Health and Economic Outcomes of Racial and Ethnic Tuberculosis Disparities

Significant progress has been made in reducing tuberculosis (TB) incidence in the United States, achieving one of the lowest rates globally. However, racial and ethnic disparities remain among US-born individuals, with higher TB incidence and case-fatality rates observed in marginalized communities. These disparities stem from systemic health inequities influenced by social, economic, and environmental disadvantages.[1]

Data from the National Tuberculosis Surveillance System (NTSS) from 2010 to 2019 highlight the disproportionate TB burden among racial and ethnic groups. Of the 31,811 reported TB cases in US-born persons, Black individuals accounted for 38%, followed by Hispanic (21%) and White (32%) populations. Case-fatality rates were also disproportionately high, with Black individuals experiencing 42% of TB-related deaths. Limited access to prevention services, delayed medical care, and lower quality of healthcare contribute to these disparities.[1]

Projections for 2023-2035 estimate 26,203 TB cases and 3,264 TB deaths among US-born persons, with case-fatality rates increasing by 7% due to age-related factors. Nearly half of TB cases (45%) are expected to be linked to racial and ethnic disparities, with Native Hawaiian or Other Pacific Islander persons experiencing the highest proportion of disparity-associated cases at 75%. Black and American Indian/Alaska Native individuals will bear the greatest loss in quality-adjusted life years (QALYs), reflecting the severe health burden of TB inequities.[1]

The economic impact of TB disparities is substantial, with projected costs reaching $1.397 billion between 2023 and 2035. Racial and ethnic disparities will account for up to 66% of these costs, highlighting the urgent need for targeted public health interventions. Addressing these inequities through improved access to healthcare, early detection, and prevention strategies is critical to reducing TB incidence and ensuring health equity for all US-born populations.[1] 

Tuberculosis (TB) continues to impose serious health and financial burdens in the United States, particularly when the disease progresses. Over half of those diagnosed with active TB require hospitalization, with treatment costs ranging from $16,000 to $23,000 per person—excluding additional indirect costs such as lost income, stigma, and public health interventions. Prioritizing testing and treatment for individuals at increased risk of TB infection is both cost-effective and potentially cost-saving, offering the most impactful path toward achieving U.S. TB elimination goals.[2]

Despite advancements in TB screening and treatment—such as interferon-gamma release assays (IGRAs) and rifamycin-based regimens—preventive care remains underutilized. This gap is especially concerning given the disproportionate impact of TB on non–U.S.-born individuals and racial or ethnic minorities. For example, TB incidence is eight times higher in Black Americans and seventy times higher in non–U.S.-born Asian persons compared to their White or U.S.-born counterparts. Medicare recipients, who often have health conditions that increase TB risk, also face high treatment costs, with Medicare and Medicaid covering 69% of TB-related hospitalizations in 2014.[2]

To address these disparities, a coalition of 25 organizations has urged the Centers for Medicare & Medicaid Services (CMS) to adopt a national coverage determination for TB infection screening using IGRAs in high-risk Medicare populations. Such a policy would improve reimbursement, lower patient costs, enhance data tracking, and support quality care standards. Most importantly, it would recognize TB as a health equity issue and remove systemic barriers to preventive care for historically underserved communities.[2]

References:

1. Swartwood, N.A., Li, Y., Regan, M., Marks, S.M., Barham, T., Asay, G.R.B., Cohen, T., Hill, A.N., Horsburgh, C.R., Khan, A.D. and McCree, D.H., 2024. Estimated Health and Economic Outcomes of Racial and Ethnic Tuberculosis Disparities in US-Born Persons. JAMA Network Open, 7(9), pp.e2431988-e2431988.

2. Murrill, M.T., Salcedo, K., Tschampl, C.A., Ahamed, N., Coates, E.S., Flood, J., Wegener, D.H. and Shete, P.B., 2025. Policy Impediments to Tuberculosis Elimination: Consequences of an Absent Medicare National Coverage Determination for Tuberculosis Prevention. Journal of Immigrant and Minority Health, pp.1-6.

