Burden of multidrug-resistant and extensively drug-resistant tuberculosis in China, India and Russia: estimates from the GBD 2023 study

Introduction

Tuberculosis remains a major challenge in global public health, and the prevalence of multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB) further exacerbates the difficulty of prevention and control, becoming a core bottleneck hindering the achievement of global TB control targets (1). According to the World Health Organization, there are over 400,000 new cases of MDR-TB/XDR-TB globally each year (2). These cases have long treatment periods, significant adverse drug reactions, and low cure rates, not only imposing a heavy economic burden on patients’ families but also posing a serious threat to global public health security (3). Accurately understanding the epidemiological trends and disease burden of MDR-TB/XDR-TB is a prerequisite for developing targeted prevention and control strategies and optimizing healthcare resource allocation, and has significant public health implications.

National-level disease burden assessment is a key component of global drug-resistant tuberculosis control, and comparative analysis of representative countries can provide important references for the development of regional and global prevention and control strategies (4). India and Russia have the highest incidence rates of MDR-TB/XDR-TB globally, with widespread prevalence and high transmission risk, making the prevention and control situation extremely challenging (5). China, as one of the high-burden TB countries globally, has an MDR-TB/XDR-TB incidence rate that follows closely behind India and Russia (6,7). Due to its large population and significant regional differences, China faces unique challenges and pressures in drug-resistant tuberculosis control (8). These three countries are all located in the core region of the global drug-resistant tuberculosis epidemic (9), and their prevention and control efforts directly affect the global drug-resistant tuberculosis control process. However, there is currently a lack of systematic comparative studies on the disease burden of MDR-TB/XDR-TB in these three countries, especially a comprehensive assessment based on the latest the Global Burden of Disease Study (GBD) 2023 data.

This study, based on the GBD 2023 database (10), systematically analyzes the epidemiological trends, driving factors, and future development trends of drug-resistant tuberculosis in China, India, and Russia. This study aims to fill the current gap in comparative research on drug-resistant tuberculosis across the three countries, clarify the epidemiological differences and key control priorities in each country, and provide a scientific basis for developing precise and differentiated strategies for drug-resistant tuberculosis control. We present this article in accordance with the GATHER reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-0761/rc).

Methods

Data acquisition

In this study, data on the incidence, prevalence, mortality, and disability-adjusted life years (DALYs) of MDR-TB and XDR-TB in China, India, and Russia were obtained from the GBD 2023 public database (11). Case definitions strictly followed the GBD 2023 research standards, where MDR-TB was defined as mycobacterium tuberculosis resistant to both isoniazid and rifampicin, but still susceptible to any fluoroquinolone and at least one second-line injectable drug; while XDR-TB was defined as MDR-TB with additional resistance to fluoroquinolones and at least one second-line injectable drug. To eliminate the interference of differences in population age structure among different countries on epidemiological indicators, the above data were age-standardized to make the epidemiological indicators of different countries comparable. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Joinpoint regression analysis

A Joinpoint regression model was used to analyze the time trends of age-standardized incidence, prevalence, mortality, and DALYs of MDR-TB and XDR-TB (12). The maximum number of joinpoints was set to two, and the optimal number of joinpoints was determined through a Monte Carlo permutation test. The annual percentage change (APC) for each time segment and the average annual percentage change (AAPC) for the entire study period were calculated, and their statistical significance was assessed using 95% confidence intervals (CIs): if the 95% CI did not include zero, the trend was considered statistically significant (P<0.05).

Decomposition analysis

A population attributable decomposition model was used to decompose the changes in the incidence of MDR-TB and XDR-TB into the contributions of three core driving factors: population aging effect, epidemiological effect, and population size effect (13).

Age-period-cohort analysis

An age-period-cohort model was used to assess the independent effects of age, period, and cohort on the mortality rates of MDR-TB and XDR-TB (14). Net drift and local drift were calculated. The net drift reflects the overall trend of changes in mortality with increasing age, while the local drift reflects the rate of change in mortality with increasing age during a specific period.

Autoregressive integrated moving average (ARIMA) prediction analysis

An ARIMA model was used to predict age-standardized incidence, prevalence, mortality, and DALYs for the years 2024–2050, outputting predicted values and 95% prediction intervals (15).

Statistical analysis

For each metric, the 95% uncertainty intervals (UI) were derived from 1000 bootstrap draws, with boundaries defined by the 2.5th and 97.5th percentiles of the resulting distribution, using the mean estimate as the central reference.

