TB death audits, like maternal mortality model, can aid elimination

Key Highlights:

• India reduced TB mortality from 35 (2015) to 22 per 1,00,000, but daily deaths still range between 800–900.
• TB case fatality rate remains 5–10%, higher for drug-resistant TB.
• Proposal for TB death audits led by district collectors and independent health institutions.
• Tamil Nadu’s TN-KET model achieved a 30% reduction target in TB deaths through triaging and inpatient care.
• Sub-clinical TB accounts for 40–50% of cases, missed in symptom-based screenings.
• X-ray screening with AI and upfront molecular testing are being promoted for early diagnosis.
• Nutritional support shown to reduce secondary household TB cases by 50% in Jharkhand’s RATIONS trial.

Detailed Insights:

• TB continues to kill silently despite being treatable and preventable, largely affecting vulnerable and poor populations.
• Media and public health focus on TB deaths remains limited compared to other diseases like dengue.
• District-wise TB death audits can help identify avoidable deaths and strengthen local health systems.
• Tamil Nadu's TN-KET model captured health status data to prioritize care for severely ill patients.
• Many TB deaths occur in people with co-morbidities like malnutrition, diabetes, alcoholism, or anaemia.
• Symptom-only screening has led to under-detection; AI-supported portable X-rays have improved case detection by 10–12%.
• Upfront molecular testing helps detect both TB and drug resistance, enabling faster and more accurate treatment.
• Nutrition as intervention plays a pivotal role; higher caloric and protein intake acts as a natural protector.
• Government’s DBT support doubled to ₹1,000/month, but delivery in remote areas remains a challenge.
• Nikshay Mitra initiative relies on voluntary participation, leading to geographical inconsistencies in nutrition delivery.

Way Forward: 

• Institutionalize district-level TB death audits nationwide to identify avoidable deaths and service gaps.
• Involve district collectors and independent health institutions for accountability and transparency.
• Scale up AI-enabled X-ray screening and upfront molecular testing for early, accurate TB detection.
• Expand nutritional support schemes and ensure effective delivery, especially in remote and vulnerable areas.

Scientific/Technical Concepts Involved:

• Tuberculosis: TB is a bacterial infection caused by Mycobacterium tuberculosis. It primarily affects the lungs, but can also impact other organs like the kidneys, spine, and brain. 
• TB Death Audit: Systematic review of TB-related deaths to identify service gaps and improve response.
• Sub-clinical TB: Infection without symptoms, often missed by conventional screening methods.
• AI-Enabled X-ray Screening: Uses artificial intelligence to detect TB in radiographs, enhancing early diagnosis.
• Molecular Testing (CBNAAT/TrueNat): Detects TB bacteria and drug resistance simultaneously for better treatment planning.
• Nutritional Immunity: Strengthening immune response through adequate nutrition to combat infectious diseases.