Non-standard anti-tuberculosis regimens at the initial prescription

A retrospective observational study sought to evaluate the real-world use and clinical implications of non-standard initial tuberculosis (TB) treatment among patients diagnosed with drug-susceptible pulmonary TB. Specifically, the researchers aimed to determine how often non-standard regimens were prescribed, identify the risk factors influencing this treatment decision, and examine the resulting impact on clinical outcomes. The study population included patients aged 20 years or older who received anti-TB medications between January 2010 and December 2020 at a single hospital. Patients were categorized into two groups: those who received the standard intensive-phase regimen and those who did not, with the latter group referred to as the NSTB (non-standard TB) group.

To create a reliable comparison, patients in the NSTB group were matched 1:1 with patients in the STB (standard TB) group based on age, sex, and treatment year. The researchers found that non-standard regimens were relatively uncommon, prescribed in only 3.7% of cases. However, the analysis revealed specific pre-existing health conditions that significantly increased the likelihood of receiving a non-standard regimen. Liver disease emerged as the strongest predictor, with an adjusted odds ratio (aOR) of 12.79. Eye disease and gout or hyperuricemia were also significant independent risk factors, with aORs of 8.87 and 4.01, respectively.

Patients receiving non-standard regimens experienced less favorable treatment outcomes. The NSTB group had a significantly longer average treatment duration (281 days compared to 223 days) and a higher rate of treatment interruption (26% versus 8%). Additionally, the rate of loss to follow-up was notably higher in this group. These clinical challenges may be attributed in part to the omission of key drugs in the initial treatment regimens. The most frequently excluded medications in the NSTB group were pyrazinamide (60%) and ethambutol (34%), both of which are cornerstone components of standard TB therapy.

The findings highlight an important clinical dilemma: while non-standard regimens are sometimes necessary due to comorbidities that increase the risk of adverse drug reactions, they are linked with poorer treatment adherence and extended therapy durations. This raises concerns about the long-term effectiveness and management of TB in patients with complex health profiles.

In conclusion, this study underscores the need for tailored TB treatment strategies for patients with contraindications to standard medications. Identifying individuals at risk for non-standard regimen use allows for proactive care planning. Moreover, developing and validating alternative treatment options may help minimize adverse outcomes and improve overall TB control in these vulnerable patient populations.

Source: Chen, R.T., Liu, C.Y., Lin, S.Y., Shu, C.C. and Sheng, W.H., 2024. The prevalence, clinical reasoning and impact of non-standard anti-tuberculosis regimens at the initial prescription. Scientific Reports, 14(1), p.5631.

Application of the age-period-cohort model in tuberculosis

The age-period-cohort (APC) model, despite facing challenges like unidentifiability due to collinearity among its variables, offers a valuable lens for examining long-term trends in diseases such as tuberculosis (TB). While it has been widely used in analyzing diseases like influenza and HIV/AIDS, its full potential in TB research has not been extensively explored. This study aims to fill that gap by reviewing literature from multiple databases, summarizing how the APC model has been applied in TB research, and highlighting its capabilities and limitations in tracking disease patterns across different time dimensions.

The APC model works by dissecting the effects of age, period, and cohort—three intertwined but distinct time-related factors. “Age” captures individual physiological and social changes over life stages, “period” reflects events and trends at a specific point in time (like pandemics or policy changes), and “cohort” encapsulates the shared experiences of people born around the same time. This framework allows researchers to interpret fluctuations in disease incidence or mortality by isolating and analyzing these temporal components independently, giving more nuanced insight into epidemic trends.

Mathematically, the APC model is built on a generalized linear model framework where incidence or mortality is predicted using age, period, and cohort as independent variables. The model typically assumes a Poisson distribution for observed events and calculates relative risk (RR), net drift (average annual change), and local drift (age-specific trends). However, a major issue is the linear dependency among the three factors (period = age + cohort), which creates a collinearity problem and makes it difficult to uniquely estimate their effects. Over time, various methodological advances—like the intrinsic estimator (IE) and estimable function approaches—have been developed to overcome this limitation.

