Does Phone Messaging improves Tuberculosis Treatment Success? A Systematic review and Meta-analysis

doi:10.21203/rs.2.14047/v1

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Does Phone Messaging improves Tuberculosis Treatment Success? A Systematic review and Meta-analysis

https://doi.org/10.21203/rs.2.14047/v1

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Background: Compliance to anti-TB treatment is crucial in achieving cure and avoiding the emergence of drug resistance. Electronic health (eHealth) interventions are included in the strategy to end the global Tuberculosis (TB) epidemic by 2035. Evidences showed that mobile messaging systems could improve patient adherence to clinic appointment for diagnosis and treatment. This review aimed to assess the effect of mobile-phone messaging on anti-TB treatment success.

Methods: All randomized controlled trial (RCT) and quasi-experimental studies done prior to August 26, 2019 were included in the review. Studies were retrieved from PubMed, EMBASE, Cochrane and ScienceDirect databases. In addition, gray literatures and non-indexed articles were searched on Google and Google scholar. Searching strategies was based on PICO criteria and using Medical Subject Headings (MeSH), Boolean search operators, truncation symbols to robust the searching scheme. Articles were independently screened and appraised by two authors. PRISMA flow diagrams were used to summarize article selection process, see Additional file 1.

Results: A total of 1237 articles were identified, with 14 meeting the eligibility criteria for qualitative synthesis. Nine studies with a total of 6030 TB patients (2893 in intervention and 3137 in control groups) were included in meta-analysis. The pooled effect of mobile-phone messaging revealed a small increase in treatment success compared to standard of care (RR 1.04, 95% CI 1.02 to 1.06), with low heterogeneity (I 2 =25%, p<0.0001). In the review, performance, detection and attrition biases were reported as major risk of biases.

Conclusions: Mobile-phone messaging showed a modest effect in improving anti-TB treatment success; however, the quality of evidence was low. Further controlled studies are needed to increase the evidence-base on the role of mHealth interventions to improve TB care. Protocol registration number: CRD420170744339 http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42017074439 Key Words: Mobile phone, Treatment success, Text messaging, Tuberculosis

Infectious Diseases

Mobile phone

Treatment success

Text messaging

Tuberculosis

Globally, mobile cellular subscription coverage is growing exponentially [1]. The unprecedented spread and advancements in innovative application of mobile technologies is an opportunity to address health priorities [2]. Some mobile health applications have shown improvement in patients’ adherence to medications [3]. Evidences demonstrated that mobile-phone text messaging were effective in ART programs [4–7]. Electronic Health interventions has now brought attention in the strategy to end the global TB epidemic by 2035 [8]. In 2015, World Health Organization (WHO) established its Global Task Force on Digital Health for TB to advocate and support the development of digital health innovations in global efforts to improve TB care and prevention[9]. Electronic based patient education was components of a WHO conceptual framework for digital health in the TB response. This was intended to be accomplished using video (virtually) observed therapy [10], Short Message Service (SMS) and eLearning [11].

Tuberculosis is one of the critical public health threat which is the leading cause of mortality worldwide [12]. WHO estimated that globally one-third of the world population harbors latent TB infections[13]. An estimated 10.5 million cases of TB incident [12] and the 22 High Burden Countries (HBCs) accounted about 83% of all incident cases worldwide since 2000 [14]. Compliance to anti-TB treatment is crucial in achieving cure and avoiding the emergence of drug resistance (MDR- and XDR-TB).

Regular and complete medication intake gives individual TB patients the best chance of cure and also protects the community from the spread of TB[15]. One untreated infectious tuberculosis patient is likely to infect 10 to 15 persons annually [16]. The consequences of poor adherence to long-term therapies are poor health outcomes and increased health care costs interventions aimed at improving adherence would provide a significant positive return on investment through primary prevention and secondary prevention of adverse health outcomes[17–22].

