Evaluating the impact of cash transfers on TB

Tuberculosis (TB) continues to pose a significant global health challenge, particularly for the most vulnerable populations. In 2021 alone, 4 million of the estimated 10 million new TB cases went unaccounted for, while many who were diagnosed struggled to complete the evaluation process due to economic barriers. TB disproportionately affects low-income individuals and families, leading to severe financial strain—causing months of lost productivity, a significant portion of annual household earnings, and even impacting national economies. Addressing these socioeconomic challenges is crucial, as financial insecurity often forces patients to prioritize immediate survival over seeking and completing medical care.[2]

Person-centered approaches, such as cash transfers, have emerged as promising strategies to bridge these gaps in TB care. These financial supports help patients afford transportation costs, ensuring they can attend crucial diagnostic and treatment appointments. Even a modest, one-time cash transfer has been shown to significantly improve adherence to the TB diagnostic process, increasing the completion rate of each step by two to four times. While the intervention did not significantly impact treatment initiation, it successfully enabled more patients to complete their diagnostic evaluations—an essential step toward better TB control. These findings highlight the need for more sustained and comprehensive economic support to improve long-term health outcomes for TB-affected individuals, reinforcing the importance of integrating financial interventions into TB care strategies.[2] See also: https://tbreadingnotes.blogspot.com/2024/09/effects-of-diabetes-mellitus-on.html

The intervention program combining conditional cash transfers with comprehensive TB counseling proved to be a powerful approach in improving treatment adherence and success rates. Patients in the intervention group showed remarkable retention, with significantly lower rates of pretreatment and on-treatment loss to follow-up compared to the control group. More importantly, the intervention led to a notably higher treatment success rate of 82.0%, compared to only 66.9% in the control group. By reducing the risk of unsuccessful outcomes by nearly half, this approach demonstrated its effectiveness in ensuring that more patients complete their TB treatment and recover successfully.[1] See also: https://tbreadingnotes.blogspot.com/2024/09/tb-and-dm-increased-hospitalisations.html

Beyond clinical outcomes, the study highlighted the positive impact of socioeconomic stability on treatment success. Patients with full-time employment and food security had a significantly lower risk of unsuccessful outcomes, emphasizing the importance of social support in healthcare. The intervention itself was highly efficient, with 93.8% of eligible cash transfers successfully issued, despite minor logistical challenges. With a large sample size and real-world setting, the study provides robust evidence that integrating financial and behavioral support into TB programs can significantly improve patient retention and health outcomes. These findings offer valuable insights for policymakers aiming to enhance TB care, particularly in resource-limited settings.[1] See also: https://tbreadingnotes.blogspot.com/2024/09/nutritional-status-in-patients-with-tb.html

References:

1. Ismail, Nazir, Harry Moultrie, Judith Mwansa-Kambafwile, Andrew Copas, Alane Izu, Sizulu Moyo, Donald Skinner et al. "Effects of conditional cash transfers and pre-test and post-test tuberculosis counselling on patient outcomes and loss to follow-up across the continuum of care in South Africa: a randomised controlled trial." The Lancet Infectious Diseases (2025).

2. Shete, P.B., Kadota, J.L., Nanyunja, G., Namale, C., Nalugwa, T., Oyuku, D., Turyahabwe, S., Kiwanuka, N., Cattamanchi, A. and Katamba, A., 2023. Evaluating the impact of cash transfers on tuberculosis (ExaCT TB): a stepped wedge cluster randomised controlled trial. ERJ open research, 9(3).

Exploring Diagnostic Methods for Drug-Resistant Tuberculosis

In high-burden settings, tuberculosis (TB) diagnosis often relies on sputum smear microscopy, a method with limited sensitivity, particularly among HIV-infected patients. Traditional culture-based methods, though accurate, are slow and expensive, making them impractical for resource-limited areas. The introduction of Xpert MTB/RIF revolutionized TB diagnosis by providing rapid, automated detection of TB and rifampicin resistance using real-time PCR. This cartridge-based system is user-friendly, allowing relatively unskilled healthcare workers to obtain results in under two hours. Recognizing its potential, the World Health Organization (WHO) recommended Xpert as the initial diagnostic tool for suspected multidrug-resistant TB (MDR-TB) and HIV-associated TB cases in 2010. Its implementation has been particularly impactful in southern Africa, improving the detection of smear-negative TB cases and enabling early treatment initiation.[3]

Despite its advantages, Xpert alone is insufficient to drastically reduce TB incidence in the long term. The persistence of a large pool of latently infected individuals and the lower transmission likelihood of smear-negative cases mean that TB remains a significant public health challenge even after decades of Xpert use. Furthermore, its widespread adoption places additional demands on healthcare systems, increasing the need for first-line TB treatment, HIV management, and second-line therapies for drug-resistant cases. While Xpert has enhanced TB control efforts, reducing the global burden of TB requires a comprehensive approach, including preventive strategies, improved treatment regimens, and strengthened healthcare infrastructure.[3]

MTB drug resistance is categorized into intrinsic (cell wall permeability, efflux pumps, metabolism) and acquired mechanisms (gene mutations like inhA and rrs). Capreomycin (CPM) resistance stems from tlyA and rrs mutations, altering ribosomal methylation and drug targets.[1]

