**6. Interventions**

## **6.1 Directly observed treatment (DOT)**

Indeed, DOT is a part of the WHO-recommended 'Directly Observed Treatment Short Course' (DOTS) strategy. Although it cannot be denied that this strategy has saved the lives of millions of TB patients, the strategy itself is not flawless. Several authors have questioned the effectiveness of DOT as summarized in a review article by Otu [92]. The 2015 Cochrane systematic review and meta-analysis on DOT compared it with self-administered treatment, and the authors concluded that "TB cure and treatment completion were low with self-administered therapy in these trials, and direct observation did not substantially improve this" [93]. They called for complementary and alternative strategies in addition to DOT. Since DOT is a well-known and well-documented intervention in the field of TB, we felt that it need not be described in further detail in this chapter. Some interventions that have the potential to correct the weaknesses of DOT will be discussed below.

### **6.2 mHealth**

Recently, mHealth has emerged as a popular choice for health programs around the world. The Global Observatory for eHealth (GOe) has defined mHealth as "medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices" [94]. Among these mHealth initiatives, appointment reminders and treatment compliance initiatives are of interest in reducing the rate of LTFU. However, there are limited interventional studies evaluating the effectiveness of these interventions in reducing the risk of LTFU.

In 2017, Hermans et al. have evaluated a text message service in the Infectious Diseases Institute (IDI) in Kampala, Uganda [95]. In this quasi-experimental study, appointment reminders were sent the day before the appointment, and adherence reminders were sent on days 2, 7, and 11 after the appointment. A total of 96% of the participants rated the messages as being helpful, and qualitative results also confirm these findings. However, data analysis has revealed that there was no statistically significant difference in the risk of LTFU between the intervention and control group. The lack of statistical significance may be due to the small sample size. Therefore, further studies with larger sample sizes are needed to further evaluate the program.

#### **6.3 eCompliance**

eCompliance is a biometric-based program, developed by Operation ASHA (OpASHA) [96], an Indian not-for-profit organization founded in 2006. The

**119**

*LTFU."*

**6.5 Social support programs**

*Loss to Follow-Up (LTFU) during Tuberculosis Treatment*

system is similar to mHealth in using text message alerts to inform the missed dose. However, the unique fingerprint verification system for the patient and the health worker takes mHealth to the next level. The OpASHA website explains the working

*"During each patient visit, the patient and healthcare worker simultaneously scan their finger in the system, the medication is dispensed, and the treatment is recorded in the system's database. If a patient misses a dose, an SMS message alert is sent to the patient, healthcare worker and supervisor. The healthcare worker is then responsible to meet the patient within 24–48 hours to administer and record the* 

This system can be used to reduce the risk of LTFU since the data from OpASHA

This claim by OpASHA has been put to test in Uganda by Snidal et al. in 2012 [97]. Community health workers (CHWs) were selected and trained to use the system. The intervention was conducted at the Millennium Villages Project (MVP) cluster in Ruhiira, Uganda. The patients were followed-up by CHWs until the end of the treatment period. The proportion of LTFU is surprisingly 0% in the intervention group, which is a significant reduction compared to the control group, yielding an excellent result. However, since this study suffers from a limited sample size, a large-scale interventional study is still necessary to confirm the results. Local adaptation to the software is available from OpASHA, and they should be incorporated into local national tuberculosis programs to lower the proportions of LTFU.

An innovative community-based intervention to improve TB treatment outcomes was conducted in Sidama zone, Ethiopia [98, 99]. The core health workers mainly responsible for delivering the intervention to the grass-root level were called the health extension workers (HEWs). The HEWs were trained and salaried female health workers from the respective intervention regions. Active case finding and sputum smear preparation were conducted by the HEWs. The supervisors process the smears and initiate anti-TB treatment. Again, HEWs provide treatment support which includes provision and monitoring of treatment. Evaluation of the program over 4.5 years revealed that the proportion of patients lost to follow-up decreased

*"We have thus demonstrated that bringing simple services that detect disease and provide treatment support close to where patients live is critical to increase access to TB diagnosis and treatment adherence and minimise the number of patients* 

Therefore, such community-based programs should be implemented in modified forms in different countries around the world to reduce the proportion of LTFU. Another important thing to note is that both this program and eCompliance mentioned above employed 'task shifting' toward basic health workers (CHWs and

In 2013, a novel social support program was developed in India by forming

HEWs) to support TB treatment at the grass-root level, not the experts.

stated that the LTFU rate is less than 4% using their system [96].

