Factors Associated with the Incidence of Pulmonary TB in Patients with Type 2 DM [TBN 075]

Who

The study included 110 adult patients with type 2 diabetes mellitus (T2DM) treated at Adam Malik Hospital, Medan, consisting of:

• 55 cases: T2DM patients with pulmonary TB
• 55 controls: T2DM patients without pulmonary TB

Eligibility criteria:

• age ≥18 years
• physician-diagnosed type 2 DM
• complete medical record data

Exclusion criteria:

• extrapulmonary TB
• incomplete clinical documentation

Case definition:

• pulmonary TB confirmed by GeneXpert MTB/RIF

Control definition:

• T2DM patients with no history of pulmonary TB, based on medical records

Reported characteristics:

• In cases, the largest subgroup was age 45–54 years (50.9%), male (62.1%), senior high school educated (54.0%), unemployed (59.6%), smokers (69.0%), and underweight (56.4%)
• In controls, the largest subgroup was age 45–54 years (38.2%), female (68.2%), junior high school educated (66.7%), employed (60.4%), smokers (84.6%), and underweight (34.5%)

Important caution: the case and control groups appear imbalanced on several baseline characteristics, which complicates interpretation of reported odds ratios.

What

The study aimed to identify factors associated with pulmonary TB among patients with type 2 DM.

Outcome variable

• Presence or absence of pulmonary TB

Independent variables

• age
• sex
• education level
• employment status
• body mass index (BMI)
• smoking history
• HbA1c level

HbA1c classification

• controlled: <7.0%
• uncontrolled: ≥7.0%

Main findings

Bivariate analysis showed significant associations between pulmonary TB and:

• age <55 years: OR 4.741; 95% CI 2.099–10.710
• male sex: OR 3.514; 95% CI 1.569–7.869
• unemployment: OR 2.253; 95% CI 1.050–4.834
• smoking: OR 12.250; 95% CI 4.485–33.460
• abnormal BMI: OR 4.225; 95% CI 1.905–9.371
• low education: OR 3.148; 95% CI 1.430–6.931

A separate table reportedly showed that:

• HbA1c >7% was associated with an approximately 11-fold higher risk of pulmonary TB compared with HbA1c ≤7%

In multivariable logistic regression:

• smoking remained a significant factor and had the strongest adjusted association with pulmonary TB among T2DM patients (p < 0.001)

Authors’ interpretation: smoking was the dominant risk factor for pulmonary TB in patients with T2DM.

Careful interpretation: this study supports an association, not causation. The very large ORs for smoking and HbA1c should be interpreted cautiously given the apparent imbalance between groups and incomplete reporting of the adjusted model.

When

The study was conducted from January to June 2024.

Where

The study took place at Adam Malik Hospital, Medan, Indonesia, using hospital medical record data.

Why

The study sought to address which patient-related factors are associated with the occurrence of pulmonary TB in people with type 2 DM, a clinically important question because diabetes may increase TB susceptibility and worsen outcomes.

How

This was an observational analytical study using a case-control design.

Methods:

• 1:1 case-control ratio
• non-probability consecutive sampling
• hospital-based recruitment from medical records
• pulmonary TB diagnosis confirmed with GeneXpert MTB/RIF
• factors assessed from clinical and demographic data

Statistical approach:

• chi-square test for bivariate analysis
• logistic regression for multivariable analysis

Strength of evidence

This is a hospital-based case-control study, which provides moderate observational evidence for association but is limited for causal inference. It is stronger than a purely descriptive study, but still vulnerable to selection bias, confounding, and measurement limitations.

Major limitations and interpretation issues

1. Case and control groups may not be comparable
   The groups appear substantially different by sex, employment, education, BMI, and possibly smoking distribution. This raises concern for confounding and unstable effect estimates.
2. Very large odds ratios need caution
   The reported ORs for smoking and HbA1c >7% are large and may partly reflect residual confounding, selection issues, or model instability.
3. Sampling method may introduce bias
   Consecutive non-probability sampling limits representativeness and may affect internal validity.
4. Hospital-based design
   Findings may not generalize well to all T2DM patients in the community.
5. Control selection details are limited
   Controls were defined by absence of pulmonary TB history in records, but the text does not clarify whether they were systematically screened to exclude undiagnosed TB.
6. Inconsistency in table presentation
   Table 1 reportedly mixes column percentages and row percentages, making interpretation less transparent.
   