The Roche™ solid ratio method, though cost-effective, requires long culture times, while the nitrate reductase test offers faster MTB sensitivity assessment. Line probe assay (LPA) detects rifampicin resistance with high accuracy and is WHO-approved for MDR-TB diagnosis. Digital PCR enhances heterologous resistance detection, and next-generation sequencing (NGS) enables high-throughput pathogen analysis but may overlook resistance data.[1]

Emerging technologies continue to refine Mycobacterium tuberculosis (MTB) drug resistance testing, balancing speed, accuracy, and accessibility. Laboratories are well-equipped to test resistance to established drugs like fluoroquinolones (FQs) and second-line injectables (SLIDs) but face challenges with novel drugs like bedaquiline (BDQ). Phenotypic drug susceptibility tests (pDSTs) remain the gold standard, classifying bacteria as resistant based on growth in drug-containing media. While accurate, pDSTs are slow, requiring up to six weeks for results. Automated systems like BACTEC MGIT 960 and Sensititre streamline pDST by detecting mycobacterial growth through fluorescence or microdilution, respectively. Colorimetric assays such as the Alamar Blue Assay and Thin-Layer Agar (TLA) method provide low-cost alternatives, particularly for low-resource settings.[2]

Molecular drug susceptibility tests (DSTs) offer faster results by detecting resistance-associated gene mutations, bypassing the need for culture. PCR-based assays like Xpert MTB/RIF and Xpert MTB/XDR, endorsed by WHO, enable rapid detection of MTB and rifampicin resistance within hours. Line probe assays (LPAs) such as GenoType MTBDRplus and MTBDRsl extend detection to isoniazid and second-line drugs, aiding multidrug-resistant tuberculosis (MDR-TB) diagnosis. Whole-genome sequencing (WGS) provides the most comprehensive resistance profiling, analyzing known and emerging mutations, though its complexity and cost limit widespread use. Platforms like Illumina and MinION offer sequencing flexibility, with MinION being particularly cost-effective and portable.[2]

Advanced methodologies like MALDI-TOF MS and QuantaMatrix Multiplexed Assay Platform (QMAP) refine resistance detection by analyzing bacterial protein profiles or magnetic microparticles. These approaches, alongside bioinformatics tools like TBProfiler and Resistance Sniffer, enhance accuracy while reducing turnaround times. Despite technological advancements, traditional phenotypic testing remains irreplaceable for confirming drug efficacy. Integrating molecular, sequencing, and phenotypic methods ensures a robust framework for MTB resistance surveillance, enabling tailored treatment strategies and reducing transmission risks.[2]

Nanopore-targeted sequencing improves pneumonia pathogen detection. Gene-core technology identifies rpoB, katG, and inhA mutations. Rifampicin (RIF) inhibits MTB transcription, but rpoB mutations drive resistance, with metabolic changes offering new drug targets. Spectral analysis aids MTB detection. MALDI-TOF MS, first used in 2004, provides rapid, accurate, high-throughput MTB identification and resistance screening.[1]

Phenotypic drug sensitivity testing is accurate but slow, while molecular methods are fast but struggle with heterogeneous resistance and silent mutations. Emerging technologies show promise but face challenges like false positives and incomplete reverse transcription.[1]

References:

1. Xiong, X.S., Zhang, X.D., Yan, J.W., Huang, T.T., Liu, Z.Z., Li, Z.K., Wang, L. and Li, F., 2024. Identification of Mycobacterium tuberculosis resistance to common antibiotics: an overview of current methods and techniques. Infection and Drug Resistance, pp.1491-1506.

2. Sanchini, A., Lanni, A., Giannoni, F. and Mustazzolu, A., 2024. Exploring Diagnostic Methods for Drug-Resistant Tuberculosis: A Comprehensive Overview. Tuberculosis, p.102522.

3. Menzies, N.A., Cohen, T., Lin, H.H., Murray, M. and Salomon, J.A., 2012. Population health impact and cost-effectiveness of tuberculosis diagnosis with Xpert MTB/RIF: a dynamic simulation and economic evaluation. PLoS medicine, 9(11), p.e1001347.

Tuberculosis in Pakistan

Thirty high-TB-burden countries account for 87% of global cases, with incidence rates exceeding 150 per 100,000 people. Two-thirds of these cases are concentrated in just eight countries: India, Indonesia, China, the Philippines, Pakistan, Nigeria, Bangladesh, and the Democratic Republic of the Congo. In contrast, England has been classified as a low-incidence area (<10/100,000) since 2017, reporting a rate of 7.3 per 100,000 in 2021. Despite this classification, disparities persist, with 72.8% of TB cases occurring in individuals born outside the UK (36.3/100,000). In 2020, 12.7% of TB patients in England had at least one social risk factor, such as alcohol or drug misuse, homelessness, or imprisonment. Latent TB infection (LTBI) remains a concern, with a 5-10% risk of progression to active TB, typically within five years.[3] See also: TB and Diabetes mellitus

