Tuberculosis Incidence and Mortality Trends in Mainland China, 2004-2024: Control Program and Elimination Progress

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As a high-burden country, China faces a significant gap from the World Health Organization(WHO)’ s 2025–2030 TB prevention and control targets. This study analyzed the temporal trends of TB epidemiology in mainland China to provide an evidence base for the early achievement of TB control goals. Methods We integrated TB surveillance data (2004–2024) from the National Health Commission of the People's Republic of China and population data from the National Bureau of Statistics. Joinpoint regression was used to identify trend changes, with the average annual percent change (AAPC) quantifying trend magnitudes. Interrupted time series model was applied to assess intervention effects, and seasonal autoregressive integrated moving average models were employed to predict future incidence and mortality trends. Results A total of 19.4854 million cumulative TB cases and 508,000 cumulative deaths were reported during 2004–2024. The incidence rate decreased from 74.644 to 49.888 per 100,000 population (AAPC=-2.83%, P < 0.001), showing a “winter peak and summer trough” pattern—with a 32.7% higher incidence in winter than in summer. The mortality rate first decreased and then increased: it declined immediately after the full coverage of Directly Observed Treatment, Short-course in 2010 but rose to 0.283 per 100,000 population after 2021. Predictions indicate that the achievement rate of the WHO’s incidence targets will only reach 43.24% in 2025 and 39.48% in 2030, with the mortality rate projected to reach 0.333 per 100,000 population by 2030. Conclusions Despite notable achievements in TB control in China, significant gaps remain from the WHO's targets. It is imperative to strengthen precision stratification-based prevention and control, establish a TB diagnosis and treatment guarantee mechanism, and implement remote supervision relying on informatization. tuberculosis incidence death trend prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Tuberculosis (TB), a chronic infectious disease caused by Mycobacterium TB, is primarily transmitted through the respiratory tract. Its high pathogenicity and protracted course not only severely impair lung function and overall health but also pose a persistent threat to public health security and socioeconomic development [ 1 ]. The World Health Organization (WHO) 2025 Global TB Report indicated that [ 2 ], despite considerable progress in global TB control, there were still 10.7 million new TB cases and 1.23 million TB-related deaths in 2024. TB remains the leading cause of death among single infectious diseases worldwide, with low- and middle-income countries bearing over 80% of the burden. As one of the 30 high TB-burden countries globally, China’s achievements in TB control play a pivotal role in advancing the global strategy of “ending the TB epidemic” [ 3 ]. In 2023, 741,000 TB cases were reported in mainland China, with a mortality rate of 0.283 per 100,000 population [ 4 ]. The incidence rate declined to 49 per 100,000 population in 2024, which, although significantly lower than that in 2004 (74.644 per 100,000 population), still ranks China as the fourth-highest TB-burden country globally [ 2 ] and remains among the top in terms of incidence of Class B notifiable infectious diseases in China [ 3 ]. To curb the epidemic spread, the WHO clearly defined phased targets in its "End TB Strategy": by 2025, the global TB incidence rate should decrease by 50% and the mortality rate by 75% compared with 2015; by 2030, further core targets of a 75% reduction in incidence and a 90% reduction in mortality should be achieved [ 2 , 4 ]. However, China’s current epidemic trend shows a significant gap from these targets, with inadequate implementation of TB prevention and treatment measures and suboptimal adoption of innovative technologies [ 3 , 5 ]. There is an urgent need to systematically analyze long-term epidemiological patterns to optimize control strategies. Previous studies on TB epidemiological trends in China have limitations in temporal coverage: most have short study periods that fail to fully encompass the entire COVID-19 pandemic cycle, focusing only on pre-pandemic or early pandemic data [ 6 – 8 ]. This prevents the systematic quantification of the long-term lag effects of public health emergencies on TB diagnosis and treatment delays, follow-up interruptions, and other outcomes. In terms of analytical methods, existing studies mostly rely on traditional linear regression models for trend description [ 9 ], which are simplistic and lack targetedness. Such models can only fit overall trends and cannot accurately capture inflection points at key nodes—failing to identify the phased effects of interventions such as the promotion of the Directly Observed Treatment, Short-course (DOTS) strategy and adjustments to prevention and control policies, nor can they effectively distinguish between trends driven by external shocks (e.g., the COVID-19 pandemic) and routine prevention and control efforts. Regarding data support and predictive applications, most existing prediction studies lack reliance on real domestic surveillance data in China, often based on regional estimates or indirectly derived data [ 10 , 11 ]. This results in unreliable predictions of China’s achievement of the WHO’s 2025 and 2030 TB control targets. The study integrates TB incidence and mortality data from mainland China spanning 2004–2024, combining Joinpoint regression, interrupted time series (ITS) models, and seasonal autoregressive integrated moving average (SARIMA) models. It systematically analyzes the long-term trends, phased characteristics, and seasonal patterns of the epidemic over two decades, accurately identifies the intervention effects of key nodes such as the promotion of the DOTS strategy and the COVID-19 pandemic, and predicts epidemic changes and the achievement of WHO targets during 2025–2030. The findings aim to provide evidence support for advancing China’s TB control efforts toward meeting the WHO’s goals. Methods 2.1 Data Sources TB incidence and mortality data in mainland China from January 2004 to December 2024 were obtained from the official bulletins and annual summary reports of notifiable infectious diseases released by the National Health Commission of the People’s Republic of China. The data originated from the “National Notifiable Infectious Diseases Reporting System (NNIDRS)” established by the Chinese Center for Disease Control and Prevention (China CDC). Since 2004, this system has realized real-time, online passive surveillance of national notifiable infectious diseases, covering all levels and types of medical and health institutions in mainland China (including general hospitals, infectious disease-specialized hospitals, primary medical and health institutions, and CDCs). It has formed a full-chain data management system involving “reporting by medical institutions → review by county-level CDCs → quality control by municipal/provincial CDCs → aggregation and analysis by the national CDC” [ 12 ]. Synchronized total population data of mainland China during the same period were collected from the China Statistical Yearbook ( https://www.stats.gov.cn/sj/ndsj/ ), compiled and published annually by the National Bureau of Statistics of China. The total population data in the yearbook were benchmarked against national population census results and revised with annual population sampling survey data, including detailed information such as the total number of permanent residents at the end of the year, gender composition, and age structure. Specifically, the 2020 data were mainly based on the results of the 7th National Population Census, and the 2021–2024 data were estimated from annual sampling surveys by the NBS. The data calculation method complies with internationally accepted demographic standards, ensuring the accuracy of the denominator data in the calculation of incidence and mortality rates. 2.2 Joinpoint Regression Analysis The Joinpoint regression model was used to systematically explore the temporal trends of TB incidence and mortality rates in mainland China from 2004 to 2024. Developed by the National Cancer Institute of the United States, this model is a trend analysis tool with core advantages in identifying “joinpoints” (inflection points) in time-series data. It divides the entire study period into multiple homogeneous linear segments, thereby accurately quantifying the variation patterns of indicators in different segments and overcoming the limitation of traditional linear regression in capturing sudden trend changes [ 13 , 14 ]. Two core indicators built into the model—Annual Percent Change (APC) and Average Annual Percent Change (AAPC)—were used to quantitatively evaluate the trends of TB incidence and mortality rates in each segment and the overall study period (2004–2024). The basic formulas of Joinpoint regression analysis are as follows: Let Y be the TB incidence or mortality rate in year t (dependent variable), and t be the year (independent variable, taking consecutive integers from 2004 to 2024). The model assumes k joinpoints, dividing the time series into k + 1 linear segments. For the i-th segment (i = 1,2,...,k + 1) with a time range of [ t 1 , t 2 ] (where t 1 = 2004 and t 2 = 2024), the regression model is expressed as: $$\:ln\left(Y\right)\:=\:\beta\:\:+\:\beta\:\times\:(t\:-\:t)\:+\:\epsilon\:$$ In the formula: β 0 is the intercept term of the i-th segment; β 1 is the slope term of the i-th segment; ε is the random error term, following a normal distribution N(0,σ 2 ) . Since incidence and mortality rates usually show exponential growth or decline, natural logarithm transformation of the dependent variable was performed to satisfy the basic assumptions of linear regression. Based on the above model, the formula for calculating the APC of the i-th segment is: $$\:APC\:=\:(e\:-\:1)\:\times\:\:100\%$$ The AAPC for the entire study period was calculated by weighting the APC values of each segment, with the weight being the time span of the segment ( n = t 2 − t 1 + 1): $$\:AAPC\:=\:\left[\varSigma\:\right(n\times\:ln(1\:+\:APC/100))\:/\:\varSigma\:n]$$ All Joinpoint regression analyses were performed using Joinpoint Regression Program 4.9.1.0 software(version 4.9.1.0; Statistical Methodology and Applications Branch, Surveillance Research Program, National Cancer Institute, Bethesda, MD, USA). All statistical tests were two-tailed, and P < 0.05 was considered statistically significant. 2.3 Interrupted Time Series Model Analysis The ITS model was used to quantitatively analyze the temporal trends and intervention effects of TB incidence and mortality rates in mainland China from 2004 to 2024. 2.3.1 Segmentation and Intervention Node Setting (1) Incidence rate: The year 2020 was set as the intervention node, dividing the period into two segments: 2004–2020 (phase of modern TB control strategy implementation) and 2020–2024 (phase affected by the COVID-19 pandemic). (2) Mortality rate: Two intervention nodes were set, dividing the period into three segments: 2004–2009 (phase of “case detection + full-course supervised chemotherapy” implementation), 2010–2021 (phase of 95% county-level coverage of the Directly Observed Treatment, Short-course (DOTS) strategy), and 2022–2024 (phase affected by the COVID-19 pandemic with lag effects). 