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Overall concise interpretation

This case-control study of 110 T2DM patients at Adam Malik Hospital found that younger age (<55 years), male sex, unemployment, low education, abnormal BMI, smoking, and HbA1c >7% were associated with pulmonary TB in bivariate analyses. In multivariable analysis, smoking remained the most important associated factor. However, the findings should be interpreted carefully because the case and control groups appear imbalanced, some reported effect sizes are very large, and the adjusted model is not fully presented.

Source: Harahap K, Sinaga BY, Syarani F, Eyanoer PC. Factors Associated with the Incidence of Pulmonary Tuberculosis in Patients with Type 2 Diabetes Mellitus. Mutiara Medika: Jurnal Kedokteran dan Kesehatan. 2025 Jul 31;25(2):123-31.

Environmental housing conditions of TB patients in a tidal-flooded area [TBN 074]

Who

The study included 25 households of registered pulmonary tuberculosis (TB) patients living in tidal-flood–affected coastal areas of Pekalongan, Indonesia. These households were selected from the service areas of Tirto II, Kramat Sari, and Dukuh primary health centers using total sampling from the eligible population. Of 52 registered TB cases, only 25 households met inclusion criteria.

Inclusion criteria were:

• confirmed active pulmonary TB,
• residence in tidal-flood–affected zones,
• willingness to participate.

Exclusion criteria were:

• untraceable address,
• duplicate residence,
• relocation,
• death.

Respondent characteristics reported were:

• age range 8–70 years, mean 46.12 ± 17.78 years,
• 56% male and 44% female,
• 80% married,
• 84% had education at elementary level or lower,
• 52% laborers, 32% housewives,
• 84% earned < IDR 1,000,000/month,
• 16% had BCG immunization history,
• mean TB illness duration 3.64 ± 2.29 months,
• mean residence duration 33.24 ± 21.38 years,
• 12% reported household transmission.

Important ambiguity: the report describes “respondents” as TB patients, but the actual unit of analysis appears to be the household/house, not the individual patient. The presence of respondents aged 8 years is also insufficiently explained in the methods.

What

The study examined the physical environmental characteristics of houses occupied by TB patients, focusing on housing conditions as a composite variable with six indicators:

1. occupancy density,
2. ventilation area,
3. indoor humidity,
4. indoor temperature,
5. lighting intensity,
6. floor type.

Key findings:

• 20% of households had occupancy density below the standard of 9 m²/person.
• 92% failed to meet the ventilation standard of ≥10% of floor area.
• Mean household humidity was 78.31% RH, above the recommended 40–60% RH.
• 84% of houses exceeded the recommended temperature limit of 30°C, with room temperatures around 32°C.
• 64% of households had inadequate lighting below 60 lux.
• 92% had waterproof flooring; 8% did not.
• Overall, only 1 household (4%) had adequate housing conditions, while 96% were classified as inadequate.

Authors’ apparent conclusion: most houses of TB patients in these tidal-flood–affected areas had poor environmental housing conditions, especially ventilation, humidity, temperature, and lighting.

Interpretation that is justified: the study shows a high prevalence of substandard housing conditions among households with TB patients.

Interpretation that is not justified: the study does not establish that these housing conditions caused TB, because there was no comparison group and the design was purely descriptive.

When

Data were collected between October 2023 and August 2024.

Where

The study was conducted in tidal-flood–affected coastal areas of Pekalongan, Indonesia, specifically in the service areas of:

• Tirto II Primary Health Center,
• Kramat Sari Primary Health Center,
• Dukuh Primary Health Center.

The setting was household-based, with direct observation and environmental measurement in:

• bedrooms,
• living rooms,
• kitchens,
• bathrooms.

Why

The study aimed to describe whether the homes of TB patients in flood-affected coastal areas met healthy housing standards. The implied rationale is that poor housing environments may facilitate TB transmission or persistence, especially in settings affected by chronic tidal flooding, dampness, heat, and poor ventilation.

The knowledge gap addressed appears to be the limited description of housing environmental conditions among TB-affected households in a coastal tidal-flood context.

How

This was a descriptive quantitative study. The single main variable was housing condition, measured using six environmental indicators.

Data collection methods:

• structured environmental assessment questionnaire adapted from prior validated instruments,
• dichotomous scoring for each indicator,
• classification into good, moderate, or poor housing condition categories,
• direct measurements in all major rooms.

Tools used:

• digital thermohygrometer for temperature and humidity,
• Smart Sensor AS803 lux meter for lighting intensity,
• measuring tape for room dimensions and ventilation area.

Environmental standards used:

• occupancy density: ≥9 m²/person,
• ventilation: ≥10% of floor area,
• humidity: 40–60% RH,
• temperature: 18–30°C,
• lighting: ≥60 lux,
• waterproof flooring as acceptable.

