Alcohol consumption and risk of developing tuberculosis

In individuals with type 2 diabetes mellitus (T2DM), heavy alcohol consumption was associated with an increased risk of developing tuberculosis (TB). Conversely, mild-to-moderate alcohol consumption was linked to a decreased risk of TB development. Notably, an alcohol intake of 20 grams per day or more was identified as a threshold beyond which the risk of TB substantially increased. Interestingly, even mild-to-moderate alcohol consumption was associated with an increased TB risk among current smokers, highlighting the complex interaction between alcohol intake and smoking status.

Specifically, mild-to-moderate alcohol consumption was associated with a lower overall risk of TB development, with an adjusted hazard ratio (aHR) of 0.92 (95% confidence interval [CI] 0.89–0.96). In contrast, heavy alcohol consumption was linked to a higher risk of TB, with an aHR of 1.21 (95% CI 1.16–1.27). Among all alcohol drinkers, those consuming less than 5 grams of alcohol per day exhibited the lowest risk of TB development (aHR 0.85, 95% CI 0.81–0.90), suggesting a potential protective effect at very low levels of consumption.

When stratifying by age, the protective association of mild-to-moderate alcohol consumption was observed only in individuals aged 65 years and older (aHR 0.85, 95% CI 0.81–0.89). No significant protective effect was found among individuals younger than 65 years. In analyses stratified by smoking status, mild-to-moderate alcohol consumption reduced the risk of TB among noncurrent smokers (aHR 0.85, 95% CI 0.81–0.89). However, among current smokers, even mild-to-moderate drinking increased the risk of TB (aHR 1.08, 95% CI 1.01–1.14). Heavy drinkers who were also current smokers faced an even greater risk of TB development (aHR 1.36, 95% CI 1.27–1.46).

Overall, these findings suggest that heavy alcohol consumption significantly increases the risk of TB in individuals with T2DM. Mild-to-moderate drinking may have a protective effect, particularly among older adults and nonsmokers. However, even low levels of alcohol intake could elevate TB risk among current smokers. Importantly, a daily alcohol consumption threshold of 20 grams was identified as critical for a notable increase in TB risk.

Source: Chung, C., Lee, K.N., Han, K., Park, J., Shin, D.W. and Lee, S.W., 2024. Association between alcohol consumption and risk of developing tuberculosis in patients with diabetes: a nationwide retrospective cohort study. Respiratory Research, 25(1), p.420.

Key Determinants of Tuberculosis Prevention Behaviors

A study sought to answer the research question: What factors support or inhibit tuberculosis (TB) prevention behavior among families with a history of TB? Recognizing the role of the family as a foundational social unit, the research aimed to explore key behavioral drivers and barriers to TB prevention at the household level.

To investigate this, the researchers employed a cross-sectional study design, conducted over a three-month period from October to December 2024. The study was situated in the working area of the Batunadua Health Center in Padangsidimpuan City. Using purposive sampling, 129 families were selected based on specific inclusion criteria: having a family member with a history of TB, residing in the area for at least one year, and a willingness to participate in the study.

The findings revealed strong associations between several variables and TB prevention behavior. Notably, families with less knowledge about TB exhibited destructive prevention behaviors at a rate of 88.9%, in contrast to only 4.2% among families with good knowledge. This relationship was statistically significant (X² = 88.454, p = 0.001). Similarly, 82% of families who reported poor access to health services showed destructive behaviors, compared to just 2.5% among those with good access (X² = 71.355, p = 0.001). Cultural factors also played a significant role: 89.9% of families in an anti-prevention cultural environment displayed poor prevention behavior, while only 20% did so in a pro-prevention setting (X² = 64.031, p = 0.001). Personal experience with TB was another influencing factor; families with no personal TB experience showed destructive behaviors at a rate of 94%, whereas those with such experience reported this behavior at 17.7% (X² = 76.625, p = 0.001).

Among all factors examined, knowledge about TB emerged as the most influential determinant of preventive behavior, with an Exp(B) value of 46.888, indicating a strong predictive effect.

In conclusion, TB prevention behavior among families is significantly influenced by knowledge, access to health services, cultural environment, and personal health experiences. Of these, knowledge stands out as the most critical factor in shaping consistent and effective preventive practices. This highlights the urgent need for health education interventions and accessible, culturally sensitive health services to foster more robust TB prevention at the community level.

Source: Sani, H.A., Hadi, A.J. and Hatta, H., 2025. Key Determinants of Tuberculosis Prevention Behaviors Among Families in Indonesia: A Cross-Sectional Study Analysis. Media Publikasi Promosi Kesehatan Indonesia (MPPKI), 8(2), pp.118-130.

Tuberculosis in Papua New Guinea

A study sought to examine the epidemiology, clinical presentation, and treatment outcomes of multidrug-resistant or rifampicin-resistant tuberculosis (MDR/RR-TB) in Morobe Province, Papua New Guinea (PNG), while also evaluating the impact of the COVID-19 pandemic on case detection and treatment outcomes during 2020 and 2021. To achieve this, researchers conducted a retrospective descriptive and analytic review of programmatic data collected over a 10-year period, from 2012 to 2021. The population studied included all individuals diagnosed with MDR/RR-TB in Morobe Province who initiated treatment within this timeframe.

The findings revealed that MDR/RR-TB case notifications in Morobe were stable from 2012 to 2015 but showed a steady increase until peaking at 42 cases in 2020. This rise was followed by a notable decline in 2021, coinciding with the major disruptions brought on by the COVID-19 pandemic. In terms of demographics, the median age of individuals diagnosed was 33 years, with the largest age group being those aged 25 to 34. Most cases occurred among males and urban residents, while children under 15 years made up only a small proportion (2.5%).

Clinically, nearly all identified cases were pulmonary TB, with only one extrapulmonary TB case reported. The HIV co-infection rate was low at 4%; however, the HIV status was unknown in 42% of patients, indicating a significant data gap. Over the study period, treatment outcomes improved substantially. The rate of unfavourable outcomes dropped from 91% in 2014 to 22% in 2019, before increasing again during the pandemic years—rising to 24% in 2020 and 29% in 2021. These reversals highlight the adverse effects of the pandemic on TB service delivery and patient care.