Evidences showed that mobile messaging systems could improve patient adherence to clinic appointment for diagnosis and treatment [23]; TB prevention and promotion of Anti-TB treatment adherence [24, 25]. But evidences were limited on effect of phone messaging intervention specifically on Anti-TB treatment success. According to WHO, Anti-TB treatment success is defined as the sum of cured and treatment completed. Cure rate is defined as the percentage of patients who completed treatment and were culture negative during the last month of treatment and on at least one other occasion for non-MDR-TB and who had at least 5 consecutive results in the previous 12–15 months for MDR-TB. Whereas, completion rate is the percentage of MDR-TB and non-MDR-TB patients who completed treatment according to guidelines but did not meet the definition for cure or treatment failure due to a lack of bacteriological results [26]. According to the latest treatment outcome, the global treatment success rate was 82% in 2016 that showed a reduction from 86% in 2013 and 83% in 2015 [27].

The output of this review could be useful evidence for policy makers, researchers, care providers, community members and patients to consider in due plan and implementations. This systematic review was aimed:

To appraise existing evidences of SMS to improve Anti-TB Treatment Success Rate(TSR)
To assess pooled effect of SMS to improve Anti-TB TSR
To assess the effect of frequency of messaging to improve TSR
To identify type of SMS (reminder/educational messaging) more important for TSR.

The protocol has been registered at PROSPERO with ID: CRD420170744339; http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID = CRD42017074439

Criteria for considering studies for this review

Types of study

This study included RCT and Quasi-experimental studies comparing DOTS with and without mobile-phone reminder for patients on Anti-TB treatment. Studies written only in English were include in the review. Studies that measure treatment success including treatment completeness and cure were included in the review.

Types of participants

TB patients aged ≥ 15 years were included in the review.

Types of interventions

Studies that used mobile-phone based messaging service for patients on DOTS as interventions group and those patients on DOTS without messaging was control groups.

Information source and search strategies

In this systematic review, databases including PubMed, EMBASE, Cochrane and ScienceDirect were searched. In addition, gray literatures and non-indexed articles were searched on Google and Google scholar. Searching strategies was based on Population, Intervention, Comparison and Outcome (PICO) criteria and using Medical Subject Headings (MeSH), Boolean search operators, truncation symbols to robust the searching scheme. Searching was held for articles disseminated up to August 26, 2019. Please see the MeSH searching terms on Additional file 2.

Study Selection

Searched results were exported to Endnote X7 citation manager, duplicates were removed and studies were examined based on selection criteria. Abstracts of studies were examined for eligibility for meta-analysis and the full text of articles was searched when abstracts did not provide sufficient information to make a decision. Searching was independently carried out by two reviewers (KDG and ZAM) to reduce selection bias.

Data collection proccess

In this review, data collection and analysis were based on the Cochrane Handbook of Systematic Reviews for Interventions [28].

Risk of bias assessment

Quality of studies were independently assessed by two authors (KDG and ZAM) using Cochrane Risk of Bias Assessment Tool including: use of random sequence generation; concealment of allocation to conditions; blinding of participant and personnel; blinding of outcome assessors; completeness of outcome data and other; selective reporting and other biases. Each study was rated as low risk of bias when there was no concern regarding bias; as high risk of bias when there was concern regarding bias; or unclear risk of bias if the information was absent[28]. The level of agreement between the two reviewers were measured using Cohen’s Kappa level of agreement [29]. Third body was planned to decide if the two reviews couldn’t able to agree on a certain point of bias assessment.

Data extraction and Analysis

All shortlisted articles were independently reviewed by two reviewers and decided to include in the systematic review and then to the meta-analysis. Both authors (KDG and ZAM) independently extracted the selected measures of Anti-TB treatment success. Cure and treatment completeness were directly used for measuring TB treatment success. Treatment success was also indirectly estimated from unsuccessful treatment outcomes like, death, loss to follow-up, treatment failure and transfer-out. Cochrane Collaboration Review Manager Version 5.3 software was used for data extraction and analysis.

Effect size determination

Anti-TB treatment success rate is measured by adding patients cured and those completed their treatment [26]. The pooled effects of intervention (mobile-phone messaging) was measured by using risk ratios (RR) and with 95% confidence intervals. The statistical heterogeneity between studies and its impact on meta-analysis was examined using the I² statistic. Where, I² (0% = no, 25% = low, 50% = moderate, 75% = high heterogeneity of effect sizes) [28]. A fixed and random effect models were used to estimate the RR (95% CI) based on the level of heterogeneity (I²) of the included studies [30, 31]. Sensitivity and subgroup analyses were used to identify the potential effect of heterogeneity of studies was evaluated using.

