Effect of comorbidities on mortality in patients with nontuberculous mycobacterial infection in Korea: National Health Insurance Service-National Sample Cohort data

Abstract

Patients with nontuberculous mycobacteria (NTM) infection have multiple comorbidities, but the impact of comorbidities on mortality are not well known. We aimed to compare the mortality between people with and without NTM infection and associated comorbidities and their prognostic value on mortality using National Health Insurance Service-National Sample Cohort data from 2006 to 2019. In this matched cohort study, people with and without NTM infection aged 20–89 years were matched 1:4 by sex, age, region, and income. The hazard ratios (HRs) with 95% confidence intervals (CIs) of mortality in patients with NTM infection were estimated using a Cox proportional hazard regression model. In total, 2421 patients with NTM infection (mean age, 54.8 years) and 9684 controls were included. NTM-infected patients had a significantly increased risk of mortality than matched controls in the multivariable model adjusted for age, sex, region, income, and Charlson comorbidity index (aHR = 1.88, 95% CI 1.65–2.14). Among patients with NTM infection, respiratory comorbidities including chronic obstructive pulmonary disease, asthma, interstitial lung disease, and moderate to severe liver disease and malignancy were positively associated with mortality. NTM infection was independently associated with an increased risk of mortality, and mortality risk in patients with NTM infection may be increased by coexisting comorbidities.

Background

Nontuberculous mycobacteria (NTM) are a group of more than 200 species of environmental mycobacteria found in a wide range of sources in our daily lives (e.g., soil, dust, and water)¹. Exposure to NTM, can cause opportunistic infections and lead to debilitating illnesses in humans, primarily involving the lungs.

NTM pulmonary disease (NTM-PD) is a public health problem with steadily increasing incidence and prevalence worldwide^2–7. In Germany, the annual prevalence of NTM-PD increased from 2.3 cases per 100,000 people to 3.3 cases per 100,000 between 2009 and 2014⁸. From 2008 to 2015, the annual incidence of NTM-PD increased from 3.13 (95% confidence interval [CI], 2.88–3.40) to 4.73 (95% CI 4.43–5.05) per 100,000 person-years, and the annual prevalence increased from 6.78 (95% CI 6.45–7.14) to 11.70 (95% CI 11.26–12.16) per 100,000 persons in the United States⁹. In South Korea, the age-adjusted prevalence of NTM infection between 2003 and 2017 increased nearly 30-fold from 1.2 per 100,000 to 33.3 per 100,000 persons². Because NTM-PD can cause irreversible lung damage^1–3,10 it may also be associated with increased mortality, and the recent increase in the prevalence and incidence of NTM infection is placing a significant burden on health care systems in many countries^8,11. Previous studies in the United States have shown that patients with NTM-PD have lower survival rates than expected even without comorbidities, and that mortality rates are further increased in patients with comorbidities¹². In addition, chronic obstructive pulmonary disease (COPD), bronchiectasis, interstitial lung disease, and malignancy, reported as common comorbidities of NTM-infected patients, were also reported to have a significant relationship with mortality^13–15.

However, the effect of NTM infection on mortality in the general population and the comorbidities associated with mortality in patients with NTM infection have not been well established. South Korea has a compulsory single-payer national health care coverage system, the National Health Insurance Service (NHIS). It provides health insurance services to nearly all Korean residents and contains large-scale medical information. Thus, we aimed to compare the mortality of people with and without NTM infection using this national representative database of South Korea. In addition, comorbidities associated with mortality in NTM patients and their prognostic effect on mortality were investigated in this study.

Methods

Data source

This study used data from the National Health Insurance Service-National Sample Cohort (NHIS-NSC) 2.2 database of the NHIS, a large-scale, population-based cohort comprising a representative sample of approximately 2% (1,000,000 people) of the general Korean population from 2002 to 2019. The database contains a de-identified research dataset including demographic information, diagnostic codes, therapeutic procedures, and drug prescriptions. A more detailed description of NHIS-NSC has been reported previously¹⁶. NHIS-NSC was missing some claim data from those eligible for medical benefits from 2002 to 2005, so only data from 2006 to 2019 was used for analysis.