A key finding was the improved success associated with shorter, all-oral treatment regimens lasting 9 to 11 months. Patients on these regimens had a 73.7% favourable outcome rate, compared to 45.8% among those on longer, injectable-based regimens. Statistical analysis identified male sex and treatment with longer injectable regimens as significant risk factors for unfavourable outcomes. Overall, the study underscored the effectiveness and better tolerability of shorter, all-oral regimens, while also emphasizing the urgent need for enhanced case finding, diagnostic capabilities, and decentralization of TB services in Morobe Province.

Source: Bumbu, L., Vaccher, S., Holmes, A., Sodeng, K., Graham, S.M. and Lin, Y.D., 2024. Drug-resistant TB in Morobe Province, Papua New Guinea, 2012–2021. Public Health Action, 14(4), pp.146-151.

Co-Infection of Tuberculosis and Diabetes

A cross-sectional study conducted at Ad-din Barrister Rafique-ul Huq Hospital in Dhaka between January and December 2023 explored the clinical profile, treatment adherence, and factors influencing poor treatment outcomes among elderly patients with coexisting tuberculosis (TB) and diabetes mellitus (DM). The study included 130 patients over the age of 60, with a majority being male (64.6%) and within the 60–64 age range (32.3%). Among the participants, 37.7% were underweight and 58.5% had a history of smoking—both known risk factors for TB and DM complications. Pulmonary TB was more prevalent (75.4%) than extrapulmonary TB (24.6%), and a significant proportion (64.6%) had uncontrolled diabetes (HbA1c ≥7.0). Additionally, 20.8% were diagnosed with multidrug-resistant TB (MDR-TB), further complicating treatment. Comorbid conditions such as hypertension (54.6%), cardiovascular disease (32.3%), and chronic kidney disease (30.0%) were also commonly observed.[2]

Regarding treatment, 79.2% of patients received standard anti-tubercular therapy, while 20.8% required MDR-specific regimens. Diabetes was managed using metformin in 47.7% of cases and insulin in 31.5%. Encouragingly, 70.8% of the patients demonstrated good adherence to treatment. However, several factors were significantly associated with unfavorable treatment outcomes, including advanced age, hypertension, poor treatment adherence, uncontrolled diabetes (adjusted odds ratio [AOR]: 4.12, p<0.001), and MDR-TB (AOR: 5.01, p<0.001). The study highlights the complex health challenges faced by elderly TB-DM patients in Dhaka and underscores the urgent need for integrated care strategies to address the dual burden of TB and diabetes, particularly focusing on managing glycemic control and drug resistance.[2]

A study aimed to assess the effect of prediabetes on tuberculosis (TB) treatment outcomes. Patients were divided into two groups: those with normoglycemia (Group I) and those with prediabetes (Group II). Significant demographic differences were observed between the groups. Group II patients were older, had lower literacy rates, and a higher proportion of unskilled workers. However, other factors such as gender distribution, income, BMI, smoking, alcohol use, and family history of TB were similar across both groups. A notable difference was the higher prevalence of a positive family history of diabetes in Group I.[1]

Biochemically, fasting and postprandial glucose levels were elevated in Group II, with HbA1c levels being significantly higher (6.0 ± 0.21 vs. 5.3 ± 0.23, p < 0.0001). Other parameters, including urea, creatinine, total cholesterol, and hemoglobin levels, showed no significant variation. Despite these metabolic differences, TB treatment outcomes were largely similar. The overall cure rate was 72.7%, with no significant difference between groups (p = 0.38). Treatment failure and defaulter rates were also comparable. While a higher proportion of deaths occurred in the prediabetes group (6.3% vs. 1.3%), the difference was not statistically significant (p = 0.09). Similarly, relapse rates were slightly higher in Group II but did not reach significance.[1]

One critical finding was the delay in sputum conversion among TB patients with prediabetes. The average number of days taken for sputum conversion was significantly higher in Group II (62.4 ± 3.8 vs. 64.2 ± 4.7, p = 0.03). At the end of the intensive treatment phase, a significantly larger proportion of patients in Group II remained sputum smear-positive (23.8% vs. 8.6%, p = 0.019), with an estimated relative risk of 3.0 (95% CI: 1.2–7.6). Baseline chest X-ray scores were higher in Group II, indicating more severe lung involvement, but the difference was not statistically significant. Both groups showed improvement in X-ray scores after treatment, with no significant difference in the degree of reduction.[1]

A key result from the logistic regression analysis was the strong association between HbA1c levels at enrollment and unfavorable TB treatment outcomes. Patients with higher HbA1c were nearly four times more likely to experience poor outcomes (OR = 3.98, p = 0.007). Although male gender showed a trend towards significance as a risk factor, it did not reach statistical significance.[1]

In conclusion, while prediabetes did not significantly impact overall TB treatment success rates, it was associated with delayed sputum conversion and a higher likelihood of remaining smear-positive at the end of the intensive treatment phase. This finding suggests that glycemic control plays a crucial role in TB prognosis. Given that HbA1c emerged as a significant predictor of poor TB outcomes, screening and early intervention for prediabetes in TB patients could improve treatment response. Further research is needed to determine whether targeted glycemic control strategies can enhance TB treatment outcomes in prediabetic individuals.[1]

Source:

1. Viswanathan, V., Devarajan, A., Kumpatla, S., Dhanasekaran, M., Babu, S. and Kornfeld, H., 2023. Effect of prediabetes on tuberculosis treatment outcomes: A study from South India. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 17(7), p.102801.

2. Rima, U.S., Islam, J., Mim, S.I., Roy, A., Dutta, T., Dutta, B. and Ferdaus, F.F., 2024. Co-Infection of Tuberculosis and Diabetes: Implications for Treatment and Management. Asia Pacific Journal of Surgical Advances, 1(2), pp.51-58.

