Global burden of MDR-TB and XDR-TB: trends, inequities, and future implications for public health planning

Abstract

Background

Drug-resistant tuberculosis (TB) remains a major global health threat, reflecting disparities in healthcare capacity, access, and socioeconomic development. Previous research often lacks geographic breadth. This study provides a comprehensive assessment of the global, regional, and national burden of Multidrug-resistant TB without extensive drug resistance (MDR-TB) and extensively drug-resistant TB(XDR-TB) from in Global Burden of Disease Study (GBD) 2021 Study 1990 to 2021, with a focus on distributional inequities. The findings aim to guide resource prioritization, inform targeted interventions, and reduce the burden in high-risk populations.

Methods

We systematically assessed the global, regional, and national burden of MDR-TB and XDR-TB, along with their change trends from 1990 to 2021, using data from the GBD 2021 database. The indicators included age-standardized incidence rate (ASIR), prevalence rate (ASPR), mortality rate (ASMR), and disability-adjusted life-years rate (ASDR). ASDR was analyzed in conjunction with the sociodemographic index (SDI) for a comprehensive assessment. Health inequalities were quantified using the slope index of inequality (SII) and concentration index (CCI). Frontier analysis estimated the achievable outcomes across different development levels, while decomposition analysis identified the key factors driving changes in disease burden.

Results

In 2021, the global ASIR of MDR-TB was 5.42 per 100,000 population [95% uncertainty interval(UI): 3.17, 9.34]), and the ASIR of XDR-TB was 0.29 per 100,000 population (95% UI: 0.21, 0.42). From 1990 to 2021, the ASIR of MDR-TB [AAPC = 0.14%, 95% confidence interval (CI): 0.13, 0.14] and XDR-TB (AAPC = 0.01%, 95% CI: 0.01, 0.02) both showed an increasing trend. The ASIR and ASMR of MDR-TB increased in low and low-middle SDI regions. Similarly, the ASIR and ASMR of XDR-TB increased in all five SDI regions. The ASIR of MDR-TB increased in 155 countries, with the largest increase observed in Somalia (AAPC = 1.79%, 95% CI: 1.67, 1.92). The ASIR of XDR-TB increased in all countries. From 1990 to 2021, both absolute and relative health inequalities in the ASDR of MDR-TB and XDR-TB have grown. In addition, the ASIR and incidence of MDR-TB and XDR-TB are negatively correlated with SDI.

Conclusion

The burden of MDR-TB/XIDR-TB is projected to increase, with persistent disparities concentrated in low-SDI settings. Targeted public health strategies—including improved resource allocation, infrastructure development, and community health education—are essential to reduce inequities. Strengthening these efforts may enhance global TB control and advance progress toward health equity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-025-11566-2.

Introduction

Tuberculosis (TB) has likely regained its position as the leading cause of death from a single infectious pathogen (Mycobacterium tuberculosis) globally [1–3]. Despite significant public health efforts that have saved millions of lives, progress in controlling, let alone eliminating, TB remains insufficient.

Drug-resistant TB has emerged as one of the deadliest infectious diseases, responsible for approximately one-quarter of TB-related deaths [4, 5]. The 2024 Global Tuberculosis Report highlights the ongoing global burden of drug-resistant TB, including multidrug-resistant TB without extensive drug resistance (MDR-TB), rifampicin-resistant TB (RR-TB), and extensively drug-resistant TB (XDR-TB) [1, 2]. The Report estimates that in 2023, there were 10.80 million incident TB cases worldwide, with 1.20 million deaths. Notably, approximately 0.40 millions cases were MDR/RR-TB, highlighting the persistent challenge of drug-resistant tuberculosis in global TB control efforts [1]. It remains one of the leading infectious causes of morbidity and mortality globally. The economic impact of drug-resistant TB is profound, as over 81% of affected households experience catastrophic healthcare costs related to treatment expenses [1].

The burden of drug-resistant TB is characterized by significant global and regional disparities, disproportionately affecting low-income and high-TB-burden countries. The ongoing conflict, such as the crisis in Ukraine, has exacerbated the spread of TB through refugee movements and the collapse of healthcare infrastructures. This has not only intensified TB transmission within high-burden regions but also facilitated the spread of drug-resistant TB to neighboring European countries via population displacement, thereby amplifying the global threat of TB [1, 2]. Drug-resistant TB is associated with low treatment success rates, extended treatment regimens, severe adverse effects, and considerable healthcare and societal costs. These challenges place immense pressure on individuals, communities, and national public health systems [6, 7]. In recent years, while the incidence of MDR-TB has plateaued at high levels, cases of XDR-TB continue to increase, presenting a significant obstacle to global efforts aimed at TB control [1, 4].

This study leverages the Global Burden of Disease (GBD) 2021 to systematically assess the global, regional, and national burdens of MDR-TB and XDR-TB [8, 9]. Health inequalities were measured using the slope index of inequality (SII) and concentration index (CCI), while frontier analysis estimated the achievable disease burden based on sociodemographic index (SDI) levels. Decomposition analysis quantified the effects of demographic and epidemiologic changes, and Joinpoint regression identified key inflection points in temporal trends. Projections to 2035 were generated using a Bayesian age-period-cohort (BAPC) model to inform public health strategies and resource allocation [10, 11]. The study aims to provide data support for the development of public health strategies, resource allocation, and policy interventions, thereby helping to reduce the burden of drug-resistant TB and address global health inequalities, particularly in low- and middle-income countries.

Methods

Data source

The GBD 2021 evaluated 371 diseases and injuries and 88 risk factors across 204 countries and territories. Estimates of incidence, prevalence, mortality, and disability-adjusted life years (DALYs) were generated using age-, sex-, location-, and time-specific modeling frameworks. Data harmonization was achieved by addressing heterogeneity in definitions and data sources through advanced statistical methods, including Bayesian meta-regression (DisMod-MR 2.1) and meta-regression-based Bayesian regularized trimming, ensuring internal consistency across the estimates. Mortality estimates were primarily derived from vital registration systems, surveillance data, and census information, while incidence and prevalence inputs were obtained from disease registries, national surveys, and peer-reviewed Literature. DALY estimates were based on data from clinical reporting systems, hospital records, household surveys, and cohort studies. Comprehensive details regarding study design, data collection protocols, and estimation methodologies can be found in the GBD 2021 documentation [8, 9].

MDR-TB is a form of TB characterized by resistance to the two most important first-line anti-TB drugs (isoniazid, rifampicin), while remaining susceptible to fluoroquinolones and second-line injectable agents, including amikacin, kanamycin, or capreomycin. XDR-TB represents a more severe variant, which is resistant to isoniazid and rifampicin, as well as to all fluoroquinolones and at least one second-line injectable drug, such as amikacin, kanamycin, or capreomycin [8, 12, 13]. It is important to clarify that the MDR-TB/XDR-TB cases included in this study are limited to individuals who are HIV-negative.

Data on MDR-TB and XDR-TB were obtained from the Global Burden of Disease Study 2021 via the Institute for Health Metrics and Evaluation (IHME) database (http://ghdx.healthdata.org). Estimates included incidence, prevalence, mortality, and DALYs at global, regional, and national levels for 204 countries and territories. Disease coding followed International Classification of Diseases (10th Revision, ICD-10) classifications (A10–A19.9, B90–B90.9, K67.3, K93.0, M49.0, P37.0) and corresponding ICD-9 codes (010–019.9, 137–137.9, 138.0, 138.9, 139.9, 320.4, 730.4–730.6) [2, 8].

