Tuberculosis in China

A study in China aimed to estimate the prevalence of tuberculosis infection (TBI) and disease (TB) among close contacts of pulmonary tuberculosis (PTB) patients using an interferon-gamma release assay (IGRA). Conducted within a larger controlled trial, the study enrolled participants from three locations—Henan, Hunan, and Chongqing—between January 1, 2018, and August 31, 2020. It found a TB prevalence of 443.51 per 100,000 among close contacts and a TBI prevalence of 39.12%. Several risk factors were significantly associated with TBI, including increasing age (with odds ratios rising from 1.77 for those aged 35–44 to 2.74 for those aged 55–64), longer contact duration (OR 1.44 for 400–599 hours in three months), and sharing a bedroom with the index case (OR 1.39).[1]

The study highlights the importance of close contact tracing and early screening for TBI, particularly among high-risk groups such as older individuals and those with prolonged exposure to PTB patients. Additional risk factors, including higher BMI, smoking, and alcohol consumption, also showed positive trends with TBI. These findings provide crucial data for optimizing intervention strategies to prevent TB progression, emphasizing the need for targeted screening and preventive treatment in vulnerable populations.[1]

A meta-analysis of diverse study designs assesses TB control in China’s primary health care system, highlighting service effectiveness and barriers. Key findings: (1) TB tracing and referral rates were high in East China but lower in the West. (2) Migrant and MDR-TB patients had poorer screening, referral, and adherence rates. (3) TB stigma, especially in West China, hindered treatment uptake. (4) Workforce shortages, limited training, and weak coordination reduced service efficiency. (5) Digital health tools improved adherence but had limited reach among older and less tech-savvy groups. (6) Multimedia and community-based health education enhanced TB awareness and self-management. Despite improvements, regional disparities and systemic challenges persist, requiring targeted interventions.[2]

References:

1. Zhang, C., Liu, Y., Yao, Y., Gong, D., Lei, R., Xia, Y., Xu, C., Chen, H., Cheng, J. and Zhang, H., 2024. Tuberculosis infection among close contacts of patients with pulmonary tuberculosis in China: a population-based, multicentered study. Clinical Microbiology and Infection, 30(9), pp.1176-1182.

2. Chen, X., Zhou, J., Yuan, Q., Zhang, R., Huang, C. and Li, Y., 2024. Challenge of ending TB in China: tuberculosis control in primary healthcare sectors under integrated TB control model–a systematic review and meta-analysis. BMC Public Health, 24(1), p.163.

Multidrug-resistant tuberculosis in Taiwan

In a population-based study, 297 cases of MDR-TB accounted for 1.0% of the 30,193 TB cases reported from 2019 to 2022. Among these, 219 (73.7%) were male, and 78 (26.3%) were female, with a median age of 63 years (ranging from 50 to 76 years). Of the cases, 242 (81.5%) were new, 52 (17.5%) were previously treated, and 3 (1.0%) had an unknown treatment history.[1]

A maximum-likelihood phylogenetic tree was constructed based on 3,453 SNPs in the non-repetitive regions of the studied isolates, which included 45 (15.2%) pre-XDR and 6 (2.0%) XDR-TB cases. Lineage 2 isolates were predominant, comprising 43 (14.5%) sublineage 2.1 (proto-Beijing) and 145 (48.8%) sublineage 2.2 (modern Beijing) isolates. Using a 12-SNP cut-off, 25.3% (75/297) of MDR-TB cases were grouped into 20 clusters, with cluster sizes ranging from 2 to 13 cases.[1]

Univariate analysis revealed a correlation between MDR-TB clusters and male sex. Individuals carrying the sublineage 2.1 proto-Beijing genotype had a higher risk of transmitting the infection. We identified putative compensatory mutations in the rpoA, rpoC, or non-RRDR regions of the rpoB genes in 161 (54.2%) MDR isolates. The most predominant mutation was rpoC V483G (17/161, 10.6%), followed by rpoC A172V (14/161, 8.7%) and rpoC E750D (14/161, 8.7%).[1]

The putative compensatory mutation rpoC E750D was not previously reported and was found exclusively among sublineage 2.1 isolates. Additionally, 43.4% (129/297) of M. tuberculosis isolates harbored concurrent compensatory mutations with the rpoB S450L mutation. Notably, these mutations were significantly associated with clustering. Specifically, rpoC E750D in sublineage 2.1 and rpoC D485Y/rpoC E1140D in sublineage 2.2 were associated with MDR-TB transmission.[1] 

