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

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

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

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

The preference of women living with HIV for the HPV self-sampling of urine at a rural HIV clinic in Uganda

BACKGROUND: Women living with HIV have a double risk of acquiring cervical cancer (CC) due to repeated human papilloma virus (HPV) infections resulting from reduced immunity, with CC screening being low at 46.7%. OBJECTIVES: To determine the factors associated with the preference for HPV self-sampling using urine as well as establish its feasibility among women living with HIV attending a rural HIV clinic in Uganda. METHOD: A cross-sectional study design using quantitative data collection methods was used at the HIV clinic, Luweero District Hospital, among 426 women aged between 30 and 65 years. Data were analysed using descriptive statistics and modified Poisson regression. Urine samples were analysed using a Liferiver high-risk HPV genotyping real-time polymerase chain reaction (PCR) kit to determine the prevalence of the 15 HPV subtypes. Cervical intraepithelial neoplasia 2 (CIN2) was determined by visual inspection under acetic acid (VIA) using the nurse-led approach. RESULTS: Most women (296/426, 70%) preferred nurse-led screening. Preference for HPV self-sampling using urine was associated with older age (46–65 years) (adjusted prevalence risk ratios [aPRR] 1.59; 95% confidence interval [CI]: 1.13–2.24), history of sexually transmitted infections (aPRR 0.74: 95% CI: 0.55–0.98) and acquisition of CC information from the television (aPRR 1.48: 95% CI: 1.09–2.02). Approximately 97% (68/70) of women living with HIV tested HPV positive with one or more subtypes. The most prevalent subtype of HPV was HPV 58 (87.1%). Only one woman tested positive with VIA. CONCLUSION: Nurse-led CC screening is preferred among women living with HIV, and HPV self-sampling using urine is feasible at the HIV clinic. Therefore, educational programmes to reassure the masses about urine HPV self-sampling need to be designed. CONTRIBUTION: This study’s findings provide early insights into the merits and demerits of the current HPV sample collection approaches. Hence, HPV testing should be tailored to routine HIV care in rural communities