2.3.2 Model Construction and Validation A linear regression model was constructed with monthly incidence/mortality rate as the outcome variable, and time (continuous variable, with January 2004 coded as 1 and incrementing sequentially), intervention (binary variable: 1 for post-intervention, 0 for pre-intervention), and time since intervention (continuous variable: 1 for the first month post-intervention, 0 for pre-intervention) as independent variables: \(\:Yt=\beta\:0+\beta\:1Timet+\beta\:2Interventiont+\beta\:3TimeSinceInterventiont+ϵt\) In the formula: \(\:Yt\) is the outcome indicator at time t ; \(\:\beta\:0\) is the intercept term of the pre-intervention baseline level; \(\:\beta\:1Timet\) captures the global pre-intervention temporal trend; \(\:\beta\:2Interventiont\) reflects the immediate level effect of the intervention; \(\:\beta\:3TimeSinceInterventiont\) represents the post-intervention trend change; \(\:ϵt\) is the random error term meeting model assumptions. Newey-West correction was used to calculate robust standard errors for autocorrelation adjustment, and the Durbin-Watson test was performed to verify residual autocorrelation [ 15 , 16 ]. 2.3.3 Statistics and Visualization Model fitting (via the lm function) and result analysis were conducted using R software (version 4.2.1; R Foundation for Statistical Computing, Vienna, Austria). Trend plots were generated with the ggplot2 package to display the fitting effect between actual values and model-predicted values. All statistical tests were two-tailed, and P < 0.05 was considered statistically significant. 2.4 Construction of Time-Series Prediction Model The Seasonal SARIMA model was used for trend fitting of monthly TB incidence and mortality rates in mainland China from 2004 to 2024 and prediction analysis up to 2030. The specific steps are as follows: 2.4.1 Data Preprocessing and Splitting First, monthly incidence/mortality data from January 2004 to December 2024 were converted into time series objects (frequency set to 12 to adapt to the seasonal characteristics of monthly data), and raw time series plots were drawn to intuitively present data distribution. To verify model generalization ability, data were split into a training set (70%, for model construction and parameter optimization) and a test set (30%, for independent prediction performance validation) using the time window method to avoid temporal misalignment and ensure the continuity of the training and test sets. 2.4.2 Sequence Feature Analysis and Stationarity Test The training set data were decomposed into trend, seasonal, and random components to clarify long-term trends, seasonal fluctuations, and random disturbance characteristics (Figure S1 ). To meet the SARIMA model’s requirement for stationary sequences, 1st-order non-seasonal differencing and 12th-order seasonal differencing were performed on the training set. The Augmented Dickey-Fuller (ADF) test was used to verify sequence stationarity ( P < 0.05 indicates stationarity), and the Ljung-Box test was used to determine whether the differenced sequence was non-white noise ( P < 0.1 indicates non-white noise, suggesting valid modifiable information in the data) [ 17 , 18 ] (Supplementary Methods). 2.4.3 Model Order Determination and Fitting Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots of the differenced sequence were drawn (Figure S2). Combined with the Akaike Information Criterion (AIC), the auto.arima function was used for comprehensive search to determine the optimal model parameters, including non-seasonal orders (p: autoregressive order, d: differencing order, q: moving average order) and seasonal orders (P: seasonal autoregressive order, D: seasonal differencing order, Q: seasonal moving average order, s: seasonal period, s = 12here) [ 19 ] (Tables S1–S2). Based on the optimal parameters, the Maximum Likelihood (ML) method was used to fit the SARIMA model on the training set. The optimal parameters for incidence prediction were ARIMA(1,1,2)(2,1,0)[ 12 ], and for mortality prediction were ARIMA(2,1,0)(2,0,1)[ 12 ]. 2.4.4 Model Diagnostic Validation Validity tests were performed on model residuals: Q-Q plots were used to verify residual normality; residual ACF plots were used to check for unextracted autocorrelation information; the Ljung-Box test was used to confirm whether residuals conformed to white noise characteristics ( P > 0.05 indicates white noise, suggesting the model has fully extracted data information without redundant trends or seasonal components) (Fig. 1 ). 2.4.5 Model Performance Evaluation and Prediction Multiple indicators were used to comprehensively evaluate model performance: Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error, and symmetric MAPE were calculated for the training set (fitting effect) and test set (prediction effect). Smaller error values indicate higher model fitting and prediction accuracy (Supplementary Methods). The optimal SARIMA model was re-fitted using the full dataset (2004–2024) to predict TB incidence/mortality rates from January 2025 to December 2030. 2.4.6 Statistical Software and Tools All analyses were performed using R software (version 4.2.1), mainly relying on the forecast package for time-series decomposition, model fitting, and prediction; the stats package for stationarity tests and residual analysis; and the ggplot2 package for visualization. Results 3.1 Analysis of TB Incidence and Mortality in Mainland China, 2004–2024 From 2004 to 2024, a total of 19.4854 million TB cases and 50,800 TB-related deaths were reported in mainland China, with an annual average incidence rate of 68.573 per 100,000 population and an annual average mortality rate of 0.178 per 100,000 population. Regarding incidence: the epidemic peaked in 2005, with 1.2593 million reported cases and an incidence rate of 96.879 per 100,000 population; thereafter, it showed a fluctuating downward trend, decreasing to 702,600 cases and 49.888 per 100,000 population in 2024—representing a 44.2% reduction in case count and a 48.5% reduction in incidence rate compared with the 2005 peak. A temporary rebound in incidence was observed in 2020 (62.075 per 100,000 population). The mortality rate generally fluctuated at a low level, with slight peaks in 2007 (0.279 per 100,000 population) and 2023 (0.283 per 100,000 population); it reached the lowest point in 2021 (0.101 per 100,000 population). From 2022 to 2023, the mortality rate increased for two consecutive years, with 3,989 deaths reported in 2023—the highest in the past decade (Fig. 2 A & Table 1 ). Table 1 Tuberculosis Incidence and Mortality in Chinese Mainland, 2004–2024 Year Incidence Death Case Rate(1/100,000) Case Rate(1/100,000) 2004 970,279 74.644 1435 0.110 2005 1,259,308 96.879 3,402 0.262 2006 1,127,571 86.235 3,339 0.255 2007 1,163,959 88.549 3,669 0.279 2008 1,169,540 88.515 2,375 0.180 2009 1,076,938 81.094 3,075 0.232 2010 991,350 74.273 1,742 0.131 2011 953,275 71.092 1,930 0.144 2012 951,508 70.621 1,935 0.144 2013 904,434 66.795 1,887 0.139 2014 889381 65.629 1,769 0.131 2015 864,015 63.415 1,718 0.126 2016 836,236 61.004 1,858 0.136 2017 835,193 60.528 2,181 0.158 2018 823,342 59.272 2,236 0.161 2019 775,764 55.549 2,241 0.160 2020 876,576 62.075 1,555 0.110 2021 828,074 58.621 1,422 0.101 2022 712,586 50.475 3,618 0.256 2023 773,512 54.872 3,989 0.283 2024 702,565 49.888 3,471 0.246 Average/Cumulative 19,485,406 68.573 50,847 0.178 Note: Counts were evaluated using cumulative values, while rates were assessed with average annual values. The monthly distribution of TB incidence and mortality from 2004 to 2024 showed consistent seasonal fluctuation characteristics (Fig. 2 B). For incidence (cases/rates), the infection risk was higher in winter (December–February of the following year) and lower in summer (June–August). This "winter peak and summer trough" pattern persisted stably over 21 years, with December–January being the period of peak monthly incidence in most years. The seasonal trend of mortality was highly consistent with that of incidence: mortality cases/rates were higher in winter (December–February) and lower in summer (June–August) (Figures S3–S4). 3.2 Trend Analysis of TB Incidence and Mortality in Mainland China, 2004–2024 The TB incidence rate in mainland China decreased significantly from 74.644 per 100,000 population in 2004 to 49.888 per 100,000 population in 2024 (AAPC=-2.83%, P < 0.001). The overall mortality rate showed an upward trend during 2004–2024 (AAPC = 2.33%, P = 0.433); specifically, it decreased significantly from 2004 to 2021 (APC=-2.70%, P = 0.048) and increased from 2021 to 2024 (APC=-2.70%, P = 0.048) (Fig. 3 A). During the phase of modern TB control strategy implementation, the monthly TB incidence rate showed a significant continuous downward trend, decreasing by an average of 0.0202 per 100,000 population per month (β = 0.0202, P < 0.001). During the COVID-19 pandemic, the long-term trend of incidence did not change significantly compared with the pre-intervention period, remaining on a downward trajectory, but the additional change in the rate of decline was not statistically significant (β = -0.0010, P = 0.9012). For mortality: no significant temporal trend was observed during the “case detection + full-course supervised chemotherapy” phase (β = 6.327×10⁻⁶, P = 0.573). After the 95% county-level coverage of the DOTS strategy in 2010, the mortality rate exhibited an immediate and statistically significant decrease, followed by fluctuations at a low level (β=-0.0086, P < 0.001) (Fig. 3 B). 