Determinants of tuberculosis treatment interruption among patients in Vihiga County, Kenya

Lived experiences of persons on tuberculosis treatment in Nairobi County, Kenya: a mixed methods study

BACKGROUND

Tuberculosis program effectiveness is majorly measured by disease severity and treatment response without integrating patient perspectives. Yet, it's a critical dimension in clinical decision-making that enhances health worker-patient interactions and increases individuals' sustained engagement with treatment, thereby benefiting the people affected and the wider public by mitigating the infection risk. This study assessed the lived experiences of persons affected by tuberculosis who were on treatment in Nairobi County, Kenya.

METHODS

A cross-sectional study was conducted in May 2023 among 392 persons with drug-susceptible pulmonary tuberculosis in five facilities in Nairobi County. Participants were selected through simple random sampling and interviewed by semi-structured questionnaires and focused group discussions. Data on prevention and control strategies, facility preference, medication burden, interaction with healthcare workers, and the socio-economic effects of the disease were collected. Quantitative data was analyzed descriptively using frequencies, percentages, means, and standard deviations while qualitative data was transcribed, coded, and thematically analyzed.

RESULTS

The sample consisted of 245 males and 147 females aged between 3 and 74 years. Despite the high rating of their interactions with the healthcare workers, the findings show insufficient knowledge of the prevention and control strategies of TB. Additionally, food insecurity resulting from an inability to afford recommended meals, medication burden such as high pill burden especially where there are coexisting medical conditions, undesirable taste and size of the TB tablets, adverse drug events, economic burden due to loss of income, and stigma from the family and community were reported to affect treatment outcomes.

CONCLUSION

Treatment outcomes are influenced by multi-level factors such as low knowledge of TB prevention and control strategies, stigma, food insecurity, medication burdens like pill number, size, taste, and adverse drug reactions, facility preference, and economic hardships including loss of income. Understanding the individual needs of persons with TB will help develop interventions that are specific to them for better treatment outcomes.

Determinants of an unfavorable treatment outcome among tuberculosis patients in the Jimma Zone, Southwest Ethiopia

BACKGROUND

Tuberculosis (TB) is a major public health challenge in Ethiopia. TB treatment outcomes were suboptimal compared to the expected target of the national TB control Program. The provision of standard anti-TB treatment is the primary component of the directly observed treatment, short-course strategy. The aim of this study was to assess the TB treatment outcomes and the determinants of an unfavorable treatment outcome. The study used a cross-sectional study design at baseline and record review to identify treatment outcomes. A total of 1,161 TB patients were recruited from eight randomly selected districts and one town administration in the Jimma Zone, Ethiopia. Treatment outcomes were grouped into favorable and unfavorable. Of the total participants, 86.9% had a favorable treatment outcome, and 5.7% an unfavorable treatment outcome. The rest were transferred out and not recorded cases. Women were more likely to experience an unfavorable treatment outcome [adjusted odds ratio (AOR) = 1.96, 95% CI 1.06, 3.64]. Patients who were perceived to not be stigmatized were less likely to have an unfavorable treatment outcome (AOR = 0.32, 95% CI 0.15, 0.73). Patients who had a monthly income of > 3,500 Ethiopian birr were less likely to have an unfavorable outcome than patients who did not have a regular income (AOR = 0.04, 95% CI 0.01, 0.45). The observed treatment success rate is lower than the World Health Organization's target of successfully treating > 90% of detected TB cases. It is imperative to ensure that information, education and communication/behavior change communication strategies consider the needs of women and patients with perceived TB stigma. Furthermore, designing locally acceptable and affordable interventions may help to address the financial challenges of TB treatment adherence.