 

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.

Prediction of tuberculosis-specific mortality for older adult patients

A retrospective cohort study aimed to identify the risk factors associated with tuberculosis-specific mortality (TSM) in older adult patients with pulmonary tuberculosis and to develop a predictive model using a competing risk framework. The study analyzed data from 528 patients aged 65 years and older, collected between 2015 and 2020 across 14 infectious disease hospitals in Henan Province, China. Among the study population, there were 97 deaths—50 were attributed to tuberculosis (9.5%) and 47 to non-tuberculosis-specific causes (8.9%). The 5-year cumulative incidence functions (CIFs) were 9.7% for TSM and 9.4% for non-TSM, highlighting the need to distinguish between different mortality risks in this population.

The analysis identified several significant predictors for TSM. Patients aged 85 years and older had nearly a threefold increased risk (HR 2.96), while those undergoing retreatment (HR 2.53), presenting with chest X-ray cavities (HR 5.11), hypoalbuminemia (HR 3.17), or elevated CRP levels (≥10 mg/L) were also at higher risk. For non-TSM, advanced age (≥85 years; HR 4.48) and elevated CRP levels (HR 2.67) were the most influential factors. These findings underscore the importance of specific clinical and inflammatory markers in shaping patient prognosis and directing care priorities for older adults with pulmonary TB.

A competing risk nomogram was developed using the Fine and Gray model to predict 3- and 5-year TSM probabilities, demonstrating strong predictive performance with a C-index of 0.844. The calibration plot confirmed close agreement between predicted and observed outcomes. Overall, the study concludes that age, treatment category, chest radiographic findings, nutritional status, and systemic inflammation are independent predictors of TSM. This nomogram provides a valuable tool to aid clinicians in risk stratification and personalized treatment planning for elderly TB patients.

Source: 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.

TB Risk-Prediction Model Using Health Administrative Data

A study set out to develop and validate a pre-tuberculosis (TB) screening risk-prediction tool using demographic and clinical data derived from health administrative records in two Canadian provinces: British Columbia and Ontario. This retrospective cohort study aimed to support earlier identification of individuals at high risk for developing TB, particularly within migrant populations.

Researchers analyzed data from 715,423 individuals in British Columbia, among whom 1,407 developed TB, and 958,131 individuals in Ontario, where 1,361 developed the disease. Using this large-scale, linked administrative dataset, the model demonstrated good discrimination ability, with concordance values around 0.75, indicating it could reliably differentiate between individuals at high and low risk of TB. The model also showed strong internal validity, evidenced by low optimism scores ranging from 0.005 to 0.012. Calibration-in-the-large values were close to zero, suggesting minimal overestimation of overall risk, though calibration slopes under 1 indicated a degree of overfitting.

Key predictors of TB risk within a two-year period included several strong risk factors: individuals aged 75 years or older (hazard ratio [HR] 15.80), originating from countries with TB incidence rates of 300 or more per 100,000 population (HR 21.34), living with HIV (HR 16.45), undergoing dialysis for chronic kidney disease (HR 16.09), and having had close (HR 6.57) or non-close (HR 3.99) contact with a TB case.

External validation, referred to as Model F, performed best when prescription variables were excluded and a 5-year landmark period was applied. However, the model tended to underestimate risk over a 2-year window and overestimate it over 5 years, as reflected in the expected/observed risk ratios. Subgroup analyses revealed a tendency to overestimate TB risk among older adults, refugees, individuals living with HIV, and patients on dialysis.

In conclusion, the study successfully created and externally validated a TB risk prediction model using existing health administrative data. The model shows significant potential for identifying individuals at elevated risk for TB, especially in migrant populations. Nonetheless, further calibration and cost-effectiveness assessments are necessary before widespread implementation in clinical or public health settings.

Source: 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.

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.

Optimizing the cascade of prevention to protect people from tuberculosis

Estimates of the global burden of TB infection (TBI) have traditionally been based on immune reactivity to TB antigens, with the assumption that infection is lifelong. This approach suggests that about one-fourth of the global population—roughly 1.8 billion people—has been infected, with around 1% acquiring the infection within the past two years. However, current diagnostic tests such as IGRAs and TSTs cannot measure bacterial viability or distinguish recent from remote infection. As a result, many individuals receive TB preventive treatment (TPT) despite having little or no risk of disease progression, raising concerns about cost, unnecessary drug exposure, and ethical implications.

Recent research challenges the binary view of TB as either latent or active, instead presenting it as a spectrum from early infection to clinical disease. It also suggests that the assumption of lifelong infection may be flawed—many individuals likely self-clear the infection without treatment. Studies have shown that over 90% of those previously infected may no longer harbor viable mycobacteria. Therefore, identifying those at real risk of disease progression is critical. Breakthroughs in diagnostics that can distinguish persistent from cleared infection or predict progression would significantly improve TPT targeting and cost-effectiveness.

The TB prevention cascade—identifying at-risk individuals, ruling out active TB, testing for infection, providing TPT, and supporting adherence—faces high attrition at each stage, particularly during initial assessment. Fewer than 20% of those eligible for TPT complete the full cascade. While HIV-positive individuals have higher completion rates, coverage among other groups, such as migrants, is much lower. Most studies and interventions to improve this cascade have been conducted in low-burden countries, with limited data from high-burden settings. However, strategies like patient incentives, digital tools, and workforce training have shown promise in improving early steps of the cascade.

Despite progress, TPT coverage remains inadequate. As of 2022, only 52% of people living with HIV had received TPT, falling short of the 90% target. Coverage among household contacts is even lower, with only 4.2 million initiated on TPT between 2018 and 2022—far below the 24 million target. The new 2027 UN goal of reaching 45 million people, including 30 million contacts, will require intensified efforts and investment. Effective delivery models must prioritize contact evaluation, community-based testing, and engagement strategies that increase access and completion.