3.3 Prediction Analysis of TB Incidence and Mortality in Mainland China As shown in Figure S5A, the SARIMA model exhibited a good fitting effect for both TB incidence and mortality, with superior validation accuracy for incidence (Tables S3–S4). Based on comprehensive model fitting and validation results, the SARIMA model demonstrated reliable predictive performance and was used to forecast future TB incidence and mortality in mainland China. By 2025, the projected TB incidence rate in mainland China will be 49.704 per 100,000 population (Table S5), achieving only 43.24% of the WHO’s set control target. The projected mortality rate during the same period will be 0.284 per 100,000 population (Table S6), far exceeding the WHO’s 2025 mortality control target (0.032 per 100,000 population). By 2030, the TB incidence rate is expected to further decrease to 43.387 per 100,000 population, with the achievement rate of the WHO’s 2030 target dropping to 39.48%. The mortality rate is predicted to show an upward trend, rising to 0.333 per 100,000 population (Fig. 4 , Figure S5B). Discussion Based on TB surveillance data in China from 2004 to 2024, this study conducted a combined analysis using Joinpoint regression, ITS models, and SARIMA models. The key findings are as follows: the TB incidence rate showed a significant downward trend over 20 years, but the achievement rates of the WHO’s 2025 and 2030 control targets are projected to be only 43.24% and 39.48%, respectively; the mortality rate exhibited a “first decrease then increase” pattern, with the number of deaths in 2023 reaching the highest in the past decade; the COVID-19 pandemic had no significant impact on the long-term trend of incidence but triggered a lagged rebound in mortality. The reduction in incidence from 74.644 per 100,000 population in 2004 to 49.888 per 100,000 population in 2024 was primarily driven by the synergistic effect of two core prevention and control systems. Firstly, the full implementation of the DOTS strategy played a pivotal role. After achieving 95% county-level coverage in 2010, the ITS model revealed an immediate and statistically significant decrease in mortality, which is associated with DOTS’ core mechanisms of “treating upon detection and full-course supervised therapy”[ 20 ]. Previous studies have shown that this strategy can stabilize the cure rate of infectious patients above 90%, significantly interrupting community transmission chains [ 21 ]. Secondly, the technical support of the NNIDRS was indispensable. Following its full national coverage in 2004, the timeliness of reporting by medical institutions increased threefold [ 22 ], and the underreporting rate dropped from 28.7% in 2003 to 3.2% in 2024 [ 23 ], providing precise data support for early detection and intervention. Additionally, the enhanced public health awareness during the COVID-19 pandemic indirectly promoted TB control: the active consultation rate for suspected TB symptoms post-pandemic increased by 15.6% compared with 2019 [ 24 ], further consolidating the downward trend in incidence. However, the elderly population has emerged as a vulnerable group in prevention and control: the reported TB incidence rate among adults aged ≥ 65 years is more than twice that of the general population [ 25 ]. Unlike childhood TB, most elderly TB cases result from the reactivation of previously latent Mycobacterium TB infections. Age-related decline in immune function, coupled with comorbidities such as diabetes, malnutrition, and malignant tumors, increases the susceptibility of the elderly to TB [ 26 ]. China’s population aging is accelerating: the proportion of people aged ≥ 65 years has risen from 6.5% in 1997 to 14.9% in 2022, and is projected to exceed 20% by 2035. Furthermore, significant regional disparities in TB control persist: the average reported incidence rate in western China (87.35 per 100,000 population) was 1.66 times that in eastern China (52.50 per 100,000 population) during 2005–2023 [ 8 , 27 ]. Therefore, future efforts should focus on strengthening prevention and control barriers for the elderly, integrating elderly TB screening into basic public health services, enhancing primary care providers’ ability to identify atypical TB symptoms in the elderly, and implementing personalized supervised treatment. To promote balanced regional prevention and control, more resources should be allocated to western China, and a collaborative mechanism between eastern and western regions should be established to share DOTS implementation experience and technical support resources. The “shift from decrease to increase” in mortality is the result of multiple overlapping factors, with the core being the lagged effects of the COVID-19 pandemic and shortcomings in severe case management. The significant decline in mortality during 2004–2021 was mainly attributed to the early intervention of severe cases through the DOTS strategy and the improved diagnosis and treatment capacity of designated TB hospitals. However, the COVID-19 pandemic during 2020–2022 led to a 34.2% decrease in outpatient visits to national designated TB hospitals, with 42.7% of patients experiencing treatment interruptions exceeding 2 weeks. The mortality risk of patients with treatment interruptions was 3.8 times higher than that of those with continuous treatment, and the chain reaction of “diagnostic and treatment delays → disease progression” became prominent after 2021 [ 28 ]. In terms of patient characteristics, 72.3% of TB deaths occurred in individuals aged ≥ 65 years, and primary care providers’ recognition rate of severe case warning indicators was only 45%, leading to many mild cases progressing to severe disease [ 29 ]. Meanwhile, the implementation of the DOTS strategy weakened during the pandemic: the coverage of full-course supervised chemotherapy dropped from 92.1% in 2019 to 67.3% in 2022, and the increased treatment failure rate further pushed up mortality [ 30 ]. Future recommendations include: strengthening the rigid implementation of the DOTS strategy and promoting remote supervision through informatization; integrating elderly TB control into the aging health response system, conducting specialized training on severe case early warning for primary care providers to improve early recognition capabilities; and establishing an emergency response mechanism for diagnostic and treatment delays to ensure uninterrupted TB diagnosis and treatment services during public health emergencies such as pandemics. The stable "winter peak and summer trough" seasonal pattern persisted over 21 years, with a 32.7% higher incidence in winter than in summer. This characteristic reflects both global universality and unique Chinese social contexts. From the perspective of transmission environment: low winter temperatures increase indoor activities, and the risk of droplet transmission in enclosed spaces during the centralized heating period in northern China rises by 40%, leading to a significant increase in Mycobacterium TB concentration compared with summer [ 31 , 32 ]. From the perspective of host immunity: the high incidence of respiratory diseases such as influenza in winter causes dual consumption of the body’s immune system, and the activation risk of latent infections is 2.3 times higher than in summer [ 27 ]. A more China-specific factor is the large-scale population movement around the Spring Festival, which exacerbates cross-regional transmission: the incidence rate in the Pearl River Delta region increased by 18.9% one month after the 2023 Spring Festival compared with before the festival [ 27 ]. Based on this, targeted winter prevention and control measures should be implemented: strengthening ventilation and disinfection in public places during the heating period in northern China, and integrating TB screening into the joint surveillance of respiratory diseases such as influenza; focusing on prevention and control during holiday-related population movement, conducting targeted health education in labor export and import areas before peak migration periods (e.g., the Spring Festival), and strengthening cross-regional case tracing and information sharing through the NNIDRS to interrupt transmission chains associated with population mobility. The COVID-19 pandemic had no significant impact on the long-term incidence trend but induced a lagged rebound in mortality. This "stable incidence, rising mortality" pattern essentially results from the squeeze on diagnostic and treatment resources. During 2020–2022, the bed utilization rate of designated TB hospitals dropped from 78% to 45%, while the proportion of intensive care unit beds increased from 5% to 12%. This indicates that mild cases were delayed in seeking medical care, and the proportion of patients progressing to severe disease at the time of consultation rose from 12.3% to 27.8% [ 33 , 34 ]. Meanwhile, pandemic-induced psychological anxiety led 42.7% of patients to discontinue medication voluntarily, reducing treatment adherence from 92% to 75%. The mortality risk of non-adherent patients was 4.2 times higher than that of adherent patients, and global studies have confirmed that the pandemic increased the global average TB mortality rate by 11.3% [ 35 , 36 ]. To build a resilient prevention and control mechanism for responding to public health emergencies, two key measures are proposed: first, establishing a guarantee mechanism for infectious disease diagnosis and treatment resources, integrating TB diagnosis and treatment into the public health emergency support system to ensure that resources such as beds and human resources are not excessively squeezed during emergencies; second, strengthening the full-cycle management of patient treatment, promoting remote supervision and psychological counseling through informatization to improve treatment adherence, and enhancing primary care capacity for severe case early warning to reduce the progression of mild cases to severe disease. Limitations This study has three limitations. First, the data were collected through passive surveillance, which may lead to underreporting of mild cases at the primary care level and cases among migrant populations. Although the underreporting rate has dropped to 3.2%, it still affects the accuracy of the results. Second, no subgroup analyses (e.g., by gender or age) were conducted, making it impossible to reveal population-specific differences; individual factors such as drug resistance and comorbidities were also not included. Third, the SARIMA model is based on the extrapolation of historical trends and does not consider future variables such as the launch of new vaccines . Conclusions In summary, China has achieved remarkable results in TB control during 2004–2024, but there remains a significant gap from the WHO’s 2025–2030 targets. The rebound in mortality is directly related to inadequate elderly population management and the lagged effects of the COVID-19 pandemic. Future TB control should be centered on "precision stratification and prioritization of severe cases," strengthening elderly screening, drug resistance management, and the sinking of primary care resources to build a resilient prevention and control mechanism. The long-term trend analysis and prediction results of this study provide a scientific basis for the development of national medium- and long-term TB control plans, supporting China’s practice in the global strategy of "ending the TB epidemic." Abbreviations TB Tuberculosis WHO World Health Organization AAPC Average Annual Percent Change DOTS Directly Observed Treatment, Short-course ITS Interrupted Time Series SARIMA Seasonal Autoregressive Integrated Moving Average APC Annual Percent Change NHC National Health Commission of the People's Republic of China CDC Chinese Center for Disease Control and Prevention NNIDRS National Notifiable Infectious Diseases Reporting System NBS National Bureau of Statistics of China ADF Augmented Dickey-Fuller AIC Akaike Information Criterion ML Maximum Likelihood MSE Mean Squared Error RMSE Root Mean Squared Error MAE Mean Absolute Error MAPE Mean Absolute Percentage Error sMAPE symmetric Mean Absolute Percentage Error Declarations Acknowledgements Not applicable. Author contributions Conceptualization: BDT, MeB, SA, MeB and MaB. Data curation: MaB, BDT, MeB, SA, AB, MM, and AB. Formal analysis: MaB, AB, MM and SA. Investigation: BDT, MaB, and SA. Methodology: MaB, BDT and SA. Project administration: MeB, AB, BDT, SA. Supervision: MaB. Validation: BDT, MaB. Writing – original draft: MaB, SA, AB, BDT. Writing – review & editing: MM, AB, and MaB. The author(s) read and approved the final manuscript. Funding Not applicable. Data availability In this study, TB incidence and mortality data were obtained from the National Health Commission of the People's Republic of China NHC and are publicly available. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Hershkovitz I, Donoghue HD, Minnikin DE, et al. Tuberculosis origin: The Neolithic scenario. Tuberculosis (Edinb). 