A cost analysis of implementing mobile health facilitated tuberculosis contact investigation in a low-income setting

Patricia Turimumahoro; Austin Tucker; Amanda J Gupta; Radhika P Tampi; Diana Babirye; Emmanuel Ochom; Joseph M Ggita; Irene Ayakaka; Hojoon Sohn; Achilles Katamba; David Dowdy; J Lucian Davis

doi:10.1371/journal.pone.0265033

. 2022 Apr 1;17(4):e0265033. doi: 10.1371/journal.pone.0265033

A cost analysis of implementing mobile health facilitated tuberculosis contact investigation in a low-income setting

Patricia Turimumahoro ¹, Austin Tucker ², Amanda J Gupta ^1,^3,⁴, Radhika P Tampi ², Diana Babirye ¹, Emmanuel Ochom ¹, Joseph M Ggita ¹, Irene Ayakaka ¹, Hojoon Sohn ⁵, Achilles Katamba ^1,⁶, David Dowdy ^1,², J Lucian Davis ^1,^2,^7,^*

Editor: Limakatso Lebina⁸

¹Uganda Tuberculosis Implementation Research Consortium, Kampala, Uganda

²Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America

³Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, United States of America

⁴Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America

⁵Department of Preventative Medicine, Seoul National University College of Medicine, Seoul, South Korea

⁶Clinical Epidemiology Unit, Makerere University, College of Health Sciences Kampala, Kampala, Uganda

⁷Pulmonary, Critical Care and Sleep Medicine Section, Yale School of Medicine, New Haven, CT, United States of America

⁸University of the Witwatersrand, SOUTH AFRICA

Competing Interests: The authors have declared that no competing interests exist.

^✉

* E-mail: lucian.davis@yale.edu

Roles

Patricia Turimumahoro: Conceptualization, Formal analysis, Methodology, Project administration, Writing – original draft, Writing – review & editing

Austin Tucker: Formal analysis, Writing – original draft, Writing – review & editing

Amanda J Gupta: Project administration, Supervision, Writing – original draft, Writing – review & editing

Radhika P Tampi: Conceptualization, Investigation, Methodology, Writing – review & editing

Diana Babirye: Data curation, Software, Writing – review & editing

Emmanuel Ochom: Data curation, Investigation, Writing – review & editing

Joseph M Ggita: Data curation, Investigation, Writing – review & editing

Irene Ayakaka: Conceptualization, Data curation, Supervision, Writing – review & editing

Hojoon Sohn: Conceptualization, Formal analysis, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing

Achilles Katamba: Conceptualization, Methodology, Supervision, Writing – review & editing

David Dowdy: Conceptualization, Formal analysis, Methodology, Writing – original draft, Writing – review & editing

J Lucian Davis: Conceptualization, Funding acquisition, Investigation, Methodology, Supervision, Writing – review & editing

Limakatso Lebina: Editor

PMCID: PMC8975098 PMID: 35363783

Abstract

Introduction

Mobile health (mHealth) applications may improve timely access to health services and improve patient-provider communication, but the upfront costs of implementation may be prohibitive, especially in resource-limited settings.

Methods

We measured the costs of developing and implementing an mHealth-facilitated, home-based strategy for tuberculosis (TB) contact investigation in Kampala, Uganda, between February 2014 and July 2017. We compared routine implementation involving community health workers (CHWs) screening and referring household contacts to clinics for TB evaluation to home-based HIV testing and sputum collection and transport with test results delivered by automated short messaging services (SMS). We carried out key informant interviews with CHWs and asked them to complete time-and-motion surveys. We estimated program costs from the perspective of the Ugandan health system, using top-down and bottom-up (components-based) approaches. We estimated total costs per contact investigated and per TB-positive contact identified in 2018 US dollars, one and five years after program implementation.

Results

The total top-down cost was $472,327, including $358,504 (76%) for program development and $108,584 (24%) for program implementation. This corresponded to $320-$348 per household contact investigated and $8,873-$9,652 per contact diagnosed with active TB over a 5-year period. CHW time was spent primarily evaluating household contacts who returned to the clinic for evaluation (median 30 minutes per contact investigated, interquartile range [IQR]: 30–70), collecting sputum samples (median 29 minutes, IQR: 25–30) and offering HIV testing services (median 28 minutes, IQR: 17–43). Cost estimates were sensitive to infrastructural capacity needs, program reach, and the epidemiological yield of contact investigation.

Conclusion

Over 75% of all costs of the mHealth-facilitated TB contact investigation strategy were dedicated to establishing mHealth infrastructure and capacity. Implementing the mHealth strategy at scale and maintaining it over a longer time horizon could help decrease development costs as a proportion of total costs.

Introduction

Tuberculosis (TB) is among the leading causes of death due to an infectious disease worldwide, with approximately 7 million new TB cases diagnosed in 2020 [1]. Low TB case detection rates represent a major gap in the TB care cascade in high burden countries, and more than 30% of estimated incident TB cases continue to go undiagnosed and/or unreported [1–3]. This problem has impeded progress toward the global End TB targets [4]. Patient-centered interventions that can facilitate early case detection and reduce barriers to TB care are thus an important public health and global health priority.

Contacts of TB patients have a substantial risk of developing active TB within the first one to two years after exposure [5]. and in high-income settings, interventions focusing on contacts of index TB patients (e.g., contact investigation) have become an important priority for TB control and elimination [6]. TB contact investigation involves teams of health care workers visiting households or workplaces of people diagnosed with active TB to identify and refer those with TB-specific symptoms or key risk factors for further clinical and bacteriologic TB evaluation. Recent evidence suggests that contact investigation has the potential to improve TB case detection in high prevalence settings relative to passive case-detection services in health facilities [7]. However, barriers to acceptance and completion, operational complexities, and resource constraints have limited wide adoption of contact investigation in low-and middle-income countries[2,8,9].

To address these challenges, we developed a home-based, mHealth-facilitated household contact investigation strategy and evaluated it in a pragmatic, prospective, household randomized trial [10]. Compared to routine contact investigation delivered by community health workers (CHWs), the mHealth-facilitated contact investigation intervention included home-based HIV testing and TB evaluation, collection and transport of sputum samples, and follow-up communications using automated short messaging services (SMS). The strategy was feasible and acceptable but not more effective than routine contact investigation because of implementation challenges [11,12]. Nonetheless, another area of uncertainty in the mHealth field is the limited and heterogeneous evidence on the costs and cost effectiveness of mHealth strategies [13] including some evidence of high up-front costs [14], which may in turn act as a barrier to ongoing research and innovation. Therefore, to characterize the resource implications of mobile health interventions more fully, we conducted a comprehensive assessment of the costs of development, implementation, and maintenance of home-based, mHealth-facilitated TB contact investigation in Kampala, Uganda.

Materials and methods

Study design and setting

We estimated the costs of developing, implementing, and maintaining a home-based, mHealth-facilitated household TB contact investigation intervention in Kampala, Uganda, from the health system perspective, using both a “top-down” and “bottom-up” (components-based) approach. In this setting, TB contact investigation involved CHWs visiting the homes of TB patients, screening all contacts for TB symptoms, and recording their findings using a customized electronic survey application (CommCare, Dimagi, Boston, USA). The application employed decision-support logic to identify contacts requiring evaluation for TB and prompted CHWs to collect a sputum sample and offer HIV testing to eligible household members. The application also delivered personalized, automated text messages to each participant providing follow-up instructions, clinic visit reminders, and TB test results. In the routine care arm, automated text messages were not sent, and all contacts needing TB evaluation were referred to the clinic. The home-based strategy sought to increase the proportion of contacts fully evaluated for TB by reducing the need for contacts to travel to clinics.