Another study examined the impact of new and repurposed anti-TB drugs on treatment outcomes in fluoroquinolone (FQ)-resistant multidrug-resistant tuberculosis (MDR-TB) patients. Among 109 FQ-resistant and 218 FQ-susceptible MDR-TB patients treated at the Taiwan MDR-TB Consortium (TMTC) between 2009 and 2019, FQ-resistant cases showed higher resistance to other TB drugs and were more common in females (p < 0.01). The use of new or repurposed drugs, particularly clofazimine, was more frequent in FQ-resistant MDR-TB (55.1%) than in FQ-susceptible cases (28.9%) (p < 0.01). Notably, patients treated with at least two such drugs had no treatment failure, significantly improving outcomes in FQ-resistant cases (p = 0.03).[2]

Despite these benefits, FQ-resistant MDR-TB patients had lower treatment success (71.6%) compared to FQ-susceptible patients (85.8%) (p < 0.01), and treatment failure was observed only in the FQ-resistant group (8.3%). Mortality was higher in FQ-resistant cases (19.3% vs. 13.8%), though most deaths were unrelated to TB. Multivariable analysis identified older age (≥65 years) and comorbidities as major predictors of poor outcomes. While the use of at least two new or repurposed drugs eliminated treatment failure in FQ-resistant MDR-TB, further research is needed to explore shorter treatment regimens for improving outcomes.[2]

References:

1. Liu, K.H., Xiao, Y.X. and Jou, R., 2024. Multidrug-resistant tuberculosis clusters and transmission in Taiwan: a population-based cohort study. Frontiers in Microbiology, 15, p.1439532. 

2. Huang, Y.W., Yu, M.C., Lin, C.B., Lee, J.J., Lin, C.J., Chien, S.T., Lee, C.H. and Chiang, C.Y., 2024. Mitigating treatment failure of pulmonary pre-extensively drug-resistant tuberculosis: The role of new and repurposed drugs. Journal of Microbiology, Immunology and Infection, 57(4), pp.617-628.

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.

TBC 054

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).

TBC 053

Tuberculosis in Brazil

A study analyzes tuberculosis incidence in Brazil from 2001 to 2022 and forecasts new cases until 2030, following the RECORD (Reporting of Studies Conducted Using Observational Routinely Collected Health Data) statement.

New tuberculosis cases recorded in the Notifiable Diseases Information System (Sinan) between January 2001 and December 2022 were included. Cases were defined as individuals diagnosed via sputum smear microscopy, culture, or clinical assessment based on epidemiological and complementary examinations, provided they had never received tuberculosis treatment.

Population estimates for 2001–2030 were obtained from the Brazilian Institute of Geography and Statistics (IBGE). Population projections and monthly tuberculosis cases were compiled into a dataset containing year, month, number of cases, and population size. Monthly incidence was calculated as cases per 100,000 inhabitants, ensuring a standardized metric over time. No data imputation was performed.

Among time series analysis methods, segmented regression was used to detect structural breaks, and the seasonal autoregressive integrated moving average (SARIMA) model was applied to account for seasonal patterns, trends, and non-seasonal variations. Exploratory analysis identified trends and seasonality, with segmented regression detecting inflection points in trendlines.

A total of 1,956,616 tuberculosis cases were included, with monthly incidence ranging from 4.92 cases per 100,000 inhabitants (May 2002) to 3.01 cases per 100,000 inhabitants (February–March 2015).

If current trends persist, tuberculosis incidence is projected to rise, reaching levels similar to the early 2000s by 2030. The SARIMA model effectively estimated incidence, predicting 124,245 new cases in 2030, averaging over 10,000 cases per month, with a projected monthly incidence of 4.64 cases per 100,000 inhabitants. 

References:

1. Silva, M.T. and Galvão, T.F., 2024. Tuberculosis incidence in Brazil: time series analysis between 2001 and 2021 and projection until 2030. Revista Brasileira de Epidemiologia, 27, p.e240027.

Non-communicable diseases in TB household contacts

A study using data from the Global Burden of Disease (GBD) 2019 analyzed the impact of high fasting plasma glucose (HFPG) on tuberculosis (TB) burden from 1990 to 2019. The research highlighted a global decline in TB mortality (ASMR) and disability burden (ASDR) due to HFPG, with the most significant reductions in high socio-demographic index (SDI) regions and minimal improvements in low SDI regions. The findings aim to inform government policies on resource allocation, promote early screening, and enhance glycemic control in high-risk populations.[3]

Regional and country-specific trends showed significant variation. East Asia and high-income regions experienced the greatest reductions in TB burden, while Southern Sub-Saharan Africa, Central Asia, and Eastern Europe saw increasing trends. Low SDI countries like the Central African Republic and Somalia had persistently high TB rates, whereas countries such as Hungary and Singapore saw the largest decreases. Economic development correlated strongly with TB burden reduction, demonstrating that higher SDI levels effectively mitigate HFPG-related TB risks.[3]