Compared to traditional trend analysis methods, the APC model excels at addressing confounding among variables and offers a more robust framework for analyzing disease dynamics across age, period, and cohort. This makes it particularly valuable in public health and social science for studying phenomena such as aging, economic shifts, or life-threatening diseases. However, practical issues like overlapping cohorts and interpretability of parameters persist. Despite these, improvements in modeling techniques and computational tools have made APC analyses more accessible and accurate, with notable applications in TB research emerging in recent years.

In conclusion, the APC model stands out as a powerful statistical tool to monitor, predict, and understand TB epidemics by parsing out the temporal components that influence disease patterns. As data collection and modeling strategies continue to improve, the model’s use in identifying high-risk groups and informing targeted interventions will become even more critical. Nonetheless, its results should be interpreted with caution and combined with contextual and qualitative research to guide policy decisions and public health responses effectively. 

Source: 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.

 

Isoniazid monoresistance in Taiwan

This study investigated the impact of isoniazid monoresistance on early treatment outcomes in patients with pulmonary tuberculosis (TB). Specifically, it aimed to determine whether resistance to isoniazid affects sputum culture conversion (SCC) and the likelihood of unfavourable outcomes within the first two months of treatment. Additionally, the researchers sought to identify which patient subgroups—especially those with isoniazid-resistant TB—might be at greater risk for poor outcomes, with the goal of informing more targeted interventions and closer monitoring strategies.

The researchers conducted a retrospective cohort study using data from Taiwan's CDC un-transitioned TB database, which was linked to national health insurance records for comprehensive patient information. The study included adults aged 20 years and older who were diagnosed with culture-positive pulmonary TB between 2008 and 2017 and received standard four-drug therapy for at least 14 days. Patients with resistance to other key drugs (rifampicin or ethambutol), prior exposure to second-line TB medications, those under 20 years old, or who died within the first month of treatment were excluded from the analysis.

In the main analysis of over 40,000 patients, isoniazid resistance—whether low-level or high-level—was not significantly associated with delays in SCC, persistent culture positivity after two months, early mortality, or other unfavourable outcomes. Key findings showed that 27.1% of patients did not achieve SCC by two months, 29.2% experienced an unfavourable outcome (death, loss to follow-up, or failure to convert), and 2.1% died within the first two months. Multivariable regression models consistently showed no significant impact of isoniazid resistance on these outcomes in the general cohort.

However, subgroup analyses revealed that isoniazid resistance may influence outcomes in certain populations. Among patients aged 20 to 65 and those without comorbidities, isoniazid resistance was associated with delayed SCC and higher odds of not achieving conversion within two months. These groups also had a modestly increased risk of experiencing unfavourable outcomes. More specifically, low-level isoniazid resistance was linked to delayed SCC in younger adults and higher early mortality in smear-positive patients, while high-level resistance was associated with delayed SCC in otherwise healthy individuals. Thus, while isoniazid monoresistance does not independently affect overall early treatment outcomes, certain subgroups may benefit from intensified care and closer follow-up.

Source: 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.

Air pollution and tuberculosis

A large-scale ecological time-series study was conducted to assess the impact of outdoor air pollution on the risk of pulmonary tuberculosis (PTB) in China. The study spanned from January 2014 to December 2019 and analyzed 172,160 PTB cases across 67 sites located in five provinces, representing various geographic regions of the country. This comprehensive dataset allowed researchers to investigate pollutant-specific and time-lagged effects on PTB incidence.

The results showed significant associations between several air pollutants and the risk of PTB. Sulfur dioxide (SO₂) had the most immediate effect, with a 1.97% increase in PTB risk per 10 μg/m³ at a lag of zero weeks. Nitrogen dioxide (NO₂) was associated with a 1.30% increase in risk per 10 μg/m³, also strongest at lag zero. Particulate matter had more delayed effects: PM₁₀ increased PTB risk by 0.55% per 10 μg/m³ at a lag of eight weeks, and PM₂.₅ by 0.59% per 10 μg/m³ at a lag of ten weeks. Carbon monoxide (CO) showed the largest effect, with a 5.80% increase in PTB risk per 1 mg/m³, peaking at a lag of fifteen weeks.