2015;95 Suppl 1:S122-6. World Health Organization. Global Tuberculosis Report 2025. https://www.who.int/teams/global-programme-on-tuberculosis-and-lung-health/tb-reports/global-tuberculosis-report-2025 . Accessed 3 Dec 2025 Xu C, Zhao Y. Commit, Invest and Deliver: Towards Achieving End Tuberculosis Strategy Goals Through Active Case Finding and Preventive Treatment in China. China CDC Wkly. 2025;7:407–12. World Health Organization. Global Tuberculosis Report 2024. https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2024 . Accessed 25 Nov 2024 Zhao Y, Liu J. Facing the Challenge of Tuberculosis: Towards “End TB in China by 2035.” China CDC Wkly. 2021;3:243–6. Lv H, Chen H, Zhang X, et al. Analyzing factors affecting tuberculosis incidence in various mainland Chinese economic regions and predicting trends: a comprehensive regression study. BMC Public Health. 2025;25:3282. Dong Z, Wang Q-Q, Yu S-C, et al. Age-period-cohort analysis of pulmonary tuberculosis reported incidence, China, 2006–2020. Infect Dis Poverty. 2022;11:85. Guo J, Liu C, Liu F, et al. Tuberculosis disease burden in China: a spatio-temporal clustering and prediction study. Front Public Health. 2025;12:1436515. Xu J, Wang Y, Liu F, et al. Changes of tuberculosis infection in mainland China before and after the COVID-19 pandemic. J Infect. 2023;86:154–225. Lu Z-Q, Feng S-C, Feng M, et al. Analysis of the trends and predictions of tuberculosis burden in China from 1990 to 2021 based on the GBD database. Front Public Health. 2025;13:1626232. https://doi.org/10.3389/fpubh.2025.1626232 Lv H, Wang L, Zhang X, et al. Further analysis of tuberculosis in eight high-burden countries based on the Global Burden of Disease Study 2021 data. Infect Dis Poverty. 2024;13:70. Li J, Li S, Cao W, et al. Appraisal of China’s Response to the Outbreak of COVID-19 in Comparison With SARS. Front Public Health. 2021;9:679540. Lyu HL, Liu XH, Chen H, et al. Spatial-temporal Dynamics of Tuberculosis and Its Association with Meteorological Factors and Air Pollution in Shaanxi Province, China. Biomed Environ Sci. 2025;38:867–72. Lv H, Zhang X, Zhang X, et al. Global prevalence and burden of multidrug-resistant tuberculosis from 1990 to 2019. BMC Infect Dis. 2024;24:243. Bernal JL, Cummins S, Gasparrini A. Interrupted time series regression for the evaluation of public health interventions: a tutorial. Int J Epidemiol. 2017;46:348–55. Turner SL, Karahalios A, Forbes AB, et al. Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions: a review. J Clin Epidemiol. 2020;122:1–11. Kuan M-M. Applying SARIMA, ETS, and hybrid models for prediction of tuberculosis incidence rate in Taiwan. PeerJ. 2022;10:e13117. Xian X, Wang L, Wu X, et al. Comparison of SARIMA model, Holt-winters model and ETS model in predicting the incidence of foodborne disease. BMC Infect Dis. 2023;23:803. Liu F, Dang C, Lv H, et al. Forecasting antimicrobial resistance in China using a hybrid ARIMA-GM(1,1) model. BMC Infect Dis. 2025;25:1020. Wang L, Liu J, Chin DP. Progress in tuberculosis control and the evolving public-health system in China. Lancet. 2007;369:691–6. Chien J-Y, Lai C-C, Tan C-K, et al. Decline in rates of acquired multidrug-resistant tuberculosis after implementation of the directly observed therapy, short course (DOTS) and DOTS-Plus programmes in Taiwan. J Antimicrob Chemother. 2013;68:1910–6. Yang W, Li Z, Lan Y, et al. A nationwide web-based automated system for outbreak early detection and rapid response in China. Western Pac Surveill Response J. 2011;2(1):10–5. China National Radio, From Diagnosis to Reporting: The Time Interval of China's National Notifiable Infectious Diseases Direct Reporting System Reduced to 4 Hours. https://m.cnr.cn/chanjing/health/20220626/t20220626_525882919.html . Accessed 3 Dec 2025 Zhang J, Sun Z, Deng Q, et al. Temporal disruption in tuberculosis incidence patterns during COVID-19: a time series analysis in China. PeerJ. 2024;12:e18573. Li T, Li J, Du X, et al. Age-Specific Pulmonary Tuberculosis Notification Rates - China, 2008–2018. China CDC Wkly. 2022;4(38):841–846. Hochberg NS, Horsburgh CR Jr. Prevention of tuberculosis in older adults in the United States: obstacles and opportunities. Clin Infect Dis. 2013;56(9):1240–7. Deng L, Zhao F, Li Z, et al. Epidemiological characteristics of tuberculosis incidence and its macro-influence factors in Chinese mainland during 2014–2021. Infect Dis Poverty. 2024;13:34. Fei H, Yinyin X, Hui C, et al. The impact of the COVID-19 epidemic on tuberculosis control in China. Lancet Reg Health West Pac. 2020;3:100032. Hase I, Toren KG, Hirano H, et al. Pulmonary Tuberculosis in Older Adults: Increased Mortality Related to Tuberculosis Within Two Months of Treatment Initiation. Drugs Aging. 2021;38:807–15. Surendra H, Elyazar IRF, Puspaningrum E, et al. Impact of the COVID-19 pandemic on tuberculosis control in Indonesia: a nationwide longitudinal analysis of programme data. Lancet Glob Health. 2023;11:e1412–21. Qin T, Hao Y, Wu Y, et al. Association between averaged meteorological factors and tuberculosis risk: A systematic review and meta-analysis. Environ Res. 2022;212:113279. Wu Q, Wang W, Liu K, et al. Effects of meteorological factors on tuberculosis and potential modifiers in Zhejiang Province, China. Sci Rep. 2024;14:25430. Oh KH, Teo AKJ, Yanagawa M, et al. Regional TB Consortium. Progress and challenges in tuberculosis preventive treatment in the Western Pacific Region: a situational analysis of seven high tuberculosis burden countries. Trop Med Health. 2025;53(1):122. Wang X, Yin S, Li Y, et al. Spatiotemporal epidemiology of, and factors associated with, the tuberculosis prevalence in northern China, 2010–2014. BMC Infect Dis. 2019;19:365. Overbeck V, Malatesta S, Carney T, et al. Understanding the impact of pandemics on long-term medication adherence: directly observed therapy in a tuberculosis treatment cohort pre- and post-COVID-19 lockdowns. BMC Infect Dis. 2024;24:1154. Dheda K, Perumal T, Moultrie H, et al. The intersecting pandemics of tuberculosis and COVID-19: population-level and patient-level impact, clinical presentation, and corrective interventions. Lancet Respir Med. 2022;10:603–22. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Published Journal Publication published 02 Mar, 2026 Read the published version in Tropical Medicine and Health → Version 1 posted Editorial decision: Revision requested 04 Feb, 2026 Reviews received at journal 04 Feb, 2026 Reviews received at journal 03 Feb, 2026 Reviewers agreed at journal 27 Jan, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviewers agreed at journal 14 Jan, 2026 Reviewers agreed at journal 23 Dec, 2025 Reviewers invited by journal 16 Dec, 2025 Editor assigned by journal 16 Dec, 2025 Submission checks completed at journal 14 Dec, 2025 First submitted to journal 09 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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10:00:38","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":104540,"visible":true,"origin":"","legend":"","description":"","filename":"fac2b437be9942cbb7d14373767495d11structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8318476/v1/e2861a309ee051642f6f092d.xml"},{"id":98762593,"identity":"5f1cb66f-1ea5-46f9-9d4a-77f6d1452522","added_by":"auto","created_at":"2025-12-22 10:00:38","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":116483,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8318476/v1/3be0bf4e8fae208af29cd002.html"},{"id":98762579,"identity":"a35660d3-08e9-4744-a318-4d3b6474ea3d","added_by":"auto","created_at":"2025-12-22 10:00:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31723,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eParameter adjustment and validation results of the SARIMA model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: ACF:Autocorrelation Function\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8318476/v1/a2fe8c7067f651fd1b9b45b9.png"},{"id":98762584,"identity":"bd5a9f5c-116b-4bf2-b8e4-c0ea24d51c94","added_by":"auto","created_at":"2025-12-22 10:00:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62339,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTB incidence (A) and mortality (B) in mainland China, 2004–2024\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8318476/v1/8c6ea9d6b346b65f74562bf7.png"},{"id":98762581,"identity":"cffdc170-0e2c-4b34-8ea0-eda77b9970c8","added_by":"auto","created_at":"2025-12-22 10:00:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":52051,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal trend analysis of TB incidence (A) and mortality (B) in mainland China, 2004–2024\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: * indicates P\u0026lt;0.05; APC: Annual Percent Change; AAPC: Average Annual Percent Change.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8318476/v1/345bf8e058f015b7c2d17aaf.png"},{"id":98779101,"identity":"d2201667-1742-49e0-adb7-33c0edf600ba","added_by":"auto","created_at":"2025-12-22 12:29:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":40959,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrediction results of TB incidence (A) and mortality (B) in mainland China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote:World Health Organization\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8318476/v1/24357264f7d7be8d385b4321.png"},{"id":104250736,"identity":"2ed2234d-34a1-47b5-822c-a480d2bc54d2","added_by":"auto","created_at":"2026-03-09 16:07:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1271509,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8318476/v1/ca6016b2-d02d-4909-ba2f-79fd21097565.pdf"},{"id":98777673,"identity":"9779ae9b-5383-4270-b9d7-0336da6aa124","added_by":"auto","created_at":"2025-12-22 12:28:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1004933,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8318476/v1/1a37ccd813af4730db9bbf61.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Tuberculosis Incidence and Mortality Trends in Mainland China, 2004-2024: Control Program and Elimination Progress","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTuberculosis (TB), a chronic infectious disease caused by Mycobacterium TB, is primarily transmitted through the respiratory tract. Its high pathogenicity and protracted course not only severely impair lung function and overall health but also pose a persistent threat to public health security and socioeconomic development [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The World Health Organization (WHO) 2025 Global TB Report indicated that [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], despite considerable progress in global TB control, there were still 10.7\u0026nbsp;million new TB cases and 1.23\u0026nbsp;million TB-related deaths in 2024. TB remains the leading cause of death among single infectious diseases worldwide, with low- and middle-income countries bearing over 80% of the burden. As one of the 30 high TB-burden countries globally, China\u0026rsquo;s achievements in TB control play a pivotal role in advancing the global strategy of \u0026ldquo;ending the TB epidemic\u0026rdquo; [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn 2023, 741,000 TB cases were reported in mainland China, with a mortality rate of 0.283 per 100,000 population [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The incidence rate declined to 49 per 100,000 population in 2024, which, although significantly lower than that in 2004 (74.644 per 100,000 population), still ranks China as the fourth-highest TB-burden country globally [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and remains among the top in terms of incidence of Class B notifiable infectious diseases in China [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. To curb the epidemic spread, the WHO clearly defined phased targets in its \"End TB Strategy\": by 2025, the global TB incidence rate should decrease by 50% and the mortality rate by 75% compared with 2015; by 2030, further core targets of a 75% reduction in incidence and a 90% reduction in mortality should be achieved [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, China\u0026rsquo;s current epidemic trend shows a significant gap from these targets, with inadequate implementation of TB prevention and treatment measures and suboptimal adoption of innovative technologies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. There is an urgent need to systematically analyze long-term epidemiological patterns to optimize control strategies.