Integrated Disease Management

1. Diabetes and TB Risk & Progression

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

2. Immune System Interactions & Inflammatory Response

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

3. Public Health & Integrated Disease Management

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

References:

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

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Geo-Spatial Insights on TB

1. Geo-Spatial Mapping and TB Surveillance

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

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

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

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

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

4. Challenges and Strategies in TB-DM Management

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

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

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

References:

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

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Tuberculosis in Brazil

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

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

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

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

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

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

References:

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

Non-communicable diseases in TB household contacts

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

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

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

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

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

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

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

References:

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

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

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

Tuberculosis in Sumatera

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

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

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

References:

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

BMI, diabetes, and risk of tuberculosis

A population-based cohort study in Eastern China (2013–2021) followed 27,807 individuals to examine tuberculosis (TB) incidence and risk factors. The median age was 50 years, with 18.3% over 65 years old. About half were female, and 34.8% were overweight or obese. Smoking and alcohol consumption were reported by 20.9% and 17.4%, respectively, while 58.8% had BCG vaccination. Diabetes prevalence was 6%.[1] See also: https://tbreadingnotes.blogspot.com/2024/08/assessing-spatiotemporal-patterns-of.html

Over seven years, 108 individuals developed TB (incidence rate: 50.4 per 100,000 person-years). Diabetes was linked to a higher TB incidence (131.2 vs. 47.1 per 100,000 person-years, P = 0.008). Among those with a BMI ≤24 kg/m², diabetics had a 3.4 times higher TB incidence than non-diabetics, whereas no difference was seen among those with a BMI >24 kg/m².[1] See also: https://tbreadingnotes.blogspot.com/2024/08/a-modelling-framework-to-support.html

Males had a higher TB incidence than females, while BCG scars were associated with lower TB rates. Risk factors in a multivariable model included male sex, older age, and diabetes. Higher BMI was protective against TB. Among those with BMI ≤24 kg/m², diabetes significantly increased TB risk, but not in those with BMI >24 kg/m². BCG scars reduced TB risk in individuals with low BMI.[1]

A study using data from Korea's National Health Insurance Service analyzed 4.4 million adults and found that diabetes increases TB risk by 48%, with a 57% higher risk in those diabetic for 5+ years. The association is stronger in men and younger adults, and new diabetics with the highest fasting plasma glucose levels face a 79% increased TB risk.[2]

A Taiwan NHIRD study (2002–2013) found that adults face a higher risk of diabetes, AMI, and stroke after TB treatment, particularly if treatment lasts 7–12 months. Age, gender, and pre-existing NCDs are key predictors, highlighting the need for vigilant post-TB monitoring.[3]

References:

1. 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.

2. 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.

3. 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.

Health System Strengthening

Tuberculosis Preventive Therapy (TPT) Initiation & Health System Strengthening

• Health system strengthening interventions increased TPT initiation among household contacts, especially in LMICs.
• Countries with policies supporting TPT for all age groups had higher initiation rates (up to 78%).
• Cost-effective implementation when tailored to local contexts and sustained with training/support.

TB Diagnosis & Infection Control in Healthcare Facilities

• Transition to auramine-rhodamine fluorescence microscopy improved early TB detection and reduced patient infectiousness.
• The proportion of hospitalized TB cases dropped from 45% to 27%; median non-isolated infectiousness reduced from 12.5 days to 3 days.
• Upgraded ventilation, UV germicidal systems, and early physician alertness enhance TB infection control.
• High-traffic outpatient areas (internal medicine, family medicine) should prioritize TB screening.

Diabetes & Tuberculosis Interaction

• TB-DM patients had lower treatment success rates (74.4% vs. 84.9%) and increased multidrug-resistant TB risk.
• Prediabetes is prevalent among TB patients and affects immune response, potentially worsening TB outcomes.
• Managing dyslipidemia in T2D patients may reduce TB risk; statins might lower active TB incidence.
• BCG vaccination may help modulate lipid metabolism and reduce TB risk in high-risk populations.

TB Diagnosis & Screening in Resource-Limited Settings

• Simple clinical risk scores (age, sex, symptoms, HIV, diabetes) improve early TB identification in clinics with limited diagnostics.
• Combining WHO symptom screening with tuberculin skin tests enhances sensitivity.
• Predictive models incorporating BMI, CD4 count, and ART duration improve TB screening in co-infected populations.
• Immediate, standardized empirical diagnosis needed to prevent TB treatment delays.

TB & Prediabetes

• TB patients with prediabetes face higher risks of treatment failure, recurrence, and modifications.
• The role of prediabetes in TB progression remains unclear, but chronic inflammation and immune dysfunction are potential factors.
• Paradoxically, one study found a 27% lower TB risk in prediabetic individuals, warranting further research.

