Determinants of treatment failure among tuberculosis patients in Kandahar City, Afghanistan: A 5-year retrospective cohort study

Rahimi, Bilal Ahmad; Rahimy, Najeebullah; Mukaka, Mavuto; Ahmadi, Qudratullah; Hayat, Mohammad Sami; Wasiq, Abdul Wahed

doi:10.4103/ijmy.ijmy_142_19

Cited by 6 publications

(5 citation statements)

References 40 publications

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“…TB transmission may continue in neighborhoods with high TB incidences, such as multigenerational households and homeless populations. [26][27][28][29] Furthermore, the decline in the CFR rate is a good indicator of the assessment of TB management programs and strategies in East Java Province during the pandemic. The decline in CFR in East Java is based on H.L Blum's theory that health status is determined by 40% environmental, 30% behavioral, 20% health services, and 10% genetic factors.…”

Section: Discussionmentioning

confidence: 99%

An Analysis of The Trend of Incidences and Fatality of Pulmonary Tuberculosis in East Java from 2015-2020: A Lesson From COVID-19

Setyawan

Mertaniasih

Utomo

et al. 2023

Natl J Community Med

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Background: Pulmonary tuberculosis is still a public health problem, and surveillance data analysis has not been done much. Recently a global pandemic of COVID-19 has the potential in disturbing TB elimination programs and treatment. This study aims to comprehensively analyse the incidence rate (IR) and Case Fatality Rate (CFR) of pulmonary tuberculosis in East Java from 2015–2020 and during COVID-19 and the strategies for optimizing tuberculosis disease control. Methodology: The study analyzed annual surveillance data using an analytical descriptive design. The Variables were analyzed with Spearman correlation with a level of evidence of 95% (p<0.05). Results: The prevalence of pulmonary tuberculosis in East Java fluctuated from 2015–2020. In 2020 and during the COVID-19 pandemic, the number of cases and morbidity rates increased. Statistic results confirm the presence of a significant correlation between the values of Incidence rate (IR) and Case Fatality rate (CFR) (p = 0.032), IR and Treatment Success Rate (TSR) (p = 0.020), and CFR and TSR (p = 0.002). Population density is not correlated with the number of new cases (p = 0.667). Treatment rates have increased to 51%; cure and treatment rates have decreased to 76% and 89%, respectively, and there was a 4% increase in mortality during COVID-19. Conclusions: COVID-19 has tremendously affected the treatment of pulmonary TB cases in East Java, Indonesia by increasing the incidence rate and decreasing the fatality rate. The pandemic promotes fear in the community to check their medical status and improve the quality of their health in East Java.

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Section: Discussionmentioning

confidence: 99%

An Analysis of The Trend of Incidences and Fatality of Pulmonary Tuberculosis in East Java from 2015-2020: A Lesson From COVID-19

Setyawan

Mertaniasih

Utomo

et al. 2023

Natl J Community Med

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“…Tuberculosis, due to its infectious nature, increasing antimicrobial resistance and prevalence in low-and-middle-income (LMIC) countries, for example, India [28], Pakistan [29] Afghanistan [30], Morocco [13,31], Kenya [32,33], and Uganda [3,[34][35][36][37] has been widely studied. Characteristically, these previous studies gathered demographic and medical histories of a cohort and observed their adherence and outcomes.…”

Section: Previous Machine Learning Related Work In Tuberculosismentioning

confidence: 99%

“…Characteristically, these previous studies gathered demographic and medical histories of a cohort and observed their adherence and outcomes. The researchers then retrospectively applied machine learning algorithms e.g., logistic regression [ 13 , 29 , 30 ], support vector machine [ 29 ], and random forest [ 29 ], to determine variables predictive of treatment failure or non-adherence.…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