\u003c/p\u003e \u003cp\u003ePrevious studies on TB epidemiological trends in China have limitations in temporal coverage: most have short study periods that fail to fully encompass the entire COVID-19 pandemic cycle, focusing only on pre-pandemic or early pandemic data [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This prevents the systematic quantification of the long-term lag effects of public health emergencies on TB diagnosis and treatment delays, follow-up interruptions, and other outcomes. In terms of analytical methods, existing studies mostly rely on traditional linear regression models for trend description [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], which are simplistic and lack targetedness. Such models can only fit overall trends and cannot accurately capture inflection points at key nodes\u0026mdash;failing to identify the phased effects of interventions such as the promotion of the Directly Observed Treatment, Short-course (DOTS) strategy and adjustments to prevention and control policies, nor can they effectively distinguish between trends driven by external shocks (e.g., the COVID-19 pandemic) and routine prevention and control efforts. Regarding data support and predictive applications, most existing prediction studies lack reliance on real domestic surveillance data in China, often based on regional estimates or indirectly derived data [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This results in unreliable predictions of China\u0026rsquo;s achievement of the WHO\u0026rsquo;s 2025 and 2030 TB control targets.\u003c/p\u003e \u003cp\u003eThe study integrates TB incidence and mortality data from mainland China spanning 2004\u0026ndash;2024, combining Joinpoint regression, interrupted time series (ITS) models, and seasonal autoregressive integrated moving average (SARIMA) models. It systematically analyzes the long-term trends, phased characteristics, and seasonal patterns of the epidemic over two decades, accurately identifies the intervention effects of key nodes such as the promotion of the DOTS strategy and the COVID-19 pandemic, and predicts epidemic changes and the achievement of WHO targets during 2025\u0026ndash;2030. The findings aim to provide evidence support for advancing China\u0026rsquo;s TB control efforts toward meeting the WHO\u0026rsquo;s goals.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Sources\u003c/h2\u003e \u003cp\u003eTB incidence and mortality data in mainland China from January 2004 to December 2024 were obtained from the official bulletins and annual summary reports of notifiable infectious diseases released by the National Health Commission of the People\u0026rsquo;s Republic of China. The data originated from the \u0026ldquo;National Notifiable Infectious Diseases Reporting System (NNIDRS)\u0026rdquo; established by the Chinese Center for Disease Control and Prevention (China CDC). Since 2004, this system has realized real-time, online passive surveillance of national notifiable infectious diseases, covering all levels and types of medical and health institutions in mainland China (including general hospitals, infectious disease-specialized hospitals, primary medical and health institutions, and CDCs). It has formed a full-chain data management system involving \u0026ldquo;reporting by medical institutions \u0026rarr; review by county-level CDCs \u0026rarr; quality control by municipal/provincial CDCs \u0026rarr; aggregation and analysis by the national CDC\u0026rdquo; [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSynchronized total population data of mainland China during the same period were collected from the China Statistical Yearbook (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.stats.gov.cn/sj/ndsj/\u003c/span\u003e\u003cspan address=\"https://www.stats.gov.cn/sj/ndsj/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), compiled and published annually by the National Bureau of Statistics of China. The total population data in the yearbook were benchmarked against national population census results and revised with annual population sampling survey data, including detailed information such as the total number of permanent residents at the end of the year, gender composition, and age structure. Specifically, the 2020 data were mainly based on the results of the 7th National Population Census, and the 2021\u0026ndash;2024 data were estimated from annual sampling surveys by the NBS. The data calculation method complies with internationally accepted demographic standards, ensuring the accuracy of the denominator data in the calculation of incidence and mortality rates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Joinpoint Regression Analysis\u003c/h2\u003e \u003cp\u003eThe Joinpoint regression model was used to systematically explore the temporal trends of TB incidence and mortality rates in mainland China from 2004 to 2024. Developed by the National Cancer Institute of the United States, this model is a trend analysis tool with core advantages in identifying \u0026ldquo;joinpoints\u0026rdquo; (inflection points) in time-series data. It divides the entire study period into multiple homogeneous linear segments, thereby accurately quantifying the variation patterns of indicators in different segments and overcoming the limitation of traditional linear regression in capturing sudden trend changes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Two core indicators built into the model\u0026mdash;Annual Percent Change (APC) and Average Annual Percent Change (AAPC)\u0026mdash;were used to quantitatively evaluate the trends of TB incidence and mortality rates in each segment and the overall study period (2004\u0026ndash;2024). The basic formulas of Joinpoint regression analysis are as follows:\u003c/p\u003e \u003cp\u003eLet Y be the TB incidence or mortality rate in year t (dependent variable), and t be the year (independent variable, taking consecutive integers from 2004 to 2024). The model assumes k joinpoints, dividing the time series into k\u0026thinsp;+\u0026thinsp;1 linear segments. For the i-th segment (i\u0026thinsp;=\u0026thinsp;1,2,...,k\u0026thinsp;+\u0026thinsp;1) with a time range of [\u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e,\u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e] (where \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2004 and \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2024), the regression model is expressed as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:ln\\left(Y\\right)\\:=\\:\\beta\\:\\:+\\:\\beta\\:\\times\\:(t\\:-\\:t)\\:+\\:\\epsilon\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the formula: \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e is the intercept term of the \u003cem\u003ei-th\u003c/em\u003e segment; \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e is the slope term of the \u003cem\u003ei-th\u003c/em\u003e segment; \u003cem\u003eε\u003c/em\u003e is the random error term, following a normal distribution \u003cem\u003eN(0,σ\u003c/em\u003e \u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e. Since incidence and mortality rates usually show exponential growth or decline, natural logarithm transformation of the dependent variable was performed to satisfy the basic assumptions of linear regression.\u003c/p\u003e \u003cp\u003eBased on the above model, the formula for calculating the APC of the \u003cem\u003ei-th\u003c/em\u003e segment is:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:APC\\:=\\:(e\\:-\\:1)\\:\\times\\:\\:100\\%$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe AAPC for the entire study period was calculated by weighting the APC values of each segment, with the weight being the time span of the segment (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u0026minus;\u0026thinsp;\u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;1):\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:AAPC\\:=\\:\\left[\\varSigma\\:\\right(n\\times\\:ln(1\\:+\\:APC/100))\\:/\\:\\varSigma\\:n]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAll Joinpoint regression analyses were performed using Joinpoint Regression Program 4.9.1.0 software(version 4.9.1.0; Statistical Methodology and Applications Branch, Surveillance Research Program, National Cancer Institute, Bethesda, MD, USA). All statistical tests were two-tailed, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Interrupted Time Series Model Analysis\u003c/h2\u003e \u003cp\u003eThe ITS model was used to quantitatively analyze the temporal trends and intervention effects of TB incidence and mortality rates in mainland China from 2004 to 2024.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Segmentation and Intervention Node Setting\u003c/h2\u003e \u003cp\u003e(1) Incidence rate: The year 2020 was set as the intervention node, dividing the period into two segments: 2004\u0026ndash;2020 (phase of modern TB control strategy implementation) and 2020\u0026ndash;2024 (phase affected by the COVID-19 pandemic). (2) Mortality rate: Two intervention nodes were set, dividing the period into three segments: 2004\u0026ndash;2009 (phase of \u0026ldquo;case detection\u0026thinsp;+\u0026thinsp;full-course supervised chemotherapy\u0026rdquo; implementation), 2010\u0026ndash;2021 (phase of 95% county-level coverage of the Directly Observed Treatment, Short-course (DOTS) strategy), and 2022\u0026ndash;2024 (phase affected by the COVID-19 pandemic with lag effects).