to one round trip to the TB clinic. For computation, we established the adjusted projected cost of travel over the duration of treatment by multiplying the actual days of travel to the clinic by the total cost of daily travel. To ascertain the adjusted project cost on VDOT over the duration of treatment, we considered the average per-patient cost of VDOT as a program, i.e., the summation of all cost drivers of VDOT as a program for each of the 71 participants.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eHypothetical, Real-life, and VDOT Scenarios of Costs Associated with Travel\u003c/h3\u003e\n\u003cp\u003eTo illustrate the cost savings from travel, we evaluated three scenarios that were based on the two-month intensive phase of TB treatment. First, the \u0026lsquo;hypothetical scenario\u0026rsquo; represents an ideal situation where there are no missed days of DOT. We assumed that a typical patient with TB who undergoes directly observed treatment at the health facility for five days a week. The patient is expected to incur round trip travel costs to the health facility for 20-week days (excluding weekends) per month without missing any schedule visit. Second, the \u0026lsquo;real-life scenario\u0026rsquo; represents a situation that accounts for missed DOT days. We used the information from video submissions as equivalent to in-person hospital visits. For example, we considered the number of videos missed out of the two-month intensive phase of treatment to represent potential DOT face-to-face visits that would have been missed. Since the participants would have missed the visits, the associated travel costs were avoided\u0026mdash;resulting in a coincidental savings. Third, the \u0026lsquo;VDOT-aided\u0026rsquo; scenario represents a situation where the patient\u0026rsquo;s treatment adherence is monitored remotely by healthcare workers using digital adherence technology. This approach eliminated the need for daily travel, thereby resulting in a substantial cost saving. The only travel costs that are incurred by the patients are associated with the trips for the initial visit, intensive phase (visit one), and continuation phase (visits two \u0026amp; three). In all these three scenarios, we consider a constant baseline cost for the initial hospital visit for drug initiation and the monthly travel costs (20 days, excluding weekends). Therefore, the ultimate cost savings in transportation for patients in VDOT was the total costs incurred in Hypothetical ideal situation \u003cb\u003eminus\u003c/b\u003e the total costs incurred in VDOT aided situation.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u0026rsquo; Baseline Characteristics\u003c/h2\u003e\u003cp\u003eOf the 72 participants who had active TB, 50% were male, and the median age was 29.5 (24\u0026ndash;42) years. The majority of the participants, 68 (96%), owned a cell phone, of which only 50 (70%) owned smart phones. The study loaned 21 smart phones to the 30% participants who did not own a smart phone. All 72 (100%) participants received the VDOT app except for one participant who was randomized to VDOT but refused to initiate his TB treatment soon after enrollment. The participant was included in the baseline analysis but excluded from any further analyses because he did not have the study outcomes of interest, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of VDOT study participants (N\u0026thinsp;=\u0026thinsp;72)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVDOT Arm (%)n\u0026thinsp;=\u0026thinsp;72\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36 (50.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36 (50.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge in years, median (IQR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eAge in years, median (IQR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.5 (24\u0026ndash;42)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge Category\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eAge Category\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19 (70.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27 (56.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10 (33.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u0026ndash;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16 (41.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHighest level of education\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eHighest level of education\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo education or primary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27 (37.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28 (38.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTertiary/University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17 (23.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCurrently married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23 (31.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePreviously married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13 (18.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNever married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36 (50.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCurrently employed\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eCurrently employed\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29 (40.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43 (59.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMonthly personal income in USD\u0026sect;, median (IQR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMonthly personal income in USD\u0026sect;, median (IQR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42.85 (14.29\u0026ndash;114.30)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMonthly total household income in USD\u0026sect;, median (IQR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMonthly total household income in USD\u0026sect;, median (IQR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71.43 (28.57\u0026ndash;157.14)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eCurrently owned cellphone\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14 (9.