INTRODUCTION

Pulmonary tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a major global health challenge despite being preventable and curable. Although first-line regimens have high efficacy under clinical trial conditions, real-world programmatic treatment success for drug-susceptible TB remains suboptimal [1-3]. In 2024, the global burden remained substantial, with an estimated 10.7 million incident cases (95% uncertainty interval, 9.9 to 11.5 million) and approximately 1.23 million deaths [4]. In Korea, overall notification rates continue to decline, but the epidemiological landscape is changing [5]. TB is increasingly concentrated among older adults, a population characterized by atypical clinical presentations, delayed diagnosis, more frequent adverse drug reactions, and poorer treatment outcomes [6-8].

Concurrently, the burden of non-tuberculous mycobacterial pulmonary disease (NTM-PD) is increasing [9-11]. Non-tuberculous mycobacteria (NTM) are ubiquitous environmental organisms found in soil and water and can cause pulmonary and extrapulmonary disease; pulmonary disease is the most common manifestation [12]. Although the pathogenesis of NTM-PD has not been fully elucidated, it appears to involve complex interactions between host susceptibility and organism virulence [13]. Mycobacterium avium complex (MAC) is the predominant pathogen worldwide [14]. The incidence and prevalence of NTM-PD are increasing globally, and this trend is also evident in Korea, where both isolation frequency and patient volume have increased substantially over the past decade [15,16]. Treatment outcomes for NTM-PD remain unsatisfactory; reported treatment success rates for MAC-PD vary widely, at approximately 32–65% [17], and pooled estimates for M. abscessus complex are approximately 45% [18].

Despite the substantial burden of mycobacterial disease, important data gaps remain. National TB programs generate valuable surveillance statistics, but these data often lack granular clinical information on comorbidities, long-term outcomes, and resource utilization [19]. These limitations are greater for NTM-PD. Unlike TB, NTM-PD is not currently a legally notifiable infectious disease in Korea; consequently, robust epidemiological data are scarce. Most existing NTM studies have relied on single-center cohorts, which may be affected by referral bias, or health insurance claims databases, which lack microbiological validation [20]. Comprehensive multicenter clinical registries with harmonized patient-level data on diagnostics, treatments, and outcomes are therefore needed.

To address these limitations, the TB–NTM registry was established. The primary objectives of this initiative were to (1) define and standardize core clinical variables for TB and NTM across major tertiary hospitals and (2) integrate site-level data into a high-quality, de-identified registry. This cohort profile describes the methodology and baseline characteristics of the cohort, which was designed to support clinical, epidemiological, and health-services research.

COHORT DESCRIPTION

Study design and setting

This retrospective multicenter cohort was based on electronic medical records (EMRs) from 4 tertiary hospitals in Korea: Chonnam National University Hospital, Pusan National University Hospital, Kyungpook National University Hospital, and Jeonbuk National University Hospital. Each institution extracted all clinical data from January 1, 2015, to December 31, 2024, through a 1-time batch extraction. The records were de-identified according to a common data specification, and the standardized datasets were transferred to a central coordinating center. Standardized core and extended data elements were organized for analysis (Table 1).

Raw data—including diagnoses, prescriptions, laboratory tests, microbiology results, imaging reports, hospitalization records, and healthcare costs—were retrieved directly from institutional EMR databases. At the central site, the data were harmonized into a curated analytic dataset through variable standardization, range and consistency checks, temporal validation, and code mapping using the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), logical observation identifiers names and codes (LOINC), and anatomical therapeutic chemical (ATC) terminology systems.

Participants and case definitions

TB

TB cases were identified as patients registered in the Korean National Tuberculosis Surveillance System, a nationwide web-based notification system established in 2000 [21]. Reporting of all TB patients within 24 hours of diagnosis is mandatory in this system.

NTM-PD

NTM-PD cases were identified from the EMRs of the participating hospitals. Cases were defined according to the microbiological criteria of the 2007 American Thoracic Society/Infectious Diseases Society of America (ATS/IDSA) guidelines [22], with classification criteria, such as “definite” and “possible,” modified for this study and applied in combination with compatible clinical and radiological features.