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Model Construction and Validation\u003c/h2\u003e \u003cp\u003eA linear regression model was constructed with monthly incidence/mortality rate as the outcome variable, and time (continuous variable, with January 2004 coded as 1 and incrementing sequentially), intervention (binary variable: 1 for post-intervention, 0 for pre-intervention), and time since intervention (continuous variable: 1 for the first month post-intervention, 0 for pre-intervention) as independent variables:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:Yt=\\beta\\:0+\\beta\\:1Timet+\\beta\\:2Interventiont+\\beta\\:3TimeSinceInterventiont+ϵt\\)\u003c/span\u003e \u003c/span\u003eIn the formula: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Yt\\)\u003c/span\u003e\u003c/span\u003e is the outcome indicator at time \u003cem\u003et\u003c/em\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:0\\)\u003c/span\u003e\u003c/span\u003e is the intercept term of the pre-intervention baseline level; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:1Timet\\)\u003c/span\u003e\u003c/span\u003e captures the global pre-intervention temporal trend; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:2Interventiont\\)\u003c/span\u003e\u003c/span\u003ereflects the immediate level effect of the intervention; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:3TimeSinceInterventiont\\)\u003c/span\u003e\u003c/span\u003e represents the post-intervention trend change; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ϵt\\)\u003c/span\u003e\u003c/span\u003e is the random error term meeting model assumptions. Newey-West correction was used to calculate robust standard errors for autocorrelation adjustment, and the Durbin-Watson test was performed to verify residual autocorrelation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Statistics and Visualization\u003c/h2\u003e \u003cp\u003eModel fitting (via the lm function) and result analysis were conducted using R software (version 4.2.1; R Foundation for Statistical Computing, Vienna, Austria). Trend plots were generated with the ggplot2 package to display the fitting effect between actual values and model-predicted values. All statistical tests were two-tailed, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Construction of Time-Series Prediction Model\u003c/h2\u003e \u003cp\u003eThe Seasonal SARIMA model was used for trend fitting of monthly TB incidence and mortality rates in mainland China from 2004 to 2024 and prediction analysis up to 2030. The specific steps are as follows:\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Data Preprocessing and Splitting\u003c/h2\u003e \u003cp\u003eFirst, monthly incidence/mortality data from January 2004 to December 2024 were converted into time series objects (frequency set to 12 to adapt to the seasonal characteristics of monthly data), and raw time series plots were drawn to intuitively present data distribution. To verify model generalization ability, data were split into a training set (70%, for model construction and parameter optimization) and a test set (30%, for independent prediction performance validation) using the time window method to avoid temporal misalignment and ensure the continuity of the training and test sets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Sequence Feature Analysis and Stationarity Test\u003c/h2\u003e \u003cp\u003eThe training set data were decomposed into trend, seasonal, and random components to clarify long-term trends, seasonal fluctuations, and random disturbance characteristics (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). To meet the SARIMA model\u0026rsquo;s requirement for stationary sequences, 1st-order non-seasonal differencing and 12th-order seasonal differencing were performed on the training set. The Augmented Dickey-Fuller (ADF) test was used to verify sequence stationarity (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates stationarity), and the Ljung-Box test was used to determine whether the differenced sequence was non-white noise (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1 indicates non-white noise, suggesting valid modifiable information in the data) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] (Supplementary Methods).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Model Order Determination and Fitting\u003c/h2\u003e \u003cp\u003eAutocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots of the differenced sequence were drawn (Figure S2). Combined with the Akaike Information Criterion (AIC), the auto.arima function was used for comprehensive search to determine the optimal model parameters, including non-seasonal orders (p: autoregressive order, d: differencing order, q: moving average order) and seasonal orders (P: seasonal autoregressive order, D: seasonal differencing order, Q: seasonal moving average order, s: seasonal period, s\u0026thinsp;=\u0026thinsp;12here) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] (Tables S1\u0026ndash;S2). Based on the optimal parameters, the Maximum Likelihood (ML) method was used to fit the SARIMA model on the training set. The optimal parameters for incidence prediction were ARIMA(1,1,2)(2,1,0)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and for mortality prediction were ARIMA(2,1,0)(2,0,1)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4 Model Diagnostic Validation\u003c/h2\u003e \u003cp\u003eValidity tests were performed on model residuals: Q-Q plots were used to verify residual normality; residual ACF plots were used to check for unextracted autocorrelation information; the Ljung-Box test was used to confirm whether residuals conformed to white noise characteristics (\u003c/p\u003e \u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicates white noise, suggesting the model has fully extracted data information without redundant trends or seasonal components) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.4.5 Model Performance Evaluation and Prediction\u003c/h2\u003e \u003cp\u003eMultiple indicators were used to comprehensively evaluate model performance: Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error, and symmetric MAPE were calculated for the training set (fitting effect) and test set (prediction effect). Smaller error values indicate higher model fitting and prediction accuracy (Supplementary Methods). The optimal SARIMA model was re-fitted using the full dataset (2004\u0026ndash;2024) to predict TB incidence/mortality rates from January 2025 to December 2030.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.4.6 Statistical Software and Tools\u003c/h2\u003e \u003cp\u003eAll analyses were performed using R software (version 4.2.1), mainly relying on the forecast package for time-series decomposition, model fitting, and prediction; the stats package for stationarity tests and residual analysis; and the ggplot2 package for visualization.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Analysis of TB Incidence and Mortality in Mainland China, 2004\u0026ndash;2024\u003c/h2\u003e \u003cp\u003eFrom 2004 to 2024, a total of 19.4854\u0026nbsp;million TB cases and 50,800 TB-related deaths were reported in mainland China, with an annual average incidence rate of 68.573 per 100,000 population and an annual average mortality rate of 0.178 per 100,000 population. Regarding incidence: the epidemic peaked in 2005, with 1.2593\u0026nbsp;million reported cases and an incidence rate of 96.879 per 100,000 population; thereafter, it showed a fluctuating downward trend, decreasing to 702,600 cases and 49.888 per 100,000 population in 2024\u0026mdash;representing a 44.2% reduction in case count and a 48.5% reduction in incidence rate compared with the 2005 peak. A temporary rebound in incidence was observed in 2020 (62.075 per 100,000 population). The mortality rate generally fluctuated at a low level, with slight peaks in 2007 (0.279 per 100,000 population) and 2023 (0.283 per 100,000 population); it reached the lowest point in 2021 (0.101 per 100,000 population). From 2022 to 2023, the mortality rate increased for two consecutive years, with 3,989 deaths reported in 2023\u0026mdash;the highest in the past decade (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTuberculosis Incidence and Mortality in Chinese Mainland, 2004\u0026ndash;2024\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eIncidence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRate(1/100,000)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRate(1/100,000)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e970,279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,259,308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,127,571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,163,959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,169,540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2,375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,076,938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e991,350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e953,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e951,508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e904,434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e889381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e864,015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e836,236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e835,193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2,181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e823,342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2,236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e775,764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2,241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e876,576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e828,074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e712,586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e773,512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e702,565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage/Cumulative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19,485,406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50,847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: Counts were evaluated using cumulative values, while rates were assessed with average annual values.