In terms of treatment regimens, isoniazid preventive therapy (IPT) has been widely used but is limited by long duration and side effects. Shorter rifamycin-based regimens, such as 3HR, 3HP, and 1HP, have improved adherence and safety profiles, though some have higher rates of adverse events or cost barriers. Pediatric-friendly formulations are in development to expand access among children. Ultimately, a pan-TPT regimen—simple, safe, and universally applicable—could revolutionize TB prevention. Innovations like slow-release, single-dose treatments would make mass preventive strategies feasible, much like vaccines or deworming programs, especially in high-burden regions.

Source: 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.

Trends and changes in tuberculosis mortality, Taiwan, 1978–2022

Tuberculosis (TB) was a significant public health burden in Taiwan during the last century. In 1978, TB was the eighth leading cause of death, with 2,809 deaths (16.55 per 100,000) and accounted for 3.56% of all deaths. By 2022, it had declined to the 24th leading cause, with 477 deaths (2.05 per 100,000) and 0.23% of total mortality. Notably, 83.65% (399 of 477) of TB-related deaths in 2022 occurred in individuals aged 65 years or older. As of May 2024, 18.7% of Taiwan's population was aged ≥65 years, and the country is projected to become a super-aged society—defined by over 21% of the population aged ≥65—by 2026. These demographics highlight that TB continues to pose a health challenge among older populations in Taiwan.

To analyze long-term trends, the study employed both Joinpoint regression and age-period-cohort (A–P–C) models. Joinpoint regression identifies statistically significant changes in trend over time and computes the annual percentage change (APC), which indicates the rate and direction of change. In contrast, the A–P–C model disaggregates the effects of age, time period, and birth cohort on mortality trends, offering deeper insight into underlying epidemiological dynamics. This study is the first to apply both models simultaneously to Taiwan’s TB mortality data from 1978 to 2022.

Mortality data were obtained from Health Statistics issued by Taiwan’s Ministry of Health and Welfare, using ICD-8 (1978–1980: codes 010–019), ICD-9 (1981–2007: codes 010–018), and ICD-10 (2008–2022: codes A15–A19). Corresponding population data were sourced from Demographic Statistics by the Department of Household Registration, Ministry of the Interior. Age-standardized mortality rates (ASMRs) were calculated using the WHO World Standard Population 2000.

All crude and standardized mortality rates from 1978 to 2022 were calculated using Microsoft Excel. Joinpoint regression analyses used annual crude and age-standardized TB mortality rates, with the rate calculation function implemented in the Joinpoint Regression Program (Version 5.0.2, May 2023). The maximum number of joinpoints allowed was seven, and model selection was based on the Weighted Bayesian Information Criterion (WBIC). Confidence intervals for APC and average APC (AAPC) were calculated using the Empirical Quantile Method.

For A–P–C analysis, TB mortality rates were calculated by sex and age group across nine 5-year calendar periods (1978–1982 through 2018–2022) and 17 age groups (0–4 to 80–84 years). The oldest age group (85+) was excluded to meet the model’s 5-year interval requirement. As a result, 25 birth cohorts (from 1898–1902 to 1998–2002) were included. The average annual mortality rate was calculated for each age group within each period. All computations were performed using Excel.

Ethical approval was not required, as the data used were aggregated and derived from official government publications. To compare TB mortality rates before and during the COVID-19 pandemic, Poisson regression was used—a standard log-linear model for count data, assuming the mean equals the variance. Model dispersion was checked, and no overdispersion was detected. These analyses were conducted using SAS software.

In the Joinpoint analysis, APCs were estimated for sex-specific crude and age-standardized mortality rates. For A–P–C models, log-linear regressions were fit to estimate overall net drift (analogous to the APC for age-standardized rates) and local drift (age-specific APCs over time). The longitudinal age curve illustrated TB mortality across 5-year age groups for the reference cohort (1958–1962). Period rate ratios (RRs) were estimated by comparing each period to the reference period (1998–2002), and cohort RRs were computed by comparing each birth cohort to the reference cohort (1958–1962).

The age-standardized mortality rate (ASMR) for tuberculosis (TB) in Taiwan decreased substantially from 1978 to 2022. Among males, it declined from 41.70 to 1.59 per 100,000, and among females, from 14.58 to 0.45 per 100,000—representing reductions of 96.19% and 96.91%, respectively. The ASMR rate ratios (1978 vs. 2022) were 26.23 for males and 32.40 for females, indicating a steeper proportional decline among females.

Crude mortality rates also decreased, though less sharply. In males, the crude rate dropped from 23.46 to 3.02 per 100,000, and in females, from 8.84 to 1.09 per 100,000—equivalent to reductions of 87.13% and 87.67%, respectively. The crude rate ratios were 7.77 in males and 8.11 in females. Compared to ASMR trends, crude mortality trends showed a more gradual decline, likely reflecting Taiwan’s aging population.

During the COVID-19 pandemic, TB crude mortality further declined in males compared to 2019 but rebounded in both sexes in 2022. For crude mortality, the average annual percentage change (AAPC) was −4.68% in males and −4.99% in females. In males, all Joinpoint segments except 1987–1990 showed statistically significant declines (p < 0.05), with the steepest drop in 2003–2006 (APC: −15.89%) and a slight increase in 1987–1990 (APC: +1.47%). In females, all segments showed a decline, but the reductions in 1987–2001 and 2007–2022 were not statistically significant. The sharpest decrease occurred during 2001–2007 (APC: −8.21%).

For ASMR, the AAPC was −7.17% in males and −7.64% in females. All segments significantly declined (p < 0.05) except 2020–2022 in females. Among males, the greatest drop was in 2000–2010 (APC: −12.01%), while the least was in 2010–2022 (APC: −5.35%). Among females, the sharpest decline occurred from 1999–2020 (APC: −9.81%), followed by an increase during 2020–2022 (APC: +11.44%).

