In Indonesia, the implementation of tuberculosis (TB) contact investigation is limited, with low detection rates. We report the yield of and risk factors for TB disease and infection for household contacts (HHCs) investigated using chest X-ray (CXR) screening. We identified HHCs aged five years and above of bacteriologically confirmed index cases from 2018 to 2022 in Yogyakarta City and Kulon Progo. All HHCs were offered screening for TB symptoms; TB infection testing with either tuberculin skin testing or interferon gamma release assay; and referral for CXR. Sputum from those with symptoms or CXR suggestive of TB was tested with Xpert MTB/RIF. Risk factors for active TB disease and latent TB infection (LTBI) were identified by logistic regression models. We screened 2857 HHCs for TB between June 2020 and December 2022, with 68 (2.4%) diagnosed with active TB. Of 2621 HHCs eligible for LTBI investigation, 1083 (45.7%) were diagnosed with LTBI. The factors associated with active TB were age, being underweight, diabetes mellitus, urban living, and sleeping in the same house as an index case. Factors associated with LTBI were increasing age and male gender. Conclusions: Screening for HHC including CXR and TST/IGRA yielded a moderate prevalence of TB disease and infection.

Coverage of tuberculosis preventive treatment (TPT) in Indonesia is inadequate, and persons who start TPT often do not complete treatment. In 2020, Zero TB Yogyakarta implemented person-centered contact investigation and shorter TPT regimen provision in collaboration with primary health care centers. Between 1 January 2020 and 31 August 2022, we assessed eligibility for TPT among household contacts of persons with bacteriologically confirmed TB (index cases) and offered them a 3-month TPT regimen (3RH or 3HP). A dedicated nurse monitored contacts on TPT for treatment adherence and side effects every week in the first month and every two weeks in the next months. Contacts were also able to contact a nurse by phone or ask for home visits at any point if they had any concerns. A total of 1016 contacts were eligible for TPT: 772 (78.8%) started short regimen TPT with 706 (91.5%) completing their TPT. Side effects were reported in 26 (39%) of the non-completion group. We conclude that high rates of TPT uptake and completion among contacts assessed as eligible for TPT can be achieved through person-centered care and the use of shorter regimens. Side-effect monitoring and management while on TPT is vital for improving TPT completion.

The World Health Organization recommends using chest X-ray (CXR) in active case finding (ACF) to improve case detection. This study aimed to describe the spectrum and outcomes of TB disease diagnosed through a mobile community based ACF program in Yogyakarta. This prospective cohort study included people attending a TB ACF program in Yogyakarta between 1 January 2021 to 30 June 2022. Participants ≥10 years old underwent CXR, symptom screening, and Xpert MTB/RIF testing of sputum. Subclinical TB was defined as asymptomatic active TB, whether bacteriologically confirmed or not. Treatment outcome data were obtained from the national program TB database. 47,735 people attended the ACF program; the yield of TB disease was 0.86% (393/45,938). There were 217 symptomatic cases, of whom 72 (33.2%) were bacteriologically confirmed, and 176 asymptomatic cases, with 52 (29.5%) bacteriologically confirmed. Treatment success was 70.7% with high loss to follow up (9%) and not evaluated (17.1%). Multivariate analysis demonstrated weak evidence for lower unsuccessful outcomes in symptomatic versus subclinical TB (aOR 0.6, 95% CI 0.36–0.998). TB ACF programs utilizing CXR may diagnose a high proportion of subclinical TB. Linkage to care in ACF program is important to increase successful treatment outcomes.

Clinical algorithms for child tuberculosis (TB) are a valuable guide for healthcare workers to initiate treatment. We evaluated the agreement of pediatric TB diagnosis using the current Indonesia diagnostic algorithms with the 2022 WHO treatment decision algorithm and developed a new Indonesia algorithm for child TB based upon our findings and expert opinion. We conducted a retrospective study at 10 hospitals in Indonesia, involving children (0–10 years), who were evaluated for TB diagnosis in 2022. A panel of child TB experts used participants’ records to make a diagnosis using the 2022 WHO algorithm and the 2016 Indonesian algorithm. We assessed agreement between the diagnosis made by the attending doctor and those determined by the expert panel. A new Indonesia guideline was developed based on the findings and consensus of various stakeholders. Of 523 eligible children, 371 (70.9%) were diagnosed with TB by the attending doctors, 295 (56.4%) by the WHO algorithm, and 246 (47%) by the Indonesia algorithm. The Cohen’s Kappa of TB diagnosis was: attending doctor vs. WHO algorithm (0.27), attending doctor vs. Indonesia algorithm (0.45), and WHO algorithm vs. Indonesia algorithm (0.42). A review of both algorithms revealed challenges for implementation. An algorithmic approach for child TB diagnosis may not be universally applicable or implementable due to variable access to diagnostic tests and the wide variety of clinical presentations.

Clinical algorithms for child tuberculosis (TB) are a valuable guide for healthcare-workers to initiate treatment. We evaluated the 2022 WHO treatment decision algorithm and developed a new Indonesia algorithm for child TB. We conducted a retrospective study at 10 hospitals in Indonesia, involving children (0-10 years), who were evaluated for TB diagnosis in 2022. A panel of child TB experts used participant’s records to make a diagnosis using the 2022 WHO algorithm and the 2016 Indonesian algorithm. We assessed agreement between the diagnosis made by the attending doctor and those determined by the expert panel. A new Indonesia guideline was developed based on the findings and consensus of various stakeholders. Of 523 eligible children, 371 (70.9%) were diagnosed with TB by the attending doctors, 295 (56.4%) by WHO algorithm, and 246 (47%) by Indonesia algorithm. The Cohen’s Kappa of TB diagnosis was: attending doctor vs WHO algorithm (0.27), attending doctor vs Indonesia algorithm (0.45), and WHO algorithm vs Indonesia algorithm (0.42). A detailed review of both algorithms revealed limitations for implementation. An algorithmic approach for child TB diagnosis may not be universally applicable or implementable due to variable access to diagnostic tests and wide variety of clinical presentations.