To comprehensively evaluate the costs of the mHealth-facilitated intervention, we divided the program into two phases and evaluated the costs accrued in each phase. The development phase, which lasted 30 months (February 2014-July 2016), consisted of formative research, software customization, and pilot testing. Activities during this phase included, but were not limited to, development of decision-support logic, integration of fingerprint identification technology and automated short messaging service (SMS) technology; pilot testing; and optimization of technological components. This was followed by the implementation phase, which occurred over 12 months (July 2016 –July 2017) in the context of a cluster-randomized trial. A total of 919 contacts were randomized at the household level, including 471 contacts in the intervention arm who are the focus of this cost analysis. The trial observed a marginal probability of completing TB evaluation of 14% (95% CI 8–20) in intervention households and 15% (95% CI 9–21) in routine care households, representing a difference of -1% (95% CI -9% to 7%, p = 0.81) [10]. Fig 1 shows a conceptual outline of project phases, activities, and timelines.

Estimation of program development costs

To estimate the cost of developing, implementing, and maintaining the mHealth-facilitated contact investigation intervention, we retrospectively collected programmatic costs of each phase of development from the health system perspective using health facility and study budget estimates. All cost and volume estimates extracted from study and health center financial records, and key informant interviews were performed with study staff and health facility administrators to confirm which budgetary items mapped to specific expenditures for program development. Cost components were appropriately mapped to specific thematic expenditure categories: human resource costs, capital costs, recurrent costs, overhead costs, and building space costs. Human resource costs included salaries of a coordinator, data manager, laboratory manager, IT officer and CHWs. Capital costs included investment in hardware and software for mHealth, a vehicle, and cost to adapt the intervention to the local setting. Recurrent costs included expenditure on consumables such as laboratory supplies, internet, and text messages. Overhead costs included operational costs such as those for supervision teams and patient care at the clinic. Building space included the space occupied by the supervision teams and patient rooms. Since budget allocations were similar across facilities with similar clinical capacity, patient visit volumes, and scope of service delivery, we compiled cost data from four of the seven participating health centers and extrapolated these costs for the remaining three health facilities. Full details on cost components, according to study phase, are summarized in the Supporting Information, S1 Table.

Estimation of program implementation costs

We assessed the cost of program implementation using both a top-down and bottom-up approach. Top-down costing of program implementation was performed similarly to the top-down estimation of program development costs described above. Bottom-up costing was performed using an activity-based approach. Specifically, we conducted a time-and-motion (TAM) study on consecutive days between March and August 2017 asking CHWs to record start and end times for each discrete contact investigation activity, as defined in the Supporting Information, S2 Table. Clinic-based activities included: TB index patient recruitment, waiting time, contact evaluation, and other activities. Community-based activities included: travel to and from the household, TB education and counseling, household contact screening for TB symptoms, HIV testing, sputum collection, HIV testing, and confirmation of phone contacts for SMS messaging. Using the top-down approach, we summed unit costs in five primary categories: human resources, capital investments, building space, overhead costs, and recurrent costs. Human resource costs were estimated by enumerating staffing levels for the entire development phase and multiplying monthly staff salaries by the percent effort contributed to program development (50%), based on opinions from key informants. Capital investments included software, hardware, and formative work. The cost of building space utilized for patient services was approximated as 5% of the cost of the entire building and operational costs for the program as 6–7% of clinic operational costs. The cost estimate for one square meter of building space was based on local rates suggested by clinic administrators and multiplied by the measured area of the building. Recurrent costs accrued during the development of the program were summarized as software, lab consumables, and program evaluation costs for routine quality assurance. The total cost of program development was then calculated as the total of all five categories over the entirety of the development phase. The cost of program implementation was calculated in a similar fashion, and total program costs were estimated as the sum of program development plus program implementation costs.

Total program costs: Bottom-up

For bottom-up costing, program development costs were assessed as annual costs based on corresponding estimates of useful life years for each component (between 5 and 30 years, based on key informant interviews). These were converted to an estimated cost per minute-use, based on the estimated total number of operational minutes per year: 8 hours per day, 5 days per week, 46 weeks per year). The bottom-up costs of program implementation were estimated by multiplying the median time estimates to perform each activity (estimated using TAM studies as above) by the cost per minute for each resource type. Resource use was categorized as 1) direct human resources; 2) capital equipment; 3) program overhead (operational costs and recurrent costs); and 4) building space costs. We summed the unit costs for each category to calculate the total activity-based cost per household contact investigated. Some activities (e.g., TB index patient recruitment) were associated with a specific index case, not a specific household contact. For these activities, we divided these unit time estimates by the average number of household contacts observed per index patient to get the time estimate for each activity per household contact investigated.

Analysis

We assumed five useful life years for all program development costs except for building space and vehicles, for which we assumed an expected useful life of 30 years. All capital costs were depreciated linearly using a 3% discount rate. We divided the total program costs by the total number of patients enrolled and the total number of new TB diagnoses made to estimate the cost per household contact investigated and the cost per TB diagnosis made. All costs are reported in 2018 US dollars. All costs measured in Ugandan Shillings were updated to 2018 using the Ugandan GDP deflator [15] and converted to US dollars at the average annual exchange rate for 2018 [16].

Sensitivity analysis

This program required a large initial investment in technology development, infrastructure, and equipment with a low marginal cost for including additional patients during program implementation. As such, we performed a sensitivity analysis to estimate costs under “continued implementation”, under the assumption that the one-year intervention could be continued for four additional years at the same volume without any additional development costs (i.e., continued implementation costs only). To perform this sensitivity analysis, we used top-down cost estimates and allocated costs incurred at the program level, clinic level, and contact level. We then multiplied costs at the clinic level by the number of clinics included in the program and costs at the contact level by the number of contacts screened per clinic. To explore differences in clinic capacity over different years, we performed a three-way sensitivity analysis in which we simultaneously varied the numbers of clinics that might be covered by the mHealth program (in increments of 10, from 10 to 50), the mean annual contacts evaluated per clinic (in increments of 50, from 100 to 350), and the average annual contact positivity rate (at four levels, from 0.025 to 0.042). The maximum number of clinics that could be covered was based on the expert opinion of research staff and implementers, and the annual number of contacts who would be screened per clinic was estimated using the observed number of patients per facility (plus or minus 2 standard deviations from the mean as minimum and maximum values).

Ethical considerations

The School of Medicine Research Ethics Committee at Makerere University; the Uganda National Council for Science and Technology; and the Yale University Human Investigation Committee approved the study protocol, informed consent forms, and assent forms.

Results

In the 12 months of program implementation, 190 index TB patients with 471 household contacts were randomized to receive the intervention. The TB case notification rate at the seven contributing health centers ranged from 15 to 67 TB cases per month. Of the 471 contacts, 106 (23%) had TB symptoms, ranging from 14% to 33% across health centers (Table 1).

Table 1. Clinic characteristics.