Detailed segment analysis of crude rates also revealed that in males, the largest drop occurred in 2003–2006 (APC: −13.23%), and the only increase (though not significant) was seen in 1986–1989 (APC: +0.94%). In females, the sharpest drop occurred in 1982–1988 (APC: −8.16%), and a notable rise in 2020–2022 (APC: +7.33%). For ASMR, the most significant male decline occurred in 2003–2006 (APC: −16.60%), with the smallest decline in 1986–1989 (APC: −1.34%). In females, the largest drop occurred in 2000–2007 (APC: −11.52%), with an increase in 2020–2022 (APC: +7.36%).

Regarding age-specific trends, only older age groups (≥55 years) were analyzed due to sparse data in younger groups. The age-sex-specific APCs from Joinpoint models ranged from −3.55% to −10.53%. Differences between Joinpoint and age-period-cohort (A–P–C) model estimates ranged from −0.52% to +1.42%.

After adjusting for A–P–C effects, all estimated APCs and age-specific APCs remained negative, confirming a consistent decline in TB mortality. The average annual reduction was −8.83% for males and −9.77% for females. The most significant drop in males was observed in the 25–29 age group (EAPC: −10.19%, 95% CI: −11.53% to −8.82%), while in females, it was in the 0–4 age group (EAPC: −11.92%, 95% CI: −21.36% to −1.35%).

TB mortality trends between ages 45 and 74 showed significantly steeper declines in females than males. However, among those aged ≥65 years, mortality declined more slowly in both sexes. The highest longitudinal age-specific mortality rates were observed in the 0–4 age group: 6.31 per 100,000 in males (95% CI: 4.28–9.29) and 7.87 per 100,000 in females (95% CI: 5.14–12.05).

The longitudinal mortality curves were similar for both sexes under age 24 but diverged after age 30, with consistently higher rates in males. In males, mortality increased with age except for a peak in the 0–4 age group. In females, mortality slightly rose to a second peak at 20–24 years, declined to a nadir at 55–59 years, and then rose again slightly.

When comparing period rate ratios (RRs) to the reference period (1998–2002), the highest RRs occurred in 1978–1982 (RR: 7.20 in males; 10.26 in females), while the lowest were observed in 2018–2022 (RR: 0.19 in males, 95% CI: 0.16–0.23; RR: 0.17 in females, 95% CI: 0.13–0.21). These declines were steeper in females.

Cohort RRs also showed consistent declines across successive birth cohorts, with peaks in the 1898–1902 cohort for males and 1903–1907 cohort for females. Similar to period RRs, cohort-based declines were more pronounced in females than in males.

The difference in annual percentage change (APC) estimates between the Joinpoint regression and the age–period–cohort (A–P–C) model can be attributed to differences in adjustment factors and the number of age groups included in the analyses. In Joinpoint regression, crude rate APCs were unadjusted, while ASMR APCs were adjusted only for age. In contrast, the A–P–C model adjusted simultaneously for age, period, and cohort effects.

Another methodological distinction is the inclusion of age groups. For Joinpoint analysis, ASMRs were calculated using 18 age groups, including the oldest group (85+). However, in the A–P–C model, only 17 age-specific rates were used, excluding the 85+ group due to the required 5-year age interval structure. Since TB mortality is typically higher and decreases more gradually in the oldest age groups, excluding the 85+ group likely contributed to a slightly larger APC estimate in the A–P–C model.

TB mortality in Taiwan has shown a steady decline over the past 45 years, with the exception of a slight increase in 2020–2022 among females. Compared to previous studies, TB mortality in Taiwan remains higher than that reported in Japan. The overall trends of age-standardized mortality rates in this study resemble those observed in high-middle sociodemographic index (SDI) countries. The accelerated decline in mortality after 1999–2000 may reflect improved health interventions and policy implementation.

Sex differences in TB mortality may stem from a combination of biological factors (e.g., hormonal and genetic differences), behavioral risk exposures (e.g., smoking and alcohol use), disparities in treatment compliance and response, and changing gender-related health inequalities. Taiwan's public health advancements—such as the introduction of 9-year compulsory education in 1968, the national vaccination program (since 1949), the launch of National Health Insurance in 1995, and the implementation of directly observed treatment, short-course (DOTS)—have played important roles in reducing sex-based disparities in TB outcomes.

TB mortality declined less among older age groups. While Bacillus Calmette–Guérin (BCG) vaccination has been effective in preventing TB-related deaths, particularly among children under 15, the vaccine's protective effect wanes with age. Older adults face a combination of reduced immunity, greater exposure to risk factors, and immunosenescence, which may explain the smaller estimated APCs with increasing age.

The significant period and cohort effects observed in this study reflect Taiwan’s socioeconomic transformation. Between 1978 and 2022, Taiwan transitioned from an agricultural society to an industrialized, high-income nation, with GDP per capita increasing from USD 1,606 to USD 32,756. National nutrition status improved markedly, shifting from deficiency to sufficiency. The institutional response to TB evolved as well—from a single TB ward established in 1915 for treatment to a centralized public health system under the Taiwan CDC focusing on prevention. As outlined in Supplementary Table S1, anti-TB programs expanded from treatment-only to integrated prevention strategies.

The BCG vaccination program, introduced in 1949, has maintained a coverage rate of approximately 98% over the past decade. In addition, free TB treatment and widespread implementation of DOTS have ensured high treatment completion rates. The observed period and cohort effects in TB mortality are thus likely the result of Taiwan’s socioeconomic development, improved nutrition, strengthened healthcare infrastructure, universal BCG coverage, and access to free TB treatment.

Source: Chen, S.Y., 2024. Trends and annual percentage changes in tuberculosis mortality estimated by Joinpoint regression and age-period-cohort analyses, Taiwan, 1978–2022. Scientific Reports, 14(1), p.29101.