Tuberculosis in Vietnam

A study aimed to assess the effectiveness and feasibility of the Double X (2X) strategy for improving tuberculosis (TB) detection and treatment in Vietnam, both in community and healthcare facility settings. The 2X strategy involves using chest X-ray (CXR) triage followed by Xpert testing for TB diagnosis. By investigating its performance across various implementation sites, the research sought to determine how well this strategy works under real-world conditions, and whether it can be scaled and sustained within Vietnam’s broader TB control framework.

To explore this, researchers conducted a programmatic implementation and evaluation study. They employed a descriptive analysis of routine health data collected from 2020 to 2022 across nine Vietnamese provinces with differing TB prevalence levels. The study design included both retrospective data reviews and prospective data collection via monthly reporting systems. This mixed-method approach made it particularly suitable for evaluating public health interventions in operational settings, allowing a detailed look into outcomes, implementation barriers, and cost-effectiveness metrics of the 2X strategy.

The study tracked multiple variables. Independent variables included the implementation of the 2X strategy, the type of population screened (such as household contacts and vulnerable groups), and demographic factors like age and sex. The dependent variables measured outcomes such as the number of TB cases detected, the initiation rate of TB preventive treatment (TPT), and the positivity rate of TB infection tests. Potential confounding factors like regional differences in TB prevalence and disparities in access to diagnostic infrastructure were also considered. Technical indicators such as CXR and Xpert test coverage, number needed to screen (NNS), and program costs were used to further interpret findings.

Results showed significant successes. From 2020 to 2022, the study screened over 21,000 household contacts and 79,000 TB-vulnerable individuals in the community, yielding 140 and 1,255 TB cases respectively. In health facility settings, over half a million people were evaluated, with the highest TB detection among older adults, smokers, and those with alcohol use disorders. Infection testing indicated a 15.7% positivity rate, and over 63% of eligible individuals began TPT. Importantly, the project demonstrated higher TB detection in men and older populations. From a cost perspective, although it varied by model, the strategy was generally efficient, and the government incorporated it into national guidelines in October 2020, with project provinces seeing a faster uptake of Xpert testing than control areas.

In conclusion, the implementation of the 2X strategy proved highly effective in detecting TB and integrating disease and infection diagnosis into routine practice. Its strong performance across diverse settings, combined with favorable cost efficiency and policy adoption, suggests that the 2X approach is both a feasible and impactful method for TB control in Vietnam. These findings support continued expansion and long-term integration of the strategy within national health systems.

Source: Innes, A.L., Lebrun, V., Hoang, G.L., Martinez, A., Dinh, N., Nguyen, T.T.H., Huynh, T.P., Quach, V.L., Nguyen, T.B., Trieu, V.C. and Tran, N.D.B., 2024. An effective health system approach to end TB: implementing the double X strategy in Vietnam. Global Health: Science and Practice, 12(3).

 

Tuberculosis in Pakistan

Between March 2018 and May 2019, a population-based survey was conducted in Karachi, Pakistan, to estimate the prevalence of adult pulmonary tuberculosis (TB) and M. tuberculosis infection in children. Using cluster-based random sampling, the study compared areas with and without prior active case finding (ACF) interventions. Participation was high overall, especially among adult women (90.1%), though lower among men (67.0%). The large sample—over 34,000 adults and 1,500 young children—provided a robust basis for estimating TB prevalence and infection risk.

The findings revealed a lower prevalence of pulmonary TB in areas with previous ACF efforts. The overall prevalence among adults was estimated at 275 per 100,000, but this was notably lower in the prior ACF zones (183 per 100,000) compared to zones without prior interventions (288 per 100,000). A sex-specific trend also emerged: TB prevalence in no prior ACF zones was significantly higher in men than women, while in prior ACF areas, the burden was more evenly distributed between sexes. This suggests that ACF may help reduce disparities and lower overall TB rates.

Despite these promising trends, the study highlighted persistent challenges in TB diagnosis and care. While over 10% of adults were eligible for sputum testing, submission rates were higher in prior ACF areas. However, among those diagnosed with TB, only 26% began treatment, and 57% could not be contacted or refused follow-up. Additionally, a significant proportion of microbiologically confirmed cases were Xpert Ultra ‘trace positive’ only, complicating diagnostic clarity and treatment decisions.

