Management of drug-resistant tuberculosis in Indonesia

Lestari, B.W., Nijman, G., Larasmanah, A., Soeroto, A.Y., Santoso, P., Alisjahbana, B., Chaidir, L., Andriyoko, B., Van Crevel, R. and Hill, P.C., 2024. Management of drug-resistant tuberculosis in Indonesia: a four-year cascade of care analysis. The Lancet Regional Health-Southeast Asia, 22:100294.

Low Identification and Diagnosis: Only about a third of estimated TB cases at risk of DR-TB were identified and reported, with only a tenth of the estimated true DR-TB cases diagnosed.

Inadequate Diagnostic Support: Approximately half of the treatment regimens were supported by phenotypic drug susceptibility testing (pDST).

High Unsuccessful Treatment Outcomes: Nearly half of all patients initiating treatment had an unsuccessful outcome.

Significant Delays in Treatment: Delays between diagnosis and treatment were substantial, particularly for patients living further away, those with employment, and those with a history of private sector engagement.

Impact of Undetected DR-TB: Undetected DR-TB contributes to ongoing transmission, which hampers global efforts to end TB. Increased testing and treatment could overburden health systems, worsening issues in the care cascade.

Potential of Active Case Finding: Active case finding strategies, when sustainably implemented within a national TB program, are cost-effective and aid in detecting DR-TB cases, especially new cases.

Challenges in Urban Settings: In this urban setting in Indonesia, significant losses and delays were found in DR-TB case finding, pDST testing, and treatment outcomes, despite relatively high treatment initiation. Improving access to diagnostics and linking patients to subsequent care steps could improve outcomes and reduce transmission.

 

Association between tobacco smoking and active TB in Taiwan

Lin, H.H., Ezzati, M., Chang, H.Y. and Murray, M., 2009. Association between tobacco smoking and active tuberculosis in Taiwan: prospective cohort study. American journal of respiratory and critical care medicine, 180(5), pp.475-480.

·       Current smoking is associated with a twofold increase in active TB risk compared to never-smokers, with the risk escalating based on the number of cigarettes, years of smoking, and pack-years.

·       Smoking accounts for 17% of TB cases in the studied population, highlighting its significant contribution to TB incidence.

·       The risk of TB is higher in current smokers than in former smokers, suggesting a reduced hazard among those who quit smoking.

·       The risk of smoking-related TB is greater in individuals under 65, potentially due to early depletion of susceptible populations among older smokers.

·       Smoking impairs key pulmonary defense mechanisms, increasing susceptibility to TB upon exposure to the pathogen.

A modelling framework to support the implementation of new TB diagnostic tools

Lin, H.H., Langley, I., Mwenda, R., Doulla, B., Egwaga, S., Millington, K.A., Mann, G.H., Murray, M., Squire, S.B. and Cohen, T., 2011. A modelling framework to support the selection and implementation of new tuberculosis diagnostic tools. The International journal of tuberculosis and lung disease, 15(8), pp.996-1004.

· Different diagnostic strategies may be more effective in regions with varying levels of HIV-associated or drug-resistant TB and differing healthcare infrastructure.

· Tests that don't require multiple visits can reduce patient costs and minimize follow-up losses.

· Early detection of TB may lead to better treatment outcomes for patients.

· New diagnostic tools might reduce delays in the lab but could create new bottlenecks in other parts of the healthcare system.

· Changes in diagnostic patterns can shift demand in other areas of the health system.

· While accurate diagnostic tools are important, they alone won't guarantee better TB control.

· The impact of these tools depends on whether they speed up the administration of effective treatment.

· Evaluating the epidemiological impact of new tools is challenging due to TB's slow progression.

· Operational and dynamic epidemiological models can help assess the overall effects of different diagnostic strategies.

The risk of tuberculosis disease among persons with DM

Baker, M.A., Lin, H.H., Chang, H.Y. and Murray, M.B., 2012. The risk of tuberculosis disease among persons with diabetes mellitus: a prospective cohort study. Clinical Infectious Diseases, 54(6), pp.818-825.

Diabetes mellitus (DM) increases the risk of developing tuberculosis disease. See also: https://tbreadingnotes.blogspot.com/2024/09/exploring-diagnostic-methods-for-drug.html

DM shares several risk factors for tuberculosis, including older age, unemployment, and low educational status.