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58 (80.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eOwned smartphone\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22 (30.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50 (69.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eHIV Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24 (33.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48 (66.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCost Analysis of VDOT for 71 Participants\u003c/h2\u003e\u003cp\u003eOur analysis categorized eight main cost drivers as shown in Fig.\u0026nbsp;1 and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below. These were further analyzed to establish both per-patient cost and the total costs incurred in the respective cost category. The largest proportion of the total cost was generated by the smartphones and accessories (31.9%), followed by the upfront cost of the VDOT platform (21.8%) and program support costs (19.6%). Others included prepaid internet subscription used by patients to submit their videos (10.2%), escalation costs for follow-up of patients (10%), of which 7.2% were related to unscheduled phone calls or home visits, while 3.8% were related to VDOT technology malfunction, app upgrade, or phone repairs. The lowest costs were related to adherence monitoring by HCWs on the VDOT platform costs (2.6%); VDOT systems and data management costs (2.3%), and the costs related to human resources training in using VDOT (0.2%).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCost Analysis of VDOT for 71 Patients in Kampala, Uganda\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCost Category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer Patient Cost in UGX\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePer Patient Cost in USD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePer Patient Cost (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal Cost in UGX\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTotal Cost in USD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTotal Cost (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmart Phone and Accessories\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e253,973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17,016,191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4726.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrepaid Internet Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81,750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5,886,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVDOT Platform / Infrastructure Costs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e173,625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12,501,120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3472.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProgram support costs (i.e. internet for viewing videos, airtime for phone communication\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e156,389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11,260,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3127.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdherence Monitoring by HCWs on VDOT Platform\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21,196.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,483,748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e412.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystems and Data Management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18,374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5,659,192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1572.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEscalation costs related to unscheduled phone calls or home visits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57,397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2,382,336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e661.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEscalation costs related to VDOT technology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30,500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e610,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e169.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVDOT training for HCWs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e937,500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e260.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGrand total\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e795,374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e220.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e57,736,087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16037.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOne (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) United States Dollar (USD)\u0026thinsp;~\u0026thinsp;3600 Uganda Shillings (UGX) in 2025\u003c/p\u003e\u003cp\u003eHCW- Health Care Worker\u003c/p\u003e\u003cp\u003e\u003cem\u003eFigure 1Per Patient Costs for Implementing VDOT Intervention\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eProjected Cost Savings from Patients\u0026rsquo; Travel to the Health Facility over 6 months\u003c/h2\u003e\u003cp\u003eIllustration of the cost analysis in three scenarios based on the two-month intensive phase of treatment:\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eThe Hypothetical Situation with no Missed In-person DOT Days\u003c/h2\u003e\u003cp\u003eBased on the cost data collected from participants, the average cost of a daily round trip at baseline was UGX 11,450.70 (\u003cspan\u003e$\u003c/span\u003e3.18). For 71 participants, the cumulative total cost for the initial visit to the clinic was UGX 813,000 (\u003cspan\u003e$\u003c/span\u003e226) \u0026mdash; these costs are the same in all three scenarios. To attend in-person DOT services daily for the first 20 weekdays of the first month, the per-patient average cost of travel would be UGX 229,014.10 (\u003cspan\u003e$\u003c/span\u003e63.62). Over a six-month treatment period, each participant would require an average of UGX 1,374,084.60 (\u003cspan\u003e$\u003c/span\u003e381.69) to cover daily travel costs. Including the baseline visit, the total average cost per participant for the entire treatment period would be UGX 1,385,535.30 (\u003cspan\u003e$\u003c/span\u003e384.88). Therefore, for 71 participants, the total estimated travel cost would be UGX 98,373,000 (\u003cspan\u003e$\u003c/span\u003e27,325.83), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e below.