Definite NTM-PD was defined using modified microbiological criteria based on the 2007 ATS/IDSA guidelines. Cases were classified as definite when any of the following conditions were met:

Positive cultures from sputum samples

• • ≥2 positive sputum cultures obtained from separate samples, with species identification confirmed at least twice using standard identification methods, such as molecular or sequencing-based assays;

• • For strongly pathogenic species, including M. avium, M. intracellulare, M. abscessus subsp. abscessus (hereafter referred to as M. abscessus), M. abscessus subsp. massiliense (hereafter referred to as M. massiliense), and M. kansasii, at least 1 confirmed species identification.

Positive cultures from bronchoscopic samples

• • ≥1 positive culture and ≥1 confirmed species identification.

Possible NTM-PD was defined as partial fulfillment of the modified microbiological criteria and required any of the following conditions:

Positive cultures from sputum samples

• • ≥2 positive sputum cultures, but species identification was not performed or not reported; or

• • 1 positive sputum culture, with species identification performed on the isolate.

Positive cultures from bronchoscopic samples

• • ≥1 positive culture from a bronchoscopic sample, but species identification was not performed or not reported.

Episode linkage

Multiple TB or NTM-PD episodes occurring within the study period were linked at the patient level using site-specific encrypted identifiers. Recurrent disease was considered part of the same individual’s clinical course. For patient-level summaries, the outcome of the most recent episode was used.

Data processing and quality assurance

At the coordinating center, all institutional datasets underwent standardized quality checks, including value-range validation, temporal consistency checks, logical cross-verification among diagnoses, tests, and treatments, assessment of missingness, and outlier identification. Discrepancies were resolved in collaboration with each institution. Data were stored in a secure environment and retained for 3 years after study completion.

Statistical analysis

Descriptive statistics for baseline laboratory variables were calculated and are presented as means with standard deviations or medians with interquartile ranges, as appropriate. Continuous variables were compared using Student’s t-test or the Mann–Whitney U-test, as appropriate. Frequencies of symptoms, comorbidities, and laboratory abnormalities were compared between the TB and NTM-PD groups using the chi-square test or Fisher’s exact test when expected cell counts were less than 5. Laboratory abnormalities were defined using baseline laboratory values within ±7 days of the reference date as follows: leukocytosis, white blood cell count >11×10³/μL; anemia, hemoglobin <13 g/dL in male or <12 g/dL in female; abnormal liver function, aspartate aminotransferase or alanine aminotransferase >40 U/L; and abnormal renal function, serum creatinine >1.2 mg/dL. Temporal trends in annual case counts were analyzed using Poisson regression. The Cochran–Armitage test for trend was used to assess linear trends in proportions over time, including the proportion of NTM among mycobacterial respiratory isolates and antimicrobial prescription rates among patients with NTM-PD. All statistical analyses were performed using Python version 3.11 (https://www.python.org/downloads/release/python-3110/) with the SciPy and statsmodels libraries. A 2-sided p-value <0.05 was considered statistically significant.

Ethics statement

The study was approved by the Institutional Review Boards of Chonnam National University Hospital (IRB No. CNUH-2025-276), Jeonbuk National University Hospital (IRB No. CUH 2025-07-076), Kyungpook National University Hospital (IRB No. KNUH 2025-08-003), and Pusan National University Hospital (IRB No. 2508-012-154). The requirement for informed consent was waived by these boards. All data were kept confidential, and all personal identifiers, including resident registration numbers, were removed before analysis.