Practical Recommendations

• Strengthen health system interventions to increase TPT uptake, particularly in LMICs.
• Enhance early TB detection through improved diagnostic tools and physician alertness.
• Implement targeted TB screening and infection control in high-risk hospital areas.
• Improve diabetes management in TB patients to enhance treatment success.
• Use cost-effective clinical risk scores for TB diagnosis in resource-limited settings.
• Further investigate the complex relationship between TB, prediabetes, and metabolic disorders for refined public health strategies.

Curated by Yoseph Leonardo Samodra.

References:

1. Sun H-Y, Wang J-Y, Chen Y-C, Hsueh PR, Chen Y-H, Chuang Y-C, et al. (2020) Impact of introducing fluorescent microscopy on hospital tuberculosis control: A before-after study at a high caseload medical center in Taiwan. PLoS ONE 15(4): e0230067.
2. Pan, S.C., Chen, C.C., Chiang, Y.T., Chang, H.Y., Fang, C.T. and Lin, H.H., 2016. Health care visits as a risk factor for tuberculosis in Taiwan: a population-based case–control study. American journal of public health, 106(7), pp.1323-1328.
3. Oxlade, O., Benedetti, A., Adjobimey, M., Alsdurf, H., Anagonou, S., Cook, V.J., Fisher, D., Fox, G.J., Fregonese, F., Hadisoemarto, P. and Hill, P.C., 2021. Effectiveness and cost-effectiveness of a health systems intervention for latent tuberculosis infection management (ACT4): a cluster-randomised trial. The Lancet Public Health, 6(5), pp.e272-e282.
4. Baik, Y., Rickman, H.M., Hanrahan, C.F., Mmolawa, L., Kitonsa, P.J., Sewelana, T., Nalutaaya, A., Kendall, E.A., Lebina, L., Martinson, N. and Katamba, A., 2020. A clinical score for identifying active tuberculosis while awaiting microbiological results: development and validation of a multivariable prediction model in sub-Saharan Africa. PLoS medicine, 17(11), p.e1003420.
5. Van Wyk, S.S., Lin, H.H. and Claassens, M.M., 2017. A systematic review of prediction models for prevalent pulmonary tuberculosis in adults. The International Journal of Tuberculosis and Lung Disease, 21(4), pp.405-411.
6. Lee, E.H., Lee, J.M., Kang, Y.A., Leem, A.Y., Kim, E.Y., Jung, J.Y., Park, M.S., Kim, Y.S., Kim, S.K., Chang, J. and Kim, S.Y., 2017. Prevalence and impact of diabetes mellitus among patients with active pulmonary tuberculosis in South Korea. Lung, 195, pp.209-215.
7. Segura-Cerda, C.A., López-Romero, W. and Flores-Valdez, M.A., 2019. Changes in host response to mycobacterium tuberculosis infection associated with type 2 diabetes: beyond hyperglycemia. Frontiers in cellular and infection microbiology, 9, p.342.
8. Liang, L. and Su, Q., 2024. Prediabetes and the treatment outcome of tuberculosis: A meta‐analysis. Tropical Medicine & International Health, 29(9), pp.757-767.
9. Ko, T.H., Chang, Y.C., Chang, C.H., Liao, K.C.W., Magee, M.J. and Lin, H.H., 2023. Prediabetes and risk of active tuberculosis: a cohort study from Northern Taiwan. International Journal of Epidemiology, 52(3), pp.932-941.

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Biological, Social, and Environmental Factors in TB

1. Tuberculosis (TB) Epidemiology in Spain

• In 2021, Spain reported 3,754 TB cases, with a notification rate (NR) of 7.61 per 100,000; this marked a 2.18% decline from 2020 and a 28.07% reduction from 2015.
• Spain surpassed its TB control goals by 2020, reducing overall TB rates by 26.5% and pulmonary TB by 6% annually, maintaining its status as a low-incidence TB country.
• Higher TB rates were reported in northern regions—Ceuta, Galicia, Catalonia, Rioja, and the Basque Country—while the Canary Islands and parts of southern Spain had lower rates.
• TB incidence was 1.7 times higher in men than women, with nearly half of the cases among foreign-born individuals, typically younger than native-born cases; TB mortality was 2.6 times higher in males.
• Foreign-born TB cases primarily came from high-burden countries (Morocco, Romania, Bolivia, Peru, Pakistan); over half had lived in Spain for over 10 years, while only 13% were recent arrivals (<2 years).

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2. Geographic, Seasonal, and Environmental Factors in TB

• A clear north–south gradient exists in TB incidence across Spain, with the highest rates in northern provinces (e.g., A Coruña, Pontevedra), well above the national average.
• Regions with higher rainfall and fewer sunshine hours (e.g., Gipuzkoa, Asturias) had significantly higher TB rates, particularly extrapulmonary TB.
• Spatial regression analyses confirmed a strong link between reduced sunlight and higher TB incidence, reinforcing the potential role of vitamin D in TB susceptibility.
• TB cases peak seasonally in spring (May), about four months after winter’s lowest sunlight levels, suggesting an environmental influence on TB trends.
• These findings highlight the relevance of environmental and seasonal factors, particularly sunlight exposure, in modulating TB incidence and informing prevention strategies.