Patients with more severe DM are at a higher risk for tuberculosis. See also: https://tbreadingnotes.blogspot.com/2024/08/clinical-tuberculosis.html

The mechanisms by which DM increases susceptibility to tuberculosis (and possibly other infectious diseases) are not yet well understood.

Tuberculosis elimination efforts must include a focus on DM, a prevalent condition expected to increase in many areas with high and moderate tuberculosis burdens. See also: https://tbreadingnotes.blogspot.com/2024/09/tuberculosis-mortality-in-brunei.html

Abstract

Background: Evidence suggests a causal link between diabetes mellitus and tuberculosis risk. However, to date, few studies have used a prospective design to estimate the impact of diabetes on tuberculosis in a general population. In this study, we prospectively investigated the risk of tuberculosis among persons with diabetes stratified by severity.

Methods: A cohort study was performed involving 17,715 Taiwanese persons, on whom baseline data were collected during Taiwan’s 2001 National Health Interview Survey. Participants’ subsequent medical care until December 2004 was captured from the National Health Insurance database. The diagnosis and severity of diabetes were established using self-reports, International Classification of Diseases, Ninth Revision, Clinical Modification codes, and pharmacy records; incident tuberculosis disease was identified using these codes and pharmacy records. Covariates were obtained through in-person interviews. We used Cox proportional hazards regression analyses to measure the association between tuberculosis and both diabetes and diabetes severity.

Results: Diabetes in general and treated diabetes were significantly associated with tuberculosis (adjusted hazard ratio, 2.09 [95% confidence interval {CI}, 1.10–3.95] and 2.60 [95% CI, 1.34–5.03], respectively). Compared with persons without treated diabetes, participants' risk of tuberculosis increased as the number of complications of diabetes mellitus increased (P = .0016), with a greater than 3-fold risk among those with ≥2 diabetes-related complications (odds ratio, 3.45; 95% CI, 1.59–7.50). Similarly, the risk increased among those with higher Diabetes Complications Severity Index scores (P = .0002).

Conclusions: The risk of developing tuberculosis increased among those with increasing diabetes severity. 

Identifying MDRTB transmission hotspots using routinely collected data

Manjourides, J., Lin, H.H., Shin, S., Jeffery, C., Contreras, C., Santa Cruz, J., Jave, O., Yagui, M., Asencios, L., Pagano, M. and Cohen, T., 2012. Identifying multidrug resistant tuberculosis transmission hotspots using routinely collected data. Tuberculosis, 92(3), pp.273-279.

Identification of MDRTB transmission hotspots in parts of the study area is crucial.

Resources to interrupt the transmission of resistant disease should be prioritized in these regions.

Further investigation, such as a molecular epidemiological study, might validate these findings and identify causes or specific high-risk locations. See also: https://tbreadingnotes.blogspot.com/2024/08/scientific-advances-and-end-of.html

Possible reasons for higher transmission in these areas:

• Delayed diagnosis and treatment of infectious MDRTB patients
• Higher population density or more respiratory contacts
• Circulation of particularly transmissible MDR strains

Detecting high-risk areas suggests that geographically targeted interventions could be effective.

Programmatically, resources for detecting and treating MDRTB should be concentrated in these areas. See also: https://tuberculosis101.blogspot.com/2024/11/evaluation-of-xpert-mtb-host-response.html

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Wulandari, D.A., Hartati, Y.W., Ibrahim, A.U. and Pitaloka, D.A.E., 2024. Multidrug-resistant tuberculosis. Clinica Chimica Acta, 559, p.119701.

• Multidrug-resistant tuberculosis (MDR-TB) is defined by resistance to at least rifampicin (RIF) and isoniazid (INH).
• MDR-TB arises from inadequate treatment practices, such as incomplete treatment, insufficient drug doses and durations, poor drug quality, and transmission from individuals with drug-resistant TB.
• Resistance in MTB (Mycobacterium tuberculosis) results from spontaneous chromosomal mutations. There are ten gene variants linked to resistance against first-line anti-TB medications, including katG, inhA, ahpC, kasA, and Ndh for INH, and rpoB for RIF.
• Drug resistance occurs primarily through two mechanisms: primary resistance from exposure to a resistant MTB strain and secondary resistance due to poor treatment adherence.