Clinic Name^*	Total	Naguru	Kawaala	Kisenyi	Kisugu	Kiswa	Kitebi	Komamboga
		Hospital	HC III	HC IV	HC III	HC III	HC III	HC III
Location		Urban	Urban	Urban	Urban	Urban	Rural	Rural
Service Statistics
Average monthly TB case notifications^**		32	39	67	15	15	16	16
Total households enrolled^***	163	37	27	29	10	30	11	19
Average monthly household visits made	13.6	3.1	2.3	2.4	0.83	2.5	0.92	1.6
Total contacts enrolled	471	117	76	97	21	91	24	45
Average contacts per household	2.9	3.2	2.8	3.3	2.1	3.0	2.2	2.4
Total symptomatic TB household contacts	106	25	13	32	4	13	5	14
Proportion of contacts needing TB evaluation	22.5%	21%	17%	33%	19%	14%	21%	31%
Staffing ^****
Doctors/Clinical officer	1	1	0	0	0	0	0	0
Nurses	14	2	2	2	2	2	2	2
CHWs	14	1	3	3	1	2	2	2
Total		4	5	5	3	4	4	4
Monthly Workload/Staffing Ratio
Average monthly contacts screened^*****	39	9.8	6.3	8.1	1.8	7.6	2.0	3.8
CHW-to-Contact Ratio	1:34	1:117	1:25	1:32	1:21	1:46	1:12	1:23

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Abbreviations: CHW, Community health worker; HC, Health Centre; TB, tuberculosis.

Legend: * Health care delivery is through a decentralized framework consisting of Village Health Teams, Health Centre (HC) II, Health Centre III, Health Centre IV/Referral Hospital, Regional Referral Hospital and a National Referral Hospital.

** The average monthly TB case notification rate was calculated based on TB case notifications between January 2017 and December 2017.

*** The households enrolled between July 2016 and July 2017 that were eligible to enroll into the study and were randomized to receive the intervention.

**** These staffing characteristics and numbers include only those working in the TB specialty units in these facilities.

*****The average monthly household contacts screened was calculated by dividing the total contacts screened by the period of implementation (12 months).

Using a top-down approach, the total cost of the mHealth TB contact investigation intervention was estimated at $472,327, of which program development accounted for $358,504 (76%) and program implementation for $113,823 (24%). Human resource costs accounted for $178,542 (38%), capital assets for $156,091 (33%), and recurrent costs for $74,965 (16%), overhead costs for $51,202, and building space for $11,525 (2%) (Table 2A).

Table 2.

a. Granular unit cost estimates for each phase of implementation of an mHealth-facilitated TB contact investigation program. b. Top-down cost estimates for household contact investigation of tuberculosis in Uganda.

A.
Resource Category^*	Development Phase								Implementation Phase
	Formative		Software		Program		Development		Implementation	Total
	Research		Customization		Pilot		Costs		Costs	Costs
Human resource costs	$ 14,979		$ 61,369		$ 49,487		$ 125,835 (35%)		$ 52,707 (46%)	$178,542 (38%)
Community health workers	-		-		$ 8,714		$ 8,714		$ 9,506	$ 18,220
Administrative staff	$ 9,400		$ 39,950		$ 25,850		$ 75,200		$ 28,200	$ 103,400
Software consultants	$ 5,579		$ 21,419		$ 14,923		$ 41,921		$ 15,001	$ 56,922
Capital costs	$ 100,200		$ 28,855		$ 24,656		$ 153,710 (43%)		$ 2,381 (2%)	$ 156,091 (33%)
Software	$ 55,792		-		-		$ 55,792		-	$ 55,792
Hardware	-		$ 18,277		$ 22,410		$ 40,687		-	$ 40,741
Training	$ 25,104		$ 10,577		$ 2,246		$ 37,927		$ 2,381	$ 40,309
Vehicle	$ 16,738		-		-		$ 16,738		-	$ 16,738
Adaptation to local setting	$ 2,566		-		-		$ 2,566		-	$ 2,566
Recurrent costs	-		$ 20,390		$ 21,517		$ 41,907 (12%)		$ 27,819 (24%)	$ 74,965 (16%)
Software hosting plan	-		$ 20,390		$ 12,536		$ 32,926		$ 13,115	$ 46,040
Supplies	-		-		$ 8,927		$ 8,927		$ 14,705	$ 23,631
Program evaluation	-		-		-		-		$ 5,239	$ 5,239
SMS service	-		-		$ 54		$ 54		-	-
Overhead costs	$ 4,646		$ 16,636		$ 15,770		$ 37,051 (10%)		$ 14,150 (12%)	$ 51,202 (11%)
Overhead (Patient care)	-		-		$ 1,132		$ 1,132		$ 1,235	$ 2,367
Overhead (Administrative)	$ 4,646		$ 16,636		$ 14,638		$ 35,919		$ 12,915	$ 84,754
Building space	-		-		-		-		$ 11,525 (10%)	$ 11,525 (2%)
Patient care	-		-		-		-		$ 11,525	$ 11,525
Total (Row Proportion)	$ 119,825 (25%)		$ 127,249 (27%)		$ 111,430 (24%)		$ 358,504 (76%)		$ 113,823 (24%)	$ 472,327
B.
		Cost per contact investigated				Cost per TB positive contact found
		n = 471				n = 17
Resource Category	Total	Program		Continued		Program		Continued
	Program	Only		Implementation		Only		Implementation
	Costs	Costs^*		Costs^**		Costs^*		Costs^**
Human resource costs	$178,542 (38%)	$379		$150		$10,502		$4,143
Capital costs	$156,091 (33%)	$331		$72		$9,182		$1,999
Recurrent costs	$74,965 (16%)	$159		$78		$4,407		$2,170
Overhead costs	$51,202 (11%)	$109		$47		$3,012		$1,304
Building space	$11,525 (2%)	$24		$1		$678		$35
Total	$472,327	$1,003		$348		$27,784		$9,652

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Abbreviations: SMS, short-messaging service.

Table 2a Legend: ^*Costs in 2018 US dollars; percentages displayed are column percentages, unless otherwise specified.

Table 2b Legend: *Program only costs assume that all intervention activities stop at the end of the observed 12-month program implementation period.

**Continued implementation costs assume continued implementation of the program for a total of five years (at a similar annual volume as observed in the first year).

The mHealth TB contact investigation intervention was estimated to cost $1,003 per household contact investigated or $27,748 per positive contact found. Assuming that the intervention was continued for an additional four years at similar capacity, the program was estimated to cost $348 per household contact investigated, or $9,652 per positive contact diagnosed (Table 2B).

In the time and motion survey, we observed a total of 12,100 person-minutes from 11 discrete activities across seven trial clinics. A total of 5,496 (45%) person-minutes of clinic-based activities and 6,604 (55%) person-minutes of community-based activities were observed. In the clinic, CHWs spent approximately 1.2 hours per household contact investigated, with the most time spent evaluating contacts returning to the clinic (median 30 person-minutes per contact evaluated enrolled, interquartile range [IQR]: 30–70). In the community, CHWs spent approximately 3.5 hours per contact investigated, with sputum collection (median 29 person-minutes per contact investigated, IQR 25–30) and offering HIV testing services (median 28 minutes per contacts investigated, IQR: 17–43) found to be the most time-consuming activities (Supporting Information, S3 Table). After enumerating all costs and activity times, our bottom-up cost estimate of program costs was $320 per household contact investigated or $8,873 per TB positive contact diagnosed, with component cost estimates as summarized in Table 3.

Table 3. Bottom-up unit cost estimates for each activity and resource category per household TB contact investigated.