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCost Saving Scenarios using Participant Travel Cost Data (n\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothetical (Ideal) facility based DOT (no missed doses) Scenario\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHypothetical (Ideal) facility based DOT (no missed doses) Scenario\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHypothetical (Ideal) facility based DOT (no missed doses) Scenario\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHypothetical (Ideal) facility based DOT (no missed doses) Scenario\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHypothetical (Ideal) facility based DOT (no missed doses) Scenario\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreatment Period\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCumulative cost for N\u0026thinsp;=\u0026thinsp;71 (UGX)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCumulative cost for N\u0026thinsp;=\u0026thinsp;71 (USD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean (SD) per patient cost (UGX)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean (SD) per patient cost (USD)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline (Cost of daily round trip per participant)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e813,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11450.7 (8883)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.18 (2.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonthly travel cost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16,260,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4516.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e229,014.1 (177,660.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63.615(49.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTravel cost for six months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97,560,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27100.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,374,084.6 (1,065,963.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e381.69 (296.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cumulative ideal costs over six months treatment period\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98,373,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27,325.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,385,535.3 (1074,846.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e384.88 (298.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReal-world facility based DOT/ (859 missed doses) Scenario\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eReal-world facility based DOT/ (859 missed doses) Scenario\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eReal-world facility based DOT/ (859 missed doses) Scenario\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eReal-world facility based DOT/ (859 missed doses) Scenario\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eReal-world facility based DOT/ (859 missed doses) Scenario\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e813,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11450.7 (8883)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.18 (2.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTravel cost at visit one (intensive phase\u0026mdash; 321 videos were missed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28,883,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e406802.0 (316174)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e113 (87.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTravel cost at visit two (Continuation phase\u0026mdash; 236 videos were missed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30,218,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e425605.6 (339041.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e118.2 (94.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTravel cost at visit three (Continuation phase\u0026mdash; 302 videos were missed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29,534,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e415971.8 (352288.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e115.5 (97.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cumulative real-world costs over six months treatment period\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89,448,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,259,830.1 (1,016,386.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e350 (282)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVDOT-aided Scenario\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eVDOT-aided Scenario\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eVDOT-aided Scenario\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eVDOT-aided Scenario\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eVDOT-aided Scenario\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline (Cost of daily round trip per participant)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e813,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11450.7 (8883)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.18 (2.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonthly travel cost at each visit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e813,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11450.7 (8883)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.18 (2.47)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCumulative Travel cost over six months treatment period\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,252,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45,802.8 (35,532.