QuantiFERON-TB Gold Plus CD8+ T cell responses in contacts with TB disease and recent TB infection

Who

• Participants: Close contacts of pulmonary or laryngeal tuberculosis (TB) cases in Singapore

• Population size:

  • 22,355 contacts attended screening

  • 19,397 had valid QFT-Plus results

• Eligibility: Immunocompetent individuals ≥5 years old (QFT-Plus); immunocompromised contacts received T-SPOT.TB; children <5 years received TST

• Key subgroups:

  • Contacts with TB disease

  • “Stringent Converters” (recent TB infection)

  • All others with TB infection (likely remote infection)

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What

• Study focus: Evaluation of the CD8⁺ T-cell response measured by the QFT-Plus assay for its ability to:

  • Predict active TB disease

  • Identify recent TB infection (TBI)

• Key findings:

  • TB disease and recent TBI (stringent conversion) were significantly associated with stronger CD8⁺ responses (TB2–TB1 IFN-γ >0.6 IU/mL)

  • TB2 tube responses increased diagnostic sensitivity for TB disease (from 84.6% to 95.2%)

  • Higher IFN-γ levels in TB1 and TB2 tubes were observed in TB disease and recent TBI compared with other TBI cases

• Conclusion: CD8⁺ IFN-γ responses in QFT-Plus may help identify individuals at higher risk of TB disease or recent infection who could benefit from further investigation or TB preventive treatment (TPT)

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When

• Data collection period: January 2018 to October 2018

• Relevant program timeline:

  • TPT implemented since 1998

  • QFT-GIT introduced from 2006

  • QFT-Plus used from December 2017

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Where

• Setting: Nationwide TB contact screening in Singapore

• Clinical site: TB Contact Clinic (TBCC)

• Laboratory: Tan Tock Seng Hospital Microbiology Laboratory

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Why

• To improve identification of:

  • Individuals with active TB disease

  • Those with recent TB infection, who are at higher risk of progression

• To assess whether CD8⁺ responses in QFT-Plus add value beyond standard IGRA positivity in a setting with high background TB infection

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How

• Study design: Retrospective observational analysis

• Testing strategy:

  • Initial and post-window QFT-Plus testing (≥8 weeks after exposure)

  • Chest radiography for IGRA-positive or high-risk contacts

• Key definitions:

• Stringent Converters: IFN-γ increase from <0.35 to >0.7 IU/mL, excluding results in the uncertainty zone

• CD8⁺ response: TB2–TB1 IFN-γ difference >0.6 IU/mL

Source: Chee, C.B.E., Kyi-Win, K., Tan, S. and Wang, Y.T., 2025. QuantiFERON-TB Gold Plus CD8+ T cell responses in contacts with tuberculosis disease and recent tuberculosis infection. Microbiology Spectrum, 13(12), pp.e01353-25.

Joint associations of multiple lifestyle factors with risk of active TB in the population

Who

• 63,257 Chinese adults (men and women), aged 45–74 years at recruitment

• Belonged to Hokkien and Cantonese dialect groups

• Participants of the Singapore Chinese Health Study

• A subset of 39,528 participants contributed updated lifestyle data at follow-up

• Participants were born in the first half of the 20th century, many likely exposed to latent tuberculosis infection early in life

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What

• The study examined the joint association of five lifestyle risk factors—smoking, underweight BMI, physical inactivity, daily alcohol consumption, and poor diet quality—with the risk of active tuberculosis (TB).

• Each individual risk factor was independently associated with higher TB risk.

• A dose–response relationship was observed: increasing numbers of lifestyle risk factors were associated with stepwise increases in TB risk.

• Participants with all five risk factors had a ~9-fold higher risk of active TB compared with those with none.

• The combined effect of all five factors was greater than expected under a purely multiplicative model, suggesting synergistic effects.

• The association was stronger among participants with diabetes, indicating effect modification.

• Smoking showed synergistic interactions with alcohol drinking and poor diet quality.

• Findings support multifactorial prevention strategies targeting lifestyle behaviors to reduce TB risk.

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When

• Baseline recruitment: April 1993 – December 1998

• Second follow-up: 2006–2010 (mean 12.7 years after baseline)

• Mean follow-up duration: 18.2 years (SD 5.9)

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Where

• Singapore, among residents living in government housing flats (where ~86% of the population lived during recruitment)

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Why

• Tuberculosis incidence has declined slowly, and effective preventive strategies remain limited.