Activity category	Activity type	Total person minutes per contact (%)	Median person minutes per contact (IQR)	Human resources		Capital costs		Recurrent costs			Overhead costs	Building space	Total cost per contact investigated
Activity category	Activity type	Total person minutes per contact (%)	Median person minutes per contact (IQR)	CHWs	Program staff & mHealth team	IT	Other	SMS	Software	Supplies	Overhead costs	Building space	Total cost per contact investigated
Clinic activities	TB patient recruitment	1,002 (8%)	12 (5–26)	$0.06	$7.45	$2.27	$1.42	$0.01	$2.20	$2.38	$2.41	$0.47	$18.68
	Waiting for clients	3,565 (29%)	21 (10–42)	$0.11	$13.04	$3.97	$2.49	$0.01	$3.85	$4.16	$4.22	$0.83	$32.68
	Contact evaluation	419 (3%)	30 (30–70)	$0.16	$18.63	$5.67	$3.56	$0.01	$5.50	$5.94	$6.02	$1.18	$46.68
	Other	510 (4%)	10 (5–21)	$0.05	$6.21	$1.89	$1.19	$0.00	$1.83	$1.98	$2.01	$0.39	$15.56
Community activities	Travel	1,707 (14%)	21 (14–32)	$0.11	$13.04	$3.97	$2.49	$0.01	$3.85	$4.16	$4.22	$0.83	$32.68
	TB education & counselling	335 (3%)	7 (5–16)	$0.04	$4.35	$1.32	$0.83	$0.01	$1.28	$1.39	$1.41	$0.28	$10.89
	Contact screening	2,104 (17%)	20 (11–38)	$0.11	$12.42	$3.78	$2.37	$0.01	$3.67	$3.96	$4.02	$0.79	$31.13
	HIV testing	1,089 (9%)	28 (17–43)	$0.15	$17.39	$5.30	$3.32	$0.01	$5.13	$3.71	$5.62	$1.11	$41.74
	Sputum collection & HIV testing	1,063 (9%)	19 (5–30)	$0.10	$11.80	$3.59	$2.26	$0.01	$3.48	$4.29	$3.82	$0.75	$30.09
	Sputum collection	258 (2%)	29 (25–30)	$0.15	$18.01	$5.49	$3.44	$0.01	$5.32	$0.57	$5.82	$1.14	$39.96
	Phone number confirmation	50 (0%)	13 (9–16)	$0.07	$8.07	$2.46	$1.54	$0.01	$2.38	$2.58	$2.61	$0.51	$20.23
Total cost per contact investigated, by cost category				$1.10	$130.40	$39.72	$24.93	$0.10	$38.50	$35.12	$42.17	$8.29	$320.35

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Abbreviations: IQR, Interquartile range; TB, tuberculosis; CHWs, community health workers; SMS, short messaging service.

Program capacity (both the number of participating facilities and the number of household contacts investigated per facility) had a large effect on the estimated cost per positive contact diagnosed (Fig 2). At the observed average annual contact positivity rate (0.036), over half of the capacity scenarios projected a cost per positive contact diagnosed of less than $600 –substantially lower than the estimates of $8,873 (bottom-up) and $9,652 (top-down) at the observed capacity of 7 clinics and 67 contacts screened per facility. Under the highest capacity projected (50 facilities with an average of 300 contacts investigated per facility per year), and the average annual contact positivity rate (0.036), we estimated that this program could be developed and implemented at a total cost of $459 per contact diagnosed with TB. Higher TB prevalence among contacts resulted in even lower costs per person diagnosed.

Fig 2 — Each graph represents the cost of an mHealth-facilitated TB contact investigation program as the average annual contact positivity rate (number of contacts diagnosed with TB divided by the number of contacts evaluated) varies from 0.0252 to 0.0307 to 0.0361 to 0.0415 among household contacts of TB patients. The y-axis represents the number of clinics covered by the program and the x-axis the total annual contacts at each clinic. The orange hue gradient represents the cost gradient associated with different program coverage capacities, with the darkest shade representing scenarios with the lowest programmatic costs and the lightest shade representing scenarios with the highest programmatic costs. Abbreviations: CHW, Community health worker; RCT, randomized-controlled trial; SMS, short-messaging service; TB, tuberculosis.

Discussion

In resource-limited settings, mHealth technologies are being widely implemented, not only for TB contact investigation [17–20], but also for a wide array of other health-related interventions. More broadly, eHealth is being rapidly adopted worldwide and has been endorsed by the WHO [18]. In this economic evaluation we provide context on the adaptation of mHealth for a home-based TB contact investigation intervention in a low-income setting. Specifically, we found that 76% of the total cost of the program was incurred during development, before the recruitment of a single participant. The total program cost was therefore estimated at $320-$348 per contact investigated and $8,873-$9,652 per new diagnosis of TB–a high cost relative to many other programs [13,21,22]. Importantly, these costs could be reduced to under $600 per new TB diagnosis simply by expanding capacity (extending from one to five years, increasing the number of clinics participating, and optimizing the volume of household contacts evaluated in each clinic). These findings illustrate that, when implementing mHealth and other interventions with substantial development costs in resource-limited settings, the feasible scope and duration of the program, as well as the expected yield, must be considered to evaluate whether a meaningful return on investment is likely.

Assuming that the maximum capacity of participants could be achieved, our projected costs of mHealth-facilitated contact investigation are comparable to the estimated costs of contact investigation (without mHealth) in other low-income settings, with one economic evaluation of contact investigation in Uganda estimating a cost of US$878 per new TB case identified [23,24]. This finding suggests that mHealth implementation may be economically viable in this context if sufficient patient volumes can be achieved. The drivers of cost in the development phase of this project included capital investments in technology (42%) and human resources–including highly trained IT staff and implementers (36%). During the implementation phase, human resource costs for supervision became more prominent (46%), reflecting the relatively small number of participants engaged relative to high-level supervisory staff. Again, this finding highlights the need to achieve economies of scale to make such programs more cost-effective.

More broadly, the cost implications of digital health interventions in low- and middle-income countries vary depending on their scope and purpose. For example, a digital adherence program in Brazil applying two-way SMS cited a cost of $65 ($53–$105) per person enrolled [25], while an mHealth support intervention for HIV in Uganda cited an annual cost of $2.35 per patient enrolled [22]. A study in India on an mHealth-facilitated intervention to improve CHW counseling skills in maternal and newborn health suggested that start-up costs represented only 9% of total costs; this program was able to operate at a cost of $20 per woman registered [21]. Taken as a whole, these findings illustrate the heterogeneous economic implications of mHealth-facilitated health interventions designed for resource-limited settings, as well as the importance of considering what is feasible in terms of program scope and the costs of program development before embarking on large investments in mHealth infrastructure and capacity.

Our analysis adheres to transparency recommendations on scope and accuracy, and captures unit cost estimates of all the key program components [26]. While we do not report granular expenditures (e.g., number of sputum cups purchased), all macro cost calculations were derived from itemized unit costs and quantities. Furthermore, our method of micro-cost estimation also provides the recommended highest level of accuracy, estimating the cost of each discrete implementation activity based on actual consumption. The observed breakdown of costs for program development versus implementation may serve as a reference for future cost-effectiveness analyses [27], and help potential implementers concisely project the budgetary implications of such a program over time. Our cost estimates using a top-down and bottom-up approach were reasonably similar, suggesting that our estimates are robust to the precise method of costing employed [28]. The inputs for this analysis were derived from real-world implementation; while the trial did not find a significant improvement in TB case detection [10], it identified important process-related challenges that, if addressed, could improve program adoption, implementation, and maintenance [2,11,29]. A follow-up trial using human-centered design and communities of practice to overcome these barriers is currently underway [30,31], and the cost implications of the revised design and implementation processes will also be important to consider.