In 2020, 48.6% of TB cases in England were pulmonary, with symptoms including cough, fever, night sweats, and weight loss. The WHO’s ‘End TB’ strategy promotes new diagnostic tools, such as Xpert MTB/RIF, though its sensitivity is lower in smear-negative or extrapulmonary cases. The Xpert MTB/RIF Ultra, introduced in 2017, offers improved sensitivity, particularly for CNS disease and HIV-positive patients. Advanced resistance profiling is available through the FluoroType MTBDR assay and the emerging FluoroType XDR-TB. Point-of-care strategies, including urinary lipoarabinomannan detection, aim to decentralize diagnostics in low-resource settings. For MDR/RR-TB, all-oral regimens are now recommended, avoiding injectables. In England, NICE advises LTBI treatment for patients under 65 with close contact history to drug-sensitive pulmonary or laryngeal TB, though hepatotoxicity risks must be considered in those aged 35-65. Treatment typically involves rifampicin and isoniazid, with WHO-endorsed regimens like weekly rifapentine facing accessibility challenges in Western Europe due to licensing issues.[3]

A study examined the implementation of various Public-Private Mix (PPM) approaches across Pakistan’s four provinces, assessing their contribution to tuberculosis (TB) case notifications and treatment outcomes compared to the public sector within the National TB Program (NTP). Analyzing data from 122 districts, the study included all new and relapse TB cases, revealing that pulmonary TB was more prevalent (79.2%), with clinically diagnosed cases (43.5%) outnumbering bacteriologically confirmed cases (32.5%). Private hospitals played a significant role in detecting bacteriologically confirmed and relapse cases. The overall treatment success rate for PPM-notified cases was 90.6%, with the highest success in NGO facilities (94.9%) and the lowest in parastatal facilities (46.7%). PPM cases had better treatment completion rates (69.4% vs. 64.5%), but also higher unfavorable outcomes (9.4% vs. 6%), with parastatal facilities showing the worst failure rate (53.3%).[1] See also: TB and dialysis

The findings highlight the vital role of PPM in TB case detection across different demographics and regions, emphasizing the effectiveness of GPs, NGOs, and private hospitals in improving treatment outcomes. To enhance TB control efforts, these successful PPM models should be expanded, while parastatal facilities require urgent reforms due to their poor performance. Strengthening PPM implementation is essential for identifying undiagnosed TB cases and curbing the epidemic in Pakistan.[1] See also: TB Predictive Modelling

Smoking remains a significant risk factor for tuberculosis and a major public health concern in Pakistan. However, another study unexpectedly found no statistical association between tuberculosis and diabetes comorbidity, despite existing evidence that diabetic patients face a higher risk of developing TB compared to non-diabetic individuals. With the rising prevalence of diabetes mellitus (DM), particularly in middle- and low-income countries, this trend poses a growing challenge to TB control efforts, emphasizing the need for integrated strategies to address both diseases effectively.[2] See also: Lin TB Lab

References:

1. Ullah, W., Wali, A., Haq, M.U., Yaqoob, A., Fatima, R. and Khan, G.M., 2021. Public–private mix models of tuberculosis care in Pakistan: a high-burden country perspective. Frontiers in public health, 9, p.703631.

2. Khalid N, Ahmad F, Qureshi FM. Association amid the comorbidity of Diabetes Mellitus in patients of Active Tuberculosis in Pakistan: A matched case control study. Pak J Med Sci. 2021;37(3):816-820.

3. Meghji, J., Kon, O.M. and Ainley, A., 2023. Clinical tuberculosis. Medicine, 51(11), pp.768-773.

Immune-endocrine network in diabetes-tuberculosis nexus

Patients with both tuberculosis (TB) and diabetes may experience more severe illness and have a higher risk of spreading infection. A study found a lower prevalence of latent TB infection (LTBI) in individuals with prediabetes (23%) and newly diagnosed diabetes (23%) compared to healthy controls (33%) and chronic diabetes patients (33%). The findings suggest that metabolic changes in prediabetes and early diabetes may influence susceptibility to LTBI.[1]

Prediabetes is associated with meta-inflammation, which leads to insulin resistance (IR) and increased insulin production by pancreatic beta cells. This stage is characterized by a unique immune response, marked by increased levels of innate immune markers such as interferon-β (IFN-β), pro-inflammatory cytokines (TNF-α, IL-1β, IL-6), and anti-inflammatory cytokines (IL-10, IL-1Ra, TGF-β). These cytokines activate macrophages, which play a critical role in restricting Mycobacterium tuberculosis (M. tb) growth. Additionally, newly identified cytokines, IL-27 and IL-38, show contrasting patterns—IL-27 is elevated, while IL-38 remains low. Among adaptive immune cytokines, IL-17 increases, while IL-9 decreases, influencing neutrophil activation and recruitment to the lungs for M. tb clearance.[1]

Macrophages are key players in the inverse relationship between insulin resistance and LTBI. During insulin resistance, adipocytes secrete pro-inflammatory cytokines (TNF-α, IL-6, IL-1β), which polarize macrophages to a pro-inflammatory state. These macrophages can either restrict M. tb growth or exacerbate meta-inflammation, worsening IR. Meanwhile, alveolar macrophages infected with M. tb produce IL-10, which can reduce inflammation but also serve as a reservoir for LTBI. This dual role suggests that immune-metabolic interactions influence TB susceptibility in individuals with prediabetes and diabetes.[1]