Classification of Drug-Resistant TB

• Mono-resistant TB: Resistant to one first-line anti-TB drug only.
• Isoniazid-resistant TB: Resistant to Isoniazid, but susceptible to Rifampicin.
• Poly-resistant TB: Resistant to more than one first-line anti-TB drug, excluding both Isoniazid and Rifampicin.
• Rifampicin-resistant TB (RR): Resistance to rifampicin detected using phenotypic or genotypic methods, with or without resistance to other anti-TB drugs. Includes mono-resistance, poly-resistance, MDR, or XDR.
• Multidrug-resistant TB (MDR-TB): Resistant to at least both Isoniazid and Rifampicin.
• Pre-extensively drug-resistant TB: Resistant to Rifampicin, Isoniazid, and either Fluoroquinolones or one injectable drug (Amikacin or Kanamycin).
• Extensively drug-resistant TB (XDR-TB): Resistance to any fluoroquinolone, and at least one of three second-line injectable drugs (capreomycin, kanamycin, or amikacin), in addition to being multidrug-resistant.

Rifampicin:

• A lipophilic antibiotic that inhibits RNA synthesis by binding to the β-subunit of DNA-dependent RNA polymerase, blocking RNA transcription.
• Common side effects include hepatotoxicity, immunological allergies, skin syndromes, gastrointestinal issues, influenza-like symptoms, hemolytic anaemia, shock, and acute renal failure.

Isoniazid:

• Requires activation by the mycobacterial enzyme katG to inhibit mycolic acid synthesis essential for the mycobacterial cell wall.
• Side effects include peripheral neuropathy, seizures from overdose, lupus erythematosus, rheumatoid-like syndrome, and various hematological disorders.

Ethambutol:

• Inhibits mycobacterial arabinosyltransferase, crucial for bacterial cell wall biosynthesis.
• It mimics arabinofuranosyl, disrupting the cell wall synthesis process and causing bacterial aggregation and morphological changes.

Pyrazinamide:

• A prodrug activated in acidic conditions to pyrazinoic acid, which inhibits fatty acid synthesis in MTB.

MDR-TB Detection:

• Phenotypic testing: Culture-based method using solid (Lowenstein Jensen) or liquid media (mycobacterium growth indicator tube, MGIT), taking 2-3 months for results.
• Genotypic testing: Faster molecular tests identify mutations causing drug resistance, including probe-based assays and sequence-based assays.
• The GeneXpert test, a NAAT, detects TB and RIF resistance in about 2 hours using real-time PCR with molecular beacons.
• Despite advancements, culture-based methods remain the gold standard for their high identification sensitivity.

 

Modelling the impacts of new diagnostic tools for TB in developing countries

Different diagnostic strategies for tuberculosis (TB) may vary in cost-effectiveness based on local factors such as prevalence of HIV, drug resistance, and access to health facilities.

The discrete-event simulation (DES) tool helps assess diagnostic methods, particularly for multidrug-resistant TB (MDR-TB), at central reference facilities.

DES is useful for policymakers to evaluate the impact of TB diagnostic tools in resource-limited settings, enhancing decision-making processes.

Incorporating a disease transmission component into the DES enhances the model’s predictive capabilities, providing insights into TB incidence and its effects on health system and patient outcomes.

A visual and interactive DES tool aids national policymakers in validating diagnostic strategies, exploring new approaches, and engaging with simulation outcomes effectively.[1]

Patients with Type 2 Diabetes Mellitus (T2DM) and Pulmonary Tuberculosis (PTB) are more likely to experience poor glycemic control, increased frequency of infections, and a higher prevalence of smoking, alcohol consumption, and lack of physical activity. Independent risk factors for concurrent T2DM and PTB include lymphopenia, smoking, a history of TB exposure, and poor glycemic control. Conversely, being overweight or obese is associated with a reduced risk of concurrent PTB in patients with T2DM.[2]

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. [TB0044]

2. Shi H, Yuan Y, Li X, Li YF, Fan L, Yang XM. Analysis of the influencing factors and clinical related characteristics of pulmonary tuberculosis in patients with type 2 diabetes mellitus. World J Diabetes 2024; 15(2): 196-208.