Gichuhi

Magumba

Kumar

et al. 2023

PLOS Glob Public Health

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Despite the availability and implementation of well-known efficacious interventions for tuberculosis treatment by the Ministry of Health, Uganda (MoH), treatment non-adherence persists. Moreover, identifying a specific tuberculosis patient at risk of treatment non-adherence is still a challenge. Thus, this retrospective study, based on a record review of 838 tuberculosis patients enrolled in six health facilities, presents, and discusses a machine learning approach to explore the individual risk factors predictive of tuberculosis treatment non-adherence in the Mukono district, Uganda. Five classification machine learning algorithms, logistic regression (LR), artificial neural networks (ANN), support vector machines (SVM), random forest (RF), and AdaBoost were trained, and evaluated by computing their accuracy, F1 score, precision, recall, and the area under the receiver operating curve (AUC) through the aid of a confusion matrix. Of the five developed and evaluated algorithms, SVM (91.28%) had the highest accuracy (AdaBoost, 91.05% performed better than SVM when AUC is considered as evaluation parameter). Looking at all five evaluation parameters globally, AdaBoost is quite on par with SVM. Individual risk factors predictive of non-adherence included tuberculosis type, GeneXpert results, sub-country, antiretroviral status, contacts below 5 years, health facility ownership, sputum test results at 2 months, treatment supporter, cotrimoxazole preventive therapy (CPT) dapsone status, risk group, patient age, gender, middle and upper arm circumference, referral, positive sputum test at 5 and 6 months. Therefore, machine learning techniques, specifically classification types, can identify patient factors predictive of treatment non-adherence and accurately differentiate between adherent and non-adherent patients. Thus, tuberculosis program management should consider adopting the classification machine learning techniques evaluated in this study as a screening tool for identifying and targeting suited interventions to these patients.

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“…In a study in Ethiopia, known risk factors for treatment failure were listed as follows: age over 55 years, male gender, re-treatment, and distance to treatment center [9] . In another study in Afghanistan, determinants that may cause treatment failure were shown to be re-treatment, absence of fever, absence of cough, living in rural areas, and no weight loss [10] . Treatment outcome is one of the best measures to explain how current TB regimens work [10] .…”

Section: Introductionmentioning

confidence: 97%

“…In another study in Afghanistan, determinants that may cause treatment failure were shown to be re-treatment, absence of fever, absence of cough, living in rural areas, and no weight loss [10] . Treatment outcome is one of the best measures to explain how current TB regimens work [10] . Khuzestan is in the third place of TB infection after Sistan and Baluchistan and Golestan provinces [11] .…”

Section: Introductionmentioning

confidence: 97%

Risk Factors of Tuberculosis Treatment Failure among Tuberculosis Patients in Khuzestan Province: A Retrospective Cohort Study

Mousavi

Fararouei

Rashidi

et al. 2021

IEM

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Background: Tuberculosis (TB) is an infectious and communicable disease and one of the top ten causes of death throughout the world. Monitoring and evaluating TB treatment outcomes provides the required data for taking the necessary measures to control TB. Thus, this study was carried out to find determinants of treatment failure among patients with smear-positive pulmonary TB in Khuzestan province during 2006-2014 Material and Methods: This retrospective cohort study was conducted over a 9-year period in Khuzestan province. Predictors of treatment failure were analyzed using multivariate logistic regression Findings: Among 5342 patients, the cumulative incidence of unsuccessful TB treatment was 1.85%. More than half of TB patients (59.2%) enrolled in this study were male, and most of them were living in urban areas (79.8%). Significant predictors of treatment failure were age (p=•001), weight (p= •039), number of delayed days in diagnosis (p=•01), isoniazid resistance (p≤•001), and number of bacilli in patients` sputum at the beginning of treatment (p≤•001). Conclusion:In this study, the rate of successful treatment was quite high; nevertheless, new cases of treatment failure could be prevented with special efforts such as prompt diagnosis and precise follow-up under Direct Observation Treatment Short course (DOTS) strategy.

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Determinants of treatment failure among tuberculosis patients in Kandahar City, Afghanistan: A 5-year retrospective cohort study

Cited by 6 publications

References 40 publications

An Analysis of The Trend of Incidences and Fatality of Pulmonary Tuberculosis in East Java from 2015-2020: A Lesson From COVID-19

An Analysis of The Trend of Incidences and Fatality of Pulmonary Tuberculosis in East Java from 2015-2020: A Lesson From COVID-19

A machine learning approach to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

Risk Factors of Tuberculosis Treatment Failure among Tuberculosis Patients in Khuzestan Province: A Retrospective Cohort Study

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