Study population

Figure 1 displays the selection process for the study population. Individuals with two or more claims for a diagnosis of NTM infection (International Classification of Disease Tenth Revision [ICD-10] A31) between January 2006 and December 2019 were identified from NHIS-NSC (n = 2,764). Among these individuals, we excluded 81 people with a history of NTM infection in 2006 to ensure inclusion only of people with newly developed NTM infection. Additionally, we excluded individuals under 20 years or over 90 years of age (n = 176), or whose income information is missing (n = 86). The reason for excluding those under 20 years of age was that we aimed to investigate the risk of mortality related to NTM infection in adults. Additionally, only one person was over 90 years old, so they were excluded. The date of the initial claim for each patient’s NTM infection was defined as the index date.

Fig. 1.

Flow diagram of the study population. NHIS-NSC, National Health Insurance Service-National Sample Cohort; NTM, nontuberculous mycobacteria; PS, propensity score.

Given the differences in the baseline characteristics and the risk of mortality between the people with NTM and without NTM, we used 1:4 propensity score (PS) matching and calculated the PS, with predicted probability of mortality conditional on baseline covariates, by multivariable logistic regression. The selected people diagnosed with NTM infection disease were matched by sex, age, region of residence, and household income class in the year of the index date with four controls who had no history of claims for NTM infection from 2007 to 2019 using PS matching. We performed PS matching using the SAS ‘psmatch’ syntax by applying the nearest-neighbor algorithm with 1:4 matching and discarding in both groups. To reduce immortal time bias, a control group was defined according to the characteristics corresponding to the year of diagnosis of patients with NTM infection¹⁷. Matching the case group and the control group in a 1:4 ratio allows for a better balance of covariates between the case group and the control group by using multiple control groups, enabling more precise estimates and reliable analyses. Finally, 2421 people diagnosed with NTM and 9,684 matched controls were included in the study. This study was conducted according to the 2008 Declaration of Helsinki and approved by the Institutional Review Board of Severance Hospital (4-2020-1473), and Korea National Health Insurance Service Medical Request Review Committee (NHIS-2021-2-227). All methods were performed in accordance with the approved guidelines and regulations. Obtaining informed consent was waived owing to the retrospective nature of the study using public deidentified.

Study variables

The study outcome of interest was all-cause mortality after the index date. For mortality analysis, individuals were followed up until the end of the study period or death.

Covariates included in the analysis were age, sex, region, household income class, and Charlson comorbidity index (CCI). Age was classified into four groups (20–39, 40–59, 60–79, and 80–89 years old). The household income groups initially comprising 11 classes (class 0, lowest income; class 10, highest income) in the NHIS database were recategorized into three groups (low, class 0–2; medium, class 3–7; high, class 8–10). Region of residence was recategorized into metropolitan area (Seoul, Busan, Daegu, Incheon, Gwangju, Daejeon, and Ulsan) and other area (Gyeonggi, Gangwon, Chungcheongbuk, Chungcheongnam, Jeollabuk, Jeollanam, Gyeongsangbuk, Gyeongsangnam, and Jeju).

The comorbidities used as covariates in this study were those included in the CCI that have been validated in several previous studies^18,19. Comorbidities were included in this study that were identified with the ICD-10 codes when there were two or more claims, and only claims up to 1 year before the index date were included (Additional File: Table 1).

Statistical analysis

Baseline characteristics of people with and without NTM infection are presented as mean ± standard deviation or mean and percentage using an independent t-test for continuous variables and the chi-squared test for categorical variables. The cumulative incidence of mortality was estimated using the Kaplan–Meier method and log-rank tests. Crude and adjusted Cox proportional hazards model were used to estimate the hazard ratios (HRs) and 95% CIs for mortality in people with NTM infection versus those in the control group. The covariates included in adjusted model were sex, age, income, region, and CCI scores as covariates in model 1 and sex, age, income, region and multiple each comorbidities in model 2. We additionally conducted a sub-analysis stratified by sex and age. We also compared the prevalence of comorbidities between survivors and non-survivors and further investigated stratified analysis with multivariable Cox proportional hazards models for comorbidities that appeared to influence mortality in NTM patients. All analyses were performed using the SAS Enterprise Guide 7.1 (SAS Institute Inc., Cary, NC, USA). Statistical significance was defined as a two-sided p value of < 0.05.