Results

Epidemiological situation and trends of MDR-TB/XDR-TB in China, India, and Russia (1990‒2023)

Table 1 summarizes the incidence, prevalence, mortality, absolute number of DALYs, and age-standardized rates of MDR-TB/XDR-TB in different genders in China, India, and Russia in 2023. In terms of the absolute number of MDR-TB/XDR-TB cases, India, Russia, and China ranked among the top three globally. Notably, India’s absolute numbers for MDR-TB incidence, prevalence, mortality, and DALYs were significantly higher than those of Russia and China; in XDR-TB-related indicators, India’s absolute numbers for prevalence, mortality, and DALYs also markedly exceeded those of the other two countries, while Russia’s absolute number of XDR-TB incidence was markedly higher than that of India and China.

Figure 1 shows the dynamic trends of age-standardized rates of MDR-TB/XDR-TB in different genders in China, India, and Russia over the past three decades. Joinpoint regression analysis results showed that China’s age-standardized incidence, prevalence, mortality, and DALY rates for MDR-TB all showed a significant downward trend (AAPC <0, P<0.05), a trend that contrasts sharply with the upward trend in India and Russia (Table 1). Analysis of XDR-TB showed that only China’s age-standardized mortality rate showed a significant downward trend, while the age-standardized incidence, prevalence, and DALY rates of XDR-TB in all three countries showed a significant upward trend.

Figure 1 Temporal trends in age-standardized incidence, prevalence, mortality, and disease burden of tuberculosis among different genders in China, India, and Russia. The x-axis denotes calendar year and the y-axis denotes age-standardized rate (per 100,000 population). ASDR, age-standardized DALYs rate; ASIR, age-standardized incidence rate; ASMR, age-standardized mortality rate; ASPR, age-standardized prevalence rate; DALYs, disability-adjusted life years; MDR-TB, multidrug-resistant tuberculosis; XDR-TB, extensively drug-resistant tuberculosis.

Further time-segmented trend analysis results showed that in recent years, the age-standardized incidence, prevalence, mortality, and DALY rates of MDR-TB in all three countries have shown a significant downward trend (APC <0, P<0.05) (Table 2). Regarding XDR-TB, in recent years, China’s age-standardized incidence, prevalence, mortality, and DALY rates have all shown a significant downward trend (APC <0, P<0.05). Russia’s age-standardized prevalence, mortality, and DALY rates for XDR-TB also showed a significant decrease. While India’s age-standardized prevalence, mortality, and DALY rates for XDR-TB showed no significant change or maintained an upward trend.

Core driving factors behind the changes in MDR-TB/XDR-TB incidence rates in China, India, and Russia

Decomposition analysis revealed that epidemiological changes were the main driving factor behind the decline in MDR-TB incidence in China, contributing 182.86% (Figure 2). It should be noted that a contribution exceeding 100% reflects that the positive epidemiological effect has more than offset the counteracting effects of other drivers (e.g., population growth and aging) that would otherwise increase the disease burden. The increase in MDR-TB incidence in India was primarily driven by two factors, including epidemiological changes (70.26%) and population growth (20.05%). In Russia, the core driving factor for the increase in MDR-TB incidence was epidemiological changes (96.82%), followed by population aging (4.37%). Regarding XDR-TB, epidemiological changes were the core driving factor behind the increasing incidence trends in China, India, and Russia.

Figure 2 Decomposition analysis of tuberculosis incidence rates in China, India, and Russia. MDR-TB, multidrug-resistant tuberculosis; XDR-TB, extensively drug-resistant tuberculosis.

Age-period-cohort analysis of MDR-TB/XDR-TB mortality rates in China, India, and Russia

Age-period-cohort analysis results showed that the net drift values of MDR-TB mortality in China, India, and Russia were all significantly less than zero (Figure 3). Specifically, China’s MDR-TB mortality rate decreased rapidly in the 2.5–7.5 years age group, then remained at a low level overall, with its rate ratio (RR) showing a decreasing trend year by year; India’s MDR-TB mortality rate increased rapidly after the 57.5–62.5 years age group, and both India and Russia showed a rise-then-fall trend in their MDR-TB mortality rate RRs.

Figure 3 Age-period-cohort analysis of tuberculosis mortality rates in China, India, and Russia. MDR-TB, multidrug-resistant tuberculosis; RR, rate ratio; XDR-TB, extensively drug-resistant tuberculosis.

Analysis of XDR-TB showed that China’s mortality net drift value was significantly less than zero [−5.63 (95% CI: −6.17, −5.07)], indicating an overall decline across age groups over time, while India’s and Russia’s mortality net drift values were both significantly greater than zero, indicating an overall increase across age groups. Further age stratification revealed that the local drift values of XDR-TB mortality in the 2.5–92.5 years age group in India were all significantly greater than zero, while Russia showed significantly greater than zero local drift values in the 32.5–92.5 years age group. Notably, the XDR-TB mortality rate was higher in the 1964–2019 birth cohort in India compared to those born before 1959.