This study had several limitations. First, volume-based costs were collected retrospectively from budget estimates and financial records. This may have led to underestimation of the absolute unit cost estimates of mHealth TB contact investigation due to missing records or poor recall by implementing staff. This retrospective data collection may also have compromised the delineation between programmatic and research-related costs. Second, clinic estimates of overhead costs were not readily available because of variations and inconsistency of funding at the clinic, with many of the inputs based on verbal estimates by clinic administrators. These limitations could have led to overestimation or underestimation of the cost of overheads due to recall bias. Third, we conducted a self-reported time-and-motion study by CHWs; these results are therefore subject to potential social desirability bias. Self-reported time-and-motion forms were collected daily, and data quality checks were done immediately to minimize this bias, but future evaluations could attempt to use more automated approaches to track activity times. Fourth, as a focused estimate of the cost of contact investigation, broader costs (such as patients’ cost of accessing TB diagnostic and treatment services if found to be positive for TB or recommended for TB preventive therapy) that are of relevance to society were not included. Finally, scenarios for sensitivity analysis were constructed based on expert opinion and may not fully represent the potential scope of the program if scaled up more broadly.

Conclusions

In summary, this investigation of the costs of mHealth-facilitated contact investigation for TB revealed high up-front costs for design and development of the mHealth infrastructure and capacity; these costs amounted to three-fourths of total program costs after one year of program implementation. In deciding whether to invest in similar interventions in Uganda and other resource-limited settings, careful consideration must be made as to the feasible duration and scope of program implementation to ensure a favorable return on investment.

Supporting information

S1 Table. Detailed list of cost components considered for the mHealth-facilitated contact investigation program.

(DOCX)

Click here for additional data file.^{(17.2KB, docx)}

S2 Table. Community health worker activities identified during time-and-motion surveys.

(DOCX)

Click here for additional data file.^{(15.4KB, docx)}

S3 Table. Distribution of community health worker activity time by health center per household TB contact investigated.

(DOCX)

Click here for additional data file.^{(17.4KB, docx)}

Acknowledgments

The authors thank all of the community health workers on the mHealth Contact Investigation Study; AIDS Information Centre staff; all administrative, TB unit, and laboratory staff at the participating Kampala City Council Authority clinics; staff at the Department of Health at the Kampala City Council Authority; National TB and Leprosy Program staff at the Uganda Ministry of Health, and all participants of the mHealth Contact Investigation Study.

Data Availability

Data are held in the Dryad online repository, available at https://datadryad.org/stash/share/02ECnaLd-vQstsxpf3_L8tWynB6DG8ILjKcAHqqdK3g.

Funding Statement

This study was supported by National Institutes of Health through grants awarded to JLD (R01AI104824 and D43TW009607). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0265033.r001

Decision Letter 0

Limakatso Lebina

2 Nov 2021

PONE-D-21-01299A cost analysis of implementing mobile-health facilitated tuberculosis contact investigation in a low income settingPLOS ONE

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Additional Editor Comments (if provided):

We apologies for delays in providing feedback. It was not easy to find appropriate reviewers and those agreed to review requested additional time.

Please also address the following to improve clarity and flow of the manuscript

1. Provide a clear description of the program, and indicate which activities were done in the clinics, and which ones were part of the community initiative and why.

2. The cost calculations are said to be done from the health system perspective, yet waiting times and travel costs for patients have been included. Please separate the health system costs and the patient costs.

3. Capital costs for the overall TB treatment have also been included, however this was an add on program, TB services costs were already incurred, it would be more important to the policy makers to understand the additional costs that will be required to include the mHealth component.

4. There is no information on the messages that were sent and the associated costs of sending the messages.

5. It appears that the research related costs such as patient enrolment and program evaluation have been included in the costing, as indicated above, policy makers would be more interested in the additional costs of implementing the program

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Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: No

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Reviewer #1: The manuscript is well-written and methodologically sound. For further improvement the authors can check the manuscript against the transparency standards for cost estimates from the reference: Fukuda H, Imanaka Y: Assessment of transparency of cost estimates in economic evaluations of patient safety programmes. J Eval Clin Pract. 2009, 15: 451-459. 10.1111/j.1365-2753.2008.01033.x.

Reviewer #2: Specific feedback to the Author:

Abstract

Introduction: Mobile health (mHealth) applications may improve timely access to health services and improve patient-provider communication but may be costly to implement, especially in resource-limited settings. This statement does not quite reflect the rationale for the study. Suggest changing to reflect that development/ upfront investment in m- interventions maybe resource intensive.

- The authors need to provide a clear aim/ research question in their abstract that aligns with the title and accurately represents the intervention. In the abstract, the authors state: We assessed the cost of development and execution of an mHealth-based program for contact investigation of tuberculosis (TB) in Kampala, Uganda however, this program has a m-health component alongside face to face intervention (i.e., community Health Worker (CHW)-led home-based specimen collection) therefore the authors should continue to refer to this program as a mobile-health facilitated tuberculosis contact investigation as the m-health is only one component of the intervention.

- Suggest changing reference to ‘ingredients based’ to ‘components based’ or ‘micro-costing’ approach.

- The authors have not provided the time-horizon of the study in the abstract. The time horizon for the cost analysis will impact the results reported and therefore, should be reported here in the abstract also.

- Conclusion- the statement ‘the cost of mHealth-facilitated contact investigation for TB was high ‘is not fully substantiated. High in what context? Over a one- year time horizon? Most technologies would not be developed for just one year of use. Suggest editing this sentence to align with the sentiment noted in the discussion i.e, that while they have high upfront development costs, they become more efficient with time, scale/coverage and the prevalence of TB in the community.

Introduction (main body of paper)

- With more than 10 million newly reported cases and 1.6 million deaths in 2018, tuberculosis (TB) is the leading global infectious disease cause of mortality (1). This refers to cases in 2018. Is TB still the leading global infectious disease cause of mortality in 2021? Edit wording to reflect that it is still the case in 2021.

- CI should be in parenthesis.

- However, barriers to acceptance and completion, operational complexities and resource constraints have limited wide adoption of CI in resource-limited setting – back this up with references please.

- It is unclear whether the authors have sufficiently summarised all relevant background literature. What were the results of the efficacy trial? The reference Davis et al, 2019 concluded that Home-based, SMS-facilitated evaluation did not improve completion or yield of household TB contact investigation, likely due to challenges delivering the intervention components. This needs to be reported as it is relevant to this study.

- Also, what is the breadth of the literature in this space too? Are there any other comparable costing /cost-effectiveness studies?

As with many other interventions utilizing mobile health (mHealth) platforms, however, mHealth-facilitated CI for TB requires substantial up-front resource outlays to establish the necessary infrastructure (9,10) . Reference 10 - Htet KKK, Liabsuetrakul T, Thein S, evaluates an intervention appeared to not have an M-health component? so can this reference support this statement?

- The authors state we conducted a comprehensive assessment of the costs of development, implementation, and operation of mHealthfacilitated, patient-centered CI for TB in Kampala, Uganda. The rationale for a partial cost analysis (and not a full cost-effectiveness analysis) is not quite clear at this point.

Setting:

- Suggest using the word implementation instead of execution. Implementation is more widely accepted terminology.

