Predictors of loss to follow-up of tuberculosis cases under the DOTS programme in Namibia

Kibuule, Dan; Aiases, Philomein; Ruswa, Nunurai; Rennie, Timothy; Verbeeck, Roger K.; Godman, Brian; Mubita, Mwangana

doi:10.1183/23120541.00030-2019

Cited by 13 publications

(12 citation statements)

References 14 publications

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“…The DOTS strategy was among the three (Stop TB and End TB) scaled-up essential strategies recommended by World Health Organization (WHO) to interrupt TB transmission and the period of infectiousness [12,13]. While the DOTS strategy has been globally adopted, TB patients still end up with poor treatment outcomes [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Tuberculosis poor treatment outcomes and its determinants in Kilifi County, Kenya: a retrospective cohort study from 2012 to 2019

et al. 2022

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Background Tuberculosis (TB) is one of the leading causes of deaths in Africa, monitoring its treatment outcome is essential to evaluate treatment effectiveness. The study aimed to evaluate proportion of poor TB treatment outcomes (PTO) and its determinants during six-months of treatment at Kilifi County, Kenya. Methods We conducted a retrospective analysis of data from the TB surveillance system (TIBU) in Kilifi County, Kenya from 2012 to 2019. The outcome of interest was PTO (lost-to-follow-up (LTFU), death, transferred out, treatment failure, drug resistance) or successful treatment (cured or completed treatment). We performed time-stratified (at three months follow-up) survival regression analyses accounting for sub-county heterogeneity to determine factors associated with PTO. Results We included 14,706 TB patients, their median (IQR) age was 37 (28–50) years and 8,791 (60%) were males. A total of 13,389 (91%) were on first line anti-TB treatment (2RHZE/4RH), 4,242 (29%) were HIV infected and 192 (1.3%) had other underlying medical conditions. During 78,882 person-months of follow-up, 2,408 (16%) patients had PTO: 1,074 (7.3%) deaths, 776 (5.3%) LTFU, 415 (2.8%) transferred out, 103 (0.7%) treatment failure and 30 (0.2%) multidrug resistance. The proportion of poor outcome increased from 7.9% in 2012 peaking at 2018 (22.8%) and slightly declining to 20% in 2019 (trend test P = 0.03). Over two-thirds 1,734 (72%) poor outcomes occurred within first three months of follow-up. In the first three months of TB treatment, overweight ((aHR 0.85 (95%CI 0.73–0.98), HIV infected not on ARVS (aHR 1.72 (95% CI 1.28–2.30)) and year of starting treatment were associated with PTO. However, in the last three months of treatment, elderly age ≥50 years (aHR 1.26 (95%CI 1.02–1.55), a retreatment patient (aHR 1.57 (95%CI 1.28–1.93), HIV infected not on ARVs (aHR 2.56 (95%CI 1.39–4.72), other underlying medical conditions (aHR 2.24 (95%CI 1.41–3.54)) and year of starting treatment were positively associated with PTO while being a female (aHR 0.83 (95%CI 0.70–0.97)) was negatively associated with PTO. Conclusions Over two-thirds of poor outcomes occur in the first three months of TB treatment, therefore greater efforts are needed during this phase. Interventions targeting HIV infected and other underlying medical conditions, the elderly and retreated patients provide an opportunity to improve TB treatment outcome.

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Section: Introductionmentioning

confidence: 99%

Tuberculosis poor treatment outcomes and its determinants in Kilifi County, Kenya: a retrospective cohort study from 2012 to 2019

et al. 2022

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“… 6 , 7 , 20 Fortunately, with the adjustment of treatment strategies in recent years, there are declining trends in loss to follow up (LTFU) and increasing trends in the success rate of TB treatment. 21 , 22 Kibuule et al reported that since the expansion of facility-based DOTS programme to community-based DOTS programme in Namibia in 2005, 23 the mean annual treatment success rate increased by approximately 20% when comparing the pre-intervention (1996–2005) and the post-intervention (2005–2015) periods.…”

Section: Discussionmentioning

confidence: 99%

Development of a nomogram for predicting treatment default under facility-based directly observed therapy short-course in a region with a high tuberculosis burden