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe monthly distribution of TB incidence and mortality from 2004 to 2024 showed consistent seasonal fluctuation characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). For incidence (cases/rates), the infection risk was higher in winter (December\u0026ndash;February of the following year) and lower in summer (June\u0026ndash;August). This \"winter peak and summer trough\" pattern persisted stably over 21 years, with December\u0026ndash;January being the period of peak monthly incidence in most years. The seasonal trend of mortality was highly consistent with that of incidence: mortality cases/rates were higher in winter (December\u0026ndash;February) and lower in summer (June\u0026ndash;August) (Figures S3\u0026ndash;S4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Trend Analysis of TB Incidence and Mortality in Mainland China, 2004\u0026ndash;2024\u003c/h2\u003e \u003cp\u003eThe TB incidence rate in mainland China decreased significantly from 74.644 per 100,000 population in 2004 to 49.888 per 100,000 population in 2024 (AAPC=-2.83%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The overall mortality rate showed an upward trend during 2004\u0026ndash;2024 (AAPC\u0026thinsp;=\u0026thinsp;2.33%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.433); specifically, it decreased significantly from 2004 to 2021 (APC=-2.70%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048) and increased from 2021 to 2024 (APC=-2.70%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring the phase of modern TB control strategy implementation, the monthly TB incidence rate showed a significant continuous downward trend, decreasing by an average of 0.0202 per 100,000 population per month (β\u0026thinsp;=\u0026thinsp;0.0202, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). During the COVID-19 pandemic, the long-term trend of incidence did not change significantly compared with the pre-intervention period, remaining on a downward trajectory, but the additional change in the rate of decline was not statistically significant (β = -0.0010, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.9012). For mortality: no significant temporal trend was observed during the \u0026ldquo;case detection\u0026thinsp;+\u0026thinsp;full-course supervised chemotherapy\u0026rdquo; phase (β\u0026thinsp;=\u0026thinsp;6.327\u0026times;10⁻⁶, P\u0026thinsp;=\u0026thinsp;0.573). After the 95% county-level coverage of the DOTS strategy in 2010, the mortality rate exhibited an immediate and statistically significant decrease, followed by fluctuations at a low level (β=-0.0086, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Prediction Analysis of TB Incidence and Mortality in Mainland China\u003c/h2\u003e \u003cp\u003eAs shown in Figure S5A, the SARIMA model exhibited a good fitting effect for both TB incidence and mortality, with superior validation accuracy for incidence (Tables S3\u0026ndash;S4). Based on comprehensive model fitting and validation results, the SARIMA model demonstrated reliable predictive performance and was used to forecast future TB incidence and mortality in mainland China. By 2025, the projected TB incidence rate in mainland China will be 49.704 per 100,000 population (Table S5), achieving only 43.24% of the WHO\u0026rsquo;s set control target. The projected mortality rate during the same period will be 0.284 per 100,000 population (Table S6), far exceeding the WHO\u0026rsquo;s 2025 mortality control target (0.032 per 100,000 population). By 2030, the TB incidence rate is expected to further decrease to 43.387 per 100,000 population, with the achievement rate of the WHO\u0026rsquo;s 2030 target dropping to 39.48%. The mortality rate is predicted to show an upward trend, rising to 0.333 per 100,000 population (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Figure S5B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on TB surveillance data in China from 2004 to 2024, this study conducted a combined analysis using Joinpoint regression, ITS models, and SARIMA models. The key findings are as follows: the TB incidence rate showed a significant downward trend over 20 years, but the achievement rates of the WHO\u0026rsquo;s 2025 and 2030 control targets are projected to be only 43.24% and 39.48%, respectively; the mortality rate exhibited a \u0026ldquo;first decrease then increase\u0026rdquo; pattern, with the number of deaths in 2023 reaching the highest in the past decade; the COVID-19 pandemic had no significant impact on the long-term trend of incidence but triggered a lagged rebound in mortality.\u003c/p\u003e \u003cp\u003eThe reduction in incidence from 74.644 per 100,000 population in 2004 to 49.888 per 100,000 population in 2024 was primarily driven by the synergistic effect of two core prevention and control systems. Firstly, the full implementation of the DOTS strategy played a pivotal role. After achieving 95% county-level coverage in 2010, the ITS model revealed an immediate and statistically significant decrease in mortality, which is associated with DOTS\u0026rsquo; core mechanisms of \u0026ldquo;treating upon detection and full-course supervised therapy\u0026rdquo;[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Previous studies have shown that this strategy can stabilize the cure rate of infectious patients above 90%, significantly interrupting community transmission chains [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Secondly, the technical support of the NNIDRS was indispensable. Following its full national coverage in 2004, the timeliness of reporting by medical institutions increased threefold [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and the underreporting rate dropped from 28.7% in 2003 to 3.2% in 2024 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], providing precise data support for early detection and intervention. Additionally, the enhanced public health awareness during the COVID-19 pandemic indirectly promoted TB control: the active consultation rate for suspected TB symptoms post-pandemic increased by 15.6% compared with 2019 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], further consolidating the downward trend in incidence. However, the elderly population has emerged as a vulnerable group in prevention and control: the reported TB incidence rate among adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years is more than twice that of the general population [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Unlike childhood TB, most elderly TB cases result from the reactivation of previously latent Mycobacterium TB infections. Age-related decline in immune function, coupled with comorbidities such as diabetes, malnutrition, and malignant tumors, increases the susceptibility of the elderly to TB [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. China\u0026rsquo;s population aging is accelerating: the proportion of people aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years has risen from 6.5% in 1997 to 14.9% in 2022, and is projected to exceed 20% by 2035. Furthermore, significant regional disparities in TB control persist: the average reported incidence rate in western China (87.35 per 100,000 population) was 1.66 times that in eastern China (52.50 per 100,000 population) during 2005\u0026ndash;2023 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, future efforts should focus on strengthening prevention and control barriers for the elderly, integrating elderly TB screening into basic public health services, enhancing primary care providers\u0026rsquo; ability to identify atypical TB symptoms in the elderly, and implementing personalized supervised treatment. To promote balanced regional prevention and control, more resources should be allocated to western China, and a collaborative mechanism between eastern and western regions should be established to share DOTS implementation experience and technical support resources.\u003c/p\u003e \u003cp\u003eThe \u0026ldquo;shift from decrease to increase\u0026rdquo; in mortality is the result of multiple overlapping factors, with the core being the lagged effects of the COVID-19 pandemic and shortcomings in severe case management. The significant decline in mortality during 2004\u0026ndash;2021 was mainly attributed to the early intervention of severe cases through the DOTS strategy and the improved diagnosis and treatment capacity of designated TB hospitals. However, the COVID-19 pandemic during 2020\u0026ndash;2022 led to a 34.2% decrease in outpatient visits to national designated TB hospitals, with 42.7% of patients experiencing treatment interruptions exceeding 2 weeks. The mortality risk of patients with treatment interruptions was 3.8 times higher than that of those with continuous treatment, and the chain reaction of \u0026ldquo;diagnostic and treatment delays \u0026rarr; disease progression\u0026rdquo; became prominent after 2021 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In terms of patient characteristics, 72.3% of TB deaths occurred in individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, and primary care providers\u0026rsquo; recognition rate of severe case warning indicators was only 45%, leading to many mild cases progressing to severe disease [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Meanwhile, the implementation of the DOTS strategy weakened during the pandemic: the coverage of full-course supervised chemotherapy dropped from 92.1% in 2019 to 67.3% in 2022, and the increased treatment failure rate further pushed up mortality [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Future recommendations include: strengthening the rigid implementation of the DOTS strategy and promoting remote supervision through informatization; integrating elderly TB control into the aging health response system, conducting specialized training on severe case early warning for primary care providers to improve early recognition capabilities; and establishing an emergency response mechanism for diagnostic and treatment delays to ensure uninterrupted TB diagnosis and treatment services during public health emergencies such as pandemics.\u003c/p\u003e \u003cp\u003eThe stable \"winter peak and summer trough\" seasonal pattern persisted over 21 years, with a 32.7% higher incidence in winter than in summer. This characteristic reflects both global universality and unique Chinese social contexts. From the perspective of transmission environment: low winter temperatures increase indoor activities, and the risk of droplet transmission in enclosed spaces during the centralized heating period in northern China rises by 40%, leading to a significant increase in \u003cem\u003eMycobacterium TB\u003c/em\u003e concentration compared with summer [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. From the perspective of host immunity: the high incidence of respiratory diseases such as influenza in winter causes dual consumption of the body\u0026rsquo;s immune system, and the activation risk of latent infections is 2.3 times higher than in summer [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A more China-specific factor is the large-scale population movement around the Spring Festival, which exacerbates cross-regional transmission: the incidence rate in the Pearl River Delta region increased by 18.9% one month after the 2023 Spring Festival compared with before the festival [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Based on this, targeted winter prevention and control measures should be implemented: strengthening ventilation and disinfection in public places during the heating period in northern China, and integrating TB screening into the joint surveillance of respiratory diseases such as influenza; focusing on prevention and control during holiday-related population movement, conducting targeted health education in labor export and import areas before peak migration periods (e.g., the Spring Festival), and strengthening cross-regional case tracing and information sharing through the NNIDRS to interrupt transmission chains associated with population mobility.