Treatment success and associated factors among drug-susceptible TB patients

Who

• Study population: Individuals diagnosed with drug-susceptible tuberculosis (TB) and initiated on TB treatment.

• Sample size: 1,009 individuals included in final analysis (from 1,062 records).

• Demographics:

  • Median age: 45 years (IQR 28–60)

  • 9.5% <15 years; 48.1% aged 15–49 years

  • 52.9% male

  • 30.9% HIV-positive

• Exclusions: Individuals with incomplete key data, unknown HIV status, or rifampicin-resistant TB.

---

What

• Focus: Determination of TB treatment success rate (TSR) and factors associated with treatment success among drug-susceptible TB patients.

• Key findings:

  • Overall TSR was 91.9% (95% CI: 90.0–93.4%), exceeding prior regional estimates and aligning with the national target.

  • Treatment success comprised 47.4% treatment completion and 44.5% cure.

  • Unsuccessful outcomes included death (5.1%), lost to follow-up (0.3%), and not evaluated (2.8%).

• Associated factors: Older age (>49 years), male sex, and HIV positivity were associated with lower treatment success.

• Implications: Despite a historically lower TSR in the Teso region, current outcomes are strong; however, targeted interventions are needed for older adults, males, and people living with HIV to sustain progress toward the End TB Strategy by 2030.

---

When

• TB treatment period reviewed: 1 October 2021 – 30 December 2023

• Data collection period: 1 March 2025 – 28 March 2025

---

Where

• Setting: Five large public health facilities in the Teso region, Northeastern Uganda.

• Districts: Kumi, Serere, Bukedea, and Ngora

• Facilities: Atutur Hospital, Kumi HC IV, Serere HC IV, Bukedea HC IV, and Ngora HC IV

• Context: Predominantly rural population with subsistence farming livelihoods and low HIV prevalence relative to the national average.

---

Why

• The Teso region has a high TB burden but historically suboptimal TSR compared to national averages.

• Understanding drivers of treatment success and failure in rural, resource-limited settings is essential for designing targeted interventions and achieving global TB elimination goals.

---

How

• Study design: Retrospective quantitative study.

• Data source: TB treatment registers from selected health facilities.

• Outcome definition:

  • Treatment success: Cure + treatment completion (WHO criteria).

  • Unsuccessful outcomes: Death, loss to follow-up, treatment failure, or not evaluated.

• Independent variables: Age, sex, HIV status, TB classification, treatment category, referral source, GeneXpert access, and nutritional status (MUAC).

• Analysis:

• Bivariate analysis for associations.

• Modified Poisson regression with robust standard errors to estimate adjusted prevalence ratios (aPR), chosen due to outcome prevalence >10%.

Source: Ssentongo, S.M., Oryokot, B., Opito, R., Ochieng, G., Sekiranda, P., Bakashaba, B. and Mugisha, K., 2025. Treatment success and associated factors among drug-susceptible tuberculosis patients in Teso region, Uganda: a retrospective study. Therapeutic Advances in Infectious Disease, 12, pp.1-12.

A Clinical Prediction Model for Atypical TB Manifestations Among Older Adults

Who

• Population: Older adults aged ≥75 years

• Sample size: 5,651 patients with culture-confirmed pulmonary tuberculosis (aPTB) and atypical symptom presentation

• Subgroups:

  • Group a (Ga): 1,155 patients with aPTB not initially suspected by non-pulmonologists

  • Group b (Gb): 4,496 non-TB comparators within the first 24 hours

• Setting of care: Evaluated initially by non-chest physicians

• Radiology review: 2 radiologists + 1 pulmonologist (blinded)

---

What

• Objective: Development and validation of a TRIPOD-compliant clinical prediction score to identify atypical pulmonary TB (aPTB) in late-elderly patients.

• Key findings:

  • Five independent predictors of delayed aPTB diagnosis were identified:

    1. Age >85 years (strongest predictor)

    2. Hypoalbuminemia (<3.5 g/dL)

    3. Cardiovascular disease

    4. Diabetes mellitus

    5. Predominant lower-lung field involvement

  • A score cutoff ≥7 showed excellent diagnostic performance:

    • AUC: 0.95–0.96

    • Sensitivity: 91–94%

    • Specificity: 97–99%

• Clinical implication: The model reliably detects aPTB even in patients without classic TB symptoms (≈70% had symptom scores ≤1).

