Improving Treatment Adherence with Integrated Patient Management for TB Patients in Morocco

Park, Seup; Moon, Narae; Oh, ByungKwon; Park, Miyeon; Kang, Kilho; Sentissi, Ilham; Bae, Sung Heui

doi:10.3390/ijerph18199991

Cited by 8 publications

(7 citation statements)

References 15 publications

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“…The introduction of telehealth reduced the rate of absenteeism for both video and in-person visits. In this small sample, it is a valuable tool for follow-up TBI and is preferred by patients living further away from the clinic 9 [ 19 ] This study aimed to examine TB management delivery models and outcomes from integrated patient management systems using patient-centered and community-based approaches, along with health technology Sample Population : A total of 3605 TB patients were enrolled in the program in five Moroccan prefectures (Rabat, Salé, Kénitra, Khemisset, Skhirat–Témara) from January 2018 to December 2019. Sample technique : A randomize sampling Retrospektif studi dengan tes chi-square Integrated patient management in the form of short message service (SMS) technology, and electronic pillboxes results in higher treatment success rates than existing programs in Morocco.…”

Section: Resultsmentioning

confidence: 99%

Telenursing Intervention for Pulmonary Tuberculosis Patients - A Scoping Review

Shalahuddin,

Pebrianti,

Eriyani

et al. 2024

JMDH

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Management of care for people with Pulmonary TB at home using Information and Communication Technology (ICT) tools and innovative approaches, such as tools for telecommunications online education. In the nursing sector, the use of telecommunications is called telenursing. The purpose of this study is to determine the types of telecommunications-based interventions, especially telenursing, in monitoring the level of treatment adherence in patients with pulmonary TB. This study used a scoping review method approach to determine the type of telecommunications-based interventions, especially telenursing, in monitoring the level of treatment adherence in patients with pulmonary TB. Literature distribution using several sources, namely EBSCOhost, Scopus (Elsevier), and PubMed. To facilitate obtaining appropriate literature, PICO techniques are used in conducting literature searches, P (population/problem/patient), I (intervention, prognostic factor, exposure), C (comparison, control), and O (outcome). Keywords used in English “Tuberculosis OR Pulmonary Tuberculosis AND Telehealth OR Telehealth Nursing AND Medication Adherence OR Medication Compliance OR Behavior”. In total from the 13 articles obtained, the articles used several telehealth to pulmonary TB patients with various implementations, such as the Directly Observed Therapy, Short Course (DOTS) (n = 7). These four implementations have the same goal and support adherence to taking medication and daily recovery in pulmonary TB patients. Overall characteristics of pulmonary TB patients in the reviewed articles are pediatric TB patients (0–14 years), adult TB (18–30 years). A total of 9 studies used a sample population of adult TB patients (18–30 years), TB undergoing Directly Observed Treatment Short-course (DOTS), TB receiving medical care and TB patients actively receiving treatment. The most influential effectiveness of telehealth in helping the treatment process of Pulmonary TB patients is Directly Observed Treatment Short-course (DOTS) because the main focus of the DOTS strategy is patient discovery and cure, priority is given to infectious TB patients without ruling out other types of TB.

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Section: Resultsmentioning

confidence: 99%

Telenursing Intervention for Pulmonary Tuberculosis Patients - A Scoping Review

Shalahuddin,

Pebrianti,

Eriyani

et al. 2024

JMDH

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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%

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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“…The copyright holder for this preprint this version posted December 6, 2022. ; https://doi.org/10.1101/2022.12.01.22283003 doi: medRxiv preprint have not adequately used machine learning (ML) as a method to generate predictors of treatment non-adherence. Yet, ML models have been proven effective in accurately illustrating the target parameters for implementing stakeholders to ensure adherence to TB treatment (32,33). Therefore, a machine learning approach, incorporating the comprehensive patient and clinical features as captured in the health facility register for modeling, could prove beneficial.…”

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

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

Gichuhi

Magumba

Kumar

et al. 2022

Preprint

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Despite the availability and implementation of well-known efficacious interventions for Tuberculosis (TB) prevention and treatment by the Ministry of Health, Uganda (MoH), treatment non-adherence persists. Moreover, identifying a specific patient at risk of non-adherence is still a challenge. Thus, this study set out to utilize machine learning modeling to explore individual risk factors predictive of tuberculosis treatment non-adherence in the Mukono district. This was a retrospective study based on a record review of 838 TB patients enrolled in six health facilities (3 government, 3 private-not-for-profit) in the Mukono district. We developed five machine learning algorithms (Logistic regression (LR), artificial neural networks (ANN), support vector machines (SVM), Random Forest (RF), and AdaBoost) to explore the individual risk factors for tuberculosis treatment non-adherence. Also, we evaluated their accuracy by computing the positive predictive value (PPV) and negative predictive value (NPV) through the aid of a confusion matrix. Of the five developed and evaluated models, SVM performed the best with an accuracy of 91.28 % compared to RF (89.97%), LR (88.30%), ANN (88.30%), and AdaBoost (91.05%) respectively. Individual risk factors predictive of non-adherence included; TB type, GeneXpert results, sub-country, ART status, contacts below 5 years, health facility ownership, sputum test results at 2 months, treatment supporter, CPT Dapson status, risk group, patient age, gender, middle and upper arm circumference, referral, positive sputum test at 5 months and 6 months. This study shows that classification machine learning techniques can identify patient factors predictive of treatment non-adherence and accurately differentiate between adherent and non-adherent patients. Thus, TB program management should consider adopting the machine learning techniques evaluated in this study as a screening tool for both identity and target-suited interventions for these patients.

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Improving Treatment Adherence with Integrated Patient Management for TB Patients in Morocco

Cited by 8 publications

References 15 publications

Telenursing Intervention for Pulmonary Tuberculosis Patients - A Scoping Review

Telenursing Intervention for Pulmonary Tuberculosis Patients - A Scoping Review

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

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

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