Figure 1- information in Figure 1 is not readable. Please re- do this figure so that all content can be read.

Estimates of program development costs and program execution costs:

- These sections provide clear information of methods but do need to align closely with the headings in the supplementary tables and should at least explain the phases so that the reader understands this without needing to go to the supplementary tables. Perhaps set out in the phases?

- Be clear how much of the overhead building costs are being apportioned to this intervention- I imagine those costs are being apportioned to many other interventions/ programs in reality?

- As you are only costing this arm of the original trial, it is important that all costs are associated in some way to the m-health component, otherwise what is being described may be more reflective of the cost of a tuberculosis contact investigation program generally rather than the addition of m-health.

Supplementary table 1:

- Supplementary table 1 has many acronyms that are not described. Please add a key providing description of all acronyms.

Figure 2 needs to explain somewhere what the contact positivity rate is (i.e., what makes up this rate?)

Analysis:

- This section is clear.

Sensitivity analysis:

- Inclusion of extended time horizon is appropriate as m-health technology usually would continue to be useful for a number of years not just 1 year as per the trial so this is good to see.

- Testing coverage/scale is also important as m-health likely works on economies of scale to offset the initial investment or sunk costs. The prevalence of disease clearly impacts here as well.

- These SA’s are important for interpreting results.

Discussion section:

The discussion section does summarise key study findings and describes how they support conclusions however a couple of things:

- In relation to the statement ‘Specifically, we found that 76% of the total cost of the program was incurred during development, before the enrollment of a single participant’. This is often the case with investment in new technologies. It would be good to note this fact with reference to the broader literature.

- All other sections of the discussion are good including the section on limitations of the study which is comprehensive.

Conclusion marries up with the discussion.

References

Please check that all references are using the standard PLOS style. There seems to be some variation in style. Reference 1 for example has fully capitalised title and states world health organ. This needs editing.

**********

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Reviewer #1: Yes: Denny John

Reviewer #2: No

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PLoS One. 2022 Apr 1;17(4):e0265033. doi: 10.1371/journal.pone.0265033.r002

Author response to Decision Letter 0

8 Feb 2022

February 7, 2022

Dr Joerg Heber, Editor in Chief

PLOS ONE

Dear Dr. Heber,

Thank you for your review of our manuscript entitled “A cost analysis of implementing mobile-health facilitated tuberculosis contact investigation in a low-income setting.” We appreciate the careful review that the reviewers have provided, and we have responded in bold text below to each of their comments, with new text added to the manuscript quoted in orange text:

Responses to Overall Review from the Editor:

1. Provide a clear description of the program, and indicate which activities were done in the clinics, and which ones were part of the community initiative and why.

Thank you for this suggestion. We have described the program in more detail in the Materials and Methods section, under the Study Design and Setting subheading, in Paragraph 1 on Page 4.

“In this setting, TB contact investigation involved CHWs visiting the homes of TB patients, screening all contacts for TB symptoms, and recording their findings using a customized electronic survey application (CommCare, Dimagi, Boston, USA). The application employed decision-support logic to identify contacts requiring evaluation for TB and prompted CHWs to collect a sputum sample and offer HIV testing to eligible household members. The application also delivered personalized, automated text messages to each participant providing follow-up instructions, clinic visit reminders, and TB test results. In the routine care arm, automated text messages were not sent, and all contacts needing TB evaluation were referred to the clinic. The home-based strategy sought to increase the proportion of contacts fully evaluated for TB by reducing the need for contacts to travel to clinics.”

2. The cost calculations are said to be done from the health system perspective yet waiting times and travel costs for patients have been included. Please separate the health system costs and the patient costs.

Thank you for this comment. To clarify, we did not collect any data on patient costs for this study. Waiting times actually refer to the time spent by CHWs unoccupied while waiting to enroll new patients at the clinic. Similarly, travel costs refer to the amount of money that CHWs spent on travel to visit households. A full description of how these costs apply to the health system is provided on Pages 4 and 5 of the Materials and Methods Section, under sub-heading, Estimation of Program Implementation Costs.

Thank you for this comment. To clarify, we did not include capital costs for the overall TB treatment program in these estimates, but only the incremental costs. As shown in Table 2a, capital costs included software purchases, information technology (IT) hardware for the mHealth intervention, training in the use of the mHealth component, a vehicle for overall site supervision, and activities to adapt the intervention that our Ugandan partners felt would be necessary for programmatic implementation in different local settings.

4. There is no information on the messages that were sent and the associated costs of sending the messages.

Thank you for this question. We originally aggregated messaging costs with capital costs for IT hardware, but we now recognize that these are better classified as recurrent costs. We have added these costs (i.e., of the 10,000 text messages costing $50 in total that were purchased to cover 5 years of the program) as an independent cost component in Table 2a and Table 3. We have also updated the capital costs in Table 2a, Table 2b, and Table 3, and in the relevant narratives in the Results sections (see Page 11, Paragraph 2). Given the low cost of messaging, we observed no change in the overall distribution of macro- or micro-cost estimates.

Thank you for this comment. We agree that only programmatic costs should be included. Our assumption in including these costs was that patient enrollment includes standard contact investigation activities such as counselling, inviting participation, and obtaining standard clinical consent for the household visit. We also assume that program evaluation is a standard part of implementation and quality assurance, and consequently the estimated costs of program evaluation are small, consistent with this purpose.

To clarify our approach in the manuscript, we have now replaced “enrollment” with “recruitment” throughout. The nature of patient recruitment activities is described in Supplemental Table 2. Finally, we have added to our description of program evaluation the qualifying phrase “for routine quality assurance” in Paragraph 1 on Page 6 in the Materials and Methods section, under the subheading Estimation of Program Implementation Costs.

Revisions made to meet journal requirements:

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We have corrected the grant numbers in both locations.

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Thank you for the guidance. All data used in this analysis was de-identified and have been made available through the Dryad public repository.

Yes, of course. We have added a full ethics statement at the end of the Materials and Methods section, in the second -to-last paragraph on Page 7.

“Ethical Considerations

5. Please upload a new copy of Figure 2 as the detail is not clear. Please follow the link for more information: https://blogs.plos.org/plos/2019/06/looking-good-tips-for-creating-your-plos-figures-graphics/" https://blogs.plos.org/plos/2019/06/looking-good-tips-for-creating-your-plos-figures-graphics/. [Note: HTML markup is below. Please do not edit.]

Thank you for these suggestions. We have prepared a new figure in the recommended format.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g., participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

4. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: No

We have now edited the manuscript to improve its clarity and readability.

5. Review Comments to the Author

Thank you for sharing this reference. We have verified our adherence to these standards and stated this in the “Discussion” section at the bottom of Page 9.

“Our analysis adheres to transparency recommendations on scope and accuracy, and captures unit cost estimates of all the key program components.(26) While we do not report granular expenditures (e.g., number of sputum cups purchased), all macro cost calculations were derived from itemized unit costs and quantities. Furthermore, our method of micro cost estimation also provides the recommended highest level of accuracy, estimating the cost of each discrete implementation activity based on actual consumption.”

Reviewer #2: Specific feedback to the Author:

Abstract

We agree, and we have revised the Introduction section of the Abstract on Page 2) as follows:

“Mobile health (mHealth) applications may improve timely access to health services and improve patient-provider communication, but the upfront costs on its implementation may be prohibitive, especially in resource-limited settings.”

Thank you for this suggestion. We now describe the implementation strategy as the “mHealth-facilitated TB contact investigation” throughout the manuscript.