---

When

• Derivation cohort: 2000–2020

• Temporal validation cohort: 2021–2023

• Study design: Retrospective analysis

---

Where

• Geographic setting: Chia-Yi, southern Taiwan

• Health system context: TB diagnosis following Taiwan’s tiered strategy using AFB smear, selective PCR, and culture confirmation.

---

Why

• Rationale: Atypical TB presentations are common in the late-elderly, leading to diagnostic delays, especially outside pulmonology settings.

• Existing symptom-based tools underperform in this population, and frailty markers (e.g., sarcopenia, osteoporosis) do not adequately capture risk.

• Early identification is critical to reduce missed diagnoses and improve outcomes in this vulnerable group.

---

How

• Design: Retrospective cohort study with derivation and temporal validation

• Inclusion criteria: Age ≥75 years, WHO/CDC symptom score <5, culture-confirmed TB

• Analysis:

  • Univariate screening (p < 0.05)

  • Multivariate logistic regression with stepwise selection

  • Model robustness assessed via:

    • ROC/AUC

    • Calibration plots

    • Decision curve analysis (DCA)

    • Subgroup analyses (age strata, diabetes status)

• Validation: Independent temporal cohort; no significant AUC differences (DeLong test p > 0.70)

• Reliability: Excellent inter-observer agreement for radiographic assessment (Fleiss’ κ = 0.91)

---

Overall conclusion

The study presents a simple, highly accurate, and externally validated clinical scoring tool for early detection of atypical pulmonary TB in adults aged ≥75 years. Extreme old age (>85 years) emerged as the most powerful predictor, surpassing traditional frailty indicators. Integration of this score into electronic medical records could meaningfully reduce diagnostic delays in non-pulmonology settings.

Source: Yeh, J.J., Chen, J.H., Kuo, Y.L., Tsai, C.H. and Ko, Y.E., 2025. A Clinical Prediction Model for Atypical Tuberculosis Manifestations Among Older Adults. Medicina, 61(10), p.1888.

Determinants of The Incidence of Tuberculosis in Malang Raya Area

Who

The study population consisted of all recorded tuberculosis (TB) cases in the Malang Raya area (Malang City, Malang Regency, and Batu City). The unit of analysis was groups (sub-districts) rather than individuals. The sample included the entire population of TB cases recorded by local Health Offices during 2020–2021. Individual-level demographic characteristics were not specified.

---

What

This ecological study examined the correlation between environmental, housing, behavioral, demographic, and climatic factors and TB incidence. The key findings were:

• Significant correlations were found between:

  • Coverage of healthy houses and TB incidence (p = 0.029)

  • Coverage of households adopting a healthy lifestyle and TB incidence (p = 0.005)

• No significant correlations were found between TB incidence and:

  • Population density

  • Air humidity

  • Rainfall

The authors conclude that housing quality and household healthy lifestyle coverage are important determinants of TB incidence in Malang Raya and should be strengthened through community-based health promotion.

---

When

The primary analysis focused on TB incidence data for the year 2021, using recorded TB cases from 2020–2021.

---

Where

The study was conducted in the Malang Raya area, East Java, Indonesia, which includes:

• Malang City

• Malang Regency

• Batu City

---

Why

The study aimed to address the need to understand contextual and environmental determinants of TB incidence at the population level. TB remains a public health problem in Indonesia, and identifying modifiable ecological factors, such as housing conditions and healthy lifestyle coverage, is essential for improving TB prevention strategies.

---

How

• Study design: Quantitative ecological study

• Data sources: Secondary data from:

  • Local Health Offices

  • Central Bureau of Statistics (BPS)

• Independent variables:

  • Coverage of healthy houses

  • Coverage of households with healthy lifestyle

  • Population density

  • Air temperature

  • Humidity

  • Rainfall

• Dependent variable: TB incidence

• Data analysis:

  • Descriptive analysis using frequency and percentage distributions

  • Correlation analysis using Pearson Product Moment and Spearman’s rho tests

Source: Alma, L.R., Olivionita, V. and Wardani, H.E., 2024. An Ecological Study of Determinants of The Incidence of Tuberculosis in Malang Raya Area. Preventia: The Indonesian Journal of Public Health, 9(1), 112–120.