\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic had no significant impact on the long-term incidence trend but induced a lagged rebound in mortality. This \"stable incidence, rising mortality\" pattern essentially results from the squeeze on diagnostic and treatment resources. During 2020\u0026ndash;2022, the bed utilization rate of designated TB hospitals dropped from 78% to 45%, while the proportion of intensive care unit beds increased from 5% to 12%. This indicates that mild cases were delayed in seeking medical care, and the proportion of patients progressing to severe disease at the time of consultation rose from 12.3% to 27.8% [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Meanwhile, pandemic-induced psychological anxiety led 42.7% of patients to discontinue medication voluntarily, reducing treatment adherence from 92% to 75%. The mortality risk of non-adherent patients was 4.2 times higher than that of adherent patients, and global studies have confirmed that the pandemic increased the global average TB mortality rate by 11.3% [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. To build a resilient prevention and control mechanism for responding to public health emergencies, two key measures are proposed: first, establishing a guarantee mechanism for infectious disease diagnosis and treatment resources, integrating TB diagnosis and treatment into the public health emergency support system to ensure that resources such as beds and human resources are not excessively squeezed during emergencies; second, strengthening the full-cycle management of patient treatment, promoting remote supervision and psychological counseling through informatization to improve treatment adherence, and enhancing primary care capacity for severe case early warning to reduce the progression of mild cases to severe disease.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThis study has three limitations. First, the data were collected through passive surveillance, which may lead to underreporting of mild cases at the primary care level and cases among migrant populations. Although the underreporting rate has dropped to 3.2%, it still affects the accuracy of the results. Second, no subgroup analyses (e.g., by gender or age) were conducted, making it impossible to reveal population-specific differences; individual factors such as drug resistance and comorbidities were also not included. Third, the SARIMA model is based on the extrapolation of historical trends and does not consider future variables such as the launch of new vaccines .\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, China has achieved remarkable results in TB control during 2004\u0026ndash;2024, but there remains a significant gap from the WHO\u0026rsquo;s 2025\u0026ndash;2030 targets. The rebound in mortality is directly related to inadequate elderly population management and the lagged effects of the COVID-19 pandemic. Future TB control should be centered on \"precision stratification and prioritization of severe cases,\" strengthening elderly screening, drug resistance management, and the sinking of primary care resources to build a resilient prevention and control mechanism. The long-term trend analysis and prediction results of this study provide a scientific basis for the development of national medium- and long-term TB control plans, supporting China\u0026rsquo;s practice in the global strategy of \"ending the TB epidemic.\"\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTB Tuberculosis\u003c/p\u003e \u003cp\u003eWHO World Health Organization\u003c/p\u003e \u003cp\u003eAAPC Average Annual Percent Change\u003c/p\u003e \u003cp\u003eDOTS Directly Observed Treatment, Short-course\u003c/p\u003e \u003cp\u003eITS Interrupted Time Series\u003c/p\u003e \u003cp\u003eSARIMA Seasonal Autoregressive Integrated Moving Average\u003c/p\u003e \u003cp\u003eAPC Annual Percent Change\u003c/p\u003e \u003cp\u003eNHC National Health Commission of the People's Republic of China\u003c/p\u003e \u003cp\u003eCDC Chinese Center for Disease Control and Prevention\u003c/p\u003e \u003cp\u003eNNIDRS National Notifiable Infectious Diseases Reporting System\u003c/p\u003e \u003cp\u003eNBS National Bureau of Statistics of China\u003c/p\u003e \u003cp\u003eADF Augmented Dickey-Fuller\u003c/p\u003e \u003cp\u003eAIC Akaike Information Criterion\u003c/p\u003e \u003cp\u003eML Maximum Likelihood\u003c/p\u003e \u003cp\u003eMSE Mean Squared Error\u003c/p\u003e \u003cp\u003eRMSE Root Mean Squared Error\u003c/p\u003e \u003cp\u003eMAE Mean Absolute Error\u003c/p\u003e \u003cp\u003eMAPE Mean Absolute Percentage Error\u003c/p\u003e \u003cp\u003esMAPE symmetric Mean Absolute Percentage Error\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: BDT, MeB, SA, MeB and MaB. Data curation: MaB, BDT, MeB, SA, AB, MM, and AB. Formal analysis: MaB, AB, MM and SA. Investigation: BDT, MaB, and SA. Methodology: MaB, BDT and SA. Project administration:\u0026nbsp;MeB, AB, BDT, SA. Supervision:\u0026nbsp;MaB. Validation: BDT, MaB. Writing \u0026ndash; original draft: MaB, SA, AB, BDT. Writing \u0026ndash; review \u0026amp; editing: MM, AB, and MaB. The author(s) read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, TB incidence and mortality data were obtained from the National Health Commission of the People\u0026apos;s Republic of China NHC and are publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHershkovitz I, Donoghue HD, Minnikin DE, et al. 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Decline in rates of acquired multidrug-resistant tuberculosis after implementation of the directly observed therapy, short course (DOTS) and DOTS-Plus programmes in Taiwan. J Antimicrob Chemother. 2013;68:1910\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang W, Li Z, Lan Y, et al. A nationwide web-based automated system for outbreak early detection and rapid response in China. 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Regional TB Consortium. Progress and challenges in tuberculosis preventive treatment in the Western Pacific Region: a situational analysis of seven high tuberculosis burden countries. Trop Med Health. 2025;53(1):122.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Yin S, Li Y, et al. Spatiotemporal epidemiology of, and factors associated with, the tuberculosis prevalence in northern China, 2010\u0026ndash;2014. BMC Infect Dis. 2019;19:365.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOverbeck V, Malatesta S, Carney T, et al. Understanding the impact of pandemics on long-term medication adherence: directly observed therapy in a tuberculosis treatment cohort pre- and post-COVID-19 lockdowns. BMC Infect Dis. 2024;24:1154.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDheda K, Perumal T, Moultrie H, et al. The intersecting pandemics of tuberculosis and COVID-19: population-level and patient-level impact, clinical presentation, and corrective interventions. Lancet Respir Med. 2022;10:603\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"tropical-medicine-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tmah","sideBox":"Learn more about [Tropical Medicine and Health](https://tropmedhealth.biomedcentral.com/)","snPcode":"41182","submissionUrl":"https://submission.springernature.com/new-submission/41182/3","title":"Tropical Medicine and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"tuberculosis, incidence, death, trend, prediction","lastPublishedDoi":"10.21203/rs.3.rs-8318476/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8318476/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe global tuberculosis (TB) epidemic imposes a substantial burden. As a high-burden country, China faces a significant gap from the World Health Organization(WHO)\u0026rsquo; s 2025\u0026ndash;2030 TB prevention and control targets. This study analyzed the temporal trends of TB epidemiology in mainland China to provide an evidence base for the early achievement of TB control goals.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe integrated TB surveillance data (2004\u0026ndash;2024) from the National Health Commission of the People's Republic of China and population data from the National Bureau of Statistics. Joinpoint regression was used to identify trend changes, with the average annual percent change (AAPC) quantifying trend magnitudes. Interrupted time series model was applied to assess intervention effects, and seasonal autoregressive integrated moving average models were employed to predict future incidence and mortality trends.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 19.4854\u0026nbsp;million cumulative TB cases and 508,000 cumulative deaths were reported during 2004\u0026ndash;2024. The incidence rate decreased from 74.644 to 49.888 per 100,000 population (AAPC=-2.83%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), showing a \u0026ldquo;winter peak and summer trough\u0026rdquo; pattern\u0026mdash;with a 32.7% higher incidence in winter than in summer. The mortality rate first decreased and then increased: it declined immediately after the full coverage of Directly Observed Treatment, Short-course in 2010 but rose to 0.283 per 100,000 population after 2021. Predictions indicate that the achievement rate of the WHO\u0026rsquo;s incidence targets will only reach 43.24% in 2025 and 39.48% in 2030, with the mortality rate projected to reach 0.333 per 100,000 population by 2030.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDespite notable achievements in TB control in China, significant gaps remain from the WHO's targets. It is imperative to strengthen precision stratification-based prevention and control, establish a TB diagnosis and treatment guarantee mechanism, and implement remote supervision relying on informatization.\u003c/p\u003e","manuscriptTitle":"Tuberculosis Incidence and Mortality Trends in Mainland China, 2004-2024: Control Program and Elimination Progress","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 10:00:33","doi":"10.21203/rs.3.rs-8318476/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-04T11:43:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-04T07:42:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-03T17:12:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165307053628324574974307880958096127903","date":"2026-01-27T12:53:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197589350493938265289519150586932842955","date":"2026-01-27T01:47:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89467920344088659395783188061114506488","date":"2026-01-14T06:41:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"293334764880508648908433732260299469991","date":"2025-12-23T09:06:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-17T03:36:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-16T06:52:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-15T02:52:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Tropical Medicine and Health","date":"2025-12-09T13:46:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"tropical-medicine-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tmah","sideBox":"Learn more about [Tropical Medicine and Health](https://tropmedhealth.biomedcentral.com/)","snPcode":"41182","submissionUrl":"https://submission.springernature.com/new-submission/41182/3","title":"Tropical Medicine and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a06f2aba-a25f-4f43-bf88-40ed6de31b99","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:03:32+00:00","versionOfRecord":{"articleIdentity":"rs-8318476","link":"https://doi.org/10.1186/s41182-026-00928-4","journal":{"identity":"tropical-medicine-and-health","isVorOnly":false,"title":"Tropical Medicine and Health"},"publishedOn":"2026-03-02 15:59:38","publishedOnDateReadable":"March 2nd, 2026"},"versionCreatedAt":"2025-12-22 10:00:33","video":"","vorDoi":"10.1186/s41182-026-00928-4","vorDoiUrl":"https://doi.org/10.1186/s41182-026-00928-4","workflowStages":[]},"version":"v1","identity":"rs-8318476","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8318476","identity":"rs-8318476","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}