Sensitivity analysis

Effect size analysis was run with and without the outlier to assess its effect on the overall findings. To assess whether individual studies had impact on summary effect, summary analysis was undertaken after removing each studies effect[28].

Subgroup analysis

Four subgroups were pre-determined based on a priori chosen sub-variables aimed to understand their independent contributions in enhancing mobile-phone messaging to improve Anti-TB treatment success. These sub-variables were: purpose of messaging which classified as medication reminder and educational messaging; disease burden which classified as High Burden Countries (HBCs) and Non-HBCs; type of mobile-phone messaging used (Text, call, graphic reminder, etc.) and frequency of messaging (Daily, Weekly & Both Daily & Weekly)

Assessment of publication bias

Publication bias was assessed using a funnel plot. When asymmetry was indicated by the funnel plot of the effect sizes by their standard error, the impact on the summary effect size was assessed. Eger’s test was used to see statistical test of publication bias. When an outlier was detected, the relevant study was re-examined.

Rating quality of evidence

The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system was used to rate the quality of evidence of this review. Five factors, namely; limitations, inconsistency, indirectness, imprecision and publication bias were used to rate the quality of evidence [32].

Selection of Studies

Articles were retrieved from data bases including: PubMed (552), EMBASE (344), ScienceDirect (168), Cochrane (92) and gray literatures from Google Scholar (78) and reference lists (3), as shown in the flow diagram for selection processes (Figure 1)

[Insert Figure 1 here]

From the total of 1237 articles retrieved, 152 articles excluded due to duplication; 1039 excluded with title review and 24 articles excluded due to ineligibility. Finally 23 articles were fully read and 9 studies were excluded from the review with different reasons, as shown in Table 1. Some articles were excluded because of using video based directly observed therapy [33–37] the intervention was different from phone text, audio, graphic and video messages. SMS intervention has unrelated purpose [38] and study participants were not matching with this review [5, 39, 40].

[Insert Table 1 here]

Characteristics of selected Studies

After exclusion of ineligible studies, fourteen studies were selected and reviewed for qualitative synthesis of evidences. Among included studies, four in Africa, seven in Asia, one in South in one in North America, and one study enrolled patients from North America, Europe, Asia and Africa. See details in Table 3 in Appendix.

All studies implemented a mobile phone messaging interventions on top of the standard TB treatment and care. The various types of phone messaging interventions were applied. Nine of fourteen studies used text based phone messaging, two studies used only phone call and two studies applied a text and/or voice messages. One study implemented text and graphic messages for TB patients to adhere on their treatment. Among all studies, the messaging intervention was interactive (two-way) in seven studies, one-way in five and two studies didn’t report on messaging model. The ultimate purpose of mobile messaging in 11 studies were to remind patients towards their anti-TB medication. In 3 studies the messaging intervention aimed to identify patients concerns, educate and motivate patients to engage on their own medication.

A study in Thailand [41] reported a significant effect of SMS messaging on TB treatment success rate. Whereas, other randomized control study in Argentina [42], in Pakistan [43, 44], in China [45] found no significant difference between the SMS or control groups for treatment success.

Two studies didn’t calculate significance test on the effect of SMS messaging on TSR of TB due to inappropriate or lack of comparator group [46, 47]. One study reported only about level of patient adherence on TB treatment but not on treatment outcome [48]. The characteristics of each selected studies have explained in Table 2. Two randomized controlled studies in USA, Spain, Hong Kong and South Africa [49], and in Canada [50], reported that phone messaging did not significantly improve LTBI completion rates compared to standard care. A successful treatment of Latent Tuberculosis Infection (LTBI) also plays key role in eliminating TB [51]; however, difficult to ascertain treatment success in LTBI cases.

Quantitative synthesis of evidences

Only nine studies [41–45, 52–55] were found to be fitted for the pooled estimation of the effect of mobile phone messaging on successful TB treatment outcome. Five studies were exclude with the following reasons. Two studies focused on LTBI treatment completion [49, 50] couldn’t be pooled to determining treatment success. One study used different tool to measure the outcome variable [48] and studies excluded due to lack of comparison groups [46, 47].