Results

Characteristics of the study population

The baseline characteristics of the study population are shown in Table 1. In total, 2421 patients with NTM infection (mean age, 54.8 years) and 9684 controls (mean age, 54.8 years) were included. Among patients with NTM infection, 838 (34.6%) were male and 1583 (65.4%) were female, with mean ages of 60.2 and 52.0 years, respectively. No significant differences were observed in sex, age, region, or household income class between patients with NTM infection and controls after PS matching.

Table 1.

Baseline characteristics of the study population after propensity score matching.

Variables	People with NTM (n = 2421)		People without NTM (n = 9684)
Sex
Male	838	(34.6)	3379	(34.9)
Female	1,583	(65.4)	6305	(65.1)
Age, years	54.8	± 17.4	54.8	± 17.5
20–29	248	(10.2)	974	(10.1)
30–39	316	(13.1)	1255	(13.0)
40–49	342	(14.1)	1368	(14.1)
50–59	465	(19.2)	1878	(19.4)
60–69	457	(18.9)	1837	(19.0)
70–79	422	(17.4)	1688	(17.4)
80–89	171	(7.1)	684	(7.1)
Household income
Low (0–2)	581	(24.0)	2297	(23.7)
Medium (3–7)	772	(31.9)	3115	(32.2)
High (8–10)	1,068	(44.1)	4272	(44.1)
Residential area
Metropolitan area	1,150	(47.5)	4627	(47.8)
Other area	1,271	(52.5)	5057	(52.2)
CCI score*	4.25	± 3.11	3.34	± 2.91
0	97	(4.0)	1354	(14.0)
1	308	(12.7)	1524	(15.7)
2	401	(16.6)	1738	(17.9)
≥ 3	1,615	(66.7)	5068	(52.3)
Respiratory comorbidity
COPD*	242	(10.0)	152	(1.6)
Bronchiectasis*	428	(17.7)	55	(0.6)
Asthma*	548	(22.6)	925	(9.6)
Interstitial lung disease*	55	(2.3)	9	(0.1)
Other comorbidity
Myocardial infarction	70	(2.9)	253	(2.6)
Congestive heart failure	250	(10.3)	875	(9.0)
Peripheral vascular disease*	682	(28.2)	2417	(25.0)
Cerebrovascular disease*	545	(22.5)	1943	(20.1)
Dementia	95	(3.9)	406	(4.2)
Rheumatologic disease*	383	(15.8)	1174	(12.1)
Peptic ulcer*	1,683	(69.5)	5445	(56.2)
Mild liver disease*	1,211	(50.0)	3909	(40.4)
Moderate or severe liver disease*	48	(2.0)	122	(1.3)
Diabetes without complications*	776	(32.1)	2687	(27.8)
Diabetes with complications	278	(11.5)	1156	(11.9)
Paraplegia/hemiplegia	57	(2.4)	270	(2.8)
Renal disease	102	(4.2)	333	(3.4)
Cancer*	311	(12.8)	515	(5.3)

Data are presented as mean ± standard deviation or mean and percentage (%).

*P < 0.05.

^aAn independent t-test for continuous variables and the chi-squared test for categorical variables were used.

^bHousehold income was divided into 11 classes (class 1, lowest income; class 11, highest income), which were reclassified into three groups (low, classes 1–3; medium, classes 4–8; high, classes 9–11).

^cResidential area was categorized into metropolitan area (Seoul, Busan, Daegu, Incheon, Gwangju, Daejeon, and Ulsan) and other area (Gyeonggi, Gangwon, Chungcheongbuk, Chungcheongnam, Jeollabuk, Jeollanam, Gyeongsangbuk, Gyeongsangnam, and Jeju).

Abbreviations: CCI, Charlson comorbidity index; COPD, chronic obstructive pulmonary disease; NTM, nontuberculous mycobacteria.