The SDI is a composite metric that captures the level of national development. It is derived as the geometric mean of standardized estimates for fertility rate among women aged < 25 years, mean years of schooling for individuals aged ≥ 15 years, and lag-distributed income per capita. In the GBD 2021 Study, a total of 204 countries and territories were categorized into five SDI groups: low (0–0.46), low-middle (0.47–0.62), middle (0.63–0.71), high-middle (0.72–0.81), and high (0.82–1.00) [10, 14, 15].

Statistical analysis

The disease burden of MDR-TB and XDR-TB was evaluated using both rates and total case counts for incidence, prevalence, mortality, and DALYs. Rates are presented as estimates per 100,000 population to reflect the relative burden, while case counts represent the absolute burden. Both metrics are accompanied by 95% uncertainty intervals (UIs).

All statistical analyses were conducted using R software (version 4.4.1, R Foundation for Statistical Computing, Vienna, Austria; available at https://cran.r-project.org). Detailed descriptions of specific analytical methods were provided in subsequent sections [10, 16].

BAPC model

The study employed the BAPC model to project age-standardized rate (ASR) of MDR-TB/XDR-TB, including age-standardized incidence rate (ASIR), prevalence rate (ASPR), mortality rate (ASMR), and disability-adjusted life-years rate (ASDR) from 2022 to 2035. In this analysis, we utilized the "BAPC" R package, and incorporated data from GBD 2021 and population projections from the IHME, to forecast these metrics of MDR-TB and XDR-TB [10, 16, 17].

In this model,  represents time points, denotes age groups,  represents the intercept, represents the age effect,  represents the period effect,  represents the cohort effect.

Joinpoint regression

The average annual percentage change (AAPC) in both the ASR and absolute case count of MDR-TB/XDR-TB reflects long-term trends in disease burden and the effectiveness of population-level interventions. Estimates of the annual percentage change (APC) and AAPC, along with 95% confidence intervals (CIs), were calculated at the global level, across five SDI regions, 21 geographical regions, and 204 countries or territories [18, 19].

In the model, is the number of segments, is the regression coefficient from the log-linear model ln(y) = β × year + constant. represented by the length of each corresponding segment. When the AAPC is greater than 0 with a P value less than 0.05, it indicates a statistically significant upward trend. Conversely, an AAPC less than 0 with a P value below 0.05 denotes a statistically significant downward trend [16, 17].

Decomposition analysis

To evaluate the factors contributing to changes in the ASDR of MDR-TB and XDR-TB from 1990 to 2021, we employed the Das Gupta decomposition method. This approach dissects the overall change in ASDR into components attributable to demographic shifts, including population aging and growth, as well as changes in disease-specific rates. This method thereby clarifies the distinct contributions of each factor to the evolving disease burden [10, 11].

Cross-country inequality analysis

To examine disparities in the ASDR of MDR-TB and XDR-TB across different SDI levels, we employed the SII and the CCI. The SII quantifies absolute inequality by measuring the difference in ASDR between the most and least advantaged populations, considering the entire distribution of the SDI. To mitigate the influence of outliers and heteroscedasticity, we applied robust regression techniques, thereby improving the robustness of our inequality assessments [10, 11, 20, 21].

Frontier analysis

To examine the association between the burden of MDR-TB/XDR-TB and sociodemographic development, we employed frontier analysis incorporating the ASDR and the SDI. Locally weighted regression was applied with varying smoothing parameters (0.3, 0.4, 0.5) to model the nonlinear relationship between SDI and ASDR. To ensure the robustness of our findings, 1,000 bootstrap samples were generated to calculate the mean ASDR for each SDI value. The potential for improvement was quantified by calculating the absolute distance between each country’s ASDR and the frontier line, a measure referred to as the"effective difference [10, 11].

Results

Global

In 2021, the global ASIR of MDR-TB was 5.42 per 100,000 population (95% UI: 3.17, 9.34), with an ASMR of 1.28 (95% UI: 0.50, 2.53) and ASDR of 50.76 (95% UI: 21.28, 99.37). From 1990 to 2021, ASIR (AAPC = 0.14%, 95% CI: 0.13, 0.14), ASPR (AAPC = 0.21%, 95% CI: 0.20, 0.22), ASMR (AAPC = 0.03%, 95% CI: 0.02, 0.04), and ASDR (AAPC = 1.19%, 95% CI: 1.12, 1.26) all increased significantly (Table 1). In 2021, MDR-TB accounted for an estimated 0.44 million incident cases (95% UI: 0.26, 0.77), 0.65 million prevalent cases (95% UI: 0.38, 1.14), 0.11 million deaths (95% UI: 0.04, 0.21), and 4.13 million DALYs (95% UI: 1.71, 8.06), all showing upward trends (Supplementary Table S1).

Table 1.

Trends of age-standardized rates in MDR-TB at the global level and across major GBD regions