Risk of bias assessment for the included studies

Among the selected studies for meta-analysis, performance bias was the major challenge in the studies, because of the nature of the intervention, participants could not be blinded. Blinding of outcome assessors was also major gap that could lead to detection bias. All studies were jugged free of selection and reporting bias. Two studies have attrition bias (Figure 2). Based on the Cohen’s Kappa level of agreement, the two reviewers (KDG and ZAM) have shown 83.3% agreement with k = 0.686, p<0.0001 for the included papers.

[Insert Figure 2 here]

Pooled estimation on effect of phone messaging on TB treatment success

Overall, 6030 TB patients, 2893 in intervention and 3137 in control groups were involved in the pooled analysis. The overall Anti-TB treatment success was 2544/2893 (87.9%) in intervention and 2655/3137 (84.6%) in control groups. The meta-analysis finding indicated that heterogeneity between individual studies was low (I² = 25%, p<0.0001) [28]. A fixed effect model shown that phone messaging group had higher treatment success rate compared to standard care (RR 1.04, 95% CI 1.02 to 1.06), see the Forest plot in Figure 3.

[Insert Figure 3 here]

Sensitivity analysis was carried out to see the effect size of each models by excluding every one of the studies. All studies were taken out of the analysis one by one and the output indicated that no single study separately influenced the pooled effect of phone messaging on TB treatment success. The Forest plot for sensitivity analysis for the two big weighted studies (Mohammed et al and Liu et al) presented on Figure on additional file 5and 6.

Sub-group analysis

Sub-group analysis was carried out to identify the effect of interventions by level of national Tb burden (high Vs low) and model of phone messaging (one-way Vs two-way) applied by individual studies. The finding indicated that phone messaging has modest effect on TSR in high TB burden (RR = 1.05; 95% CI 1.01–1.08) and in low TB burden countries (RR = 1.06; 95% CI 1.01–1.11) with moderate (I² = 53%) and no (I² = 0%) heterogeneity of studies in both groups respectively (Figure 4). Similarly, a sub-group analysis by model of phone messaging (one-way Vs two-way) shown that both interactive and non-interactive phone messaging have modest effect on TSR of TB (Figure 5). Further sub-group analyses (purpose of the messaging, frequency of messaging, type of messaging and national income) were not conducted due to unable to get sufficient studies in the pre-determined sub-groups.

Publication bias

A subjective visual inspection of funnel plot of the included studies pointed out symmetrical observation that shows less likely occurrence of publication bias. We used Eger’s test to objectively measured publication bias. The finding revealed that there was no evidence of publication bias (P = 0.986), (Figure 6).

[Insert Figure 6b here]

Rating quality of evidence

Based on GRADE quality of evidence assessment approach, the overall quality of evidence of this review was rated low, mainly due to limitations of performance, detection and selection biases for more details see Table 2.

[Insert Table 2 here]

The substantial growth of mobile phones throughout the world has brought opportunities for integrating mobile phones as a health care intervention tools for TB patients[56]. Mobile Health is now inevitable to play big role to end the global TB epidemic [57]. This systematic review aimed to assess the effect of phone messaging on TB treatment success.

The pooled treatment success rate has shown a small increase 87.9% (2544/2893) in intervention groups than 84.6% (2655/3137) in control groups. However, the finding in both groups conveyed higher than latest global treatment success rate 82% in 2016 [27]. The difference might be the effect of latest studies included the review. The meta-analysis shown that phone messaging group had modest increment in treatment success rate compared to standard care (RR 1.04, 95% CI 1.02 to 1.06). The result implies that mobile-phone messaging has a promising effect in improving Anti-TB treatment success. Systematic reviews on ART showed that text-messaging interventions were effective for self-management of diabetes, weight loss, smoking cessation, physical activities and medication adherence for antiretroviral therapy [58, 59]. The variation could be the difference in patient’s perceived risk and benefit for their own TB and HIV status. The other probable justification could be; evidence on the effect of m-Health in improving TB patient care remains limited [60–62]. Whereas, the effect of m-Health has been well studied on ART.