*DOI: http://dx.doi.org/10.5772/intechopen.81900*

mechanism of eCompliance as follows.

*treatment."*

**6.4 Community-based programs**

significantly up to 3% [99]. The authors concluded that

groups called "treatment support group (TSG)" [100].

*Healthcare Access - Regional Overviews*

Based on the factors associated with LTFU, Rodrigo et al. have developed a scoring instrument to predict the probability of LTFU (**Table 1**) [91]. According to their original paper, "Scores of 0, 1, 2, 3, 4 and 5 points were associated with a lost to follow-up probability of 2.2% 5.4% 9.9%, 16.4%, 15%, and 28%, respectively." Incorporating the instrument in the process of history taking could help the healthcare providers in identifying patients who have the potential to be LTFU. Further interventions should be carried out to prevent these patients from becoming LTFU. Similar scoring systems could be developed in different regions, since there are always country-specific variations.

Indeed, DOT is a part of the WHO-recommended 'Directly Observed Treatment Short Course' (DOTS) strategy. Although it cannot be denied that this strategy has saved the lives of millions of TB patients, the strategy itself is not flawless. Several authors have questioned the effectiveness of DOT as summarized in a review article by Otu [92]. The 2015 Cochrane systematic review and meta-analysis on DOT compared it with self-administered treatment, and the authors concluded that "TB cure and treatment completion were low with self-administered therapy in these trials, and direct observation did not substantially improve this" [93]. They called for complementary and alternative strategies in addition to DOT. Since DOT is a well-known and well-documented intervention in the field of TB, we felt that it need not be described in further detail in this chapter. Some interventions that have the potential to correct the weaknesses of DOT will be discussed below.

Recently, mHealth has emerged as a popular choice for health programs around

the world. The Global Observatory for eHealth (GOe) has defined mHealth as "medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices" [94]. Among these mHealth initiatives, appointment reminders and treatment compliance initiatives are of interest in reducing the rate of LTFU. However, there are limited interventional studies evaluating the effectiveness

In 2017, Hermans et al. have evaluated a text message service in the Infectious Diseases Institute (IDI) in Kampala, Uganda [95]. In this quasi-experimental study, appointment reminders were sent the day before the appointment, and adherence reminders were sent on days 2, 7, and 11 after the appointment. A total of 96% of the participants rated the messages as being helpful, and qualitative results also confirm these findings. However, data analysis has revealed that there was no statistically significant difference in the risk of LTFU between the intervention and control group. The lack of statistical significance may be due to the small sample size. Therefore, further studies with larger sample sizes are needed to further evaluate the program.

eCompliance is a biometric-based program, developed by Operation ASHA (OpASHA) [96], an Indian not-for-profit organization founded in 2006. The

of these interventions in reducing the risk of LTFU.

**5.6 Scoring instrument**

**6. Interventions**

**6.2 mHealth**

**6.1 Directly observed treatment (DOT)**

**118**

**6.3 eCompliance**

system is similar to mHealth in using text message alerts to inform the missed dose. However, the unique fingerprint verification system for the patient and the health worker takes mHealth to the next level. The OpASHA website explains the working mechanism of eCompliance as follows.

*"During each patient visit, the patient and healthcare worker simultaneously scan their finger in the system, the medication is dispensed, and the treatment is recorded in the system's database. If a patient misses a dose, an SMS message alert is sent to the patient, healthcare worker and supervisor. The healthcare worker is then responsible to meet the patient within 24–48 hours to administer and record the treatment."*

This system can be used to reduce the risk of LTFU since the data from OpASHA stated that the LTFU rate is less than 4% using their system [96].

This claim by OpASHA has been put to test in Uganda by Snidal et al. in 2012 [97]. Community health workers (CHWs) were selected and trained to use the system. The intervention was conducted at the Millennium Villages Project (MVP) cluster in Ruhiira, Uganda. The patients were followed-up by CHWs until the end of the treatment period. The proportion of LTFU is surprisingly 0% in the intervention group, which is a significant reduction compared to the control group, yielding an excellent result. However, since this study suffers from a limited sample size, a large-scale interventional study is still necessary to confirm the results. Local adaptation to the software is available from OpASHA, and they should be incorporated into local national tuberculosis programs to lower the proportions of LTFU.