Tuberculosis in Healthcare Workers

This study investigates the factors affecting the implementation of active tuberculosis (TB) surveillance in rural and urban districts of the Eastern Cape, South Africa, from the perspective of healthcare workers. Utilizing a cross-sectional survey method, data was gathered via an electronic questionnaire through REDCap software. The approach effectively captures healthcare workers' views on systemic and contextual challenges at a specific time, though self-reported data may introduce bias, and the focus solely on healthcare workers might overlook patient and community stakeholder insights.[1]

Variables in this study include independent variables such as geographical settings (rural vs. urban districts) and healthcare worker demographics and roles. Dependent variables involve factors impacting TB surveillance like training, transportation, coordination, and community acceptance. Confounding factors include socioeconomic disparities and variations in clinic resources, while control variables were demographic comparatives of the survey participants. Key results indicated significant challenges like CHW transport issues and community distrust, alongside a substantial discrepancy in resource allocation between rural and urban settings. The study concludes that multiple barriers, including leadership and resource deficiencies, affect TB surveillance, necessitating tailored interventions for different locales.[1]

In Taiwan, Tuberculosis (TB) poses a significant occupational risk for healthcare workers (HCWs), who exhibit a higher incidence of active TB compared to the general population when adjusted for age, sex, and diagnosis year. Notably, the outcomes of TB in HCWs are more favorable than those of non-HCW patients treated in the same settings, primarily due to factors such as the healthy worker effect, expedited diagnosis, and reduced treatment delays, all contributing to lower TB mortality rates among healthcare workers.[2]

A nested randomized controlled trial, part of the BRACE phase 3 study, assessed the effect of BCG-Denmark revaccination on preventing Mycobacterium tuberculosis infection among adult healthcare workers in Brazil—a country with high TB burden. A total of 1,985 participants with valid baseline QFT Plus results were enrolled across three sites (Campo Grande, Manaus, and Rio de Janeiro), with 996 receiving BCG and 989 receiving placebo. Initial QFT Plus conversion rates were similar between groups (BCG: 3.4%; placebo: 3.2%; RR 1.09, 95% CI 0.67–1.77; p = 0.791), and sustained conversion rates showed no significant difference either (BCG: 1.5%; placebo: 1.9%; RR 0.80, 95% CI 0.41–1.57; p = 0.510).[3]

Alternative thresholds for QFT positivity (0.7 IU/mL and 2.0 IU/mL) and subgroup analysis of participants with low baseline IFN-γ levels (0.2 IU/mL) also revealed no meaningful difference between BCG and placebo arms. These findings suggest that BCG-Denmark revaccination does not significantly reduce the risk of initial or sustained QFT Plus conversion in previously uninfected adult healthcare workers, indicating limited benefit for TB infection prevention in this population.[3]

References:

1. Ajudua, F.I. and Mash, R.J., 2024. Implementing active surveillance for TB: A descriptive survey of healthcare workers in the Eastern Cape, South Africa. African Journal of Primary Health Care & Family Medicine, 16(1), p.4217.

2. Pan S-C, Chen Y-C, Wang J-Y, Sheng W-H, Lin H-H, Fang C-T, et al. (2015) Tuberculosis in Healthcare Workers: A Matched Cohort Study in Taiwan. PLoS ONE 10(12): e0145047.

3. Dos Santos, P.C.P., Messina, N.L., de Oliveira, R.D., da Silva, P.V., Puga, M.A.M., Dalcolmo, M., Dos Santos, G., de Lacerda, M.V.G., Jardim, B.A., e Val, F.F.D.A. and Curtis, N., 2024. Effect of BCG vaccination against Mycobacterium tuberculosis infection in adult Brazilian health-care workers: a nested clinical trial. The Lancet Infectious Diseases, 24(6), pp.594-601.

Managing TB-Diabetes Comorbidity

• TB-diabetes co-occurrence is shaped by both biological and socio-environmental factors, including poverty, malnutrition, smoking, and poor healthcare access. Older adults and males are at heightened risk due to weakened immunity and higher TB incidence, respectively. See also: Lin TB Lab
• Diabetes increases TB risk by weakening the immune system via chronic hyperglycemia and inflammation. TB worsens glycemic control through stress-induced insulin resistance, and some TB drugs interfere with diabetes medications, complicating treatment.
• Managing TB in diabetic patients requires strict glucose monitoring and tailored medication plans. Integrated care models and preventive measures like latent TB screening are essential to improve outcomes. See also: Benang Merah RC
• Diabetic TB patients tend to be older males with more comorbidities, yet symptoms and X-rays often resemble non-diabetic cases. Poor glycemic control is tied to more severe TB presentations and longer treatment times.
• At IBIT in Brazil, 11.8% of symptomatic adults had PTB, with many showing poor nutrition, high alcohol use, and smoking. Among them, 63.1% had glucose metabolism issues and poor glycemic control significantly raised TB risk.
• In Shenzhen and Kunming, 16.8% of confirmed PTB cases also had diabetes, with these patients being older, predominantly male, and exhibiting worse metabolic health. Low education and high alcohol use were more common among the PTB-DM group.
• Risk factors for diabetes among TB patients include male sex, age, family history of diabetes, and elevated glucose levels. Heavy alcohol use increases risk, while awareness of diabetes offers significant protection.
• Diabetes is emerging as a major obstacle to TB control, especially in low- and middle-income countries, mirroring the threat level of HIV/AIDS. Despite this, research funding and large-scale clinical trials for TB-DM comorbidity remain scarce.
• A nationwide South Korean study found TB-DM patients had worse disease severity, more comorbidities, and higher death rates. TB was the leading cause of death, with male and low-income patients especially vulnerable.
• In Eastern China, diabetics had significantly higher TB incidence, especially those with lower BMI. This suggests that underweight diabetics are at particular risk, while higher BMI may offer some protection.
• Males and older individuals had higher TB rates, while BCG vaccination scars were linked to lower TB incidence. Diabetes significantly raised TB risk in lean individuals but had no effect in those with higher BMI.
• Korean national data showed diabetes raises TB risk by 48%, and long-standing diabetes (>5 years) increases this further to 57%. Younger adults and men with high fasting glucose were particularly affected.
• A Taiwan study found TB survivors face higher risks of diabetes, heart attacks, and strokes post-treatment. Longer TB treatment durations and existing chronic conditions worsen these outcomes, requiring post-TB health monitoring.