As prediabetes progresses to diabetes, the immune response undergoes significant changes. Prediabetes is characterized by increased innate immune markers, while adaptive immune responses, such as IL-2, IL-4, and IFN-γ, begin to decline. A rise in defensins and a drop in adaptive immune cytokines distinguish prediabetes from diabetes. In chronic diabetes, glycemic control and treatment stabilize cytokine levels, with pro- and anti-inflammatory cytokines returning to normal. However, increased levels of TGF-β, IL-1Ra, IFN-β, and α-defensin-1, along with reduced IL-15, IL-27, and IL-38, highlight an altered immune environment.[1]

LTBI further modifies this immune landscape by downregulating key cytokines, including IL-12, IL-2, IL-4, IL-9, IL-15, and IFN-β, while upregulating IL-10, IL-27, IFN-γ, and IL-17. The suppression of IL-12 and increase in IFN-γ suggest alternative pathways for Th1 activation in LTBI-positive diabetic patients, while the reduction in IL-33 and IL-4 indicates a weakened Th2 response. Notably, the downregulation of IFN-β and α-defensin-1 signals impaired TB immunity in chronic diabetes, raising concerns about the accuracy of IGRA tests for LTBI detection in these patients.[1]

The role of other immune cells in this interplay is also significant. Dendritic cells contribute to both insulin resistance and anti-TB immunity, while B cells and neutrophils may exacerbate IR. The impact of humoral immunity on TB remains uncertain, with studies showing both beneficial and harmful effects of antibodies. Additionally, the relationship between BCG vaccination and diabetes risk is debated. Some research suggests that neonatal BCG vaccination may induce meta-inflammation and increase diabetes risk, while a Canadian study found a small protective effect of BCG against type 2 diabetes. Animal studies further suggest that BCG may improve glycemic control, potentially offering protection similar to LTBI.[1]

Reference:

1. Aravindhan, V. and Yuvaraj, S., 2024. Immune-endocrine network in diabetes-tuberculosis nexus: does latent tuberculosis infection confer protection against meta-inflammation and insulin resistance?. Frontiers in Endocrinology, 15, p.1303338.

TB Risk Factors & Progression Risk

· TB Diagnostic Strategies & Cost-Effectiveness

• Varies based on HIV prevalence, drug resistance, and healthcare access.
• Discrete-event simulation (DES) helps assess MDR-TB diagnostics.
• DES tool enhances decision-making in resource-limited settings.
• Incorporating disease transmission models improves predictions.

· Novel TB Vaccines & Impact

• Delay beyond 2025 could reduce effectiveness.
• Adolescent/adult-targeted vaccines may prevent 44M cases & 5M deaths by 2050.
• Accelerated rollout could prevent 65.5M cases & 7.9M deaths.
• Greatest impact in Africa, South-East Asia, and low-income nations.
• High-efficacy, long-lasting vaccines could cut TB mortality by 27%.
• Urgency for policymakers to fast-track vaccine introduction.

· TB & Air Pollution (PM2.5 Exposure)

• PM2.5 linked to higher MDR-TB infection risk and lung damage.
• Different exposure durations impact radiographic severity.
• Smoking, indoor air pollution, and biomass fuel use increase TB risk.
• Air pollution’s TB impact may be underestimated due to socioeconomic factors.

· Household & Environmental Risk Factors

• Solid fuel use contributes to TB risk but evidence remains weak.
• Fine particles, nitrogen oxides, and CO exposure linked to TB.
• Tobacco taxes could fund TB control and clean energy programs.

· Latent TB Infection (LTBI) & Progression Risk

• WHO guidelines prioritize high-risk groups for screening & treatment.
• 11 key risk populations include HIV-positive individuals, healthcare workers, and prisoners.
• Preventive treatment is crucial in the absence of an effective TB vaccine.

· TB Risk Factors by Health Condition

• Corticosteroids: Highest risk when used 30 days before TB diagnosis.
• Diabetes: TB risk 2.33x higher.
• Glomerular Diseases: TB risk 23.36x higher.
• HCV Infection: Higher risk in untreated cases (HR 2.9).
• Cancer: Children with cancer have a 16.82x higher TB risk.
• Rheumatoid Arthritis & Psoriasis: Increased risk with corticosteroid use.
• Vitamin D Deficiency: 5.68x higher risk of progressing to active TB.

See also: Lin TB Lab

Feasible TB Intervention Suggestions

1. Expand Rapid Diagnostic Tools: Increase access to cost-effective and rapid TB diagnostic methods like GeneXpert in resource-limited settings. Implement discrete-event simulation (DES) models to optimize diagnostic strategies for MDR-TB.
2. Accelerate TB Vaccine Development & Rollout: Prioritize fast-track introduction of novel TB vaccines to prevent millions of cases and deaths. Focus on high-burden regions (Africa, South-East Asia) and at-risk populations (adolescents, adults).
3. Strengthen Air Pollution Control Policies: Enforce air quality regulations to reduce PM2.5 and other TB-aggravating pollutants. Promote clean energy solutions (e.g., LPG, electricity) over biomass fuel for cooking and heating.
4. Enhance LTBI Screening & Preventive Treatment: Implement systematic LTBI screening in high-risk groups (HIV-positive individuals, healthcare workers, prisoners). Expand access to preventive therapy (e.g., isoniazid, rifapentine) to reduce progression to active TB.
5. Integrate TB Control into Non-Communicable Disease (NCD) Programs: Strengthen TB screening in diabetes, cancer, and immunosuppressed patients, given their increased TB risk. Provide corticosteroid alternatives or monitor TB risk in patients requiring immunosuppressants.
6. Tax & Regulate Tobacco to Reduce TB Risk: Increase tobacco taxes to discourage smoking, a major TB risk factor. Use tax revenue to fund TB treatment and prevention programs in low-income communities.
7. Improve TB Awareness & Health Education: Conduct public health campaigns on TB transmission, symptoms, and prevention. Educate healthcare workers on early TB detection, drug-resistant TB, and infection control practices.