Quality procedures:

• instrument calibration against hospital-standard instruments,
• trained researchers,
• standardized protocols,
• informed consent and administrative authorization.

Strength of evidence

This is a descriptive cross-sectional household environmental assessment, which is a low level of evidence for causality. It is useful for describing conditions but not for determining whether poor housing conditions increase TB risk.

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Overall concise interpretation

Among 25 TB-affected households in tidal-flood–affected Pekalongan, 96% were classified as having inadequate housing conditions. The most prominent deficits were poor ventilation, high humidity, high indoor temperature, and insufficient lighting. However, because the study was small, descriptive, and lacked a non-TB comparison group, it supports only a description of housing conditions, not a conclusion that these conditions caused TB.

Source: Irnawati, I., Wulan, D. R., Rahmatullah, R., Kartikasari, D., Permadi, Y. W., & Arifin, M. (2026). Environmental housing conditions of tuberculosis patients in a tidal-flooded area: Evidence from one of the world’s second-highest tb-burden countries. BIS Health and Environmental Science, 3, V326012.

Linking Vulnerability, Adherence, and Outcomes in TB

(Yoseph Samodra)

Unequal TB burden shaped by demography, comorbidity, and place

• Tuberculosis burden is strongly patterned by age, with consistently higher prevalence among older adults, particularly those aged 55 years and above, as shown in national Indonesian survey data and hospital based cohorts.
• Men experience substantially higher TB prevalence than women in Indonesia, aligning with both population level epidemiology and clinic based samples of drug resistant and pulmonary TB patients.
• Socioeconomic vulnerability is a recurrent determinant, with higher TB prevalence among individuals with low educational attainment, informal or physically demanding occupations, and lower economic status.
• Occupational risk is pronounced in livelihoods tied to physical strain and environmental exposure, such as fishermen, farmers, agricultural laborers, drivers, and domestic workers.
• Urban settings, especially dense and informal settlements, show higher TB prevalence than rural areas, reflecting crowding, mobility, and health system fragmentation.
• Spatial analyses demonstrate that TB cases are not randomly distributed but clustered, with strong spatial autocorrelation and concentration in western Indonesia, particularly on Java Island.
• Comorbid conditions significantly amplify TB risk, most notably diabetes mellitus, where TB prevalence among diabetic patients is many times higher than in the general population.
• Among diabetic populations, younger age, smoking, HIV infection, poor glycemic control, insulin only treatment, and longer duration of diabetes independently increase TB risk.
• Environmental vulnerability in coastal urban areas, including flooding, tidal intrusion, and erosion, normalizes chronic illness and weakens perceived urgency for TB diagnosis and prevention.

See also: https://lintblab.weebly.com/research-topics.html

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Treatment adherence as a social and relational process

• Medication adherence varies widely across TB populations, with drug resistant TB patients showing particularly low adherence, where only a very small minority achieve high adherence.
• Female patients and those in productive adult age groups tend to show better adherence, suggesting gendered and life course influences on treatment behavior.
• Educational level, occupation, and physical distance to health facilities do not consistently predict adherence, challenging assumptions that structural access alone determines compliance.
• The presence of a treatment supporter or PMO is one of the most consistent positive determinants of adherence in pulmonary TB patients.
• Patient motivation to recover is strongly associated with adherence, underscoring the importance of psychosocial and behavioral dimensions of TB care.
• Long and complex treatment regimens, especially for drug resistant TB, erode adherence over time and require sustained support beyond initial treatment phases.
• In coastal urban communities, irregular livelihoods and daily survival priorities disrupt routine medication intake and clinic attendance.
• Communication failures within health systems, including euphemistic language and avoidance of explicitly naming TB, weaken understanding of treatment necessity and infectious risk.
• Stigma drives concealment of illness and resistance to screening, indirectly undermining adherence and continuity of care.

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Prognosis, mortality, and the importance of holistic clinical assessment

• Older adults with TB experience high mortality, even when respiratory function appears relatively preserved at admission.
• Functional status, measured by performance status, is the strongest predictor of TB related death, outweighing chronological age alone.
• Corticosteroid use is independently associated with both TB related and TB unrelated mortality, reflecting the impact of immune suppression.
• Hypoalbuminemia emerges as a powerful marker of poor prognosis, highlighting the central role of nutritional status in TB outcomes.
• Mortality risk increases stepwise as multiple risk factors accumulate, supporting the use of simple composite risk stratification in clinical practice.
• TB unrelated deaths in older patients are strongly influenced by age, comorbidities, and systemic frailty, reinforcing the need for integrated geriatric care.
• In diabetic patients, poor metabolic control not only increases TB risk but likely worsens disease course and recovery.
• Fragmented care pathways and delayed diagnosis, particularly in socially marginalized settings, contribute indirectly to worse outcomes by prolonging infectious periods and disease severity.
• Comprehensive TB management requires attention to daily functioning, nutrition, immune status, and comorbid disease alongside microbiological cure.

