Factors influencing TB treatment interruption and treatment outcomes among patients in Kiambu County, 2016-2019

Kimani, E; Muhula, Samuel; Kiptai, T.; Orwa, James; Odero, Theresa; Gachuno, Onesmus

doi:10.1371/journal.pone.0248820

Cited by 18 publications

(26 citation statements)

References 11 publications

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“…About one in every ten TB patients interrupted their treatment, predominantly during intensive phase of treatment. The treatment interruption rate in our study is higher than national treatment interruption rate of 4.5% among new TB patients and 8.5% among retreatments [ 8 ] but lower compared to Kiambu county, Kenya (20.9%) [ 15 ], Ethiopia (21.21%) [ 23 ] and Mbarara hospital, Uganda (25%) [ 21 ]. This is consistent with a regional widespread problem.…”

Section: Discussionmentioning

confidence: 67%

“…This suggests the need for continuous health education and adherence counselling. Previous studies in Kenya [ 8 , 14 , 15 ] and other Ethiopia [ 17 ] have also demonstrated higher incidences of treatment interruption during intensive phase of treatment. Nevertheless, studies in Mbarara hospital, Uganda [ 21 ] and Gondar town Ethiopia [ 25 ] indicated that interruption of TB treatment occur during continuation phase.…”

Section: Discussionmentioning

confidence: 96%

“…Several studies [ 8 , 14 , 15 ] have associated TB treatment interruption with male gender, possible reasons being cited as males’ mobility and poor health seeking behaviour. Several other studies [ 13 , 18 , 21 , 25 , 29 ] have shown that there is no sex deference in TB treatment interruption.…”

Section: Discussionmentioning

confidence: 99%

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

Wekunda¹,

Aduda

Guyah

2021

PLoS ONE

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Background Despite robust Tuberculosis (TB) program with effective chemotherapy and high coverage, treatment interruption remains a serious problem. Interrupting TB treatment means that patients remain infectious for longer time and are at risk of developing drug resistance and death. This study was conducted to identify and describe predictors of TB treatment interruption. Methods A cohort of 291 notified TB patients from 20 selected health facilities in Vihiga County were enrolled in to the study and followed up until the end of treatment. Patient characteristics that potentially predict treatment interruption were recorded during treatment initiation using structured questionnaires. Patients who interrupted treatment were traced and reasons for stoppage of treatment recorded. Kaplan Meier method was used to estimate probabilities of treatment interruption by patient characteristics and determine time intervals. The Log rank test for the equality of survival distributions analyzed significance of survival differences among categorical variables. For multivariable analysis, Cox proportional hazard model, was fitted to identify predictors of TB treatment interruption through calculation of hazard ratios with 95% Confidence Intervals (CIs). For variable analysis, statistical significance was set at P ≤ 0.05. Reasons for treatment interruption were categorized according to most recurrent behavioral or experiential characteristics. Results Participants’ median age was 40 years (IQR = 32–53) and 72% were male. Of the 291 patients, 11% (n = 32) interrupted treatment. Incidences of treatment interruption significantly occurred during intensive phase of treatment. Independent predictors of treatment interruption included alcohol consumption (HR = 9.2, 95% CI; 2.6–32.5, p < 0.001), being female (HR = 5.01, 95% CI; 1.68–15.0, p = 0.004), having primary or lower education level (HR = 3.09, 95% CI; 1.13–8.49, p < 0.029) and having a treatment supporter (HR = 0.33, 95% CI; 0.14–0.76, p = 0.009). Reasons for interrupting treatment were categorized as: alcoholism, feeling better after treatment initiation, associated TB stigma, long distance to health facility, lack of food, perception of not having TB and pill burden. Conclusion TB treatment interruption was high and largely associated with patients’ socio-demographic and behavioral characteristics. These multidimensional factors suggest the need for interventions that not only target individual patients but also environment in which they live and receive healthcare services.