Location	ASIR (per 100,000 population, 95% UI) 2021	ASIR AAPC (%, 95%CI) 1990–2021	ASPR (per 100,000 population, 95% UI) 2021	ASPR AAPC (%, 95% CI) 1990–2021	ASMR (per 100,000 population, 95% UI) 2021	ASMR AAPC (%,95% CI) 1990–2021	ASDR (per 100,000 population, 95% UI) 2021	ASDR AAPC(%, 95% CI) 1990–2021
Global	5.42 (3.17, 9.34)	0.14 (0.13, 0.14)	8.02 (4.63, 13.96)	0.21 (0.20, 0.22)	1.28 (0.50, 2.53)	0.03 (0.02, 0.04)	50.76 (21.28, 99.37)	1.19 (1.12, 1.26)
High SDI	0.21 (0.12, 0.38)	−0.01 (−0.02, −0.01)	0.25 (0.15, 0.48)	−0.01 (−0.02, −0.01)	0.02 (0.01, 0.05)	−0.01 (−0.02, −0.01)	0.74 (0.31, 1.52)	−0.05 (−0.05, −0.04)
High-middle SDI	3.77 (2.33, 5.85)	0.07 (0.06, 0.07)	3.75 (2.23, 7.03)	0.05 (0.04, 0.06)	0.27 (0.15, 0.46)	−0.02 (−0.02, −0.01)	11.04 (6.33, 18.03)	−0.09 (−0.16, −0.03)
Middle SDI	4.63 (2.22, 8.39)	0.08 (0.08, 0.09)	6.92 (3.42, 12.61)	0.12 (0.11, 0.13)	0.78 (0.28, 1.58)	−0.02 (−0.02, 0.01)	27.94 (10.72, 55.83)	0.01 (−0.06, 0.08)
Low-middle SDI	10.35 (4.21, 21.99)	0.34 (0.33, 0.35)	15.39 (6.45, 33.03)	0.50 (0.49, 0.51)	3.45 (1.07, 7.70)	0.10 (0.10, 0.11)	113.99 (37.12, 248.83)	3.43 (3.10, 3.75)
Low SDI	11.13 (6.51, 19.05)	0.34 (0.33, 0.35)	18.22 (10.70, 30.46)	0.54 (0.53, 0.55)	5.09 (2.05, 10.44)	0.16 (0.15, 0.17)	157.61 (66.11, 316.55)	4.90 (4.65, 5.15)
East Asia	1.67 (0.43, 4.73)	−0.06 (−0.07, −0.05)	3.05 (0.63, 9.22)	−0.06 (−0.08, −0.05)	0.19 (0.05, 0.48)	−0.03 (−0.04, −0.03)	6.68 (1.81, 16.83)	−1.18 (−1.30, −1.05)
Southeast Asia	3.55 (1.87, 6.11)	0.09 (0.08, 0.10)	6.29 (3.33, 10.73)	0.16 (0.14, 0.18)	0.95 (0.35, 2.03)	0.02 (0.02, 0.02)	28.46 (11.07, 60.35)	0.48 (0.35, 0.61)
Oceania	4.47 (1.33, 10.23)	0.14 (0.14, 0.15)	12.98 (3.77, 29.61)	0.42 (0.41, 0.43)	2.36 (0.52, 6.26)	0.07 (0.07, 0.08)	77.00 (17.39, 198.67)	2.45 (2.33, 2.57)
Central Asia	11.07 (7.45, 15.82)	0.34 (0.32, 0.36)	12.83 (8.56, 18.78)	0.36 (0.31, 0.40)	1.21 (0.67, 1.86)	0.03 (0.03, 0.04)	55.32 (31.87, 85.01)	2.39 (1.83, 2.95)
Central Europe	0.26 (0.12, 0.50)	0.02 (0.00, 0.01)	0.29 (0.14, 0.56)	−0.01 (−0.02, 0.01)	0.03 (0.01, 0.07)	−0.02 (−0.02, −0.01)	1.31 (0.44, 2.88)	−0.02 b(−0.04, −0.01)
Eastern Europe	16.73 (10.38, 24.72)	0.48 (0.46, 0.51)	12.08 (7.83, 17.33)	0.37 (0.35, 0.40)	0.97 (0.55, 1.39)	0.03 (0.02, 0.04)	42.49 (24.80, 59.65)	1.38 (1.11, 1.66)
High-income Asia Pacific	0.17 (0.05, 0.55)	−0.02 (−0.02, −0.01)	0.10 (0.03, 0.31)	−0.02 (−0.02, −0.01)	0.02 (0.00, 0.07)	−0.02 (−0.01, −0.01)	0.45 (0.10, 1.45)	−0.07 (−0.08, −0.06)
Australasia	0.15 (0.06, 0.32)	0.01 (0.00, 0.01)	0.11 (0.04, 0.24)	0.01 (0.00, 0.01)	0.01 (0.00, 0.02)	0.01 (−0.01, 0.01)	0.22 (0.07, 0.55)	0.01 (−0.01, 0.01)
Western Europe	0.13 (0.08, 0.21)	0.01 (0.00, 0.01)	0.11 (0.07, 0.17)	0.01 (0.00, 0.01)	0.01 (0.01, 0.02)	−0.02 (−0.02, −0.01)	0.29 (0.14, 0.55)	−0.01 (−0.01, −0.01)
Southern Latin America	0.17 (0.05, 0.54)	0.01 (0.00, 0.01)	0.20 (0.06, 0.60)	0.01 (0.00, 0.01)	0.04 (0.01, 0.11)	−0.02 (−0.02, −0.01)	1.23 (0.26, 3.76)	−0.02 (−0.03,- 0.01)
High-income North America	0.03 (0.01, 0.08)	−0.02 (−0.02, −0.01)	0.04 (0.02, 0.10)	−0.02 (−0.02, −0.01)	0.00 (0.00, 0.01)	−0.02 (−0.02, −0.01)	0.13 (0.04, 0.37)	−0.04 (−0.04, −0.04)
Caribbean	0.16 (0.06, 0.38)	−0.02 (−0.02, −0.01)	0.25 (0.09, 0.55)	−0.02 (−0.01, −0.01)	0.05 (0.01, 0.15)	−0.02 (−0.02, −0.01)	2.15 (0.40, 6.66)	−0.07 (−0.09, −0.06)
Andean Latin America	4.20 (2.07, 8.29)	0.08 (0.07, 0.09)	5.07 (2.47, 9.76)	0.09 (0.08, 0.10)	0.89 (0.32, 1.97)	0.01 (−0.01, 0.02)	31.59 (11.90, 68.16)	−0.14 (−0.26, −0.02)
Central Latin America	0.57 (0.24, 1.13)	0.02 (0.02, 0.02)	0.86 (0.35, 1.75)	0.02 (0.02, 0.03)	0.14 (0.05, 0.30)	0.01 (0.01, 0.02)	4.74 (1.64, 10.35)	0.12 (0.09, 0.14)
Tropical Latin America	0.92 (0.21, 2.64)	0.03 (0.03, 0.03)	0.95 (0.22, 2.75)	0.03 (0.03, 0.03)	0.13 (0.03, 0.39)	0.01 (0.00, 0.01)	5.10 (1.02, 14.91)	0.16 (0.14, 0.17)
North Africa and Middle East	0.84 (0.47, 1.55)	0.02 (0.02, 0.02)	1.01 (0.60, 1.75)	0.03 (0.03, 0.03)	0.28 (0.09, 0.70)	0.01 (0.01, 0.01)	9.97 (3.13, 25.00)	0.33 (0.26, 0.39)
South Asia	14.46 (4.55, 32.75)	0.49 (0.47, 0.51)	21.18 (6.90, 47.38)	0.68 (0.64, 0.71)	4.22 (1.04, 9.67)	0.13 (0.12, 0.14)	139.31 (35.46, 313.45)	4.13 (3.87, 4.39)
Central Sub-Saharan Africa	9.83 (2.91, 25.87)	0.25 (0.24, 0.26)	19.93 (5.76, 53.80)	0.52 (0.50, 0.54)	5.10 (1.24, 17.43)	0.13 (0.12, 0.13)	166.56 (42.73, 566.43)	3.96 (3.69, 4.23)
Eastern Sub-Saharan Africa	11.61 (6.94, 19.19)	0.37 (0.36, 0.38)	18.26 (11.03, 30.10)	0.58 (0.57, 0.59)	6.21 (2.40, 12.52)	0.19 (0.18, 0.20)	189.74 (75.50, 390.11)	5.93 (5.64, 6.22)
Southern Sub-Saharan Africa	15.65 (7.14, 32.63)	0.43 (0.41, 0.46)	21.31 (9.41,4 4.94)	0.58 (0.56, 0.60)	4.53 (1.65, 10.15)	0.12 (0.10, 0.13)	172.30 (63.88, 382.47)	4.32 (3.80, 4.83)
Western Sub-Saharan Africa	6.52 (3.06, 13.88)	0.18 (0.16, 0.19)	10.93 (5.28, 21.85)	0.28 (0.26, 0.30)	3.07 (1.07, 6.62)	0.08 (0.08, 0.09)	90.03 (31.43, 197.30)	2.37 (2.18, 2.55)

Abbreviations: ASIR Age-standardized incidence rate, ASPR Age-standardized prevalence rate, ASMR Age-standardized mortality rate, ASDR Age-standardized disability-adjusted life years rate, DALYs Disability-adjusted life years, MDR-TB Multidrug-resistant TB without extensive drug resistance, SDI Sociodemographic Index, TB Tuberculosis

For XDR-TB in 2021, the ASIR was 0.29 (95% UI: 0.21, 0.42), ASPR was 0.34 (95% UI: 0.23, 0.52), ASMR was 0.01 (95% UI: 0.00, 0.01), and ASDR was 3.47 (95% UI: 1.51, 6.43). From 1990 to 2021, significant increases were observed in ASIR (AAPC = 0.01%, 95% CI: 0.01, 0.02), ASPR (AAPC = 0.01%, 95% CI: 0.01, 0.02), ASMR (AAPC = 0.01%, 95% CI: 0.00, 0.01), and ASDR (AAPC = 0.11%, 95% CI: 0.11, 0.12) (Table 2). In 2021, XDR-TB was associated with 24,036 incident cases (95% UI: 17,144, 34,587), 27,933 prevalent cases (95% UI: 18,731, 43,751), 7,950 deaths (95% UI: 3,326, 14,859), and 287,736 DALYs (95% UI: 124,816, 531,852). All these indicators showed upward trajectories (Supplementary Table S2).

