Remote monitoring of Xpert® MTB/RIF testing in Mozambique: results of programmatic implementation of GxAlert

Cowan, James; Michel, Cathy; Manhiça, Ivan; Mutaquiha, Cláudia; Monivo, C.; Saize, D.; Beste, Jason; Creswell, Jacob; Codlin, Andrew James; Gloyd, Stephen

doi:10.5588/ijtld.15.0535

Cited by 17 publications

(13 citation statements)

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“…A preliminary study from South Africa 151 found that Xpert MTB/RIF probe information, which differed substantially by geographical region, could be used for near real-time national surveillance using connected software, such as GXalert (open-source data connectivity for the GeneXpert). 152 By documenting the increasing frequency of a specific mutation, these data could be used for identifying the emergence of drug-resistant tubercu losis or hotspots of MDR tuberculosis. 69 Targeting these hotspots might be highly effective for controlling drug-resistant tuberculosis.…”

Section: Next-generation Molecular Epidemiologymentioning

confidence: 99%

The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis

Dheda

Gumbo

Maartens

et al. 2017

The Lancet Respiratory Medicine

521

474

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Section: Next-generation Molecular Epidemiologymentioning

confidence: 99%

The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis

Dheda

Gumbo

Maartens

et al. 2017

The Lancet Respiratory Medicine

521

474

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“…In many countries in sub-Saharan Africa, access to mobile phones is nearly universal, and mHealth apps such as mobile data collection software and SMS are showing great promise for enhancing capacity in resource-constrained health systems [ 3 - 5 ]. For example, mHealth technologies can promote treatment adherence [ 6 - 10 ], enhance access to and uptake of maternal and child health care services [ 11 , 12 ], and improve the quality and reliability of services by connecting external quality assurance teams directly to diagnostic instruments [ 13 ]. However, mHealth studies have met with mixed success in achieving their aims in these settings: many interventions have been piloted, yet few have made it into routine practice [ 1 , 3 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Implementing mHealth Interventions in a Resource-Constrained Setting: Case Study From Uganda

Meyer¹,

Armstrong-Hough²,

Babirye³

et al. 2020

JMIR Mhealth Uhealth

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Background Mobile health (mHealth) interventions are becoming more common in low-income countries. Existing research often overlooks implementation challenges associated with the design and technology requirements of mHealth interventions. Objective We aimed to characterize the challenges that we encountered in the implementation of a complex mHealth intervention in Uganda. Methods We customized a commercial mobile survey app to facilitate a two-arm household-randomized, controlled trial of home-based tuberculosis (TB) contact investigation. We incorporated digital fingerprinting for patient identification in both study arms and automated SMS messages in the intervention arm only. A local research team systematically documented challenges to implementation in biweekly site visit reports, project management reports, and minutes from biweekly conference calls. We then classified these challenges using the Consolidated Framework for Implementation Research (CFIR). Results We identified challenges in three principal CFIR domains: (1) intervention characteristics, (2) inner setting, and (3) characteristics of implementers. The adaptability of the app to the local setting was limited by software and hardware requirements. The complexity and logistics of implementing the intervention further hindered its adaptability. Study staff reported that community health workers (CHWs) were enthusiastic regarding the use of technology to enhance TB contact investigation during training and the initial phase of implementation. After experiencing technological failures, their trust in the technology declined along with their use of it. Finally, complex data structures impeded the development and execution of a data management plan that would allow for articulation of goals and provide timely feedback to study staff, CHWs, and participants. Conclusions mHealth technologies have the potential to make delivery of public health interventions more direct and efficient, but we found that a lack of adaptability, excessive complexity, loss of trust among end users, and a lack of effective feedback systems can undermine implementation, especially in low-resource settings where digital services have not yet proliferated. Implementers should anticipate and strive to avoid these barriers by investing in and adapting to local human and material resources, prioritizing feedback from end users, and optimizing data management and quality assurance procedures. Trial Registration Pan-African Clinical Trials Registration PACTR201509000877140; https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=877

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“…For example, acceptability and satisfaction within the perception category were defined differently from one study to another [15,28-30,32,35,36,40,44]. Also, SMS-only intervention studies did not focus on technical outcome [12,15,22,29,30,32,35, 36,42,43,45,46] whereas studies involving other mHealth technologies such as an app or mobile data collection did so [21,28,33,37,39-41]. On the contrary, outcomes related to cost or cost-effectiveness were reported only via studies involving SMS [29,45,46].…”

Section: Resultsmentioning

confidence: 99%

Toward Developing a Standardized Core Set of Outcome Measures in Mobile Health Interventions for Tuberculosis Management: Systematic Review

Lee

et al. 2019

JMIR Mhealth Uhealth

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Background Tuberculosis (TB) management can be challenging in low- and middle-income countries (LMICs) not only because of its high burden but also the prolonged treatment period involving multiple drugs. With rapid development in mobile technology, mobile health (mHealth) interventions or using a mobile device for TB management has gained popularity. Despite the potential usefulness of mHealth interventions for TB, few studies have quantitatively synthesized evidence on its effectiveness, presumably because of variability in outcome measures reported in the literature. Objective The aim of this systematic review was to evaluate the outcome measures reported in TB mHealth literature in LMICs. Methods MEDLINE, EMBASE, and the Cochrane Database of Systematic Reviews were searched to identify mHealth intervention studies for TB (published up to May 2018) that reported any type of outcome measures. The extracted information included the study setting, types of mHealth technology used, target population, study design, and categories of outcome measures. Outcomes were classified into 13 categories including treatment outcome, adherence, process measure, perception, technical outcome, and so on. The qualitative synthesis of evidence focused on the categories of outcome measures reported by the type of mHealth interventions. Results A total of 27 studies were included for the qualitative synthesis of evidence. The study designs varied widely, ranging from randomized controlled trials to economic evaluations. A total of 12 studies adopted short message service (SMS), whereas 5 studies used SMS in combination with additional technologies or mobile apps. The study populations were also diverse, including patients with TB, patients with TB/HIV, health care workers, and general patients attending a clinic. There was a wide range of variations in the definition of outcome measures across the studies. Among the diverse categories of outcome measures, treatment outcomes have been reported in 14 studies, but only 6 of them measured the outcome according to the standard TB treatment definitions by the World Health Organization. Conclusions This critical evaluation of outcomes reported in mHealth studies for TB management suggests that substantial variability exists in reporting outcome measures. To overcome the challenges in evidence synthesis for mHealth interventions, this study can provide insights into the development of a core set of outcome measures by intervention type and study design.

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Remote monitoring of Xpert® MTB/RIF testing in Mozambique: results of programmatic implementation of GxAlert

Abstract: Remote monitoring of diagnostic platforms is feasible in LMICs. While promising, this effort needs to address issues around patient data ownership, confidentiality, interoperability, unique patient identifiers, and data security.

Cited by 17 publications

References 0 publications

The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis

The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis

Implementing mHealth Interventions in a Resource-Constrained Setting: Case Study From Uganda

Toward Developing a Standardized Core Set of Outcome Measures in Mobile Health Interventions for Tuberculosis Management: Systematic Review

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