• Older adults in Singapore, many with latent TB infection acquired earlier in life, remain at risk of reactivation.

• The study aimed to clarify how modifiable lifestyle factors jointly influence active TB risk, addressing a gap in population-level prevention evidence.

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How

• Prospective population-based cohort study

• Lifestyle factors assessed via structured interviewer-administered questionnaires at baseline and follow-up

• Diet assessed using a validated 165-item food-frequency questionnaire

• A combined lifestyle risk score (0–5) was constructed, assigning one point per at-risk factor

• Incident active TB cases identified through mandatory linkage with the National TB Notification Registry

• Cox proportional hazards models used to estimate hazard ratios, with adjustment for confounders

• Sensitivity analyses and time-varying covariate analyses confirmed robustness of findings

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Overall conclusion:
An increasing number of unhealthy lifestyle factors is associated with a markedly higher risk of active tuberculosis in older Chinese adults, underscoring the importance of integrated, multisectoral lifestyle interventions for TB prevention at the population level.

Source: Li, H., Chee, C.B., Geng, T., Pan, A. and Koh, W.P., 2022. Joint associations of multiple lifestyle factors with risk of active tuberculosis in the population: the Singapore Chinese Health Study. Clinical Infectious Diseases, 75(2), pp.213-220.

Risk factors for TB among close IGRA-negative contacts of persons with infectious TB

Who

• Study population: Close contacts aged ≥2 years of laboratory-confirmed pulmonary TB patients in Singapore.

• Sample size: 60,377 unique contacts (62,724 observations) linked to 7,737 index TB cases.

• Key characteristics: Majority male, predominantly Chinese ethnicity, mostly Singapore residents; 75% of contacts were aged >25 years.

• Outcome group: 150 contacts (0.3%) who developed active TB disease despite being IGRA-negative.

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What

• Focus: Identification of risk factors for developing active TB disease among IGRA-negative close contacts.

• Key findings: Independent risk factors included:

  • Age >25 years

  • Malay ethnicity

  • Diabetes mellitus

  • End-stage renal failure

  • Exposure to smear-positive index cases

  • Family relationship with the index case

  • Exposure in dormitories or nursing homes

• Implications: IGRA-negative contacts with these risk factors have a substantially higher TB incidence than the general population, suggesting possible false-negative IGRA results and need for enhanced follow-up.

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When

• Study period: January 2014 to December 2022.

• Follow-up: Median time to TB disease development was 92 weeks after index case notification.

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Where

• Setting: Singapore.

• Program context: National Tuberculosis Programme (NTBP), using data from the national TB registry.

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Why

• To inform context-specific follow-up strategies for IGRA-negative close contacts, addressing uncertainty about residual TB risk after a negative IGRA result and supporting Singapore’s goal of reducing TB incidence to 10 per 100,000 by 2040.

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How

• Design: Retrospective cohort study.

• Inclusion criteria: Close contacts with a negative QuantiFERON-TB Gold Plus (QFT-Plus) result at 8 weeks post-exposure.

• Exclusions: Prior TB treatment or preventive therapy; screening with TB-SPOT only.

• Analysis:

  • Univariate analyses (chi-square, Fisher’s exact, Mann–Whitney U tests).

  • Multivariable logistic regression including variables with P <0.1 or strong prior evidence.

  • Model performance assessed using ROC curve (AUC = 0.79).

• Conclusion: Authors recommend reviewing IGRA retesting timing and extending follow-up to 24 months for high-risk IGRA-negative contacts.

Source: Tavitian-Exley, I., Kyaw, W.M., Kang-Yang, L.L., Foo, K., Boudville, I.C., Cutter, J.L. and Ng, D.H.L., 2024. Risk factors for tuberculosis among close IGRA-negative contacts of persons with infectious tuberculosis in Singapore. International Journal of Infectious Diseases, 147, p.107166.