Nutritional status and other associated factors of patients with TB in urban areas

Who

• Population: 314 adult (≥18 years) patients with active tuberculosis (pulmonary or extrapulmonary) enrolled in DOTS centers.

• Demographics: Mean age 35.2±15.0 years (range 18–80); 51.3% male. Most were 21–50 years old (65.9%).

• Socioeconomic profile: 12.7% had no schooling; 26.8% primary, 26.4% secondary, 34.1% higher secondary/other education. 32.5% were in service, 38.5% dependent. Over half (52.9%) had monthly family income <20,000 taka; 54.8% lived in concrete houses.

• Clinical profile: 44.9% had pulmonary TB; 55.1% extrapulmonary TB; 91.4% on anti-TB treatment <6 months; 10.8% had diabetes; 17.5% had hypertension.

---

What (focus, main findings, conclusions, implications)

• Focus: To determine the prevalence of undernutrition and identify factors associated with nutritional status among adult TB patients in selected urban areas of Bangladesh.

• Nutritional status (primary outcome):

  • Underweight (BMI <18.5): 33.4%

  • Normal BMI (18.5–24.9): 45.5%

  • Overweight/obese (>24.9): 21%
    

• Key bivariate associations with nutritional status (significant):

  • Sociodemographic/clinical factors: age group, educational status, occupational status, housing condition, type of TB, TB treatment duration, and diabetes status (all p<0.05).

  • Dietary/lifestyle factors: frequency of meals per day, daily protein intake, receiving dietary counseling, safe drinking water facilities, and fortified oil intake (all p<0.05).

• Key multivariable (logistic regression) findings (underweight vs normal):

  • Age <20 years vs ≥50 years: higher odds of being underweight (OR 2.494; 95% CI 0.994–6.253; p=0.051 – borderline).

  • TB treatment duration <6 months vs ≥6 months: significantly higher odds of underweight (OR 3.639; 95% CI 1.193–11.085; p=0.023).

  • Having safe drinking water and eating three meals per day were protective against underweight (safe water OR 0.151, p=0.017; three meals/day OR 0.339, p=0.037).

• Authors’ conclusions:

  • About one-third of urban TB patients are underweight, indicating a substantial burden of undernutrition.

  • Nutritional status is closely linked with demographic, clinical, and dietary factors (e.g., age, TB type, occupation, family size, diabetes, diet, water and oil use).

  • TB programs in urban Bangladesh should integrate nutritional assessment and support, including food assistance, nutritional care guidelines, and health education on undernutrition and its consequences.

---

When

• Study period/data collection: January–June 2023 (6 months).

• Contextual timeframe: Conducted against ongoing national TB control efforts in Bangladesh and the post-1993 WHO TB emergency era, but no longer-term follow-up was performed.

---

Where

• Geographic setting: Selected DOTS centers in three urban city corporations in Bangladesh: Dhaka, Gazipur, and Narayanganj.

• Facilities: 12 DOTS centers in total – six in Dhaka City Corporation, three in Narayanganj, and three in Gazipur.

---

Why (purpose/rationale)

• TB and malnutrition are major overlapping public health problems in Bangladesh, and malnutrition impairs cell-mediated immunity, increasing risk of TB disease and poor outcomes.

• Urban settings like Dhaka have high TB burden due to overcrowding, poor hygiene, and poverty, making nutritional problems particularly relevant.

• The study aimed to quantify undernutrition among adult TB patients in urban areas and identify associated factors in order to inform nutritional interventions within the National TB Program.

---

How (design, methods, analysis)

• Study design: Descriptive cross-sectional study.

• Sampling and inclusion: Adult (≥18 years) patients with active TB confirmed by sputum microscopy and GeneXpert, enrolled in DOTS at the 12 selected centers and currently receiving anti-TB treatment. Only those present and consenting on data collection days were included.

• Data collection tools:

  • Semi-structured questionnaire (developed from prior literature, drafted in English then translated into Bangla, pretested on 10% of sample).

  • Sections on sociodemographics, lifestyle (water source, sanitation, tobacco, exercise, iodized salt and fortified oil use), health status (type of TB, treatment duration, functional status, comorbidities), dietary patterns (meal frequency, protein intake, appetite, dietary changes, counseling), and nutritional status.

