Characteristics and determinants of loss to follow-up among tuberculosis (TB) patients who smoke in an industrial state of Malaysia: a registry-based study of the years 2013-2017

Retreatment and Anti-tuberculosis Therapy Outcomes in Brazil Between 2015 and 2022: A Nationwide Study

Background

Adherence to anti-tuberculosis treatment (ATT) in Brazil remains a challenge in achieving the goals set by the World Health Organization (WHO). Patients who are lost to follow-up during treatment pose a significant public health problem. This study aimed to investigate the factors associated with unfavorable ATT outcomes among those undergoing retreatment in Brazil.

Methods

We conducted an observational study of patients aged ≥18 years with tuberculosis (TB) reported to the Brazilian National Notifiable Disease Information System between 2015 and 2022. Clinical and epidemiologic variables were compared between the study groups (new cases and retreatment). Regression models identified variables associated with unfavorable outcomes.

Results

Among 743 823 reported TB cases in the study period, 555 632 cases were eligible, consisting of 462 061 new cases and 93 571 undergoing retreatments (44 642 recurrent and 48 929 retreatments after loss to follow-up [RLTFU]). RLTFU (odds ratio [OR], 3.96 [95% confidence interval {CI}, 3.83-4.1]) was a significant risk factor for any type of unfavorable ATT. Furthermore, RLTFU (OR, 4.93 [95% CI, 4.76-5.11]) was the main risk factor for subsequent LTFU. For death, aside from advanced age, living with HIV (OR, 6.28 [95% CI, 6.03-6.54]) was the top risk factor.

Conclusions

Retreatment is a substantial risk factor for unfavorable ATT outcomes, especially after LTFU. The rates of treatment success in RLTFU are distant from the WHO End TB Strategy targets throughout Brazil. These findings underscore the need for targeted interventions to improve treatment adherence and outcomes in persons who experience RLTFU.

Enhancing tuberculosis treatment adherence and motivation through gamified real-time mobile app utilization: a single-arm intervention study

BACKGROUND

Finding innovative methods to enhance Tuberculosis treatment adherence in Malaysia is imperative, given the rising trend of non-adhere TB patients. Direct Observed Therapy (DOTS) has been used to ensure Tuberculosis (TB) drug compliance worldwide. However, due to its inconvenience, digitalizing this system into a virtual monitoring system via a mobile app can help deliver a more efficient tuberculosis management system. A gamified video-observed therapy is developed that connects three users the patient, supervisor, and administrator, allowing drug monitoring and patient loss to follow up with the patient tracking system. Thus, the objective of this study is to determine the impact of Gamified Real-time Video Observed Therapy (GRVOTS) mobile apps on patient medication adherence rates and motivation.

METHODS

71 patients from 18 facilities participated in the 8-week single-arm intervention study. GRVOTS mobile apps were installed in their mobile apps, and patients were expected to fulfill tasks such as providing Video Direct Observe Therapy (VDOTS) daily as well as side effect reporting. At 3-time intervals of baseline,1-month, and 2-month intervals, the number of VDOT taken, the Malaysian Medication Adherence Assessment Tool (MyMAAT), and the Intrinsic Motivation Inventory (IMI) questionnaire were collected. One-sample t-test was conducted comparing the VDOT video adherence to the standard rate of 80%. RM ANOVA was used to analyze any significant differences in MyMAAT and IMI scores across three-time intervals.

RESULTS

This study involved 71 numbers of patients from 18 healthcare facilities who showed a significantly higher treatment adherence score of 90.87% than a standard score of 80% with a mean difference of 10.87(95% CI: 7.29,14.46; p < 0.001). The participants' MyMAAT and IMI scores significantly increased over 3-time intervals with the IMI Interest domain showing the highest mean difference 19.76 (95% CI: 16.37, 21.152: p < 0.001).

CONCLUSIONS

By utilizing GRVOTS, a mobile application based on gamification and real-time features, we can enhance motivation and medication adherence among TB patients, while also addressing the limitations of physical DOTS.

TRIAL REGISTRATION

IRCT20230308057657N1, Registered on (15/03/23).

T-BACCO SCORE: A predictive scoring tool for tuberculosis (TB) loss to follow-up among TB smokers

INTRODUCTION

Loss to follow-up (LTFU) and smoking during TB treatment are major challenges for TB control programs. Smoking increases the severity and prolongs TB treatment duration, which lead to a higher rate of LTFU. We aim to develop a prognostic scoring tool to predict LTFU among TB patients who smoke to improve successful TB treatment outcomes.

MATERIALS AND METHODS

The development of the prognostic model utilized prospectively collected longitudinal data of adult TB patients who smoked in the state of Selangor between 2013 until 2017, which were obtained from the Malaysian Tuberculosis Information System (MyTB) database. Data were randomly split into development and internal validation cohorts. A simple prognostic score (T-BACCO SCORE) was constructed based on the regression coefficients of predictors in the final logistic model of the development cohort. Estimated missing data was 2.8% from the development cohort and was completely at random. Model discrimination was determined using c-statistics (AUCs), and calibration was based on the Hosmer and Lemeshow goodness of fit test and calibration plot.

RESULTS

The model highlights several variables with different T-BACCO SCORE values as predictors for LTFU among TB patients who smoke (e.g., age group, ethnicity, locality, nationality, educational level, monthly income level, employment status, TB case category, TB detection methods, X-ray categories, HIV status, and sputum status). The prognostic scores were categorized into three groups that predict the risk for LTFU: low-risk (<15 points), medium-risk (15 to 25 points) and high-risk (> 25 points). The model exhibited fair discrimination with a c-statistic of 0.681 (95% CI 0.627-0.710) and good calibration with a nonsignificant chi-square Hosmer‒Lemeshow's goodness of fit test χ2 = 4.893 and accompanying p value of 0.769.

CONCLUSION

Predicting LTFU among TB patients who smoke in the early phase of TB treatment is achievable using this simple T-BACCO SCORE. The applicability of the tool in clinical settings helps health care professionals manage TB smokers based on their risk scores. Further external validation should be carried out prior to use.