The Advancing Lung Health in Zambia project aims to improve access to integrated screening, diagnosis, and management of tuberculosis (TB) and other priority respiratory diseases at community and primary healthcare levels. The project recognizes that many lung diseases present with similar symptoms, such as persistent cough, shortness of breath, chest pain, and difficulty breathing. Because of this overlap, focusing on a single disease can result in missed or delayed diagnoses for patients with other serious respiratory conditions. Guided by the World Health Organization's Practical Approach to Lung Health (PAL), the project promotes integrated assessment of people presenting with respiratory symptoms. The main conditions of interest include TB, post-TB lung disease (PTLD), pneumonia, silicosis, and chronic obstructive pulmonary disease (COPD). This approach ensures that patients receive comprehensive evaluation and appropriate care regardless of the underlying cause of their symptoms. A key feature of the project is the use of AI-enabled digital chest X-ray technology to support early and accurate detection of lung abnormalities. The technology assists healthcare workers in identifying signs of TB and other respiratory diseases, improving clinical decision-making and timely referral for further diagnosis and treatment. The project also strengthens healthcare worker capacity, expands access to diagnostic services, enhances community awareness, and improves referral and follow-up systems. In addition, it seeks to generate evidence on the burden and patterns of non-TB respiratory diseases in Zambia while documenting good practices and lessons learned from integrated lung health screening. The evidence generated will help inform policy, strengthen primary healthcare services, and support the scale-up of integrated lung health approaches, ultimately improving respiratory health outcomes and access to quality care across Zambia.
Study Type
OBSERVATIONAL
Enrollment
20,300
We conduct screening for TB and other respiratory diseases through established integrated screening points at community and primary healthcare facilities. All clients undergo both subjective and objective clinical assessment, including symptom screening, medical history review, and physical examination. Individuals are then referred for AI-enabled digital chest X-ray screening to support the detection of lung abnormalities and guide clinical decision-making. Based on the findings from the clinical assessment and chest X-ray, patients are assigned a working diagnosis, which may include tuberculosis (TB), post-TB lung disease (PTLD), pneumonia, chronic obstructive pulmonary disease (COPD), silicosis, or other respiratory conditions. Patients are subsequently investigated and managed according to standardized diagnostic and treatment pathways specific to their suspected condition.
Participants will undergo screening using an AI-enabled cough sound analysis tool. Cough sounds will be digitally recorded and analyzed by a machine-learning algorithm trained to identify acoustic patterns associated with pulmonary tuberculosis. The AI-generated screening result will be compared with GeneXpert results, the study reference standard, to determine the tool's diagnostic accuracy. The intervention seeks to evaluate the effectiveness, feasibility, acceptability, and cost implications of using cough sound artificial intelligence as a scalable TB screening strategy within integrated lung health services.
This is a nested substudy evaluating participants diagnosed with bacteriologically confirmed TB (with GeneXpert or smear microscopy) will undergo a structured assessment at treatment initiation and at the end of TB treatment to identify post-TB lung disease and related sequelae. Evaluations will include symptom assessment, chest radiography, laboratory investigations (CRP, HbA1c, and FBC), pulmonary function testing (peak flowmetry and spirometry), functional status assessment (sit-to-stand and 2-minute walk tests), and quality-of-life evaluation using the WHOQOL-BREF instrument.
Kafue District Hospital, Nangongwe Health Centre, Railway Clinic, Kafue Estates Clinic, and Chisankane Rural Health Centre
Kafue, Lusaka Province, Zambia
Number of Participants Diagnosed With Tuberculosis or Other Respiratory Diseases Through the Integrated Screening and Detection Model
Total number of participants diagnosed with tuberculosis, post-TB lung disease, silicosis, asthma, COPD, or other respiratory diseases through implementation of the Integrated Screening and Detection model.
Time frame: 18 Months
Number of Individuals Screened for Tuberculosis and Other Respiratory Diseases
Total number of individuals screened for tuberculosis and other respiratory diseases through the Integrated Screening and Detection (ISD) model.
Time frame: 18 months
Number of Presumptive Tuberculosis Cases Identified
Total number of individuals identified as having presumptive tuberculosis based on screening results through the ISD model.
Time frame: 18 months
Number of Individuals Evaluated for Tuberculosis
Total number of individuals who underwent diagnostic evaluation for tuberculosis following identification as presumptive TB cases.
Time frame: 18 Months
Number of All-Form Tuberculosis Cases Notified
Total number of tuberculosis cases, including bacteriologically confirmed and clinically diagnosed cases, notified through the ISD model.
Time frame: 18 months
Number of Bacteriologically Confirmed Tuberculosis Cases Diagnosed
Total number of tuberculosis cases confirmed by bacteriological testing among individuals evaluated through the ISD model.
Time frame: 18 Months
Number of Participants Diagnosed With Other Respiratory Diseases other than Tuberculosis
Total number of participants diagnosed with other respiratory diseases, including post-TB lung disease (PTLD), silicosis, asthma, and chronic obstructive pulmonary disease (COPD), through the ISD model.
Time frame: 18 Months
The Prevalence of Post-Tuberculosis Lung Disease (PTLD)
Proportion of individuals with bacteriologically confirmed TB completing TB treatment who meet the criteria for post-TB lung disease at the end of treatment. Assessment will include persistent respiratory symptoms, radiological abnormalities, lung function impairment, exercise limitation, and quality-of-life measures.
Time frame: From Enrollment at the start of TB treatment up to the end of treatment in 6 months
Diagnostic Accuracy of the AI-Enabled Cough Sound Screening Tool for Tuberculosis (Sub-Study)
Diagnostic accuracy of the AI-enabled cough sound screening tool for tuberculosis, measured by sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC), using GeneXpert as the reference standard.
Time frame: Each participant spends approximately 30 minutes to 2 hours participating in the evaluation.
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