Development of a proactive case-finding model for early detection and treatment of new tuberculosis cases to reduce mortality in Lopburi Province, Thailand

Abstract

This study aimed to develop a proactive model for identifying new tuberculosis (TB) patients in the communities in Lopburi province, Thailand. This was a research and development study conducted in two phases. Phase 1 involved developing a proactive TB case-finding model through analysis of mortality data, assessment of hospital TB care standards, and identification of operational challenges. The sample included TB program personnel and related stakeholders. Findings were synthesized to design and develop the model, which was subsequently piloted in five selected areas. Phase 2 evaluated the effectiveness of the developed model and expanded its implementation province-wide. Data were analyzed using frequencies, percentages, means, and Z-test to compare the proportion of newly detected TB cases before and after implementation. Treatment registration outcomes were also compared with World Health Organization estimated targets. The developed model implemented a one-day proactive community-based case-finding approach using mobile digital chest X-ray units with artificial intelligence (AI)-assisted interpretation, integrated through multisectoral collaboration within and beyond the Ministry of Public Health. The provincial TB case detection rate increased significantly from 21.8% to 30.7%, while the treatment enrollment rate significantly increased from 17.0% to 63.7%. Recommendations include sustaining continuous community-based case finding, implementing systematic hospital-based screening in the medium term according to national standards, and strengthening the primary healthcare system in the long term to ensure timely access to medical and public health services, improve treatment outcomes, and reduce mortality.