References:

1. Langley, I., Doulla, B., Lin, H.H., Millington, K. and Squire, B., 2012. Modelling the impacts of new diagnostic tools for tuberculosis in developing countries to enhance policy decisions. Health care management science, 15, pp.239-253.
2. Clark, R.A., Mukandavire, C., Portnoy, A., Weerasuriya, C.K., Deol, A., Scarponi, D., Iskauskas, A., Bakker, R., Quaife, M., Malhotra, S. and Gebreselassie, N., 2023. The impact of alternative delivery strategies for novel tuberculosis vaccines in low-income and middle-income countries: a modelling study. The Lancet Global Health, 11(4), pp.e546-e555.
3. Makrufardi, F., Chuang, H.C., Suk, C.W., Lin, Y.C., Rusmawatiningtyas, D., Murni, I.K., Arguni, E., Chung, K.F. and Bai, K.J., 2024. Particulate matter deposition and its impact on tuberculosis severity: A cross-sectional study in Taipei. Science of the Total Environment, 924, p.171534.
4. Lin, H.H., Suk, C.W., Lo, H.L., Huang, R.Y., Enarson, D.A. and Chiang, C.Y., 2014. Indoor air pollution from solid fuel and tuberculosis: a systematic review and meta-analysis. The International journal of tuberculosis and lung disease, 18(5), pp.613-621.
5. Lai, T.C., Chiang, C.Y., Wu, C.F., Yang, S.L., Liu, D.P., Chan, C.C. and Lin, H.H., 2016. Ambient air pollution and risk of tuberculosis: a cohort study. Occupational and environmental medicine, 73(1), pp.56-61.
6. Lin, H.H., Murray, M., Cohen, T., Colijn, C. and Ezzati, M., 2008. Effects of smoking and solid-fuel use on COPD, lung cancer, and tuberculosis in China: a time-based, multiple risk factor, modelling study. The Lancet, 372(9648), pp.1473-1483.
7. Bigio, J., Viscardi, A., Gore, G., Matteelli, A. and Sulis, G., 2023. A scoping review on the risk of tuberculosis in specific population groups: can we expand the World Health Organization recommendations?. European Respiratory Review, 32(167).

TBC 042 

Paradoxical Link Between Obesity and TB

1. BMI and Drug-Resistant TB

• Underweight individuals show higher susceptibility to isoniazid (INH)-resistant TB.
• Overweight and obese patients have an increased risk of MDR-TB.
• Comorbidities like diabetes and hypertension correlate with higher drug resistance.
• Suggestion: Implement BMI-based TB screening protocols to identify at-risk individuals early.

2. Paradoxical Link Between Obesity and TB

• Obesity is directly protective against TB despite its association with diabetes.
• Higher BMI reduces TB risk even in diabetic individuals.
• Socioeconomic factors may partially explain this protective effect.
• Suggestion: Investigate mechanisms behind obesity’s protective role to refine TB prevention strategies.

3. Gaps in the TB Care Cascade

• Delays in diagnosis and treatment worsen TB outcomes.
• Country-specific factors (e.g., HIV in Kenya, MDR-TB in Moldova) influence TB burden.
• Addressing care gaps can significantly reduce TB incidence and mortality.
• Suggestion: Strengthen TB care pathways with faster diagnosis and treatment initiation.

4. Economic and Healthcare Factors in TB Control

• Higher GDP and healthcare expenditure correlate with lower TB incidence.
• Cost-effective interventions improve access to TB care.
• Financial barriers hinder TB elimination efforts in lower-income settings.
• Suggestion: Increase TB funding through sustainable health financing models.

5. Strategies for TB Elimination

• Country-specific interventions (e.g., nutrition in India, latent TB treatment in China) are essential.
• Active Case Finding (ACF) is hindered by logistical, administrative, and social barriers.
• Integrating TB screening with other health programs enhances outreach.
• Suggestion: Streamline ACF processes with digital tools and better community incentives.