On the other hand, there was a significant difference in CCI score between the NTM group and the control group. Among patients with NTM, 4% had a CCI score of 0, compared with 14% of controls. Those with a CCI score of 3 or higher were 66.7% in NTM patients and 52.3% in controls. At baseline, the prevalence of most comorbidities was higher in the people with NTM than in the control group. Respiratory diseases (COPD, bronchiectasis, asthma, and interstitial lung disease) were all statistically significantly higher in the NTM-infected group, and peripheral vascular disease, cerebrovascular disease, rheumatologic disease, peptic ulcer, mild liver disease, moderate or severe liver disease, diabetes without complications, and cancer were also higher in the NTM-infected group than in the control group at baseline.

Comparison of mortality between the NTM and control groups

Overall, 13.4% of NTM-infected patients and 7.7% of controls died during a median follow-up duration of 3.6 years [quartile range, 1.9–6.1]. Kaplan–Meier survival analysis of mortality in people with and without NTM infection demonstrated that the risk of mortality was significantly higher in patients with NTM infection than that in people without NTM infection (log-rank p < 0.0001; Fig. 2).

Fig. 2.

Kaplan–Meier survival curves for death in people with and without NTM infection. NTM, nontuberculous mycobacteria.

The Cox regression model adjusted for age, sex, income, region, and CCI score (adjusted model 1 in Table 2) showed that the risk of mortality in people with NTM infection was significantly higher than that of people without NTM infection (aHR = 1.88, 95% CI 1.65–2.14). The positive association between NTM infection and mortality did not differ according to sex (aHR = 1.88, 95% CI 1.59–2.22 for male, aHR = 1.88, 95% CI 1.52–2.33 for female in adjusted model 1, Table 2). Even when the analysis included individual diseases included in the CCI rather than the total CCI score in the adjusted model 2, the risk of mortality was found to be higher in NTM patients than in the control group (aHR = 1.77, 95% CI 1.52–2.06).

Table 2.

Mortality in patients with NTM relative to people without NTM.

				Crude model			Adjusted model 1a			Adjusted model 2b
		Number of death	%	HR	95% CI	p value	aHR	95% CI	p value	aHR	95% CI	p value
Total	NTM	325	(13.42)	1.84	(1.62–2.10)	< 0.0001	1.88	(1.65–2.14)	< 0.0001	1.77	(1.52–2.06)	< 0.0001
	Non-NTM	747	(7.71)		Ref			Ref			Ref
Male	NTM	201	(23.99)	1.89	(1.60–2.23)	< 0.0001	1.88	(1.59–2.22)	< 0.0001	1.81	(1.49–2.19)	< 0.0001
	Non-NTM	468	(13.85)		Ref			Ref			Ref
Female	NTM	124	(7.83)	1.85	(1.49–2.28)	< 0.0001	1.88	(1.52–2.33)	< 0.0001	1.81	(1.40–2.33)	< 0.0001
	Non-NTM	279	(4.43)		Ref			Ref			Ref

aHR, adjusted hazard ratio; CCI, Charlson comorbidity index; CI, confidence interval; COPD, chronic obstructive pulmonary disease; HR, hazard ratio; NTM, nontuberculous mycobacteria; Ref, reference.

^aModel 1 Adjusted for age, sex, income, residential area, and CCI scores.

^bModel 2 Adjusted for age, sex, income, residential area, respiratory comorbidities (COPD, bronchiectasis, asthma, and interstitial lung disease), and other comorbidity (myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, peptic ulcer, mild liver disease, moderate or severe liver disease, diabetes without complication, diabetes with complications, paraplegia/hemiplegia, renal disease, and cancer).

In a sub-analysis stratified by age (Additional File: Table 2), patients with NTM had a higher risk of mortality than the control group in all groups: 40–59 years, 60–79 years, and 80 years or older.