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.72 (9.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCost savings in travel for patients in VDOT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eCost savings in travel for patients in VDOT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eCost savings in travel for patients in VDOT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eCost savings in travel for patients in VDOT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eCost savings in travel for patients in VDOT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCost saving\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eCost saving\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eCost saving\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eTotal amount (UGX)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eTotal amount (USD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual travel cost saving (Cumulative per-patient travel cost incurred in the hypothetical ideal situation - Cumulative per-patient travel cost incurred in the VDOT aided scenario)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndividual travel cost saving (Cumulative per-patient travel cost incurred in the hypothetical ideal situation - Cumulative per-patient travel cost incurred in the VDOT aided scenario)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndividual travel cost saving (Cumulative per-patient travel cost incurred in the hypothetical ideal situation - Cumulative per-patient travel cost incurred in the VDOT aided scenario)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,339,732.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e372.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReal beneficial cost saving (Total costs incurred in Hypothetical ideal situation - Total costs incurred in VDOT aided situation)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReal beneficial cost saving (Total costs incurred in Hypothetical ideal situation - Total costs incurred in VDOT aided situation)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReal beneficial cost saving (Total costs incurred in Hypothetical ideal situation - Total costs incurred in VDOT aided situation)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95,121,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26,422.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoincidental cost saving (total missed doses * Average daily round trip cost)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoincidental cost saving (total missed doses * Average daily round trip cost)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoincidental cost saving (total missed doses * Average daily round trip cost)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9,836,151.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2732.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eThe Real-Life Scenario Accounting for Missed In-Person DOT Days\u003c/h2\u003e\u003cp\u003eIn this scenario, we not only accounted for weekends but also for missed days of observation. A total of 859 videos were missed. During the intensive phase (visit one), each participant would incur an average travel cost of UGX 406,802 (\u003cspan\u003e$\u003c/span\u003e113), in the continuation phase, UGX 425,605.60 (\u003cspan\u003e$\u003c/span\u003e118.20) at visit two, and UGX 415,971.80 (\u003cspan\u003e$\u003c/span\u003e115.50) at visit three. In total, each participant would incur an average of UGX 1,259,830.10 (\u003cspan\u003e$\u003c/span\u003e350) for the entire treatment period. For all 71 participants, the total estimated travel cost would be UGX 89,448,000 (\u003cspan\u003e$\u003c/span\u003e24,847).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eVDOT Aided Scenario\u003c/h2\u003e\u003cp\u003eIn this scenario, we considered costs for the round-trip travel for the baseline visit UGX 11,450.70 (\u003cspan\u003e$\u003c/span\u003e3.18) and the three subsequent medication refill visits, as opposed to the 40 days of travel required in a traditional in-person DOT setup. Therefore, each participant would incur a total average of UGX 45,802.80 (\u003cspan\u003e$\u003c/span\u003e12.72) over the treatment period. For all 71 participants, the total cost was UGX 3,252,000 (\u003cspan\u003e$\u003c/span\u003e904).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe performed a cost analysis for the implementation of the VDOT for monitoring treatment among patients with TB to inform future adoption and potential scale up of digital adherence technologies in Uganda and similar settings. To our knowledge, this is the first study to assess the costs of a VDOT intervention among patients with drug-susceptible TB in Uganda. Our findings show that VDOT can offer a cost-saving alternative for monitoring TB treatment in this setting. The overall per-person cost of \u003cspan\u003e$\u003c/span\u003e220.93 over the 6 months of treatment was estimated for implementing VDOT in Uganda. Our findings report a slightly lower per-patient cost compared to a multi-country study that reported \u003cspan\u003e$\u003c/span\u003e304 in Moldova (n\u0026thinsp;=\u0026thinsp;173), \u003cspan\u003e$\u003c/span\u003e1,154 in Haiti (n\u0026thinsp;=\u0026thinsp;87), \u003cspan\u003e$\u003c/span\u003e661 in the Philippines (n\u0026thinsp;=\u0026thinsp;110) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). This is largely attributed to a relatively smaller sample considered and leveraging on participants who used their own devices, which could have reduced the costs if a study provided smartphones to all the participants. Our findings are consistent with the conclusion from a U.S based study that showed that the VDOT approach contributes to the reduction in costs of care for both patients and health departments compared to the traditional approach of direct DOT (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) and also in tandem with a study by Rosu and colleagues that reported that VDOT would reduce direct and indirect costs incurred by patients with multidrug resistant (MDR) TB by more than 90% compared