Table 2.

Trends of age-standardized rates in XDR-TB at the global level and across major GBD regions

Location	ASIR (per 100,000 population, 95% UI) 2021	ASIR AAPC(%, 95%CI) 1990–2021	ASPR (per 100,000 population, 95% UI) 2021	ASPR AAPC(%,95% CI) 1990–2021	ASMR (per 100,000 population, 95% UI) 2021	ASMR AAPC(%, 95% CI) 1990–2021	ASDR (per 100,000 population, 95% UI) 2021	ASDR AAPC(%,95% CI) 1990–2021
Global	0.29 (0.21, 0.42)	0.01 (0.01, 0.02)	0.34 (0.23, 0.52)	0.01 (0.01, 0.02)	0.09 (0.04, 0.18)	0.01 (0.00, 0.01)	3.47 (1.51, 6.43)	0.11 (0.11, 0.12)
High SDI	0.02 (0.01, 0.04)	0.01 (0.00, 0.01)	0.03 (0.02, 0.04)	0.00 (0.00, 0.01)	0.01 (0.00, 0.01)	0.01 (0.00, 0.01)	0.15 (0.07, 0.30)	0.01 (0.00, 0.01)
High-middle SDI	0.66 (0.41, 0.95)	0.01 (0.00, 0.01)	0.55 (0.35, 0.85)	0.02 (0.02, 0.03)	0.09 (0.05, 0.15)	0.01 (0.00, 0.01)	3.72 (2.06, 5.94)	0.11 (0.10, 0.12)
Middle SDI	0.25 (0.15, 0.40)	0.01 (0.01, 0.02)	0.37 (0.21, 0.66)	0.01 (0.01, 0.02)	0.08 (0.03, 0.16)	0.01 (0.00, 0.01)	2.82 (1.18, 5.16)	0.09 (0.09, 0.10)
Low-middle SDI	0.36 (0.18, 0.70)	0.01 (0.01, 0.02)	0.43 (0.21, 0.84)	0.01 (0.01, 0.02)	0.21 (0.07,0.46)	0.01 (0.01, 0.01)	6.76 (2.26, 14.63)	0.22 (0.21, 0.23)
Low SDI	0.23 (0.12, 0.47)	0.01 (0.01, 0.02)	0.27 (0.13, 0.56)	0.01 (0.01, 0.02)	0.16 (0.05, 0.36)	0.01 (0.00, 0.01)	4.60 (1.59, 10.13)	0.15 (0.15, 0.15)
East Asia	0.15 (0.04, 0.42)	0.01 (0.00, 0.02)	0.27 (0.06, 0.81)	0.01 (0.01, 0.02)	0.04 (0.01, 0.10)	0.01 (0.00, 0.01)	1.21 (0.30, 3.13)	0.05 (0.04, 0.06)
Southeast Asia	0.37 (0.20, 0.64)	0.01 (0.01, 0.02)	0.55 (0.29, 0.94)	0.02 (0.02, 0.03)	0.19 (0.06, 0.43)	0.01 (0.01, 0.01)	5.36 (1.92, 12.43)	0.17 (0.16, 0.19)
Oceania	0.64 (0.19, 1.49)	0.02 (0.02, 0.02)	1.14 (0.33, 2.60)	0.04 (0.03, 0.05)	0.46 (0.10, 1.26)	0.02 (0.01, 0.02)	14.59 (3.29, 39.97)	0.49 (0.47, 0.51)
Central Asia	2.35 (1.59, 3.35)	0.08 (0.07, 0.08)	2.70 (1.80, 3.95)	0.09 (0.08, 0.10)	0.56 (0.31, 0.90)	0.03 (0.02, 0.04)	24.86 (14.07, 39.55)	0.78 (0.73, 0.83)
Central Europe	0.06 (0.03, 0.11)	0.01 (0.00, 0.01)	0.06 (0.03, 0.12)	0.01 (0.00, 0.01)	0.02 (0.01, 0.04)	0.01 (0.00, 0.01)	0.58 (0.19, 1.39)	0.02 (0.01, 0.02)
Eastern Europe	3.52 (2.18, 5.20)	0.11 (0.11, 0.12)	2.54 (1.65, 3.64)	0.08 (0.07, 0.09)	0.45 (0.25, 0.70)	0.01 (0.01, 0.02)	18.96 (10.51, 29.19)	0.59 (0.54, 0.65)
High-income Asia Pacific	0.02 (0.01, 0.07)	0.01 (0.00, 0.01)	0.01 (0.00, 0.04)	0.01 (0.00, 0.01)	0.01 (0.00, 0.02)	0.01 (0.00, 0.01)	0.11 (0.02, 0.35)	0.01 (0.00, 0.01)
Australasia	0.02 (0.01, 0.04)	0.01 (0.00, 0.01)	0.01 (0.01, 0.03)	0.01 (0.00, 0.01)	0.01 (0.00, 0.01)	0.01 (0.00, 0.01)	0.05 (0.01, 0.13)	0.01 (0.00, 0.01)
Western Europe	0.02 (0.01, 0.03)	0.01 (0.00, 0.01)	0.01 (0.01, 0.02)	0.01 (0.00, 0.01)	0.01 (0.00, 0.01)	0.01 (0.00, 0.01)	0.07 (0.03, 0.15)	0.01 (0.00, 0.01)
Southern Latin America	0.02 (0.01, 0.08)	0.01 (0.00, 0.01)	0.02 (0.01, 0.07)	0.01 (0.00, 0.01)	0.01 (0.00, 0.03)	0.01 (0.00, 0.01)	0.32 (0.06, 1.07)	0.01 (0.01, 0.01)
High-income North America	0.01 (0.01, 0.02)	0.01 (0.00, 0.01)	0.00 (0.00, 0.01)	0.01 (0.00, 0.01)	0.01 (0.00, 0.01)	0.01 (0.00, 0.01)	0.03 (0.01, 0.09)	0.01 (0.00, 0.01)
Caribbean	0.01 (0.00, 0.04)	0.01 (0.00, 0.01)	0.02 (0.01, 0.04)	0.01 (0.00, 0.01)	0.01 (0.00, 0.03)	0.01 (0.00, 0.01)	0.36 (0.07, 1.22)	0.01 (0.01, 0.01)
Andean Latin America	0.35 (0.18, 0.69)	0.01 (0.01, 0.01)	0.39 (0.19, 0.74)	0.01 (0.01, 0.02)	0.15 (0.05, 0.35)	0.01 (0.00, 0.01)	5.20 (1.84, 11.92)	0.18 (0.16, 0.19)
Central Latin America	0.05 (0.02, 0.09)	0.01 (0.00, 0.01)	0.07 (0.03, 0.13)	0.01 (0.00, 0.01)	0.02 (0.01, 0.05)	0.01 (0.00, 0.01)	0.78 (0.27, 1.80)	0.02 (0.02, 0.03)
Tropical Latin America	0.07 (0.02, 0.21)	0.01 (0.00, 0.01)	0.07 (0.02, 0.21)	0.01 (0.00, 0.01)	0.02 (0.00, 0.07)	0.01 (0.00, 0.01)	0.84 (0.14, 2.58)	0.03 (0.03, 0.03)
North Africa and Middle East	0.04 (0.02, 0.08)	0.01 (0.00, 0.01)	0.03 (0.02, 0.06)	0.01 (0.00, 0.01)	0.02 (0.01, 0.06)	0.01 (0.00, 0.01)	0.75 (0.21, 2.16)	0.03 (0.02, 0.03)
South Asia	0.40 (0.13, 0.91)	0.01 (0.00, 0.01)	0.50 (0.16, 1.12)	0.02 (0.02, 0.02)	0.22 (0.05, 0.53)	0.01 (0.01, 0.01)	7.19 (1.79, 16.99)	0.23 (0.23, 0.24)
Central Sub-Saharan Africa	0.10 (0.03, 0.26)	0.01 (0.00, 0.01)	0.12 (0.04, 0.34)	0.00 (0.00, 0.00)	0.07 (0.02, 0.24)	0.01 (0.00, 0.01)	2.29 (0.55, 7.85)	0.08 (0.07, 0.08)
Eastern Sub-Saharan Africa	0.12 (0.07, 0.19)	0.01 (0.00, 0.01)	0.11 (0.07, 0.19)	0.01 (0.00, 0.01)	0.09 (0.03, 0.18)	0.01 (0.00, 0.01)	2.60 (1.01, 5.36)	0.09 (0.08, 0.09)
Southern Sub-Saharan Africa	0.13 (0.06, 0.29)	0.01 (0.00, 0.01)	0.13 (0.06, 0.28)	0.01 (0.00, 0.01)	0.06 (0.02, 0.15)	0.01 (0.00, 0.01)	2.35 (0.80, 5.57)	0.08 (0.07, 0.08)
Western Sub-Saharan Africa	0.06 (0.03, 0.13)	0.01 (0.00, 0.01)	0.07 (0.03, 0.14)	0.01 (0.00, 0.01)	0.04 (0.01, 0.09)	0.01 (0.00, 0.01)	1.23 (0.39, 2.75)	0.04 (0.04, 0.04)