• Anthropometry:

  • Weight measured with bathroom scale; height with measuring tape.

  • BMI (kg/m²) used to classify nutritional status: underweight <18.5; normal 18.5–24.9; overweight/obese >24.9.

• Statistical analysis:

  • Data entry and analysis using SPSS v25.

  • Descriptive statistics: means, standard deviations, frequencies, percentages.

  • Bivariate analysis: chi-square tests to explore associations between nutritional status and explanatory variables (sociodemographic, clinical, dietary, lifestyle).

  • Multivariable analysis: multiple logistic regression to identify independent predictors of underweight vs normal nutritional status; significance level p<0.05. 

Source: Nabi, S.G., Aziz, M.M., Uddin, M.R., Tuhin, R.A., Shuchi, R.R., Nusreen, N., Jahan, R., Afroz, F. and Islam, M.S., 2024. Nutritional status and other associated factors of patients with tuberculosis in selected urban areas of Bangladesh. Well Testing Journal, 33(S2), pp.571-590.

Association of CKD with incident TB

Who

• Adults in South Korea (≥19 years) enrolled in the National Health Insurance Service (NHIS) screening program.

• 408,873 people with predialysis CKD (stages 1–5 not on dialysis), each matched 1:1 to 408,873 controls without CKD by age, sex, smoking history, and low-income status (total ≈ 817,746 participants).

---

What

• Focus: To assess whether predialysis CKD (not yet on dialysis or transplanted) is associated with higher risk of incident active tuberculosis (TB), and to examine short-term mortality after TB.

• Main findings:

  • Incidence of active TB was higher in predialysis CKD vs matched controls:

    • CKD: 1704 TB cases; 137.5 per 100,000 person-years

    • Controls: 1518 TB cases; 121.9 per 100,000 person-years

    • Adjusted hazard ratio (HR) for active TB with CKD: 1.21 (95% CI 1.13–1.30).

  • By CKD stage (vs controls, fully adjusted):

    • Stage 1 (eGFR ≥90 + albuminuria): HR ≈ 1.82

    • Stage 2 (eGFR 60–89 + albuminuria): HR ≈ 1.19 (borderline)

    • Stage 3 (eGFR 30–59): HR ≈ 1.16

    • Stage 4/5 without dialysis (eGFR <30): HR ≈ 1.85.

  • CKD was associated with higher risks of pulmonary, non-pulmonary, and miliary TB.

  • Among those who developed TB, 1-year all-cause mortality was higher in the CKD group (adjusted HR 1.68; 95% CI 1.28–2.22).

• Authors’ conclusion: Predialysis CKD is associated with increased incidence of active TB and worse short-term prognosis after TB. Particular concern is warranted for CKD stage 1 and advanced CKD (stages 4/5) not on dialysis.

---

When

• Health screenings to define CKD status: 2012–2016.

• Follow-up for TB events and mortality: from each participant’s index examination date until TB, death, or December 31, 2016.

• Median follow-up: about 3.0 years in both CKD and control groups.

---

Where

• Republic of Korea, using the nationwide National Health Insurance Database (NHID) and its general health screening program.

---

Why

• CKD prevalence is increasing globally and in countries where TB remains endemic.

• Dialysis and kidney transplantation are known TB risk factors, but large-scale data on TB risk in predialysis CKD were lacking.

• Because CKD is associated with immune dysfunction and infection risk, the authors aimed to clarify if earlier CKD stages already confer significantly increased TB risk to guide surveillance and management.

---

How

• Design: Nationwide retrospective population-based cohort study.

• Data source: Korean NHID, including:

  • General health screening data (serum creatinine, urine dipstick albuminuria, BMI, smoking, etc.).

  • Claims data with mandatory TB insurance codes and ICD-10 diagnoses to identify active TB (A15–A19).

• Population definition:

  • Included adults with ≥2 health screenings (2012–2016) using Jaffe-based creatinine.

  • Predialysis CKD: persistent CKD indicators (eGFR <60 mL/min/1.73 m² and/or dipstick albuminuria ≥1+) in two or more consecutive screenings.