Time interval for QuantiFERON-TB Gold Plus conversion after last exposure with TB

Who

• Study population: Close contacts of notified tuberculosis (TB) cases

• Inclusion criteria:

  • Aged ≥15 years

  • Initial negative QFT within 56 days of last exposure date (LED)

  • Follow-up QFT within 180 days of LED

• Sample size: 23,236 contacts

• Demographics:

  • Largest age groups: 30–39 years (23.9%), 20–29 years (20.0%)

  • QFT conversion increased with age, highest in those aged 70–79 years (5.8%)

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What

• Primary finding:

  • 3.5% (804/23,236) of contacts experienced QFT conversion on follow-up

  • Median time to QFT conversion was 10 weeks post-LED (IQR 9–11 weeks)

• Timing of conversion:

  • 73% converted within ≤10 weeks

  • 27% converted after 10 weeks, up to 25 weeks

• Clinical outcomes:

  • 45 contacts were diagnosed with active TB disease

  • 69% (31/45) had QFT conversion

  • Some active TB cases were identified only because repeat QFT occurred after 10 weeks

• Authors’ conclusion:

  • A longer window period (≥10 weeks) is more effective for detecting later QFT conversions

  • Repeating QFT too early may miss TB infection and early active disease

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When

• Study period: 1 January 2018 – 31 December 2022

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Where

• Setting: National TB contact investigation program in Singapore

• Data source: National TB Registry

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Why

• To determine the optimal timing for repeat QFT testing after TB exposure

• To address uncertainty around the QFT window period, particularly regarding later conversions that may be clinically significant

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How

• Study design: Retrospective cohort study

• Exposure reference: Last exposure date (LED) to an infectious TB case

• Outcome definition:

  • QFT conversion = negative initial QFT → positive follow-up QFT

• Analysis:

  • Timing of QFT conversion relative to LED

  • Stratification by age and follow-up interval

  • Identification of active TB disease following QFT results

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Summary implication:
This large national cohort study demonstrates that while most QFT conversions occur within 10 weeks of TB exposure, over one-quarter occur later. Extending the repeat QFT window to at least 10 weeks post-exposure may improve detection of TB infection and prevent missed diagnoses of active TB, especially in programmatic contact investigations.

Source: Kyaw, W.M., Tay, J.Y., Lim, L.K.Y. and Ng, D.H.L., 2025. Time interval for QuantiFERON-TB Gold Plus conversion after last exposure with tuberculosis. ERJ Open Research, 11(3).

Subclinical disease among people with culture-confirmed pulmonary TB in Singapore

Who

• Population: Singapore citizens and permanent residents with sputum culture–confirmed pulmonary tuberculosis (TB)

• Sample size: 18,693 pulmonary TB cases

• Key characteristics: Subclinical TB patients were older (median age 62–63 years) and predominantly male (~75%). High-risk groups included individuals aged ≥70 years and those with immunocompromising conditions (renal failure, steroid therapy, malignancy, HIV).

• Exclusions: Patients with extrapulmonary TB

What

• Focus: Description of the subclinical TB disease spectrum, identification of risk factors, and evaluation of diagnostic methods to improve TB control.

• Findings:

  • Subclinical TB was common: 41.6% met definition 1 (culture-positive with no cough or cough <2 weeks) and 31.6% met definition 2 (culture-positive with no cough).

  • Most subclinical cases had abnormal chest X-rays (~96%), and a substantial proportion had high sputum smear positivity, indicating potential infectiousness despite minimal symptoms.

  • Subclinical TB was independently associated with older age, male sex, immunocompromising conditions, known TB contact, and positive sputum TB PCR.

  • Diagnostic performance of models was moderate (AUC 0.69–0.72).

• Implications: Symptom-based screening alone is insensitive; relying solely on culture confirmation may delay diagnosis and increase transmission.

When

• Study period: January 1, 2004 to December 31, 2023

Where

• Setting: Singapore, using data from the Singapore National TB Registry

Why

• To address gaps in TB control by characterizing subclinical TB, which may be missed by symptom-based screening, particularly in moderate-incidence settings, and to inform more effective screening strategies.

How

• Design: Retrospective registry-based analysis

• Data sources: Mandatory TB notification data, including demographics, clinical presentation, laboratory results, and treatment outcomes

• Definitions: Two operational definitions of subclinical TB based on cough duration/absence

• Analysis: Logistic regression with stepwise forward selection; odds ratios and adjusted odds ratios with 95% confidence intervals; ROC curve analysis to assess model fit

Source: Chew, Y.R., Tay, J.Y., Kyaw, W.M., Chia, P.Y. and Ng, D.H.L., 2025. Subclinical disease among people with culture-confirmed pulmonary tuberculosis in Singapore-a retrospective study. International Journal of Infectious Diseases, 153, p.107768.