Subgroup analyses indicated that the pollution effects were generally consistent across sexes and age groups. However, the risk was notably higher during colder seasons, particularly in winter and autumn, suggesting a role of seasonal environmental factors in amplifying pollution-related PTB risk. Although there were no significant differences by demographic subgroup, the seasonal variations were statistically significant.

Sensitivity analyses confirmed the robustness of these associations even after controlling for other co-pollutants. Notably, the effect of carbon monoxide displayed a non-linear dose-response pattern, with risk increasing up to a concentration threshold of 2.3 mg/m³ before tapering off.

In conclusion, outdoor air pollution was found to be significantly associated with elevated PTB risk in China, with distinct lag patterns and seasonal effects across different pollutants. These findings emphasize the importance of air quality control as a potential public health strategy to reduce the burden of tuberculosis.

Source: 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.

Cost-effectiveness analysis of a prediction model for community-based screening of active tuberculosis

A study investigated whether a newly developed prediction model, transformed into a practical scoring system, could enhance the cost-effectiveness of active TB case finding in community settings compared to existing WHO symptom-based screening tools. The central question addressed the potential for improved TB detection and resource optimization using a data-driven, stratified approach rather than relying solely on broad symptom checklists.

To answer this, the researchers used a robust methodological design combining prediction model development and external validation with a cost-effectiveness analysis (CEA). The model was built using data from the ZAMSTAR trial in South Africa and validated using an independent dataset from Zambia. This separation between development and validation populations supports the model’s external validity. Stratifying analyses by HIV status was also appropriate, reflecting how TB symptoms can differ significantly between HIV-positive and HIV-negative individuals. Moreover, the inclusion of CEA is a strong match to the study objective, which centers on efficient use of limited public health resources.

Model performance was consistently better than existing WHO tools. Among HIV-positive individuals, the area under the curve (AUC) was significantly higher for the new model compared to the WHO-recommended four-symptom screen (W4SS), both in South Africa (0.652 vs. 0.568) and Zambia (0.778 vs. 0.725). Similar improvements were observed in HIV-negative or unknown-status populations, where the model outperformed standard symptom-based tools like “any TB symptom” or “prolonged cough.” These results suggest stronger predictive power and a higher potential to identify true TB cases.

Cost-effectiveness was also a key outcome. Across different cut-off strategies in the scoring system, 17 approaches consistently outperformed the WHO tools in both countries in terms of average cost-effectiveness ratio (ACER), which ranged from USD 246 to 1670 in South Africa and USD 164 to 7074 in Zambia. The model identified flexible screening thresholds that allowed programs to balance case detection efficiency with available budgets, making it a pragmatic solution for real-world implementation.

In conclusion, the study demonstrated that a tailored TB scoring system, based on predictive modeling and stratified by HIV status, is not only more accurate than current WHO symptom-based tools but also more cost-effective. Its practicality in field settings through simple score sheets enhances its implementation potential, especially in resource-limited areas. The findings support broader use of data-driven tools to optimize TB screening programs and improve public health outcomes.

Source: 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.

Integrated Disease Management

1. Diabetes and TB Risk & Progression

• Diabetes mellitus (DM) increases the risk of active TB, poor treatment outcomes, and higher relapse rates.
• Prediabetes (PDM) also raises TB susceptibility due to chronic low-grade inflammation and immune dysfunction.
• Even mild glucose imbalances can impair immunity, increasing TB risk, especially in high-burden regions.
• TB can induce temporary hyperglycemia, sometimes unmasking undiagnosed diabetes or worsening metabolic dysfunction.

2. Immune System Interactions & Inflammatory Response

• Chronic inflammation in DM and PDM alters immune responses, making individuals more prone to TB.
• Studies show mixed immune profiles in TB patients with PDM—some indicating heightened inflammation, others showing suppressed immunity.
• Mycobacterium tuberculosis infections in hyperglycemic states can further weaken immune defenses, promoting TB progression.
• Obesity appears to lower TB risk, potentially due to immune modulation, though the mechanisms remain unclear.

3. Public Health & Integrated Disease Management

• Screening for TB in diabetic patients and glucose monitoring in TB patients should be routine.
• TB patients with DM have worse treatment outcomes, including multidrug-resistant TB and lower survival rates.
• Integrated care models addressing both TB and DM can improve patient outcomes and support TB control efforts.
• Countries like Korea, with declining TB rates, face new challenges with aging populations, requiring targeted interventions.