The child M. tuberculosis infection survey further underscored the potential community-level impact of ACF. Among children aged 2 to 4 years, the estimated annual risk of infection (ARTI) was significantly lower in prior ACF zones (0.6%) compared to those without prior interventions (1.1%). This suggests that targeted case-finding in adults may contribute to reducing transmission risk to children. Together, these findings advocate for the continued implementation and expansion of ACF strategies, while also calling attention to the urgent need to improve linkage to care and treatment uptake for those diagnosed.

Source: Khan, P.Y., Paracha, M.S., Grundy, C., Madhani, F., Saeed, S., Maniar, L., Dojki, M., Page-Shipp, L., Khursheed, N., Rabbani, W. and Riaz, N., 2024. Insights into tuberculosis burden in Karachi, Pakistan: A concurrent adult tuberculosis prevalence and child Mycobacterium tuberculosis infection survey. PLOS global public health, 4(8), p.e0002155.

BMI, diabetes, and risk of tuberculosis

The relationship between obesity, diabetes mellitus (DM), and tuberculosis (TB) is multifaceted and often contradictory. Numerous studies highlight that a higher body mass index (BMI) is generally associated with a lower risk of pulmonary TB. This potential protective effect may stem from better nutritional reserves and energy stores in individuals with overweight or obesity. However, this benefit appears to plateau or even reverse at extremely high BMI levels, particularly in specific populations such as young women. These findings suggest that there may be an optimal BMI range where the protective effects are most pronounced. Additionally, demographic factors like age and gender further influence this association, with older adults and women showing stronger protective effects from higher BMI.

Diabetes mellitus, in contrast, consistently emerges as a strong and independent risk factor for TB, regardless of BMI. Individuals with DM are significantly more likely to develop TB, indicating that metabolic dysfunction plays a critical role in TB susceptibility. Some studies suggest that DM may act as a mediator between BMI and TB risk, with obesity-related metabolic impairments such as impaired fasting glucose (IFG) potentially exacerbating the progression of TB. Interestingly, individuals who are both obese and diabetic may not experience a heightened TB risk compared to non-diabetic individuals with normal BMI, indicating a complex interplay of protective and risk factors. Moreover, the association between BMI and TB is stronger for active TB cases than for latent infections (LTBI), highlighting differences in disease dynamics.

These insights have important implications for both clinical practice and public health policy. Clinicians must consider not just BMI, but also the presence of metabolic conditions like DM when assessing a patient's risk for TB. Public health strategies should aim to address the shared and synergistic effects of obesity and DM in TB prevention efforts, particularly among vulnerable populations. While obesity may offer some protection against TB, it also poses other health risks and can contribute to metabolic disorders that increase TB risk. Therefore, a balanced, individualized approach to managing body weight and metabolic health is essential. Further research is needed to clarify the underlying biological mechanisms and to develop targeted interventions that can effectively reduce TB incidence while also addressing the growing global burden of obesity and diabetes.

Source: Herman, D., Machmud, R. and Lipoeto, N.I., 2025. Unraveling the Link between Obesity and Tuberculosis: A Systematic Review of the Underlying Mechanisms. Bioscientia Medicina: Journal of Biomedicine and Translational Research, 9(2), pp.6453-6466.

Tuberculosis with CKD in Ethiopia

A hospital-based prospective cross-sectional study was conducted between January and December 2023 among 381 patients with chronic kidney disease (CKD) attending six selected hospitals across five regions in Ethiopia. These included facilities in Addis Ababa, Dire Dawa, Oromia, Sidama, and Southern Ethiopia. The study aimed to assess TB prevalence, symptomatology, and associated factors among CKD patients.

The study population had a mean age of 45.08 years (ranging from 15 to 90), with males constituting 54.1% of participants. A majority (59.8%) resided in rural areas, over half had no formal education, and more than 80% were married. Notably, 11.8% had a history of cigarette smoking, and 21.8% reported alcohol consumption. About a quarter (24.9%) lived in rooms without windows, indicating poor ventilation, a known risk factor for TB.

All participants reported at least one TB-suggestive symptom, with decreased appetite (89.5%), weight loss (82.4%), and fatigue (76.6%) being the most common. More than two-thirds experienced six or more symptoms. Among those tested, 8.1% had a prior history of TB treatment, and 6.8% were underweight (BMI <18.5 kg/m²). A large majority (94%) were in pre-dialysis stages, with stage 3 CKD being the most common. Only 9.7% had progressed to end-stage renal disease. Elevated serum creatinine was found in 98.2% of participants, and 81.6% reported a history of hospitalization.