Prevalence and risk factors of active TB disease in contacts of TB cases in Nigeria

Who

• Index cases: Patients diagnosed with active tuberculosis (TB) disease attending the Chest/TB clinic of Chukwuemeka Odumegwu Ojukwu University Teaching Hospital.

  • Modal age group: 36–45 years

  • Mean age: 40.49 years

  • Predominantly male

  • 99.2% had pulmonary TB

  • 82.6% were smear-positive

  • 28.1% were HIV-positive

• Contacts: Individuals identified through index cases, predominantly household contacts.

  • Modal age group: ≤15 years

  • Mean age: 24.01 years

  • Predominantly female

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What

• Study focus: Determination of the prevalence and risk factors of active TB disease among contacts of patients with active TB.

• Key findings:

  • 17.5% of contacts had at least one symptom suggestive of TB.

  • Active TB disease was detected in 2.7% of contacts.

  • Presence of TB symptoms among contacts was significantly associated with active TB disease (p = 0.000).

• Conclusion: Contacts of active TB patients have a higher risk of developing active TB compared to the general population. Systematic contact investigation is crucial for early case detection and TB control.

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When

• Not specified.

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Where

• Chest/TB clinic of Chukwuemeka Odumegwu Ojukwu University Teaching Hospital, Awka, southeastern Nigeria.

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Why

• To address the increased risk of active TB disease among contacts of TB patients and to support TB control efforts through early identification of previously undiagnosed cases, particularly in high TB burden settings.

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How

• Study design: Cross-sectional study.

• Sampling: Consecutive enrollment of all diagnosed active TB patients and their identified contacts.

• Data collection:

  • In-depth interviews with index cases to identify contacts.

  • Interviewer-administered questionnaires for both index cases and contacts.

• Screening and diagnosis:

• Clinical screening of contacts for cardinal TB symptoms (e.g., prolonged cough, fever, weight loss, night sweats; failure to thrive in children).

• Laboratory testing using Xpert MTB/RIF Ultra assay on sputum samples (adults) or stool samples (children unable to produce sputum).

Source: Njelita, I.A., Nwachukwu, C.C., Eyisi, I.G., Ezenyeaku, C.A. and Okeke, H.N., 2025. Prevalence and risk factors of active tuberculosis disease in contacts of tuberculosis cases treated in a teaching hospital in southeast Nigeria: a cross-sectional study. International Journal of Healthcare Sciences, 13(1), pp.80-89.

The Role of Youths in Within-Household Tuberculosis Transmission

Who

• Participants:

  • Index patients: 2,771 individuals aged 15–60 years with microbiologically confirmed pulmonary TB.

  • Household contacts (HHCs): 10,745 contacts aged 0–60 years (participants >60 excluded).

• Key subgroups:

  • Index patients categorized as Youth (15–24 years) or Adults (25–60 years).

  • HHCs categorized as Children (0–14 years), Youth (15–24 years), and Adults (25–60 years).

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What

• Main findings:

  • Child household contacts exposed to youth index patients had a lower prevalence of TB infection at enrollment compared with those exposed to adult index patients (adjusted PRR = 0.77; 95% CI: 0.67–0.87).

  • Index patient age was not associated with the incidence of TB infection among household contacts over 12 months.

  • Children and youth contacts had significantly lower incidence of TB infection than adult contacts, regardless of index patient age.

  • Whole-genome sequencing (WGS) showed a lower proportion of genetically linked transmission pairs for youth index patients compared with adults, though this difference was not statistically significant.

• Interpretation:

  • Youths appear to contribute less to within-household TB transmission than adults, suggesting that their transmission risk may occur more often outside the household.

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When

• Study period: September 2009 to August 2012.

• Follow-up duration: 12 months after household enrollment.

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Where

• Setting: Lima, Peru.

• Healthcare context: 106 public health centers serving approximately 3 million people.

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Why

• To determine whether the age of TB index patients, particularly youth (15–24 years), influences the risk of TB transmission to household contacts, with a focus on children as a marker of recent transmission.