KEY FINDINGS

Patients’ characteristics

A total of 11,790 patients with TB and 5,145 patients with NTM-PD were identified across the 4 tertiary hospitals during the study period (Table 2). Among patients with TB, the mean age was 62.1±18.3 years, and 59.2% were male. The mean body mass index (BMI) was 21.9±3.5 kg/m², and 15.5% of patients were classified as underweight (BMI <18.5 kg/m²). Cough, sputum, hemoptysis, and dyspnea were reported more frequently in patients with NTM-PD than in those with TB (all p<0.001), whereas the prevalence of fever was comparable between the groups (Table 2). Bronchiectasis was more frequent in patients with NTM-PD than in those with TB (44.7 vs. 4.8%, p<0.001), whereas diabetes mellitus was more common in patients with TB (34.3 vs. 18.2%, p<0.001). Abnormal liver function (19.6 vs. 14.2%, p<0.001) and abnormal renal function (11.7 vs. 7.9%, p<0.001) were more frequent in patients with TB than in those with NTM-PD, whereas leukocytosis (10.5 vs. 9.1%, p=0.127) and anemia (53.3 vs. 50.6%, p=0.079) did not differ significantly between the groups. Detailed descriptive statistics for baseline laboratory parameters are provided in Supplementary Material 1. Among patients with TB, the treatment success rate, defined as the sum of “cured” and “treatment completed” according to World Health Organization standard definitions [23], was 71.6% (8,436/11,790) (Table 2). Detailed outcome distributions are presented in Supplementary Material 2.

Trends in mycobacterial species in respiratory cultures, 2015–2024

The annual distribution of mycobacterial species isolated from respiratory samples is shown in Figure 1. Mycobacterial isolates were classified as either M. tuberculosis or NTM. Over the study period, the proportion of isolates identified as NTM increased significantly, from 53.9% in 2015 to 79.2% in 2024 (Cochran–Armitage test for trend, p<0.001).

Among NTM species, the relative distribution remained generally stable over time (Figure 2). M. intracellulare was consistently the most frequently isolated species, followed in descending order by M. avium, M. abscessus, M. kansasii, and M. massiliense.

Trends in tuberculosis and non-tuberculous mycobacterial pulmonary disease, 2015–2024

The annual number of newly diagnosed TB cases declined consistently over the 10-year period, from 1,657 cases in 2015 to 580 cases in 2024 (p for trend <0.001; Table 3). This downward trend was observed in both sexes: male cases decreased from 959 to 359 (p for trend <0.001), and female cases decreased from 698 to 221 (p for trend <0.001) during the same period. The decrease in TB notifications became particularly pronounced after 2020, with the annual percent decrease accelerating from 5.8% in 2015–2019 to 13.2% in 2020–2024.

In contrast, NTM-PD showed a long-term upward trend (p for trend <0.001). Annual NTM-PD cases increased from 493 in 2015 to 582 in 2019, reached 600 in 2022 and 673 in 2023, and then decreased to 450 cases in 2024. Despite the decrease in the final year, the overall pattern indicates an increased NTM-PD burden across the decade in these tertiary referral centers.

With respect to the etiological distribution of NTM-PD, MAC accounted for most cases (78.7%; Table 3). Overall, M. intracellulare was the most common species, representing 58.9% of all NTM-PD cases, followed by M. avium (19.8%). The second most frequent pathogen group was the M. abscessus complex (8.9%), comprising M. abscessus (6.0%) and M. massiliense (2.9%). M. kansasii accounted for 3.8% of NTM-PD cases.

Antimicrobial prescription patterns in non-tuberculous mycobacterial pulmonary disease

Antimicrobial prescription patterns showed sustained use of macrolide-based multidrug regimens throughout the study period (Table 4). The proportion of patients receiving azithromycin increased significantly from 8.9% in 2015 to 24.2% in 2024 (Cochran–Armitage test for trend, p<0.001), whereas the proportion receiving clarithromycin decreased from 14.8% to 3.3% over the same period (Cochran–Armitage test for trend, p<0.001). Azithromycin was prescribed 39–181 times annually, whereas clarithromycin prescriptions ranged from 13 to 73 annually and declined in later years. Rifampin and ethambutol were used at similar frequencies, typically with 64–206 prescriptions per year, and their highest use occurred in 2023.

Parenteral therapies were used more selectively. Amikacin was prescribed 4–51 times per year, consistent with selective use in severe or refractory disease. In contrast, imipenem and cefoxitin were prescribed infrequently, generally fewer than 10 times per year, suggesting targeted use for M. abscessus complex infections. Other agents, including fluoroquinolones, clofazimine, linezolid, and tigecycline, were used intermittently and in smaller numbers, although clofazimine prescriptions increased in certain years and reached 26 in 2023.