Wang

2021

Therapeutic Advances in Infection

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Background: Poor adherence to tuberculosis (TB) treatment is a substantial barrier to global TB control. The aim of this study was to construct a nomogram for predicting the probability of TB treatment default. Methods: A total of 1185 TB patients who had received treatment between 2010 and 2011 in Peru were analyzed in this study. Patient demographics, social, and medical information were recorded. Predictors were selected by least absolute shrinkage and selection operator (LASSO) regression analysis, and a nomogram for predicting TB treatment default was constructed by using multivariable logistic regression analysis. Bootstrapping method was applied for internal validation. Calibration and clinical utility of the nomogram was also evaluated. Results: The incidence of TB treatment default among the study patients was 11.6% (138/1185). Six predictors (secondary education status, alcohol use, illegal drug use, body mass index, multidrug-resistant tuberculosis, and human immunodeficiency virus serostatus) were selected through the LASSO regression analysis. A nomogram was developed based on the six predictors and it yielded an area under the curve (AUC) value of 0.797 [95% confidence interval (CI), 0.755–0.839]. In the internal validation, the AUC achieved 0.805 (95% CI, 0.759–0.844). Additionally, the nomogram was well-calibrated, and it showed clinical utility in decision curve analysis. Conclusion: A nomogram was constructed that incorporates six characteristics of the TB patients, which provides a good reference for predicting TB treatment default.

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“…16,17 Thus, it is not surprising that limited utility of quality PIS data remains an important bottleneck to healthcare delivery in these settings in public healthcare. 18,19 Previously, the authors have described challenges with quality and utility of PIS data and indicators, as well as misalignment of PIS indicators with National Standard Treatment Guidelines (NSTGs) for Namibia implemented in 2011. [20][21][22] The utility of PIS data among focal persons at public health facilities in Namibia was estimated at 60.7% (target >80%).…”

Section: Introductionmentioning

confidence: 99%

“… 16 , 17 Thus, it is not surprising that limited utility of quality PIS data remains an important bottleneck to healthcare delivery in these settings in public healthcare. 18 , 19 …”

Section: Introductionmentioning

confidence: 99%

A model to strengthen utility of quality pharmaceutical health systems data in resource-limited settings

Kagoya

Kibuule

Rennie

et al. 2020

Medicine Access @ Point of Care

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Background: Limited utility of quality health data undermines efforts to strengthen healthcare delivery, particularly in resource-limited settings. Few studies model the effective utility of quality pharmaceutical information system (PIS) data in sub-Saharan Africa, typified with weak health systems. Aim: To develop a model and guidelines for strengthening utility of quality PIS data in public healthcare in Namibia, a resource-limited setting. Methods: A qualitative model based on Dickoff et al. practice-oriented theory, Chinn and Jacobs’ systematic approach to theory, and applied consensus techniques. Data from nationwide studies on quality and utility of PIS data in public healthcare conducted between 2018 and March 2020 informed the development of the model concepts. Pharmaceutical and public health systems experts validated the final model. Results: Overall, four preliminary national studies that recruited 58 PIS focal persons at 38 public health facilities and national level informed the development of four model concepts. The model describes concepts on access, management, dissemination, and utility of quality PIS data. Activities to implement the model in practice include grass-root integration of real-time automated pharmaceutical intelligence systems to collect, consolidate, monitor, and report PIS data. Strengthening coordination, human resources, and technical capacity through support supervisory systems at grass-root facilities are key activities. PIS focal persons at health facility and national level are agents to implement these activities among recipients, that is, healthcare professionals at points of care. Guidelines for implementation of the model at point of care are included. Experts described the model as clear, simple, comprehensive, and integration of pharmaceutical intelligence systems at point of care as novel and of importance to enhance utility of quality PIS data in resource-limited settings. Conclusion: While utility of quality PIS data is limited in Namibia, advantages of the model are encouraging, toward building resilient pharmaceutical intelligence systems at grass roots in resource-limited countries, where there are not only weak health systems, but high burden of misuse of medicines.

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Predictors of loss to follow-up of tuberculosis cases under the DOTS programme in Namibia

Cited by 13 publications

References 14 publications

Tuberculosis poor treatment outcomes and its determinants in Kilifi County, Kenya: a retrospective cohort study from 2012 to 2019

Tuberculosis poor treatment outcomes and its determinants in Kilifi County, Kenya: a retrospective cohort study from 2012 to 2019

Development of a nomogram for predicting treatment default under facility-based directly observed therapy short-course in a region with a high tuberculosis burden

A model to strengthen utility of quality pharmaceutical health systems data in resource-limited settings

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