Comparison of MDR-TB/XDR-TB DALYs across different genders and age groups in China, India, and Russia

Analysis of the disease burden pyramid charts by sex and age group showed that the burden of MDR-TB/XDR-TB was generally higher in men than in women in China, India, and Russia, and there were significant differences in the peak age of disease burden among the three countries (Figure 4). Specifically, the burden of MDR-TB/XDR-TB in Chinese men peaked in the 65–69 years age group, while in women it peaked in the 70–74 years age group. In India, the peak age of disease burden showed no gender difference, consistently concentrated in the 55–59 years age group. In Russia, the peak age of disease burden was even earlier, and also showed no gender difference, both occurring in the 45–49 years age group.

Figure 4 Comparative analysis of the burden of tuberculosis across different genders and age groups in China, India, and Russia. DALY, disability-adjusted life year; MDR-TB, multidrug-resistant tuberculosis; XDR-TB, extensively drug-resistant tuberculosis.

Predicted trends of MDR-TB/XDR-TB in China, India, and Russia (2024‒2050)

The prediction results based on the ARIMA model show that by 2050, the age-standardized incidence, prevalence, mortality, and DALY rates of MDR-TB/XDR-TB in China will all stabilize (Figure 5). In India, the age-standardized prevalence of MDR-TB is expected to continue to rise; the age-standardized prevalence, mortality, and DALY rates of XDR-TB will respectively climb to 1.36/100,000, 0.35/100,000, and 10.82/100,000. The age-standardized incidence and prevalence of XDR-TB in Russia are also expected to show a potential increase.

Figure 5 Time series prediction analysis of age-standardized incidence, prevalence, mortality, and burden of tuberculosis in China, India, and Russia from 2024 to 2050. ASDR, age-standardized DALYs rate; ASIR, age-standardized incidence rate; ASMR, age-standardized mortality rate; ASPR, age-standardized prevalence rate; DALYs, disability-adjusted life years; MDR-TB, multidrug-resistant tuberculosis; XDR-TB, extensively drug-resistant tuberculosis.

Discussion

The prevalence of MDR-TB/XDR-TB poses a serious challenge to global tuberculosis control and continues to threaten public health security (16). China, India, and Russia, as core countries with a high burden of drug-resistant tuberculosis, have a direct impact on the global control process. Based on the latest GBD 2023 data, the present study uses multi-dimensional statistical analysis to examine the epidemiological characteristics and driving mechanisms of drug-resistant tuberculosis in these three countries, thus providing a scientific basis for precise joint prevention and control efforts.

This study reveals a pattern of drug-resistant tuberculosis prevalence in the three countries that is consistent with previous understandings of high-burden regions (17,18), but further clarifies the core differences in dynamic trends. The burden of MDR-TB in China shows an overall downward trend, while India and Russia show an overall upward trend. Specifically, both India and Russia initially showed an upward trend, but have recently begun to decline, although the burden of XDR-TB in India remains high. This divergence essentially reflects the differences in the effectiveness of prevention and control policies and treatment systems (19,20). China’s “Trinity” tuberculosis prevention and control model and comprehensive early screening measures have effectively interrupted the transmission of drug-resistant strains (21,22), confirming the critical value of precise prevention and control. Moreover, the early increase in burden in India and Russia may be related to insufficient treatment coverage and improper drug use (23,24), while the recent common decline in MDR-TB reflects the effectiveness of the implementation of global prevention and control technologies and strategic consensus. The continued high incidence of XDR-TB in India suggests that there are still significant shortcomings in its second-line drug management and drug resistance monitoring (25).

Decomposition analysis of incidence rates reveals that epidemiological changes are the core factors driving the trends of drug-resistant tuberculosis epidemics in the three countries, essentially stemming from differences in the transmission efficiency of drug-resistant strains and the effectiveness of diagnosis and treatment interventions. China’s significant contribution to the decline in MDR-TB may due to the effective interruption of transmission chains by control measures (26,27). The combined effect of population growth and high infection rates in India has exacerbated the accumulation of drug-resistant cases. In India, the rapid scale-up of second-line anti-TB drug access under the National Tuberculosis Elimination Programme (NTEP) has outpaced the establishment of robust regulatory frameworks and effective antibiotic stewardship programs, particularly at peripheral health facilities. This policy-implementation gap likely facilitates the inappropriate use of fluoroquinolones and second-line injectable agents, thereby accelerating resistance amplification and contributing to the observed epidemiological changes (28,29). Notably, the rise in XDR-TB in China, India, and Russia is driven by epidemiological changes, suggesting that the risk of resistance escalation caused by the inappropriate use of second-line drugs cannot be ignored, and there is an urgent need to strengthen drug management and resistance monitoring (30). However, these epidemiological differences are also compounded by underlying socio-economic gradients, such as poverty levels, healthcare access, and urban density, which likely modulate the real-world effectiveness of diagnosis and treatment interventions beyond the demographic variables captured in our decomposition model.