to in-person DOT (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn our study, the largest total cost drivers were the smartphones and accessories (29.5%) and the patient costs (31.9%). This is in tandem with other global findings (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), which highlighted smartphones as a major contributor to VDOT costs. Therefore, given that VDOT is dependent on smartphones, implementers should ensure that participants have compatible devices or consider the loaning options for those who don\u0026rsquo;t have them (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). This model was also used in a study by Drabarek and colleagues in Vietnam that loaned smartphones to participants who did not own smart phones(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Loaned smartphones increase the upfront costs of VDOT in low-resource settings (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e); however, it foster a sustainable model of reusability that can be scaled up, given that different phones have different qualities, which may result in incompatibility, which affects usability (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The smartphone loaning system has been reported to minimize the heterogeneity in quality and incompatibility of personal phone devices across patients (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). It is important to note that the cost of smartphones is gradually decreasing globally; thus the prospects of phone ownership in low income settings like Uganda will be high over time (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe costs related to the VDOT platform (21.7%) were another high cost driver. This is consistent with literature on expenditure of digital adherence technologies as essential one-time costs (Nsengiyumva et al. 2024). These VDOT platform costs are unavoidable should one decide to implement VDOT, for they are the lever on which the whole process is hinged. These costs are largely dependent on the existence of prior infrastructure, the ability to leverage shared cloud-based resources, and the degree of platform customization and tailoring (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). However, costs can be reduced if the same platform is customized to accommodate as many participants as possible, which is dependent on the study design and the period of treatment follow-up (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrepaid internet subscriptions were among the high cost drivers in this cost evaluation. This was because an internet subscription is required by both patients and healthcare providers for the VDOT program implementation. This is consistent with what has been reported in other settings (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) and our previous study that reported internet costs as a potential barrier to the uptake of VDOT in Uganda (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). In the future, the National TB scale-up programs should explore public-private partnerships with telecommunications companies. These partnerships could, in turn, allocate corporate social responsibility resources to subsidize internet data costs. However, with the new advances in internet technology like 5G and 6G that enhance high data communication rates, low latency, and reliability (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), access to the internet is projected to expand in LRS like Uganda. On the other hand, developers should think of standalone applications that do not require the internet for patients to upload videos (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur cost analysis shows that there is substantial cost savings in transportation associated with VDOT. For example, a patient who uses VDOT could save thirty times more (\u003cspan\u003e$\u003c/span\u003e12.72 vs \u003cspan\u003e$\u003c/span\u003e384.88) in transportation costs than if the standard in-person facility based DOT is used. This comparison assumes that a participant doesn\u0026rsquo;t miss any clinic visits for the whole treatment period. When we further compared the real life scenario where a participant may miss going to the facility for in-person DOT (considering that a missed video submission is a missed hospital visit), we found that a VDOT patient would on average still incur a total of \u003cspan\u003e$\u003c/span\u003e12.72 compared to \u003cspan\u003e$\u003c/span\u003e350 that he would have incurred in the real life scenario of in-person DOT during the entire six-month treatment period. This implies that VDOT is less costly, as confirmed by a study by Lam and colleagues that reported a significant cost saving while using VDOT (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Therefore, considering both travel costs and per-patient costs associated with the implementation of the VDOT in this setting (e.g., internet costs, smartphone accessories, airtime) on the side of the user and platform/infrastructure costs, system/data management, and training on the side of the facility, VDOT is associated with lower implementation costs.\u003c/p\u003e\u003cp\u003eOur cost analysis has some strengths and limitations. This study is the first to conduct a preliminary cost evaluation of VDOT implemented in the local Uganda context; therefore, the results could inform the next steps of scale-up. We conducted a partial economic evaluation that does not account for the real cost of usual care and a full scale of societal costs; therefore, a cost-effectiveness analysis study is recommended. The secondary cost analysis is based on a limited sample size from the pilot VDOT trial conducted in an urban setting; therefore, the findings may not be generalizable to rural settings. We recommend future research that explores the costs of VDOT in both urban and rural populations, as there might be context specific differences in cost drivers.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe implementation of VDOT for monitoring medication adherence among patients with drug susceptible TB was cost saving compared to a hypothetical scenario of using in-person facility-based DOT in an urban Ugandan setting. We recommend further research to evaluate the cost-effectiveness of VDOT.