Overall, the core structure of NTM-PD treatment regimens remained stable across the decade, but the absolute number of prescriptions increased in parallel with the growing number of patients with NTM-PD. This pattern reflects the increasing clinical burden of NTM-PD in these tertiary referral centers.

STRENGTHS AND WEAKNESSES

The main value of this registry lies in its integration of multidomain clinical, microbiological, radiological, and health-economic data from 4 regional national university hospitals over a 10-year period. Because these institutions function as tertiary referral centers for their catchment areas, the registry captures a broad spectrum of routine clinical practice, including complex cases. This integrated data framework strengthens research capacity in 3 complementary areas.

First, the registry supports clinical and epidemiological research. By incorporating standardized information on imaging, pulmonary function, and longitudinal microbiological results, the TB-NTM Registry can provide detailed descriptions of temporal epidemiological changes, diagnostic practices, and treatment strategies for mycobacterial disease in Korea.

Second, the registry strengthens microbiological research. It includes comprehensive laboratory data on mycobacterial cultures performed in participating hospital laboratories. The ability to distinguish M. tuberculosis from specific non-tuberculous mycobacterial species and to examine changes in their relative proportions over time makes it possible to characterize shifts in species distribution within these centers. Because analyses of mycobacterial species and disease trends are based on laboratory-confirmed culture results in addition to diagnostic codes, this registry can reduce the misclassification that may occur in studies based only on diagnostic codes [20,24-26].

Third, the registry enables health service and economic research. The TB-NTM Registry integrates laboratory parameters, radiological findings, detailed drug exposure, and information on health service utilization and costs within a single harmonized framework. This breadth of data supports future clinical and health-services research, including comparative effectiveness and safety evaluations and economic analyses.

Several limitations should also be acknowledged. First, the registry is limited to 4 tertiary university hospitals and may not fully represent all patients with TB or NTM-PD in Korea, particularly those treated exclusively in primary or secondary care settings. Therefore, the trends observed in this registry should be interpreted as reflecting referral-center practice patterns rather than national incidence.

Second, because this is a retrospective EMR-based registry, the data are restricted to information documented during routine clinical care. Symptoms, medical history, or adverse drug reactions that were not recorded in the medical records could not be captured, which may have led to under-ascertainment and missing data for some variables.

Third, diagnostic approaches, follow-up schedules, and treatment strategies were not standardized across institutions or physicians. For TB, national guidelines and the public–private mix program likely reduced variation in clinical practice, although some heterogeneity remained [27,28]. In contrast, NTM-PD diagnosis and management are less standardized, and no national program exists; therefore, practice patterns may vary considerably by institution and attending physician [14,16,20,29]. Although this variability can be viewed as a limitation, it also reflects routine clinical practice and provides an opportunity to investigate how differences in management are associated with patient outcomes.

Beyond these limitations, the trends observed in this registry should be interpreted with attention to representativeness and denominator issues. Because the cohort was restricted to 4 tertiary referral centers, the annual counts of TB and NTM-PD reflect case burden within these hospitals rather than population-based incidence. Changes in referral patterns, healthcare access, and institutional case mix over time may therefore have influenced the observed trends. Moreover, complete data were not available on the total number of respiratory visits, imaging studies, or mycobacterial cultures performed each year; therefore, standardized rates per population or per number of tests could not be calculated. The rising proportion of non-tuberculous mycobacterial isolates and cases may partly reflect increased culture intensity and heightened clinical awareness, in addition to underlying epidemiological changes [15,30,31].