Comorbidities associated with mortality in patients with NTM

In multivariable adjusted Cox proportional hazards model (Table 3), which included each comorbidity variable to assess the influence of comorbidities on mortality among people with NTM infection, the risk of mortality was significantly increased in the presence of COPD (aHR = 1.53, 95% CI 1.17–2.00), asthma (aHR = 1.31, 95% CI 1.03–1.68), interstitial lung disease (aHR = 2.27, 95% CI 1.51–3.42), moderate or severe liver disease (aHR = 3.05, 95% CI 1.91–4.87), and cancer (aHR = 1.52, 95% CI 1.19–1.95). Additionally, the risk of mortality increased as the patients’ age increased (aHR = 1.10, 95% CI 1.08–1.11) (Additional File: Table 3), and depending on the age group, the risk significantly increased in the 60 s and older age group compared to the 40 s age group (Table 3).

Table 3.

Crude and multivariable Cox proportional model for mortality among people with NTM infection.

			Crude model			Multivariable model 1			Multivariable analysis 2
	Number of death	(%)	HR	95% CI	P-value	aHR	95% CI	p-value	aHR	95% CI	p-value
Sex
Male	201	(24.0)	2.88	(2.31 –3.61)	< 0.0001	2.09	(1.67 –2.63)	< 0.0001	1.82	(1.42–2.33)	< 0.0001
Female	124	(7.8)		Ref			Ref			Ref
Age, year
20–29	–	(0.0)		NA			NA			NA
30–39	–	(0.0)		NA			NA			NA
40–49	14	(4.1)		Ref			Ref			Ref
50–59	18	(3.9)	0.91	(0.46 –1.84)	0.8013	0.89	(0.44–1.79)	0.7467	0.83	(0.41–1.68)	0.6120
60–69	68	(14.9)	3.78	(2.13 –6.72)	< 0.0001	3.16	(1.77–5.66)	0.0001	3.04	(1.70–5.45)	0.0002
70–79	144	(34.1)	9.49	(5.48 –16.44)	< 0.0001	7.75	(4.43–13.57)	< 0.0001	7.10	(4.01–12.58)	< 0.0001
80–89	81	(47.4)	18.32	(10.37–32.35)	< 0.0001	14.96	(8.37–26.75)	< 0.0001	14.19	(7.78–25.87)	< 0.0001
Income
Low (0–3)	78	(13.4)		Ref			Ref			Ref
Medium (4–7)	89	(11.5)	0.87	(0.64–1.18)	0.3665	1.07	(0.79–1.46)	0.6712	1.14	(0.83–1.27)	0.4254
High (8–10)	158	(14.8)	1.07	(0.82–1.41)	0.6165	0.88	(0.67–1.15)	0.3513	0.94	(0.71–1.24)	0.6643
Residential area
Metropolitan area	140	(12.2)		Ref			Ref			Ref
Other area	185	(14.6)	1.23	(0.99–1.53)	0.0660	1.12	(0.90–1.40)	0.3169	1.13	(0.90–1.41)	0.2897
CCI scores
0	–	(0.0)		NA			NA
1	6	(2.0)		Ref			Ref
2	15	(3.7)	1.91	(0.74–4.92)	0.1809	1.29	(0.50–3.34)	0.5973
≥ 3	304	(18.8)	8.48	(3.78–19.03)	< 0.0001	1.63	(0.72–3.70)	0.2406
Respiratory comorbidity
COPD	97	(40.1)	3.63	(2.86–4.61)	< 0.0001				1.53	(1.17–2.00)	0.0020
Bronchiectasis	65	(15.2)	0.90	(0.68–1.18)	0.4297
Asthma	128	(23.4)	2.14	(1.71–2.67)	< 0.0001				1.31	(1.03–1.68)	0.0307
Interstitial lung disease	28	(50.9)	5.99	(4.06–8.85)	< 0.0001				2.27	(1.51–3.42)	< 0.0001
Other comorbidity
Myocardial infarction	23	(32.9)	2.29	(1.50–3.50)	0.0001				1.05	(0.67–1.65)	0.8358
Congestive heart failure	76	(30.4)	2.84	(2.19–3.67)	< 0.0001				1.02	(0.77–1.36)	0.8770
Peripheral vascular disease	134	(19.7)	1.71	(1.37–2.13)	< 0.0001				0.80	(0.63–1.01)	0.0559
Cerebrovascular disease	141	(25.9)	2.32	(1.86 –2.89)	< 0.0001				0.96	(0.76 –1.23)	0.7575
Dementia	134	(19.7)	1.71	(1.37–2.22)	< 0.0001				0.81	(0.52–1.25)	0.3355
Rheumatologic disease	57	(14.9)	1.06	(0.80–1.41)	0.6946
Peptic ulcer	247	(14.7)	1.28	(0.99–1.65)	0.0615
Mild liver disease	177	(14.6)	1.09	(0.88–1.36)	0.4190
Moderate or severe liver disease	20	(41.7)	3.22	(2.05–5.08)	< 0.0001				3.05	(1.91–4.87)	< 0.0001
Diabetes without complications	186	(24.0)	2.61	(2.10–3.26)	< 0.0001				1.17	(0.92–1.50)	0.1950
Diabetes with complications	78	(28.1)	2.24	(1.74–2.89)	< 0.0001				1.09	(0.82 –1.45)	0.5571
Paraplegia/hemiplegia	22	(38.6)	2.55	(1.64 –3.95)	< 0.0001				1.19	(0.75–1.89)	0.4735
Renal disease	33	(32.4)	2.42	(1.69–3.48)	< 0.0001				0.91	(0.62–1.34)	0.6483
Cancer	99	(31.8)	2.94	(2.32–3.73)	< 0.0001				1.52	(1.19–1.95)	0.0010