References:

1. Munir, M.A., Khan, S., Rehman, S., Ahmed, D. and Jabbar, A., 2024. Tuberculosis among diabetes patients: a review of epidemiology, pathophysiology, clinical manifestations, and management. Chronicles of Biomedical Sciences, 1(3), pp. PID26-PID26.
2. Park, S.W., Shin, J.W., Kim, J.Y., Park, I.W., Choi, B.W., Choi, J.C. and Kim, Y.S., 2012. The effect of diabetic control status on the clinical features of pulmonary tuberculosis. European journal of clinical microbiology & infectious diseases, 31, pp.1305-1310.
3. Almeida-Junior JL, Gil-Santana L, Oliveira CAM, Castro S, Cafezeiro AS, Daltro C, et al. (2016) Glucose Metabolism Disorder Is Associated with Pulmonary Tuberculosis in Individuals with Respiratory Symptoms from Brazil. PLoS ONE 11(4):e0153590.
4. Li, J., Zhao, Y., Jiang, Y., Zhang, Y., Zhang, P., Shen, L. and Chen, Z., 2024. Prevalence and risk factors of diabetes in patients with active pulmonary tuberculosis: a cross-sectional study in two financially affluent China cities. Diabetes, Metabolic Syndrome and Obesity, pp.1105-1114.
5. Bao, J., Hafner, R., Lin, Y., Lin, H.H. and Magee, M.J., 2018. Curbing the tuberculosis and diabetes co-epidemic: strategies for the integration of clinical care and research. The International Journal of Tuberculosis and Lung Disease, 22(10), pp.1111-1112.
6. Kwak SH, Jeong D, Mok J, Jeon D, Kang H-Y, Kim HJ, et al. (2023) Association between diabetes mellitus and cause of death in patients with tuberculosis: A Korean nationwide cohort study. PLoS ONE 18(12): e0295556.
7. Lu, P., Zhang, Y., Liu, Q., Ding, X., Kong, W., Zhu, L. and Lu, W., 2021. Association of BMI, diabetes, and risk of tuberculosis: a population-based prospective cohort. International Journal of Infectious Diseases, 109, pp.168-173.
8. Yoo JE, Kim D, Han K, Rhee SY, Shin DW, Lee H. Diabetes status and association with risk of tuberculosis among Korean adults. JAMA network open. 2021 Sep 1;4(9):e2126099.
9. Salindri, A.D., Wang, J.Y., Lin, H.H. and Magee, M.J., 2019. Post-tuberculosis incidence of diabetes, myocardial infarction, and stroke: retrospective cohort analysis of patients formerly treated for tuberculosis in Taiwan, 2002–2013. International Journal of Infectious Diseases, 84, pp.127-130.

TBC 056

Tuberculosis in Southeast Asia Region

A recent study highlights that optimal implementation of tuberculosis preventive therapy (TPT) in Southeast Asia Region (SEAR) countries could potentially reduce the annual TB incidence rate by 8.30% compared to 2015 figures. TPT services are widely provided to household contacts over five years of age and to other high-risk groups in these countries. Notably, Indonesia and Maldives extend these services to prisoners, and Timor-Leste to individuals who are undernourished or have diabetes.[1]

Sri Lanka diverges in its approach by considering all pulmonary TB cases for TPT, not just bacteriologically confirmed ones, focusing primarily on contacts below 15 or over 50 years of age. In terms of treatment protocols, India specifies different TPT regimens depending on the resistance profile of the index patient. All countries in the region are committed to monitoring TPT adherence closely, often coordinated with routine healthcare visits or, for PLHIV, synchronized with their ART medication refills.[1]

Operational research identified several priorities to enhance TPT implementation, including developing comprehensive strategies for provider training and patient counseling, improving diagnostic access like X-ray for contact investigation, and strengthening health system capacities. These initiatives are aimed at integrating TPT into a broader TB management framework to ensure effective and targeted TB prevention, ultimately contributing to the global goal of reducing TB incidence.[1]

A study evaluates whether Southeast Asian Region (SEAR) countries are poised to meet the WHO's interim End TB targets of reducing tuberculosis incidence by 50% and mortality by 75% by 2025 compared to 2015 levels. The analysis uses data from the WHO Global Health Observatory and Global Health Estimates, applying Joinpoint Regression to examine trends from 2000–2018 for incidence and until 2016 for mortality, alongside ARIMA forecasting to project trends through to 2025. Region-wide, slight declines in age-standardized incidence rates (ASIR) and stronger declines in age-standardized mortality rates (ASMR) are noted, but the projections fall short of the WHO targets, indicating an overall 17.3% reduction in incidence and 25.8% in mortality by 2025.[2]

The study highlights significant variations among individual countries within SEAR. India, Myanmar, and Indonesia are projected to see declines in both ASIR and ASMR, with Myanmar nearing target levels (50% reduction in ASIR and 60.3% in ASMR). Conversely, Bangladesh, Nepal, Sri Lanka, and Timor-Leste are expected to either experience increases in these rates or only minimal changes. The Maldives, due to low numbers, had minimal or no forecast data available. The forecasting models underscore the disparate trends across the region, with some countries showing promising declines while others face stagnating or worsening tuberculosis burdens. The study concludes that most SEAR countries are unlikely to achieve the WHO's ambitious End TB targets by the stipulated 2025 deadline.[2]

References:

1. Giridharan, P., Suseela, R.P., Zangpo, T., Joshi, R.B., Cader, M., Isbaniah, F., Velayudham, B., Rafeeg, F.N., da Cruz Santos, A., Shah, N.P. and Mathew, M., 2024. Tuberculosis preventive treatment in eight SEAR countries–Current practices, implementation challenges and operations research priorities. Public Health in Practice, 8, p.100518.

2. Krishnamoorthy, Y., Nagarajan, R., Rajaa, S., Majella, M.G., Murali, S. and Jayaseelan, V., 2021. Progress of South East Asian Region countries towards achieving interim End TB Strategy targets for TB incidence and mortality: a modelling study. Public Health, 198, pp.9-16.