The study period also spans the coronavirus disease 2019 (COVID-19) pandemic, which had substantial effects on healthcare utilization and respiratory infection surveillance [32-34]. Reduced healthcare-seeking behavior, disruptions in routine services, and competing clinical priorities during the pandemic may have contributed to the marked decline in TB notifications after 2020, while changes in diagnostic practices may have influenced NTM detection. Finally, as with any retrospective registry based on EMRs, missing or incomplete data were unavoidable. Information on symptoms, comorbidities, adverse drug reactions, and lifestyle factors such as smoking or alcohol use may have been under-recorded, and multiple imputation for missing values was not performed in this cohort profile. These limitations should be considered when interpreting the findings and underscore the need for caution when drawing causal inferences.

DATA ACCESSIBILITY

Supplementary materials

NOTES

Conflict of interest

The authors have no conflicts of interest to declare for this study.

Funding

This work was supported by a grant from the Korea National Enterprise for Clinical Trials (KoNECT) under the project titled “Disease-specific Registry Data Development and Utilization Research” (2025).

Acknowledgements

None.

Author contributions

Conceptualization: Kwon YS, Kim MG, Kang G. Data curation: Woo HJ, Kim MG. Formal analysis: Woo HJ, Kim MG, Kwon YS. Funding acquisition: Kwon YS. Methodology: Woo HJ, Kim MG, Kang G, Lee J, Mok J, Choe YH, Kwon YS. Writing – original draft: Woo HJ. Writing – review & editing: Woo HJ, Kim MG, Kang G, Lee J, Mok J, Choe YH, Kwon YS.

Figure 1.

Annual trend in mycobacterial isolates from respiratory specimens. The left y-axis shows the number of isolates, with bars indicating the annual counts of TB (magenta) and NTM (lime green). The right y-axis shows the proportion of NTM among all mycobacterial isolates, shown as a line. TB, tuberculous; NTM, non-tuberculous mycobacteria.

Figure 2.

Annual distribution of non-tuberculous mycobacterial species among mycobacterial isolates from respiratory specimens. 1Indicates a mycobacterial isolate in which more than 1 non-tuberculous mycobacterial species was identified.