References:

1. Abbas, U., Masood, K.I., Khan, A., Irfan, M., Saifullah, N., Jamil, B. and Hasan, Z., 2022. Tuberculosis and diabetes mellitus: Relating immune impact of co-morbidity with challenges in disease management in high burden countries. Journal of clinical tuberculosis and other mycobacterial diseases, 29, p.100343.
2. Byers, M.; Guy, E. The Complex Relationship Between Tuberculosis and Hyperglycemia. Diagnostics 2024, 14, 2539.
3. Lee P-H, Fu H, Lai T-C, Chiang C-Y, Chan C-C, Lin H-H (2016) Glycemic Control and the Risk of Tuberculosis: A Cohort Study. PLoS Med 13(8): e1002072.
4. Lee, P.H., Fu, H., Lee, M.R., Magee, M. and Lin, H.H., 2018. Tuberculosis and diabetes in low and moderate tuberculosis incidence countries. The International Journal of Tuberculosis and Lung Disease, 22(1), pp.7-16.
5. Salindri, A.D., Haw, J.S., Amere, G.A., Alese, J.T., Umpierrez, G.E. and Magee, M.J., 2021. Latent tuberculosis infection among patients with and without type-2 diabetes mellitus: results from a hospital case-control study in Atlanta. BMC Research Notes, 14(1), p.252.
6. Barron, M.M., Shaw, K.M., Bullard, K.M., Ali, M.K. and Magee, M.J., 2018. Diabetes is associated with increased prevalence of latent tuberculosis infection: Findings from the National Health and Nutrition Examination Survey, 2011–2012. Diabetes research and clinical practice, 139, pp.366-379.
7. Kang, Y.A., Kim, S.Y., Jo, K.W., Kim, H.J., Park, S.K., Kim, T.H., Kim, E.K., Lee, K.M., Lee, S.S., Park, J.S. and Koh, W.J., 2014. Impact of diabetes on treatment outcomes and long-term survival in multidrug-resistant tuberculosis. Respiration, 86(6), pp.472-478.
8. Jeong D, et al. Prevalence and associated factors of diabetes mellitus among patients with tuberculosis in South Korea from 2011 to 2018: a nationwide cohort study. BMJ Open 2023;13:e069642.
9. Min, J., Jeong, Y., Kim, H.W. and Kim, J.S., 2024. Tuberculosis notification and incidence: Republic of Korea, 2022. Tuberculosis and Respiratory Diseases, 87(3), p.411.

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Geo-Spatial Insights on TB

1. Geo-Spatial Mapping and TB Surveillance

• Geographic mapping of TB cases in Anambra State, Nigeria, helped identify high-burden areas for targeted interventions.
• Urban and peri-urban regions, particularly those with high population density, are major TB hotspots.
• Geo-spatial mapping, using low-cost tools like Google Earth, can enhance TB surveillance and guide early, targeted interventions in resource-limited settings. See also: Yoseph Samodra

2. TB and Diabetes Mellitus (DM) Co-Infection

• Studies in Uganda, Thailand, and other regions highlight the high prevalence of DM in TB patients.
• DM increases the risk of TB, leading to poorer outcomes like higher mortality, treatment complications, and a higher incidence of cavities and lung lesions in TB patients.
• DM-TB co-infection is more common in older individuals and those living in semi-urban areas, with TB-DM patients showing an altered immune response and increased susceptibility to severe disease.

3. Risk Factors and Predictors for MDR-TB and TB-DM

• Geo-spatial analysis aids in identifying MDR-TB hotspots, enabling earlier detection and improved management.
• Key risk factors for MDR-TB include age, history of TB treatment, and HIV status. In TB-DM patients, poor glycemic control and insulin resistance exacerbate susceptibility to MDR-TB.
• Age ≥40 and smoking history are significant predictors for TB-DM co-infection, and HIV co-infection may have a protective effect against DM in certain populations.