aHR, adjusted hazard ratio; CCI, Charlson comorbidity index; CI, confidence interval; COPD, chronic obstructive pulmonary disease; HR, hazard ratio; NA, not available; NTM, nontuberculous mycobacteria; Ref, reference.

When the prevalence of comorbidities was examined by distinguishing between those who died and those who survived among NTM patients during follow-up, the prevalence of all comorbidities included in Table 1 were statistically significantly higher in the deceased than in the survivors. (Additional File: Fig. 1).

Discussion

This matched cohort study investigated the mortality risk due to NTM infection using a national representative sample of the Korean adult population. Further, comorbidities related to mortality and their effect on mortality were assessed in NTM patients. People diagnosed with NTM infection have approximately twice the risk of mortality than age-, sex-, region-, and household income-matched controls. Additionally, in a multivariable analysis using the Cox proportional hazard model targeting NTM patients, COPD, asthma, interstitial lung disease, moderate or severe liver disease, and cancer statistically significantly increased the risk of mortality.

NTM infection has been reported to increase mortality in several studies; however, the exact mechanism by which NTM increases mortality has not yet been clearly elucidated. For that, several potential hypotheses can be considered. First, NTM-PD can cause irreversible lung damage and impaired lung function²⁰. NTM-PD typically develops in patients with structural lung disease, such as COPD, bronchiectasis, or prior pulmonary TB²¹. Thus, underlying structural lung disease and progressive inflammation due to NTM infection can accelerate the decline in lung function²² and increase the risk of mortality in a patient with NTM-PD. Second, concomitant pulmonary disease and other comorbidities in patients with NTM may be exacerbated²³. Active inflammation caused by NTM can act as an aggravating factor of chronic co-morbid conditions such as interstitial lung disease, COPD and asthma. In addition, combined NTM infection might make underlying lung disease difficult to treat²⁴. Third, a previous study reported that the higher prevalence of cancer as a comorbidity among patients with NTM-PD than various control group^5,25. Similar to previous studies, our study results showed a higher prevalence of cancer in deceased NTM patients, and multivariable analysis showed that having cancer increased the risk of death in NTM patients.

Our study found that NTM infection independently increased the risk of death in people, and suggested that the risk of death may be further increased in people with NTM infection who also had other comorbidities, such as COPD, asthma, interstitial lung disease, moderate or severe liver disease, or cancer. To elucidate the reason of increased mortality in patients with NTM infection with comorbidities, we should further investigate the cause of death in this population. While NTM species and disease severity contribute to mortality²⁶, comorbidities have been considered as predominant contributor to increased risk of mortality². However, there are only a few studies assessing the cause of death in patients with NTM infection. In a previous Korean study using NHIS data, cardiovascular disease and lung cancer were the common cause of death among patients with NTM infection².