REFERENCES

• 1. Joint Committee for the Development of Korean Guidelines for Tuberculosis, Korea Centers for Disease Control and Prevention. Korean guidelines for tuberculosis. 5th ed. Cheongju: Joint Committee for the Development of Korean Guidelines for Tuberculosis, Korea Centers for Disease Control and Prevention; 2024, (Korean).
• 2. World Health Organization. Global tuberculosis report 2024 [cited 2025 Nov 26]. Available from: https://www.who.int/teams/global-programme-on-tuberculosis-and-lung-health/tb-reports/global-tuberculosis-report-2024
• 3. Paton NI, Cousins C, Suresh C, Burhan E, Chew KL, Dalay VB, et al. Treatment strategy for rifampin-susceptible tuberculosis. N Engl J Med 2023;388:873-887. https://doi.org/10.1056/NEJMoa2212537ArticlePubMedPMC
• 4. World Health Organization. Global tuberculosis report, 2025 [cited 2025 Nov 26]. Available from: https://www.who.int/teams/global-programme-on-tuberculosis-and-lung-health/tb-reports/global-tuberculosis-report-2025
• 5. Korea Disease Control and Prevention Agency. Annual report on the notified TB in the Republic of Korea. Cheongju: Korea Disease Control and Prevention Agency; 2024, (Korean).
• 6. Cho KS. Tuberculosis control in the Republic of Korea. Epidemiol Health 2018;40:e2018036. https://doi.org/10.4178/epih.e2018036ArticlePubMedPMC
• 7. Kwon YS, Chi SY, Oh IJ, Kim KS, Kim YI, Lim SC, et al. Clinical characteristics and treatment outcomes of tuberculosis in the elderly: a case control study. BMC Infect Dis 2013;13:121. https://doi.org/10.1186/1471-2334-13-121ArticlePubMedPMC
• 8. Mok J, An D, Kim S, Lee M, Kim C, Son H. Treatment outcomes and factors affecting treatment outcomes of new patients with tuberculosis in Busan, South Korea: a retrospective study of a citywide registry, 2014-2015. BMC Infect Dis 2018;18:655. https://doi.org/10.1186/s12879-018-3574-yArticlePubMedPMC
• 9. Daley CL, Iaccarino JM, Lange C, Cambau E, Wallace RJ Jr, Andrejak C, et al. Treatment of nontuberculous mycobacterial pulmonary disease: an official ATS/ERS/ESCMID/IDSA clinical practice guideline. Clin Infect Dis 2020;71:e1-e36. https://doi.org/10.1093/cid/ciaa241ArticlePubMedPMC
• 10. Zhou Y, Mu W, Zhang J, Wen SW, Pakhale S. Global prevalence of non-tuberculous mycobacteria in adults with non-cystic fibrosis bronchiectasis 2006-2021: a systematic review and meta-analysis. BMJ Open 2022;12:e055672. https://doi.org/10.1136/bmjopen-2021-055672ArticlePubMedPMC
• 11. Prevots DR, Marshall JE, Wagner D, Morimoto K. Global epidemiology of nontuberculous mycobacterial pulmonary disease: a review. Clin Chest Med 2023;44:675-721. https://doi.org/10.1016/j.ccm.2023.08.012ArticlePubMedPMC
• 12. Falkinham JO 3rd. Environmental sources of nontuberculous mycobacteria. Clin Chest Med 2015;36:35-41. https://doi.org/10.1016/j.ccm.2014.10.003ArticlePubMed
• 13. Matsuyama M, Matsumura S, Nonaka M, Nakajima M, Sakai C, Arai N, et al. Pathophysiology of pulmonary nontuberculous mycobacterial (NTM) disease. Respir Investig 2023;61:135-148. https://doi.org/10.1016/j.resinv.2022.12.002ArticlePubMed
• 14. Kwon YS, Koh WJ, Daley CL. Treatment of Mycobacterium avium complex pulmonary disease. Tuberc Respir Dis (Seoul) 2019;82:15-26. https://doi.org/10.4046/trd.2018.0060ArticlePubMedPMC
• 15. Kim JY, Kwak N, Yim JJ. The rise in prevalence and related costs of nontuberculous mycobacterial diseases in South Korea, 2010-2021. Open Forum Infect Dis 2022;9:ofac649. https://doi.org/10.1093/ofid/ofac649ArticlePubMedPMC
• 16. Kwon YS, Koh WJ. Diagnosis and treatment of nontuberculous mycobacterial lung disease. J Korean Med Sci 2016;31:649-659. https://doi.org/10.3346/jkms.2016.31.5.649Article
• 17. Kwak N, Park J, Kim E, Lee CH, Han SK, Yim JJ. Treatment outcomes of Mycobacterium avium complex lung disease: a systematic review and meta-analysis. Clin Infect Dis 2017;65:1077-1084. https://doi.org/10.1093/cid/cix517ArticlePubMed
• 18. Kwak N, Dalcolmo MP, Daley CL, Eather G, Gayoso R, Hasegawa N, et al. Mycobacterium abscessus pulmonary disease: individual patient data meta-analysis. Eur Respir J 2019;54:1801991. https://doi.org/10.1183/13993003.01991-2018ArticlePubMed