Abbreviations: ASIR Age-standardized incidence rate, ASPR Age-standardized prevalence rate, ASMR Age-standardized mortality rate, ASDR Age-standardized disability-adjusted life years rate, DALYs Disability-adjusted life years, SDI Sociodemographic Index, TB Tuberculosis, XDR-TB Extensively drug-resistant TB

5 SDI regions

In 2021, the ASIR, ASPR, ASMR, and ASDR for MDR-TB were highest in low SDI regions and lowest in high SDI regions. Between 1990 and 2021, these indicators increased in low and low-middle SDI regions, while they declined in high SDI regions. Similarly, the number of incident cases, prevalent cases, deaths, and DALYs associated with MDR-TB rose across low-SDI, low-middle SDI, middle-SDI, and high-middle SDI regions (Table 1. Supplementary Table S1).

For XDR-TB, the ASIR and ASPR were highest in high-middle SDI regions, while the ASMR and ASDR were highest in low SDI regions, with the lowest values observed in high SDI regions. From 1990 to 2021, all indicators for XDR-TB increased across the five SDI regions (Table 2. Supplementary Table S2).

Geographic regions

In 2021, the ASIR was highest in Eastern Europe (16.73 per 100,000 population, 95% UI: 10.38, 24.72), ASPR in Southern Sub-Saharan Africa (21.31 per 100,000 population, 95% UI: 9.41, 44.94), ASMR in Eastern Sub-Saharan Africa (6.21 per 100,000 population, 95% UI: 2.40, 12.52), and ASDR in Eastern Sub-Saharan Africa (189.74 per 100,000 population, 95% UI: 75.50, 390.11). The ASIR declined in East Asia, High-income Asia Pacific, High-income North America, and the Caribbean, while it increased in 17 other regions, with the greatest increase observed in South Asia (AAPC = 0.49%, 95% CI: 0.47, 0.51). The ASPR decreased in East Asia, Central Europe, Australasia, High-income North America, and the Caribbean, but increased in 16 other regions, with the highest increase observed in South Asia (AAPC = 0.68%, 95% CI: 0.64, 0.71). The ASMR declined in East Asia, Central Europe, High-income Asia Pacific, Western Europe, Southern Latin America, High-income North America, and the Caribbean, while it increased in 11 regions, with the greatest increase observed in Eastern Sub-Saharan Africa (AAPC = 0.19%, 95% CI: 0.18, 0.20). The ASDR decreased in 8 regions, with the largest decline in East Asia (AAPC = −1.18%, 95% CI: −1.30, −1.05), and increased in 12 regions, with the greatest increase observed in Eastern Sub-Saharan Africa (AAPC = 5.93%, 95% CI: 5.64, 6.22) (Table 1). The number of incident cases of MDR-TB decreased in East Asia (AAPC = −234.50%, 95% CI: −323.18, −145.82) and High-income North America (AAPC = −7.74%, 95% CI: −7.87, −7.62), remained stable in High-income Asia Pacific (AAPC = −0.08%, 95% CI: −2.51, 2.36), and increased in all other regions (Supplementary Table S1).

In 2021, the ASIR of XDR-TB was highest in Oceania (0.64 per 100,000 population, 95% UI: 0.19, 1.49), while the ASPR (2.71 per 100,000 population, 95% UI: 1.80, 3.95), ASMR (0.56 per 100,000 population, 95% UI: 0.31, 0.90), and ASDR (24.86 per 100,000 population, 95% UI: 14.07, 39.55) were highest in Central Asia. From 1990 to 2021, the ASIR, ASPR, ASMR, and ASDR for XDR-TB increased across all 21 geographic regions, with the most significant increases observed in Central Asia (Table 2). Additionally, the number of incident cases, prevalent cases, deaths, and DALYs associated with XDR-TB also showed an upward trend in all 21 geographic regions (Supplementary Table S2).

204 countries and territories

In 2021, the ASIR of MDR-TB was highest in Somalia (57.25 per 100,000 population, 95%UI: 14.12, 169.56), as was the ASPR (66.95 per 100,000 population, 95%UI: 16.78, 191.63), ASMR (37.58 per 100,000 population, 95%UI: 7.63, 109.59), and ASDR (1066.78 per 100,000 population, 95%UI: 219.93, 3124.42) (Fig. 1A-D). From 1990 to 2021, the ASIR of MDR-TB increased in 155 countries, with the largest increase observed in Somalia (AAPC = 1.79%, 95%CI: 1.67, 1.92). It decreased in 40 countries, with the largest decline in Latvia (AAPC = −0.21%, 95%CI: −0.23, −0.19). The ASPR rose in 147 countries, with Somalia again seeing the largest increase (AAPC = 2.20%, 95%CI: 2.11, 2.28), while 44 countries experienced a decline, the greatest of which was in Latvia (AAPC = −0.31%, 95%CI: −0.36, −0.27). The ASMR increased in 120 countries, with the largest rise in Somalia (AAPC = 1.16%, 95%CI: 1.05, 1.27), while it decreased in 70 countries, with China seeing the largest reduction (AAPC = −0.04%, 95%CI: −0.04, −0.03). The ASDR increased in 120 countries, with Somalia showing the highest increase (AAPC = 37.62%, 95%CI: 34.13, 41.12), while 69 countries saw a decline, with China experiencing the largest drop (AAPC = −1.21%, 95%CI: −1.29, −1.12) (Fig. 1A-D. Supplementary Table S3 and S4).

