Background: Drug-resistant tuberculosis (DR-TB) is the barrier for global TB elimination efforts with a lower treatment success rate. Loss to follow-up (LTFU) in DR-TB is a serious problem, causes mortality and morbidity for patients, and leads to wide spreading of DR-TB to their family and the wider community, as well as wasting health resources. Prevention and management of LTFU is crucial to reduce mortality, prevent further spread of DR-TB, and inhibit the development and transmission of more extensively drug-resistant strains of bacteria. A study about the factors associated with loss to follow-up is needed to develop appropriate strategies to prevent DR-TB patients become loss to follow-up. This study was conducted to identify the factors correlated with loss to follow-up in DR-TB patients, using questionnaires from the point of view of patients. Methods: An observational study with a cross-sectional design was conducted. Study subjects were all DR-TB patients who have declared as treatment success and loss to follow-up from DR-TB treatment. A structured questionnaire was used to collect information by interviewing the subjects as respondents. Obtained data were analyzed potential factors correlated with loss to follow-up in DR-TB patients. Results: A total of 280 subjects were included in this study. Sex, working status, income, and body mass index showed a significant difference between treatment success and loss to follow-up DR-TB patients with p-value of 0.013, 0.010, 0.007, and 0.006, respectively. In regression analysis, factors correlated with increased LTFU were negative attitude towards treatment (OR = 1.2; 95% CI = 1.1–1.3), limitation of social support (OR = 1.1; 95% CI = 1.0–1.2), dissatisfaction with health service (OR = 2.1; 95% CI = 1.5–3.0)), and limitation of economic status (OR = 1.1; 95% CI = 1.0–1.2)). Conclusions: Male patients, jobless, non-regular employee, lower income, and underweight BMI were found in higher proportion in LTFU patients. Negative attitude towards treatment, limitation of social support, dissatisfaction with health service, and limitation of economic status are factors correlated with increased LTFU in DR-TB patients. Non-compliance to treatment is complex, we suggest that the involvement and support from the combination of health ministry, labor and employment ministry, and social ministry may help to resolve the complex problems of LTFU in DR-TB patients.

Purpose: Patients with tuberculosis need to religiously take medication daily. However, they experience several side effects from these medications. The main reason for measuring the quality of life is to explain closely related factors that affect the patient’s daily life that have been compromised with illness, while considering a patients’ well-being that has associations with individual characteristics. Patients and Methods: This study included 157 patients with tuberculosis at 5 primary health-care centers and 2 hospitals in Surabaya. Quality of life is determined based on eight domains: general health, pain, social functioning, physical functioning, role limitation due to physical health, role limitation due to emotional problems, energy, and emotional well-being. The research instrument used to measure the quality of life is the RAND-36 Item Health Survey, whereas that used to measure mental distress is the Self-Reporting Questionnaire. Results: Our study results show that, of the eight domains measuring the quality of life, only age exhibited a significant effect on general health (P = 0.018); sex did not significantly affect the quality of life in all domains. The level of education exhibited a significant effect only on role limitation due to emotional problems (P = 0.014). Mental distress demonstrated a significant effect on the quality of life in all domains. Conclusion: There are several factors affecting TB patients’ quality of life. The study found that age, level of education, and comorbidity affect quality of life in several domains. However, mental distress affects quality of life in all domains.

Tuberculosis (TB) treatment failure is a health burden, as the patient remains a source of infection and may lead to the development of multi-drug resistance (MDR). Information from cases of treatment failure that develop into MDR, which is related to a history of previous TB treatment, in accordance with the pharmacokinetic aspect, is one important thing to prevent TB treatment failure and to prevent drug resistance. This was an observational descriptive study in an acquired MDR-TB patient who had a prior history of treatment failure. A structured questionnaire was used to collect information. The questionnaire consisted of a focus on the use of TB drug formulas during the treatment period, as well as when and how to take them. This study included 171 acquired MDR-TB patients from treatment failure cases. An amount of 64 patients received the separated TB drug, and 107 patients received the fixed dose combination (FDC) TB drug. An amount of 21 (32.8%) patients receiving separated TB drug and six (5.6%) patients receiving FDC TB drug took their drug in divided doses. In addition, three (4.7%) patients receiving separated TB drug and eight (7.5%) patients receiving FDC TB drug took their drug with food. An amount of 132 out of 171 (77.2%) patients had a history of incorrect treatment that developed into MDR-TB. Education on how to take the correct medication, both the separate version and the FDC TB drug, according to the pharmacokinetic aspect, is important before starting TB treatment.

Tuberculosis and COVID-19 are among the infectious diseases that constitute a public health concern. Therefore, this study aims to examine the recent epidemiology of tuberculosis and COVID-19 in East Java Province, Indonesia, in 2020. Case-based surveillance data were acquired with a retrospective design between January and December 2020 by the East Java Health Officer. The data were analyzed using Quantum Geographic Information System (QGIS) for mapping, and Microsoft Excel for recording. Furthermore, the statistical analysis (Spearman correlation test) was carried out via Statistical Package for Social Science (SPSS) applications. A total number of 38,089 confirmed cases of tuberculosis was recorded, with an incidence rate of 95.49/100,000 population, a case fatality rate (CFR) of 3.6%, and an average treatment success rate of 87.78%. COVID-19 is a new viral disease, with a total of 84,133 confirmed COVID-19 cases in East Java, with an incidence rate of 232.9/100,000 population. The highest incidence rate was found in Mojokerto city, while the lowest was found in Sampang. Furthermore, the CFR values of tuberculosis and COVID-19 were 1.4% and 6.8%, respectively. The regional survey in East Java Province showed that the incidence of tuberculosis remains high. This indicated that the search for active cases and preventive promotion was not completed. Therefore, inter-sectoral collaboration can be adapted to provide suitable tuberculosis health care.

No population pharmacokinetics (PK) model of isoniazid (INH) has been reported for the Indonesian population with tuberculosis (TB). Therefore, we aimed to develop a population PK model to optimize pharmacotherapy of INH on the basis of therapeutic drug monitoring (TDM) implementation in Indonesian patients with TB. INH concentrations, N-acetyltransferase 2 (NAT2) genotypes, and clinical data were collected from Dr. Soetomo General Academic Hospital, Indonesia. A nonlinear mixed-effect model was used to develop and validate the population PK model. A total of 107 patients with TB (with 153 samples) were involved in this study. A one-compartment model with allometric scaling for bodyweight effect described well the PK of INH. The NAT2 acetylator phenotype significantly affected INH clearance. The mean clearance rates for the rapid, intermediate, and slow NAT2 acetylator phenotypes were 55.9, 37.8, and 17.7 L/h, respectively. Our model was well-validated through visual predictive checks and bootstrapping. We established the population PK model for INH in Indonesian patients with TB using the NAT2 acetylator phenotype as a significant covariate. Our Bayesian forecasting model should enable optimization of TB treatment for INH in Indonesian patients with TB.