• 19. Jeong D, Kang HY, Kim J, Lee H, Yoo BN, Kim HS, et al. Cohort profile: Korean tuberculosis and post-tuberculosis cohort constructed by linking the Korean National Tuberculosis Surveillance System and National Health Information Database. J Prev Med Public Health 2022;55:253-262. https://doi.org/10.3961/jpmph.21.635ArticlePubMedPMC
• 20. Park JH, Shin S, Kim TS, Park H. Clinically refined epidemiology of nontuberculous mycobacterial pulmonary disease in South Korea: overestimation when relying only on diagnostic codes. BMC Pulm Med 2022;22:195. https://doi.org/10.1186/s12890-022-01993-1ArticlePubMedPMC
• 21. Go U, Park M, Kim UN, Lee S, Han S, Lee J, et al. Tuberculosis prevention and care in Korea: evolution of policy and practice. J Clin Tuberc Other Mycobact Dis 2018;11:28-36. https://doi.org/10.1016/j.jctube.2018.04.006ArticlePubMedPMC
• 22. Griffith DE, Aksamit T, Brown-Elliott BA, Catanzaro A, Daley C, Gordin F, et al. An official ATS/IDSA statement: diagnosis, treatment, and prevention of nontuberculous mycobacterial diseases. Am J Respir Crit Care Med 2007;175:367-416. https://doi.org/10.1164/rccm.200604-571STArticlePubMedPMC
• 23. World Health Organization. Definitions and reporting framework for tuberculosis – 2013 revision: updated December 2014 and January 2020 [cited 2025 Nov 26]. Available from: https://www.who.int/publications/i/item/9789241505345
• 24. Ringshausen FC, Ewen R, Multmeier J, Monga B, Obradovic M, van der Laan R, et al. Predictive modeling of nontuberculous mycobacterial pulmonary disease epidemiology using German health claims data. Int J Infect Dis 2021;104:398-406. https://doi.org/10.1016/j.ijid.2021.01.003ArticlePubMed
• 25. Winthrop KL, Marras TK, Adjemian J, Zhang H, Wang P, Zhang Q. Incidence and prevalence of nontuberculous mycobacterial lung disease in a large U.S. managed care health plan, 2008-2015. Ann Am Thorac Soc 2020;17:178-185. https://doi.org/10.1513/AnnalsATS.201804-236OCArticlePubMedPMC
• 26. Izumi K, Morimoto K, Hasegawa N, Uchimura K, Kawatsu L, Ato M, et al. Epidemiology of adults and children treated for nontuberculous mycobacterial pulmonary disease in Japan. Ann Am Thorac Soc 2019;16:341-347. https://doi.org/10.1513/AnnalsATS.201806-366OCArticlePubMed
• 27. Min J, Jeong Y. Public-private partnership for tuberculosis care and prevention in Korea. Tuberc Respir Dis (Seoul) 2024;87:543-546. https://doi.org/10.4046/trd.2024.0022ArticlePubMedPMC
• 28. Yu S, Sohn H, Kim HY, Kim H, Oh KH, Kim HJ, et al. Evaluating the impact of the nationwide public-private mix (PPM) program for tuberculosis under National Health Insurance in South Korea: a difference in differences analysis. PLoS Med 2021;18:e1003717. https://doi.org/10.1371/journal.pmed.1003717ArticlePubMedPMC
• 29. Jeon D. Infection source and epidemiology of nontuberculous mycobacterial lung disease. Tuberc Respir Dis (Seoul) 2019;82:94-101. https://doi.org/10.4046/trd.2018.0026ArticlePubMedPMC
• 30. Ko RE, Moon SM, Ahn S, Jhun BW, Jeon K, Kwon OJ, et al. Changing epidemiology of nontuberculous mycobacterial lung diseases in a tertiary referral hospital in Korea between 2001 and 2015. J Korean Med Sci 2018;33:e65. https://doi.org/10.3346/jkms.2018.33.e65Article
• 31. Park Y, Kim CY, Park MS, Kim YS, Chang J, Kang YA. Age- and sex-related characteristics of the increasing trend of nontuberculous mycobacteria pulmonary disease in a tertiary hospital in South Korea from 2006 to 2016. Korean J Intern Med 2020;35:1424-1431. https://doi.org/10.3904/kjim.2019.395ArticlePubMedPMC
• 32. Jeong Y, Min J. Impact of COVID-19 pandemic on tuberculosis preventive services and their post-pandemic recovery strategies: a rapid review of literature. J Korean Med Sci 2023;38:e43. https://doi.org/10.3346/jkms.2023.38.e43Article
• 33. Kwak N, Hwang SS, Yim JJ. Effect of COVID-19 on tuberculosis notification, South Korea. Emerg Infect Dis 2020;26:2506-2508. https://doi.org/10.3201/eid2610.202782Article
• 34. Min J, Kim HW, Kim JS. Tuberculosis: Republic of Korea, 2021. Tuberc Respir Dis (Seoul) 2023;86:67-69. https://doi.org/10.4046/trd.2022.0111Article

Figure & Data

References

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