Fig. 1.

The global disease burden of MDR-TB in 204 countries and territories in 2021. A ASIR. B ASPR. C ASMR. D ASDR. Abbreviations: ASIR, Age-standardized incidence rate. ASPR, Age-standardized prevalence rate. ASMR, Age-standardized mortality rate. ASDR, Age-standardized disability-adjusted life years rate. DALYs, Disability-adjusted life years. MDR-TB, Multidrug-resistant TB without extensive drug resistance. TB, Tuberculosis

In 2021, India had the highest number of new cases of MDR-TB (0.22 million individuals, 95% UI: 0.05, 0.53), the highest number of prevalent cases (0.32 million individuals, 95% UI: 0.07, 0.77), the most deaths (0.05 million individuals, 95% UI: 0.01, 0.13), and the highest number of DALY cases (1.93 million individuals, 95% UI: 0.39, 4.79). Between 1990 and 2021, the number of new cases, prevalent cases, and DALYs all increased the most in India, while China experienced the greatest decreases (Supplementary Table S5 and S6).

In 2021, the ASIR of XDR-TB was highest in Kyrgyzstan (4.25 per 100,000 population, 95%UI: 1.41, 8.44), the ASPR was highest in Moldova (4.74 per 100,000 population, 95%UI: 2.79, 7.01), and the ASDR was highest in Mongolia (43.31 per 100,000 population, 95%UI: 9.55, 109.82) (Fig. 2A-D). Between 1990 and 2024, the ASIR, ASPR, ASMR, and ASDR of XDR-TB increased in all 204 countries and territories (Fig. 2A-D. Supplementary Table S7 and S8).

Fig. 2.

The global disease burden of XDR-TB in 204 countries and territories in 2021. A ASIR. B ASPR. C ASMR. D ASDR. Abbreviations: ASIR, Age-standardized incidence rate. ASPR, Age-standardized prevalence rate. ASMR, Age-standardized mortality rate. ASDR, Age-standardized disability-adjusted life years rate. DALYs, Disability-adjusted life years. TB, Tuberculosis. XDR-TB, Extensively drug-resistant TB

In 2021, the highest number of new cases of XDR-TB was in the Russian Federation (5,942 individuals, 95%UI: 3,172, 9,373), the highest number of prevalent cases was in India (7,512 individuals, 95%UI: 1,597, 18,276), the highest number of deaths was in India (2,815 individuals, 95%UI: 502, 7,178), and the highest number of DALY cases was in India (99,599 individuals, 95%UI: 17,968, 250,417). From 1990 to 2021, the number of new cases, prevalent cases, deaths, and DALYs due to XDR-TB increased in all 204 countries and territories (Supplementary Table S9 and S10).

Association between ASR and SDI

Between 1990 and 2021, significant inverse associations were observed between the SDI and ASRs for MDR-TB globally. Specifically, higher SDI levels were associated with lower ASRs for incidence (r = –0.50, P < 0.01), prevalence (r = –0.58, P < 0.01), mortality (r = –0.69, P < 0.01), and DALYs (r = –0.67, P < 0.01). Similarly, negative correlations were observed between SDI and the absolute numbers of incident cases (r = –0.33, P < 0.01), prevalent cases (r = –0.40, P < 0.01), deaths (r = –0.44, P < 0.01), and DALY counts (r = –0.49, P < 0.01) (Supplementary Fig. S1A-D. Table S11).

For XDR-TB during the same period, weaker yet statistically significant negative correlations were noted between SDI and ASRs: incidence (r = –0.09, P = 0.02), prevalence (r = –0.12, P < 0.01), mortality (r = –0.18, P < 0.01), and DALYs (r = –0.19, P < 0.01). However, associations between SDI and the absolute numbers of incident cases (r = –0.04, P = 0.02), prevalent cases (r = –0.04, P = 0.52), deaths (r = –0.04, P = 0.66), and DALY counts (r = –0.04, P = 0.08) were not statistically significant (Supplementary Fig. S2A-D. Table S11).

BAPC model prediction

By 2035, the ASIR of MDR-TB is projected to increase to 11.01 per 100,000 population (95%CI: 0.01, 23.98), while the ASMR is expected to rise to 2.47 per 100,000 population (95%CI: 0.01, 5.11). The ASIR of XDR-TB is projected to rise to 3.84 per 100,000 population (95%CI: 0.01, 23.52), and the ASMR to 1.34 per 100,000 population (95%CI: 0.01, 10.16). Between 2022 and 2035, the ASIR, ASPR, ASMR, and ASDR for both MDR-TB and XDR-TB are expected to show a declining trend (Table 3. Supplementary Fig. S3A-D, S4 A-D).

Table 3.

The Prediction of global burden of MDR-TB and XDR-TB for 2022–2035 based on the BAPC model

Disease	Index	Incidence	Prevalence	Mortality	DALYs
MDR-TB	Rate (per 100,000 population) (95% CI). 2035	11.01 (0.01, 23.98)	17.36 (0.01, 39.37)	2.47 (0.01, 5.11)	96.01 (0.01, 201.75)
	AAPC (95% CI) 2022–2035	0.41 (0.40, 0.42)	0.69 (0.68, 0.71)	0.08 (0.08, 0.10)	3.13 (3.11, 3.15)
XDR-TB	Rate (per 100,000 population) (95% CI). 2035	3.84 (1.08, 23.52)	4.20 (0.01, 29.06)	1.34 (0.01, 10.16)	72.45 (0.01, 595.38)
	AAPC (95% CI) 2022–2035	0.27 (0.26, 0.28)	0.30 (0.29, 0.31)	0.09 (0.09, 0.10)	5.23 (5.11, 5.35)

Abbreviations: BAPC Bayesian age-period-cohort, CI Confidence interval, AAPC estimated annual percentage change, DALYs Disability-adjusted life years, MDR-TB Multidrug-resistant TB without extensive drug resistance, TB Tuberculosis, XDR-TB Extensively drug-resistant TB

Decomposition analysis on DALYs cases

For MDR-TB, the global burden is predominantly driven by epidemiological changes (69.43%), with aging and population factors contributing 6.97% and 23.60%, respectively. High-middle and low-middle SDI regions show significant variations, with the high-middle SDI region experiencing a negative contribution from aging (−33.98%) and a large positive contribution from population change (122.84%). In contrast, the low-middle SDI region shows a large share from epidemiological change (71.20%) and a more balanced distribution across factors (Fig. 3A. Supplementary Table S12).

Fig. 3.