TB was diagnosed in 12.9% of the patients, with 10.5% confirmed bacteriologically. Smear-positive TB was detected in 3.7% of cases, while the Xpert MTB/RIF Ultra assay identified TB in 10.5%. Culture-confirmed TB occurred in 7.9%, and an additional 2.4% were diagnosed based on clinical or radiological findings. Most TB cases (67.4%) were pulmonary, while extrapulmonary and disseminated TB each accounted for 16.3%.

TB prevalence was notably higher among individuals aged 25–44, rural residents, smokers, and those living in rooms without windows. Higher TB positivity was also seen in patients reporting night sweats, hemoptysis, or chest pain. In the multivariable logistic regression analysis, factors independently associated with TB included current or past smoking, decreased appetite, night sweats, diabetes mellitus, dipstick-positive albuminuria, short renal follow-up duration (<1 year), low BMI, and being on maintenance hemodialysis.

Overall, this study highlights a significant burden of TB among patients with CKD, emphasizing the need for targeted TB screening and prevention strategies, especially for high-risk subgroups within this vulnerable population.

Source: Alemu, A., Diriba, G., Seid, G., Wondimu, A., Moga, S., Tadesse, G., Haile, B., Berhe, N., Mariam, S.H. and Gumi, B., 2025. Active tuberculosis among patients with presumptive tuberculosis with chronic kidney disease in a high tuberculosis burden country, Ethiopia: a multi-center study. IJID regions, 14, p.100551.

Tuberculosis in Kenya

The study aimed to investigate the factors influencing tuberculosis (TB) medication adherence among patients at Kiambu Level Five Hospital in Kiambu County, Kenya. Despite the presence of various treatment interventions, non-adherence to TB medication remains a persistent challenge in the region. Using an analytical cross-sectional design, the study sampled 141 participants systematically from the hospital's TB clinic.

Demographically, the majority of participants were male (59.6%), while females accounted for 40.4%. The average age was 33.8 years (±12.60), with the largest age group being 25–34 years (39.0%), followed by 18–24 years (22.0%). Only a small proportion (2.8%) were aged 65 and above. Most participants were married (57.4%), while 27.0% had never married, and 15.6% were divorced or separated. Age was found to have a statistically significant association with adherence to TB medication (χ² = 22.873, df = 5, p = 0.001), whereas sex and marital status did not show significant associations.

In terms of education, 34.0% had completed secondary education, 31.9% had college or tertiary education, 22.0% had primary-level education, and 4.3% had attained university-level education. The average monthly income was Ksh. 19,990 (±18,532), with nearly half (46.8%) earning between Ksh. 10,000 and 19,000. A notable portion (17.1%) earned less than Ksh. 10,000, while another 17.7% earned more than Ksh. 30,000. Regarding employment, 27.7% were in informal (casual) work, 24.8% in formal employment, and 22.0% were unemployed. Both occupation (χ² = 27.056, df = 5, p = 0.001) and ownership of agricultural land (χ² = 4.626, df = 1, p = 0.031) were significantly associated with adherence to medication.

Knowledge about TB varied among participants, with 41.8% having moderate knowledge, 39.0% low knowledge, and only 19.2% possessing high knowledge. The mean knowledge score was 58.425 (±16.417). Knowledge was significantly linked to adherence (χ² = 10.102, df = 2, p = 0.006), with those having moderate and high knowledge levels being 14.8% and 18.2% more likely, respectively, to adhere to medication compared to those with low knowledge.

Health-related behaviors revealed that 30.5% had experienced a recent ailment, most commonly flu (32.6%), followed by diarrhea (18.6%), vomiting (18.6%), pneumonia (16.3%), and headache (14.0%). Only 4.3% of participants smoked tobacco, and 15.6% consumed alcohol—predominantly males (77.3%).

Medication adherence behaviors showed that 75.0% of respondents had high adherence, and 25.0% had moderate adherence. There were no reports of low adherence. However, some challenges were reported: 4.4% occasionally forgot to take their medication, 10.2% missed doses for reasons other than forgetfulness, 4.4% forgot medication while traveling, 1.4% missed a dose the previous day, 3.7% felt hassled about sticking to the regimen, and 5.2% often had difficulty remembering to take all their medication.