Tuberculosis in Ethiopia

This systematic review and meta-analysis aimed to estimate the pooled prevalence of pulmonary tuberculosis (PTB) among key and vulnerable populations (KVPs) in Ethiopian hotspot settings. Following PRISMA guidelines, researchers conducted comprehensive searches in databases such as PubMed, Scopus, ScienceDirect, Google Scholar, and institutional repositories. Eligible studies were those conducted in hotspot environments like prisons, refugee camps, universities, healthcare facilities, spiritual holy water sites, and shelters, using accepted PTB diagnostic methods and meeting language and quality criteria. Studies outside Ethiopia, non-English articles, and those lacking sufficient data or focused only on extrapulmonary TB or drug resistance were excluded.[1]

In total, 34 studies involving 32,909 participants met the inclusion criteria, with 2,044 confirmed PTB cases. The pooled PTB prevalence was 11.7% (95% CI: 7.97–15.43%), with substantial heterogeneity (I² = 99.91%, p < 0.001) and publication bias detected via Egger’s test (p < 0.00001). However, the trim-and-fill method indicated no change in the prevalence estimate. Subgroup analysis showed notable variation in PTB burden: refugees had the highest prevalence at 28.4%, followed by university students (23.1%), spiritual sites (12.3%), healthcare settings (11.1%), prisons (8.8%), homeless individuals (5.8%), and other hotspot areas (4.3%).[1]

The findings indicate that PTB prevalence among KVPs in Ethiopian hotspot settings is significantly higher than in the general population. Despite a slight decline in PTB rates from 2000 to 2023, certain groups remain disproportionately affected. These results call for urgent national TB control measures, including routine screening, mandatory testing for symptomatic individuals, and stricter infection prevention strategies in high-risk environments.[1]

References:

1. Reta, M.A., Asmare, Z., Sisay, A., Gashaw, Y., Getachew, E., Gashaw, M., Dejazmach, Z., Jemal, A., Gedfie, S., Kumie, G. and Nigatie, M., 2024. Prevalence of pulmonary tuberculosis among key and vulnerable populations in hotspot settings of Ethiopia. A systematic review and meta-analysis. Plos one, 19(8), p.e0309445.

Tuberculosis in Semarang, Indonesia

The research conducted in Semarang, Indonesia, aimed to assess the effectiveness of the TB elimination program within primary health centers (PHCs). The study focused on identifying the strengths, weaknesses, and areas for improvement to guide the city towards its goal of becoming TB-free by 2028. Utilizing a qualitative methodology through focus group discussions, the research was tailored to uncover nuanced perspectives from healthcare staff. This approach was vital for understanding operational challenges but highlighted limitations such as potential participant bias and the specificity of findings to the studied PHCs. Key findings include systemic inefficiencies, resource shortages, and significant community-based challenges like stigma and cultural barriers affecting treatment adherence and early diagnosis.[1]

To address the identified issues, several strategic recommendations emerge. First, there is an urgent need for system integration to avoid duplication and ensure real-time data availability for effective patient follow-up. Secondly, bolstering resource availability, particularly diagnostic tools and medications, is crucial to reduce treatment delays. Community outreach should be intensified to combat stigma and educate on TB, while capacity building for healthcare workers will help in managing latent TB cases more effectively. Lastly, aligning local strategies with national policies will ensure that efforts are both consistent and scalable, contributing to a sustainable path towards TB elimination in Semarang by 2028.[1]

In Semarang City, Indonesia, a research study was conducted to explore the influence of housing quality and sanitation conditions on tuberculosis (TB) treatment outcomes. The study utilized a quantitative observational method, where data were gathered through home visits and analyzed using linear regression to determine causal relationships. The results indicated a modest but statistically significant effect of housing adequacy on the success of TB treatment; however, the analysis found no significant correlation between household sanitation and the outcomes of TB treatment. These findings underline the complexity of TB management, suggesting that while environmental conditions contribute to treatment success, they are not the sole factors.[2]

Further analysis of the data revealed specific details about the impact of housing on TB treatment. Inadequate housing conditions were associated with slightly poorer treatment outcomes, accounting for only 0.3% of the variability in these outcomes, which highlights the limited but non-negligible role of housing quality in managing TB. In contrast, the lack of a significant relationship between sanitation and treatment outcomes suggests that improvements in sanitation, while essential for public health, may not directly influence TB treatment success. This discrepancy emphasizes the need to address broader public health issues alongside targeted disease management strategies.[2]

A study aimed to identify the factors associated with the occurrence of Multi-Drug Resistant Tuberculosis (MDR-TB) in Semarang City. It used an observational case-control design, conducted between November and December 2020 at community health centers (Puskesmas) in Semarang. A total of 70 participants were involved, consisting of 35 cases and 35 controls. Data were collected through structured interviews using pre-prepared questionnaires. The analysis showed no significant association between MDR-TB and age over 45, gender, Body Mass Index (BMI), education level, or the presence of Diabetes Mellitus (DM). However, a significant relationship was found between MDR-TB and economic status, history of contact with MDR-TB patients, previous TB treatment, and stress levels. These four factors were identified as the main contributors to the occurrence of MDR-TB in the study area.[3]

References:

1. Handayani, S. and Isworo, S., 2024. Evaluation of Tuberculosis program implementation in Primary Health Care, Semarang, Indonesia. International Journal of Public Health Asia Pacific, pp.1-11.

2. Hakam, M.A., Safitri, B.D., Wandastuti, A.D., Husni, M.F., Setiawan, A.W., Konoralma, A.R., Radja, B.L., Setiono, O. and Wulan, W.R., 2024. The Relationship Between Adequate Housing And Household Sanitation With The Success Of Tuberculosis Patient Treatment In Semarang City. International Journal of Health Literacy and Science, 2(2), pp.14-19.

3. Buryanti, S., 2021. Faktor-Faktor yang Mempengaruhi Kejadian TB MDR di Kota Semarang. Journal Health & Science: Gorontalo Journal Health and Science Community, 5(1), pp.146-154.

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.