Impact of aging, population growth, and epidemiological changes on DALYs cases for MDR-TB and XDR-TB globally and across five SDI regions from 1990 to 2021 (A: MDR-TB. B XDR-TB). Black dots represent the total change in DALYs cases due to the combined effects of aging, population growth, and epidemiological shifts. Positive values indicate an increase in DALYs cases driven by the respective factor, while negative values indicate a net reduction in DALYs cases. Abbreviations: DALYs, disability-adjusted life-years. MDR-TB Multidrug-resistant TB without extensive drug resistance. SDI, sociodemographic index. TB, Tuberculosis. XDR-TB Extensively drug-resistant TB

For XDR-TB, the global burden is influenced heavily by epidemiological changes (100.87%), with negative contributions from aging (−19.44%) and minimal impact from population change (18.56%). High-middle SDI regions show positive contributions from population growth (15.41%) and aging (6.61%), while low SDI regions exhibit significant impact from epidemiological changes (132.79%) (Fig. 3B. Supplementary Table S12).

Cross-country inequality analysis for ASDR

For MDR-TB, the SII decreased from − 0.26 (95% CI: − 0.45, − 0.14) in 1990 to − 0.45 (95% CI: − 0.60, − 0.34) in 2021, indicating a widening absolute disparity, while the CCI exhibited a pronounced decline from − 12.15 (95% CI: − 14.03, − 10.27) to − 67.68 (95% CI: − 79.08, − 56.28), reflecting a substantial increase in relative inequality (Fig. 4A, B. Table S13). Similarly, XDR-TB demonstrated a marked expansion in inequality, with the SII decreasing from − 0.01 (95% CI: − 0.02, − 0.01) in 1992 to − 0.40 (95% CI: − 0.53, − 0.27) in 2021, and the CCI shifting from − 0.09 (95% CI: − 0.30, − 0.14) to − 0.24 (95% CI: − 0.38, − 0.08). These findings underscore a progressive concentration of both MDR-TB and XDR-TB burden among socioeconomically disadvantaged populations, highlighting the need for targeted public health interventions to mitigate growing disparities in drug-resistant TB (Fig. 4C, D. Supplementary Table S13).

Fig. 4.

Regression and concentration curves illustrating health inequalities in the ASDR of MDR-TB and extensively XDR-TB across 204 countries and territories. Panels A and C present the SII for MDR-TB and XDR-TB, respectively. These panels depict the gradient of ASDR across countries, with data points scaled according to population size, thereby reflecting demographic weight. Panels B and D show the concentration curves, from which the CCI is derived. These curves illustrate the degree of relative inequality by comparing the cumulative ASDR distributions against cumulative population ranks stratified by SDI. Temporal shifts are color-coded: blue indicates baseline estimates (1990), while red denotes estimates for 2021. Abbreviations: ASDR, Age-Standardized disability-adjusted life-years rate. CCI, Concentration Index of Inequality. MDR-TB, Multidrug-resistant TB without extensive drug resistance. SDI, Sociodemographic Index. SI, Slope index of inequality. TB, Tuberculosis. XDR-TB, Extensively drug-resistant TB

Frontier analysis for ASDR

For MDR-TB, there was substantial potential for improvement, particularly in low- and middle-SDI countries such as Somalia, Lesotho, and South Sudan. Notably, several high-SDI regions, including Lithuania and Greenland, also exhibited considerable gaps in their ASDR levels (Fig. 5A, B. Table S14). Similarly, analysis of XDR-TB demonstrated considerable opportunities for ASDR reduction in countries across Central Asia and Eastern Europe, regions with moderate to high SDI. Countries like Kazakhstan, Ukraine, and Korea showed significant shortfalls in their expected performance, given their development levels (Fig. 5C, D. Supplementary Table S15).

Fig. 5.

Frontier analysis examining the relationship between SDI and ASDR for MDR-TB and XDR-TB across 204 countries and territories. Panels A and C illustrate temporal shifts, where the gradient transition from Light to dark blue tracks the trajectory of national ASDRs in relation to changes in SDI from 1990 to 2021. Panels B and D present the ASDR cross-sectional distribution in 2021, with each point representing an individual country or territory. The black curve represents the estimated efficiency frontier, reflecting the lowest achievable ASDR for a given SDI level. Countries exhibiting the greatest deviations from the frontier are highlighted with labeled markers: red dots indicate high-SDI countries that are underperforming, while Light blue dots denote low-SDI countries that are positioned closer to the frontier. The change in ASDR between 1990 and 2021 is color-coded, with orange indicating a net decline and light blue denoting a net increase. Abbreviations: ASDR, Age-Standardized disability-adjusted life-years rate. MDR-TB, Multidrug-resistant TB without extensive drug resistance. SDI, Sociodemographic Index. TB, Tuberculosis. XDR-TB, Extensively drug-resistant TB

Discussion

The study provides a comprehensive analysis of the long-term trends in MDR-TB/XDR-TB incidence across countries and regions, quantifying disparities in disease burden and control effectiveness between countries at different development levels. It highlights the unique and persistent challenges faced by lower-income countries in managing drug-resistant TB, while also revealing areas where high-income countries have not met expected control outcomes. The findings support the design of more targeted public health strategies that prioritize health equity, emphasizing policy innovations in resource allocation, treatment optimization, and global cooperation to reduce the gap in MDR-TB/XDR-TB control between countries and territories.

The study indicated that the global ASIR of MDR-TB has remained persistently high. Moreover, over the past 30 years, the ASIR of XDR-TB has risen across virtually all regions worldwide, particularly in South Asia and Sub-Saharan Africa. Moreover, the relative inequalities in TB control have become increasingly pronounced, with low-income countries, burdened by a high disease load, facing insufficient policy and resource investment [22]. These trends can be attributed to the interplay of several factors. First, the improper use of anti-TB treatments, including incomplete treatment courses, improper drug use, and drug misuse, is a primary driver of drug-resistant TB, particularly in low-income and resource-limited areas(South Asia, Sub-Saharan Africa, etc.), where standardized treatment regimens and regulatory mechanisms are lacking, exacerbating the spread of resistance [23, 24]. Second, many countries or regions, especially low- and middle-income ones (South Asia, Sub-Saharan Africa, etc.), struggle with inadequate early diagnosis and treatment, allowing drug-resistant TB strains to spread unchecked and further fueling the rise of MDR-TB/XDR-TB [25]. In addition, as Mycobacterium tuberculosis strains continue to be exposed to anti-TB treatments, resistant strains naturally evolve into more complex and lethal forms of drug-resistant TB. It leads to significant treatment challenges, particularly in low-resource settings, thereby exacerbating the burden on public health systems [26].