References:

1. Song, W.M., Guo, J., Xu, T.T., Li, S.J., Liu, J.Y., Tao, N.N., Liu, Y., Zhang, Q.Y., Liu, S.Q., An, Q.Q. and Li, Y.F., 2021. Association between body mass index and newly diagnosed drug-resistant pulmonary tuberculosis in Shandong, China from 2004 to 2019. BMC pulmonary medicine, 21, pp.1-14.
2. Lin, H.H., Wu, C.Y., Wang, C.H., Fu, H., Lönnroth, K., Chang, Y.C. and Huang, Y.T., 2018. Association of obesity, diabetes, and risk of tuberculosis: two population-based cohorts. Clinical Infectious Diseases, 66(5), pp.699-705.
3. Vesga, J.F., Hallett, T.B., Reid, M.J., Sachdeva, K.S., Rao, R., Khaparde, S., Dave, P., Rade, K., Kamene, M., Omesa, E. and Masini, E., 2019. Assessing tuberculosis control priorities in high-burden settings: a modelling approach. The Lancet Global Health, 7(5), pp.e585-e595.
4. Menzies, N.A., Gomez, G.B., Bozzani, F., Chatterjee, S., Foster, N., Baena, I.G., Laurence, Y.V., Qiang, S., Siroka, A., Sweeney, S. and Verguet, S., 2016. Cost-effectiveness and resource implications of aggressive action on tuberculosis in China, India, and South Africa: a combined analysis of nine models. The Lancet global health, 4(11), pp.e816-e826.
5. Sorokina, M., Ukubayev, T. and Koichubekov, B., 2023. Tuberculosis incidence and its socioeconomic determinants: developing a parsimonious model. Annali di Igiene, Medicina Preventiva e di Comunita, 35(4): 468-479.
6. Houben, R.M., Menzies, N.A., Sumner, T., Huynh, G.H., Arinaminpathy, N., Goldhaber-Fiebert, J.D., Lin, H.H., Wu, C.Y., Mandal, S., Pandey, S. and Suen, S.C., 2016. Feasibility of achieving the 2025 WHO global tuberculosis targets in South Africa, China, and India: a combined analysis of 11 mathematical models. The Lancet Global Health, 4(11), pp.e806-e815.
7. Shewade, H.D., Ravichandran, P., Pradeep, S.K., Kiruthika, G., Shanmugasundaram, D., Chadwick, J., Iyer, S., Chowdhury, A., Tumu, D., Shah, A.N. and Vadera, B., 2024. Bridging the “know-do” gap to improve active case finding for tuberculosis in India: A qualitative exploration into national tuberculosis elimination program staffs’ perspectives. PloS one, 19(11), p.e0309750.

TBC 041

Diabetes mellitus and tuberculosis

A systematic review of studies on diabetes mellitus (DM) and tuberculosis (TB) risk encompassed multiple WHO regions, including five in the Americas (Peru, USA, Canada, Brazil), six in Europe (Spain, Greenland/Denmark, UK), one in Africa (Ethiopia), one in the Eastern Mediterranean (Yemen), 25 in the Western Pacific (China, Singapore), and 11 in Southeast Asia (India, South Korea, Thailand). These studies primarily focused on adults, with nine also including children and adolescents. The diagnosis of DM was generally based on clinical records, fasting blood glucose levels, or glucose-lowering prescriptions, with limited differentiation between type 1 and type 2 diabetes.[1] See also: https://tbreadingnotes.blogspot.com/2024/07/feasibility-of-achieving-2025-who.html

The evidence from these studies suggests that DM may increase the risk of TB. Various metrics showed a heightened risk, with a hazard ratio (HR) of 1.90 (95% CI 1.51–2.40), an odds ratio (OR) of 1.61 (95% CI 1.27–2.04), and a relative risk (RR) of 1.60 (95% CI 1.42–1.80), though all findings had low to moderate certainty due to potential biases and inconsistencies. Furthermore, DM appears to elevate the risk of TB recurrence, with a hazard ratio of 1.35 (95% CI 0.76–2.42), although results varied significantly. The data suggests that the risk of TB may be particularly high within the first decade after DM diagnosis, and possibly extends beyond 10 years. Reducing the burden of diabetes could play a crucial role in TB elimination efforts, highlighting the need for integrated strategies to address both conditions.[1] See also: https://tbreadingnotes.blogspot.com/2024/07/cost-effectiveness-and-resource.html

The study of Type 2 Diabetes Mellitus (DM2) as a risk factor for Tuberculosis (TB) is complicated by the vast heterogeneity of populations worldwide. Factors such as age, access to medical care, the level of glucose control, the types and number of complications associated with DM2, and the availability of medications can all influence the outcomes of research in this area. This complexity is further compounded by regional differences in healthcare infrastructure and socioeconomic conditions, making it challenging to generalize findings across different populations. Despite these challenges, the study indicates that military personnel with DM2 experience a higher prevalence of recurrent TB compared to those without the condition, as well as a more rapid increase in cumulative risk for recurrent TB over time.[2] See also: https://tbreadingnotes.blogspot.com/2024/07/ambient-air-pollution-and-risk-of.html

In countries with limited to moderate healthcare resources, such as Peru, where both TB and DM2 are prevalent, military personnel with DM2 may be at heightened risk, particularly due to exposure in fieldwork conditions. However, despite these findings suggesting a possible link, the relationship between DM2 and recurrent TB in military personnel was not found to be statistically significant. This highlights the need for further research to better understand the role of contextual factors, such as resource availability and environmental exposure, in shaping the relationship between DM2 and TB. Understanding these nuances is crucial for developing targeted interventions, especially in resource-limited settings where both diseases are major public health concerns.[2] See also: https://tbreadingnotes.blogspot.com/2024/07/tuberculosis-in-healthcare-workers.html

References:

1. Franco, J.V., Bongaerts, B., Metzendorf, M.I., Risso, A., Guo, Y., Silva, L.P., Boeckmann, M., Schlesinger, S., Damen, J.A., Richter, B. and Baddeley, A., 2024. Diabetes as a risk factor for tuberculosis disease. The Cochrane database of systematic reviews, 8, p.CD016013. 