Incidence and predictors of treatment interruption among patients on anti-tuberculosis treatment in Nekemte public healthcare facilities, Oromia, Western Ethiopia

Introduction

Tuberculosis treatment interruption increases the risk of poor treatment outcomes and the occurrence of drug resistant Tuberculosis. However, data on the incidence and predictors of tuberculosis treatment interruption are still scarce in Ethiopia, as well as in the study area. Therefore, this study aimed to assess the incidence and predictors of treatment interruption among patients on tuberculosis treatment in Nekemte public healthcare facilities, Oromia region, Western Ethiopia, from July 1, 2017, to June 30, 2021.

Methods

A retrospective cohort study design was conducted among 800 patients enrolled in anti-tuberculosis treatment during the study period. Data were collected from patient cards who were enrolled in treatment from July 1, 2017 to June 30, 2021. Epidata version 3.2 was used for data entry, and STATA version 14 was used for analysis. A multivariable Cox regression model with a 95% confidence interval (CI) and adjusted hazard ratio (AHR) was used to identify the significant predictors at a p value < 0.05. Finally, the log likelihood ratio, and a Cox-Snell residual graph was used to check the adequacy of the model.

Results

A total of 800 patients were followed for a median time of 2.3 (95% CI: 2.20-2.36) months, and with a maximum follow-up time of 11.7 months. The overall incidence rate of treatment interruption was 27.4 per 1000 (95% CI: 22.8-32.8) person-month observations. Age 18-34 years (AHR = 1.8, 95% CI: 1.02-3.18), male (AHR = 1.63, 95% CI: 1.1-2.42), rural residence (AHR = 3, 95% CI: 1.98-4.64), presence of comorbidity (AHR = 10, 95% CI: 5.47-18.27) and lack of treatment supporters on the treatment follow-up (AHR = 2.82, 95% CI: 1.9-4.41) were found to be significant predictors of treatment interruption.

Conclusion

A high incidence rate of interruption was observed among TB patients in public health facilities in Nekemte town. Health facilities should provide supportive care for patients with co-morbidities and consider interventions that target middle-aged patients from rural areas that reduce treatment interruptions.

Development and validation of a prediction model for unsuccessful treatment outcomes in patients with multi-drug resistance tuberculosis

BACKGROUND

The World Health Organization has reported that the treatment success rate of multi-drug resistance tuberculosis is approximately 57% globally. Although new drugs such as bedaquiline and linezolid is likely improve the treatment outcome, there are other factors associated with unsuccessful treatment outcome. The factors associated with unsuccessful treatment outcomes have been widely examined, but only a few studies have developed prediction models. We aimed to develop and validate a simple clinical prediction model for unsuccessful treatment outcomes in patients with multi-drug resistance pulmonary tuberculosis (MDR-PTB).

METHODS

This retrospective cohort study was performed between January 2017 and December 2019 at a special hospital in Xi'an, China. A total of 446 patients with MDR-PTB were included. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were used to select prognostic factors for unsuccessful treatment outcomes. A nomogram was built based on four prognostic factors. Internal validation and leave-one-out cross-validation was used to assess the model.

RESULTS

Of the 446 patients with MDR-PTB, 32.9% (147/446) cases had unsuccessful treatment outcomes, and 67.1% had successful outcomes. After LASSO regression and multivariate logistic analyses, no health education, advanced age, being male, and larger extent lung involvement were identified as prognostic factors. These four prognostic factors were used to build the prediction nomograms. The area under the curve of the model was 0.757 (95%CI 0.711 to 0.804), and the concordance index (C-index) was 0.75. For the bootstrap sampling validation, the corrected C-index was 0.747. In the leave-one-out cross-validation, the C-index was 0.765. The slope of the calibration curve was 0.968, which was approximately 1.0. This indicated that the model was accurate in predicting unsuccessful treatment outcomes.

CONCLUSIONS

We built a predictive model and established a nomogram for unsuccessful treatment outcomes of multi-drug resistance pulmonary tuberculosis based on baseline characteristics. This predictive model showed good performance and could be used as a tool by clinicians to predict who among their patients will have an unsuccessful treatment outcome.