TB Prevention Challenges

1. TB Infection Estimates and Changing Paradigms

• Traditional TB burden estimates suggest ~1.8 billion people infected globally, based on immune reactivity, assuming lifelong infection.
• Current tests (IGRA, TST) can't determine bacterial viability or differentiate recent from old infections.
• New research challenges the latent/active TB binary, suggesting TB exists on a spectrum and many may self-clear infection.
• More precise diagnostics are needed to identify those truly at risk of disease progression and improve cost-effectiveness of TB preventive treatment (TPT).

See also: Lin TB Lab

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2. Challenges and Gaps in TB Preventive Treatment (TPT)

• The TB prevention cascade (risk identification to treatment adherence) sees major drop-offs, with <20% completing all steps.
• Coverage among HIV-positive individuals is better but still suboptimal; even lower among other groups like migrants.
• Global TPT targets (e.g., 90% for PLHIV, 24M contacts by 2022) have not been met; new 2027 UN goals aim to reach 45M people, requiring intensified efforts.
• Community-based testing, digital tools, patient incentives, and improved contact tracing are promising strategies for boosting coverage and completion.

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3. Evolving TPT Regimens and Future Innovations

• Traditional isoniazid preventive therapy (IPT) is limited by side effects and long duration.
• Shorter rifamycin-based regimens (3HR, 3HP, 1HP) improve adherence and safety but may still face cost/adverse event issues.
• Pediatric-friendly formulations are in development.
• A future pan-TPT regimen (e.g., single-dose, slow-release) could transform prevention, similar to vaccines or mass deworming campaigns.

See also: Yoseph Samodra

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4. Data-Driven Screening and Risk Prediction Tools

• Community Scoring Model: A new predictive model outperforms WHO symptom-based TB screening tools, especially when stratified by HIV status; it improves detection and cost-effectiveness in real-world settings.
• Administrative Risk Model (Canada): A validated tool based on health records identifies high-risk individuals, particularly migrants, but needs further calibration for certain subgroups (e.g., elderly, refugees, HIV+).
• These tools support more targeted screening and resource use, enhancing early intervention.

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5. Clinical Predictors of TB Mortality in the Elderly

• A competing-risk model in China found age (≥85), retreatment, cavities, hypoalbuminemia, and elevated CRP as strong predictors of TB-specific mortality in older adults.
• Developed a nomogram with high predictive accuracy to support personalized treatment planning for elderly TB patients.

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6. Isoniazid Monoresistance and Early Treatment Outcomes

• Large Taiwanese cohort showed isoniazid resistance does not broadly impact early treatment outcomes.
• However, younger adults and patients without comorbidities may face delayed culture conversion and slightly worse outcomes.
• These subgroups may need closer monitoring and tailored care despite overall neutral findings.

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7. Environmental Risk Factor: Air Pollution and TB

• A large Chinese study linked outdoor air pollutants—especially CO, SO₂, NO₂, PM₁₀, and PM₂.₅—to increased PTB risk, with pollutant-specific lag effects.
• Stronger impacts were seen during colder seasons.
• Highlights air quality control as a potential strategy in TB prevention.

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8. Long-Term Trend Analysis with Age-Period-Cohort (APC) Models

• APC models help disentangle the roles of age, period, and cohort in TB incidence trends.
• Despite methodological challenges, they provide valuable insights into shifting epidemiology and help identify high-risk groups.
• Growing use in TB research supports better-targeted public health strategies when paired with contextual understanding.

References:

1. Matteelli, A., Churchyard, G., Cirillo, D., den Boon, S., Falzon, D., Hamada, Y., Houben, R.M., Kanchar, A., Kritski, A., Kumar, B. and Miller, C., 2024. Optimizing the cascade of prevention to protect people from tuberculosis: A potential game changer for reducing global tuberculosis incidence. PLOS Global Public Health, 4(7), p.e0003306.
2. Yang, C.C., Shih, Y.J., Ayles, H., Godfrey-Faussett, P., Claassens, M. and Lin, H.H., 2024. Cost-effectiveness analysis of a prediction model for community-based screening of active tuberculosis. Journal of Global Health, 14, p.04226.
3. Li, Z., Liu, Q., Chen, L., Zhou, L., Qi, W., Wang, C., Zhang, Y., Tao, B., Zhu, L., Martinez, L. and Lu, W., 2024. Ambient air pollution contributed to pulmonary tuberculosis in China. Emerging Microbes & Infections, 13(1), p.2399275.
4. Puyat, J.H., Brode, S.K., Shulha, H., Romanowski, K., Menzies, D., Benedetti, A., Duchen, R., Huang, A., Fang, J., Macdonald, L. and Marras, T.K., 2025. Predicting Risk of Tuberculosis (TB) Disease in People Who Migrate to a Low-TB Incidence Country: Development and Validation of a Multivariable, Dynamic Risk-Prediction Model Using Health Administrative Data. Clinical Infectious Diseases, 80(3), pp.644-652.
5. Wang, S., Gu, R., Ren, P., Chen, Y., Wu, D. and Li, L., 2025. Prediction of tuberculosis-specific mortality for older adult patients with pulmonary tuberculosis. Frontiers in Public Health, 12, p.1515867.
6. Lee, M.R., Keng, L.T., Lee, M.C., Chen, J.H., Lee, C.H. and Wang, J.Y., 2024. Impact of isoniazid monoresistance on overall and vulnerable patient populations in Taiwan. Emerging Microbes & Infections, 13(1), p.2417855.
7. Luo, D., Wang, F., Chen, S., Zhang, Y., Wang, W., Wu, Q., Ling, Y., Zhou, Y., Li, Y., Liu, K. and Chen, B., 2025. Application of the age-period-cohort model in tuberculosis. Frontiers in Public Health, 13, p.1486946.

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