2. Alvarado-Valdivia, N.T., Flores, J.A., Inolopú, J.L. and Rosales-Rimache, J.A., 2024. Type 2 diabetes mellitus and recurrent Tuberculosis: A retrospective cohort in Peruvian military workers. Journal of Clinical Tuberculosis and Other Mycobacterial Diseases, 35, p.100432.

Tuberculosis in Kenya

A study aimed to assess the extent of pre-treatment loss to follow-up (PTLFU) among adults with pulmonary tuberculosis (PTB) in western Kenya and to identify associated patient factors. The research utilized a retrospective record review from January 2018 to December 2021, examining laboratory and treatment registers at the Jaramogi Oginga Odinga Teaching and Referral Hospital (JOOTRH) in Kisumu. The population studied comprised adults (≥18 years) with bacteriologically confirmed PTB. This method proved suitable for determining PTLFU rates and associated factors, though it depended on the accuracy of recorded data and did not account for patient behaviors or external systemic influences.[1]

The study reviewed independent variables including demographics, contact information, residence, HIV status, TB history, diagnosis methods, and linkage to treatment. The primary dependent variable was the time from diagnosis to treatment initiation. The analysis found a PTLFU rate of 42.4% among the 476 participants studied. Significant risk factors included limited contact details, with those having only a physical address or a telephone number facing markedly higher odds of PTLFU compared to those with both types of contact information. Additionally, older adults (≥55 years) were more likely to experience PTLFU. Factors such as sex, HIV status, place of residence, and prior TB treatment did not significantly impact PTLFU after adjusting for confounders. The study concluded that a significant proportion of adults with PTB in western Kenya are lost to follow-up before treatment, with restricted contact details and older age being key risk factors.[1]

Another paper highlights that enhancing the screening of asymptomatic and latently infected individuals is crucial for decreasing infection transmission among susceptible populations. It posits that the most effective strategy for reducing tuberculosis (TB) transmission involves a combined approach of vaccination, screening, and treatment of all forms of the disease. Specifically, screening and treating all pulmonary tuberculosis forms is more beneficial than merely vaccinating and treating symptomatic individuals. The least effective method identified is treating only those who exhibit symptoms, which minimally curtails transmission.[2]

The study emphasizes the importance of focusing on latent infections and asymptomatic carriers. By screening and treating these groups, the development of pulmonary tuberculosis can be curtailed, significantly reducing transmission rates. Furthermore, managing the asymptomatic infectious population effectively decreases the spread of infections to susceptible individuals, further reducing transmission rates.[2]

Men face a disproportionately high burden of tuberculosis (TB) but often exhibit poor health-seeking behavior, leading to higher mortality and treatment failure. Structural barriers, such as the concentration of TB diagnostic facilities in urban areas and an overburdened healthcare system, further limit access, particularly for rural populations. Social stigma, cultural beliefs favoring traditional medicine, and weak governance also contribute to delayed treatment and poor outcomes. Additionally, TB policies in Kenya lack gender-specific interventions, and limited research funding prevents evidence-based policy improvements. Heavy reliance on donor funding further raises sustainability concerns for TB care programs.[3]

To address these challenges, targeted interventions should focus on gender-responsive TB strategies, decentralizing healthcare facilities, and implementing community-based anti-stigma initiatives. Strengthening collaboration between national and county governments, increasing domestic TB funding, and promoting evidence-informed decision-making are also crucial. Male-friendly outreach programs, such as screenings at social gathering places, can improve TB case detection and treatment adherence. By prioritizing these solutions, Kenya can enhance TB care outcomes and reduce disparities in treatment access.[3]

References:

1. Mulaku, M.N., Ochodo, E., Young, T. and Steingart, K.R., 2024. Pre-treatment loss to follow-up in adults with pulmonary TB in Kenya. Public Health Action, 14(1), pp.34-39.

2. Kirimi, E.M., Muthuri, G.G., Ngari, C.G. and Karanja, S., 2024. A Model for the Propagation and Control of Pulmonary Tuberculosis Disease in Kenya. Discrete Dynamics in Nature and Society, 2024(1), p.5883142.

3. Abdullahi, L.H., Oketch, S., Komen, H., Mbithi, I., Millington, K., Mulupi, S., Chakaya, J. and Zulu, E.M., 2024. Gendered gaps to tuberculosis prevention and care in Kenya: a political economy analysis study. BMJ open, 14(4), p.e077989.