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Practical actions to strengthen TB disease management

• Integrate routine TB screening into diabetes care, with emphasis on glycemic control and smoking cessation.
• Prioritize treatment supporter programs, especially for drug resistant TB and socially vulnerable patients.
• Incorporate simple functional and nutritional assessments, such as performance status and serum albumin, into routine TB care for older adults.
• Design patient centered adherence strategies that align treatment schedules with local livelihoods and daily realities.
• Address stigma directly through clear, explicit, and culturally sensitive communication that names TB and explains transmission.
• Use spatial analysis to target high burden clusters with intensified case finding and resource allocation.
• Strengthen coordination between clinical services, community health workers, and social support systems to reduce fragmentation.

References:

1. Harahap, D.W.S., Andrajati, R., Sari, S.P. and Handayani, D., 2024. Medication Adherence among Drug-Resistant Tuberculosis (DR-TB) Patients at Universitas Indonesia Hospital. Jurnal Respirologi Indonesia, 44(3), pp.196-200.
2. Handayani, L., 2024. Studi Epidemiologi Tuberkulosis Paru (TB) di Indonesia: Temuan Survey Kesehatan Indonesia (SKI) 2023. Jurnal Kendari Kesehatan Masyarakat, 4(1), pp.59-67.
3. Kusmiyani, O.T., Hermanto, H. and Rosela, K., 2024. Analisis faktor yang berhubungan dengan kepatuhan minum obat anti tuberkulosis pada pasien TB paru di Puskesmas Samuda dan Bapinang Kotawaringin Timur. Jurnal Surya Medika, 10(1), pp.139-151.
4. Alemu, A., Seid, G., Diriba, G., Hailu, M., Dange, B., Moga, S., Melese, D., Tadesse, G., Mariam, S.H., Berhe, N. and Gumi, B., 2025. Prevalence and associated factors of tuberculosis among diabetic patients attending public health facilities in Ethiopia: a multicenter study. Archives of Public Health, 83(1), pp.1-15.
5. Rahmawati, Y., Jamil, I.R., Hidayah, I., Kusumawardani, D. and Wibowo, W., 2026. Spatial Econometric Analysis of the Impact of Health Infrastructure on TBC Patients Study Case in Indonesia Provinces Level. International Review for Spatial Planning and Sustainable Development, 14(1), pp.96-117.
6. Miyoshi, S., Semba, M., Tanabe, M., Sato, C., Watanabe, A., Ito, R., Kubota, M. and Abe, M., 2025. Assessment of Risk Factors for Death in Older Adult Patients With TB in Japan. CHEST Pulmonary, 3(2).
7. Shaluhiyah, Z., Handayani, S., Sriatmi, A., Agushybana, F. and Rimawati, E., 2025. Understanding tuberculosis as a wicked problem: a qualitative study in coastal urban settlements of Semarang, Indonesia. Frontiers in Communication, 10, p.1719819.

TBN 008

Missed Diagnoses, Overdiagnosis, and Improving Treatment Outcomes

(Yoseph Samodra)

Tuberculosis (TB) control requires coordinated improvements across diagnosis, prevention, and clinical management. Across these six studies, a consistent picture emerges: health systems face substantial diagnostic error at scale, TB burden continues to rise in key populations and geographies, and outcomes can be improved through stronger patient-centered supports and practical clinical tools, particularly for high-risk and drug-resistant TB patients.

See also: Lin TB Lab

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TB diagnosis at scale is still error-prone, with major consequences

• Real-world diagnostic algorithms in LMICs show only “moderate” performance, with estimated sensitivity 82.6% and specificity 88.0%, meaning large numbers of cases are still missed or wrongly labeled as TB.
• In 2023 alone (across 111 LMICs), the model estimated approximately: ~1.00 million false-negative diagnoses (missed TB cases); ~2.05 million false-positive diagnoses (people treated for TB who likely did not have it).
• Clinical diagnosis is a key driver of overdiagnosis, contributing to: ~22% of true-positive diagnoses, but ~75% of false-positive diagnoses.
• Diagnostic performance varies widely by region, largely reflecting differences in access to rapid diagnostic tests (RDTs) and reliance on clinical judgment.
• System-level improvements could substantially reduce both missed cases and overtreatment, including wider RDT adoption, better clinical decision algorithms, and more sensitive diagnostic technologies.

