Care seeking and attitudes towards treatment compliance by newly enrolled tuberculosis patients in the district treatment programme in rural western Kenya: a qualitative study

<p>Abstract</p> <p>Background</p> <p>The two issues mostly affecting the success of tuberculosis (TB) control programmes are delay in presentation and non-adherence to treatment. It is important to understand the factors that contribute to these issues, particularly in resource limited settings, where rates of tuberculosis are high. The objective of this study is to assess health-seeking behaviour and health care experiences among persons with pulmonary tuberculosis, and identify the reasons patients might not complete their treatment.</p> <p>Methods</p> <p>We performed qualitative one-on-one in-depth interviews with pulmonary tuberculosis patients in nine health facilities in rural western Kenya. Thirty-one patients, 18 women and 13 men, participated in the study. All reside in an area of western Kenya with a Health and Demographic Surveillance System (HDSS). They had attended treatment for up to 4 weeks on scheduled TB clinic days in September and October 2005.</p> <p>The nine sites all provide diagnostic and treatment services. Eight of the facilities were public (3 hospitals and 5 health centres) and one was a mission health centre.</p> <p>Results</p> <p>Most patients initially self-treated with herbal remedies or drugs purchased from kiosks or pharmacies before seeking professional care. The reported time from initial symptoms to TB diagnosis ranged from 3 weeks to 9 years. Misinterpretation of early symptoms and financial constraints were the most common reasons reported for the delay.</p> <p>We also explored potential reasons that patients might discontinue their treatment before completing it. Reasons included being unaware of the duration of TB treatment, stopping treatment once symptoms subsided, and lack of family support.</p> <p>Conclusions</p> <p>This qualitative study highlighted important challenges to TB control in rural western Kenya, and provided useful information that was further validated in a quantitative study in the same area.</p

Machine learning to predict bacteriologic confirmation of Mycobacterium tuberculosis in infants and very young children.

Diagnosis of tuberculosis (TB) among young children (<5 years) is challenging due to the paucibacillary nature of clinical disease and clinical similarities to other childhood diseases. We used machine learning to develop accurate prediction models of microbial confirmation with simply defined and easily obtainable clinical, demographic, and radiologic factors. We evaluated eleven supervised machine learning models (using stepwise regression, regularized regression, decision tree, and support vector machine approaches) to predict microbial confirmation in young children (<5 years) using samples from invasive (reference-standard) or noninvasive procedure. Models were trained and tested using data from a large prospective cohort of young children with symptoms suggestive of TB in Kenya. Model performance was evaluated using areas under the receiver operating curve (AUROC) and precision-recall curve (AUPRC), accuracy metrics. (i.e., sensitivity, specificity), F-beta scores, Cohen's Kappa, and Matthew's Correlation Coefficient. Among 262 included children, 29 (11%) were microbially confirmed using any sampling technique. Models were accurate at predicting microbial confirmation in samples obtained from invasive procedures (AUROC range: 0.84-0.90) and from noninvasive procedures (AUROC range: 0.83-0.89). History of household contact with a confirmed case of TB, immunological evidence of TB infection, and a chest x-ray consistent with TB disease were consistently influential across models. Our results suggest machine learning can accurately predict microbial confirmation of M. tuberculosis in young children using simply defined features and increase the bacteriologic yield in diagnostic cohorts. These findings may facilitate clinical decision making and guide clinical research into novel biomarkers of TB disease in young children

Diagnostic Advances in Childhood Tuberculosis-Improving Specimen Collection and Yield of Microbiological Diagnosis for Intrathoracic Tuberculosis

There is no microbiological gold standard for childhood tuberculosis (TB) diagnosis. The paucibacillary nature of the disease, challenges in sample collection in young children, and the limitations of currently available microbiological tests restrict microbiological confirmation of intrathoracic TB to the minority of children. Recent WHO guidelines recommend the use of novel rapid molecular assays as initial diagnostic tests for TB and endorse alternative sample collection methods for children. However, the uptake of these tools in high-endemic settings remains low. In this review, we appraise historic and new microbiological tests and sample collection techniques that can be used for the diagnosis of intrathoracic TB in children. We explore challenges and possible ways to improve diagnostic yield despite limitations, and identify research gaps to address in order to improve the microbiological diagnosis of intrathoracic TB in children