Tuberculosis in Tanzania

A study included a large number of participants in Tanzania, mostly adults aged 25–49, with a high proportion being male. Coastal and lake regions had the most participants. A significant portion was HIV positive, and the majority had pulmonary TB. Most patients were self-referred and managed at hospitals, with nearly all treated using community-based DOT and first-line TB treatment. Bacteriological diagnosis was more common.[1]

Newly diagnosed TB patients were the vast majority, while recurrent TB cases were rare. Key risk factors for TB recurrence included older age, male sex, HIV positivity, referral from CTC, bacteriological diagnosis, and facility-based DOT. Patients in Zanzibar had a notably higher recurrence risk. Among recurrent TB cases, some experienced poor treatment outcomes, with death being the most common. Risk factors for poor outcomes included HIV positivity, treatment in certain regions (central, coastal, Zanzibar), bacteriological diagnosis, and facility-based DOT.[1]

Expanding new diagnostic methods and algorithms could enhance tuberculosis detection while reducing delays in treatment initiation. Among available options, the full rollout of Xpert (B1) offers the most significant patient benefits. It decreases the number of visits required for diagnosis, shortens the time to treatment by nearly a week, and reduces diagnostic loss to follow-up, ultimately increasing successful treatment completion. At the health-system level, scaling up Xpert significantly lowers the need for sputum samples and laboratory staff time, easing resource burdens. Additionally, its implementation is expected to have the greatest impact on reducing tuberculosis prevalence, mortality, and incidence. Over a decade, Xpert could prevent tens of thousands of tuberculosis cases and related deaths, particularly improving survival rates for tuberculosis and HIV co-infected patients by expanding access to antiretroviral therapy.[2]

Despite its advantages, Xpert's implementation requires substantial financial investment. However, it remains one of the three most cost-effective diagnostic strategies in Tanzania. Full Xpert rollout is estimated to cost $169 per DALY averted, making it a viable option despite higher initial resource demands. Alternative strategies, such as same-day LED fluorescence microscopy (A3) and standard LED fluorescence microscopy (A2), offer lower-cost solutions at $45 and $29 per DALY averted, respectively. While these approaches may be more affordable, they do not match Xpert's comprehensive benefits in improving patient outcomes and reducing tuberculosis burden. Balancing cost-effectiveness with epidemiological impact will be crucial in determining the optimal diagnostic strategy for widespread implementation.[2] 

References:

1. Njiro, B.J., Kisonga, R., Joachim, C., Sililo, G.A., Nkiligi, E., Ibisomi, L., Chirwa, T. and Francis, J.M., 2024. Epidemiology and treatment outcomes of recurrent tuberculosis in Tanzania from 2018 to 2021 using the National TB dataset. PLOS Neglected Tropical Diseases, 18(2), p.e0011968.

2. Langley, I., Lin, H.H., Egwaga, S., Doulla, B., Ku, C.C., Murray, M., Cohen, T. and Squire, S.B., 2014. Assessment of the patient, health system, and population effects of Xpert MTB/RIF and alternative diagnostics for tuberculosis in Tanzania: an integrated modelling approach. The Lancet Global Health, 2(10), pp.e581-e591.

Tuberculosis in Madagascar

A study in the Antanimora prison in Antananarivo, the capital of Madagascar, found a high prevalence of tuberculosis (TB) among detainees, with confirmed active TB cases at 0.5% (4/748) and probable cases at 1.3% (10/748), resulting in a total active TB prevalence of 1.9%. Latent TB was significantly higher at 69.6% (517/743; 95% CI: 66.27–72.89). HIV prevalence was low at 0.4% (3/745), and no TB/HIV coinfection was detected. Univariable analysis identified key risk factors, including age ≥40 years (OR = 5.6), previous incarceration (OR = 7.1), prior TB history (OR = 8.4), and TB treatment history (OR = 9.7). Multivariable regression confirmed that older detainees were 4.4 times more likely to have active TB, while those with prior TB treatment had a 6.3-fold increased risk. Although confidence intervals were wide, the associations remained significant.[1]

These findings highlight the urgent need for targeted TB screening and prevention strategies in prison settings, particularly for older detainees and those with prior TB treatment. The study successfully addressed the prevalence of TB and HIV and identified key risk factors, aligning with its research objectives. With a high latent TB burden and a notable risk concentration among older detainees, the results underscore the importance of enhanced TB surveillance and intervention efforts.[1]

Social network analysis (SNA), enriched by ethnographic data on human interactions, can enhance the realism of compartmental models by capturing the impact of social structures on disease transmission. From another study in Madagascar, despite 15 years of intervention, latent TB infection prevalence showed only a slight decline, highlighting the persistence of TB reservoirs even after systematic treatment of active cases. The intensity of social contacts plays a crucial role in TB exposure, yet conventional transmission models often overlook these inter-community differences, underscoring the need for more nuanced approaches to understanding and controlling TB spread.[2]

References:

1. Rakotomanana, F., Dreyfus, A., Randrianarisoa, M.M., Raberahona, M., Chevallier, E., Andriamasy, H.E., Bernardson, B.A., Ranaivomanana, P., Ralaitsilanihasy, F., Rasoamaharo, M. and Randrianirisoa, S.A., 2024. Prevalence of pulmonary tuberculosis and HIV infections and risk factors associated to tuberculosis in detained persons in Antananarivo, Madagascar. Scientific Reports, 14(1), p.8640.

2. Pando, C., Hazel, A., Tsang, L.Y., Razafindrina, K., Andriamiadanarivo, A., Rabetombosoa, R.M., Ambinintsoa, I., Sadananda, G., Small, P.M., Knoblauch, A.M. and Rakotosamimanana, N., 2023. A social network analysis model approach to understand tuberculosis transmission in remote rural Madagascar. BMC Public Health, 23(1), p.1511.