See also: Jago Beasiswa

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The TB burden is increasing, with clear demographic and geographic concentration

• In Southeast Sulawesi (Indonesia), reported TB cases increased steadily from 2,087 (2021) to 2,906 (2023), showing a sustained upward trend.
• TB burden was consistently higher among males, and the most affected age group was 45–54 years, indicating concentrated transmission and/or delayed detection in working-age adults.
• Geographic clustering was clear, with Kendari City reporting the highest number of cases, while less densely populated regions reported fewer cases.
• Although latent TB infection (ILTB/LTBI) detection increased annually, participation in tuberculosis preventive therapy (TPT) declined, signaling a critical implementation gap between identifying risk and delivering prevention.
• In Kupang City, pulmonary TB cases were mostly among productive age adults (15–50 years; 73.4%), with no cases reported in children (0–14 years) in the dataset—suggesting either lower detection in children or a surveillance/diagnostic gap.
• Education level showed a statistically significant association with pulmonary TB incidence (p = 0.048), while occupation did not (p = 0.958), reinforcing that social determinants (especially education-linked health literacy and access) may shape risk more than job category alone.

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Patient-centered behavior and clinical risk tools strengthen TB outcomes

• In Surabaya (Indonesia), TB transmission prevention behavior was significantly influenced by three modifiable factors: Knowledge (each 1-point increase → +0.667 prevention behavior points); Supportive behavior (each 1-point increase → +0.370); Medication adherence (each 1-point increase → +0.720). This highlights adherence and education as high-impact levers for transmission control.
• In MDR-TB treatment (Indonesia), adding delamanid to bedaquiline-containing regimens produced: No significant differences in sputum/culture conversion compared with bedaquiline regimens without delamanid; No significant differences in QTc interval changes, suggesting comparable cardiac safety under the study’s exclusion criteria; Numerically faster conversion and lower QTc prolongation in the combination group, though not statistically significant.
• In Thailand, an externally validated TB mortality risk score using only three diagnosis-time predictors (CCI category + tuberculous meningitis) demonstrated good discrimination (AuROC 76.3%) and good calibration, supporting its use for risk stratification and resource targeting, even if individual prediction remains imperfect, especially in intermediate-risk patients.

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Conclusion

Taken together, these studies indicate that TB programs must address both technical performance gaps (diagnostic accuracy and appropriate confirmation) and implementation gaps (prevention uptake and adherence support). The most actionable direction is an integrated strategy: expand high-quality rapid diagnostics to reduce false results, target rising hotspots and high-burden demographics with prevention that actually reaches completion, and strengthen outcomes through education, adherence reinforcement, and simple clinical risk tools, particularly for patients at elevated mortality risk or facing MDR-TB treatment complexity.

References:

1. van Lieshout Titan, A., Dodd, P.J., Cohen, T. and Menzies, N.A., 2026. Estimating the number of incorrect tuberculosis diagnoses in low-and middle-income countries. Nature Medicine, pp.1-8.
2. Juliasih, N.N., Sakinah, L.F., Sari, R.M., Winarso, H., Siahaan, S.C.P. and Gunawan, E.J., 2024. Determinants of transmission prevention behavior among Tuberculosis patients in Surabaya, Indonesia. Infection Prevention in Practice, 6(4), p.100404.
3. Lestari, H., 2024. Analisis Epidemiologi Kejadian Tuberkulosis Di Provinsi Sulawesi Tenggara Tahun 2021-2023. Variable Research Journal, 1(02), pp.802-810.
4. Dewi, N.P.A.N. and Susilawati, N.M., 2024. Hubungan Pekerjaan dan Pendidikan dengan Kejadian TB Paru di Kota Kupang. Inovasi Kesehatan Global, 1(4), pp.139-148.
5. Soedarsono, S., Mertaniasih, N.M., Kusmiati, T., Permatasari, A., Subay, S. and Adiono, S.H., 2024. Comparison of Individual Regimen Containing Bedaquiline with Delamanid and Bedaquiline without Delamanid on Efficacy and Safety in Multidrug-resistant Tuberculosis Patients: Implementation in Dr. Soetomo General Academic Hospital, Indonesia. The International Journal of Mycobacteriology, 13(2), pp.140-146.
6. Saisudjarit, P., Saokaew, S., Duangjai, A., Prasatkhetragarn, A., Kanchanasurakit, S. and Phisalprapa, P., 2026. External Validation of a Simple Mortality Risk Prediction Score for Tuberculosis Patients. Journal of Health Research, 40(1), p.3.

TBN 007