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3. Active Case Finding (ACF) and TB Control in High-Risk Groups

• ACF is effective for early TB detection, especially among migrants, the homeless, prisoners, and impoverished populations, where it outperforms passive case finding.
• ACF strategies include door-to-door screening, mobile clinics, and point-of-care tools like battery-operated GeneXpert systems (e.g., XACT), improving early detection and reducing transmission.
• Mobile clinics are more efficient and scalable than labor-intensive door-to-door approaches, offering quicker treatment initiation and better community reach.
• WHO recommends contact screening and latent TB infection (LTBI) screening for migrants when resources permit; integrating support services can help overcome barriers like legal status and social isolation.
• While ACF has clear benefits in high-risk groups, its effectiveness in the general population of developing countries remains limited, necessitating further research and tailored strategies.

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4. Health Education, Self-Management, and TB Treatment Adherence

• Health education significantly improves self-efficacy, treatment adherence, and disease knowledge in pulmonary TB patients, as shown in multiple studies (Jauhar 2019; Nuwa & Kiik 2021; Haskas 2023).
• Video-based and self-management education enhances adherence, health behaviors, perceived control, and nutritional management, reinforcing the need for patient-centered interventions.
• Knowledge is the dominant factor influencing adherence; interventions based on the health belief model and audiovisual tools have proven effective in improving compliance.
• Family and community support improves psychological well-being and coping, increasing the likelihood of treatment completion in TB patients.
• Self-management education is critical not only in TB but also for chronic diseases like diabetes and hypertension, fostering comprehensive care that includes medication adherence, physical activity, and transmission prevention.

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5. TB-Diabetes Mellitus (DM) Comorbidity: Endocrine-Immune Interactions and Treatment Implications

• TB and T2D comorbidity involves complex endocrine-immune disruptions; cytokines (IL-1, IL-6, TNF-α) trigger HPA and HPT axis activation but suppress the HPG axis, contributing to endocrine dysfunction.
• TB-T2D patients show hormonal imbalances (high cortisol, low DHEA/leptin), impairing immunity (reduced PRR/MHC expression, foam cell formation) and promoting Mycobacterium tuberculosis persistence.
• Hormonal modulators like glucocorticoids, leptin, DHEA, and GH influence lung immunity; experimental therapies targeting hormone receptors (e.g., α-MSH, GHRHR inhibitors) show potential.
• Cholesterol and lipid profiles affect TB risk and treatment outcomes; low cholesterol links to higher TB risk, while elevated triglycerides predict poor outcomes—cholesterol may aid immune defense by enhancing macrophage activity.
• The link between diabetes, BMI, and TB risk remains unclear, especially in Asian populations; distinguishing transient stress-induced hyperglycemia from clinical T2D during TB treatment is essential for accurate diagnosis and management.

Curated by Yoseph Leonardo Samodra

References:

1. Galán, M.D.M.D., Redondo-Bravo, L., Gómez-Barroso, D., Herrera, L., Amillategui, R., Gómez-Castellá, J. and Herrador, Z., 2024. The impact of meteorological factors on tuberculosis incidence in Spain: a spatiotemporal analysis. Epidemiology & Infection, 152, p.e58.
2. Guillén, S.M., et al., 2023. Tuberculosis in Spain: An opinion paper. Rev Esp Quimioter, 36(6), pp.562-583.
3. Pramono, J.S., Ridwan, A., Maria, I.L., Syam, A., Russeng, S.S. and Mumang, A.A., 2024. Active case finding for tuberculosis in migrants: a systematic review. Medical Archives, 78(1), p.60.
4. Esmail, A., Randall, P., Oelofse, S. et al. Comparison of two diagnostic intervention packages for community-based active case finding for tuberculosis: an open-label randomized controlled trial. Nat Med 29, 1009–1016 (2023).
5. Rochmah, A.F., Zahroh, C., Nadatien, I., Setiyowati, E., & Hidaayah, N. (2024). Does education influence self-efficacy in tuberculosis patients? A systematic review. Journal of Applied Nursing and Health, 6(1), 128–138.
6. Yamanaka, T., Castro, M.C., Ferrer, J.P., Solon, J.A., Cox, S.E., Laurence, Y.V. and Vassall, A., 2024. Health system costs of providing outpatient care for diabetes in people with TB in the Philippines. IJTLD open, 1(3), pp.124-129.
7. Webber, T., Ronacher, K., Conradie-Smit, M. and Kleynhans, L., 2022. Interplay between the immune and endocrine systems in the lung: implications for TB susceptibility. Frontiers in immunology, 13, p.829355.
8. Ngo, M.D.; Bartlett, S.; Ronacher, K. Diabetes-Associated Susceptibility to Tuberculosis: Contribution of Hyperglycemia vs. Dyslipidemia. Microorganisms 2021, 9, 2282.

TBC 051