To effectively address the rising trend of MDR-TB/XDR-TB, a series of comprehensive measures must be implemented. These measures should focus on strengthening global cooperation to enhance technology transfer and financial support, improving early diagnostic capabilities, optimizing treatment protocols, and upgrading public health infrastructure [27–29]. First, for low-income countries and regions (South Asia, Sub-Saharan Africa, etc.), support from international organizations, Non-Governmental Organizations, and developed countries should facilitate the sharing of technology and expertise. For example, through platforms like the Global Fund and the World Health Organization, the development of anti-TBdrugs, advancements in drug resistance detection technologies, and the standardization of treatment protocols can be promoted, ensuring that low-income countries gain access to cutting-edge technologies and treatments [30, 31]. Second, strengthening early diagnostic capabilities is crucial. The introduction of advanced molecular diagnostic technologies, such as GeneXpert, can enhance the speed and accuracy of drug-resistant TB detection, ensuring timely identification and intervention [32–34]. Screening coverage should also be expanded, particularly for high-risk groups, such as individuals with HIV and those with a history of TB relapse [35, 36]. Third, optimizing treatment protocols and improving patient adherence are vital. Ensuring a steady supply of anti-TB drugs and promoting standardized treatment regimens, such as directly observed treatment, along with providing comprehensive social and psychological support, can significantly improve treatment adherence, reduce interruptions, and prevent the development of resistance [37, 38]. In addition, improving TB control and management infrastructure is key to effective prevention and control efforts. Strengthening primary healthcare infrastructure, particularly in rural and remote areas, and enhancing TB health record management systems to ensure timely updates and patient tracking, will improve treatment management and overall control capabilities [39]. Additionally, implementing medical surveillance and preventive treatment for close contacts of MDR-TB and XDR-TB patients not only helps reduce the incidence of drug-resistant tuberculosis but also effectively controls the spread of resistant strains, reduces community transmission, limits disease spread, and significantly enhances the capacity of public health systems to respond to tuberculosis [24, 40]. Finally, reinforcing public health policies and health education is essential. Promoting awareness of drug-resistant TB prevention and treatment will enhance public understanding of TB and reduce its spread [41]. The combination of these multi-layered and multi-dimensional interventions can effectively tackle the challenges of drug-resistant TB, particularly in low- and middle-income countries, ultimately contributing to the achievement of global TB control goals.

This study employed frontier analysis to assess the potential for improvement in the burden of MDR-TB/XDR-TB across 204 countries and territories. The research found that low-SDI countries and regions such as Liberia and the Solomon Islands, due to their outstanding progress in reducing disease burden, have become exemplary models in the control of drug-resistant TB [42, 43]. Existing studies have highlighted several drug-resistant TB surveillance initiatives implemented in countries like Liberia and the Solomon Islands. For example, the Southeast Asia Regional Green Light Committee supported the Solomon Islands in developing specific drug-resistant TB surveillance and management strategies, and a multi-center genetic data project tracks the spread of resistant strains across Pacific Island nations [44, 45]. Similarly, the United Nations Development Programme’s South-South Cooperation Health Project has contributed to TB control and drug resistance monitoring in the Solomon Islands and other Pacific countries [46, 47]. In Liberia, the United States Agency for International Development (USAID) conducted research on community transmission of MDR-TB/XDR-TB and implemented targeted interventions to address this issue [48, 49]. These initiatives prospectively collected and analyzed data on the incidence of drug-resistant TB and related risk factors, such as improper drug use, poor adherence, and HIV co-infection, providing a solid foundation for the development of prevention and management policies for these regions and low- and middle-income populations [50, 51]. Drawing from these successful experiences, strengthening international cooperation, enhancing monitoring and management, implementing targeted interventions, and optimizing treatment protocols will be key measures in the effective control of MDR-TB/XDR-TB.

This study has several Limitations. First, the accuracy of the GBD 2021 estimates for drug-resistant TB depends on the completeness and quality of the data, which exhibit significant variation between countries. Differences in monitoring coverage, reporting standards, and data timeliness may introduce systematic bias [52–54]. Second, variations in the classification and diagnostic criteria for drug-resistant TB Limit the comparability across countries or territories. Many studies have indicated that the GBD 2021 estimates tend to overstate the disease burden [55, 56]. Fourth, the projections based on BAPC rely on historical trends and may fail to account for future shifts in epidemiology, treatment adherence, or structural changes such as migration and conflict. Fifth, the projected ASR using the BAPC model exhibit wide prediction intervals, indicating a high degree of uncertainty in the forecasts. Therefore, while these projections offer valuable insights into potential future trends, they should be interpreted with caution, and policy decisions should be informed by a range of possible scenarios. Finally, the analysis excluded HIV-positive populations and cases of mono-resistant TB. Future research should prioritize strengthening standardized monitoring of drug-resistant TB and updating estimation models to improve the accuracy of disease burden estimates.

Conclusions

Drug-resistant TB remains a growing global health threat, particularly in low- and middle-income countries. In recent years, the incidence of MDR-TB have continued to rise globally, especially in middle-income SDI and low-SDI regions, with no signs of alleviation. Similarly, the incidence of XDR-TB have surged in both global and regional contexts, further exacerbating the global burden of drug-resistant TB. In addition, significant disparities in MDR-TB/XDR-TB control across countries and the growing health inequalities in low-income nations highlight the escalating challenges in global public health. International technical and financial assistance, including advanced diagnostic tools, adequate supply of anti-drug-resistant TB medications, and standardized treatment protocols, are crucial for improving TB control. Strengthening TB prevention and control infrastructure, including enhancing patient health record management and treatment adherence monitoring, will improve disease management capabilities and help reduce health inequalities in global TB control.

Abbreviations

AAPC

Average annual percentage changes

APC

Annual percentage change

ASR

Age-standardized rate

ASIR

Age-standardized incidence rate

ASDR

Age-standardized disability-adjusted life-years rate

ASMR

Age-standardized mortality rate

ASPR

Age-standardized prevalence rate

BAPC

Bayesian age-period-cohort

CCI

Concentration index

CI

Confidence Interval

DALYs

Disability-adjusted life-years

GBD

Global Burden of Disease Study

ICD

International Classification of Diseases

IHME

Institute for Health Metrics and Evaluation

MDR-TB

Multidrug-resistant TB without extensive drug resistance

RR-TB

Rifampicin-resistant TB.

SDI

Sociodemographic index

SII

Slope index of inequality

TB

Tuberculosis

UI

Uncertainty Interval.

XDR-TB

Extensively drug-resistant TB

Authors’ contributions

SX-Z, EL-T and QY provided and were involved in data analysis. JY, GB-Y, YJ-L, ZZ-Z, XJ-H and TQ-L created the tables and charts. SX-Z, JX-Z and JC-W analyzed the data and revised the manuscript. JC-W and ZH-L designed and supervised the research methodology. JC-W, JX-Z and SX-Z are the corresponding author. EN-T and YQ contributed equally to this paper. All authors read and approved the final version of the paper.

Funding

The study was supported by the Shanghai Natural Science Foundation (No. 23ZR1464000), Science and technology development project of Shanghai University of traditional Chinese medicine(No.24BZH07), Traditional Chinese Medicine Innovation Team of Shanghai Municipal Health Commission (No. 2022CX010), the Three-Year Action Plan for Strengthening the Construction of the Public Health System in Shanghai (2023—2025, No. GWVI-11.1–08), the Excellent Academic Leaders Program of Shanghai Science and Technology Commission (22XD1423500), the Multidisciplinary Innovation Team of Traditional Chinese Medicine of China (ZYYCXTD-D-202208), Talent Fund of Longhua Hospital affiliated to Shanghai University of Traditional Chinese Medicine (No. LH001.007).

Data availability

The datasets used and/or analyzed during the current study are publicly available and accessibly (https://ghdx.healthdata.org/gbd-2021).

Declarations

Ethics approval and consent to participate

For Global Burden of Disease 2021 Study, a waiver of informed consent was reviewed and approved by the Institutional Review Board of the University of Washington (No. STUDY00009060). All the information about ethical standards is available through the official website (http://www.healthdata.org/gbd/2021).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.