Age-period-cohort analysis provides important evidence for identifying precise targets for disease control and prevention. In China, the risk of death from MDR-TB decreases rapidly in younger age groups and then remains low, while in India it increases year by year, reflecting the effectiveness of China’s control strategies against MDR-TB. The cross-country differences in XDR-TB are even more significant, with the risk of death increasing continuously with age in both India and Russia. India across all age groups and Russia in middle-aged and elderly populations represent high-risk groups. The high mortality rate in specific birth cohorts in India also reveals the long-term impact of weak historical control measures, suggesting the need for strengthened targeted interventions for key populations.

The differences in the gender and age distribution of the disease burden provide direction for optimizing resource allocation. The burden of MDR-TB/XDR-TB is generally heavier in men than in women across the three countries, which is closely related to men’s higher exposure risk and delayed health-seeking behavior (31,32). The cross-country differences in the age at which the disease burden peaks (latest in China, earliest in Russia) are related to differences in population structure, distribution of underlying diseases, and accessibility of diagnosis and treatment in each country. Therefore, screening and service priorities need to be precisely adjusted based on the age characteristics of each country.

The ARIMA model prediction results provide crucial reference for long-term prevention and control planning. The core indicators of MDR-TB/XDR-TB in China will tend to stabilize, confirming the sustainability of current prevention and control strategies. The prevalence of MDR-TB in India will continue to rise, and the burden of XDR-TB will further increase. Russia also faces a risk of increasing XDR-TB, suggesting that both countries urgently need to strengthen prevention and control measures. Nevertheless, these long-term projections carry inherent uncertainty, as major policy shifts, healthcare reforms, or unforeseen epidemiological disruptions could substantially alter the predicted trajectories up to 2050. Based on this, this study proposes targeted recommendations. China, India, and Russia need to optimize prevention and control strategies based on their respective core driving factors and population characteristics, and simultaneously establish a transnational joint prevention and control mechanism to curb the transnational spread of drug-resistant tuberculosis through sharing technical experience (33,34).

However, this study has some limitations. GBD data may have regional gaps and estimation biases. The analysis of driving factors did not include detailed variables such as socioeconomic factors and accessibility of medical resources. The ARIMA prediction also did not fully consider the impact of uncertain factors, namely public health emergencies. Future research can conduct refined regional difference analysis using local monitoring data from each country, construct multi-factor models to quantify the impact of policy interventions and socioeconomic factors, and deepen the exploration of pathogenesis in high-risk populations through cohort studies. Moreover, the present study was unable to directly validate the GBD-derived MDR-TB and XDR-TB estimates against official national surveillance data, as disaggregated national drug-resistant TB data are not publicly accessible due to restricted permissions and institutional reporting policies. This represents a key limitation, as discrepancies may exist between modeled GBD estimates and local registry figures due to differences in case definitions and diagnostic coverage. Future studies should seek formal data-sharing agreements or linkage mechanisms to enable direct cross-validation in high-burden settings.

Conclusions

The present study reveals the core differences in the epidemiology of drug-resistant tuberculosis in China, India, and Russia, and identifies the target populations and resource allocation directions for precise prevention and control. This not only provides scientific support for the development of differentiated prevention and control strategies in these three countries but also offers important insights for collaborative prevention and control of drug-resistant tuberculosis among high-burden countries globally.

Acknowledgments

The data used in this work are provided by the GBD 2023 database, for which we are thankful.

Footnote

Reporting Checklist: The authors have completed the GATHER reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-0761/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-0761/prf

Funding: The present study was funded by Innovation Project of Guangxi Graduate Education (JGY2023068), Guangxi Medical University Digital Textbook Construction Project (Gxmuszjc2515), Guangxi Medical University Special Project on Educational and Teaching Reform for Clinical Disciplines (2025LCJG02), Guangxi Medical University “Four New” Project (SX202403), Guangxi Zhuang Autonomous Region Health Commission Scientific Research Project (Z-A20240554), and Guangxi Medical University Innovation and Entrepreneurship Training Program for College Students (202510598002X).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-0761/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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