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eInstitutional Ethical approvals for this research were obtained from the Makerere University (protocol 756) and the University of Georgia (ID PROJECT00000571) institutional review boards, and the national ethical approval from the Uganda National Council for Science and Technology (HS656ES). Written informed consent was obtained from all the study participants in either English or Luganda, depending on their language preference. Approximately US \u003cspan\u003e$\u003c/span\u003e10 was given to all the research participants per visit as compensation for transportation and the time spent participating in research activities.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eDeclaration of Interest\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study is funded by the United States National Institutes of Health, Grant #1 R21 TW011365. The funders do not have any role in study design; in the collection, management, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication. The funders did not have any authority over the study activities.\u003c/p\u003e\u003ch2\u003eAuthors' contributions\u003c/h2\u003e\u003cp\u003eConceptualization: JNS, EB. Data curation: RK, DN. Formal analysis: RK, WT. Funding acquisition: JNS. Methodology: JNS, WT, RK, DN. Supervision: JNS, EB, SZ. Literature Review: RK, DN, WT. Writing original draft: RK, WT, DN. Writing \u0026ndash; review \u0026amp; editing: WT, RK, DN, EB, SZ, AM, JNS. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eWe acknowledge Drs. Christopher Whalen, Noah Kiwanuka, and Robert Kakaire for their scientific input during the implementation of the study. We thank the VDOT research assistants, Mitchell Geno, Daphne Kyaine, and Gloria Nassanga, who collected the data and mobilized the study participants for interviews at the Lubaga study site in Uganda. Lastly, we acknowledge Everwell Health Solutions for providing the VDOT mobile technology service and the initial training of the research team.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on a reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO Tuberculosis 2024 [cited 2025 7th Feb ]. 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Sensors 24(8):2455\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTumuhimbise W, Musiimenta A (2021) A review of mobile health interventions for public private mix in tuberculosis care. Internet interventions 25:100417\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLam C, Fluegge K, Macaraig M, Burzynski J (2019) Cost savings associated with video directly observed therapy for treatment of tuberculosis. Int J Tuberc Lung Dis 23(11):1149\u0026ndash;1154\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"7d5ad8b1-0de5-4862-8a97-2ed30b471683","identifier":"10.13039/100000002","name":"National Institutes of Health","awardNumber":"1R21 TW011365","order_by":0}],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Georgia","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Video Directly Observed Treatment, cost analysis, Tuberculosis, adherence, Implementation","lastPublishedDoi":"10.21203/rs.3.rs-7911005/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7911005/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eVideo directly observed treatment (VDOT) supports person-centered care and addresses shortcomings of in-person directly observed treatment (DOTS). However, there is limited literature on implementation costs to inform TB program policy in low-resource settings, such as Uganda.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study aimed to assess the costs associated with implementing the VDOT intervention in Uganda, based on a completed randomized trial.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe carried out a micro-costing analysis. This employed both bottom-up and top-down approaches to estimate health system costs (in 2024 US dollars) of the VDOT intervention. The analysis covered 71 persons with TB from six health facilities in Kampala, Uganda.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe estimated overall costs for implementing VDOT in Uganda were \u003cspan\u003e$\u003c/span\u003e16,037.80 among 71 patients with TB. The estimated per-person costs for VDOT were \u003cspan\u003e$\u003c/span\u003e220.93. The largest proportion of the overall cost was associated with smartphones and accessories (29.5%), followed by VDOT platform infrastructure costs (21.7%), program support costs (19.5%), and internet data costs used by patients to send the videos (10.2%). The per-person costs of using VDOT are lower than the potential cost of daily transport that would be associated with facility-based DOT.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe implementation of VDOT in Kampala was associated with a relatively low cost compared to the projected cost when using the traditional DOT method. This highlights the cost saving benefit to the patients when using remote treatment monitoring with the video technology. A full economic evaluation from the programmatic and societal perspective is recommended to inform the adoption of the VDOT strategy in resource-limited settings.\u003c/p\u003e","manuscriptTitle":"A Cost Analysis of Implementing Asynchronous Video Directly Observed Therapy for Monitoring Treatment in Patients with Tuberculosis Disease in Uganda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-21 17:48:19","doi":"10.21203/rs.3.rs-7911005/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b83261ad-2678-43cf-86bc-92d1278ee113","owner":[],"postedDate":"November 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56620365,"name":"Health Economics \u0026 Outcomes Research"}],"tags":[],"updatedAt":"2025-11-21T17:48:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-21 17:48:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7911005","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7911005","identity":"rs-7911005","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}