Further statistical analysis indicated that age significantly influenced adherence (p = 0.042, t = 2.071), with older participants showing better adherence. Although sex had a minor positive association with adherence, it was not statistically significant. Income showed a significant positive correlation with adherence (p = 0.010, t = 2.654), suggesting that higher income levels support better adherence. Interestingly, while occupation was not a significant predictor (p = 0.182), owning agricultural land positively influenced adherence (p = 0.025, t = 2.293).

In conclusion, the study identified age, income, land ownership, and TB knowledge as key determinants of TB medication adherence in Kiambu County. The findings emphasize the need for targeted interventions focusing on education, economic support, and knowledge dissemination to improve adherence rates and ultimately enhance TB treatment outcomes.

Source: Kamui, I. N. (2025). Determinants of tuberculosis medication adherence among TB patients at Kiambu Level Five Hospital, Kiambu County, Kenya. Journal of Medical and Health Sciences, 4(1), 1–15.

Transmission, Comorbidities, and Treatment

1. Smear-Negative TB and Aerosol-Generating Procedures: Transmission Risks Redefined

• Theory: Smear-negative, culture-positive (SmN) TB patients were traditionally seen as less infectious than smear-positive (SmP) ones.
• Key Findings: SmN patients undergoing aerosol-generating procedures (AGPs) had a comparable transmission rate to SmP patients (LTBI: ~15% in both groups); AGPs independently raised transmission risk.
• Conclusion: Infection control protocols should expand to include SmN patients receiving AGPs, with universal LTBI screening and treatment for close contacts.

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2. Comorbidities and Treatment Customization: The Cost of Non-Standard Regimens

• Theory: Standard TB regimens are optimal, but patient-specific comorbidities (e.g., liver disease) necessitate modifications.
• Key Findings: Only 3.7% received non-standard treatments, mostly due to liver, eye, or metabolic conditions; these patients faced longer durations, more interruptions, and higher loss to follow-up.
• Conclusion: There's a critical need to design validated alternative regimens and proactively plan care for patients with contraindications to standard TB drugs.

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3. Diabetes and TB: Dual Burden and Its Clinical Consequences

• Theory: Coexisting metabolic disorders such as diabetes impair immune response and may worsen TB outcomes.
• Key Findings:
• Conclusion: Integrated care is essential, with aggressive glycemic management and early DM screening to improve TB outcomes.

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4. MDR/RR-TB: Treatment Evolution and the COVID-19 Disruption

• Theory: Drug-resistant TB strains are harder to treat and monitor, and service disruptions exacerbate outcomes.
• Key Findings: Shorter, all-oral regimens led to significantly better outcomes (74% success vs. 46%); the COVID-19 pandemic reversed years of progress in treatment success.
• Conclusion: Emphasizes the need for resilient TB programs, all-oral regimens, and decentralized care, especially during global health crises.

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5. Household Behavior and TB Prevention: Knowledge as the Core Driver

• Theory: Behavioral and cultural determinants at the family level are crucial for TB prevention.
• Key Findings: Poor TB prevention behaviors were strongly linked to low knowledge (Exp(B)=46.9), poor healthcare access, anti-prevention cultural norms, and lack of personal TB experience.
• Conclusion: Effective TB control requires targeted health education, culturally sensitive messaging, and enhanced community-based prevention strategies.

References:

1. Yang, Y.J., Pan, S.C., Lee, M.R., Chung, C.L., Ku, C.P., Liao, C.Y., Tsai, T.Y., Wang, J.Y., Fang, C.T. and Chen, Y.C., 2024. Quantifying the contribution of smear-negative, culture-positive pulmonary tuberculosis to nosocomial transmission. American Journal of Infection Control, 52(7), pp.807-812.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.

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TB Prevention Challenges

1. TB Infection Estimates and Changing Paradigms

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

See also: Lin TB Lab

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

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

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3. Evolving TPT Regimens and Future Innovations

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

See also: Yoseph Samodra

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4. Data-Driven Screening and Risk Prediction Tools

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

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5. Clinical Predictors of TB Mortality in the Elderly

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

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6. Isoniazid Monoresistance and Early Treatment Outcomes

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

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7. Environmental Risk Factor: Air Pollution and TB

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

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

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

References:

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

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