  • Excluded those with prior TB, prior kidney replacement therapy, fluctuating/transient CKD labs, or (for controls) any CKD codes.

• Exposure: Presence and stage of predialysis CKD, categorized per KDIGO into stages 1, 2, 3, and 4/5 (non-dialysis) using baseline eGFR and albuminuria.

• Outcome measures:

  • Primary: Incident active TB (pulmonary, non-pulmonary, miliary) during follow-up.

  • Secondary: TB-associated mortality, defined as 1-year all-cause death after TB diagnosis.

• Analysis:

• 1:1 matching of CKD patients to controls by age, sex, smoking history, and low-income status.

• Incidence rates calculated per 100,000 person-years.

• Cox proportional hazards models with:

  • Model 1: adjusted for matching factors.

  • Model 2: additionally adjusted for BMI, residence (urban/rural), diabetes, hypertension, cancer, COPD, immunosuppressant use.

• Subgroup analyses by CKD stage and TB site; risk factor analysis for TB within the CKD cohort using multivariable Cox with backward elimination.

Source: Park, S., Lee, S., Kim, Y., Lee, Y., Kang, M.W., Cho, S., Han, K., Han, S.S., Lee, H., Lee, J.P. and Joo, K.W., 2019. Association of CKD with incident tuberculosis. Clinical Journal of the American Society of Nephrology, 14(7), pp.1002-1010.

Potential paediatric TB incidence and deaths resulting from interruption in programmes supported by international health aid, 2025–34

Who

• Population: Infants, children, and young adolescents aged 0–14 years living in 130 low-income and middle-income countries (LMICs).

• This group represents 99.5% of global paediatric TB incidence.

• Subgroups in 63 countries were further stratified by HIV status (no HIV, on ART, or not on ART).
  

---

What

• Focus of the Study: Estimate how cuts to US bilateral health aid and Global Fund contributions could affect paediatric tuberculosis (TB) incidence and mortality between 2025 and 2034.

• Key Findings:

  • If US bilateral funding stops (Scenario 2):
    +2.5 million paediatric TB cases and +340,000 TB deaths.

  • If the US also withdraws from the Global Fund (Scenario 3):
    +5.9 million cases and +0.9 million deaths.

  • If non-US donors also reduce contributions by 50% (Scenario 4):
    +8.9 million cases and +1.5 million deaths—134% increase in deaths vs continued funding.

• Interpretation: Cuts could reverse decades of gains, especially in Africa and South-East Asia.

• Implication: Rapid restoration of funding (even within one year) could reduce excess deaths by ≥90%.
  

---

When

• Study projection period: 2025–2034.

• Historical model base: Paediatric cohorts simulated from 2010 to allow full 0–14-year population by 2025.
  

---

Where

• Geographic scope:

  • 130 LMICs across all WHO regions (largest impacts in African Region and South-East Asia Region).

  • Minimal projected impact in Europe and the Americas.
    

---

Why

• Rationale:

  • Children have higher susceptibility to TB infection and mortality than adults.

  • US government cuts in early 2025 affected USAID, PEPFAR, and potentially The Global Fund.

  • Many high-burden countries rely heavily on external TB/HIV funding; disruptions risk surging transmission, reduced treatment access, and increased deaths.
    

---

How

• Study Design: Mathematical modelling study.

• Methods:

  • Transmission-dynamic TB and HIV models calibrated for 130 countries.

  • Used country-specific data on funding shares, TB/HIV epidemiology, malnutrition, and BCG vaccination.

  • Created four funding scenarios, from no cuts to severe cuts involving both US and non-US donors.

  • Modelled:

    • Changes in force of infection (TB transmission risk),

    • Declines in treatment coverage (TB treatment & ART),

    • Resulting TB incidence and mortality among children.

  • Incorporated parameter uncertainty using 1000 Monte Carlo simulations.

• Validation: Compared model outputs to WHO and Global Burden of Disease estimates for recent years.
  

Source: Menzies, N.A., Brown, T.S., Imai-Eaton, J.W., Dodd, P.J., Cohen, T. and Martinez, L., 2025. Potential paediatric tuberculosis incidence and deaths resulting from interruption in programmes supported by international health aid, 2025–34: a mathematical modelling study. The Lancet Child & Adolescent Health, 9(11), pp.787-795.