This study has several strengths. First, this study used the NHIS-NSC 2.0 database of South Korea, which is representative of the national population and contains healthcare utilization information from all healthcare settings in the country. Second, our study is one of the few matched cohort studies to compare the risk of mortality between patients with NTM infection and the general population as a control group.

This study also has several limitations that should be addressed. First, this study confirmed that NTM infection increased the risk of death but did not investigate the specific cause of death. If it could be confirmed that the specific cause of death in patients is death from certain comorbidities related to NTM infection, it would be helpful in establishing preventive measures to prevent death. Thus, further related studies need to be conducted. Second, since the definition of NTM infection and comorbidity in this study was based on the diagnostic code of insurance claim data, there is an inherent limitation in that the diagnostic code was selected for administrative and billing purposes rather than medical research. The use of administrative claims data may have resulted in issues such as coding inaccuracy and lack of disease specificity. In this study. NTM infection was defined using two or more codes associated with NTM infection (ICD-10, A31), to reduce sensitivity but improve positive predictive values of diagnosis, similar to most previous studies analyzing claims data^2,11,27–30. Third, we cannot exclude the possibility of residual confounding by unmeasured or uncontrolled confounders such as other infectious diseases (e.g., human immunodeficiency virus or TB) and time-varying confounders. This is because this is an observational study using the health insurance claim database, and presence of several diseases is considered sensitive personal information in the NHIS-NSC 2.2 database and is not disclosed to researchers. In addition, we did not consider body mass index (BMI) or weight, smoking, NTM-related drugs, and microbiological data. Further studies using larger NTM patient data combining the missing information are needed.

Conclusions

In conclusion, this study showed that NTM infection was associated with an increased risk of mortality. In addition, it was confirmed that the risk of mortality significantly increased when NTM patients had chronic heart failure, chronic viral hepatitis, COPD, diabetes mellitus, and interstitial lung disease. To further reduce the mortality rate and public health burden in the aging population, multifaceted efforts such as prevention of NTM infection and appropriate treatment of NTM patients as well as management of comorbidities in NTM patients will be required. In particular, in order to effectively prevent death of NTM patients, it will be necessary to conduct additional investigations into specific causes of death through a larger cohort study, and intensive prevention and management of related comorbidities will be required.

Abbreviations

BMI

Body mass index

CCI

Charlson comorbidity index

CI

Confidence interval

COPD

Chronic obstructive pulmonary disease

HR

Hazard ratio

ICD-10

International Classification of Disease Tenth Revision

NHIS-NSC

National Health Insurance Service-National Sample Cohort

NTM

Nontuberculous mycobacteria

PS

Propensity score

Author contributions

S.W.L. participated in the conceptualization and design of the study, performed data analysis and interpretation, drafted the manuscript, and revised the manuscript. S.C., E.C. and Y.P. coordinated and critically reviewed the manuscript for intellectual content. Y.A.K. conceptualized and designed the study, acquired and interpreted the data, and reviewed and revised the manuscript.

Funding

This research was supported by a grant of Patient-Centered Clinical Research Coordinating Center (PACEN), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: RS-2021-KH119793). This work was also supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-RS-2023-00273095).

Data availability

The release of data by the researchers is not legally permitted. All data are available from the database of the National Health Insurance Sharing Service (NHISS) (https://nhiss.nhis.or.kr/bd/ay/bdaya001iv.do). NHISS allows the data to be used by any researcher who agrees to abide by research ethics at some cost. The data for this article can be accessed and downloaded from the website after agreeing to abide by the ethical rules.

Declarations

Ethics approval and consent to participate

This study was approved by the Institutional Review Board of Severance Hospital (4-2020-1473) and the Korea NHIS Medical Information Disclosure Committee (NHIS-2021-2-227). The need for informed consent was waived because this was a retrospective study of anonymized administrative data.

Competing interests

The authors declare no competing interests.

Consent for publication

Not applicable.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-73768-z.