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Section: Discussionmentioning

confidence: 67%

Section: Discussionmentioning

confidence: 96%

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

Wekunda¹,

Aduda

Guyah

2021

PLoS ONE

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“…It is likely that patients on retreatment had poor outcomes because they developed drug resistant strains as reported elsewhere [36]. Also, studies show that patient's behaviour in uences unsuccessful treatment [37,38]; a study looking at factors associated with poor outcomes indicated that patients who get lost to follow-up who then have to be retreated for TB are often reluctant to uptake and tend to interrupt treatment [20]. Studies conducted elsewhere have also shown that TB treatment exacerbates as age advances because old age comes with increased age-related immunosuppressant comorbidities such as diabetes mellitus that increase adverse effects of anti-TB drugs, cause drug resistance, mortality, and increases recurrence of TB in this group [28,39,40].…”

Section: Discussionmentioning

confidence: 92%

Tuberculosis Poor Treatment Outcomes and its determinants in Kilifi County, Kenya: A Retrospective Cohort Study

Katana¹,

Ngari

Maina

et al. 2021

Preprint

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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. In this study, we aimed to evaluate proportion of poor TB treatment outcome (PTO) and its determinants during six-months of treatment in Kilifi County, Kenya. Methods:This study was 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 (cured or completed treatment) 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 14706 TB patients, median (IQR) age 37 (28–50) years old and 8791 (60%) males. A total of 13389 (91%) were on first line anti-TB treatment (2RHZE/4RH), 4242 (29%) were HIV infected and 192 (1.3%) had other underlying medical conditions. During 6586 person-years of follow-up, 2408 (16%) patients had PTO: 1074 (45%) deaths, 776 (32%) LTFU, 415 (17%) transferred out, 103 (4.3%) treatment failure and 30 (1.3%) 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 1734(72%), poor outcomes occurred within first three months of follow-up. In the first three months of starting TB treatment, HIV infection (aHR 1.72 (95% CI 1.28–2.30)) and year of starting treatment were positively associated with PTO. However, in the last three months of treatment, elderly age ≥50 years, a retreatment patient, HIV infection, 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.ConclusionsOver two-thirds of poor outcomes occur in the first three months of TB treatment, therefore greater efforts are needed during this phase. Intervention targeting the HIV infected and other underlying medical conditions, the elderly and retreated patients provide an opportunity to improve TB treatment outcome.

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“…Kenya is listed among the top 30 TB high burdened countries [7]. Kenya is also among 14 countries globally that suffer from the TB, TB-HIV co-infection, and Multi-Drug Resistant TB [8] triple burden. The TB prevalence for Kenya in 2015 was 233 per 100,000 population with a mortality of 20 per 100,000.…”

Section: Introductionmentioning

confidence: 99%

Application of ARIMA, hybrid ARIMA and Artificial Neural Network Models in predicting and forecasting tuberculosis incidences among children in Homa Bay and Turkana Counties, Kenya

Siamba

Argwings

Koech

2022

Preprint

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Background Tuberculosis (TB) infections among children (below 15 years) is a growing concern, particularly in resource-limited settings. However, the TB burden among children is relatively unknown in Kenya where two-thirds of estimated TB cases are undiagnosed annually. Very few studies have used Autoregressive Integrated Moving Average (ARIMA), hybrid ARIMA, and Artificial Neural Networks (ANNs) models to model infectious diseases globally. We applied ARIMA, hybrid ARIMA, and Artificial Neural Network models to predict and forecast TB incidences among children in Homa bay and Turkana Counties in Kenya. Methods The ARIMA, ANN, and hybrid models were used to predict and forecast monthly TB cases reported in the Treatment Information from Basic Unit (TIBU) system for Homa bay and Turkana Counties between 2012 and 2021. The data were split into training data, for model development, and testing data, for model validation using an 80:20 split ratio respectively. Results The hybrid ARIMA model (ARIMA-ANN) produced better predictive and forecast accuracy compared to the ARIMA (0,0,1,1,0,1,12) and NNAR (1,1,2) [12] models. Furthermore, using the Diebold-Mariano (DM) test, the predictive accuracy of NNAR (1,1,2) [12] versus ARIMA-ANN, and ARIMA-ANN versus ARIMA (0,0,1,1,0,1,12) models were significantly different, p<0.001, respectively. The 12-month forecasts showed a TB prevalence of 175 to 198 cases per 100,000 children in Homa bay and Turkana Counties in 2022. Conclusion The hybrid (ARIMA-ANN) model produces better predictive and forecast accuracy compared to the single ARIMA and ANN models. The findings show evidence that the prevalence of TB among children below 15 years in Homa bay and Turkana Counties is significantly under-reported and is potentially higher than the national average.

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Factors influencing TB treatment interruption and treatment outcomes among patients in Kiambu County, 2016-2019

Cited by 18 publications

References 11 publications

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

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

Tuberculosis Poor Treatment Outcomes and its determinants in Kilifi County, Kenya: A Retrospective Cohort Study

Application of ARIMA, hybrid ARIMA and Artificial Neural Network Models in predicting and forecasting tuberculosis incidences among children in Homa Bay and Turkana Counties, Kenya

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