• To address gaps in understanding age-specific transmission dynamics and inform targeted TB control strategies.

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How

• Study design: Prospective household cohort study.

• TB infection assessment:

  • Baseline tuberculin skin test (TST) to measure prevalence.

  • Repeat TSTs at 6 and 12 months to measure incidence.

• TB disease classification:

  • Co-prevalent TB (≤14 days after enrollment) vs secondary TB (>14 days).

• Transmission confirmation:

  • Whole-genome sequencing of Mycobacterium tuberculosis isolates to assess genetic linkage between index and secondary cases.

• Analysis:

• Multivariable regression and survival analyses adjusting for demographic, behavioral, socioeconomic, and nutritional factors.

Source: Brooks, M.B., Lecca, L., Becerra, M.C., Calderon, R.I., Contreras, C.C., Jimenez, J., Yataco, R.M., Zhang, Z., Murray, M.B. and Huang, C.C., 2025. The Role of Youths in Within-Household Tuberculosis Transmission: A Household Contact Cohort Study. Clinical Infectious Diseases, p.ciaf490.

Microbiological aspects and clinical impact of lower lung field TB in Peru

Who

• Participants: Individuals aged ≥14 years with newly diagnosed, microbiologically confirmed pulmonary tuberculosis (PTB).

• Sample size: 1,316 patients with abnormal baseline chest X-rays (CXRs); 84 (6%) had lower lung field (LLF) TB and 1,232 (94%) had non-LLF TB.

• Key characteristics: LLF TB patients were more likely to be women, have BMI >25 kg/m², be sputum smear–negative, have lower baseline St. George’s Respiratory Questionnaire (SGRQ) scores, and be infected with Mycobacterium tuberculosis (MTB) Lineage 2.

What

• Focus: The association between microbiological characteristics (sputum smear and culture status, MTB lineage) and radiographic localization of TB (LLF vs non-LLF), and the impact of LLF disease on treatment response and outcomes.

• Main findings:

  • LLF TB was independently associated with sputum smear negativity and infection with MTB Lineage 2.

  • Patients with LLF TB showed significantly less improvement in SGRQ scores after 2 months of treatment compared with non-LLF TB patients.

  • Final treatment outcomes appeared better in LLF TB but were not statistically significant after adjustment.

• Implications: LLF TB may be underdiagnosed using conventional sputum-based tests and is associated with slower early clinical improvement, suggesting a risk of ongoing transmission and the need for improved diagnostic strategies.

When

• Study period: October 2020 to December 2022.

Where

• Setting: Primary care health centers across 16 districts in Lima, Peru, including urban, peri-urban, and informal shantytown areas.

Why

• Rationale: Lower lung field TB can be difficult to detect with routine diagnostic approaches and may differ biologically and clinically from typical upper-lung TB. The study aimed to clarify microbiological correlates of LLF TB and assess whether LLF localization affects treatment response and outcomes.

How

• Design: Prospective cohort study.

• Diagnostics: Microbiological confirmation by GeneXpert MTB/RIF and/or sputum culture; drug susceptibility testing per WHO definitions.

• Radiography: Baseline and 2-month posteroanterior CXRs classified as LLF or non-LLF TB by radiologists.

• Molecular methods: Whole-genome sequencing of culture-positive isolates; MTB lineage determined using a 62-SNP barcode.

• Outcomes: Treatment response assessed by change in SGRQ score from baseline to 2 months; end-of-treatment outcomes classified by WHO criteria.

• Analysis: Multivariable regression adjusting for demographic and clinical covariates.

Source: Tan, Q., Huang, C.C., Calderon, R., Lecca, L., Mendoza, M., Rocha, G.R., Tintaya, K., Tovar, X., Feng, J.Y., Pan, S.W. and Tseng, Y.H., 2025. Microbiological aspects and clinical impact of lower lung field tuberculosis: An observational cohort study in Peru. International Journal of Infectious Diseases, 150, p.107284.

The social determinants of tuberculosis in Peru

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Who

• Cases: 2,337 individuals aged ≥15 years diagnosed with pulmonary or extrapulmonary tuberculosis (with or without bacteriological confirmation). Median age 31 years (IQR 23–47); 64% male.

• Controls: 981 individuals aged ≥15 years from randomly selected households in the same communities. Median age 38 years (IQR 25–54); 40% male.