4. Challenges and Strategies in TB-DM Management

• Bi-directional screening for TB and DM is being implemented in high-burden countries like India and China, but challenges remain in cost-effectiveness, diagnostic sensitivity, and decentralized care systems.
• Managing TB-DM requires careful attention to drug interactions, as TB treatments (e.g., rifampicin) can reduce the efficacy of hypoglycemic drugs, while metformin remains a viable option.
• Monitoring and tailored treatment plans are critical for optimizing outcomes in TB-DM patients.

5. Immune Disruption and Diagnostic Challenges in TB-DM Patients

• Chronic hyperglycemia in DM impairs both innate and adaptive immunity, making individuals more susceptible to TB and increasing the likelihood of drug resistance.
• Immune dysfunction in TB-DM patients includes altered T-cell responses, macrophage dysfunction, and weakened ability to clear the Mycobacterium tuberculosis bacteria.
• Diagnostic tools like TST and IGRA have reduced sensitivity in TB-DM patients due to immune suppression, making early detection and accurate diagnosis more challenging.

References:

1. Ugwu, C.I., Chukwulobelu, U., Igboekwu, C., Emodi, N., Anumba, J.U., Ugwu, S.C., Ezeobi, C.L., Ibeziako, V. and Nwakaogor, G.U., 2021. Geo-spatial mapping of tuberculosis burden in Anambra State, South-East Nigeria. Journal of Tuberculosis Research, 9(01), p.51.
2. Lin, H., Shin, S., Blaya, J.A., Zhang, Z., Cegielski, P., Contreras, C., Asencios, L., Bonilla, C., Bayona, J., Paciorek, C.J. and Cohen, T., 2011. Assessing spatiotemporal patterns of multidrug-resistant and drug-sensitive tuberculosis in a South American setting. Epidemiology & Infection, 139(11), pp.1784-1793.
3. Lin HH, Shin SS, Contreras C, Asencios L, Paciorek CJ, Cohen T. Use of spatial information to predict multidrug resistance in tuberculosis patients, Peru. Emerg Infect Dis. 2012 May;18(5):811-3.
4. Kibirige, D., Andia-Biraro, I., Olum, R., Adakun, S., Zawedde-Muyanja, S., Sekaggya-Wiltshire, C. and Kimuli, I., 2024. Tuberculosis and diabetes mellitus comorbidity in an adult Ugandan population. BMC Infectious Diseases, 24(1), p.242.
5. Kibirige, D., Andia-Biraro, I., Kyazze, A.P., Olum, R., Bongomin, F., Nakavuma, R.M., Ssekamatte, P., Emoru, R., Nalubega, G., Chamba, N. and Kilonzo, K., 2023. Burden and associated phenotypic characteristics of tuberculosis infection in adult Africans with diabetes: a systematic review. Scientific Reports, 13(1), p.19894.
6. Buasroung, P., Petnak, T., Liwtanakitpipat, P. and Kiertiburanakul, S., 2022. Prevalence of diabetes mellitus in patients with tuberculosis: a prospective cohort study. International Journal of Infectious Diseases, 116, pp.374-379.
7. Bezerra, A.L., Moreira, A.D.S.R., Isidoro-Gonçalves, L., Lara, C.F.D.S., Amorim, G., Silva, E.C., Kritski, A.L. and Carvalho, A.C.C., 2022. Clinical, laboratory, and radiographic aspects of patients with pulmonary tuberculosis and dysglycemia and tuberculosis treatment outcomes. Jornal Brasileiro de Pneumologia, 48(06), p.e20210505.
8. Zheng, C., Hu, M. and Gao, F., 2017. Diabetes and pulmonary tuberculosis: a global overview with special focus on the situation in Asian countries with high TB-DM burden. Global health action, 10(1), p.1264702.
9. Al-Bari MAA, Peake N, Eid N. Tuberculosis-diabetes comorbidities: Mechanistic insights for clinical considerations and treatment challenges. World J Diabetes 2024; 15(5): 853-866.
10. Ye, Z., Li, L., Yang, L., Zhuang, L., Aspatwar, A., Wang, L. and Gong, W., 2024. Impact of diabetes mellitus on tuberculosis prevention, diagnosis, and treatment from an immunologic perspective. In Exploration (p. 20230138).

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