A sub-group analysis based on the level of national Tb burden (high Vs low) revealed that phone messaging has similar and small effect in both high TB burden (RR = 1.05; 95% CI 1.01–1.08) and low TB burden (RR = 1.06; 95% CI 1.01–1.11) countries. The sub-group analysis of this review however, limited with insufficient studies in each sub-groups. Similarly, same analysis between one-way and two-way phone messaging interventions showed small and comparable effect on TSR. But, evidences on SMS interventions for ART that run two-way SMS communication were more acceptable than non-interactive reminders [59].

This systematic review has shown that mobile-phone messaging could be encouraging approach to improve Anti-TB treatment success, however, there is insufficient strong evidence to direct policies. Detection, performance and attrition biases were observed as a limitation in the studies.

The overall quality of evidence of this review was rated low by using GRADE approach. The main reasons were biases and low sample size in many of the studies. Therefore, there is still a need to undertake RCTs in the field to get quality evidences.

This systematic review shown paucity of high-quality evidences concerning effect of mobile-phone messaging on anti-TB treatment success. Mobile-phone messaging showed a modest effect in improving anti-TB treatment success, however the quality of evidence was low. Further controlled studies are needed to increase the evidence-base on the role of mHealth interventions to improve TB care. This systematic review will be updated as new evidence emerged.

Implications for practice

The use of medication reminders alone may not be sufficient to influence the success of TB outcomes. Educational, motivational and other engagement strategies may be important.

Implications for future research

Current evidence is of low quality. This implies that further well designed and reported RCT studies are needed for better quality of evidence on Anti-TB treatment outcome.

Abbreviations

GRADE: Grading of Recommendations Assessment, Development and Evaluation; HBCs: High Burden Countries; MeSH: Medical Subject Headings; PICO: Population, Intervention, Comparison and Outcome; PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses; RCT: Randomized Controlled Trial; SMS: Short Message Service; TB: Tuberculosis.

Ethics approval and consent to participate

Not applicable.

Consent to publish

Not applicable.

Availability of data and materials

All the data was presented within the manuscript.

Competing interests

The authors declare that they have no any competing interests.

Funding

The authors of the study covered all the budget.

Authors’ contributions

KDG and BT contributed the conception and design of the review. KDG and ZAM participated in searching, selection and appraisal of articles. All authors involved in the analysis, interpretation and write-up of the article. All authors approved the manuscript for publication.

Acknowledgments

We acknowledge the authors for the original articles that were taken for pooled analysis. We are grateful to the Institute of Public Health, College of Medicine and Health Sciences, University of Gondar for direct and indirect support to undertake this review.

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Due to technical limitations, "Table 1," "Table 2," & "Appendix - Table 3" are only available for download from the Supplementary Files section.

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Editorial decision: Major revision
08 Oct, 2019
Review #3 received at journal
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03 Oct, 2019
Review #1 received at journal
25 Sep, 2019
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09 Sep, 2019
Reviewer #1 agreed at journal
05 Sep, 2019
Submission checks completed at journal
03 Sep, 2019
Reviewers invited by journal
03 Sep, 2019
Editor assigned by journal
01 Sep, 2019
Editor invited by journal
31 Aug, 2019
First submitted to journal
28 Aug, 2019

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Does Phone Messaging improves Tuberculosis Treatment Success? A Systematic review and Meta-analysis

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Methods

Criteria for considering studies for this review

Study Selection

Data collection proccess

Risk of bias assessment

Data extraction and Analysis

Effect size determination

Sensitivity analysis

Subgroup analysis

Assessment of publication bias

Rating quality of evidence

Results

Selection of Studies

Risk of bias assessment for the included studies

Pooled estimation on effect of phone messaging on TB treatment success

Sub-group analysis

Publication bias

Rating quality of evidence

Discussion

Conclusion

Declarations

Abbreviations

Ethics approval and consent to participate

Consent to publish

Availability of data and materials

Competing interests

Funding

Authors’ contributions

Acknowledgments

References

Tables

Supplementary Files

Status:

Journal Publication

Version 1