• Setting population: Residents of 32 high–tuberculosis-burden communities (~900,000 people).

• Healthcare context: Communities served by Ministry of Health (MINSA)-run health posts.

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What

• The study examined how household-level poverty and interrelated personal risk factors (e.g., smoking, alcohol use, undernutrition, education, incarceration, social capital) increase the risk of tuberculosis.

• Key findings:

  • Household poverty was strongly associated with tuberculosis (adjusted odds ratio [aOR] 3.1 for poorer vs. less poor households).

  • Tuberculosis risk increased non-linearly with worsening poverty; 21% of cases were in the poorest poverty decile.

  • Population attributable fractions (PAFs) suggested that nearly 47% of tuberculosis burden could be reduced if poorer households achieved poverty levels comparable to the less poor.

  • Several personal risk factors independently contributed to tuberculosis risk even after adjusting for poverty, including low education, alcohol excess, underweight, smoking, HIV, diabetes, prior tuberculosis, incarceration, and low social capital.

  • Most personal risk factors showed clear social gradients, being more prevalent among poorer households, except HIV (no gradient) and diabetes/other immunosuppression (more prevalent in less poor households).

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When

• Communities were followed from 2013 onward.

• Recruitment and detailed data collection occurred during the study period up to 2019.

• Tuberculosis notification data refer to 2019.

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Where

• Callao, Peru, a metropolitan area bordering Lima.

• Specifically, 32 of 45 communities in Callao with high tuberculosis rates.

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Why

• To address gaps in understanding how household poverty and downstream personal risk factors interact to shape tuberculosis risk.

• The study aimed to move beyond single risk factors and explicitly apply a social epidemiological framework to tuberculosis transmission and vulnerability.

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How

• Study design: Case–control study nested within the PREVENT TB study.

• Case identification: Passive case finding through MINSA-run health posts; cases recruited at diagnosis or during treatment.

• Control selection: Randomly selected households using satellite mapping and random number tables; all household members invited after adult consent.

• Data collection: Structured questionnaires administered by trained research nurses.

  • Household poverty assessed across physical, human, and financial capital dimensions using the Sustainable Livelihood Framework and principal component analysis (PCA).

  • Personal risk factors grouped into five domains: education/behavioural, exposure, biological, nutritional, and psychosocial.

• Analysis: Directed acyclic graphs (DAGs) guided causal assumptions; multivariable regression estimated adjusted odds ratios and population attributable fractions.

Source: Saunders, M.J., Montoya, R., Quevedo, L., Ramos, E., Datta, S. and Evans, C.A., 2025. The social determinants of tuberculosis: a case-control study characterising pathways to equitable intervention in Peru. Infectious diseases of poverty, 14(1), p.53.

Factors Influencing Adherence to Anti-tuberculosis Treatment

Who

The study involved patients with pulmonary tuberculosis receiving treatment at Puskesmas Nibung. The population consisted of 97 patients treated in 2023. The study sample included 35 pulmonary tuberculosis patients in 2024 who had been diagnosed with tuberculosis and had undergone treatment for at least two months.

What

The study examined medication adherence to anti-tuberculosis treatment and identified factors associated with adherence. The results showed that 57.1% of respondents were adherent to their medication. Multivariate analysis revealed that gender was the most dominant factor associated with medication adherence. Statistically significant relationships were found between medication adherence and gender, education level, knowledge, employment status, family support, and attitude.

When

Data collection was conducted from March 3 to May 12, 2024.

Where

The study was conducted at the Nibung Community Health Center (UPTD Puskesmas Nibung), North Musi Rawas Regency, Indonesia.

Why

The study was conducted to address the need for understanding medication adherence among pulmonary tuberculosis patients and to identify key factors influencing adherence to anti-tuberculosis treatment.

How

A quantitative analytic survey with a cross-sectional design was used. Total sampling based on inclusion criteria was applied. Data were analyzed using univariate and multivariate analyses to determine factors associated with medication adherence.

Source: Fitri, V.K., Zaman, C., Priyanto, A.D. and Ekawati, D., 2025. Analysis Factor of Compliance With Taking Anti-Pulmonary Tuberculosis Drugs in Patients With Pulmonary Tuberculosis. Lentera Perawat, 6(1), pp.59-68.