- Suggest changing reference to ‘ingredients based’ to ‘components based’ or ‘micro-costing’ approach.

Thank you for this suggestion. We now describe the bottom-up costing as “components-based.”

Thank you for this suggestion. Time horizons have now been integrated into the abstract and elsewhere in the manuscript. Figure 1 also provides a conceptual illustration of the costing timeline.

“We estimated total costs per contact investigated and per TB-positive contact identified in 2018 US dollars, one and five years after program implementation.”

- Conclusion- the statement ‘the cost of mHealth-facilitated contact investigation for TB was high ‘is not fully substantiated. High in what context? Over a one- year time horizon? Most technologies would not be developed for just one year of use. Suggest editing this sentence to align with the sentiment noted in the discussion, i.e., that while they have high upfront development costs, they become more efficient with time, scale/coverage and the prevalence of TB in the community.

Thank you again for these observations and helpful suggestions. We have revised the Abstract’s Conclusion on Page 2 to emphasize that high upfront development costs may become more manageable if the intervention can be implemented at scale and over time.

“Over 75% of all costs of the mHealth-facilitated TB contact investigation strategy were dedicated to establishing mHealth infrastructure and capacity. Implementing the mHealth strategy at scale and maintaining it over a longer time horizon could help decrease development costs as a proportion of total costs.”

Introduction (main body of paper)

Thank you for this comment. Because COVID is now the leading infectious cause of death, we have revised the language as follows:

“Tuberculosis (TB) is among the leading causes of mortality due to an infectious disease worldwide with approximately 7 million new cases of TB diagnosed in 2020.”

- CI should be in parenthesis.

We have put CI in parentheses upon first reference in the abstract and in the main text.

Thank you, references supporting this statement have now been added:

2. Armstrong-Hough M, Turimumahoro P, Meyer AJ, Ochom E, Babirye D, Ayakaka I, et al. Drop-out from the tuberculosis contact investigation cascade in a routine public health setting in urban Uganda: A prospective, multi-center study. PLoS One. 2017;12(11).

8. Ayakaka I, Ackerman S, Ggita JM, Kajubi P, Dowdy D, Haberer JE, et al. Identifying barriers to and facilitators of tuberculosis contact investigation in Kampala, Uganda: A behavioral approach. Implement Sci. 2017.

9. Ngamvithayapong-Yanai J, Luangjina S, Thawthong S, Bupachat S, Imsangaun W. Stigma against tuberculosis may hinder non-household contact investigation: a qualitative study in Thailand. Public Health Action. 2019.

- It is unclear whether the authors have sufficiently summarized all relevant background literature. What were the results of the efficacy trial? This needs to be reported as it is relevant to this study.

Yes, this is important. In addition to the above noted references, we have also added the results of the parent trial in the Study Design and Setting subsection of the Materials and Methods section in Paragraph 1 on Page 5 and cited the publication that reported the original results.

“The overall trial observed a marginal probability of completing TB evaluation of 14% (95% CI 8-20 14%) in intervention households and 15% (95% CI 9-21) in routine care households, representing a difference of -1% (95% CI -9% to 7%, p=0.81.”

- Also, what is the breadth of the literature in this space too? Are there any other comparable costing /cost-effectiveness studies?

We have now referenced and discussed the following literature that references the cost of mHealth programs in low- and middle-income settings in the Discussion section, in Paragraph 3, on Page 14.

21. Prinja S, Bahuguna P, Gupta A, Nimesh R, Gupta M, Thakur JS. Cost effectiveness of mHealth intervention by community health workers for reducing maternal and newborn mortality in rural Uttar Pradesh, India. Cost Eff Resour Alloc. 2018

22. Chang LW, Kagaayi J, Nakigozi G, Serwada D, Quinn TC, Gray RH, et al. Cost analyses of peer health worker and mHealth support interventions for improving AIDS care in Rakai, Uganda. AIDS Care - Psychol Socio-Medical Asp AIDS/HIV. 2013.

25. Nsengiyumva NP, Mappin-Kasirer B, Oxlade O, Bastos M, Trajman A, Falzon D, et al. Evaluating the potential costs and impact of digital health technologies for tuberculosis treatment support. Eur Respir J. 2018.

As with many other interventions utilizing mobile health (mHealth) platforms, however, mHealth-facilitated CI for TB requires substantial up-front resource outlays to establish the necessary infrastructure (9,10). Reference 10 - Htet KKK, Liabsuetrakul T, Thein S, evaluates an intervention appeared to not have an M-health component? so can this reference support this statement? for this question.

We have replaced the Htet KKK et al reference and reworded the literature we found on cost evaluations in the text below, found in the last paragraph on Page 3.

“The strategy was feasible and acceptable but not more effective because of implementation challenges (11,12). Nonetheless, another challenge in mHealth field is the limited and heterogeneous evidence on the costs and cost effectiveness of mHealth strategies (13), including some evidence of high up-front costs (14), which may act as a barrier to ongoing research and innovation.”

13. Iribarren SJ, Cato K, Falzon L, Stone PW. What is the economic evidence for mHealth? A systematic review of economic evaluations of mHealth solutions. PLoS One. 2017.

14. Larsen-Cooper E, Bancroft E, Rajagopal S, O’Toole M, Levin A. Scale matters: A cost-outcome analysis of an m-health intervention in Malawi. Telemed e-Health. 2016.

- The authors state we conducted a comprehensive assessment of the costs of development, implementation, and operation of mHealth facilitated, patient-centered CI for TB in Kampala, Uganda. The rationale for a partial cost analysis (and not a full cost-effectiveness analysis) is not quite clear at this point.

We had originally proposed a cost effectiveness analysis, but this was not possible because the mHealth intervention strategy was not more effective than routine contact investigation for TB. Therefore, we are reporting a cost analysis only, to provide real world estimates of the expenditure of implementing such programs. We have clarified this in the final paragraph of the Introduction on Page 3 (partially cited above, provided again here for full context):

“To address these challenges, we developed a home-based, mHealth-facilitated household CI strategy and evaluated it in a pragmatic, prospective, household randomized trial (10). Compared to routine CI delivered by community health workers (CHWs), the mHealth-facilitated CI intervention included home-based HIV testing and TB evaluation, collection and transport of sputum samples, and follow-up communications using automated short messaging services (SMS). The strategy was feasible and acceptable but not more effective than routine contact investigation because of implementation challenges (11, 12). Nonetheless, another area of uncertainty in the mHealth field is the limited and heterogeneous evidence on the costs and cost effectiveness of mHealth strategies (13), including some evidence of high up-front costs (14), which may in turn act as a barrier to ongoing research and innovation. Therefore, to characterize the resource implications of such health interventions more fully, we conducted a comprehensive assessment of the costs of development, implementation, and maintenance of home-based, mHealth-facilitated TB CI in Kampala, Uganda.”

Setting:

- Suggest using the word implementation instead of execution. Implementation is more widely accepted terminology.

Thank you for this comment; we have revised accordingly throughout the manuscript.

Figure 1- information in Figure 1 is not readable. Please re- do this figure so that all content can be read.

We apologize for this. We have uploaded a revised figure that adheres to journal guidance.