Demographic disparities were also notable. Males consistently had higher TB mortality and disability rates than females, with gender differences widening in low SDI regions. Older populations, particularly those aged 45 and above, experienced slower declines in TB burden, with the oldest age groups in high SDI regions showing the least improvement. Despite global progress, TB linked to HFPG remains a significant health challenge in economically disadvantaged areas, necessitating targeted interventions for older adults and vulnerable populations.[3]

A cross-sectional study conducted in Yangon, Myanmar, from April to December 2018 explored demographic, behavioral, and health characteristics of TB patients, their household contacts, and the general population. Most household contacts were female (68.9%), while TB patients were predominantly male (66.1%). Age distribution was similar across groups, though TB patients had fewer individuals over 65 years. Behavioral risk factors were more pronounced in TB patients, who had 2.5 times and 4.6 times higher odds of smoking and drinking, respectively, compared to the general population. Household contacts also showed increased odds for these behaviors (1.7 and 2.1 times, respectively).[1]

Nutritional and metabolic disparities were significant. TB patients were seven times more likely to be underweight and had markedly lower probabilities of being overweight or obese (62% and 92% lower, respectively). They also exhibited a 6.3 times higher likelihood of diabetes mellitus (DM), with odds reducing to 3.4 when BMI-mediated pathways were considered. Conversely, TB patients and their household contacts had lower odds of hypertension, with TB patients showing up to 71% lower odds when BMI pathways were accounted for. These findings emphasize the health and behavioral differences between TB patients, their household contacts, and the general population.[1]

Integrating non-communicable disease (NCD) screening, care, and prevention into TB contact tracing offers an efficient way to maximize resources and improve cost-effectiveness. Household contacts (HHCs) of TB patients often share risk factors that make them more susceptible to NCDs, such as diabetes mellitus (DM), compared to the general population. Incorporating NCD screening during TB contact investigations enables the identification of individuals who may be unaware of their conditions, allowing for timely intervention and management. Early detection and treatment of DM among TB contacts could also help reduce the incidence of TB.[2]

The high prevalence of NCDs observed among HHCs and even among neighborhood controls underscores the necessity for community-wide screening initiatives. Notably, a significant number of undiagnosed NCD cases, particularly DM, were identified among TB contacts and community members, demonstrating the potential of integrating NCD screening into TB contact tracing efforts. This approach not only addresses the dual burden of TB and NCDs but also provides an opportunity to improve health outcomes across the broader community.[2]

References:

[1] Zayar, N.N., Chotipanvithayakul, R., Bjertness, E., Htet, A.S., Geater, A.F. and Chongsuvivatwong, V., 2023. Vulnerability of NCDs and Mediating Effect of Risk Behaviors Among Tuberculosis Patients and Their Household Contacts Compared to the General Population in the Yangon Region, Myanmar. International Journal of General Medicine, pp.5909-5920.

[2] Hamada, Y., Lugendo, A., Ntshiqa, T., Kubeka, G., Lalashowi, J.M., Mwastaula, S., Ntshamane, K., Sabi, I., Wilson, S., Copas, A. and Velen, K., 2024. A pilot cross-sectional study of non-communicable diseases in TB household contacts. IJTLD OPEN, 1(4), pp.154-159.

[3] Bian, Q., Zhang, Y., Xue, C., Lu, W., Li, W., Pan, F. and Li, Y., 2024. Global and regional estimates of tuberculosis burden attributed to high fasting plasma glucose from 1990 to 2019: emphasis on earlier glycemic control. BMC Public Health, 24(1), p.782.

Tuberculosis in Sumatera

In a study in Aceh, Indonesia, researchers conducted a case-control study to identify risk factors contributing to tuberculosis (TB) prevalence and inform public health policy. Univariate analysis revealed that environmental factors such as insufficient light exposure (OR: 34.6), insufficient housing humidity (OR: 6.65), and high housing density (OR: 3.92) increased TB risk. Behavioral and social factors, including close contact with TB patients (OR: 18.52) and smoking (daily smokers OR: 6.04, previous smokers OR: 1.66), were also significant contributors. Additionally, comorbidities nearly tripled TB risk (OR: 2.99).

Multivariate analysis identified major TB risk factors, including insufficient light exposure (AOR: 77.69), close contact with TB patients (AOR: 25.39), poor TB knowledge (AOR: 24.2), comorbidities (AOR: 4.49), and negative preventive behaviors (AOR: 3.39). Protective factors included employment, higher income, and good nutrition. The model accounted for 76.7% of the variance in TB prevalence, demonstrating strong predictive power.

The study concluded that both individual (education, comorbidities, smoking, preventive behavior) and environmental (housing conditions, food security, TB exposure) factors significantly contribute to TB prevalence.

References:

1. Fahdhienie, F., Mudatsir, M., Abidin, T.F. and Nurjannah, N., 2024. Risk factors of pulmonary tuberculosis in Indonesia: a case-control study in a high disease prevalence region. Narra J, 4(2), p.e943.