Estimates of program development costs and program execution costs:

Thank you. We have now provided additional information in the body of the Materials and Methods subsection entitled “Estimation of program development cost” on Page 5 to expand description of the supplementary tables in the main text described below:

“Cost components were appropriately mapped to specific thematic expenditure categories: human resource costs, capital costs, recurrent costs, overhead costs and building space costs. Human resource costs included a coordinator, data manager, laboratory manager and IT officer. Capital costs included investment in hardware and software for mHealth, a vehicle, and cost to adapt the intervention to the local setting. Recurrent costs included expenditure on consumables such as lab supplies, internet, and text messages. Overhead costs included operational costs such as those for supervision teams and patient care at the clinic. Building space was the space occupied by the supervision teams and patient rooms.”

- Be clear how much of the overhead building costs are being apportioned to this intervention- I imagine those costs are being apportioned to many other interventions/ programs in reality?

Thank you for this comment. We have added text to address this question at the bottom of Page 5 and the top of Page 6, in the Materials and Methods section under the sub-header “Estimation of program implementation costs”:

“The cost of building space utilized for patient services was approximated as 5% of the cost of the entire building and operational costs for the program as 6.7% of clinic operational costs.”

Thank you for this comment. Only the study arm that was exposed to the mHealth components was considered for this analysis and we have only included costs associated with the mHealth components in some way. We have provided a general overview of our approach in the second paragraph of the Materials and Methods section on Page 4, and have edited this section to clarify that costing is focused only the mHealth components:

“To comprehensively evaluate the costs of mHealth-facilitated intervention, we divided the program into two phases and evaluated the costs accrued in each phase.”

We further elaborate on this process in the “Estimation of program development costs” and “Estimation of program implementation costs” that immediately follow, on Pages 4 and 5. Finally, Supplementary Table 1 summarized all included costs.

Supplementary table 1:

- Supplementary table 1 has many acronyms that are not described. Please add a key providing a description of all acronyms.

Yes, of course, we have now defined all abbreviations.

Figure 2 needs to explain somewhere what the contact positivity rate is (i.e., what makes up this rate?)

Thank you. We have now revised the Figure 2 Legend on Page 13 to explain that the contact positivity rate is obtained by dividing the number of contacts diagnosed with TB by the number of contacts evaluated per year.

“...the average annual contact positivity rate (number of contacts diagnosed with TB divided by the number of contacts evaluated)”

Analysis:

- This section is clear.

Sensitivity analysis:

- Inclusion of extended time horizon is appropriate as m-health technology usually would continue to be useful for a number of years not just 1 year as per the trial, so this is good to see.

Thank you.

- Testing coverage/scale is also important as m-health likely works on economies of scale to offset the initial investment or sunk costs. The prevalence of disease clearly impacts here as well.

Yes, we entirely agree and have emphasized this in the Abstract Conclusion on Page 2 and elsewhere in the manuscript:

- These SA’s are important for interpreting results.

Thank you for these comments.

Discussion section:

The discussion section does summarize key study findings and describes how they support conclusions however a couple of things:

It would be good to note this fact with reference to the broader literature.

Thank you. In the subsequent sentence in the first paragraph of the Discussion at the bottom of Page 13, we talk about how our findings compare to other mHealth programs.

“Specifically, we found that 76% of the total cost of the program was incurred during development, before the recruitment of a single participant. The total program cost was therefore estimated at $320-$348 per contact investigated and $8,873-$9,652 per new diagnosis of TB – a high cost relative to many other programs(13,21,22). Importantly, these costs could be reduced to under $600 per new TB diagnosis simply by expanding capacity (extending from one to five years, increasing the number of clinics participating, and optimizing the volume of household contacts evaluated in each clinic). These findings illustrate that, when implementing mHealth and other interventions with substantial development costs in resource-limited settings, the feasible scope and duration of the program, as well as the expected yield, must be considered to evaluate whether a meaningful return on investment is likely.”

We further expand on this statement in Paragraph 3 on Page 14 of the Discussion section.

“More broadly, the cost implications of digital health interventions in low- and middle-income countries vary depending on their scope and purpose. For example, a digital adherence program in Brazil applying two way SMS cited a cost of $65 ($53–$105) per person enrolled (25), while an mHealth support intervention for HIV in Uganda cited an annual cost of $2.35 per patient enrolled.(22) A study in India on an mHealth-facilitated intervention to improve CHW counseling skills in maternal and newborn health suggested that start-up costs represented only 9% of total costs; this program was able to operate at a cost of $20 per woman registered.(21) Taken as a whole, these findings illustrate the heterogeneous economic implications of mHealth-facilitated health interventions designed for resource-limited settings, as well as the importance of considering feasible program scope and costs of program development before embarking on large investments in mHealth infrastructure and capacity.”

- All other sections of the discussion are good including the section on limitations of the study which is comprehensive.

Conclusion marries up with the discussion.

Thank you for these comments.

References

Please check that all references are using the standard PLOS style. There seems to be some variation in style. Reference 1 for example has fully capitalized title and states world health organ. This needs editing.

Thank you, all references have been added in similar font and the referencing style is Vancouver.

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

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Reviewer #1: Yes: Denny John

Reviewer #2: No

Sincerely,

Patricia Turimumahoro, MBChB, MPH

J. Lucian Davis, MD, MAS

PLoS One. doi: 10.1371/journal.pone.0265033.r003

Decision Letter 1

Limakatso Lebina

23 Feb 2022

A cost analysis of implementing mobile-health facilitated tuberculosis contact investigation in a low-income setting

PONE-D-21-01299R1

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Additional Editor Comments (optional):

You have been able to address all comments in the revised manauscript.

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0265033.r004

Acceptance letter

Limakatso Lebina

15 Mar 2022

PONE-D-21-01299R1

A cost analysis of implementing mobile health facilitated tuberculosis contact investigation in a low-income setting

Dear Dr. Davis:

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Associated Data

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

Supplementary Materials

S1 Table. Detailed list of cost components considered for the mHealth-facilitated contact investigation program.

(DOCX)

Click here for additional data file.^{(17.2KB, docx)}

S2 Table. Community health worker activities identified during time-and-motion surveys.

(DOCX)

Click here for additional data file.^{(15.4KB, docx)}

S3 Table. Distribution of community health worker activity time by health center per household TB contact investigated.

(DOCX)

Click here for additional data file.^{(17.4KB, docx)}

Data Availability Statement

Data are held in the Dryad online repository, available at https://datadryad.org/stash/share/02ECnaLd-vQstsxpf3_L8tWynB6DG8ILjKcAHqqdK3g.

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PERMALINK

A cost analysis of implementing mobile health facilitated tuberculosis contact investigation in a low-income setting

Patricia Turimumahoro

Austin Tucker

Amanda J Gupta

Radhika P Tampi

Diana Babirye

Emmanuel Ochom

Joseph M Ggita

Irene Ayakaka

Hojoon Sohn

Achilles Katamba

David Dowdy

J Lucian Davis

Roles

Abstract

Introduction

Methods

Results

Conclusion

Introduction

Materials and methods

Study design and setting

Fig 1. A detailed description of program activities in the different phases of implementation.

Estimation of program development costs

Estimation of program implementation costs

Total program costs: Bottom-up

Analysis

Sensitivity analysis

Ethical considerations

Results

Table 1. Clinic characteristics.

Table 2.

Table 3. Bottom-up unit cost estimates for each activity and resource category per household TB contact investigated.

Fig 2. Three-way sensitivity analysis of the cost per TB case detected.

Discussion

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Limakatso Lebina

Roles

Author response to Decision Letter 0

Decision Letter 1

Limakatso Lebina

Roles

Acceptance letter

Limakatso Lebina

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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